text: import os
  from argparse import ArgumentParser
  
  from mmpose.apis import inference_pose_model, init_pose_model, vis_pose_result
  
  
  def main():
      """Visualize the demo images.
  
      Require the json_file containing boxes.
      """
      parser = ArgumentParser()
      parser.add_argument('pose_config', help='Config file for detection')
      parser.add_argument('pose_checkpoint', help='Checkpoint file')
      parser.add_argument('--img-root', type=str, default='', help='Image root')
      parser.add_argument(
          '--json-file',
          type=str,
          default='',
          help='Json file containing image info.')
      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')
  
      args = parser.parse_args()
  
      assert args.show or (args.out_img_root != '')
      assert 'cuda' in args.device
  
      skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],
                  [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],
                  [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
  
      from pycocotools.coco import COCO
      coco = COCO(args.json_file)
      # 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)
  
      img_keys = list(coco.imgs.keys())
  
      # process each image
      for i in range(len(img_keys)):
          # get bounding box annotations
          image_id = img_keys[i]
          image = coco.loadImgs(image_id)[0]
          image_name = os.path.join(args.img_root, image['file_name'])
          ann_ids = coco.getAnnIds(image_id)
  
          # make person bounding boxes
          person_bboxes = []
          for ann_id in ann_ids:
              ann = coco.anns[ann_id]
              # bbox format is 'xywh'
              bbox = ann['bbox']
              person_bboxes.append(bbox)
  
          # test a single image, with a list of bboxes.
          pose_results = inference_pose_model(
              pose_model, image_name, person_bboxes, format='xywh')
  
          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_{i}.jpg')
  
          # show the results
          vis_pose_result(
              pose_model,
              image_name,
              pose_results,
              skeleton=skeleton,
              kpt_score_thr=args.kpt_thr,
              show=args.show,
              out_file=out_file)
  
  
  if __name__ == '__main__':
      main()
  