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d036c91a
编写于
4月 11, 2021
作者:
J
Jethong
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
support two postprocess
上级
f67e6e13
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
665 addition
and
169 deletion
+665
-169
configs/e2e/e2e_r50_vd_pg.yml
configs/e2e/e2e_r50_vd_pg.yml
+1
-0
doc/doc_ch/inference.md
doc/doc_ch/inference.md
+4
-37
ppocr/data/imaug/label_ops.py
ppocr/data/imaug/label_ops.py
+7
-9
ppocr/metrics/e2e_metric.py
ppocr/metrics/e2e_metric.py
+1
-1
ppocr/postprocess/pg_postprocess.py
ppocr/postprocess/pg_postprocess.py
+10
-114
ppocr/utils/e2e_utils/extract_textpoint_fast.py
ppocr/utils/e2e_utils/extract_textpoint_fast.py
+458
-0
ppocr/utils/e2e_utils/extract_textpoint_slow.py
ppocr/utils/e2e_utils/extract_textpoint_slow.py
+8
-8
ppocr/utils/e2e_utils/pgnet_pp_utils.py
ppocr/utils/e2e_utils/pgnet_pp_utils.py
+176
-0
未找到文件。
configs/e2e/e2e_r50_vd_pg.yml
浏览文件 @
d036c91a
...
@@ -59,6 +59,7 @@ Optimizer:
...
@@ -59,6 +59,7 @@ Optimizer:
PostProcess
:
PostProcess
:
name
:
PGPostProcess
name
:
PGPostProcess
score_thresh
:
0.5
score_thresh
:
0.5
mode
:
fast
# fast or slow two ways
Metric
:
Metric
:
name
:
E2EMetric
name
:
E2EMetric
gt_mat_dir
:
# the dir of gt_mat
gt_mat_dir
:
# the dir of gt_mat
...
...
doc/doc_ch/inference.md
浏览文件 @
d036c91a
...
@@ -28,13 +28,10 @@ inference 模型(`paddle.jit.save`保存的模型)
...
@@ -28,13 +28,10 @@ inference 模型(`paddle.jit.save`保存的模型)
-
[
4. 自定义文本识别字典的推理
](
#自定义文本识别字典的推理
)
-
[
4. 自定义文本识别字典的推理
](
#自定义文本识别字典的推理
)
-
[
5. 多语言模型的推理
](
#多语言模型的推理
)
-
[
5. 多语言模型的推理
](
#多语言模型的推理
)
-
[
四、端到端模型推理
](
#端到端模型推理
)
-
[
四、方向分类模型推理
](
#方向识别模型推理
)
-
[
1. PGNet端到端模型推理
](
#PGNet端到端模型推理
)
-
[
五、方向分类模型推理
](
#方向识别模型推理
)
-
[
1. 方向分类模型推理
](
#方向分类模型推理
)
-
[
1. 方向分类模型推理
](
#方向分类模型推理
)
-
[
六
、文本检测、方向分类和文字识别串联推理
](
#文本检测、方向分类和文字识别串联推理
)
-
[
五
、文本检测、方向分类和文字识别串联推理
](
#文本检测、方向分类和文字识别串联推理
)
-
[
1. 超轻量中文OCR模型推理
](
#超轻量中文OCR模型推理
)
-
[
1. 超轻量中文OCR模型推理
](
#超轻量中文OCR模型推理
)
-
[
2. 其他模型推理
](
#其他模型推理
)
-
[
2. 其他模型推理
](
#其他模型推理
)
...
@@ -362,38 +359,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
...
