def __init__(self):
self.image_pub = rospy.Publisher("lanedetframe", Image, queue_size=1)
self.maskimg_pub = rospy.Publisher("lanedetmask", Image, queue_size=1)
self.binimg_pub = rospy.Publisher("lanedetbin", Image, queue_size=1)
self.morphoimg_pub = rospy.Publisher("lanedetmorph",
Image,
queue_size=1)
# self.bridge = CvBridge()
self.yolo_sub = rospy.Subscriber("YOLO_detect_result_boxes",
BoundingBoxes, self.callbackyolo)
self.tlight_sub = rospy.Subscriber("tlight_detect_result_boxes",
BoundingBoxes,
self.callbackTrafficLight)
# self.image_sub = rospy.Subscriber("YOLO_detect_result", Image, self.callbackRos)
# self.image_sub = rospy.Subscriber("/camera/image_color", Image, self.callbackRos)
self.image_sub = rospy.Subscriber("/wideangle/image_color", Image,
self.callbackRos)
# self.image_sub = message_filters.Subscriber("/camera/rgb/image_raw", Image，queue_size=1, buff_size=110592*6)
self.weights_file = rospy.get_param("lanenet_weight")
self.CUDA = torch.cuda.is_available()
self.postprocessor = LaneNetPostProcessor()
self.warningModule = Detection()
self.band_width = 1.5
self.image_X = CFG.IMAGE_WIDTH
self.image_Y = CFG.IMAGE_HEIGHT
self.car_X = self.image_X / 2
self.car_Y = self.image_Y
self.model = LaneNet(pretrained=False,
embed_dim=4,
delta_v=.5,
delta_d=3.)
self.save_dict = torch.load(self.weights_file, map_location='cuda:0')
self.model.load_state_dict(self.save_dict['net'])
# self.model.load_state_dict(torch.load(self.weights_file, map_location='cuda:0'))
if self.CUDA: self.model.cuda()
self.model.set_test()
self.lastlane = np.ndarray(4, )
self.bridge = CvBridge()
self.leftlane = Lane('left')
self.rightlane = Lane('right')
self.tracker = LaneTracker()
# self.out = cv2.VideoWriter(str(time.time())+'testwrite.avi',cv2.VideoWriter_fourcc(*'MJPG'), 10.0, (CFG.IMAGE_WIDTH, CFG.IMAGE_HEIGHT),True)
self.img = np.zeros([CFG.IMAGE_WIDTH, CFG.IMAGE_HEIGHT, 3], np.uint8)
self.yoloBoxes = BoundingBoxes()
self.trafficLightBoxes = BoundingBoxes()
self.warning = 0
def _load_model(self):
model=LaneNet()
if self.mode=='parallel':
model=DataParallel(model)
model.load_state_dict(torch.load(self.model_path))
model=model.cuda()
return model
def __init__(self):
self.image_pub = rospy.Publisher("lanedetframe", Image,queue_size = 1)
# self.bridge = CvBridge()
# self.image_sub = rospy.Subscriber("YOLO_detect_result", Image, self.callbackRos)
self.image_sub = rospy.Subscriber("/camera/rgb/image_raw", Image, self.callbackRos)
# self.yolobbox_sub = rospy.Subscriber("publishers/bounding_boxes/topic", BoundingBoxes, self.callbackRos)
self.weights_file = '/home/iairiv/code/lane_waring_final/experiments/exp1/exp1_best.pth'
self.CUDA = torch.cuda.is_available()
self.postprocessor = LaneNetPostProcessor()
self.warning = Detection()
self.band_width = 1.5
self.image_X = 1920
self.image_Y = 1200
self.car_X = self.image_X/2
self.car_Y = self.image_Y
self.model = LaneNet(pretrained=False, embed_dim=4, delta_v=.5, delta_d=3.)
