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 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 __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 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
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
# 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)
import keras
import pandas as pd
import numpy as np
import os
from jetracer.nvidia_racecar import NvidiaRacecar
from jetcam.csi_camera import CSICamera
import time
import vae
from vae.controller import VAEController
from stable_baselines import SAC
car = NvidiaRacecar()
throttle = 0.8
#path = "jetcar_weights.pkl"
path = "logs/sac/JetVae-v0_46/JetVae-v0.pkl"
input_array = np.zeros((1, 256))
try:
i = 0
v = VAEController()
v.load("logs/vae-256.pkl")
#model = keras.models.load_model(path)
model = SAC.load(path)
camera = CSICamera(width=112, height=112)
camera.running = True
print("Imported Camera! Ready to Start!")
while True:
print("Starting to read image")
image = camera.value.copy()
print("Image Read!")
#image = cv2.resize(image, (224//2, 224//2))
#
#Save the optimized model using the cell below
torch.save(model_trt.state_dict(), 'road_following_model_trt.pth')
#
#Load the optimized model by executing the cell below
import torch
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load('road_following_model_trt.pth'))
#
#Create the racecar class
#
from jetracer.nvidia_racecar import NvidiaRacecar
car = NvidiaRacecar()
#
#Create the camera class.
from jetcam.csi_camera import CSICamera
camera = CSICamera(width=224, height=224, capture_fps=65)
"""
Finally, execute the cell below to make the racecar move forward, steering the racecar based on the x value of the apex.
Here are some tips,
If the car wobbles left and right, lower the steering gain
If the car misses turns, raise the steering gain
If the car tends right, make the steering bias more negative (in small increments like -0.05)
If the car tends left, make the steering bias more postive (in small increments +0.05)
"""
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)
# parameter setting
# 長さの単位はcm
isLoop = True
centerLine = 30
thresWidth = 15
facingAngleThres = 15
betweenSensors = 20
timeStamp = []
timeStamp.append(time.time())
print("carmachi")
# steering setting
car = NvidiaRacecar()
car.steering_gain = 1.0
car.steering_offset = 0.0
rightMax = 1.0
leftMax = -1.0
steerParam = 1.0 # steerParam * facingAngle で facingAngle[rad]曲がる
# throttle setting
car.throttle_gain = 0.5
speedHigh = 10
speedLow = 0.5
# *Distance are the acquired distance to the wall from each sensor
# file
file = "data"
# Parent Directory path
parent_dir = "/home/petbot/jetracer/notebooks/"
# Path
path = os.path.join(parent_dir, file)
# os.mkdir(path)
print("Directory '% s' created" % file)
cap = cv2.VideoCapture(gstreamer_pipeline(flip_method=0), cv2.CAP_GSTREAMER)
print("successful")
car = NvidiaRacecar()
car.throttle = 0
car.steering = 0.5
i = 0
print("press keys")
while True:
while True:
ret_val, image = cap.read()
# cv2.imshow("CSI Camera", image)
cv2.imwrite(path + '/' + str(i) + '.jpeg', image)
i += 1
if keyboard.is_pressed("w"):
car.throttle += 0.3
elif keyboard.is_pressed("s"):
#!/usr/bin/env python3
import rospy
from jetracer.nvidia_racecar import NvidiaRacecar
from std_msgs.msg import Float32
#Initialize car variable and tune settings
car = NvidiaRacecar()
car.steering_gain = 0.65
car.steering_offset = -0.16
car.throttle_gain = 1
car.steering = 0.0
car.throttle = 0.0
#Throttle
def callback_throttle(throt):
car.throttle = throt.data
rospy.loginfo("Throttle: %s", str(throt.data))
#Steering
def callback_steering(steer):
car.steering = steer.data
rospy.loginfo("Steering: %s", str(steer.data))
#Setup node and topics subscription
def racecar():
rospy.init_node('racecar', anonymous=True)
rospy.Subscriber("throttle", Float32, callback_throttle)
rospy.Subscriber("steering", Float32, callback_steering)
rospy.spin()
from jetracer.nvidia_racecar import NvidiaRacecar
car = NvidiaRacecar()
car.steering = 0.3
print("car.steer gain {}".format(car.steering_gain))
print("car.steering_offset {}".format(car.steering_offset))
car.throttle = 0.0
print("car.throttle_gain {}".format(car.throttle_gain))
car.throttle_gain = 0.5