@@ -362,38 +359,8 @@ python3 tools/infer/predict_rec.py --image_dir="./doc/imgs_words/korean/1.jpg" -
Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
Predicts of ./doc/imgs_words/korean/1.jpg:('바탕으로', 0.9948904)
```
```
<a
name=
"端到端模型推理"
></a>
## 四、端到端模型推理
端到端模型推理,默认使用PGNet模型的配置参数。当不使用PGNet模型时,在推理时,需要通过传入相应的参数进行算法适配,细节参考下文。
<a
name=
"PGNet端到端模型推理"
></a>
### 1. PGNet端到端模型推理
#### (1). 四边形文本检测模型(ICDAR2015)
首先将PGNet端到端训练过程中保存的模型,转换成inference model。以基于Resnet50_vd骨干网络,在ICDAR2015英文数据集训练的模型为例(
[
模型下载地址
](
https://paddleocr.bj.bcebos.com/dygraph_v2.0/pgnet/en_server_pgnetA.tar
)
),可以使用如下命令进行转换:
```
python3 tools/export_model.py -c configs/e2e/e2e_r50_vd_pg.yml -o Global.pretrained_model=./en_server_pgnetA/iter_epoch_450 Global.load_static_weights=False Global.save_inference_dir=./inference/e2e
```
**PGNet端到端模型推理,需要设置参数`--e2e_algorithm="PGNet"`**
,可以执行如下命令:
```
python3 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img_10.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=False
```
可视化文本检测结果默认保存到
`./inference_results`
文件夹里面,结果文件的名称前缀为'e2e_res'。结果示例如下:
![](
../imgs_results/e2e_res_img_10_pgnet.jpg
)
#### (2). 弯曲文本检测模型(Total-Text)
和四边形文本检测模型共用一个推理模型
**PGNet端到端模型推理,需要设置参数`--e2e_algorithm="PGNet"`,同时,还需要增加参数`--e2e_pgnet_polygon=True`,**
可以执行如下命令:
```
python3.7 tools/infer/predict_e2e.py --e2e_algorithm="PGNet" --image_dir="./doc/imgs_en/img623.jpg" --e2e_model_dir="./inference/e2e/" --e2e_pgnet_polygon=True
```
可视化文本端到端结果默认保存到
`./inference_results`
文件夹里面,结果文件的名称前缀为'e2e_res'。结果示例如下:
![](
../imgs_results/e2e_res_img623_pgnet.jpg
)
<a
name=
"方向分类模型推理"
></a>
<a
name=
"方向分类模型推理"
></a>
##
五
、方向分类模型推理
##
四
、方向分类模型推理
下面将介绍方向分类模型推理。
下面将介绍方向分类模型推理。
...
@@ -418,7 +385,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
...
@@ -418,7 +385,7 @@ Predicts of ./doc/imgs_words/ch/word_4.jpg:['0', 0.9999982]
```
```
<a
name=
"文本检测、方向分类和文字识别串联推理"
></a>
<a
name=
"文本检测、方向分类和文字识别串联推理"
></a>
##
六
、文本检测、方向分类和文字识别串联推理
##
五
、文本检测、方向分类和文字识别串联推理
<a
name=
"超轻量中文OCR模型推理"
></a>
<a
name=
"超轻量中文OCR模型推理"
></a>
### 1. 超轻量中文OCR模型推理
### 1. 超轻量中文OCR模型推理
...
...
ppocr/data/imaug/label_ops.py
浏览文件 @
d036c91a
...
@@ -200,18 +200,16 @@ class E2ELabelEncode(BaseRecLabelEncode):
...
@@ -200,18 +200,16 @@ class E2ELabelEncode(BaseRecLabelEncode):
self
.
pad_num
=
len
(
self
.
dict
)
# the length to pad
self
.
pad_num
=
len
(
self
.
dict
)
# the length to pad
def
__call__
(
self
,
data
):
def
__call__
(
self
,
data
):
text_label_index_list
,
temp_text
=
[],
[]
texts
=
data
[
'strs'
]
texts
=
data
[
'strs'
]
temp_texts
=
[]
for
text
in
texts
:
for
text
in
texts
:
text
=
text
.
lower
()
text
=
text
.
lower
()
temp_text
=
[]
text
=
self
.
