drive.py

'''
Connects to game simulator and sends predicted steering angles basd on current image
'''
import cv2
import base64
import json
import time
import argparse
import socketio
import eventlet
import numpy as np
import eventlet.wsgi
from PIL import Image
from io import BytesIO
from PIL import ImageOps
import matplotlib.pyplot as plt
from flask import Flask, render_template

from keras.models import model_from_json
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array

import tensorflow as tf
tf.python.control_flow_ops = tf
from process_data import show_image


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
    imgString = data["image"]
    image = Image.open(BytesIO(base64.b64decode(imgString)))
    image_array = np.asarray(image)
    # image_array = image_array[20:160]
    # show_image(image_array)
    resized = cv2.resize(image_array, (200, 66))
    # show_image(resized)

    resized = resized.reshape((1,) + resized.shape)

    # This model currently assumes that the features of the model are just the images. Feel free to change this.
    steering_angle = float(model.predict(resized, batch_size=1))
    # if abs(steering_angle) > .1:
    #correct for model's bias towards 0 values
    steering_angle = steering_angle * 1.3
    # The driving model currently just outputs a constant throttle. Feel free to edit this.
    throttle = 0.1
    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__':
    #finding model path
    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(json.load(jfile))

    model.compile("adam", "mse")
    weights_file = args.model.replace('json', 'h5')
    #load weights into model
    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)