def main(): global client, scenario client = Client(ip='localhost', port=8000, datasetPath='D:/no_traffic/dataset' + str(i) + '.pz', compressionLevel=9) scenario = Scenario(weather=weather, vehicle='blista', time=[set_time, 0], drivingMode=-1, location=location) # 设置数据集存储位置,采集数据所用的车辆型号 client.sendMessage(Start(scenario=scenario, dataset=dataset)) print("load deepGTAV successfully! \nbegin") while True: fo = open(txt_position, "r") # 配置1 txt = fo.read() fo.close() if txt == '0': tongji = open(tongji_position, "w+") tongji.write("left:" + str(left) + " right:" + str(right) + " total:" + str(total)) tongji.close() print('=====================end=====================') exit(0) elif txt == '1': drive()
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(): # Resets position of the car to the starting location dataset = Dataset(rate=30, frame=frame, throttle=True, brake=True, steering=True, location=True, drivingMode=True) scenario = Scenario(weather=weather, vehicle=vehicle, time=time, drivingMode=drivingMode, location=location) Client.sendMessage(Config(scenario=scenario, dataset=dataset))
#,yawRate=True,time=True,vehicles=True, peds=True, trafficSigns=True, direction=True, reward=True #[-1917.06640625, 4595.87255859375, 56.853] #-737.1954345703125, 1975.72265625, 133.54100036621094 #[2723.626953125, 3224.170654296875, 54.402042388916016] #827.8175659179688, -1201.2620849609375, 45.51389694213867 街头竞速 没车 scenario = Scenario(weather='EXTRASUNNY', vehicle='blista', time=[12, 0], drivingMode=-1, location=location) client.sendMessage(Config(scenario=scenario, dataset=dataset)) if image_source == USE_GTAV: client = Client( ip='localhost', port=8000 ) #, datasetPath="self_driving.pz", compressionLevel=9) # Default interface ''' try: #client.sendMessage(Stop()) # Stops DeepGTAV client.close() except Exception as e: pass client = Client(ip='localhost', port=8000, datasetPath="self_driving.pz", compressionLevel=9) # Default interface ''' dataset = Dataset(rate=FPS, frame=[show_imgwidth, show_imgheight], throttle=True, brake=True, steering=True,
port = 8000 dataset_path = 'dataset.pz' log_freq = 10 # in minute max_stop_time = 10 # in second max_wall_time = 10 # in hour max_speed = 120 # in km rate = 30 # in HZ frame = [350, 205 + 20] logger.info( 'Rate %s hz, frame %s, max_stop_time %s, max_wall_time %s, max_speed %s, dataset_path %s.' % (str(rate), str(frame), str(max_stop_time), str(max_wall_time), str(max_speed), str(dataset_path))) client = Client(ip=host, port=port, datasetPath=dataset_path, compressionLevel=0) dataset = Dataset(rate=rate, frame=frame, throttle=True, brake=True, steering=True, speed=True, drivingMode=True, location=True) scenario = Scenario(weather='EXTRASUNNY', vehicle='voltic', time=[12, 0], drivingMode=[1074528293, max_speed / 1.6],
return bgr print("Loading Model...") model = load_model(model_path) print("Model Loaded. Compiling...") sgd = SGD(lr=1e-3, decay=1e-4, momentum=0.9, nesterov=True) # SGD Optimizer 사용 model.compile(optimizer=sgd, loss="mse") # loss function 은 논문을 따라 "mean squared error" model.summary() if input("Continue?") == "y": # Wait until you load GTA V to continue, else can't connect to DeepGTAV print("Conintuing...") # Loads into a consistent starting setting print("Loading Scenario...") client = Client(ip='localhost', port=8000) # Default interface scenario = Scenario(weather=weather, vehicle=vehicle, time=time, drivingMode=-1, location=location) client.sendMessage(Start(scenario=scenario)) hwnd_list = _get_windows_bytitle("Grand Theft Auto V", exact=True) # window 를 받아온다. count = 0 print("Starting Loop...") while True: try: # Collect and preprocess image # message = client.recvMessage() # window title 을 이용해 받아온 window 에서 frame 을 따온다. # 기존의 기능에서 이를 제대로 지원하지 않는듯 하기 때문에 추가로 작성함. frame_image = screenshot(hwnd=hwnd_list[0]) # frame_image = normalize(frame_image)
