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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import os | |
| import warnings | |
| from argparse import ArgumentParser | |
| from mmpose.apis import (inference_top_down_pose_model, init_pose_model, | |
| vis_pose_result) | |
| from mmpose.datasets import DatasetInfo | |
| try: | |
| import face_recognition | |
| has_face_det = True | |
| except (ImportError, ModuleNotFoundError): | |
| has_face_det = False | |
| def process_face_det_results(face_det_results): | |
| """Process det results, and return a list of bboxes. | |
| :param face_det_results: (top, right, bottom and left) | |
| :return: a list of detected bounding boxes (x,y,x,y)-format | |
| """ | |
| person_results = [] | |
| for bbox in face_det_results: | |
| person = {} | |
| # left, top, right, bottom | |
| person['bbox'] = [bbox[3], bbox[0], bbox[1], bbox[2]] | |
| person_results.append(person) | |
| return person_results | |
| def main(): | |
| """Visualize the demo images. | |
| Using mmdet to detect the human. | |
| """ | |
| 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('--img-root', type=str, default='', help='Image root') | |
| parser.add_argument('--img', type=str, default='', help='Image file') | |
| parser.add_argument( | |
| '--show', | |
| action='store_true', | |
| default=False, | |
| help='whether to show img') | |
| parser.add_argument( | |
| '--out-img-root', | |
| type=str, | |
| default='', | |
| help='root of the output img file. ' | |
| 'Default not saving the visualization images.') | |
| parser.add_argument( | |
| '--device', default='cuda:0', help='Device used for inference') | |
| 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') | |
| assert has_face_det, 'Please install face_recognition to run the demo. ' \ | |
| '"pip install face_recognition", For more details, ' \ | |
| 'see https://github.com/ageitgey/face_recognition' | |
| args = parser.parse_args() | |
| assert args.show or (args.out_img_root != '') | |
| assert args.img != '' | |
| # 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) | |
| image_name = os.path.join(args.img_root, args.img) | |
| # test a single image, the resulting box is (top, right, bottom and left) | |
| image = face_recognition.load_image_file(image_name) | |
| face_det_results = face_recognition.face_locations(image) | |
| # keep the person class bounding boxes. | |
| face_results = process_face_det_results(face_det_results) | |
| # optional | |
| return_heatmap = False | |
| # e.g. use ('backbone', ) to return backbone feature | |
| output_layer_names = None | |
| pose_results, returned_outputs = inference_top_down_pose_model( | |
| pose_model, | |
| image_name, | |
| face_results, | |
| bbox_thr=None, | |
| format='xyxy', | |
| dataset=dataset, | |
| dataset_info=dataset_info, | |
| return_heatmap=return_heatmap, | |
| outputs=output_layer_names) | |
| if args.out_img_root == '': | |
| out_file = None | |
| else: | |
| os.makedirs(args.out_img_root, exist_ok=True) | |
| out_file = os.path.join(args.out_img_root, f'vis_{args.img}') | |
| # show the results | |
| vis_pose_result( | |
| pose_model, | |
| image_name, | |
| pose_results, | |
| radius=args.radius, | |
| thickness=args.thickness, | |
| dataset=dataset, | |
| dataset_info=dataset_info, | |
| kpt_score_thr=args.kpt_thr, | |
| show=args.show, | |
| out_file=out_file) | |
| if __name__ == '__main__': | |
| main() | |