drive.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from deepgtav.messages import Start, Stop, Scenario, Commands, frame2numpy
from deepgtav.client import Client

import argparse
import time
import cv2

class Model:
	def run(self,frame):
		return [1.0, 0.0, 0.0] # throttle, brake, steering

# Controls the DeepGTAV vehicle
if __name__ == '__main__':
	parser = argparse.ArgumentParser(description=None)
	parser.add_argument('-l', '--host', default='localhost', help='The IP where DeepGTAV is running')
	parser.add_argument('-p', '--port', default=8000, help='The port where DeepGTAV is running')
	args = parser.parse_args()

	# Creates a new connection to DeepGTAV using the specified ip and port. 
	# If desired, a dataset path and compression level can be set to store in memory all the data received in a gziped pickle file.
	# We don't want to save a dataset in this case
	client = Client(ip=args.host, port=args.port)
	
	# We set the scenario to be in manual driving, and everything else random (time, weather and location). 
	# See deepgtav/messages.py to see what options are supported
	scenario = Scenario(drivingMode=-1) #manual driving
	
	# Send the Start request to DeepGTAV. Dataset is set as default, we only receive frames at 10Hz (320, 160)
	client.sendMessage(Start(scenario=scenario))
	
	# Dummy agent
	model = Model()

	# Start listening for messages coming from DeepGTAV. We do it for 80 hours
	stoptime = time.time() + 80*3600
	while time.time() < stoptime:
		try:
			# We receive a message as a Python dictionary
			message = client.recvMessage()	
			print(message)
				
			# The frame is a numpy array that can we pass through a CNN for example		
			image = frame2numpy(message['frame'], (320,160))
			commands = model.run(image)
			# We send the commands predicted by the agent back to DeepGTAV to control the vehicle
			client.sendMessage(Commands(commands[0], commands[1], commands[2]))
		except KeyboardInterrupt:
			break
			
	# We tell DeepGTAV to stop
	client.sendMessage(Stop())
	client.close()