def test_bottom_up_pose_tracking_demo(): # COCO demo # build the pose model from a config file and a checkpoint file pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/associative_embedding/' 'coco/res50_coco_512x512.py', None, device='cpu') image_name = 'tests/data/coco/000000000785.jpg' pose_results, _ = inference_bottom_up_pose_model(pose_model, image_name) pose_results, next_id = get_track_id(pose_results, [], next_id=0) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='BottomUpCocoDataset') pose_results_last = pose_results # oks pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id=next_id, use_oks=True) pose_results_last = pose_results # one_euro pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id=next_id, use_one_euro=True)
def test_bottom_up_pose_tracking_demo(): # COCO demo # build the pose model from a config file and a checkpoint file pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/associative_embedding/' 'coco/res50_coco_512x512.py', None, device='cpu') image_name = 'tests/data/coco/000000000785.jpg' dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info']) pose_results, _ = inference_bottom_up_pose_model(pose_model, image_name, dataset_info=dataset_info) pose_results, next_id = get_track_id(pose_results, [], next_id=0) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset_info=dataset_info) pose_results_last = pose_results # oks pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id=next_id, use_oks=True) pose_results_last = pose_results # one_euro (will be deprecated) with pytest.deprecated_call(): pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id=next_id, use_one_euro=True)
def test_pose_tracking_demo(): # COCO demo # build the pose model from a config file and a checkpoint file pose_model = init_pose_model( 'configs/top_down/resnet/coco/res50_coco_256x192.py', None, device='cpu') image_name = 'tests/data/coco/000000000785.jpg' # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model(pose_model, image_name, [[50, 50, 50, 100]], format='xywh') pose_results, next_id = get_track_id(pose_results, [], next_id=0) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results) pose_results_last = pose_results # AIC demo pose_model = init_pose_model( 'configs/top_down/resnet/aic/res50_aic_256x192.py', None, device='cpu') image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg' # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model( pose_model, image_name, [[50, 50, 50, 100]], format='xywh', dataset='TopDownAicDataset') pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='TopDownAicDataset') # OneHand10K demo # build the pose model from a config file and a checkpoint file pose_model = init_pose_model( 'configs/hand/resnet/onehand10k/res50_onehand10k_256x256.py', None, device='cpu') image_name = 'tests/data/onehand10k/9.jpg' # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model( pose_model, image_name, [[10, 10, 30, 30]], format='xywh', dataset='OneHand10KDataset') pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='OneHand10KDataset') # InterHand2D demo pose_model = init_pose_model( 'configs/hand/resnet/interhand2d/res50_interhand2d_all_256x256.py', None, device='cpu') image_name = 'tests/data/interhand2d/image2017.jpg' # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model( pose_model, image_name, [[50, 50, 0, 0]], format='xywh', dataset='InterHand2DDataset') pose_results, next_id = get_track_id(pose_results, [], next_id=0) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='InterHand2DDataset') pose_results_last = pose_results # MPII demo pose_model = init_pose_model( 'configs/top_down/resnet/mpii/res50_mpii_256x256.py', None, device='cpu') image_name = 'tests/data/mpii/004645041.jpg' # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model( pose_model, image_name, [[50, 50, 0, 0]], format='xywh', dataset='TopDownMpiiDataset') pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='TopDownMpiiDataset') with pytest.raises(NotImplementedError): vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='test')
