def work(self):
"""
Pretend this worker method does work that takes a long time. During this time, the thread's
event loop is blocked, except if the application's processEvents() is called: this gives every
thread (incl. main) a chance to process events, which in this sample means processing signals
received from GUI (such as abort).
"""
thread_name = QThread.currentThread().objectName()
thread_id = int(
QThread.currentThreadId()) # cast to int() is necessary
self.sig_msg.emit('Running worker #{} from thread "{}" (#{})'.format(
self.__id, thread_name, thread_id))
# 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
self.client = Client(ip=self.args.host, port=self.args.port)
# self.client = Client(ip="127.0.0.1", port=8000)
# 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)
self.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 and (not self.__abort)):
# We receive a message as a Python dictionary
app.processEvents()
message = self.client.recvMessage()
# 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)
self.sig_step.emit(self.__id, 'step ' + str(time.time()))
self.sig_image.emit(image.tolist())
# We send the commands predicted by the agent back to DeepGTAV to control the vehicle
self.client.sendMessage(
Commands(commands[0], commands[1], commands[2]))
# We tell DeepGTAV to stop
self.client.sendMessage(Stop())
self.client.close()
self.sig_done.emit(self.__id)
def reset(weatherIndex=0):
''' Resets position of car to a specific location '''
# Same conditions as below |
client.sendMessage(Stop())
dataset = Dataset(rate=30,
frame=frame_capture_size,
throttle=True,
brake=True,
steering=True,
location=True,
speed=True,
yawRate=True,
direction=True)
# dataset = Dataset(rate=30, frame=[400,300], throttle=True, brake=True, steering=True, location=True, speed=True, yawRate=True, direction=True, reward=[18.0, 0.5])
# Automatic driving scenario
# scenario = Scenario(weather='EXTRASUNNY',vehicle='voltic',time=[12,0],drivingMode=[786603,70.0],location=[-2573.13916015625, 3292.256103515625, 13.241103172302246])
# scenario = Scenario(weather=weatherList[weatherIndex],vehicle='voltic',time=[12,0],drivingMode=[4294967295,70.0],location=[-2573.13916015625, 3292.256103515625, 13.241103172302246])
scenario = Scenario(weather=weatherList[weatherIndex],
vehicle='voltic',
time=[12, 0],
drivingMode=[2883621, 20.0],
wander=False)
client.sendMessage(Start(scenario=scenario,
dataset=dataset)) # Start request
# 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()
count = 0
print("Starting Loop...")
while True:
try:
# Collect and preprocess image
message = client.recvMessage()
image = frame2numpy(message['frame'], (320, 160))
image = ((image / 255) - .5) * 2
# Corrects for model input shape
model_input = []
model_input.append(image)
# Converts classification to float for steering input
category_prediction = np.argmax(model.predict(np.array(model_input)))
decimal_prediction = (category_prediction - 500) / 500
print('Category: ' + str(category_prediction) + ' Decimal: ' +
str(decimal_prediction))
client.sendMessage(Commands(
0.0, 0.0, decimal_prediction *
3)) # Mutiplication scales decimal prediction for harder turning
count += 1
except Exception as e:
print("Excepted as: " + str(e))
continue
client.sendMessage(Stop()) # Stops DeepGTAV
client.close()
def __close_all(self):
self.__detection_pickleFile.close()
self.__labels_csv.close()
self.__client.sendMessage(Stop())
self.__client.close()
