def __init__(self):
# 센서 객체 생성
self.__pcf8591 = Pcf8591(0x48)
self.__distance = Distance(self.__pcf8591)
self.cm = 0
self.pre = 0
# 모터 제어
self.__motor_control = NvidiaRacecar()
self.stop_flag = False
# Oled 표시, 스레딩
self.__oled = Oled()
self.__oled_flag = True
self.__oled_thread = threading.Thread(target=self.__oled_setting, daemon=True)
self.__oled_thread.start()
# =================motor values========================
self.__handle_angle = 0
self.__dcMotor_speed = 0.55
self.__max_speed = self.__dcMotor_speed
self.__motor_direction = "stop"
# =================car status==========================
self.__working = False
self.__position = None
self.__dst = None
# =================pid controller======================
self.pid_controller = PIDController(round(datetime.utcnow().timestamp() * 1000))
# self.pid_controller.set_gain(0.63, -0.001, 0.23)
# self.pid_controller.set_gain(0.61, 0, 0.22)
self.pid_controller.set_gain(0.55, 0, 0.22)
# ==================line detect variable===============
self.road_half_width_list = deque(maxlen=10)
self.road_half_width_list.append(100)
self.prev_road_center_x = deque(maxlen=5)
self.prev_road_center_x.append(120)
# 플래그 들
self.crosswalk_flag = False
self.which_side = None
self.change_road_flag = False
self.init_flag = None
self.stop_and_forward_flag = False
self.L_lines = []
self.R_lines = []
self.L_x = 0
self.R_x = 240
# =================mode 1:auto 2:manual================
self.__mode = 0
# =======================count============================
self.count = 0
self.stop_count = 0
def __init__(self, config):
self.car = NvidiaRacecar(
steering_channel=config.jetracer_steering_channel(),
throttle_channel=config.jetracer_throttle_channel(),
steering_gain=config.jetracer_steering_gain(),
steering_offset=config.jetracer_steering_offset(),
throttle_gain=config.jetracer_throttle_gain(),
throttle_offset=config.jetracer_throttle_offset())
def __init__(self, camera_width=112, camera_height=112, fps=30):
self.car = NvidiaRacecar()
print("====== LOADING CAMERA ======")
self.camera = CSICamera(width=camera_width,
height=camera_height,
capture_fps=fps)
self.camera.running = True
print("====== CAMERA LOADED SUCCESSFULLY ======")
self.car.throttle = 0
self.car.steering = 0
def doexecute():
print("start")
CATEGORIES = ['apex']
device = torch.device('cuda')
model = torchvision.models.resnet18(pretrained=False)
model.fc = torch.nn.Linear(512, 2 * len(CATEGORIES))
model = model.cuda().eval().half()
# model.load_state_dict(torch.load('model.pth'))
model.load_state_dict(torch.load('data/model.pth'))
print("1")
data = torch.zeros((1, 3, 224, 224)).cuda().half()
model_trt = torch2trt(model, [data], fp16_mode=True)
torch.save(model_trt.state_dict(), 'road_following_model_trt.pth')
print("2")
model_trt = TRTModule()
model_trt.load_state_dict(torch.load('road_following_model_trt.pth'))
print("model load end")
car = NvidiaRacecar()
# Left Camera
camera0 = nano.Camera(device_id=0, flip=2, width=224, height=224, fps=60)
# Right Camera
camera1 = nano.Camera(device_id=1, flip=2, width=224, height=224, fps=60)
STEERING_GAIN = 0.75
STEERING_BIAS = 0.00
cnt = 0
while True:
image = camera0.read()
image = preprocess(image).half()
output = model_trt(image).detach().cpu().numpy().flatten()
x = float(output[0])
car.steering = x * STEERING_GAIN + STEERING_BIAS
car.throttle = 0.5
print(str(cnt) + ":" + str(x) + ":")
cnt = cnt + 1
def prepare():
# 走行Button
GPIO.setmode(GPIO.BOARD)
GPIO.setup(recbtn, GPIO.IN)
GPIO.add_event_detect(gobtn,
GPIO.FALLING,
callback=btn_thrd,
bouncetime=200)
# Left Camera
camera0 = nano.Camera(device_id=0, flip=2, width=224, height=224, fps=60)
# Right Camera
camera1 = nano.Camera(device_id=1, flip=2, width=224, height=224, fps=60)
CATEGORIES = ['apex']
device = torch.device('cuda')
model = torchvision.models.resnet18(pretrained=False)
model.fc = torch.nn.Linear(512, 2 * len(CATEGORIES))
