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PaddleOCR
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Adaptation of Chinese and r34/18

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release/1.1
tink2123 5 years ago
parent 7b201a3855
commit 1b19050391
5 changed files with 64 additions and 25 deletions
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48
ppocr/data/rec/dataset_traversal.py
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@ -214,6 +214,8 @@ class SimpleReader(object):
self.mode = params['mode']
self.infer_img = params['infer_img']
self.use_tps = False
if "num_heads" in params:
self.num_heads = params['num_heads']
if "tps" in params:
self.use_tps = True
self.use_distort = False
@ -251,12 +253,19 @@ class SimpleReader(object):
img = cv2.imread(single_img)
if img.shape[-1] == 1 or len(list(img.shape)) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
norm_img = process_image(
img=img,
image_shape=self.image_shape,
char_ops=self.char_ops,
tps=self.use_tps,
infer_mode=True)
if self.loss_type == 'srn':
norm_img = process_image_srn(
img=img,
image_shape=self.image_shape,
num_heads=self.num_heads,
max_text_length=self.max_text_length)
else:
norm_img = process_image(
img=img,
image_shape=self.image_shape,
char_ops=self.char_ops,
tps=self.use_tps,
infer_mode=True)
yield norm_img
else:
with open(self.label_file_path, "rb") as fin:
@ -286,14 +295,25 @@ class SimpleReader(object):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
label = substr[1]
outs = process_image(
img=img,
image_shape=self.image_shape,
label=label,
char_ops=self.char_ops,
loss_type=self.loss_type,
max_text_length=self.max_text_length,
distort=self.use_distort)
if self.loss_type == "srn":
outs = process_image_srn(
img=img,
image_shape=self.image_shape,
num_heads=self.num_heads,
max_text_length=self.max_text_length,
label=label,
char_ops=self.char_ops,
loss_type=self.loss_type)
else:
outs = process_image(
img=img,
image_shape=self.image_shape,
label=label,
char_ops=self.char_ops,
loss_type=self.loss_type,
max_text_length=self.max_text_length,
distort=self.use_distort)
if outs is None:
continue
yield outs

12
ppocr/data/rec/img_tools.py
Unescape View File

@ -410,7 +410,8 @@ def resize_norm_img_srn(img, image_shape):
def srn_other_inputs(image_shape,
num_heads,
max_text_length):
max_text_length,
char_num):
imgC, imgH, imgW = image_shape
feature_dim = int((imgH / 8) * (imgW / 8))
@ -418,7 +419,7 @@ def srn_other_inputs(image_shape,
encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype('int64')
gsrm_word_pos = np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype('int64')
lbl_weight = np.array([37] * max_text_length).reshape((-1,1)).astype('int64')
lbl_weight = np.array([int(char_num-1)] * max_text_length).reshape((-1,1)).astype('int64')
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape([-1, 1, max_text_length, max_text_length])
@ -441,17 +442,18 @@ def process_image_srn(img,
loss_type=None):
norm_img = resize_norm_img_srn(img, image_shape)
norm_img = norm_img[np.newaxis, :]
char_num = char_ops.get_char_num()
[lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
srn_other_inputs(image_shape, num_heads, max_text_length)
srn_other_inputs(image_shape, num_heads, max_text_length,char_num)
if label is not None:
char_num = char_ops.get_char_num()
text = char_ops.encode(label)
if len(text) == 0 or len(text) > max_text_length:
return None
else:
if loss_type == "srn":
text_padded = [37] * max_text_length
text_padded = [int(char_num-1)] * max_text_length
for i in range(len(text)):
text_padded[i] = text[i]
lbl_weight[i] = [1.0]

17
ppocr/modeling/backbones/rec_resnet50_fpn.py
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@ -81,6 +81,23 @@ class ResNet():
num_filters=num_filters[block],
stride=stride_list[block] if i == 0 else 1, name=conv_name)
F.append(conv)
else:
for block in range(len(depth)):
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
conv = self.basic_block(
input=conv,
num_filters=num_filters[block],
stride=stride,
if_first=block == i == 0,
name=conv_name)
F.append(conv)
base = F[-1]
for i in [-2, -3]:

8
ppocr/utils/character.py
Unescape View File

@ -26,8 +26,6 @@ class CharacterOps(object):
self.character_type = config['character_type']
self.loss_type = config['loss_type']
self.max_text_len = config['max_text_length']
if self.loss_type == "srn" and self.character_type != "en":
raise Exception("SRN can only support in character_type == en")
if self.character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
@ -160,13 +158,15 @@ def cal_predicts_accuracy_srn(char_ops,
acc_num = 0
img_num = 0
char_num = char_ops.get_char_num()
total_len = preds.shape[0]
img_num = int(total_len / max_text_len)
for i in range(img_num):
cur_label = []
cur_pred = []
for j in range(max_text_len):
if labels[j + i * max_text_len] != 37: #0
if labels[j + i * max_text_len] != int(char_num-1): #0
cur_label.append(labels[j + i * max_text_len][0])
else:
break
@ -178,7 +178,7 @@ def cal_predicts_accuracy_srn(char_ops,
elif j == len(cur_label) and j == max_text_len:
acc_num += 1
break
elif j == len(cur_label) and preds[j + i * max_text_len][0] == 37:
elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
acc_num += 1
break
acc = acc_num * 1.0 / img_num

4
tools/infer_rec.py
Unescape View File

@ -140,12 +140,12 @@ def main():
preds = preds.reshape(-1)
preds_text = char_ops.decode(preds)
elif loss_type == "srn":
cur_pred = []
char_num = char_ops.get_char_num()
preds = np.array(predict[0])
preds = preds.reshape(-1)
probs = np.array(predict[1])
ind = np.argmax(probs, axis=1)
valid_ind = np.where(preds != 37)[0]
valid_ind = np.where(preds != int(char_num-1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])

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