120 lines
3.6 KiB
Python
120 lines
3.6 KiB
Python
import os
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from argparse import ArgumentParser
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import cv2
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from mmpose.apis import (inference_bottom_up_pose_model, init_pose_model,
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vis_pose_result)
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from mmcv import Config, DictAction
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from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
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from mmcv.runner import get_dist_info, init_dist, load_checkpoint
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from models import build_posenet
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def main():
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"""Visualize the demo images."""
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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('--video-path', type=str, help='Video path')
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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 visualizations.')
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parser.add_argument(
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'--out-video-root',
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default='',
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help='Root of the output video file. '
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'Default not saving the visualization video.')
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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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args = parser.parse_args()
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assert args.show or (args.out_video_root != '')
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cfg = Config.fromfile(args.pose_config)
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#if args.cfg_options is not None:
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# cfg.merge_from_dict(args.cfg_options)
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# set cudnn_benchmark
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if cfg.get('cudnn_benchmark', False):
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torch.backends.cudnn.benchmark = True
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cfg.model.pretrained = None
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cfg.data.test.test_mode = True
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model = build_posenet(cfg.model)
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fp16_cfg = cfg.get('fp16', None)
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if fp16_cfg is not None:
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wrap_fp16_model(model)
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load_checkpoint(model, args.pose_checkpoint, map_location='cpu')
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#model = MMDataParallel(model, device_ids=[0])
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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 = cfg.data['test']['type']
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#assert (dataset == 'BottomUpCocoDataset')
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cap = cv2.VideoCapture(args.video_path)
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if args.out_video_root == '':
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save_out_video = False
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else:
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os.makedirs(args.out_video_root, exist_ok=True)
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save_out_video = True
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if save_out_video:
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fps = cap.get(cv2.CAP_PROP_FPS)
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size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
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int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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videoWriter = cv2.VideoWriter(
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os.path.join(args.out_video_root,
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f'vis_{os.path.basename(args.video_path)}'), fourcc,
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fps, size)
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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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while (cap.isOpened()):
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flag, img = cap.read()
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if not flag:
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break
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pose_results, returned_outputs = inference_bottom_up_pose_model(
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model,
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img,
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return_heatmap=return_heatmap,
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outputs=output_layer_names)
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# show the results
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vis_img = vis_pose_result(
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pose_model,
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img,
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pose_results,
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dataset=dataset,
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kpt_score_thr=args.kpt_thr,
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show=False)
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if args.show:
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cv2.imshow('Image', vis_img)
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if save_out_video:
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videoWriter.write(vis_img)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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if save_out_video:
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videoWriter.release()
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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main()
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