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import os |
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import warnings |
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from argparse import ArgumentParser |
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from mmpose.apis import (inference_top_down_pose_model, init_pose_model, |
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vis_pose_result) |
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from mmpose.datasets import DatasetInfo |
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try: |
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import face_recognition |
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has_face_det = True |
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except (ImportError, ModuleNotFoundError): |
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has_face_det = False |
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def process_face_det_results(face_det_results): |
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"""Process det results, and return a list of bboxes. |
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:param face_det_results: (top, right, bottom and left) |
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:return: a list of detected bounding boxes (x,y,x,y)-format |
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""" |
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person_results = [] |
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for bbox in face_det_results: |
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person = {} |
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person['bbox'] = [bbox[3], bbox[0], bbox[1], bbox[2]] |
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person_results.append(person) |
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return person_results |
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def main(): |
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"""Visualize the demo images. |
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Using mmdet to detect the human. |
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""" |
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parser = ArgumentParser() |
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parser.add_argument('pose_config', help='Config file for pose') |
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parser.add_argument('pose_checkpoint', help='Checkpoint file for pose') |
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parser.add_argument('--img-root', type=str, default='', help='Image root') |
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parser.add_argument('--img', type=str, default='', help='Image file') |
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parser.add_argument( |
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'--show', |
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action='store_true', |
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default=False, |
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help='whether to show img') |
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parser.add_argument( |
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'--out-img-root', |
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type=str, |
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default='', |
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help='root of the output img file. ' |
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'Default not saving the visualization images.') |
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parser.add_argument( |
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'--device', default='cuda:0', help='Device used for inference') |
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parser.add_argument( |
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'--kpt-thr', type=float, default=0.3, help='Keypoint score threshold') |
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parser.add_argument( |
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'--radius', |
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type=int, |
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default=4, |
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help='Keypoint radius for visualization') |
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parser.add_argument( |
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'--thickness', |
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type=int, |
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default=1, |
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help='Link thickness for visualization') |
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assert has_face_det, 'Please install face_recognition to run the demo. ' \ |
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'"pip install face_recognition", For more details, ' \ |
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'see https://github.com/ageitgey/face_recognition' |
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args = parser.parse_args() |
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assert args.show or (args.out_img_root != '') |
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assert args.img != '' |
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pose_model = init_pose_model( |
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args.pose_config, args.pose_checkpoint, device=args.device.lower()) |
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dataset = pose_model.cfg.data['test']['type'] |
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dataset_info = pose_model.cfg.data['test'].get('dataset_info', None) |
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if dataset_info is None: |
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warnings.warn( |
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'Please set `dataset_info` in the config.' |
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'Check https://github.com/open-mmlab/mmpose/pull/663 for details.', |
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DeprecationWarning) |
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else: |
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dataset_info = DatasetInfo(dataset_info) |
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image_name = os.path.join(args.img_root, args.img) |
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image = face_recognition.load_image_file(image_name) |
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face_det_results = face_recognition.face_locations(image) |
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face_results = process_face_det_results(face_det_results) |
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return_heatmap = False |
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output_layer_names = None |
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pose_results, returned_outputs = inference_top_down_pose_model( |
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pose_model, |
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image_name, |
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face_results, |
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bbox_thr=None, |
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format='xyxy', |
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dataset=dataset, |
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dataset_info=dataset_info, |
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return_heatmap=return_heatmap, |
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outputs=output_layer_names) |
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if args.out_img_root == '': |
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out_file = None |
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else: |
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os.makedirs(args.out_img_root, exist_ok=True) |
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out_file = os.path.join(args.out_img_root, f'vis_{args.img}') |
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vis_pose_result( |
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pose_model, |
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image_name, |
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pose_results, |
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radius=args.radius, |
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thickness=args.thickness, |
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dataset=dataset, |
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dataset_info=dataset_info, |
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kpt_score_thr=args.kpt_thr, |
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show=args.show, |
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out_file=out_file) |
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if __name__ == '__main__': |
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main() |
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