def main(): """Visualize the demo images. Using mmdet to detect the human. """ parser = ArgumentParser() parser.add_argument('det_config', help='Config file for detection') parser.add_argument('det_checkpoint', help='Checkpoint file for detection') parser.add_argument('pose_config', help='Config file for pose') parser.add_argument('pose_checkpoint', help='Checkpoint file for pose') parser.add_argument('--video-path', type=str, help='Video path') parser.add_argument('--show', action='store_true', default=False, help='whether to show visualizations.') parser.add_argument('--out-video-root', default='', help='Root of the output video file. ' 'Default not saving the visualization video.') parser.add_argument('--device', default='cuda:0', help='Device used for inference') parser.add_argument('--bbox-thr', type=float, default=0.3, help='Bounding box score threshold') parser.add_argument('--kpt-thr', type=float, default=0.3, help='Keypoint score threshold') parser.add_argument('--iou-thr', type=float, default=0.3, help='IoU score threshold') assert has_mmdet, 'Please install mmdet to run the demo.' args = parser.parse_args() assert args.show or (args.out_video_root != '') assert args.det_config is not None assert args.det_checkpoint is not None det_model = init_detector(args.det_config, args.det_checkpoint, device=args.device.lower()) # build the pose model from a config file and a checkpoint file pose_model = init_pose_model(args.pose_config, args.pose_checkpoint, device=args.device.lower()) dataset = pose_model.cfg.data['test']['type'] cap = cv2.VideoCapture(args.video_path) if args.out_video_root == '': save_out_video = False else: os.makedirs(args.out_video_root, exist_ok=True) save_out_video = True if save_out_video: fps = cap.get(cv2.CAP_PROP_FPS) size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter( os.path.join(args.out_video_root, f'vis_{os.path.basename(args.video_path)}'), fourcc, fps, size) # optional return_heatmap = False # e.g. use ('backbone', ) to return backbone feature output_layer_names = None next_id = 0 pose_results = [] while (cap.isOpened()): pose_results_last = pose_results flag, img = cap.read() if not flag: break # test a single image, the resulting box is (x1, y1, x2, y2) mmdet_results = inference_detector(det_model, img) # keep the person class bounding boxes. person_results = process_mmdet_results(mmdet_results) # test a single image, with a list of bboxes. pose_results, returned_outputs = inference_top_down_pose_model( pose_model, img, person_results, bbox_thr=args.bbox_thr, format='xyxy', dataset=dataset, return_heatmap=return_heatmap, outputs=output_layer_names) # get track id for each person instance pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id, iou_thr=args.iou_thr) # show the results vis_img = vis_pose_tracking_result(pose_model, img, pose_results, dataset=dataset, kpt_score_thr=args.kpt_thr, show=False) if args.show: cv2.imshow('Image', vis_img) if save_out_video: videoWriter.write(vis_img) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() if save_out_video: videoWriter.release() cv2.destroyAllWindows()
def test_top_down_pose_tracking_demo(): # COCO demo # build the pose model from a config file and a checkpoint file pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/' 'coco/res50_coco_256x192.py', None, device='cpu') image_name = 'tests/data/coco/000000000785.jpg' dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info']) person_result = [{'bbox': [50, 50, 50, 100]}] # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model(pose_model, image_name, person_result, format='xywh', dataset_info=dataset_info) pose_results, next_id = get_track_id(pose_results, [], next_id=0) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset_info=dataset_info) pose_results_last = pose_results # AIC demo pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/' 'aic/res50_aic_256x192.py', None, device='cpu') image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg' dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info']) # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model(pose_model, image_name, person_result, format='xywh', dataset_info=dataset_info) pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) for pose_result in pose_results: del pose_result['bbox'] pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset_info=dataset_info) # OneHand10K demo # build the pose model from a config file and a checkpoint file pose_model = init_pose_model( 'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/' 'onehand10k/res50_onehand10k_256x256.py', None, device='cpu') image_name = 