encode
(
text
)
for
c_
in
text
:
if
text
is
None
:
if
c_
in
self
.
dict
:
return
None
temp_text
.
append
(
self
.
dict
[
c_
])
text
=
text
+
[
self
.
pad_num
]
*
(
self
.
max_text_len
-
len
(
text
))
temp_text
=
temp_text
+
[
self
.
pad_num
]
*
(
self
.
max_text_len
-
temp_texts
.
append
(
text
)
len
(
temp_text
))
data
[
'strs'
]
=
np
.
array
(
temp_texts
)
text_label_index_list
.
append
(
temp_text
)
data
[
'strs'
]
=
np
.
array
(
text_label_index_list
)
return
data
return
data
...
...
ppocr/metrics/e2e_metric.py
浏览文件 @
d036c91a
...
@@ -19,7 +19,7 @@ from __future__ import print_function
...
@@ -19,7 +19,7 @@ from __future__ import print_function
__all__
=
[
'E2EMetric'
]
__all__
=
[
'E2EMetric'
]
from
ppocr.utils.e2e_metric.Deteval
import
get_socre
,
combine_results
from
ppocr.utils.e2e_metric.Deteval
import
get_socre
,
combine_results
from
ppocr.utils.e2e_utils.extract_textpoint
import
get_dict
from
ppocr.utils.e2e_utils.extract_textpoint
_slow
import
get_dict
class
E2EMetric
(
object
):
class
E2EMetric
(
object
):
...
...
ppocr/postprocess/pg_postprocess.py
浏览文件 @
d036c91a
...
@@ -22,10 +22,7 @@ import sys
...
@@ -22,10 +22,7 @@ import sys
__dir__
=
os
.
path
.
dirname
(
__file__
)
__dir__
=
os
.
path
.
dirname
(
__file__
)
sys
.
path
.
append
(
__dir__
)
sys
.
path
.
append
(
__dir__
)
sys
.
path
.
append
(
os
.
path
.
join
(
__dir__
,
'..'
))
sys
.
path
.
append
(
os
.
path
.
join
(
__dir__
,
'..'
))
from
ppocr.utils.e2e_utils.pgnet_pp_utils
import
PGNet_PostProcess
from
ppocr.utils.e2e_utils.extract_textpoint
import
*
from
ppocr.utils.e2e_utils.visual
import
*
import
paddle
class
PGPostProcess
(
object
):
class
PGPostProcess
(
object
):
...
@@ -33,10 +30,12 @@ class PGPostProcess(object):
...
@@ -33,10 +30,12 @@ class PGPostProcess(object):
The post process for PGNet.
The post process for PGNet.
"""
"""
def
__init__
(
self
,
character_dict_path
,
valid_set
,
score_thresh
,
**
kwargs
):
def
__init__
(
self
,
character_dict_path
,
valid_set
,
score_thresh
,
mode
,
self
.
Lexicon_Table
=
get_dict
(
character_dict_path
)
**
kwargs
):
self
.
character_dict_path
=
character_dict_path
self
.
valid_set
=
valid_set
self
.
valid_set
=
valid_set
self
.
score_thresh
=
score_thresh
self
.
score_thresh
=
score_thresh
self
.
mode
=
mode
# c++ la-nms is faster, but only support python 3.5
# c++ la-nms is faster, but only support python 3.5
self
.
is_python35
=
False
self
.
is_python35
=
False
...
@@ -44,113 +43,10 @@ class PGPostProcess(object):
...
@@ -44,113 +43,10 @@ class PGPostProcess(object):
self
.
is_python35
=
True
self
.
is_python35
=
True
def
__call__
(
self
,
outs_dict
,
shape_list
):
def
__call__
(
self
,
outs_dict
,
shape_list
):
p_score
=
outs_dict
[
'f_score'
]
post
=
PGNet_PostProcess
(
self
.
character_dict_path
,
self
.
valid_set
,
p_border
=
outs_dict
[
'f_border'
]
self
.
score_thresh
,
outs_dict
,
shape_list
)
p_char
=
outs_dict
[
'f_char'
]
if
self
.
mode
==
'fast'
:
p_direction
=
outs_dict
[
'f_direction'
]
data
=
post
.
pg_postprocess_fast
()
if
isinstance
(
p_score
,
paddle
.
