help='the validation images path')
parser.add_argument('--dropout_rate', default=0.5,
help='dropout rate.') #0.5
parser.add_argument('--epoch',
type=int,
default=1600,
help='number of epoches for training') #800
parser.add_argument('--batch_size', default=batch, help='batch size')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('device: ', device)
print("len CHARS: ", len(CHARS))
print('pow:', p)
print('r:', r)
print('mini:', mini)
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=args.dropout_rate,
r=r) #mini/c
lprnet.to(device)
summary(lprnet, (3, resh, resw))
#lprnet_load=torch.load('weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage.cuda)
#lprnet.load_state_dict(lprnet_load['net_state_dict'])
#lprnet.load_state_dict(torch.load('weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage))
print("LPRNet loaded")
print('LPRNet params: ', sum(p.numel() for p in lprnet.parameters()))
STN = STNet(r=r, resw=resw, resh=resh)
STN.to(device)
#summary(STN, (3,24,94))
summary(STN, (3, resh, resw))
#STN_load=torch.load('weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage.cuda)
#STN.load_state_dict(torch.load('weights/Final_STN_model.pth', map_location=lambda storage, loc: storage))
print("STN loaded")
print('STN params: ', sum(p.numel() for p in STN.parameters()))
for i, label in enumerate(pred_labels):
lb = ""
for i in label:
lb += CHARS[i]
labels.append(lb)
return labels, np.array(pred_labels)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LPR Demo')
parser.add_argument("-image", help='image path', default='data/ccpd_weather/吉BTW976.jpg', type=str)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
lprnet.to(device)
lprnet.load_state_dict(torch.load('weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage))
lprnet.eval()
STN = STNet()
STN.to(device)
STN.load_state_dict(torch.load('weights/Final_STN_model.pth', map_location=lambda storage, loc: storage))
STN.eval()
print("Successful to build network!")
since = time.time()
image = cv2.imread(args.image)
im = cv2.resize(image, (94, 24), interpolation=cv2.INTER_CUBIC)
im = (np.transpose(np.float32(im), (2, 0, 1)) - 127.5)*0.0078125
for i in label:
lb += CHARS[i]
labels.append(lb)
return labels, np.array(pred_labels)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='LPR Demo')
parser.add_argument("-image", help='image path', default='data/ccpd_weather/吉BTW976.jpg', type=str)
parser.add_argument("-lprnet_model", help='lprnet model', default='weights/Final_LPRNet_model.pth', type=str)
parser.add_argument("-stn_model", help='stn model', default='weights/Final_STN_model.pth', type=str)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
lprnet.to(device)
lprnet.load_state_dict(torch.load(args.lprnet_model, map_location=lambda storage, loc: storage))
lprnet.eval()
STN = STNet()
STN.to(device)
STN.load_state_dict(torch.load(args.stn_model, map_location=lambda storage, loc: storage))
STN.eval()
print("Successful to build network!")
since = time.time()
image = cv2.imread(args.image)
im = cv2.resize(image, (94, 24), interpolation=cv2.INTER_CUBIC)
im = (np.transpose(np.float32(im), (2, 0, 1)) - 127.5)*0.0078125
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='LPR Demo') # 00-D-112255 151-WX-694
parser.add_argument(
"-image",
help='image path',
default=
'/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/data/plate_test/152L393.png',
type=str)
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=0)
lprnet.to(device)
lprnet.load_state_dict(
torch.load(
'/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/LPRNet/saving_ckpt/lprnet_epoch_001200_model.ckpt'
)['net_state_dict'])
lprnet.eval()
STN = STNet()
STN.to(device)
STN.load_state_dict(
torch.load(
'/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/LPRNet/saving_ckpt/stn_epoch_001200_model.ckpt'
)['net_state_dict'])
STN.eval()
parser.add_argument('--img_dirs_val',
default="./data/validation",
help='the validation images path')
parser.add_argument('--dropout_rate', default=0.5, help='dropout rate.')
parser.add_argument('--epoch',
type=int,
default=33,
help='number of epoches for training')
parser.add_argument('--batch_size',
type=int,
default=128,
help='batch size')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=args.dropout_rate)
lprnet.to(device)
# load pretrained model
# lprnet.load_state_dict(torch.load('weights/LPRNet_model_Init.pth', map_location=lambda storage, loc: storage))
# print("LPRNet loaded")
def xavier(param):
nn.init.xavier_uniform(param)
def weights_init(m):
for key in m.state_dict():
if key.split('.')[-1] == 'weight':
if 'conv' in key:
nn.init.kaiming_normal_(m.state_dict()[key],
mode='fan_out')
if 'bn' in key:
parser.add_argument('--img_dirs_train',
default="data/plate_train",
help='the training images path')
parser.add_argument('--img_dirs_val',
default="./data/validation",
help='the validation images path')
parser.add_argument('--dropout_rate', default=0.5, help='dropout rate.')
parser.add_argument('--epoch',
type=int,
default=10000,
help='number of epoches for training')
parser.add_argument('--batch_size', default=24 * 4, help='batch size')
args = parser.parse_args()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=args.dropout_rate)
lprnet.to(device)
lprnet.load_state_dict(
torch.load('LPRNet/weights/LPRNet_model_Init.pth',
map_location=lambda storage, loc: storage))
# lprnet.load_state_dict(torch.load('/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/saving_ckpt/lprnet_Iter_007200_model.ckpt')['net_state_dict'])
print("LPRNet loaded")
STN = STNet()
STN.to(device)
STN.load_state_dict(
torch.load('LPRNet/weights/STN_model_Init.pth',
map_location=lambda storage, loc: storage))
# STN.load_state_dict(torch.load('/media/cbpm2016/D/liaolong/alpr/License_Plate_Detection_Pytorch/saving_ckpt/stn_Iter_007200_model.ckpt')['net_state_dict'])
print("STN loaded")
print('r:', r)
dir = '/content/gdrive/MyDrive/個別研究/車牌/LPRNet3/test_lr_mini_saving_ckpt(1.1_2.5)/test_lr_mini_saving_ckpt(1.1_2.5-2)' #
parser = argparse.ArgumentParser(description='LPR Evaluation')
parser.add_argument('--img_size',
default=(resw, resh),
help='the image size')
parser.add_argument('--img_dirs',
default="data/test2",
help='the images path')
parser.add_argument('--dropout_rate', default=0.5, help='dropout rate.')
parser.add_argument('--batch_size', default=1, help='batch size.')
args = parser.parse_args()
device = torch.device("cuda:0") #
# device = torch.device("cpu")#
print(device)
lprnet = LPRNet(class_num=len(CHARS), dropout_rate=args.dropout_rate, r=r)
lprnet.to(device)
# lprnet.load_state_dict(torch.load('weights/Final_LPRNet_model.pth', map_location=lambda storage, loc: storage))
checkpoint = torch.load(dir + '/lprnet_Iter_150000_model.ckpt') #
lprnet.load_state_dict(checkpoint['net_state_dict'])
lprnet.eval()
print("LPRNet loaded")
torch.save(lprnet.state_dict(), 'weights/Final2_LPRNet_model.pth')
STN = STNet(r=r, resw=resw, resh=resh)
STN.to(device)
# STN.load_state_dict(torch.load('weights/Final_STN_model.pth', map_location=lambda storage, loc: storage))
checkpoint = torch.load(dir + '/stn_Iter_150000_model.ckpt') #
STN.load_state_dict(checkpoint['net_state_dict'])