|
|
import numpy as np |
|
|
import torch |
|
|
|
|
|
from .ctc_postprocess import BaseRecLabelDecode |
|
|
|
|
|
|
|
|
class CharLabelDecode(BaseRecLabelDecode): |
|
|
"""Convert between text-label and text-index.""" |
|
|
|
|
|
def __init__(self, |
|
|
character_dict_path=None, |
|
|
use_space_char=True, |
|
|
**kwargs): |
|
|
super(CharLabelDecode, self).__init__(character_dict_path, |
|
|
use_space_char) |
|
|
|
|
|
def __call__(self, preds, label=None, *args, **kwargs): |
|
|
if len(preds) >= 4: |
|
|
preds_id = preds[0] |
|
|
preds_prob = preds[1] |
|
|
char_preds = preds[2] |
|
|
if isinstance(preds_id, torch.Tensor): |
|
|
preds_id = preds_id.numpy() |
|
|
if isinstance(preds_prob, torch.Tensor): |
|
|
preds_prob = preds_prob.numpy() |
|
|
if preds_id[0][0] == 2: |
|
|
preds_idx = preds_id[:, 1:] |
|
|
preds_prob = preds_prob[:, 1:] |
|
|
|
|
|
else: |
|
|
preds_idx = preds_id |
|
|
char_preds = char_preds.numpy() |
|
|
char_preds_idx = char_preds.argmax(-1) + 4 |
|
|
char_preds_prob = char_preds.max(-1) |
|
|
text, text_box = self.decode(preds_idx, preds_prob, char_preds_idx, |
|
|
char_preds_prob) |
|
|
else: |
|
|
preds_logit = preds[0].numpy() |
|
|
char_preds = preds[1].numpy() |
|
|
|
|
|
|
|
|
preds_idx = preds_logit.argmax(axis=2) |
|
|
preds_prob = preds_logit.max(axis=2) |
|
|
char_preds_idx = char_preds.argmax(-1) + 4 |
|
|
char_preds_prob = char_preds.max(-1) |
|
|
text, text_box = self.decode(preds_idx, preds_prob, char_preds_idx, |
|
|
char_preds_prob) |
|
|
|
|
|
if label is None: |
|
|
return text, text_box |
|
|
label = self.decode(label[:, 1:]) |
|
|
return text, text_box, label |
|
|
|
|
|
def add_special_char(self, dict_character): |
|
|
dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character |
|
|
return dict_character |
|
|
|
|
|
def decode( |
|
|
self, |
|
|
text_index, |
|
|
text_prob=None, |
|
|
char_text_index=None, |
|
|
char_text_prob=None, |
|
|
is_remove_duplicate=False, |
|
|
): |
|
|
"""convert text-index into text-label.""" |
|
|
result_list = [] |
|
|
box_result_list = [] |
|
|
batch_size = len(text_index) |
|
|
for batch_idx in range(batch_size): |
|
|
char_list = [] |
|
|
conf_list = [] |
|
|
char_box_list = [] |
|
|
conf_box_list = [] |
|
|
for idx in range(len(text_index[batch_idx])): |
|
|
try: |
|
|
char_idx = self.character[int(text_index[batch_idx][idx])] |
|
|
if char_text_index is not None: |
|
|
char_box_idx = self.character[int( |
|
|
char_text_index[batch_idx][idx])] |
|
|
except: |
|
|
continue |
|
|
if char_idx == '</s>': |
|
|
break |
|
|
char_list.append(char_idx) |
|
|
|
|
|
if char_text_index is not None: |
|
|
char_box_list.append(char_box_idx) |
|
|
|
|
|
if text_prob is not None: |
|
|
conf_list.append(text_prob[batch_idx][idx]) |
|
|
else: |
|
|
conf_list.append(1) |
|
|
|
|
|
if char_text_prob is not None: |
|
|
conf_box_list.append(char_text_prob[batch_idx][idx]) |
|
|
else: |
|
|
conf_box_list.append(1) |
|
|
text = ''.join(char_list) |
|
|
result_list.append((text, np.mean(conf_list).tolist())) |
|
|
|
|
|
if char_text_index is not None: |
|
|
text_box = ''.join(char_box_list) |
|
|
box_result_list.append( |
|
|
(text_box, np.mean(conf_box_list).tolist())) |
|
|
if char_text_index is not None: |
|
|
return result_list, box_result_list |
|
|
return result_list |
|
|
|