def __call__(self, v): return UNIT(self.tpe, v) def __rmul__(self, o): from numbers import Number assert isinstance(o, Number), "Can only specify numeric unit types" return UNIT(self.tpe, o * self.value) MS = UNIT(0) # Milliseconds FRAMES = UNIT(1) # Frames if __name__ == "__main__": from time import time, sleep c = Client(('localhost', 8766)) print(c) if not c.control(): print("Failed to gain control over game, just watching then ...") c.reset() t0 = time() #for i in range(100): target_list = c.listTargets() print(target_list) c.startScenarios() #c.interceptKeys(print) c.fetchGameState(print, 1 * FRAMES) import matplotlib
'--host', default='localhost', help='The IP where DeepGTAV is running') parser.add_argument('-p', '--port', default=8000, help='The port where DeepGTAV is running') parser.add_argument('-d', '--dataset_path', default='dataset_test.pz', help='Place to store the dataset') args = parser.parse_args() # Creates a new connection to DeepGTAV using the specified ip and port client = Client(ip=args.host, port=args.port, datasetPath=args.dataset_path, compressionLevel=9) # Dataset options dataset = Dataset(rate=30, frame=[320, 160], throttle=True, brake=True, steering=True, location=True, drivingMode=True) # Automatic driving scenario scenario = Scenario( weather='EXTRASUNNY', vehicle='blista', time=[12, 0], drivingMode=[786603, 20.0],
# Stores a dataset file with data coming from DeepGTAV 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') parser.add_argument('-d', '--dataset_path', default='dataset.pz', help='Place to store the dataset') args = parser.parse_args() weathers = ["CLEAR", "EXTRASUNNY", "CLOUDS", "OVERCAST", "RAIN", "CLEARING", "THUNDER", "SMOG", "FOGGY", "XMAS", "SNOWLIGHT", "BLIZZARD", "NEUTRAL", "SNOW" ] hours = [0,4,8,12,16,20] ## CREATE A REAL DATASET WITH WEATHER CONDITIONS IN DIRECTORY. for weather in weathers: # 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. for hour in hours: client = Client(ip=args.host, port=args.port, datasetPath=args.dataset_path, compressionLevel=9) infos = {} infos['weather'] = weather infos['hour'] = hour infos['time'] = int(time.time()) # Configures the information that we want DeepGTAV to generate and send to us. # See deepgtav/messages.py to see what options are supported dataset = Dataset(rate=4, frame=[1280,640], throttle=True, brake=True, steering=True, vehicles=True, peds=True, trafficSigns=True, reward=[15.0, 0.0], direction=None, speed=True, yawRate=True, location=True, time=True) # Send the Start request to DeepGTAV. scenario = Scenario(time=[hour,0],weather=weather,drivingMode=[786603,15.0]) # Driving style is set to normal, with a speed of 15.0 mph. All other scenario options are random. client.sendMessage(Start(dataset=dataset,scenario=scenario)) stoptime = time.time() + 2*60 while time.time() < stoptime: try: message = client.recvMessage(infos)
# Stores a pickled dataset file with data coming from DeepGTAV if __name__ == '__main__': parser = argparse.ArgumentParser(description=None) parser.add_argument('-l', '--host', default='10.60.124.42', help='The IP where DeepGTAV is running') parser.add_argument('-p', '--port', default=8000, help='The port where DeepGTAV is running') parser.add_argument('-d', '--dataset_path', default='dataset.pz', help='Place to store the dataset') parser.add_argument('-s', '--save_data', default=False, help='Record data?') args = parser.parse_args() # Creates a new connection to DeepGTAV using the specified ip and port client = Client(ip=args.host, port=args.port) # Dataset options dataset = Dataset( rate=10, frame=[640, 320], throttle=True, brake=True, steering=True, location=True, speed=True, yawRate=True, vehicles=True, direction=[-2573.13916015625, 2000, 13.241103172302246] ) # Automatic driving scenario
json_file = open(NAME + '.json', 'r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) # load weights into new model model.load_weights(NAME + ".h5") print("Loaded model from disk") print("Model Loaded. Compiling...") model.compile(optimizer='Adadelta', loss='mean_squared_error') ''' if input("Continue?") == "y": # Wait