drive.py

import argparse
import base64
import numpy as np
import socketio
import eventlet.wsgi
from PIL import Image
from flask import Flask
from io import BytesIO
from keras.models import model_from_json
import tensorflow as tf
from dataPreprocessing import preprocess
import os, os.path

sio = socketio.Server()
app = Flask(__name__)
model = None
@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
    image = Image.open(BytesIO(base64.b64decode(data["image"])))

    image_array = preprocess(np.asarray(image))
    transformed_image_array = image_array[None, :, :, :]

    steering_angle = float(model.predict(transformed_image_array, batch_size=1))
    throttle = .2 if float(speed) > 5 else 1.
    print("steering angle",steering_angle)
    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:
        model = model_from_json(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)