def __init__(
self,
config,
) -> None:
super(ocr, self).__init__()
self.config = config
config_base = Cfg.load_config_from_file("config/base.yml")
config = Cfg.load_config_from_file(self.config)
config_base.update(config)
config = config_base
config['vocab'] = character
self.text_r = Predictor(config)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config',
type=str,
default="config/vgg-seq2seq.yml",
help='config path ')
# parser.add_argument('--checkpoint', required=False, help='your checkpoint')
args = parser.parse_args()
logger = logging.getLogger(__name__)
config = Cfg.load_config_from_file(args.config, download_base=False)
logger.info("Loaded config from {}".format(args.config))
# print('-- CONFIG --')
dataset_params = {
'name':
'hw_word',
'data_root':
'./DATA',
'is_padding':
True,
'image_max_width':
100,
'train_lmdb': [
'train_hw_word', 'hw_word_9k_good', 'hw_word_50k_dict_3k',
'valid_hw_word', 'hw_word_70k_dict_full_filter'
],
'valid_lmdb':
'test_hw_word'
}
config['monitor']['log_dir'] = './logs/hw_word_seq2seq_finetuning_240k'
trainer_params = {
'batch_size': 32,
'print_every': 200,
'valid_every': 5 * 200,
'iters': 150000,
'metrics': 5000,
'pretrained': './logs/hw_word_seq2seq_finetuning_170k_v2/best.pt',
'resume_from': None,
'is_finetuning': False
}
config['aug']['masked_language_model'] = False
# optim_params = {
# 'max_lr': 0.00001
# }
# config['optimizer'].update(optim_params)
config['trainer'].update(trainer_params)
# config['trainer']['resume_from'] = './logs/hw_small_finetuning/last.pt'
config['dataset'].update(dataset_params)
print(config.pretty_text())
# print(config)
trainer = Trainer(config, pretrained=False)
# trainer.visualize_dataset()
trainer.train()
def load_recognition_model():
#chuan bi ocr predict model
config = Cfg.load_config_from_file('./vietocr/config.yml')
config['weights'] = "./models/transformerocr.pth"
config['cnn']['pretrained']=False
config['device'] = 'cuda:0'
config['predictor']['beamsearch']=False
recognizer = Predictor(config)
return recognizer
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--img', required=True, help='foo help')
parser.add_argument('--config', required=True, help='foo help')
args = parser.parse_args()
config_base = Cfg.load_config_from_file("config/base.yml")
config = Cfg.load_config_from_file(args.config)
config_base.update(config)
config = config_base
config['vocab'] = character
detector = Predictor(config)
img = Image.open(args.img)
s = detector.predict(img)
print(s)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('config', help='see example at ')
parser.add_argument('--checkpoint', help='your checkpoint')
args = parser.parse_args()
config_base = Cfg.load_config_from_file("config/base.yml")
config = Cfg.load_config_from_file(args.config)
config_base.update(config)
config = config_base
config['vocab'] = character
trainer = Trainer(config, pretrained=False)
# args.checkpoint = config.trainer["checkpoint"]
# if args.checkpoint:
# trainer.load_checkpoint(args.checkpoint)
# logging.info(f"Load checkpoint form {args.checkpoint}....")
trainer.train()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--img', required=True, help='foo help')
parser.add_argument('--config', required=True, help='foo help')
args = parser.parse_args()
config = Cfg.load_config_from_file(args.config)
detector = TextDetector(config)
img = Image.open(args.img)
s = detector.predict(img)
print(s)
def predict_file():
config_path = './logs/hw_word_seq2seq/config.yml'
config = Cfg.load_config_from_file(config_path, download_base=False)
config['weights'] = './logs/hw_word_seq2seq_finetuning/best.pt'
print(config.pretty_text())
detector = Predictor(config)
detector.gen_annotations(
'./DATA/data_verifier/hw_word_15k_labels.txt',
'./DATA/data_verifier/hw_word_15k_labels_preds.txt',
data_root='./DATA/data_verifier')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', required=True, help='see example at ')
parser.add_argument('--checkpoint', required=False, help='your checkpoint')
args = parser.parse_args()
config = Cfg.load_config_from_file(args.config)
trainer = Trainer(config)
if args.checkpoint:
trainer.load_checkpoint(args.checkpoint)
trainer.train()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config',
type=str,
default='./logs/hw_word_seq2seq/config.yml')
parser.add_argument('--weight',
type=str,
default='./logs/hw_word_seq2seq/best.pt')
parser.add_argument('--img', type=str, default=None, required=True)
args = parser.parse_args()
config = Cfg.load_config_from_file(args.config, download_base=False)
config['weights'] = args.weight
print(config.pretty_text())
detector = Predictor(config)
if os.path.isdir(args.img):
img_paths = os.listdir(args.img)
for img_path in img_paths:
try:
img = Image.open(args.img + '/' + img_path)
except:
continue
t1 = time.time()
s, prob = detector.predict(img, return_prob=True)
print('Text in {} is:\t {} | prob: {:.2f} | times: {:.2f}'.format(
img_path, s, prob,
time.time() - t1))
else:
t1 = time.time()
img = Image.open(args.img)
s, prob = detector.predict(img, return_prob=True)
print('Text in {} is:\t {} | prob: {:.2f} | times: {:.2f}'.format(
args.img, s, prob,
time.time() - t1))