self.save_dict = torch.load(self.weights_file, map_location='cuda:0')
self.model.load_state_dict(self.save_dict['net'])
# self.model.load_state_dict(torch.load(self.weights_file, map_location='cuda:0'))
if self.CUDA: self.model.cuda()
self.model.set_test()
self.lastlane = np.ndarray(4,)
self.bridge = CvBridge()
def main():
args = parse_args()
img_path = args.img_path
weight_path = args.weight_path
_set = "IMAGENET"
mean = IMG_MEAN[_set]
std = IMG_STD[_set]
transform_img = Resize((800, 288))
transform_x = Compose(ToTensor(), Normalize(mean=mean, std=std))
transform = Compose(transform_img, transform_x)
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB for net model input
img = transform_img({'img': img})['img']
x = transform_x({'img': img})['img']
x.unsqueeze_(0)
net = LaneNet(pretrained=False, embed_dim=4, delta_v=.5, delta_d=3.)
save_dict = torch.load(weight_path, map_location='cpu')
net.load_state_dict(save_dict['net'])
net.eval()
output = net(x)
embedding = output['embedding']
embedding = embedding.detach().cpu().numpy()
embedding = np.transpose(embedding[0], (1, 2, 0))
binary_seg = output['binary_seg']
bin_seg_prob = binary_seg.detach().cpu().numpy()
bin_seg_pred = np.argmax(bin_seg_prob, axis=1)[0]
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
seg_img = np.zeros_like(img)
lane_seg_img = embedding_post_process(embedding, bin_seg_pred,
args.band_width, 4)
color = np.array([[255, 125, 0], [0, 255, 0], [0, 0, 255], [0, 255, 255]],
dtype='uint8')
for i, lane_idx in enumerate(np.unique(lane_seg_img)):
if lane_idx == 0:
continue
seg_img[lane_seg_img == lane_idx] = color[i - 1]
img = cv2.addWeighted(src1=seg_img, alpha=0.8, src2=img, beta=1., gamma=0.)
cv2.imwrite("demo/demo_result.jpg", img)
if args.visualize:
cv2.imshow("", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
folders.insert(0, path)
break
return folders
# ------------ data and model ------------
# Imagenet mean, std
mean=(0.485, 0.456, 0.406)
std=(0.229, 0.224, 0.225)
transform = Compose(Resize(resize_shape), ToTensor(),
Normalize(mean=mean, std=std))
dataset_name = exp_cfg['dataset'].pop('dataset_name')
Dataset_Type = getattr(dataset, dataset_name)
test_dataset = Dataset_Type(Dataset_Path['Tusimple'], "test", transform)
test_loader = DataLoader(test_dataset, batch_size=32, collate_fn=test_dataset.collate, num_workers=4)
net = LaneNet(pretrained=True, **exp_cfg['model'])
save_name = os.path.join(exp_dir, exp_dir.split('/')[-1] + '_best.pth')
save_dict = torch.load(save_name, map_location='cpu')
print("\nloading", save_name, "...... From Epoch: ", save_dict['epoch'])
net.load_state_dict(save_dict['net'])
net = torch.nn.DataParallel(net.to(device))
net.eval()
# ------------ test ------------
out_path = os.path.join(exp_dir, "coord_output")
evaluation_path = os.path.join(exp_dir, "evaluate")
if not os.path.exists(out_path):
os.mkdir(out_path)
if not os.path.exists(evaluation_path):
os.mkdir(evaluation_path)
dump_to_json = []
class Lane_warning:
def __init__(self):
self.image_pub = rospy.Publisher("lanedetframe", Image, queue_size=1)
self.maskimg_pub = rospy.Publisher("lanedetmask", Image, queue_size=1)
self.binimg_pub = rospy.Publisher("lanedetbin", Image, queue_size=1)
self.morphoimg_pub = rospy.Publisher("lanedetmorph",
Image,
queue_size=1)
# self.bridge = CvBridge()
self.yolo_sub = rospy.Subscriber("YOLO_detect_result_boxes",
BoundingBoxes, self.callbackyolo)
self.tlight_sub = rospy.Subscriber("tlight_detect_result_boxes",
BoundingBoxes,
self.callbackTrafficLight)
# self.image_sub = rospy.Subscriber("YOLO_detect_result", Image, self.callbackRos)
# self.image_sub = rospy.Subscriber("/camera/image_color", Image, self.callbackRos)
self.image_sub = rospy.Subscriber("/wideangle/image_color", Image,
self.callbackRos)
# self.image_sub = message_filters.Subscriber("/camera/rgb/image_raw", Image，queue_size=1, buff_size=110592*6)
self.weights_file = rospy.get_param("lanenet_weight")
self.CUDA = torch.cuda.is_available()
self.postprocessor = LaneNetPostProcessor()
self.warningModule = Detection()
self.band_width = 1.5
self.image_X = CFG.IMAGE_WIDTH
self.image_Y = CFG.IMAGE_HEIGHT
self.car_X = self.image_X / 2
self.car_Y = self.image_Y
self.model = LaneNet(pretrained=False,
embed_dim=4,
delta_v=.5,
delta_d=3.)