Tensor
):
p_score
=
p_score
[
0
].
numpy
()
p_border
=
p_border
[
0
].
numpy
()
p_direction
=
p_direction
[
0
].
numpy
()
p_char
=
p_char
[
0
].
numpy
()
else
:
else
:
p_score
=
p_score
[
0
]
data
=
post
.
pg_postprocess_slow
()
p_border
=
p_border
[
0
]
p_direction
=
p_direction
[
0
]
p_char
=
p_char
[
0
]
src_h
,
src_w
,
ratio_h
,
ratio_w
=
shape_list
[
0
]
is_curved
=
self
.
valid_set
==
"totaltext"
instance_yxs_list
=
generate_pivot_list
(
p_score
,
p_char
,
p_direction
,
score_thresh
=
self
.
score_thresh
,
is_backbone
=
True
,
is_curved
=
is_curved
)
p_char
=
paddle
.
to_tensor
(
np
.
expand_dims
(
p_char
,
axis
=
0
))
char_seq_idx_set
=
[]
for
i
in
range
(
len
(
instance_yxs_list
)):
gather_info_lod
=
paddle
.
to_tensor
(
instance_yxs_list
[
i
])
f_char_map
=
paddle
.
transpose
(
p_char
,
[
0
,
2
,
3
,
1
])
feature_seq
=
paddle
.
gather_nd
(
f_char_map
,
gather_info_lod
)
feature_seq
=
np
.
expand_dims
(
feature_seq
.
numpy
(),
axis
=
0
)
feature_len
=
[
len
(
feature_seq
[
0
])]
featyre_seq
=
paddle
.
to_tensor
(
feature_seq
)
feature_len
=
np
.
array
([
feature_len
]).
astype
(
np
.
int64
)
length
=
paddle
.
to_tensor
(
feature_len
)
seq_pred
=
paddle
.
fluid
.
layers
.
ctc_greedy_decoder
(
input
=
featyre_seq
,
blank
=
36
,
input_length
=
length
)
seq_pred_str
=
seq_pred
[
0
].
numpy
().
tolist
()[
0
]
seq_len
=
seq_pred
[
1
].
numpy
()[
0
][
0
]
temp_t
=
[]
for
c
in
seq_pred_str
[:
seq_len
]:
temp_t
.
append
(
c
)
char_seq_idx_set
.
append
(
temp_t
)
seq_strs
=
[]
for
char_idx_set
in
char_seq_idx_set
:
pr_str
=
''
.
join
([
self
.
Lexicon_Table
[
pos
]
for
pos
in
char_idx_set
])
seq_strs
.
append
(
pr_str
)
poly_list
=
[]
keep_str_list
=
[]
all_point_list
=
[]
all_point_pair_list
=
[]
for
yx_center_line
,
keep_str
in
zip
(
instance_yxs_list
,
seq_strs
):
if
len
(
yx_center_line
)
==
1
:
yx_center_line
.
append
(
yx_center_line
[
-
1
])
offset_expand
=
1.0
if
self
.
valid_set
==
'totaltext'
:
offset_expand
=
1.2
point_pair_list
=
[]
for
batch_id
,
y
,
x
in
yx_center_line
:
offset
=
p_border
[:,
y
,
x
].
reshape
(
2
,
2
)
if
offset_expand
!=
1.0
:
offset_length
=
np
.
linalg
.
norm
(
offset
,
axis
=
1
,
keepdims
=
True
)
expand_length
=
np
.