until you load GTA V to continue, else can't connect to DeepGTAV print("Conintuing...") ''' # Loads into a consistent starting setting print("Loading Scenario...") client = Client(ip='localhost', port=8000) # Default interface dataset = Dataset(rate=60, frame=[800, 600], throttle=True, brake=True, steering=True, location=True, drivingMode=True) scenario = Scenario( weathers='EXTRASUNNY', vehicle='blista', times=[12, 0], drivingMode=-1 ) #, location=[-2573.13916015625, 3292.256103515625, 13.241103172302246]) client.sendMessage(Start(scenario=scenario)) count = 0 print("Starting Loop...") while True:
parser.add_argument( '-vl', '--video_len', default=300, type=int, help='Length of video to record in seconds, default 0.42*3600') 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. client = Client( ip=args.host, port=args.port, datasetPath="tmp.gz", compressionLevel=0) for i_trial in range(args.n_trials): # Scenario parameters: time, weather, location # with open("highway.np", "rb") as f: # x_list, y_list, _ = np.load(f) # args.time[0] = np.random.randint(5, 18) # args.time[1] = np.random.randint(0, 60) # weather = weather_list[np.random.randint(0, len(weather_list))] # rand_loc_idx = np.random.randint(len(x_list)) # while not (args.loc_x_range[0] < x_list[rand_loc_idx] < # args.loc_x_range[1] and # args.loc_y_range[0] < y_list[rand_loc_idx] < # args.loc_y_range[1]): # rand_loc_idx = np.random.randint(len(x_list))
def __init_client(self): self.__client = Client( ip=self.__args['host'], port=self.__args['port'])
class Data_Generator: def __init__(self, args_): self.__args = args_ self.__driving_mode = [786603, 55.0] self.__driving_vehicle = 'Blista' self.__starting_time = 5 self.__starting_weather = 4 self.__starting_location = [-2730, 2300] self.__frame_size = [650, 418] self.__client, self.__scenario, self.__dataset, self.__detection_pickleFile = None, None, None, None self.__total_written_images = 0 self.__images_to_capture = self.__args['images_to_capture'] self.__save_dir = self.__args['save_dir'] self.__target_shape = (200,88) self.__labels_csv = None self.__init_client() self.__init_scenairo() self.__prepare_datasets() self.__client.sendMessage(Start(scenario=self.__scenario, dataset=self.__dataset)) self.__old_location = [] def __build_env(self): self.__init_scenairo() self.__prepare_datasets(open_=False) self.__client.sendMessage(Config(scenario=self.__scenario, dataset=self.__dataset)) def __init_scenairo(self): self.__scenario = Scenario(drivingMode=self.__driving_mode, vehicle=self.__driving_vehicle, time=self.__starting_time, weather=self.__starting_weather, location=self.__starting_location) def __init_client(self): self.__client = Client( ip=self.__args['host'], port=self.__args['port']) def __prepare_datasets(self, open_=True, fps=20, throttle_=True, brake_=True, steering_=True, vehicles_=True, peds_=True, speed_=True, location_=True, detection_file_name_='detection1.pickle'): self.__dataset = Dataset(rate=fps, frame=self.__frame_size, throttle=throttle_, brake=brake_, steering=steering_, vehicles=vehicles_, time=True, peds=peds_, speed=speed_, location=location_) if open_: self.__detection_pickleFile = open(detection_file_name_, mode='wb') self.__labels_csv = open('labels.csv', 'wb') def __save_image(self, image): cv2.imwrite(self.__save_dir +str(self.__total_written_images)+'.png', image) def __process_image(self, image, up_crop=110, down_crop=40 ): return cv2.resize(image[up_crop:-down_crop, :], dsize=self.__target_shape) def __recv_message(self): message = self.__client.recvMessage() image = frame2numpy(message['frame'], (self.__frame_size[0], self.__frame_size[1])) detect = dict() detect['peds'] = message['peds'] detect['vehicles'] = message['vehicles'] recv_labels = [message['steering'], message['throttle'], message['brake'], message['speed'], message['time']] return message, self.__process_image(image= image), recv_labels, detect def __close_all(self): self.