target_path = os.path.join(output_path, "checkpoint_weights.hdf5")
if args.charset_path is not None:
with open(args.charset_path, 'r') as f:
charset_base = f.readline().strip()
print('hej')
else:
charset_base = string.printable[:95]
if args.transform:
print(os.getcwd())
print(f"{ds_names_str} dataset will be transformed...")
ds = Dataset(source=raw_path, name=args.ds_names)
ds.read_partitions()
ds.save_partitions(source_path,
input_size,
max_text_length,
binarize=args.binarize)
elif args.cv2:
with h5py.File(source_path, "r") as hf:
dt = hf['test']['dt'][:256]
gt = hf['test']['gt'][:256]
predict_file = os.path.join(output_path, "predict.txt")
predicts = [''] * len(dt)
if os.path.isfile(predict_file):
with open(predict_file, "r") as lg:
predicts = [line[5:] for line in lg if line.startswith("TE_P")]
for x in range(len(dt)):
if args.transform:
# from data.create_hdf5 import Dataset
# if os.path.isfile(source_path):
# print("Dataset file already exists")
# else:
# ds = Dataset()
# ds.save_partitions()
from data.reader import Dataset
print(f"iam dataset will be transformed...")
ds = Dataset(source=os.path.join(raw_path, "iam"), name="iam")
ds.read_partitions()
ds.save_partitions(source_path, target_image_size, maxTextLength)
elif args.predict:
input_image_path = os.path.join(output_path, "prediction")
output_image_path = os.path.join(input_image_path, "out")
os.makedirs(output_image_path, exist_ok=True)
if args.image:
images = sorted(glob(os.path.join(input_image_path, args.image)))
else:
images = sorted(glob(os.path.join(input_image_path, "*.png")))
from network.model import MyModel
from data.tokenizer import Tokenizer
from data import imgproc
args = parser.parse_args()
raw_path = os.path.join("..", "raw", args.source)
source_path = os.path.join("..", "data", f"{args.source}.hdf5")
output_path = os.path.join("..", "output", args.source, args.arch)
target_path = os.path.join(output_path, "checkpoint_weights.hdf5")
input_size = (1024, 128, 1)
max_text_length = 128
charset_base = string.printable[:95]
if args.transform:
print(f"{args.source} dataset will be transformed...")
ds = Dataset(source=raw_path, name=args.source)
ds.read_partitions()
ds.save_partitions(source_path, input_size, max_text_length)
elif args.cv2:
with h5py.File(source_path, "r") as hf:
dt = hf['test']['dt'][:256]
gt = hf['test']['gt'][:256]
predict_file = os.path.join(output_path, "predict.txt")
predicts = [''] * len(dt)
if os.path.isfile(predict_file):
with open(predict_file, "r") as lg:
predicts = [line[5:] for line in lg if line.startswith("TE_P")]
for x in range(len(dt)):
print(f"Image shape:\t{dt[x].shape}")
