code: # Copyright (c) OpenMMLab. All rights reserved.
  import argparse
  import os
  import os.path as osp
  import shutil
  
  import cv2
  import mmcv
  import numpy as np
  import torch
  from mmcv import DictAction
  
  from mmaction.apis import inference_recognizer, init_recognizer
  
  try:
      from mmdet.apis import inference_detector, init_detector
  except (ImportError, ModuleNotFoundError):
      raise ImportError('Failed to import `inference_detector` and '
                        '`init_detector` form `mmdet.apis`. These apis are '
                        'required in this demo! ')
  
  try:
      from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
                               vis_pose_result)
  except (ImportError, ModuleNotFoundError):
      raise ImportError('Failed to import `inference_top_down_pose_model`, '
                        '`init_pose_model`, and `vis_pose_result` form '
                        '`mmpose.apis`. These apis are required in this demo! ')
  
  try:
      import moviepy.editor as mpy
  except ImportError:
      raise ImportError('Please install moviepy to enable output file')
  
  FONTFACE = cv2.FONT_HERSHEY_DUPLEX
  FONTSCALE = 0.75
  FONTCOLOR = (255, 255, 255)  # BGR, white
  THICKNESS = 1
  LINETYPE = 1
  
  
  def parse_args():
      parser = argparse.ArgumentParser(description='MMAction2 demo')
      parser.add_argument('video', help='video file/url')
      parser.add_argument('out_filename', help='output filename')
      parser.add_argument(
          '--config',
          default=('configs/skeleton/posec3d/'
                   'slowonly_r50_u48_240e_ntu120_xsub_keypoint.py'),
          help='skeleton model config file path')
      parser.add_argument(
          '--checkpoint',
          default=('https://download.openmmlab.com/mmaction/skeleton/posec3d/'
                   'slowonly_r50_u48_240e_ntu120_xsub_keypoint/'
                   'slowonly_r50_u48_240e_ntu120_xsub_keypoint-6736b03f.pth'),
          help='skeleton model checkpoint file/url')
      parser.add_argument(
          '--det-config',
          default='demo/faster_rcnn_r50_fpn_2x_coco.py',
          help='human detection config file path (from mmdet)')
      parser.add_argument(
          '--det-checkpoint',
          default=('http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/'
                   'faster_rcnn_r50_fpn_2x_coco/'
                   'faster_rcnn_r50_fpn_2x_coco_'
                   'bbox_mAP-0.384_20200504_210434-a5d8aa15.pth'),
          help='human detection checkpoint file/url')
      parser.add_argument(
          '--pose-config',
          default='demo/hrnet_w32_coco_256x192.py',
          help='human pose estimation config file path (from mmpose)')
      parser.add_argument(
          '--pose-checkpoint',
          default=('https://download.openmmlab.com/mmpose/top_down/hrnet/'
                   'hrnet_w32_coco_256x192-c78dce93_20200708.pth'),
          help='human pose estimation checkpoint file/url')
      parser.add_argument(
          '--det-score-thr',
          type=float,
          default=0.9,
          help='the threshold of human detection score')
      parser.add_argument(
          '--label-map',
          default='tools/data/skeleton/label_map_ntu120.txt',
          help='label map file')
      parser.add_argument(
          '--device', type=str, default='cuda:0', help='CPU/CUDA device option')
      parser.add_argument(
          '--short-side',
          type=int,
          default=480,
          help='specify the short-side length of the image')
      parser.add_argument(
          '--cfg-options',
          nargs='+',
          action=DictAction,
          default={},
          help='override some settings in the used config, the key-value pair '
          'in xxx=yyy format will be merged into config file. For example, '
          "'--cfg-options model.backbone.depth=18 model.backbone.with_cp=True'")
      args = parser.parse_args()
      return args
  
  
  def frame_extraction(video_path, short_side):
      """Extract frames given video_path.
  
      Args:
          video_path (str): The video_path.
      """
      # Load the video, extract frames into ./tmp/video_name
      target_dir = osp.join('./tmp', osp.basename(osp.splitext(video_path)[0]))
      os.makedirs(target_dir, exist_ok=True)
      # Should be able to handle videos up to several hours
      frame_tmpl = osp.join(target_dir, 'img_{:06d}.jpg')
      vid = cv2.VideoCapture(video_path)
      frames = []
      frame_paths = []
      flag, frame = vid.read()
      cnt = 0
      new_h, new_w = None, None
      while flag:
          if new_h is None:
              h, w, _ = frame.shape
              new_w, new_h = mmcv.rescale_size((w, h), (short_side, np.Inf))
  
          frame = mmcv.imresize(frame, (new_w, new_h))
  
          frames.append(frame)
          frame_path = frame_tmpl.format(cnt + 1)
          frame_paths.append(frame_path)
  
          cv2.imwrite(frame_path, frame)
          cnt += 1
          flag, frame = vid.read()
  
      return frame_paths, frames
  
  
  def detection_inference(args, frame_paths):
      """Detect human boxes given frame paths.
  