__detection_pickleFile.close() self.__labels_csv.close() self.__client.sendMessage(Stop()) self.__client.close() def __dump_all(self, labels, detect_list): np.savetxt(self.__labels_csv, labels, delimiter=',') for object_ in detect_list: pickle.dump(object_, self.__detection_pickleFile) def generate_dataset(self): reset_images = 0 counter = 0 reset_threshold = 6000 labels = np.zeros((1000,5), dtype=np.float32) detect_list = [] for i in tqdm(np.arange(self.__images_to_capture - self.__total_written_images)): try: message, image, labels[counter], detect = self.__recv_message() self.__save_image(image) detect_list.append(detect) self.__total_written_images += 1 reset_images += 1 counter += 1 if ((counter) % 1000 == 0 and counter > 0) or ((counter) % 1000 == 0 and counter > 0 and reset_images >= reset_threshold): if (counter) % 1000 == 0 or reset_images >= reset_threshold: # print ('Saved : ',total_images) self.__dump_all(labels, detect_list) else: self.__total_written_images -= 1000 self.__build_env() if reset_images >= reset_threshold: reset_images = 0 self.__build_env() counter = 0 detect_list = [] # We send the commands predicted by the agent back to DeepGTAV to control the vehicle except KeyboardInterrupt: self.__close_all() break self.__close_all() print(self.__total_written_images)
parser.add_argument('-sp', '--speed', default=True, help='speed') parser.add_argument('-y', '--yawRate', default=True, help='yawRate') parser.add_argument('-il', '--if_location', default=True, help='location') parser.add_argument( '-nw', '--n_workers', default=4, help='Number of workers to receive messages in parallel') 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. client = Client(ip=args.host, port=args.port) with open("road.np", "rb") as f: x_list, y_list, z_list = np.load(f) args.time[0] = np.random.randint(8, 20) args.time[1] = np.random.randint(0, 60) weather = 'EXTRASUNNY' rand_loc_idx = np.random.randint(len(x_list)) while not (x_list[rand_loc_idx] > args.loc_x_range[0] and x_list[rand_loc_idx] < args.loc_x_range[1] and y_list[rand_loc_idx] > args.loc_y_range[0] and y_list[rand_loc_idx] < args.loc_y_range[1]): rand_loc_idx = np.random.randint(len(x_list)) args.location[0] = x_list[rand_loc_idx] args.location[1] = y_list[rand_loc_idx] args.des = [0, 0, 0] with open("path.np", "rb") as f:
'--video_len', default=1200, type=int, help='Length of video to record in seconds, default 0.42*3600') args = parser.parse_args() args.driving_mode = [args.policy, args.maxspeed] print(args.driving_mode) # 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. client = Client( ip=args.host, port=args.port, datasetPath="tmp.gz", compressionLevel=0) for i_trial in range(args.n_trials): # Scenario parameters: time, weather, location with open("highway.np", "rb") as f: x_list, y_list, _ = np.load(f) # with open("road.np", "rb") as f: # x_list_, y_list_, z_list_ = np.load(f) # x_list, y_list, z_list = np.hstack((x_list,x_list_)), np.hstack((y_list, y_list_)), np.hstack((z_list, z_list_)) # with open("sampled_path_points.np", "rb") as f: # x_list, y_list = np.load(f) args.time[0] = 12#np.random.randint(8, 20) args.time[1] = 51#np.random.randint(0, 60) weather = 'EXTRASUNNY'#weather_list[np.random.randint(0, len(weather_list))] rand_loc_idx = np.random.randint(len(x_list))
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 Route0 = [ -1989.000000, -468.250000, 10.562500, 1171.351563, -1925.791748, 36.220097 ] Route1 = [ -1889.000000, -368.250000, 10.562500, 1171.351563, -1925.791748, 36.220097 ] Route2 = [
