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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"c:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\groundingdino\\models\\GroundingDINO\\ms_deform_attn.py:31: UserWarning: Failed to load custom C++ ops. Running on CPU mode Only!\n",
" warnings.warn(\"Failed to load custom C++ ops. Running on CPU mode Only!\")\n"
]
}
],
"source": [
"import os\n",
"import cv2\n",
"from SegTracker import SegTracker\n",
"from model_args import aot_args,sam_args,segtracker_args\n",
"from PIL import Image\n",
"from aot_tracker import _palette\n",
"import numpy as np\n",
"import torch\n",
"import imageio\n",
"import matplotlib.pyplot as plt\n",
"from scipy.ndimage import binary_dilation\n",
"import gc\n",
"def save_prediction(pred_mask,output_dir,file_name):\n",
" save_mask = Image.fromarray(pred_mask.astype(np.uint8))\n",
" save_mask = save_mask.convert(mode='P')\n",
" save_mask.putpalette(_palette)\n",
" save_mask.save(os.path.join(output_dir,file_name))\n",
"def colorize_mask(pred_mask):\n",
" save_mask = Image.fromarray(pred_mask.astype(np.uint8))\n",
" save_mask = save_mask.convert(mode='P')\n",
" save_mask.putpalette(_palette)\n",
" save_mask = save_mask.convert(mode='RGB')\n",
" return np.array(save_mask)\n",
"def draw_mask(img, mask, alpha=0.7, id_countour=False):\n",
" img_mask = np.zeros_like(img)\n",
" img_mask = img\n",
" if id_countour:\n",
" # very slow ~ 1s per image\n",
" obj_ids = np.unique(mask)\n",
" obj_ids = obj_ids[obj_ids!=0]\n",
"\n",
" for id in obj_ids:\n",
" # Overlay color on binary mask\n",
" if id <= 255:\n",
" color = _palette[id*3:id*3+3]\n",
" else:\n",
" color = [0,0,0]\n",
" foreground = img * (1-alpha) + np.ones_like(img) * alpha * np.array(color)\n",
" binary_mask = (mask == id)\n",
"\n",
" # Compose image\n",
" img_mask[binary_mask] = foreground[binary_mask]\n",
"\n",
" countours = binary_dilation(binary_mask,iterations=1) ^ binary_mask\n",
" img_mask[countours, :] = 0\n",
" else:\n",
" binary_mask = (mask!=0)\n",
" countours = binary_dilation(binary_mask,iterations=1) ^ binary_mask\n",
" foreground = img*(1-alpha)+colorize_mask(mask)*alpha\n",
" img_mask[binary_mask] = foreground[binary_mask]\n",
" img_mask[countours,:] = 0\n",
" \n",
" return img_mask.astype(img.dtype)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set parameters for input and output"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"video_name = 'cars'\n",
"io_args = {\n",
" 'input_video': f'./assets/{video_name}.mp4',\n",
" 'output_mask_dir': f'./assets/{video_name}_masks', # save pred masks\n",
" 'output_video': f'./assets/{video_name}_seg.mp4', # mask+frame vizualization, mp4 or avi, else the same as input video\n",
" 'output_gif': f'./assets/{video_name}_seg.gif', # mask visualization\n",
"}"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tuning Grounding-DINO and SAM on the First Frame for Good Initialization"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"ename": "AssertionError",
"evalue": "Torch not compiled with CUDA enabled",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[3], line 27\u001b[0m\n\u001b[0;32m 25\u001b[0m cap \u001b[39m=\u001b[39m cv2\u001b[39m.\u001b[39mVideoCapture(io_args[\u001b[39m'\u001b[39m\u001b[39minput_video\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m 26\u001b[0m frame_idx \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m---> 27\u001b[0m segtracker \u001b[39m=\u001b[39m SegTracker(segtracker_args,sam_args,aot_args)\n\u001b[0;32m 28\u001b[0m segtracker\u001b[39m.\u001b[39mrestart_tracker()\n\u001b[0;32m 29\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mamp\u001b[39m.\u001b[39mautocast():\n",