'tests/data/onehand10k/9.jpg' dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info']) # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model( pose_model, image_name, [{ 'bbox': [10, 10, 30, 30] }], format='xywh', dataset_info=dataset_info) pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset_info=dataset_info) # InterHand2D demo pose_model = init_pose_model( 'configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/' 'interhand2d/res50_interhand2d_all_256x256.py', None, device='cpu') image_name = 'tests/data/interhand2.6m/image2017.jpg' dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info']) # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model(pose_model, image_name, [{ 'bbox': [50, 50, 0, 0] }], format='xywh', dataset_info=dataset_info) pose_results, next_id = get_track_id(pose_results, [], next_id=0) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset_info=dataset_info) pose_results_last = pose_results # MPII demo pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/' 'mpii/res50_mpii_256x256.py', None, device='cpu') image_name = 'tests/data/mpii/004645041.jpg' dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info']) # test a single image, with a list of bboxes. pose_results, _ = inference_top_down_pose_model(pose_model, image_name, [{ 'bbox': [50, 50, 0, 0] }], format='xywh', dataset_info=dataset_info) pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id) # show the results vis_pose_tracking_result(pose_model, image_name, pose_results, dataset_info=dataset_info)
def main(): """Visualize the demo images. Using mmdet to detect the human. """ parser = ArgumentParser() parser.add_argument('det_config', help='Config file for detection') parser.add_argument('det_checkpoint', help='Checkpoint file for detection') parser.add_argument('pose_config', help='Config file for pose') parser.add_argument('pose_checkpoint', help='Checkpoint file for pose') parser.add_argument('--video-path', type=str, help='Video path') parser.add_argument('--show', action='store_true', default=False, help='whether to show visualizations.') parser.add_argument('--out-video-root', default='', help='Root of the output video file. ' 'Default not saving the visualization video.') parser.add_argument('--device', default='cuda:0', help='Device used for inference') parser.add_argument('--det-cat-id', type=int, default=1, help='Category id for bounding box detection model') parser.add_argument('--bbox-thr', type=float, default=0.3, help='Bounding box score threshold') parser.add_argument('--kpt-thr', type=float, default=0.3, help='Keypoint score threshold') parser.add_argument('--use-oks-tracking', action='store_true', help='Using OKS tracking') parser.add_argument('--tracking-thr', type=float, default=0.3, help='Tracking threshold') parser.add_argument('--euro', action='store_true', help='Using One_Euro_Filter for smoothing') parser.add_argument('--radius', type=int, default=4, help='Keypoint radius for visualization') parser.add_argument('--thickness', type=int, default=1, help='Link thickness for visualization') assert has_mmdet, 'Please install mmdet to run the demo.' args = parser.parse_args() assert args.show or (args.out_video_root != '') assert args.det_config is not None assert args.det_checkpoint is not None det_model = init_detector(args.det_config, args.det_checkpoint, device=args.device.lower()) # build the pose model from a config file and a checkpoint file pose_model = init_pose_model(args.pose_config, args.pose_checkpoint, device=args.device.lower()) dataset = pose_model.cfg.data['test']['type'] dataset_info = pose_model.cfg.data['test'].get('dataset_info', None) if dataset_info is None: warnings.warn( 'Please set `dataset_info` in the config.' 'Check https://github.com/open-mmlab/mmpose/pull/663 for details.', DeprecationWarning) else: dataset_info = DatasetInfo(dataset_info) cap = cv2.VideoCapture(args.video_path) fps = None assert cap.isOpened(), f'Faild to load video file {args.video_path}' if args.out_video_root == '': save_out_video = False else: os.makedirs(args.out_video_root, exist_ok=True) save_out_video = True if save_out_video: fps = cap.get(cv2.CAP_PROP_FPS) size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter( os.path.join(args.out_video_root, f'vis_{os.path.basename(args.video_path)}'), fourcc, fps, size) # optional return_heatmap = False # e.g. use ('backbone', ) to return backbone feature