gradio_demo/barc_demo_v6.py

# python gradio_demo/barc_demo_v6.py

import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"  
os.environ["CUDA_VISIBLE_DEVICES"]="0"
try:
    # os.system("pip install --upgrade  torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html")
    os.system("pip install --upgrade  torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/cu101/torch_stable.html")
except Exception as e:
    print(e)

import numpy as np
import os
import glob
import torch
from torch.utils.data import DataLoader
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
import torchvision.transforms as T
import cv2
from matplotlib import pyplot as plt
from PIL import Image
import random
from datetime import datetime
import gradio as gr

import sys
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../', 'src'))
from stacked_hourglass.datasets.imgcropslist import ImgCrops
from combined_model.train_main_image_to_3d_withbreedrel import do_visual_epoch
from combined_model.model_shape_v7 import ModelImageTo3d_withshape_withproj
from configs.barc_cfg_defaults import get_cfg_global_updated

random.seed(0)

print(
    "torch: ", torch.__version__,
    "\ntorchvision: ", torchvision.__version__,
)



def get_prediction(model, img_path_or_img, confidence=0.5):
    """
    see https://haochen23.github.io/2020/04/object-detection-faster-rcnn.html#.YsMCm4TP3-g
    get_prediction
        parameters:
        - img_path - path of the input image
        - confidence - threshold value for prediction score
        method:
        - Image is obtained from the image path
        - the image is converted to image tensor using PyTorch's Transforms
        - image is passed through the model to get the predictions
        - class, box coordinates are obtained, but only prediction score > threshold
            are chosen.
    """
    if isinstance(img_path_or_img, str):
        img = Image.open(img_path_or_img).convert('RGB')
    else:
        img = img_path_or_img
    transform = T.Compose([T.ToTensor()])
    img = transform(img)
    pred = model([img])
    # pred_class = [COCO_INSTANCE_CATEGORY_NAMES[i] for i in list(pred[0]['labels'].numpy())]
    pred_class = list(pred[0]['labels'].numpy())
    pred_boxes = [[(int(i[0]), int(i[1])), (int(i[2]), int(i[3]))] for i in list(pred[0]['boxes'].detach().numpy())]
    pred_score = list(pred[0]['scores'].detach().numpy())
    try:
        pred_t = [pred_score.index(x) for x in pred_score if x>confidence][-1]
        pred_boxes = pred_boxes[:pred_t+1]
        pred_class = pred_class[:pred_t+1]
        return pred_boxes, pred_class, pred_score
    except:
        print('no bounding box with a score that is high enough found! -> work on full image')
        return None, None, None


def detect_object(model, img_path_or_img, confidence=0.5, rect_th=2, text_size=0.5, text_th=1):
    """
    see https://haochen23.github.io/2020/04/object-detection-faster-rcnn.html#.YsMCm4TP3-g
    object_detection_api
        parameters:
        - img_path_or_img - path of the input image
        - confidence - threshold value for prediction score
        - rect_th - thickness of bounding box
        - text_size - size of the class label text
        - text_th - thichness of the text
        method:
        - prediction is obtained from get_prediction method
        - for each prediction, bounding box is drawn and text is written 
            with opencv
        - the final image is displayed
    """
    boxes, pred_cls, pred_scores = get_prediction(model, img_path_or_img, confidence)
    if isinstance(img_path_or_img, str):
        img = cv2.imread(img_path_or_img)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    else:
        img = img_path_or_img
    is_first = True
    bbox = None
    if boxes is not None:
        for i in range(len(boxes)):
            cls = pred_cls[i]
            if cls == 18 and bbox is None:
                cv2.rectangle(img, boxes[i][0], boxes[i][1],color=(0, 255, 0), thickness=rect_th)
                # cv2.putText(img, pred_cls[i], boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
                # cv2.putText(img, str(pred_scores[i]), boxes[i][0], cv2.FONT_HERSHEY_SIMPLEX, text_size, (0,255,0),thickness=text_th)
                bbox = boxes[i]
    return img, bbox


