def load_model_from_checkpoint(checkpoint_file_name, use_gpu=False):
"""Load a pretrained CRNN model."""
model = CRNN(line_size, 1, len(vocab), 256)
checkpoint = torch.load(checkpoint_file_name,
map_location='cpu' if not use_gpu else None)
model.load_state_dict(checkpoint['state_dict'])
model.float()
model.eval()
model = model.cuda() if use_gpu else model.cpu()
return model
def ocr(orig_img, lines, checkpoint_file_name, use_gpu=False):
"""OCR on segmented lines."""
model = CRNN(line_size, 1, len(vocab), 256)
checkpoint = torch.load(checkpoint_file_name,
map_location='cpu' if not use_gpu else None)
model.load_state_dict(checkpoint['state_dict'])
model.float()
model.eval()
model = model.cuda() if use_gpu else model.cpu()
torch.set_grad_enabled(False)
result = []
for line in lines:
(x1, y1), (x2, y2) = line
line_img = image_resize(np.array(np.rot90(orig_img[y1:y2, x1:x2])),
height=line_size)
inputs = torch.from_numpy(line_img /
255).float().unsqueeze(0).unsqueeze(0)
outputs = model(inputs)
prediction = outputs.softmax(2).max(2)[1]
def to_text(tensor, max_length=None, remove_repetitions=False):
sentence = ''
sequence = tensor.cpu().detach().numpy()
for i in range(len(sequence)):
if max_length is not None and i >= max_length:
continue
char = idx2char[sequence[i]]
if char != 'B': # ignore blank
if remove_repetitions and i != 0 and char == idx2char[
sequence[i - 1]]:
pass
else:
sentence = sentence + char
return sentence
predicted_text = to_text(prediction[:, 0], remove_repetitions=True)
result.append((line_img, predicted_text))
return result