text: # Copyright (c) OpenMMLab. All rights reserved.
  import os
  from argparse import ArgumentParser
  
  import cv2
  
  from mmpose.apis import (get_track_id, inference_top_down_pose_model,
                           init_pose_model, process_mmdet_results,
                           vis_pose_tracking_result)
  
  try:
      from mmdet.apis import inference_detector, init_detector
      has_mmdet = True
  except (ImportError, ModuleNotFoundError):
      has_mmdet = False
  
  
  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']
  
      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,
              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()
  
  
  if __name__ == '__main__':
      main()
  