clip
(
offset_length
*
(
offset_expand
-
1
),
a_min
=
0.5
,
a_max
=
3.0
)
offset_detal
=
offset
/
offset_length
*
expand_length
offset
=
offset
+
offset_detal
ori_yx
=
np
.
array
([
y
,
x
],
dtype
=
np
.
float32
)
point_pair
=
(
ori_yx
+
offset
)[:,
::
-
1
]
*
4.0
/
np
.
array
(
[
ratio_w
,
ratio_h
]).
reshape
(
-
1
,
2
)
point_pair_list
.
append
(
point_pair
)
all_point_list
.
append
([
int
(
round
(
x
*
4.0
/
ratio_w
)),
int
(
round
(
y
*
4.0
/
ratio_h
))
])
all_point_pair_list
.
append
(
point_pair
.
round
().
astype
(
np
.
int32
)
.
tolist
())
detected_poly
,
pair_length_info
=
point_pair2poly
(
point_pair_list
)
detected_poly
=
expand_poly_along_width
(
detected_poly
,
shrink_ratio_of_width
=
0.2
)
detected_poly
[:,
0
]
=
np
.
clip
(
detected_poly
[:,
0
],
a_min
=
0
,
a_max
=
src_w
)
detected_poly
[:,
1
]
=
np
.
clip
(
detected_poly
[:,
1
],
a_min
=
0
,
a_max
=
src_h
)
if
len
(
keep_str
)
<
2
:
continue
keep_str_list
.
append
(
keep_str
)
detected_poly
=
np
.
round
(
detected_poly
).
astype
(
'int32'
)
if
self
.
valid_set
==
'partvgg'
:
middle_point
=
len
(
detected_poly
)
//
2
detected_poly
=
detected_poly
[
[
0
,
middle_point
-
1
,
middle_point
,
-
1
],
:]
poly_list
.
append
(
detected_poly
)
elif
self
.
valid_set
==
'totaltext'
:
poly_list
.
append
(
detected_poly
)
else
:
print
(
'--> Not supported format.'
)
exit
(
-
1
)
data
=
{
'points'
:
poly_list
,
'strs'
:
keep_str_list
,
}
return
data
return
data
ppocr/utils/e2e_utils/extract_textpoint_fast.py
0 → 100644
浏览文件 @
d036c91a
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains various CTC decoders."""
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
cv2
import
math
import
numpy
as
np
from
itertools
import
groupby
from
cv2.ximgproc
import
thinning
as
thin
def
get_dict
(
character_dict_path
):
character_str
=
""
with
open
(
character_dict_path
,
"rb"
)
as
fin
:
lines
=
fin
.
readlines
()
for
line
in
lines
:
line
=
line
.
decode
(
'utf-8'
).
strip
(
"
\n
"
).
strip
(
"
\r\n
"
)
character_str
+=
line
dict_character
=
list
(
character_str
)
return
dict_character
def
softmax
(
logits
):
"""
logits: N x d
"""
max_value
=
np
.
max
(
logits
,
axis
=
1
,
keepdims
=
True
)
exp
=
np
.
exp
(
logits
-
max_value
)
exp_sum
=
np
.
sum
(
exp
,
axis
=
1
,
keepdims
=
True
)
dist
=
exp
/
exp_sum
return
dist
def
get_keep_pos_idxs
(
labels
,
remove_blank
=
None
):
"""
Remove duplicate and get pos idxs of keep items.
The value of keep_blank should be [None, 95].
