def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
#cudnn.benchmark = True
pidcal = PidCal()
opt = opts.parse()
warper = Warper()
slidewindow = SlideWindow()
print(("device id: {}".format(torch.cuda.current_device())))
print("torch.version", torch.__version__)
print("cuda_version", torch.version.cuda)
models = importlib.import_module('models.init')
# print(models)
criterions = importlib.import_module('criterions.init')
checkpoints = importlib.import_module('checkpoints')
Trainer = importlib.import_module('models.' + opt.netType + '-train')
# Data loading
print('=> Setting up data loader')
# Load previous checkpoint, if it exists
print('=> Checking checkpoints')
checkpoint = checkpoints.load(opt)
# Create model
model, optimState = models.setup(opt, checkpoint)
model.cuda()
criterion = criterions.setup(opt, checkpoint, model)
##################################################################################
model.eval()
cap = cv2.VideoCapture("input_video/test.avi")
if cap.isOpened():
print("width : {}, height : {}".format(cap.get(3), cap.get(4)))
video_width = int(cap.get(3))
video_height = int(cap.get(4))
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
# out = cv2.VideoWriter('output_video/TEST_1.avi', fourcc, 20.0, (1280,480),0)
prev_time = 0
fps_list = []
# fourcc =cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter('input_video/processed_video.avi', fourcc, 40.0,
(480, 320), 0)
steer_list = list()
lpf_list = list()
while True:
ret, frame = cap.read()
if ret:
cur_time = time.time()
frame_new = cv2.resize(frame, (320, 180))
input_img = frame_new / 255.
input_img = preprocess_img(input_img)
# array to tensor
input_img = torch.from_numpy(input_img).float()
with torch.no_grad():
inputData_var = Variable(input_img).unsqueeze(0).cuda()
# inference
output = model.forward(inputData_var)
output = torch.sigmoid(output)
#output = F.softmax(output, dim=1)
# gpu -> cpu, tensor -> numpy
output = output.detach().cpu().numpy()
output = output[0]
output = postprocess_img(output)
output = np.clip(output, 0, 1)
output *= 255
output = np.uint8(output)
output = cv2.resize(output, (640, 360))
output[output > 80] = 255
output[output <= 80] = 0
warper_img, point_img = warper.warp(output)
ret, left_start_x, right_start_x, cf_img = slidewindow.w_slidewindow(
warper_img)
if ret:
left_x_current, right_x_current, sliding_img, steer_theta = slidewindow.h_slidewindow(
warper_img, left_start_x, right_start_x)
cv2.imshow('sliding_img', sliding_img)
steer_list.append(steer_theta)
lpf_result = lpf(steer_theta, 0.5)
lpf_list.append(lpf_result)
print("steer theta:", steer_theta)
if steer_theta < -28 or steer_theta > 28:
continue
else:
pid = round(pidcal.pid_control(int(50 * steer_theta)),
6)
print("pid :", pid)
'''
auto_drive(pid)
'''
else:
pidcal.error_sum = 0
end_time = time.time()
sec = end_time - cur_time
fps = 1 / sec
fps_list.append(fps)
print("Estimated fps {0} ".format(fps))
# out.write(add_img)
cv2.imshow("frame", frame)
out.write(warper_img)
# cv2.imshow("src", warper_img)
# cv2.imshow("out_img", output)
cv2.imshow("cf_img", cf_img)
key = cv2.waitKey(1) & 0xFF
if key == 27: break
elif key == ord('p'):
cv2.waitKey(-1)
plt.figure(1)
plt.plot(steer_list)
plt.figure(2)
plt.plot(lpf_list)
plt.show()
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
#cudnn.benchmark = True
pidcal = PidCal()
opt = opts.parse()
warper = Warper()
slidewindow = SlideWindow()
stopline = StopLine()
print(("device id: {}".format(torch.cuda.current_device())))
print("torch.version",torch.__version__)
print("cuda_version",torch.version.cuda)
models = importlib.import_module('models.init')
# print(models)
criterions = importlib.import_module('criterions.init')
checkpoints = importlib.import_module('checkpoints')
Trainer = importlib.import_module('models.' + opt.netType + '-train')
# Data loading
print('=> Setting up data loader')
# Load previous checkpoint, if it exists
print('=> Checking checkpoints')
checkpoint = checkpoints.load(opt)
# Create model
model, optimState = models.setup(opt, checkpoint)
model.cuda()
criterion = criterions.setup(opt, checkpoint, model)
##################################################################################
model.eval()
cap = None
if opt.video_idx is 0:
cap = cv2.VideoCapture("input_video/720p.mp4")
elif opt.video_idx is 1:
cap = cv2.VideoCapture("input_video/straight.avi")
elif opt.video_idx is 2:
cap = cv2.VideoCapture("input_video/test.avi")
elif opt.video_idx is 3:
cap = cv2.VideoCapture("input_video/track.avi")
elif opt.video_idx is 4:
cap =cv2.VideoCapture("output_video/field.avi")
elif opt.video_idx is 5:
cap = cv2.VideoCapture("output_video/2020-08-23 19:20:01.166517.avi")
else:
cap = cv2.VideoCapture(0)
# video test
cap.set(3,1280)
cap.set(4,720)
if cap.isOpened():
print("width : {}, height : {}".format(cap.get(3), cap.get(4)))
video_width = int(cap.get(3))
video_height = int(cap.get(4))
#fourcc = cv2.VideoWriter_fourcc('M','J','P','G')
# out = cv2.VideoWriter('output_video/TEST_1.avi', fourcc, 20.0, (1280,480),0)
prev_time = 0
fps_list = []
now = datetime.datetime.now()
fourcc = None
out = None
if opt.video_idx > 2:
fourcc =cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter('output_video/' + str(now) + '.avi',fourcc,30.0,(1280,720))
pid_list=list()
steer_list = list()
lpf_list = list()
pid_old = None
steer_theta = 0
i=0
x_location = 240
frame_cnt = 0
while True:
ret, frame = cap.read()
if frame is None:
break
frame_height, frame_width, frame_channels = frame.shape
print("Frame Info : (Height, Width, Channels) : ({}, {}, {})".format(frame_height, frame_width, frame_channels))
record_frame = cv2.resize(frame, (1280,720))
if ret:
cur_time = time.time()
frame_new = cv2.resize(frame, (320,180))
input_img = frame_new / 255.