output_layer_names = None next_id = 0 pose_results = [] while (cap.isOpened()): pose_results_last = pose_results flag, img = cap.read() if not flag: break # test a single image, the resulting box is (x1, y1, x2, y2) mmdet_results = inference_detector(det_model, img) # keep the person class bounding boxes. person_results = process_mmdet_results(mmdet_results, args.det_cat_id) # test a single image, with a list of bboxes. pose_results, returned_outputs = inference_top_down_pose_model( pose_model, img, person_results, bbox_thr=args.bbox_thr, format='xyxy', dataset=dataset, dataset_info=dataset_info, return_heatmap=return_heatmap, outputs=output_layer_names) # get track id for each person instance pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id, use_oks=args.use_oks_tracking, tracking_thr=args.tracking_thr, use_one_euro=args.euro, fps=fps) # show the results vis_img = vis_pose_tracking_result(pose_model, img, pose_results, radius=args.radius, thickness=args.thickness, dataset=dataset, dataset_info=dataset_info, kpt_score_thr=args.kpt_thr, show=False) if args.show: cv2.imshow('Image', vis_img) if save_out_video: videoWriter.write(vis_img) if args.show and cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() if save_out_video: videoWriter.release() if args.show: cv2.destroyAllWindows()
def main(): """Visualize the demo images.""" parser = ArgumentParser() parser.add_argument('pose_config', help='Config file for pose') parser.add_argument('pose_checkpoint', help='Checkpoint file for pose') parser.add_argument('--video-path', type=str, help='Video path') parser.add_argument('--show', action='store_true', default=False, help='whether to show visualizations.') parser.add_argument('--out-video-root', default='', help='Root of the output video file. ' 'Default not saving the visualization video.') parser.add_argument('--device', default='cuda:0', help='Device used for inference') parser.add_argument('--kpt-thr', type=float, default=0.5, help='Keypoint score threshold') parser.add_argument('--pose-nms-thr', type=float, default=0.9, help='OKS threshold for pose NMS') parser.add_argument('--use-oks-tracking', action='store_true', help='Using OKS tracking') parser.add_argument('--tracking-thr', type=float, default=0.3, help='Tracking threshold') parser.add_argument('--euro', action='store_true', help='Using One_Euro_Filter for smoothing') parser.add_argument('--radius', type=int, default=4, help='Keypoint radius for visualization') parser.add_argument('--thickness', type=int, default=1, help='Link thickness for visualization') args = parser.parse_args() assert args.show or (args.out_video_root != '') # build the pose model from a config file and a checkpoint file pose_model = init_pose_model(args.pose_config, args.pose_checkpoint, device=args.device.lower()) dataset = pose_model.cfg.data['test']['type'] assert (dataset == 'BottomUpCocoDataset') cap = cv2.VideoCapture(args.video_path) fps = None assert cap.isOpened(), f'Faild to load video file {args.video_path}' if args.out_video_root == '': save_out_video = False else: os.makedirs(args.out_video_root, exist_ok=True) save_out_video = True if save_out_video: fps = cap.get(cv2.CAP_PROP_FPS) size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter( os.path.join(args.out_video_root, f'vis_{os.path.basename(args.video_path)}'), fourcc, fps, size) # optional return_heatmap = False # e.g. use ('backbone', ) to return backbone feature output_layer_names = None next_id = 0 pose_results = [] while (cap.isOpened()): flag, img = cap.read() if not flag: break pose_results_last = pose_results pose_results, returned_outputs = inference_bottom_up_pose_model( pose_model, img, pose_nms_thr=args.pose_nms_thr, return_heatmap=return_heatmap, outputs=output_layer_names) # get track id for each person instance pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id, use_oks=args.use_oks_tracking, tracking_thr=args.tracking_thr, use_one_euro=args.euro, fps=fps) # show the results vis_img = vis_pose_tracking_result(pose_model, img, pose_results, radius=args.radius, thickness=args.thickness, dataset=dataset, kpt_score_thr=args.kpt_thr, show=False) if args.show: cv2.imshow('Image', vis_img) if save_out_video: videoWriter.write(vis_img) if cv2.waitKey(1) & 0xFF == ord('q'): break cap.release() if save_out_video: videoWriter.release() cv2.destroyAllWindows()