self.save_dict = torch.load(self.weights_file, map_location='cuda:0')
self.model.load_state_dict(self.save_dict['net'])
# self.model.load_state_dict(torch.load(self.weights_file, map_location='cuda:0'))
if self.CUDA: self.model.cuda()
self.model.set_test()
self.lastlane = np.ndarray(4, )
self.bridge = CvBridge()
self.leftlane = Lane('left')
self.rightlane = Lane('right')
self.tracker = LaneTracker()
# self.out = cv2.VideoWriter(str(time.time())+'testwrite.avi',cv2.VideoWriter_fourcc(*'MJPG'), 10.0, (CFG.IMAGE_WIDTH, CFG.IMAGE_HEIGHT),True)
self.img = np.zeros([CFG.IMAGE_WIDTH, CFG.IMAGE_HEIGHT, 3], np.uint8)
self.yoloBoxes = BoundingBoxes()
self.trafficLightBoxes = BoundingBoxes()
self.warning = 0
def callbackyolo(self, boxmsg):
print('callbackyolo, boxes len:', boxmsg.objNum)
self.yoloBoxes.objNum = boxmsg.objNum
self.yoloBoxes.bounding_boxes = []
for i in range(boxmsg.objNum):
print('box id:', boxmsg.bounding_boxes[i].id)
box = boxmsg.bounding_boxes[i]
self.yoloBoxes.bounding_boxes.append(box)
# cv2.rectangle(self.img, (int(box.xmin), int(box.ymin)), (int(box.xmax), int(box.ymax)), (0, 255, 0), 4)
def callbackTrafficLight(self, boxmsg):
print('callbackTrafficLight, boxes len:', boxmsg.objNum)
self.trafficLightBoxes.objNum = boxmsg.objNum
self.trafficLightBoxes.bounding_boxes = []
for i in range(boxmsg.objNum):
print('box id:', boxmsg.bounding_boxes[i].id)
box = boxmsg.bounding_boxes[i]
self.trafficLightBoxes.bounding_boxes.append(box)
def transform_input(self, img):
_set = "IMAGENET"
mean = IMG_MEAN[_set]
std = IMG_STD[_set]
# transform_img = Resize((800, 288))
transform_img = Resize((512, 256))
transform_x = Compose(ToTensor(), Normalize(mean=mean, std=std))
#img_org = img[255:945, :, :]
img = transform_img({'img': img})['img']
x = transform_x({'img': img})['img']
# print(x)
x.unsqueeze_(0)
x = x.to('cuda')
return x
def detection(self, input):
#startt = time.time()
if self.CUDA:
input = input.cuda()
with torch.no_grad():
output = self.model(input, None)
# print('detection use：', time.time()-startt)
return self.cluster(output)
def cluster(self, output):
#startt = time.time()
global g_frameCnt
embedding = output['embedding']
embedding = embedding.detach().cpu().numpy()
embedding = np.transpose(embedding[0], (1, 2, 0))
binary_seg = output['binary_seg']
bin_seg_prob = binary_seg.detach().cpu().numpy()
bin_seg_pred = np.argmax(bin_seg_prob, axis=1)[0]
# seg = bin_seg_pred * 255
postprocess_result = self.postprocessor.postprocess(
binary_seg_result=bin_seg_pred, instance_seg_result=embedding)
# cv2.imwrite(str(g_frameCnt)+'_mask.png', postprocess_result['mask_image'])
# cv2.imwrite(str(g_frameCnt)+'_binary.png', postprocess_result['binary_img'])
return postprocess_result
def process(self, frame):
startt = time.time()
cropImg = cropRoi(frame)
input_image = self.transform_input(cropImg)
# startt = time.time()
postProcResult = self.detection(input_image)
self.img = frame.copy()
# cv2.imwrite(str(g_frameCnt)+'.png', frame)
self.tracker.process(postProcResult['detectedLanes'])
llane = self.tracker.leftlane
rlane = self.tracker.rightlane
lanePoints = {'lanes': [llane.points, rlane.points]}
# self.warning = self.warningModule.detect(lanePoints)
# if self.warning == 1:
# soundplayTh = threading.Thread(target=playWarningSound)
# soundplayTh.start()
color = (0, 0, 255) if self.warning == 1 else (0, 255, 0)
