text: import os
  from argparse import ArgumentParser
  
  import cv2
  
  from mmpose.apis import (inference_bottom_up_pose_model, init_pose_model,
                           vis_pose_result)
  
  
  def main():
      """Visualize the demo images."""
      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('--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(
          '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
      parser.add_argument(
          '--pose-nms-thr',
          type=float,
          default=0.9,
          help='OKS threshold for pose NMS')
      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')
      parser.add_argument(
          '--dataset_type',
          type=str,
          default="coco",
          help="dataset type"
              )
  
      args = parser.parse_args()
  
      assert args.show or (args.out_video_root != '')
  
      # 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']
      assert (args.dataset_type == "coco" or args.dataset_type == "coco-wholebody")
  
      cap = cv2.VideoCapture(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
  
      while (cap.isOpened()):
          flag, img = cap.read()
          if not flag:
              break
  
          pose_results, returned_outputs = inference_bottom_up_pose_model(
              pose_model,
              img,
              pose_nms_thr=args.pose_nms_thr,
              return_heatmap=return_heatmap,
              outputs=output_layer_names,
              dataset_type=args.dataset_type
              )
  
          # show the results
          vis_img = vis_pose_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()
  