text: import sys
  import onnx
  import os
  import argparse
  import numpy as np
  import cv2
  import onnxruntime
  
  from tool.utils import *
  from tool.darknet2onnx import *
  
  
  def main(cfg_file, namesfile, weight_file, image_path, batch_size):
  
      if batch_size <= 0:
          onnx_path_demo = transform_to_onnx(cfg_file, weight_file, batch_size)
      else:
          # Transform to onnx as specified batch size
          transform_to_onnx(cfg_file, weight_file, batch_size)
          # Transform to onnx as demo
          onnx_path_demo = transform_to_onnx(cfg_file, weight_file, 1)
  
      session = onnxruntime.InferenceSession(onnx_path_demo)
      # session = onnx.load(onnx_path)
      print("The model expects input shape: ", session.get_inputs()[0].shape)
  
      image_src = cv2.imread(image_path)
      detect(session, image_src, namesfile)
  
  
  
  def detect(session, image_src, namesfile):
      IN_IMAGE_H = session.get_inputs()[0].shape[2]
      IN_IMAGE_W = session.get_inputs()[0].shape[3]
  
      # Input
      resized = cv2.resize(image_src, (IN_IMAGE_W, IN_IMAGE_H), interpolation=cv2.INTER_LINEAR)
      img_in = cv2.cvtColor(resized, cv2.COLOR_BGR2RGB)
      img_in = np.transpose(img_in, (2, 0, 1)).astype(np.float32)
      img_in = np.expand_dims(img_in, axis=0)
      img_in /= 255.0
      print("Shape of the network input: ", img_in.shape)
  
      # Compute
      input_name = session.get_inputs()[0].name
  
      outputs = session.run(None, {input_name: img_in})
  
      boxes = post_processing(img_in, 0.4, 0.6, outputs)
  
      class_names = load_class_names(namesfile)
      plot_boxes_cv2(image_src, boxes[0], savename='predictions_onnx.jpg', class_names=class_names)
  
  
  
  if __name__ == '__main__':
      print("Converting to onnx and running demo ...")
      if len(sys.argv) == 6:
          cfg_file = sys.argv[1]
          namesfile = sys.argv[2]
          weight_file = sys.argv[3]
          image_path = sys.argv[4]
          batch_size = int(sys.argv[5])
          main(cfg_file, namesfile, weight_file, image_path, batch_size)
      else:
          print('Please run this way:\n')
          print('  python demo_onnx.py <cfgFile> <namesFile> <weightFile> <imageFile> <batchSize>')
  