parameters: (image, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU)
return_statement: return segmentation
docstring: Prepare input for execution, run net and postprocess output to parse human.
      :param image: input image
      :param model_path: path to JPPNet model
      :param backend: name of computation backend
      :param target: name of computation target
docstring_summary: Prepare input for execution, run net and postprocess output to parse human.
      :param image: input image
      :param model_path: path to JPPNet model
      :param backend: name of computation backend
      :param target: name of computation target
docstring_tokens: Prepare input for execution run net and postprocess output to parse human . : param image : input image : param model_path : path to JPPNet model : param backend : name of computation backend : param target : name of computation target
function: def parse_human(image, model_path, backend=cv.dnn.DNN_BACKEND_OPENCV, target=cv.dnn.DNN_TARGET_CPU):
      """
      Prepare input for execution, run net and postprocess output to parse human.
      :param image: input image
      :param model_path: path to JPPNet model
      :param backend: name of computation backend
      :param target: name of computation target
      """
      input = preprocess(image)
      input_h, input_w = input.shape[2:]
      output = run_net(input, model_path, backend, target)
      grayscale_out = postprocess(output, (input_w, input_h))
      segmentation = decode_labels(grayscale_out)
      return segmentation
function_tokens: def parse_human ( image , model_path , backend = cv . dnn . DNN_BACKEND_OPENCV , target = cv . dnn . DNN_TARGET_CPU ) : input = preprocess ( image ) input_h , input_w = input . shape [ 2 : ] output = run_net ( input , model_path , backend , target ) grayscale_out = postprocess ( output , ( input_w , input_h ) ) segmentation = decode_labels ( grayscale_out ) return segmentation