text: import argparse
  import logging
  import time
  
  import cv2
  import numpy as np
  
  from tf_pose.estimator import TfPoseEstimator
  from tf_pose.networks import get_graph_path, model_wh
  
  logger = logging.getLogger('TfPoseEstimator-WebCam')
  logger.setLevel(logging.DEBUG)
  ch = logging.StreamHandler()
  ch.setLevel(logging.DEBUG)
  formatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')
  ch.setFormatter(formatter)
  logger.addHandler(ch)
  
  fps_time = 0
  
  def str2bool(v):
      return v.lower() in ("yes", "true", "t", "1")
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description='tf-pose-estimation realtime webcam')
      # parser.add_argument('--camera', type=int, default=0)
      parser.add_argument('--camera', type=int, default=cv2.CAP_DSHOW + 0)
  
      parser.add_argument('--resize', type=str, default='0x0',
                          help='if provided, resize images before they are processed. default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ')
      parser.add_argument('--resize-out-ratio', type=float, default=4.0,
                          help='if provided, resize heatmaps before they are post-processed. default=1.0')
  
      # parser.add_argument('--model', type=str, default='mobilenet_thin', help='cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small')
      parser.add_argument('--model', type=str, default='mobilenet_v2_small', help='cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small')
      parser.add_argument('--show-process', type=bool, default=False,
                          help='for debug purpose, if enabled, speed for inference is dropped.')
      
      parser.add_argument('--tensorrt', type=str, default="False",
                          help='for tensorrt process.')
      args = parser.parse_args()
  
      logger.debug('initialization %s : %s' % (args.model, get_graph_path(args.model)))
      w, h = model_wh(args.resize)
      if w > 0 and h > 0:
          e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h))
      else:
          e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368))
      logger.debug('cam read+')
      
      cam = cv2.VideoCapture(args.camera)
      # cam.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
      # cam.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
      
      ret_val, image = cam.read()
      logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))
  
      while True:
          ret_val, image = cam.read()
  
          logger.debug('image process+')
          humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)
  
          logger.debug('postprocess+')
          image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)
  
          logger.debug('show+')
          cv2.putText(image,
                      "FPS: %f" % (1.0 / (time.time() - fps_time)),
                      (10, 10),  cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                      (0, 255, 0), 2)
          cv2.imshow('tf-pose-estimation result', image)
          fps_time = time.time()
          if cv2.waitKey(1) == 27:
              break
          logger.debug('finished+')
  
      cv2.destroyAllWindows()