# if signal == 1:
# playsound.playsound('/space/warn.mp3')
for idx in range(11):
cv2.line(
self.img,
(int(llane.points[idx][0]), int(llane.points[idx][1])),
(int(llane.points[idx + 1][0]), int(llane.points[idx + 1][1])),
color, 10)
cv2.line(
self.img,
(int(rlane.points[idx][0]), int(rlane.points[idx][1])),
(int(rlane.points[idx + 1][0]), int(rlane.points[idx + 1][1])),
color, 10)
#debug
debugImg = frame.copy()
for lane in postProcResult['detectedLanes']:
if lane[0][0] == llane.detectedLane[0][0]:
color = (255, 0, 0)
elif lane[0][0] == rlane.detectedLane[0][0]:
color = (255, 255, 0)
else:
color = (0, 255, 0)
for j in range(11):
cv2.line(debugImg, (lane[j][0], lane[j][1]),
(lane[j + 1][0], lane[j + 1][1]), color, 10)
cv2.line(debugImg, (0, lane[j][1]),
(int(self.image_X), lane[j][1]), (0, 0, 255), 3)
# cv2.putText(debugImg, '{}'.format(abs(int(fit_x[5])-960)), (int(fit_x[0]), int(plot_y[0])), cv2.FONT_HERSHEY_SIMPLEX, 1.0, color, thickness=2)
cv2.imwrite(str(1) + 'debug.png', debugImg)
if postProcResult['mask_image'] is None:
print('cant find any lane!!!')
else:
self.maskimg_pub.publish(
self.bridge.cv2_to_imgmsg(postProcResult['mask_image'],
"bgr8"))
self.binimg_pub.publish(
self.bridge.cv2_to_imgmsg(postProcResult['binary_img'],
"mono8"))
self.morphoimg_pub.publish(
self.bridge.cv2_to_imgmsg(postProcResult['morpho_img'],
"mono8"))
self.image_pub.publish(self.bridge.cv2_to_imgmsg(self.img, "bgr8"))
#debug end
print('lanedet all use：', time.time() - startt)
def drawYoloResult(self, data):
for i in range(data.objNum):
box = data.bounding_boxes[i]
cv2.rectangle(self.img, (int(box.xmin), int(box.ymin)),
(int(box.xmax), int(box.ymax)), (0, 255, 0), 3)
t_size = cv2.getTextSize(box.id, cv2.FONT_HERSHEY_PLAIN, 1, 1)[0]
c2 = int(box.xmin) + t_size[0] + 3, int(box.ymin) + t_size[1] + 4
cv2.rectangle(self.img, (int(box.xmin), int(box.ymin)), c2,
(0, 255, 0), -1)
cv2.putText(self.img, box.id, (int(box.xmin), int(box.ymin) + 10),
cv2.FONT_HERSHEY_PLAIN, 1, (225, 255, 255), 1)
def callbackRos(self, data):
print('callbackros')
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
self.img = cv_image.copy()
# self.out.write(cv_image)
self.process(cv_image)
#cv2.imwrite('cvimage.png', cv_image)
# cv2.imwrite('result.png', cv_image)
self.drawYoloResult(self.yoloBoxes)
self.drawYoloResult(self.trafficLightBoxes)
self.image_pub.publish(self.bridge.cv2_to_imgmsg(self.img, "bgr8"))
class Lane_warning:
def __init__(self):
self.image_pub = rospy.Publisher("lanedetframe", Image,queue_size = 1)
# self.bridge = CvBridge()
# self.image_sub = rospy.Subscriber("YOLO_detect_result", Image, self.callbackRos)
self.image_sub = rospy.Subscriber("/camera/rgb/image_raw", Image, self.callbackRos)
# self.yolobbox_sub = rospy.Subscriber("publishers/bounding_boxes/topic", BoundingBoxes, self.callbackRos)
self.weights_file = '/home/iairiv/code/lane_waring_final/experiments/exp1/exp1_best.pth'
self.CUDA = torch.cuda.is_available()
self.postprocessor = LaneNetPostProcessor()
self.warning = Detection()
self.band_width = 1.5
self.image_X = 1920
self.image_Y = 1200
self.car_X = self.image_X/2
self.car_Y = self.image_Y
self.model = LaneNet(pretrained=False, embed_dim=4, delta_v=.5, delta_d=3.)