"File \u001b[1;32md:\\05 Dr\\Segmentation\\Segment-and-Track-Anything\\SegTracker.py:19\u001b[0m, in \u001b[0;36mSegTracker.__init__\u001b[1;34m(self, segtracker_args, sam_args, aot_args)\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\u001b[39mself\u001b[39m,segtracker_args, sam_args, aot_args) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 16\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 17\u001b[0m \u001b[39m Initialize SAM and AOT.\u001b[39;00m\n\u001b[0;32m 18\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 19\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msam \u001b[39m=\u001b[39m Segmentor(sam_args)\n\u001b[0;32m 20\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtracker \u001b[39m=\u001b[39m get_aot(aot_args)\n\u001b[0;32m 21\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdetector \u001b[39m=\u001b[39m Detector(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msam\u001b[39m.\u001b[39mdevice)\n",
"File \u001b[1;32md:\\05 Dr\\Segmentation\\Segment-and-Track-Anything\\tool\\segmentor.py:16\u001b[0m, in \u001b[0;36mSegmentor.__init__\u001b[1;34m(self, sam_args)\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdevice \u001b[39m=\u001b[39m sam_args[\u001b[39m\"\u001b[39m\u001b[39mgpu_id\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m 15\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msam \u001b[39m=\u001b[39m sam_model_registry[sam_args[\u001b[39m\"\u001b[39m\u001b[39mmodel_type\u001b[39m\u001b[39m\"\u001b[39m]](checkpoint\u001b[39m=\u001b[39msam_args[\u001b[39m\"\u001b[39m\u001b[39msam_checkpoint\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m---> 16\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msam\u001b[39m.\u001b[39;49mto(device\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdevice)\n\u001b[0;32m 17\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39meverything_generator \u001b[39m=\u001b[39m SamAutomaticMaskGenerator(model\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39msam, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39msam_args[\u001b[39m'\u001b[39m\u001b[39mgenerator_args\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m 18\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39minteractive_predictor \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39meverything_generator\u001b[39m.\u001b[39mpredictor\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1145\u001b[0m, in \u001b[0;36mModule.to\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1141\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 1142\u001b[0m non_blocking, memory_format\u001b[39m=\u001b[39mconvert_to_format)\n\u001b[0;32m 1143\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m, non_blocking)\n\u001b[1;32m-> 1145\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_apply(convert)\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:797\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 795\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[0;32m 796\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[1;32m--> 797\u001b[0m module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[0;32m 799\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[0;32m 800\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[0;32m 801\u001b[0m \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[0;32m 802\u001b[0m \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 807\u001b[0m \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[0;32m 808\u001b[0m \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:797\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 795\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[0;32m 796\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[1;32m--> 797\u001b[0m module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[0;32m 799\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[0;32m 800\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[0;32m 801\u001b[0m \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[0;32m 802\u001b[0m \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 807\u001b[0m \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[0;32m 808\u001b[0m \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:797\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 795\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_apply\u001b[39m(\u001b[39mself\u001b[39m, fn):\n\u001b[0;32m 796\u001b[0m \u001b[39mfor\u001b[39;00m module \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mchildren():\n\u001b[1;32m--> 797\u001b[0m module\u001b[39m.