"""
duplicate_len_list
=
[]
keep_pos_idx_list
=
[]
keep_char_idx_list
=
[]
for
k
,
v_
in
groupby
(
labels
):
current_len
=
len
(
list
(
v_
))
if
k
!=
remove_blank
:
current_idx
=
int
(
sum
(
duplicate_len_list
)
+
current_len
//
2
)
keep_pos_idx_list
.
append
(
current_idx
)
keep_char_idx_list
.
append
(
k
)
duplicate_len_list
.
append
(
current_len
)
return
keep_char_idx_list
,
keep_pos_idx_list
def
remove_blank
(
labels
,
blank
=
0
):
new_labels
=
[
x
for
x
in
labels
if
x
!=
blank
]
return
new_labels
def
insert_blank
(
labels
,
blank
=
0
):
new_labels
=
[
blank
]
for
l
in
labels
:
new_labels
+=
[
l
,
blank
]
return
new_labels
def
ctc_greedy_decoder
(
probs_seq
,
blank
=
95
,
keep_blank_in_idxs
=
True
):
"""
CTC greedy (best path) decoder.
"""
raw_str
=
np
.
argmax
(
np
.
array
(
probs_seq
),
axis
=
1
)
remove_blank_in_pos
=
None
if
keep_blank_in_idxs
else
blank
dedup_str
,
keep_idx_list
=
get_keep_pos_idxs
(
raw_str
,
remove_blank
=
remove_blank_in_pos
)
dst_str
=
remove_blank
(
dedup_str
,
blank
=
blank
)
return
dst_str
,
keep_idx_list
def
instance_ctc_greedy_decoder
(
gather_info
,
logits_map
,
pts_num
=
4
):
_
,
_
,
C
=
logits_map
.
shape
ys
,
xs
=
zip
(
*
gather_info
)
logits_seq
=
logits_map
[
list
(
ys
),
list
(
xs
)]
probs_seq
=
logits_seq
labels
=
np
.
argmax
(
probs_seq
,
axis
=
1
)
dst_str
=
[
k
for
k
,
v_
in
groupby
(
labels
)
if
k
!=
C
-
1
]
detal
=
len
(
gather_info
)
//
(
pts_num
-
1
)
keep_idx_list
=
[
0
]
+
[
detal
*
(
i
+
1
)
for
i
in
range
(
pts_num
-
2
)]
+
[
-
1
]
keep_gather_list
=
[
gather_info
[
idx
]
for
idx
in
keep_idx_list
]
return
dst_str
,
keep_gather_list
def
ctc_decoder_for_image
(
gather_info_list
,
logits_map
,
Lexicon_Table
,
pts_num
=
6
):
"""
CTC decoder using multiple processes.
"""
decoder_str
=
[]
decoder_xys
=
[]
for
gather_info
in
gather_info_list
:
if
len
(
gather_info
)
<
pts_num
:
continue
dst_str
,
xys_list
=
instance_ctc_greedy_decoder
(
gather_info
,
logits_map
,
pts_num
=
pts_num
)
dst_str_readable
=
''
.
join
([
Lexicon_Table
[
idx
]
for
idx
in
dst_str
])
if
len
(
dst_str_readable
)
<
2
:
continue
decoder_str
.
append
(
dst_str_readable
)
decoder_xys
.
append
(
xys_list
)
return
decoder_str
,
decoder_xys
def
sort_with_direction
(
pos_list
,
f_direction
):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def
sort_part_with_direction
(
pos_list
,
point_direction
):
pos_list
=
np
.
array
(
pos_list
).
reshape
(
-
1
,
2
)
point_direction
=
np
.
array
(
point_direction
).
reshape
(
-
1
,
2
)
average_direction
=
np
.
mean
(
point_direction
,
axis
=
0
,
keepdims
=
True
)
pos_proj_leng
=
np
.
sum
(
pos_list
*
average_direction
,
axis
=
1
)
sorted_list
=
pos_list
[
np
.
argsort
(
pos_proj_leng
)].
tolist
()
sorted_direction
=
point_direction
[
np
.
argsort
(
pos_proj_leng
)].
tolist
()
return
sorted_list
,
sorted_direction
pos_list
=
np
.
array
(
pos_list
).
