def init_args() -> Tuple[argparse.Namespace, EasyDict]:
"""
:return: parsed arguments and (updated) config.cfg object
"""
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dataset_dir', type=str,
help='Directory containing train_features.tfrecords')
parser.add_argument('-c', '--chardict_dir', type=str,
help='Directory where character dictionaries for the dataset were stored')
parser.add_argument('-m', '--model_dir', type=str,
help='Directory where to store model checkpoints')
parser.add_argument('-t', '--tboard_dir', type=str,
help='Directory where to store TensorBoard logs')
parser.add_argument('-f', '--config_file', type=str,
help='Use this global configuration file')
parser.add_argument('-e', '--decode_outputs', action='store_true', default=False,
help='Activate decoding of predictions during training (slow!)')
parser.add_argument('-w', '--weights_path', type=str, help='Path to pre-trained weights to continue training')
parser.add_argument('-j', '--num_threads', type=int, default=int(os.cpu_count()/2),
help='Number of threads to use in batch shuffling')
args = parser.parse_args()
config = load_config(args.config_file)
if args.dataset_dir:
config.cfg.PATH.TFRECORDS_DIR = args.dataset_dir
if args.chardict_dir:
config.cfg.PATH.CHAR_DICT_DIR = args.chardict_dir
if args.model_dir:
config.cfg.PATH.MODEL_SAVE_DIR = args.model_dir
if args.tboard_dir:
config.cfg.PATH.TBOARD_SAVE_DIR = args.tboard_dir
return args, config.cfg
def init_args() -> Tuple[argparse.Namespace, EasyDict]:
"""
:return: parsed arguments and (updated) config.cfg object
"""
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dataset_dir', type=str,
help='Directory containing test_features.tfrecords')
parser.add_argument('-c', '--chardict_dir', type=str,
help='Directory where character dictionaries for the dataset were stored')
parser.add_argument('-w', '--weights_path', type=str, required=True,
help='Path to pre-trained weights')
parser.add_argument('-n', '--num_classes', type=int, required=True,
help='Force number of character classes to this number. '
'Use 37 to run with the demo data. '
'Set to 0 for auto (read from files in charset_dir)')
parser.add_argument('-f', '--config_file', type=str,
help='Use this global configuration file')
parser.add_argument('-v', '--visualize', type=bool, default=False,
help='Whether to display images')
parser.add_argument('-b', '--one_batch', default=False,
action='store_true', help='Test only one batch of the dataset')
parser.add_argument('-j', '--num_threads', type=int,
default=int(os.cpu_count() / 2),
help='Number of threads to use in batch shuffling')
args = parser.parse_args()
config = load_config(args.config_file)
if args.dataset_dir:
config.cfg.PATH.TFRECORDS_DIR = args.dataset_dir
if args.chardict_dir:
config.cfg.PATH.CHAR_DICT_DIR = args.chardict_dir
return args, config.cfg
def init_args() -> Tuple[argparse.Namespace, EasyDict]:
"""
:return: parsed arguments and (updated) config.cfg object
"""
parser = argparse.ArgumentParser()
parser.add_argument('--image_path',
type=str,
help='Path to the image to be tested',
default='data/test_images/test_01.jpg')
parser.add_argument(
'--weights_path',
type=str,
help='Path to the pre-trained weights to use',
default='model/shadownet/shadownet_2017-09-29-19-16-33.ckpt-39999')
parser.add_argument('-f',
'--config_file',
type=str,
help='Use this global configuration file')
parser.add_argument(
'-c',
'--chardict_dir',
type=str,
help=
'Directory where character dictionaries for the dataset were stored')
parser.add_argument(
'-n',
'--num_classes',
type=int,
default=37,
help='Force number of character classes to this number. '
'Set to 0 for auto (read from charset_dir)')
args = parser.parse_args()
config = load_config(args.config_file)
if args.chardict_dir:
config.cfg.PATH.CHAR_DICT_DIR = args.chardict_dir
return args, config.cfg
ord_map_dict_path = os.path.join(charset_dir, "ord_map.json")
else:
char_dict_path = os.path.join("data/char_dict", "char_dict.json")
ord_map_dict_path = os.path.join("data/char_dict", "ord_map.json")
feature_io = TextFeatureIO(char_dict_path, ord_map_dict_path)
feature_io.writer.write_features(tfrecords_path=tfrecord_path,
labels=labels,
images=images,
imagenames=imagenames)
if __name__ == '__main__':
args = init_args()
config = load_config(args.config_file)
if not ops.exists(args.dataset_dir):
raise ValueError('Dataset {:s} doesn\'t exist'.format(
args.dataset_dir))
os.makedirs(args.save_dir, exist_ok=True)
print('Initializing the dataset provider...')
provider = data_provider.TextDataProvider(
dataset_dir=args.dataset_dir,
annotation_name=args.annotation_file,
validation_set=args.validation_split > 0,
validation_split=args.validation_split,
shuffle='every_epoch',