#parsing command line arguments
import argparse
#decoding camera images
import base64
#for frametimestamp saving
from datetime import datetime
#reading and writing files
import os
#high level file operations
import shutil
#matrix math
import numpy as np
#real-time server
import socketio
#concurrent networking
import eventlet
#web server gateway interface
import eventlet.wsgi
#image manipulation
from PIL import Image
#web framework
from flask import Flask
#input output
from io import BytesIO
#load our saved model
from keras.models import load_model
#helper class
import utils
#initialize our server
sio = socketio.Server()
#our flask (web) app
app = Flask(__name__)
#init our model and image array as empty
model = None
prev_image_array = None
#set min/max speed for our autonomous car
MAX_SPEED = 25
MIN_SPEED = 10
#and a speed limit
speed_limit = MAX_SPEED
#registering event handler for the server
@sio.on('telemetry')
def telemetry(sid, data):
if data:
# The current steering angle of the car
steering_angle = float(data["steering_angle"])
# The current throttle of the car, how hard to push peddle
throttle = float(data["throttle"])
# The current speed of the car
speed = float(data["speed"])
# The current image from the center camera of the car
image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(image) # from PIL image to numpy array
image = utils.preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# predict the steering angle for the image
steering_angle = float(model.predict(image, batch_size=1))
# lower the throttle as the speed increases
# if the speed is above the current speed limit, we are on a downhill.
# make sure we slow down first and then go back to the original max speed.
global speed_limit
if speed > speed_limit:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle**2 - (speed/speed_limit)**2
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
# save frame
if args.image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(args.image_folder, timestamp)
image.save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
@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 h5 file. Model should be on the same path.'
)
parser.add_argument(
'image_folder',
type=str,
nargs='?',
default='',
help='Path to image folder. This is where the images from the run will be saved.'
)
args = parser.parse_args()
#load model
model = load_model(args.model)
if args.image_folder != '':
print("Creating image folder at {}".format(args.image_folder))
if not os.path.exists(args.image_folder):
os.makedirs(args.image_folder)
else:
shutil.rmtree(args.image_folder)
os.makedirs(args.image_folder)
print("RECORDING THIS RUN ...")
else:
print("NOT RECORDING THIS RUN ...")
# 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)
