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}")
print(f"Ground truth:\t{gt[x].decode()}")
print(f"Predict:\t{predicts[x]}\n")
cv2.imshow("img", pp.adjust_to_see(dt[x]))
cv2.waitKey(0)
elif args.image:
tokenizer = Tokenizer(chars=charset_base,
max_text_length=max_text_length)
img = pp.preproc(args.image, input_size=input_size)
x_test = pp.normalization([img])
model = HTRModel(architecture=args.arch,
input_size=input_size,
vocab_size=tokenizer.vocab_size,
top_paths=10)
model.compile()
compression="gzip",
compression_opts=9)
print(f"[OK] {i} partition.")
print(f"Transformation finished.")
elif args.cv2:
with h5py.File(hdf5_src, "r") as hf:
dt = hf["test"]["dt"][:]
gt = hf["test"]["gt"][:]
for x in range(len(dt)):
print(f"Image shape: {dt[x].shape}")
print(f"Ground truth: {gt[x].decode()}\n")
cv2.imshow("img", pp.adjust_to_see(dt[x]))
cv2.waitKey(0)
elif args.train or args.test:
os.makedirs(output_path, exist_ok=True)
dtgen = DataGenerator(hdf5_src=hdf5_src,
batch_size=args.batch_size,
charset=charset_base,
max_text_length=max_text_length)
network_func = getattr(architecture, args.arch)
ioo = network_func(input_size=input_size,
output_size=(dtgen.tokenizer.vocab_size + 1),
learning_rate=0.001)