hdr.setFormatter(formatter) logger.addHandler(hdr) fd = sys.stdin.fileno() old_settings = termios.tcgetattr(fd) new_settings = old_settings new_settings[3] = new_settings[3] & ~termios.ICANON new_settings[3] = new_settings[3] & ~termios.ECHONL #print("old setting %s" %(repr(old_settings))) termios.tcsetattr(fd, termios.TCSAFLUSH, new_settings) # 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. #client = Client(ip=args.host, port=args.port, datasetPath=args.dataset_path, compressionLevel=9) #client = Client(ip=args.host, port=args.port, compressionLevel=9) client = Client(ip="10.29.1.135", port=args.port, compressionLevel=9) # Configures the information that we want DeepGTAV to generate and send to us. # See deepgtav/messages.py to see what options are supported #rate = 30 kong dataset = Dataset(rate=30, frame=[320, 160], throttle=True, brake=True, steering=True, vehicles=True, peds=True, reward=[15.0, 0.0], direction=None, speed=True, yawRate=True,
import cv2 import numpy as np print("Loading Model...") model = load_model('sample_model.h5') # Load trained model print("Model Loaded. Compiling...") model.compile(optimizer='Adadelta', loss='mean_squared_error') if input( "Continue?" ) == "y": # Wait until you load GTA V to continue, else can't connect to DeepGTAV print("Conintuing...") # Loads into a consistent starting setting print("Loading Scenario...") client = Client(ip='localhost', port=8000) # Default interface scenario = Scenario( weather='EXTRASUNNY', vehicle='blista', time=[12, 0], drivingMode=-1, location=[-2573.13916015625, 3292.256103515625, 13.241103172302246]) client.sendMessage(Start(scenario=scenario)) count = 0 print("Starting Loop...") while True: try: # Collect and preprocess image message = client.recvMessage() image = frame2numpy(message['frame'], (320, 160))
weatherCount = 0 to_remove = [] speed = [] direction = [] pred_speed = None pred_dir = None # how much data to collect data_to_collect_per_weather = 20000 rep_per_weather = 4 reset_every = int(data_to_collect_per_weather / rep_per_weather) # Creates a new connection to DeepGTAV using the specified ip and port client = Client(ip=args.host, port=args.port, datasetPath=args.dataset_path, compressionLevel=9, frame_capture_size=frame_capture_size, frame_save_size=frame_save_size) reset(weatherCount) print('Data to collect per weather:', data_to_collect_per_weather) print('Reset every:', reset_every, 'frames in each weather\n') print('{:>15} | {:^16} | {:^21} | {:^8} | {}'.format( "Weather", "Progress", "[str, thtl, brk]", "Speed", "Direction")) while True: # Main loop try: # Message recieved as a Python dictionary message = client.recvMessage(pre_count + 1 >= 0) if message is None: print('Message Error')
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:
def main(): global client, scenario client = Client(ip='localhost', port=8000) # Default interface scenario = Scenario( weather='EXTRASUNNY', vehicle='blista', time=[12, 0], drivingMode=-1, location=[-2583.6708984375, 3501.88232421875, 12.7711820602417]) client.sendMessage(Start(scenario=scenario, dataset=dataset)) print("load deepGTAV successfully! \nbegin") # load yolo v3 classes = yolo_util.read_coco_names('./files/coco/coco.names') num_classes = len(classes) input_tensor, output_tensors = yolo_util.read_pb_return_tensors( tf.get_default_graph(), "./files/trained_models/yolov3.pb", ["Placeholder:0", "concat_9:0", "mul_6:0"]) print("load yolo v3 successfully!") with tf.Session() as sess: model = load_model("files/trained_models/main_model.h5") print("load main_model successfully!") while True: fo = open(config_position, "r") # 配置1 txt = fo.read() fo.close() if txt == '0': set_gamepad(-1, -1, 0) time.sleep(0.7) print('=====================end=====================') exit(0) elif txt == '1': message = client.recvMessage() frame = frame2numpy(message['frame'], (CAP_IMG_W, CAP_IMG_H)) image_obj = Image.fromarray(frame) speed = message['speed'] boxes, scores = sess.run(output_tensors, feed_dict={ input_tensor: np.expand_dims( yolo_img_process(frame), axis=0) }) boxes, scores, labels = yolo_util.cpu_nms(boxes, scores, num_classes, score_thresh=0.4, iou_thresh=0.1) image, warning = yolo_util.draw_boxes(image_obj, boxes, scores, labels, classes, (IMAGE_H, IMAGE_W), show=False) control, throttle, breakk = drive(model=model, image=frame, speed=speed, warning=warning) print(warning) set_gamepad(control, throttle, breakk)