content: import argparse
  import base64
  import json
  
  import numpy as np
  import socketio
  import eventlet
  import eventlet.wsgi
  import time
  from PIL import Image
  from PIL import ImageOps
  from flask import Flask, render_template
  from io import BytesIO
  from random import randint
  from keras.models import model_from_json
  from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array
  import cv2
  # Fix error with Keras and TensorFlow
  import tensorflow as tf
  import matplotlib.pyplot as plt
  
  tf.python.control_flow_ops = tf
  
  
  sio = socketio.Server()
  app = Flask(__name__)
  model = None
  prev_image_array = None
  
  iDebug = 0
  
  def preprocess(image, top_offset=.375, bottom_offset=.125):
      """
      Applies preprocessing pipeline to an image: crops `top_offset` and `bottom_offset`
      portions of image, resizes to 32x128 px and scales pixel values to [0, 1].
      """
      top = int(top_offset * image.shape[0])
      bottom = int(bottom_offset * image.shape[0])
      image = image[top:-bottom, :]
      newShape = image.shape
      image= cv2.resize(image,(int(newShape[1]/2), int(newShape[0]/2)), interpolation = cv2.INTER_CUBIC)
      return image
  
  
  @sio.on('telemetry')
  def telemetry(sid, data):
      # The current steering angle of the car
      steering_angle = data["steering_angle"]
      # The current throttle of the car
      throttle = data["throttle"]
      # The current speed of the car
      speed = data["speed"]
      # The current image from the center camera of the car
      imgString = data["image"]
      image = Image.open(BytesIO(base64.b64decode(imgString)))
      image_array = np.asarray(image)
      image_array=preprocess(image_array)
      
      newShape = image_array.shape
      #image_array=cv2.resize(image_array,(newShape[1], newShape[0]),interpolation=cv2.INTER_CUBIC)
      transformed_image_array = image_array[None, :, :, :]
      if (iDebug==1):
          plt.imshow(image_array)
          plt.show()
      #transformed_image_array2 = np.zeros([1,2*64,64,3])
      #transformed_image_array2[0]=cv2.resize(transformed_image_array[0],(2*64, 64),interpolation=cv2.INTER_CUBIC)
      # This model currently assumes that the features of the model are just the images. Feel free to change this.
      steering_angle = float(model.predict(transformed_image_array, batch_size=1))
      # The driving model currently just outputs a constant throttle. Feel free to edit this.
      #steering_angle = randint(0,100)/100*randint(-1,1);
      throttle = 0.2
      print(steering_angle, throttle)
      send_control(steering_angle, throttle)
  
  
  @sio.on('connect')
  def connect(sid, environ):
      print("connect ", sid)
      send_control(0, 0)
  
  
  def send_control(steering_angle, throttle):
      sio.emit("steer", data={
      'steering_angle': steering_angle.__str__(),
      'throttle': throttle.__str__()
      }, skip_sid=True)
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description='Remote Driving')
      parser.add_argument('model', type=str,
      help='Path to model definition json. Model weights should be on the same path.')
      args = parser.parse_args()
      with open(args.model, 'r') as jfile:
          # NOTE: if you saved the file by calling json.dump(model.to_json(), ...)
          # then you will have to call:
          #
          #   model = model_from_json(json.loads(jfile.read()))\
          #
          # instead.
          #model = model_from_json(jfile.read())
          model = model_from_json(json.loads(jfile.read()))
  
      model.compile("adam", "mse")
      weights_file = args.model.replace('json', 'h5')
      model.load_weights(weights_file)
  
      # wrap Flask application with engineio's middleware
      app = socketio.Middleware(sio, app)
  
      # deploy as an eventlet WSGI server
      eventlet.wsgi.server(eventlet.listen(('', 4567)), app)