self.save_dict = torch.load(self.weights_file, map_location='cuda:0')
self.model.load_state_dict(self.save_dict['net'])
# self.model.load_state_dict(torch.load(self.weights_file, map_location='cuda:0'))
if self.CUDA: self.model.cuda()
self.model.set_test()
self.lastlane = np.ndarray(4,)
self.bridge = CvBridge()
def transform_input(self, img):
return prep_image(img)
def detection(self, input,raw):
if self.CUDA:
input = input.cuda()
with torch.no_grad():
output = self.model(input, None)
return self.cluster(output,raw)
def cluster(self,output,raw):
global i
embedding = output['embedding']
embedding = embedding.detach().cpu().numpy()
embedding = np.transpose(embedding[0], (1, 2, 0))
binary_seg = output['binary_seg']
bin_seg_prob = binary_seg.detach().cpu().numpy()
bin_seg_pred = np.argmax(bin_seg_prob, axis=1)[0]
# plt.savefig('a.png')
# i = time.time()
cv2.imwrite(str(i)+'a.png',bin_seg_pred)
i=i+1
# cv2.waitKey(0)
#plt.show()
seg = bin_seg_pred * 255
# print('postprocess_result')
postprocess_result = self.postprocessor.postprocess(
binary_seg_result=bin_seg_pred,
instance_seg_result=embedding
)
prediction = postprocess_result
prediction = np.array(prediction)
return prediction
""""没加预警"""
# def write(self, output, img):
# # output[:,:,1] = output[:,:,1]+255
# for i in range(len(output)):
# line = np.array(output[i])
# line[:,1] = line[:,1]+255
# output[i] = line.tolist()
# # for j in range(len(output[i])):
# # output[i][j][1] = output[i][j][1] + 255
# # print(arr[i][j])
# # cv.circle(image, (int(arr[i][j][0]),int(arr[i][j][1])), 5, (0, 0, 213), -1) #画成小圆点
# cv.line(img, (int(output[i][0][0]), int(output[i][0][1])), (int(output[i][-1][0]), int(output[i][-1][1])),
# (0,0,255), 3)
# # if signal == 1:
# # cv2.putText(img, "WARNING", (1300, 150), cv2.FONT_HERSHEY_SIMPLEX, 3.0, color, thickness=10)
# #plt.imshow(img)
# #plt.show()
# return img
"""""加了预警"""""
# def write(self, output, img,signal,color):
# # output[:,:,1] = output[:,:,1]+255
# for i in range(len(output)):
# line = np.array(output[i])
# # line[:,1] = line[:,1]+255
# output[i] = line.tolist()
# #for j in range(len(output[i])):
# #output[i][j][1] = output[i][j][1] + 255
# #print(output[i][j])
# #cv2.circle(img, (int(output[i][j][0]),int(output[i][j][1])), 5, color, -1) #画成小圆点
# cv2.line(img, (int(output[i][0][0]), int(output[i][0][1])), (int(output[i][-1][0]), int(output[i][-1][1])),color, 3)
# if signal == 1:
# cv2.putText(img, "WARNING", (1300, 150), cv2.FONT_HERSHEY_SIMPLEX, 3.0, color, thickness=10)
# #plt.imshow(img)
# #plt.show()
# return img
def write_nowarning(self, output, img):
# output[:,:,1] = output[:,:,1]+255
for i in range(len(output)):
line = np.array(output[i])
# line[:,1] = line[:,1]+255
output[i] = line.tolist()
#for j in range(len(output[i])):
#output[i][j][1] = output[i][j][1] + 255
#print(output[i][j])
#cv2.circle(img, (int(output[i][j][0]),int(output[i][j][1])), 5, color, -1) #画成小圆点
cv2.line(img, (int(output[i][0][0]), int(output[i][0][1])), (int(output[i][-1][0]), int(output[i][-1][1])),(0,0,255), 3)
#plt.imshow(img)
#plt.show()
return img
def color(self, signal):
if signal == 0:
color = (0, 255, 0)
else:
color = (0, 0, 255)
return color
#ros下的代码，还没测试过。无ros用另一个测。
def callbackRos(self, data):
# print('callbackros')
try:
cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
