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140 lines
4.3 KiB
140 lines
4.3 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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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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# left, top, right, bottom
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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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# build the pose model from a config file and a checkpoint file
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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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# test a single image, the resulting box is (top, right, bottom and left)
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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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# keep the person class bounding boxes.
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face_results = process_face_det_results(face_det_results)
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# optional
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return_heatmap = False
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# e.g. use ('backbone', ) to return backbone feature
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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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# show the results
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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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