# -------------------------------------------------------------------------------------------------------------------- #
model_bbox = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
model_bbox.eval()

def run_bbox_inference(input_image):
    # load configs
    cfg = get_cfg_global_updated()
    out_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples', 'test2.png')
    img, bbox = detect_object(model=model_bbox, img_path_or_img=input_image, confidence=0.5)
    fig = plt.figure()   #  plt.figure(figsize=(20,30))
    plt.imsave(out_path, img)
    plt.close()
    return img, bbox
# -------------------------------------------------------------------------------------------------------------------- #


# -------------------------------------------------------------------------------------------------------------------- #
# load configs
cfg = get_cfg_global_updated()
# Select the hardware device to use for inference.
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('---> device: ' + device)
# disable gradient calculations.
torch.set_grad_enabled(False)
# prepare complete model
complete_model = ModelImageTo3d_withshape_withproj(
    num_stage_comb=cfg.params.NUM_STAGE_COMB, num_stage_heads=cfg.params.NUM_STAGE_HEADS, \
    num_stage_heads_pose=cfg.params.NUM_STAGE_HEADS_POSE, trans_sep=cfg.params.TRANS_SEP, \
    arch=cfg.params.ARCH, n_joints=cfg.params.N_JOINTS, n_classes=cfg.params.N_CLASSES, \
    n_keyp=cfg.params.N_KEYP, n_bones=cfg.params.N_BONES, n_betas=cfg.params.N_BETAS, n_betas_limbs=cfg.params.N_BETAS_LIMBS, \
    n_breeds=cfg.params.N_BREEDS, n_z=cfg.params.N_Z, image_size=cfg.params.IMG_SIZE, \
    silh_no_tail=cfg.params.SILH_NO_TAIL, thr_keyp_sc=cfg.params.KP_THRESHOLD, add_z_to_3d_input=cfg.params.ADD_Z_TO_3D_INPUT,
    n_segbps=cfg.params.N_SEGBPS, add_segbps_to_3d_input=cfg.params.ADD_SEGBPS_TO_3D_INPUT, add_partseg=cfg.params.ADD_PARTSEG, n_partseg=cfg.params.N_PARTSEG, \
    fix_flength=cfg.params.FIX_FLENGTH, structure_z_to_betas=cfg.params.STRUCTURE_Z_TO_B, structure_pose_net=cfg.params.STRUCTURE_POSE_NET,
    nf_version=cfg.params.NF_VERSION) 
# load trained model
path_model_file_complete = os.path.join(cfg.paths.ROOT_CHECKPOINT_PATH,  'barc_complete', 'model_best.pth.tar') 
print(path_model_file_complete)
assert os.path.isfile(path_model_file_complete)
print('Loading model weights from file: {}'.format(path_model_file_complete))
checkpoint_complete = torch.load(path_model_file_complete, map_location=device)
state_dict_complete = checkpoint_complete['state_dict']
complete_model.load_state_dict(state_dict_complete, strict=False)        
complete_model = complete_model.to(device)
# create path for output files
save_imgs_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples')
if not os.path.exists(save_imgs_path):
    os.makedirs(save_imgs_path)

def run_barc_inference(input_image, bbox=None):
    input_image_list = [input_image]
    if bbox is not None:
        input_bbox_list = [bbox]
    else:
        input_bbox_list = None
    # prepare data loader
    val_dataset = ImgCrops(image_list=input_image_list, bbox_list=input_bbox_list, dataset_mode='complete')
    test_name_list = val_dataset.test_name_list
    val_loader = DataLoader(val_dataset, batch_size=1, shuffle=False,
                            num_workers=0, pin_memory=True, drop_last=False)   
    # run visual evaluation
    all_results = do_visual_epoch(val_loader, complete_model, device,
                        ImgCrops.DATA_INFO,
                        weight_dict=None,
                        acc_joints=ImgCrops.ACC_JOINTS,
                        save_imgs_path=None, # save_imgs_path,
                        metrics='all', 
                        test_name_list=test_name_list,
                        render_all=cfg.params.RENDER_ALL,
                        pck_thresh=cfg.params.PCK_THRESH, 
                        return_results=True)
    # prepare output mesh
    mesh = all_results[0]['mesh_posed']
    mesh.apply_transform([[-1, 0, 0, 0],
                            [0, -1, 0, 0],
                            [0, 0, 1, 1],
                            [0, 0, 0, 1]])
    result_path = os.path.join(save_imgs_path, test_name_list[0] + '_z')
    mesh.export(file_obj=result_path + '.glb')
    result_gltf = result_path + '.glb'
    return result_gltf
# -------------------------------------------------------------------------------------------------------------------- #