reshape
(
-
1
,
2
)
point_direction
=
f_direction
[
pos_list
[:,
0
],
pos_list
[:,
1
]]
# x, y
point_direction
=
point_direction
[:,
::
-
1
]
# x, y -> y, x
sorted_point
,
sorted_direction
=
sort_part_with_direction
(
pos_list
,
point_direction
)
point_num
=
len
(
sorted_point
)
if
point_num
>=
16
:
middle_num
=
point_num
//
2
first_part_point
=
sorted_point
[:
middle_num
]
first_point_direction
=
sorted_direction
[:
middle_num
]
sorted_fist_part_point
,
sorted_fist_part_direction
=
sort_part_with_direction
(
first_part_point
,
first_point_direction
)
last_part_point
=
sorted_point
[
middle_num
:]
last_point_direction
=
sorted_direction
[
middle_num
:]
sorted_last_part_point
,
sorted_last_part_direction
=
sort_part_with_direction
(
last_part_point
,
last_point_direction
)
sorted_point
=
sorted_fist_part_point
+
sorted_last_part_point
sorted_direction
=
sorted_fist_part_direction
+
sorted_last_part_direction
return
sorted_point
,
np
.
array
(
sorted_direction
)
def
add_id
(
pos_list
,
image_id
=
0
):
"""
Add id for gather feature, for inference.
"""
new_list
=
[]
for
item
in
pos_list
:
new_list
.
append
((
image_id
,
item
[
0
],
item
[
1
]))
return
new_list
def
sort_and_expand_with_direction
(
pos_list
,
f_direction
):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
h
,
w
,
_
=
f_direction
.
shape
sorted_list
,
point_direction
=
sort_with_direction
(
pos_list
,
f_direction
)
point_num
=
len
(
sorted_list
)
sub_direction_len
=
max
(
point_num
//
3
,
2
)
left_direction
=
point_direction
[:
sub_direction_len
,
:]
right_dirction
=
point_direction
[
point_num
-
sub_direction_len
:,
:]
left_average_direction
=
-
np
.
mean
(
left_direction
,
axis
=
0
,
keepdims
=
True
)
left_average_len
=
np
.
linalg
.
norm
(
left_average_direction
)
left_start
=
np
.
array
(
sorted_list
[
0
])
left_step
=
left_average_direction
/
(
left_average_len
+
1e-6
)
right_average_direction
=
np
.
mean
(
right_dirction
,
axis
=
0
,
keepdims
=
True
)
right_average_len
=
np
.
linalg
.
norm
(
right_average_direction
)
right_step
=
right_average_direction
/
(
right_average_len
+
1e-6
)
right_start
=
np
.
array
(
sorted_list
[
-
1
])
append_num
=
max
(
int
((
left_average_len
+
right_average_len
)
/
2.0
*
0.15
),
1
)
left_list
=
[]
right_list
=
[]
for
i
in
range
(
append_num
):
ly
,
lx
=
np
.
round
(
left_start
+
left_step
*
(
i
+
1
)).
flatten
().
astype
(
'int32'
).
tolist
()
if
ly
<
h
and
lx
<
w
and
(
ly
,
lx
)
not
in
left_list
:
left_list
.
append
((
ly
,
lx
))
ry
,
rx
=
np
.
round
(
right_start
+
right_step
*
(
i
+
1
)).
flatten
().
astype
(
'int32'
).
tolist
()
if
ry
<
h
and
rx
<
w
and
(
ry
,
rx
)
not
in
right_list
:
right_list
.
append
((
ry
,
rx
))
all_list
=
left_list
[::
-
1
]
+
sorted_list
+
right_list
return
all_list
def
sort_and_expand_with_direction_v2
(
pos_list
,
f_direction
,
binary_tcl_map
):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
binary_tcl_map: h x w
"""
h
,
w
,
_
=
f_direction
.
shape
sorted_list
,
point_direction
=
sort_with_direction
(
pos_list
,
f_direction
)
point_num
=
len
(
sorted_list
)
sub_direction_len
=
max
(
point_num
//
3
,
2
)
left_direction
=
point_direction
[:
sub_direction_len
,
:]
right_dirction
=
point_direction
[
point_num
-
sub_direction_len
:,
:]
left_average_direction
=
-
np
.