# cv_image = cv2.resize(cv_image, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_NEAREST)
input_image = self.transform_input(cv_image)
prediction = self.detection(input_image, cv_image)
if len(prediction) == 0:
result = cv_image
else:
print(prediction)
# signal = self.warning.detect(prediction)
# color = self.color(signal)
# result = self.write(prediction, cv_image, signal, color)
# result = self.write_nowarning(prediction, cv_image)
except CvBridgeError as e:
print(e)
# cv2.imshow("image windows", result)
# cv2.waitKey(3)
# try:
# self.image_pub.publish(self.bridge.cv2_to_imgmsg(result, "bgr8"))
# except CvBridgeError as e:
# print(e)
def callback(self, data):
# try:
# cv_image = self.bridge.imgmsg_to_cv2(data, "bgr8")
# cv_image = cv2.resize(cv_image, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_NEAREST)
time3 = time.time()
input_image = self.transform_input(data)
time4 = time.time()-time3
print('数据预处理时间:',time4)
#lane_detect
time5 = time.time()
prediction = self.detection(input_image,data)
# if len(prediction) == 0:
# prediction = self.lastlane
# else:
# self.lastlane = prediction
#print(prediction)
time6 = time.time()-time5
print('检测时间：', time6)
#warning
time7 = time.time()
signal = self.warning.detect(prediction)
color = self.color(signal)
time8 = time.time()-time7
print('预警时间：',time8)
#draw_line
time1 = time.time()
# img = self.write(prediction, data)
img = self.write(prediction, data, signal, color)
time2 = time.time()-time1
print('画图时间：',time2)
cv2.imshow("final",img)
cv2.waitKey(0)
# ------------ data and model ------------
# Imagenet mean, std
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
transform = Compose(Resize(resize_shape), ToTensor(),
Normalize(mean=mean, std=std))
dataset_name = exp_cfg['dataset'].pop('dataset_name')
Dataset_Type = getattr(dataset, dataset_name)
test_dataset = Dataset_Type(Dataset_Path['Tusimple'], "test", transform)
test_loader = DataLoader(test_dataset,
batch_size=16,
collate_fn=test_dataset.collate,
num_workers=4)
net = LaneNet(pretrained=False, **exp_cfg['model'])
save_name = os.path.join(exp_dir, exp_dir.split('/')[-1] + '_best.pth')
save_dict = torch.load(save_name, map_location='cuda:0')
print("\nloading", save_name, "...... From Epoch: ", save_dict['epoch'])
net.load_state_dict(save_dict['net'])
# net = torch.nn.DataParallel(net.to(device))
net = net.to(device)
net.eval()
# ------------ test ------------
out_path = os.path.join(exp_dir, "coord_output")
evaluation_path = os.path.join(exp_dir, "evaluate")
if not os.path.exists(out_path):
os.mkdir(out_path)
if not os.path.exists(evaluation_path):
os.mkdir(evaluation_path)
ap = argparse.ArgumentParser()
ap.add_argument('-e', '--epoch', default=50) #Epoch
ap.add_argument('-b', '--batch', default=2) #Batch_size
ap.add_argument('-dv', '--delta_v', default=.5) #delta_v
ap.add_argument('-dd', '--delta_d', default=3) #delta_d
ap.add_argument('-l', '--learning_rate', default=5e-4) #learning_rate
ap.add_argument('-o', '--optimizer', default='Adam') #optimizer
ap.add_argument('-d', '--device', default='GPU') #training device
ap.add_argument('-t', '--test_ratio', default=.1)
ap.add_argument('-s', '--stage', default='new')
#ap.add_argument('-cl','--class_weight',default=.5)
#ap.add_argument()
#ap.add_argument()
args = vars(ap.parse_args())
train_indices, test_indices = split_dataset(args['test_ratio'])
