def main_loop():
while True:
if learning_stage():
break
cv.destroyAllWindows()
jm.set_throttle(0)
jm.set_angle(90)
time.sleep(5)
if not debug:
jm.set_throttle(run_throttle)
while True:
real_action()
def learning_stage() -> bool:
global now_dir, run_throttle
_, raw_img = img.read()
input_tensor = cv2img2tensor(raw_img)
inputs = Variable(input_tensor.cuda()).float()
cv.imshow('judge', raw_img)
in_char = cv.waitKey(1) # getch()
if in_char == ord('a'):
now_dir = 0 # jm.MAX_STEER_DEV
jm.set_angle(130)
elif in_char == ord('s'):
now_dir = 1 # straight
jm.set_angle(90)
elif in_char == ord('d'):
now_dir = 2 # -jm.MAX_STEER_DEV
jm.set_angle(50)
elif in_char == ord('i'):
run_throttle += 0.01
if run_throttle > 1:
run_throttle = 1
elif in_char == ord('k'):
run_throttle -= 0.01
if run_throttle < 0:
run_throttle = 0
elif in_char == ord('w'):
return True
debug_print(f'\n\ninput: {in_char}')
label = Variable(torch.tensor([now_dir]).cuda(),
requires_grad=False).long()
debug_print(f'label: {label}')
outputs = net(inputs)
if not debug:
jm.set_throttle(run_throttle)
debug_print(f'output: {outputs}')
optimizer.zero_grad()
loss = criterion(outputs, label).cuda()
loss.backward()
optimizer.step()
return False
def finish_program(video_capture: cv.VideoCapture) -> None:
video_capture.release()
jm.set_throttle(0)
cv.destroyAllWindows()
else:
r = const / (640 - left_coord[0] - right_coord[0])
print(r)
jm.set_angle(steer_dampener(180 / m.pi * m.atan(car_len / r)))
return r
def debug_img_show(image_in):
if debug:
cv.imshow('debug', image_in)
debug = True
jm.set_throttle(0)
time.sleep(1)
# camera init
img = jm.cap
prev_turn = m.inf
try:
while True:
_, image_raw = img.read()
image_raw = cv.resize(image_raw, (640, 360),
interpolation=cv.INTER_AREA)
image_bird = bird_eye.bird_eye_warp(image_raw)
list_up_left, up_left_image = \
search_lane_center(image_bird[:image_bird.shape[0] // 2, :image_bird.shape[1] // 2], 1)
if debug:
print(a)
def cv2img2tensor(image):
tensor_img = numpy.transpose(image, (2, 0, 1))
tensor_img = torch.from_numpy(tensor_img)
return tensor_img.unsqueeze_(0)
net: Net = Net()
net.cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = optim.SGD(net.parameters(), lr=0.0001, momentum=0.5)
jm.set_throttle(0)
run_throttle = 0.15
time.sleep(1)
# camera init
img = jm.cap
debug = False
def main_loop():
while True:
if learning_stage():
break
cv.destroyAllWindows()
jm.set_throttle(0)