model = model.cuda().eval().half()
# model.load_state_dict(torch.load('model.pth'))
model.load_state_dict(torch.load('data/model.pth'))
data = torch.zeros((1, 3, 224, 224)).cuda().half()
model_trt = torch2trt(model, [data], fp16_mode=True)
torch.save(model_trt.state_dict(), 'road_following_model_trt.pth')
model_trt = TRTModule()
model_trt.load_state_dict(torch.load('road_following_model_trt.pth'))
car = NvidiaRacecar()
STEERING_GAIN = -0.75
STEERING_BIAS = 0.00
car.throttle = 0.25
cnt = 0
while True:
image = camera0.read()
image = preprocess(image).half()
output = model_trt(image).detach().cpu().numpy().flatten()
x = float(output[0])
car.steering = x * STEERING_GAIN + STEERING_BIAS
print(str(cnt) + ":" + str(x) + ":")
cnt = cnt + 1
# Model
if os.path.isfile(MODEL_PATH_TRT):
model_trt = TRTModule()
model_trt.load_state_dict(torch.load(MODEL_PATH_TRT))
else:
model = AutopilotModel(pretrained=False)
model.load_from_path(MODEL_PATH)
model.eval()
x = torch.ones((1, FRAME_CHANNELS, FRAME_SIZE, FRAME_SIZE)).cuda()
model_trt = torch2trt(model, [x], fp16_mode=True)
torch.save(model_trt.state_dict(), MODEL_PATH_TRT)
try:
# Car
car = NvidiaRacecar()
car.throttle_gain = THROTTLE_GAIN
car.steering_offset = STEERING_OFFSET
# Camera
camera = CSICamera(width=CAMERA_WIDTH, height=CAMERA_HEIGHT)
# Control Loop
while True:
if SHOW_LOGS:
start_time = time.time()
camera_frame = camera.read()
cropped_frame = center_crop_square(camera_frame)
resized_frame = cv2.resize(cropped_frame, (FRAME_SIZE, FRAME_SIZE))
preprocessed_frame = preprocess_image(resized_frame)
def __init__(self):
self.car = NvidiaRacecar(steering_gain=self.STEERING_GAIN,
steering_offset=self.STEERING_OFFSET,
throttle_gain=self.THROTTLE_GAIN,
throttle_offset=self.THROTTLE_OFFSET)
def collect_data(train,throttle,steering,s_gain):
# Importing NvidiaRacecar modeule and CSICamera
# Note: CSICamera object can be created once at a time
camera = CSICamera(width=112, height=112)
camera.running = True
car = NvidiaRacecar()
# initialize parameters: 0
car.throttle = throttle
car.steering = steering
car.throttle_gain = -0.5
# file name
if not os.path.exists("data/"):
os.makedirs("data/")
file_name=input("File_name: ")
try:
while(True):
# Display steering and Throttle
print("Steering: {}, Throttle {}" .format(car.steering,car.throttle))
# Increase throttle on press
if keyboard.is_pressed('i'):
if car.throttle<=0.04:
car.throttle=0.04
if car.throttle>=0.062:
car.throttle=car.throttle
else:
car.throttle=car.throttle+0.001
# Decrease throttle on release
if not keyboard.is_pressed('i'):
if car.throttle<=0.04:
car.throttle=0.04
else:
car.throttle=car.throttle-0.01
# turn left
if keyboard.is_pressed('j'):
if car.steering>=0.6:
car.steering=car.steering
else:
car.steering =car.steering+0.01
### turn right
if keyboard.is_pressed('l'):
if car.steering<=-0.6:
car.steering=car.steering
else:
car.steering =car.steering-0.01
# Resease steering back to steering angle of 0 degrees
if not keyboard.is_pressed('j') and not keyboard.is_pressed('l'):
if not keyboard.is_pressed('j'):
if car.steering<=0:
car.steering=car.steering
else:
car.steering =car.steering-0.015
if not keyboard.is_pressed('l'):
if car.steering>=0:
car.steering=car.steering
else:
car.steering =car.steering+0.015
# Emergency break
if keyboard.is_pressed('space'):
car.throttle=-100
time.sleep(0.1)
car.throttle=0
# Save the recording labelled data: Press
if keyboard.is_pressed('s'):
car.throttle=0
car.steering=0
print("saving")
np.savez("data/"+str(file_name)+".npz", train=np.array(train))
print("saved")
camera.running=False
del camera
break
# Abort
if keyboard.is_pressed('a'):
car.throttle=0
car.steering=0
camera.running=False
del camera
break
# append recording
train.append([camera.value,car.throttle,car.steering])
except:
camera.running=False
del camera
if __name__ == '__main__':
# input: train,throttle,steering,s_gain
collect_data([],0,0,-0.5)