input_img = preprocess_img(input_img)
# array to tensor
input_img = torch.from_numpy(input_img).float()
with torch.no_grad():
inputData_var = Variable(input_img).unsqueeze(0).cuda()
# inference
output = model.forward(inputData_var)
output = torch.sigmoid(output)
#output = F.softmax(output, dim=1)
# gpu -> cpu, tensor -> numpy
output = output.detach().cpu().numpy()
output = output[0]
output = postprocess_img(output)
output = np.clip(output, 0, 1)
output *= 255
output = np.uint8(output)
output = cv2.resize(output, (640, 360))
output[output>80] = 255
output[output<=80] = 0
# cv2.circle(output, (output.shape[1]/2, output.shape[0]), 9, (255,255,0), -1)
cv2.imshow("output_img", output)
print("shape_info", output.shape)
# cv2.circle(output, (output.shape[0]/2, output.shape[1]/2), 9, (0,255,0), -1)
#warper_img = warper.warp(output)
warper_img = warper.warp_test(output)
cv2.imshow("warp_img", warper_img)
# warper_img_test = warper.warp_test(output)
# cv2.imshow("warp_img_test",warper_img_test)
ret, left_start_x, right_start_x, cf_img = slidewindow.w_slidewindow(warper_img, 180)
if ret:
i+=1
left_x_current,right_x_current, sliding_img,steer_theta,center, length = slidewindow.h_slidewindow(warper_img, left_start_x, right_start_x)
#stop_test Lee youn joo
# if center != None:
# locate_x, locate_y = center
# if (warper_img[int(locate_y)][int(locate_x)] != 0):
# stopFlag, id_L, id_R = stopline.findline(warper_img,locate_x,locate_y,length,left_x_current,right_x_current)
# if stopFlag != None:
# if frame_cnt == 0:
# print('STOP!')
# cv2.line(warper_img,id_L,id_R,(0,0,255),2)
# cv2.waitKey(-1)
# frame += 1
# if (frame_cnt > 0):
# frame_cnt = 0
# print(stopFlag,frame_cnt)
# SD.stop(warper_img)
SD.stoping_tmp(warper_img)
cv2.imshow('sliding_img', sliding_img)
steer_list.append(steer_theta)
x_location = (left_x_current+right_x_current)/2
# low pass filter
steer_theta = lpf(steer_theta, 0.3)
lpf_list.append(steer_theta)
# steer theta : Degree
print("steer theta:" ,steer_theta)
#
# if steer_theta<-28.0 or steer_theta >28.0:
# # auto_drive(pid_old)
# auto_drive(steer_theta)
# else:
# degree angle
pid = round(pidcal.pid_control(steer_theta),6)
pid_list.append(pid)
print("pid :",pid)
pid_old = pid
auto_drive(steer_theta)
# auto_drive(pid)
else:
auto_drive(steer_theta)
# auto_drive(pid)
pidcal.error_sum = 0
pidcal.error_old = 0
end_time = time.time()
sec = end_time - cur_time
fps = 1/sec
fps_list.append(fps)
print("Estimated fps {0} " . format(fps))
# out.write(add_img)
cv2.imshow("frame",frame)
if opt.video_idx == -1:
print("frame.shape : {}".format(frame.shape))
out.write(frame)
# cv2.imshow("src", warper_img)
# cv2.imshow("out_img", output)
cv2.imshow("cf_img", cf_img)
key = cv2.waitKey(1) & 0xFF
if key == 27: break
elif key == ord('p'):
cv2.waitKey(-1)
cap.release()
cv2.destroyAllWindows()
plt.plot(range(i),steer_list,label='steer')
plt.legend()
plt.plot(range(i),pid_list,label='pid')
plt.legend()
plt.plot(range(i),lpf_list,label='lpf')
plt.legend()
pid_info=pidcal.info_p()
plt.savefig('output_video/video_idx:'+ str(opt.video_idx)+' '+str(pid_info) +'.png', dpi=300)