      Args:
          args (argparse.Namespace): The arguments.
          frame_paths (list[str]): The paths of frames to do detection inference.
  
      Returns:
          list[np.ndarray]: The human detection results.
      """
      model = init_detector(args.det_config, args.det_checkpoint, args.device)
      assert model.CLASSES[0] == 'person', ('We require you to use a detector '
                                            'trained on COCO')
      results = []
      print('Performing Human Detection for each frame')
      prog_bar = mmcv.ProgressBar(len(frame_paths))
      for frame_path in frame_paths:
          result = inference_detector(model, frame_path)
          # We only keep human detections with score larger than det_score_thr
          result = result[0][result[0][:, 4] >= args.det_score_thr]
          results.append(result)
          prog_bar.update()
      return results
  
  
  def pose_inference(args, frame_paths, det_results):
      model = init_pose_model(args.pose_config, args.pose_checkpoint,
                              args.device)
      ret = []
      print('Performing Human Pose Estimation for each frame')
      prog_bar = mmcv.ProgressBar(len(frame_paths))
      for f, d in zip(frame_paths, det_results):
          # Align input format
          d = [dict(bbox=x) for x in list(d)]
          pose = inference_top_down_pose_model(model, f, d, format='xyxy')[0]
          ret.append(pose)
          prog_bar.update()
      return ret
  
  
  def main():
      args = parse_args()
  
      frame_paths, original_frames = frame_extraction(args.video,
                                                      args.short_side)
      num_frame = len(frame_paths)
      h, w, _ = original_frames[0].shape
  
      # Get clip_len, frame_interval and calculate center index of each clip
      config = mmcv.Config.fromfile(args.config)
      config.merge_from_dict(args.cfg_options)
      for component in config.data.test.pipeline:
          if component['type'] == 'PoseNormalize':
              component['mean'] = (w // 2, h // 2, .5)
              component['max_value'] = (w, h, 1.)
  
      model = init_recognizer(config, args.checkpoint, args.device)
  
      # Load label_map
      label_map = [x.strip() for x in open(args.label_map).readlines()]
  
      # Get Human detection results
      det_results = detection_inference(args, frame_paths)
      torch.cuda.empty_cache()
  
      pose_results = pose_inference(args, frame_paths, det_results)
      torch.cuda.empty_cache()
  
      fake_anno = dict(
          frame_dir='',
          label=-1,
          img_shape=(h, w),
          original_shape=(h, w),
          start_index=0,
          modality='Pose',
          total_frames=num_frame)
      num_person = max([len(x) for x in pose_results])
  
      num_keypoint = 17
      keypoint = np.zeros((num_person, num_frame, num_keypoint, 2),
                          dtype=np.float16)
      keypoint_score = np.zeros((num_person, num_frame, num_keypoint),
                                dtype=np.float16)
      for i, poses in enumerate(pose_results):
          for j, pose in enumerate(poses):
              pose = pose['keypoints']
              keypoint[j, i] = pose[:, :2]
              keypoint_score[j, i] = pose[:, 2]
      fake_anno['keypoint'] = keypoint
      fake_anno['keypoint_score'] = keypoint_score
  
      results = inference_recognizer(model, fake_anno)
  
      action_label = label_map[results[0][0]]
  
      pose_model = init_pose_model(args.pose_config, args.pose_checkpoint,
                                   args.device)
      vis_frames = [
          vis_pose_result(pose_model, frame_paths[i], pose_results[i])
          for i in range(num_frame)
      ]
      for frame in vis_frames:
          cv2.putText(frame, action_label, (10, 30), FONTFACE, FONTSCALE,
                      FONTCOLOR, THICKNESS, LINETYPE)
  
      vid = mpy.ImageSequenceClip([x[:, :, ::-1] for x in vis_frames], fps=24)
      vid.write_videofile(args.out_filename, remove_temp=True)
  
      tmp_frame_dir = osp.dirname(frame_paths[0])
      shutil.rmtree(tmp_frame_dir)
  
  
  if __name__ == '__main__':
      main()
  
apis: torch.cuda.empty_cache numpy.zeros