\u001b[39;49m_apply(fn)\n\u001b[0;32m 799\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcompute_should_use_set_data\u001b[39m(tensor, tensor_applied):\n\u001b[0;32m 800\u001b[0m \u001b[39mif\u001b[39;00m torch\u001b[39m.\u001b[39m_has_compatible_shallow_copy_type(tensor, tensor_applied):\n\u001b[0;32m 801\u001b[0m \u001b[39m# If the new tensor has compatible tensor type as the existing tensor,\u001b[39;00m\n\u001b[0;32m 802\u001b[0m \u001b[39m# the current behavior is to change the tensor in-place using `.data =`,\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 807\u001b[0m \u001b[39m# global flag to let the user control whether they want the future\u001b[39;00m\n\u001b[0;32m 808\u001b[0m \u001b[39m# behavior of overwriting the existing tensor or not.\u001b[39;00m\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:820\u001b[0m, in \u001b[0;36mModule._apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m 816\u001b[0m \u001b[39m# Tensors stored in modules are graph leaves, and we don't want to\u001b[39;00m\n\u001b[0;32m 817\u001b[0m \u001b[39m# track autograd history of `param_applied`, so we have to use\u001b[39;00m\n\u001b[0;32m 818\u001b[0m \u001b[39m# `with torch.no_grad():`\u001b[39;00m\n\u001b[0;32m 819\u001b[0m \u001b[39mwith\u001b[39;00m torch\u001b[39m.\u001b[39mno_grad():\n\u001b[1;32m--> 820\u001b[0m param_applied \u001b[39m=\u001b[39m fn(param)\n\u001b[0;32m 821\u001b[0m should_use_set_data \u001b[39m=\u001b[39m compute_should_use_set_data(param, param_applied)\n\u001b[0;32m 822\u001b[0m \u001b[39mif\u001b[39;00m should_use_set_data:\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1143\u001b[0m, in \u001b[0;36mModule.to.<locals>.convert\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m 1140\u001b[0m \u001b[39mif\u001b[39;00m convert_to_format \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m t\u001b[39m.\u001b[39mdim() \u001b[39min\u001b[39;00m (\u001b[39m4\u001b[39m, \u001b[39m5\u001b[39m):\n\u001b[0;32m 1141\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39mto(device, dtype \u001b[39mif\u001b[39;00m t\u001b[39m.\u001b[39mis_floating_point() \u001b[39mor\u001b[39;00m t\u001b[39m.\u001b[39mis_complex() \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 1142\u001b[0m non_blocking, memory_format\u001b[39m=\u001b[39mconvert_to_format)\n\u001b[1;32m-> 1143\u001b[0m \u001b[39mreturn\u001b[39;00m t\u001b[39m.\u001b[39;49mto(device, dtype \u001b[39mif\u001b[39;49;00m t\u001b[39m.\u001b[39;49mis_floating_point() \u001b[39mor\u001b[39;49;00m t\u001b[39m.\u001b[39;49mis_complex() \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m, non_blocking)\n",
"File \u001b[1;32mc:\\Users\\Dubai Computers\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\cuda\\__init__.py:239\u001b[0m, in \u001b[0;36m_lazy_init\u001b[1;34m()\u001b[0m\n\u001b[0;32m 235\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[0;32m 236\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 237\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmultiprocessing, you must use the \u001b[39m\u001b[39m'\u001b[39m\u001b[39mspawn\u001b[39m\u001b[39m'\u001b[39m\u001b[39m start method\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 238\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(torch\u001b[39m.\u001b[39m_C, \u001b[39m'\u001b[39m\u001b[39m_cuda_getDeviceCount\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m--> 239\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mTorch not compiled with CUDA enabled\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 240\u001b[0m \u001b[39mif\u001b[39;00m _cudart \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 241\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\n\u001b[0;32m 242\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[39m\"\u001b[39m)\n",
