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()
pts.appendleft(center)
# loop over the set of tracked points
for i in range(1, len(pts)):
# if either of the tracked points are None, ignore
# them
if pts[i - 1] is None or pts[i] is None:
continue
# 단순 도식화용
thickness = int(np.sqrt(args["buffer"] / float(i + 1)) * 2.5)
cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)
# center point 존재시 검출된 객체의 center 값으로 pid 계산 및 컨트롤
if center != None:
pid_x = pidcal_x.pid_control(center[0])
pid_y = pidcal_y.pid_control(center[1])
print("pid_X : ", pid_x)
print("pid_Y : ", pid_y)
pid_controller(p_x,pid_x,p_y,pid_y)
p_x.ChangeDutyCycle(current_x_motor)
p_y.ChangeDutyCycle(current_y_motor)
time.sleep(0.002)
#print("pid_Y : ", pid_y)
# show the frame to our screen
cv2.imshow("Frame", frame)
#print(frame.shape[0],frame.shape[1])
key = cv2.waitKey(1) & 0xFF
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
#cudnn.benchmark = True
opt = opts.parse()
warper = Warper()
slidewindow = SlideWindow()
pidcal = PidCal()
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')
#trainLoader, valLoader = DataLoader.create(opt)
#print('opt',opt)
# 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(0)
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(*'DIVX')
video_name = time.time()
out = cv2.VideoWriter('output_video/{}.avi'.format(video_name), fourcc, 25.0, (video_width,video_height),0)
prev_time = 0
fps_list = []
while True:
ret, frame = cap.read()
if ret:
cur_time = time.time()
frame = cv2.resize(frame, (480,360))
input_img = frame / 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)
print("output.shape : ", output.shape)
# gpu -> cpu, tensor -> numpy
output = output.detach().cpu().numpy()
output = output[0]
output = postprocess_img(output)
output *= 255
output = np.clip(output, 0, 255)
output = np.uint8(output)
# resize
output = cv2.resize(output, (640, 480))
# threshold
ret, thr_img = cv2.threshold(output, 180, 255, 0)
# warp
output, warp_img = warper.warp(output, thr_img)
img, x_location = slidewindow.slidewindow(warp_img)
if x_location != None:
pid = round(pidcal.pid_control(int(x_location)), 6)
print("pid rate : ", pid)
auto_drive(pid, x_location)
else:
pid = pidcal.pid_control(320)
print("pid rate : ", pid)
auto_drive(pid)
end_time = time.time()
sec = end_time - cur_time
fps = 1/sec
fps_list.append(fps)
print("Estimated fps {0} " . format(fps))
out.write(output)
cv2.imshow("src", frame)
cv2.imshow("output", output)
cv2.imshow("thre", img)
key = cv2.waitKey(1) & 0xFF
if key == 27: break
elif key == ord('p'):
cv2.waitKey(-1)
def main():
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
#cudnn.benchmark = True
opt = opts.parse()
warper = Warper()
#slidewindow = SlideWindow()
# slidewindow = LineDetector()
pidcal = PidCal()
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')
#trainLoader, valLoader = DataLoader.create(opt)
#print('opt',opt)
# 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(
"/home/foscar/ISCC_2019/src/race/src/my_lane_detection/input_video/0.avi"
)
ret, frame = cap.read()
slidewindow = LineDetector(frame)
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(*'DIVX')
video_name = time.time()
out = cv2.VideoWriter('output_video/{}.avi'.format(video_name), fourcc,
25.0, (video_width, video_height), 0)
prev_time = 0
count = 0
while True:
ret, frame = cap.read()
count += 1
if ret:
cur_time = time.time()
frame = cv2.resize(frame, (480, 360))
input_img = frame / 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)
print("output.shape : ", output.shape)
# gpu -> cpu, tensor -> numpy
output = output.detach().cpu().numpy()
output = output[0]
output = postprocess_img(output)
# cv2.imshow("203",output)
output *= 255
output = np.clip(output, 0, 255)
output = np.uint8(output)
# resize
output = cv2.resize(output, (640, 480))
# cv2.imshow('resize',output)
# threshold
ret, thr_img = cv2.threshold(output, 20, 255, 0)
# cv2.imshow('threshold',thr_img)
# warp
warp_img = warper.warp(thr_img)
# cv2.imshow('warped',warp_img)
# cv2.imshow("new output", canny_like_output)
#canny = cv2.Canny(warp_img, 40, 255)
kernel1 = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
kernel2 = np.ones((
5,
5,
), np.uint8)
#dilate = cv2.dilate(warp_img, kernel1, iterations=2)
#closed = cv2.morphologyEx(dilate, cv2.MORPH_OPEN, kernel2)
# x_start_L, x_start_R=slidewindow.find_sliding_point(warp_img)
# img, x_location = slidewindow.slide_window(x_start_L,x_start_R,warp_img)
slided_img, x_location, point_list_left, point_list_right = slidewindow.main(
warp_img)
if x_location != None:
# cv2.circle(img,(int(x_location),300),5,(0,0,255),3)
pid = round(pidcal.pid_control(int(x_location)), 6)
#print("pid rate : ", pid)
auto_drive(pid, x_location)
else:
pid = pidcal.pid_control(slidewindow_middle)
print("pid rate : ", pid)
auto_drive(pid)
end_time = time.time()
sec = end_time - cur_time
fps = 1 / sec
fps_list.append(fps)
print("Estimated fps {0} ".format(fps))
out.write(output)
cv2.imshow("src", frame)
pid_draw.append(pid)
# cv2.imshow("th_img", thr_img)
# cv2.imshow("output", output)
# img = cv2.imread('/home/foscar/Downloads/wapped_screenshot_12.08.2020.png',cv2.IMREAD_GRAYSCALE)
# img = cv2.resize(img, (640, 480))
# ret, thr_img = cv2.threshold(img,20,255,cv2.THRESH_BINARY)
# img ,xloc, point_list_left, point_list_right = slidewindow.main(thr_img)
# plt.xlim(0,640)
# plt.ylim(0,480)
# plt.plot(point_list_left[0], point_list_left[1])
# plt.plot(point_list_right[0], point_list_right[1])
# plt.show()
# cv2.imshow('aa',img)
# key = cv2.waitKey(1) & 0xFFx
# if key == 27: break
# elif key == ord('p'):
# cv2.waitKey(-1)
cv2.imshow("ws", slided_img)
print("x_loc :", x_location)
key = cv2.waitKey(1) & 0xFF
if key == 27: break
elif key == ord('p'):
cv2.waitKey(-1)
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)