total_count = 0

def run_complete_inference(img_path_or_img, crop_choice):
    now = datetime.now()
    dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
    global total_count
    total_count += 1
    print(dt_string + ' total count: ' + str(total_count))
    # depending on crop_choice: run faster r-cnn or take the input image directly
    if crop_choice == "input image is cropped":
        if isinstance(img_path_or_img, str):
            img = cv2.imread(img_path_or_img)
            output_interm_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        else:
            output_interm_image = img_path_or_img
        output_interm_bbox = None
    else:
        output_interm_image, output_interm_bbox = run_bbox_inference(img_path_or_img.copy())
    # run barc inference
    result_gltf = run_barc_inference(img_path_or_img, output_interm_bbox)
    # add white border to image for nicer alignment
    output_interm_image_vis = np.concatenate((255*np.ones_like(output_interm_image), output_interm_image, 255*np.ones_like(output_interm_image)), axis=1)
    return [result_gltf, result_gltf, output_interm_image_vis]




########################################################################################################################

# see: https://huggingface.co/spaces/radames/PIFu-Clothed-Human-Digitization/blob/main/PIFu/spaces.py

description = '''
# BARC

#### Project Page
* https://barc.is.tue.mpg.de/

#### Description
This is a demo for BARC (*B*reed *A*ugmented *R*egression using *C*lassification). 
You can either submit a cropped image or choose the option to run a pretrained Faster R-CNN in order to obtain a bounding box.  
Please have a look at the examples below.
<details>

<summary>More</summary>

#### Citation

```
@inproceedings{BARC:2022,
    title = {BARC}: Learning to Regress {3D} Dog Shape from Images by Exploiting Breed Information,
    author = {Rueegg, Nadine and Zuffi, Silvia and Schindler, Konrad and Black, Michael J.},
    booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
    year = {2022}
}
```

#### Image Sources (Examples)
* Stanford extra image dataset
* Images from google search engine
    * https://www.dogtrainingnation.com/wp-content/uploads/2015/02/keep-dog-training-sessions-short.jpg
    * https://thumbs.dreamstime.com/b/hund-und-seine-neue-hundeh%C3%BCtte-36757551.jpg
    * https://www.mydearwhippet.com/wp-content/uploads/2021/04/whippet-temperament-2.jpg
    * https://media.istockphoto.com/photos/ibizan-hound-at-the-shore-in-winter-picture-id1092705644?k=20&m=1092705644&s=612x612&w=0&h=ppwg92s9jI8GWnk22SOR_DWWNP8b2IUmLXSQmVey5Ss=


</details>
'''






example_images = sorted(glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.jpg')) + glob.glob(os.path.join(os.path.dirname(__file__), '../', 'datasets', 'test_image_crops', '*.png')))  
random.shuffle(example_images)
examples = []
for img in example_images:
    if os.path.basename(img)[:2] == 'z_':
        examples.append([img, "use Faster R-CNN to get a bounding box"])
    else:
        examples.append([img, "input image is cropped"])

demo = gr.Interface(
    fn=run_complete_inference,
    description=description,
    # inputs=gr.Image(type="filepath", label="Input Image"),
    inputs=[gr.Image(label="Input Image"),
        gr.Radio(["input image is cropped", "use Faster R-CNN to get a bounding box"], value="use Faster R-CNN to get a bounding box", label="Crop Choice"),
    ],
    outputs=[
        gr.Model3D(
            clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
        gr.File(label="Download 3D Model"),
        gr.Image(label="Bounding Box (Faster R-CNN prediction)"),

    ],
    examples=examples,
    thumbnail="barc_thumbnail.png",
    allow_flagging="never",
    cache_examples=False,       # True
    examples_per_page=14,
)

demo.launch()