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| # python gradio_demo/barc_demo_v3.py | |
| 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 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 | |
| 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 | |
| def run_bbox_inference(input_image): | |
| model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) | |
| model.eval() | |
| out_path = os.path.join(cfg.paths.ROOT_OUT_PATH, 'gradio_examples', 'test2.png') | |
| img, bbox = detect_object(model=model, img_path_or_img=input_image, confidence=0.5) | |
| fig = plt.figure() # plt.figure(figsize=(20,30)) | |
| plt.imsave(out_path, img) | |
| return img, bbox | |
| def run_barc_inference(input_image, bbox=None): | |
| # load configs | |
| cfg = get_cfg_global_updated() | |
| model_file_complete = os.path.join(cfg.paths.ROOT_CHECKPOINT_PATH, 'barc_complete', 'model_best.pth.tar') | |
| # Select the hardware device to use for inference. | |
| if torch.cuda.is_available() and cfg.device=='cuda': | |
| device = torch.device('cuda', torch.cuda.current_device()) | |
| # torch.backends.cudnn.benchmark = True | |
| else: | |
| device = torch.device('cpu') | |
| path_model_file_complete = os.path.join(cfg.paths.ROOT_CHECKPOINT_PATH, model_file_complete) | |
| # 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 | |
| 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) | |
| state_dict_complete = checkpoint_complete['state_dict'] | |
| complete_model.load_state_dict(state_dict_complete, strict=False) | |
| complete_model = complete_model.to(device) | |
| 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) | |
| input_image_list = [input_image] | |
| if bbox is not None: | |
| input_bbox_list = [bbox] | |
| else: | |
| input_bbox_list = None | |
| 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 | |
| # remark: take ACC_Joints and DATA_INFO from StanExt as this is the training dataset | |
| 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) | |
| mesh = all_results[0]['mesh_posed'] | |
| result_path = os.path.join(save_imgs_path, test_name_list[0] + '_z') | |
| mesh.apply_transform([[-1, 0, 0, 0], | |
| [0, -1, 0, 0], | |
| [0, 0, 1, 1], | |
| [0, 0, 0, 1]]) | |
| mesh.export(file_obj=result_path + '.glb') | |
| result_gltf = result_path + '.glb' | |
| return [result_gltf, result_gltf] | |
| def run_complete_inference(input_image): | |
| output_interm_image, output_interm_bbox = run_bbox_inference(input_image.copy()) | |
| print(output_interm_bbox) | |
| # output_image = run_barc_inference(input_image) | |
| output_image = run_barc_inference(input_image, output_interm_bbox) | |
| return output_image | |
| # demo = gr.Interface(run_barc_inference, gr.Image(), "image") | |
| # demo = gr.Interface(run_complete_inference, gr.Image(), "image") | |
| # 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. While BARC is trained on image crops, this demo uses a pretrained Faster-RCNN in order to get bounding boxes for the dogs. | |
| To see your result you may have to wait a minute or two, please be paitient. | |
| <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} | |
| } | |
| ``` | |
| </details> | |
| ''' | |
| examples = 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'))) | |
| demo = gr.Interface( | |
| fn=run_complete_inference, | |
| description=description, | |
| # inputs=gr.Image(type="filepath", label="Input Image"), | |
| inputs=gr.Image(label="Input Image"), | |
| outputs=[ | |
| gr.Model3D( | |
| clear_color=[0.0, 0.0, 0.0, 0.0], label="3D Model"), | |
| gr.File(label="Download 3D Model") | |
| ], | |
| examples=examples, | |
| thumbnail="barc_thumbnail.png", | |
| allow_flagging="never", | |
| cache_examples=True | |
| ) | |
| demo.launch(share=True) |