"\u001b[1;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
]
}
],
"source": [
"# choose good parameters in sam_args based on the first frame segmentation result\n",
"# other arguments can be modified in model_args.py\n",
"# note the object number limit is 255 by default, which requires < 10GB GPU memory with amp\n",
"sam_args['generator_args'] = {\n",
" 'points_per_side': 30,\n",
" 'pred_iou_thresh': 0.8,\n",
" 'stability_score_thresh': 0.9,\n",
" 'crop_n_layers': 1,\n",
" 'crop_n_points_downscale_factor': 2,\n",
" 'min_mask_region_area': 200,\n",
" }\n",
"\n",
"# Set Text args\n",
"'''\n",
"parameter:\n",
" grounding_caption: Text prompt to detect objects in key-frames\n",
" box_threshold: threshold for box \n",
" text_threshold: threshold for label(text)\n",
" box_size_threshold: If the size ratio between the box and the frame is larger than the box_size_threshold, the box will be ignored. This is used to filter out large boxes.\n",
" reset_image: reset the image embeddings for SAM\n",
"'''\n",
"grounding_caption = \"car.suv\"\n",
"box_threshold, text_threshold, box_size_threshold, reset_image = 0.35, 0.5, 0.5, True\n",
"\n",
"cap = cv2.VideoCapture(io_args['input_video'])\n",
"frame_idx = 0\n",
"segtracker = SegTracker(segtracker_args,sam_args,aot_args)\n",
"segtracker.restart_tracker()\n",
"with torch.cuda.amp.autocast():\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)\n",
" pred_mask, annotated_frame = segtracker.detect_and_seg(frame, grounding_caption, box_threshold, text_threshold, box_size_threshold)\n",
" torch.cuda.empty_cache()\n",
" obj_ids = np.unique(pred_mask)\n",
" obj_ids = obj_ids[obj_ids!=0]\n",
" print(\"processed frame {}, obj_num {}\".format(frame_idx,len(obj_ids)),end='\\n')\n",
" break\n",
" cap.release()\n",
" init_res = draw_mask(annotated_frame, pred_mask,id_countour=False)\n",
" plt.figure(figsize=(10,10))\n",
" plt.axis('off')\n",
" plt.imshow(init_res)\n",
" plt.show()\n",
" plt.figure(figsize=(10,10))\n",
" plt.axis('off')\n",
" plt.imshow(colorize_mask(pred_mask))\n",
" plt.show()\n",
"\n",
" del segtracker\n",
" torch.cuda.empty_cache()\n",
" gc.collect()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate Results for the Whole Video"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For every sam_gap frames, we use SAM to find new objects and add them for tracking\n",
"# larger sam_gap is faster but may not spot new objects in time\n",
"segtracker_args = {\n",
" 'sam_gap': 49, # the interval to run sam to segment new objects\n",
" 'min_area': 200, # minimal mask area to add a new mask as a new object\n",
" 'max_obj_num': 255, # maximal object number to track in a video\n",
" 'min_new_obj_iou': 0.8, # the area of a new object in the background should > 80% \n",
"}\n",
"\n",
"# source video to segment\n",
"cap = cv2.VideoCapture(io_args['input_video'])\n",
"fps = cap.get(cv2.CAP_PROP_FPS)\n",
"# output masks\n",
"output_dir = io_args['output_mask_dir']\n",
"if not os.path.exists(output_dir):\n",
" os.makedirs(output_dir)\n",
"pred_list = []\n",
"masked_pred_list = []\n",
"\n",
"torch.cuda.empty_cache()\n",
"gc.collect()\n",
"sam_gap = segtracker_args['sam_gap']\n",
"frame_idx = 0\n",
"segtracker = SegTracker(segtracker_args, sam_args, aot_args)\n",
"segtracker.restart_tracker()\n",
"\n",
"with torch.cuda.amp.autocast():\n",
" while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
" frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)\n",
" if frame_idx == 0:\n",
" pred_mask, _ = segtracker.detect_and_seg(frame, grounding_caption, box_threshold, text_threshold, box_size_threshold, reset_image)\n",