def main(): """Visualize the demo images.""" parser = ArgumentParser() parser.add_argument('pose_config', help='Config file for pose') parser.add_argument('pose_checkpoint', help='Checkpoint file for pose') parser.add_argument('--video-path', type=str, help='Video path') parser.add_argument('--show', action='store_true', default=False, help='whether to show visualizations.') parser.add_argument('--out-video-root', default='', help='Root of the output video file. ' 'Default not saving the visualization video.') parser.add_argument('--device', default='cuda:0', help='Device used for inference') parser.add_argument('--kpt-thr', type=float, default=0.5, help='Keypoint score threshold') parser.add_argument('--pose-nms-thr', type=float, default=0.9, help='OKS threshold for pose NMS') parser.add_argument('--use-oks-tracking', action='store_true', help='Using OKS tracking') parser.add_argument('--tracking-thr', type=float, default=0.3, help='Tracking threshold') parser.add_argument( '--euro', action='store_true', help='(Deprecated, please use --smooth and --smooth-filter-cfg) ' 'Using One_Euro_Filter for smoothing.') parser.add_argument( '--smooth', action='store_true', help='Apply a temporal filter to smooth the pose estimation results. ' 'See also --smooth-filter-cfg.') parser.add_argument( '--smooth-filter-cfg', type=str, default='configs/_base_/filters/one_euro.py', help='Config file of the filter to smooth the pose estimation ' 'results. See also --smooth.') parser.add_argument('--radius', type=int, default=4, help='Keypoint radius for visualization') parser.add_argument('--thickness', type=int, default=1, help='Link thickness for visualization') args = parser.parse_args() assert args.show or (args.out_video_root != '') # build the pose model from a config file and a checkpoint file pose_model = init_pose_model(args.pose_config, args.pose_checkpoint, device=args.device.lower()) dataset = pose_model.cfg.data['test']['type'] dataset_info = pose_model.cfg.data['test'].get('dataset_info', None) if dataset_info is None: warnings.warn( 'Please set `dataset_info` in the config.' 'Check https://github.com/open-mmlab/mmpose/pull/663 for details.', DeprecationWarning) assert (dataset == 'BottomUpCocoDataset') else: dataset_info = DatasetInfo(dataset_info) video = mmcv.VideoReader(args.video_path) assert video.opened, f'Faild to load video file {args.video_path}' if args.out_video_root == '': save_out_video = False else: os.makedirs(args.out_video_root, exist_ok=True) save_out_video = True if save_out_video: fps = video.fps size = (video.width, video.height) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter( os.path.join(args.out_video_root, f'vis_{os.path.basename(args.video_path)}'), fourcc, fps, size) # optional return_heatmap = False # build pose smoother for temporal refinement if args.euro: warnings.warn( 'Argument --euro will be deprecated in the future. ' 'Please use --smooth to enable temporal smoothing, and ' '--smooth-filter-cfg to set the filter config.', DeprecationWarning) smoother = Smoother(filter_cfg='configs/_base_/filters/one_euro.py', keypoint_dim=2) elif args.smooth: smoother = Smoother(filter_cfg=args.smooth_filter_cfg, keypoint_dim=2) else: smoother = None # e.g. use ('backbone', ) to return backbone feature output_layer_names = None next_id = 0 pose_results = [] for cur_frame in mmcv.track_iter_progress(video): pose_results_last = pose_results pose_results, _ = inference_bottom_up_pose_model( pose_model, cur_frame, dataset=dataset, dataset_info=dataset_info, pose_nms_thr=args.pose_nms_thr, return_heatmap=return_heatmap, outputs=output_layer_names) # get track id for each person instance pose_results, next_id = get_track_id(pose_results, pose_results_last, next_id, use_oks=args.use_oks_tracking, tracking_thr=args.tracking_thr, use_one_euro=args.euro, fps=fps) # post-process the pose results with smoother if smoother: pose_results = smoother.smooth(pose_results) # show the results vis_frame = vis_pose_tracking_result(pose_model, cur_frame, pose_results, radius=args.radius, thickness=args.thickness, dataset=dataset, dataset_info=dataset_info, kpt_score_thr=args.kpt_thr, show=False) if args.show: cv2.imshow('Image', vis_frame) if save_out_video: videoWriter.write(vis_frame) if args.show and cv2.waitKey(1) & 0xFF == ord('q'): break if save_out_video: videoWriter.release() if args.show: cv2.destroyAllWindows()