data = build_sampler(TusimpleData('./data', transform=Rescale((256, 512))),
args['batch'], 1, train_indices, test_indices)
if args['stage'] == 'new':
model = LaneNet()
else:
model_file = 'model_1548830895.pkl'
model = LaneNet()
weight_dict = torch.load(os.path.join('./logs/models/', model_file))
model.load_state_dict(weight_dict.state_dict())
train(model, data, args['epoch'], args['batch'], args['delta_v'],
args['delta_d'], args['learning_rate'], args['optimizer'])
batch_size=exp_cfg['dataset']['batch_size'],
shuffle=True,
collate_fn=train_dataset.collate,
num_workers=8)
# ------------ val data ------------
transform_val = Compose(Resize(resize_shape), ToTensor(),
Normalize(mean=mean, std=std))
val_dataset = Dataset_Type(Dataset_Path[dataset_name], "val", transform_val)
val_loader = DataLoader(val_dataset,
batch_size=8,
collate_fn=val_dataset.collate,
num_workers=4)
# ------------ preparation ------------
net = LaneNet(pretrained=True, **exp_cfg['model'])
net = net.to(device)
net = torch.nn.DataParallel(net)
optimizer = optim.SGD(net.parameters(), **exp_cfg['optim'])
lr_scheduler = PolyLR(optimizer, 0.9, exp_cfg['MAX_ITER'])
best_val_loss = 1e6
def train(epoch):
print("Train Epoch: {}".format(epoch))
net.train()
train_loss = 0
train_loss_bin_seg = 0
train_loss_var = 0
train_loss_dist = 0
torch.save(
model, os.path.join('./logs/models',
'model_{}.pkl'.format(start_time)))
log.close()
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument('-e', '--epoch', default=30) #Epoch
ap.add_argument('-b', '--batch', default=16) #Batch_size
ap.add_argument('-dv', '--delta_v', default=.5) #delta_v
ap.add_argument('-dd', '--delta_d', default=3) #delta_d
ap.add_argument('-l', '--learning_rate', default=5e-4) #learning_rate
ap.add_argument('-o', '--optimizer', default='Adam') #optimizer
ap.add_argument('-d', '--device', default='GPU') #training device
ap.add_argument('-t', '--test_ratio', default=.1)
#ap.add_argument('-cl','--class_weight',default=.5)
#ap.add_argument()
#ap.add_argument()
args = vars(ap.parse_args())
train_indices, test_indices = split_dataset(args['test_ratio'])
data = build_sampler(TusimpleData('./data', transform=Rescale((256, 512))),
args['batch'], 1, train_indices, test_indices)
model = LaneNet()
train(model, data, args['epoch'], args['batch'], args['delta_v'],
args['delta_d'], args['learning_rate'], args['optimizer'])
import argparse
import cv2
import torch
from model import LaneNet
from utils.transforms import *
from utils.postprocess import embedding_post_process
net = LaneNet(pretrained=False, embed_dim=7, delta_v=.5, delta_d=3.)
transform = Compose(Resize((800, 288)), ToTensor(),
Normalize(mean=(0.3598, 0.3653, 0.3662), std=(0.2573, 0.2663, 0.2756)))
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--img_path", '-i', type=str, default="demo/demo.jpg", help="Path to demo img")
parser.add_argument("--weight_path", '-w', type=str, help="Path to model weights")
parser.add_argument("--delta_v", '-dv', type=float, default=0.5, help="Value of delta_v")
parser.add_argument("--visualize", '-v', action="store_true", default=False, help="Visualize the result")
args = parser.parse_args()
return args
def main():
args = parse_args()
img_path = args.img_path
weight_path = args.weight_path
img = cv2.imread(img_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB for net model input
x = transform(img)[0]