" # pred_mask = cv2.imread('./debug/first_frame_mask.png', 0)\n",
" torch.cuda.empty_cache()\n",
" gc.collect()\n",
" segtracker.add_reference(frame, pred_mask)\n",
" elif (frame_idx % sam_gap) == 0:\n",
" seg_mask, _ = segtracker.detect_and_seg(frame, grounding_caption, box_threshold, text_threshold, box_size_threshold, reset_image)\n",
" save_prediction(seg_mask, './debug/seg_result', str(frame_idx)+'.png')\n",
" torch.cuda.empty_cache()\n",
" gc.collect()\n",
" track_mask = segtracker.track(frame)\n",
" save_prediction(track_mask, './debug/aot_result', str(frame_idx)+'.png')\n",
" # find new objects, and update tracker with new objects\n",
" new_obj_mask = segtracker.find_new_objs(track_mask, seg_mask)\n",
" if np.sum(new_obj_mask > 0) > frame.shape[0] * frame.shape[1] * 0.4:\n",
" new_obj_mask = np.zeros_like(new_obj_mask)\n",
" save_prediction(new_obj_mask,output_dir,str(frame_idx)+'_new.png')\n",
" pred_mask = track_mask + new_obj_mask\n",
" # segtracker.restart_tracker()\n",
" segtracker.add_reference(frame, pred_mask)\n",
" else:\n",
" pred_mask = segtracker.track(frame,update_memory=True)\n",
" torch.cuda.empty_cache()\n",
" gc.collect()\n",
" \n",
" save_prediction(pred_mask,output_dir,str(frame_idx)+'.png')\n",
" # masked_frame = draw_mask(frame,pred_mask)\n",
" # masked_pred_list.append(masked_frame)\n",
" # plt.imshow(masked_frame)\n",
" # plt.show() \n",
" \n",
" pred_list.append(pred_mask)\n",
" \n",
" \n",
" print(\"processed frame {}, obj_num {}\".format(frame_idx,segtracker.get_obj_num()),end='\\r')\n",
" frame_idx += 1\n",
" cap.release()\n",
" print('\\nfinished')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save results for visualization"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# draw pred mask on frame and save as a video\n",
"cap = cv2.VideoCapture(io_args['input_video'])\n",
"fps = cap.get(cv2.CAP_PROP_FPS)\n",
"width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n",
"height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n",
"num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
"\n",
"if io_args['input_video'][-3:]=='mp4':\n",
" fourcc = cv2.VideoWriter_fourcc(*\"mp4v\")\n",
"elif io_args['input_video'][-3:] == 'avi':\n",
" fourcc = cv2.VideoWriter_fourcc(*\"MJPG\")\n",
" # fourcc = cv2.VideoWriter_fourcc(*\"XVID\")\n",
"else:\n",
" fourcc = int(cap.get(cv2.CAP_PROP_FOURCC))\n",
"out = cv2.VideoWriter(io_args['output_video'], fourcc, fps, (width, height))\n",
"\n",
"frame_idx = 0\n",
"while cap.isOpened():\n",
" ret, frame = cap.read()\n",
" if not ret:\n",
" break\n",
" frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)\n",
" pred_mask = pred_list[frame_idx]\n",
" masked_frame = draw_mask(frame,pred_mask)\n",
" # masked_frame = masked_pred_list[frame_idx]\n",
" masked_frame = cv2.cvtColor(masked_frame,cv2.COLOR_RGB2BGR)\n",
" out.write(masked_frame)\n",
" print('frame {} writed'.format(frame_idx),end='\\r')\n",
" frame_idx += 1\n",
"out.release()\n",
"cap.release()\n",
"print(\"\\n{} saved\".format(io_args['output_video']))\n",
"print('\\nfinished')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# save colorized masks as a gif\n",
"imageio.mimsave(io_args['output_gif'],pred_list,fps=fps)\n",
"print(\"{} saved\".format(io_args['output_gif']))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"21"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# manually release memory (after cuda out of memory)\n",
"del segtracker\n",
"torch.cuda.empty_cache()\n",
"gc.collect()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.8.5 64-bit ('ldm': conda)",
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"name": "python3"
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"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
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"orig_nbformat": 4,
"vscode": {
"interpreter": {
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|