def test_inference_without_dataset_info(): # Top down pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/' 'coco/res50_coco_256x192.py', None, device='cpu') if 'dataset_info' in pose_model.cfg: _ = pose_model.cfg.pop('dataset_info') image_name = 'tests/data/coco/000000000785.jpg' person_result = [] person_result.append({'bbox': [50, 50, 50, 100]}) with pytest.warns(DeprecationWarning): pose_results, _ = inference_top_down_pose_model(pose_model, image_name, person_result, format='xywh') with pytest.warns(DeprecationWarning): vis_pose_result(pose_model, image_name, pose_results) with pytest.raises(NotImplementedError): with pytest.warns(DeprecationWarning): pose_results, _ = inference_top_down_pose_model(pose_model, image_name, person_result, format='xywh', dataset='test') # Bottom up pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/associative_embedding/' 'coco/res50_coco_512x512.py', None, device='cpu') if 'dataset_info' in pose_model.cfg: _ = pose_model.cfg.pop('dataset_info') image_name = 'tests/data/coco/000000000785.jpg' with pytest.warns(DeprecationWarning): pose_results, _ = inference_bottom_up_pose_model( pose_model, image_name) with pytest.warns(DeprecationWarning): vis_pose_result(pose_model, image_name, pose_results) # Top down tracking pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/' 'coco/res50_coco_256x192.py', None, device='cpu') if 'dataset_info' in pose_model.cfg: _ = pose_model.cfg.pop('dataset_info') image_name = 'tests/data/coco/000000000785.jpg' person_result = [{'bbox': [50, 50, 50, 100]}] with pytest.warns(DeprecationWarning): pose_results, _ = inference_top_down_pose_model(pose_model, image_name, person_result, format='xywh') pose_results, _ = get_track_id(pose_results, [], next_id=0) with pytest.warns(DeprecationWarning): vis_pose_tracking_result(pose_model, image_name, pose_results) with pytest.raises(NotImplementedError): with pytest.warns(DeprecationWarning): vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='test') # Bottom up tracking pose_model = init_pose_model( 'configs/body/2d_kpt_sview_rgb_img/associative_embedding/' 'coco/res50_coco_512x512.py', None, device='cpu') if 'dataset_info' in pose_model.cfg: _ = pose_model.cfg.pop('dataset_info') image_name = 'tests/data/coco/000000000785.jpg' with pytest.warns(DeprecationWarning): pose_results, _ = inference_bottom_up_pose_model( pose_model, image_name) pose_results, next_id = get_track_id(pose_results, [], next_id=0) with pytest.warns(DeprecationWarning): vis_pose_tracking_result(pose_model, image_name, pose_results, dataset='BottomUpCocoDataset') # Pose lifting pose_model = init_pose_model( 'configs/body/3d_kpt_sview_rgb_img/pose_lift/' 'h36m/simplebaseline3d_h36m.py', None, device='cpu') pose_det_result = { 'keypoints': np.zeros((17, 3)), 'bbox': [50, 50, 50, 50], 'track_id': 0, 'image_name': 'tests/data/h36m/S1_Directions_1.54138969_000001.jpg', } if 'dataset_info' in pose_model.cfg: _ = pose_model.cfg.pop('dataset_info') pose_results_2d = [[pose_det_result]] dataset = pose_model.cfg.data['test']['type'] pose_results_2d = extract_pose_sequence(pose_results_2d, frame_idx=0, causal=False, seq_len=1, step=1) with pytest.warns(DeprecationWarning): _ = inference_pose_lifter_model(pose_model, pose_results_2d, dataset, with_track_id=False) with pytest.warns(DeprecationWarning): pose_lift_results = inference_pose_lifter_model(pose_model, pose_results_2d, dataset, with_track_id=True) for res in pose_lift_results: res['title'] = 'title' with pytest.warns(DeprecationWarning): vis_3d_pose_result(pose_model, pose_lift_results, img=pose_results_2d[0][0]['image_name'], dataset=dataset) with pytest.raises(NotImplementedError): with pytest.warns(DeprecationWarning): _ = inference_pose_lifter_model(pose_model, pose_results_2d, dataset='test')
