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drive.py
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drive.py
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# -*- coding: utf-8 -*-
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 keras.models import model_from_json
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array
import cv2
import math
def preprocess_image(image):
"""
Crop the top 1/5 of the image to remove the horizon
and the bottom 25 pixels to remove the car’s hood.
We will next rescale the image to a new size, depending on the model
"""
if model_name == 'nvidia':
new_size = (220, 66)
elif model_name == 'tiny':
new_size = (32, 16)
elif model_name == 'carputer':
new_size = (160, 120)
else:
new_size = (64, 64)
n_rows = image.shape[0]
image = image[math.floor(n_rows / 5):n_rows - 25, :, :]
image = cv2.resize(image, new_size, interpolation=cv2.INTER_AREA)
return image
sio = socketio.Server()
app = Flask(__name__)
model = None
model_name = None
prev_image_array = 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 = preprocess_image(image_array)
transformed_image_array = image_array[None, :, :, :]
# 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.
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:
model = model_from_json(json.load(jfile))
# Extract the model name
model_name = args.model.split('_')[1]
print("Model name: {}".format(model_name))
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)