def main(): """Visualize the demo images. Using mmdet to detect the human. """ parser = ArgumentParser() parser.add_argument('tracking_config', help='Config file for tracking') parser.add_argument('pose_config', help='Config file for pose') parser.add_argument('pose_checkpoint', help='Checkpoint file for pose') parser.add_argument('--video-path', type=str, help='Video path') parser.add_argument('--show', action='store_true', default=False, help='whether to show visualizations.') parser.add_argument('--out-video-root', default='', help='Root of the output video file. ' 'Default not saving the visualization video.') parser.add_argument('--device', default='cuda:0', help='Device used for inference') parser.add_argument('--bbox-thr', type=float, default=0.3, help='Bounding box score threshold') parser.add_argument('--kpt-thr', type=float, default=0.3, help='Keypoint score threshold') parser.add_argument('--radius', type=int, default=4, help='Keypoint radius for visualization') parser.add_argument('--thickness', type=int, default=1, help='Link thickness for visualization') parser.add_argument( '--smooth', action='store_true', help='Apply a temporal filter to smooth the pose estimation results. ' 'See also --smooth-filter-cfg.') parser.add_argument( '--smooth-filter-cfg', type=str, default='configs/_base_/filters/one_euro.py', help='Config file of the filter to smooth the pose estimation ' 'results. See also --smooth.') parser.add_argument( '--use-multi-frames', action='store_true', default=False, help='whether to use multi frames for inference in the pose' 'estimation stage. Default: False.') parser.add_argument( '--online', action='store_true', default=False, help='inference mode. If set to True, can not use future frame' 'information when using multi frames for inference in the pose' 'estimation stage. Default: False.') assert has_mmtrack, 'Please install mmtrack to run the demo.' args = parser.parse_args() assert args.show or (args.out_video_root != '') assert args.tracking_config is not None print('Initializing model...') tracking_model = init_tracking_model(args.tracking_config, None, device=args.device.lower()) # build the pose model from a config file and a checkpoint file pose_model = init_pose_model(args.pose_config, args.pose_checkpoint, device=args.device.lower()) dataset = pose_model.cfg.data['test']['type'] dataset_info = pose_model.cfg.data['test'].get('dataset_info', None) if dataset_info is None: warnings.warn( 'Please set `dataset_info` in the config.' 'Check https://github.com/open-mmlab/mmpose/pull/663 for details.', DeprecationWarning) else: dataset_info = DatasetInfo(dataset_info) # read video video = mmcv.VideoReader(args.video_path) assert video.opened, f'Faild to load video file {args.video_path}' if args.out_video_root == '': save_out_video = False else: os.makedirs(args.out_video_root, exist_ok=True) save_out_video = True if save_out_video: fps = video.fps size = (video.width, video.height) fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter( os.path.join(args.out_video_root, f'vis_{os.path.basename(args.video_path)}'), fourcc, fps, size) # frame index offsets for inference, used in multi-frame inference setting if args.use_multi_frames: assert 'frame_indices_test' in pose_model.cfg.data.test.data_cfg indices = pose_model.cfg.data.test.data_cfg['frame_indices_test'] # build pose smoother for temporal refinement if args.smooth: smoother = Smoother(filter_cfg=args.smooth_filter_cfg, keypoint_dim=2) else: smoother = None # whether to return heatmap, optional return_heatmap = False # return the output of some desired layers, # e.g. use ('backbone', ) to return backbone feature output_layer_names = None print('Running inference...') for frame_id, cur_frame in enumerate(mmcv.track_iter_progress(video)): if args.use_multi_frames: frames = collect_multi_frames(video, frame_id, indices, args.online) mmtracking_results = inference_mot(tracking_model, cur_frame, frame_id=frame_id) # keep the person class bounding boxes. person_results = process_mmtracking_results(mmtracking_results) # test a single image, with a list of bboxes. pose_results, returned_outputs = inference_top_down_pose_model( pose_model, frames if args.use_multi_frames else cur_frame, person_results, bbox_thr=args.bbox_thr, format='xyxy', dataset=dataset, dataset_info=dataset_info, return_heatmap=return_heatmap, outputs=output_layer_names) if smoother: pose_results = smoother.smooth(pose_results) # show the results vis_frame = vis_pose_tracking_result(pose_model, cur_frame, pose_results, radius=args.radius, thickness=args.thickness, dataset=dataset, dataset_info=dataset_info, kpt_score_thr=args.kpt_thr, show=False) if args.show: cv2.imshow('Frame', vis_frame) if save_out_video: videoWriter.write(vis_frame) if args.show and cv2.waitKey(1) & 0xFF == ord('q'): break if save_out_video: videoWriter.release() if args.show: cv2.destroyAllWindows()