mean
(
left_direction
,
axis
=
0
,
keepdims
=
True
)
left_average_len
=
np
.
linalg
.
norm
(
left_average_direction
)
left_start
=
np
.
array
(
sorted_list
[
0
])
left_step
=
left_average_direction
/
(
left_average_len
+
1e-6
)
right_average_direction
=
np
.
mean
(
right_dirction
,
axis
=
0
,
keepdims
=
True
)
right_average_len
=
np
.
linalg
.
norm
(
right_average_direction
)
right_step
=
right_average_direction
/
(
right_average_len
+
1e-6
)
right_start
=
np
.
array
(
sorted_list
[
-
1
])
append_num
=
max
(
int
((
left_average_len
+
right_average_len
)
/
2.0
*
0.15
),
1
)
max_append_num
=
2
*
append_num
left_list
=
[]
right_list
=
[]
for
i
in
range
(
max_append_num
):
ly
,
lx
=
np
.
round
(
left_start
+
left_step
*
(
i
+
1
)).
flatten
().
astype
(
'int32'
).
tolist
()
if
ly
<
h
and
lx
<
w
and
(
ly
,
lx
)
not
in
left_list
:
if
binary_tcl_map
[
ly
,
lx
]
>
0.5
:
left_list
.
append
((
ly
,
lx
))
else
:
break
for
i
in
range
(
max_append_num
):
ry
,
rx
=
np
.
round
(
right_start
+
right_step
*
(
i
+
1
)).
flatten
().
astype
(
'int32'
).
tolist
()
if
ry
<
h
and
rx
<
w
and
(
ry
,
rx
)
not
in
right_list
:
if
binary_tcl_map
[
ry
,
rx
]
>
0.5
:
right_list
.
append
((
ry
,
rx
))
else
:
break
all_list
=
left_list
[::
-
1
]
+
sorted_list
+
right_list
return
all_list
def
point_pair2poly
(
point_pair_list
):
"""
Transfer vertical point_pairs into poly point in clockwise.
"""
point_num
=
len
(
point_pair_list
)
*
2
point_list
=
[
0
]
*
point_num
for
idx
,
point_pair
in
enumerate
(
point_pair_list
):
point_list
[
idx
]
=
point_pair
[
0
]
point_list
[
point_num
-
1
-
idx
]
=
point_pair
[
1
]
return
np
.
array
(
point_list
).
reshape
(
-
1
,
2
)
def
shrink_quad_along_width
(
quad
,
begin_width_ratio
=
0.
,
end_width_ratio
=
1.
):
ratio_pair
=
np
.
array
(
[[
begin_width_ratio
],
[
end_width_ratio
]],
dtype
=
np
.
float32
)
p0_1
=
quad
[
0
]
+
(
quad
[
1
]
-
quad
[
0
])
*
ratio_pair
p3_2
=
quad
[
3
]
+
(
quad
[
2
]
-
quad
[
3
])
*
ratio_pair
return
np
.
array
([
p0_1
[
0
],
p0_1
[
1
],
p3_2
[
1
],
p3_2
[
0
]])
def
expand_poly_along_width
(
poly
,
shrink_ratio_of_width
=
0.3
):
"""
expand poly along width.
"""
point_num
=
poly
.
shape
[
0
]
left_quad
=
np
.
array
(
[
poly
[
0
],
poly
[
1
],
poly
[
-
2
],
poly
[
-
1
]],
dtype
=
np
.
float32
)
left_ratio
=
-
shrink_ratio_of_width
*
np
.
linalg
.
norm
(
left_quad
[
0
]
-
left_quad
[
3
])
/
\
(
np
.
linalg
.
norm
(
left_quad
[
0
]
-
left_quad
[
1
])
+
1e-6
)
left_quad_expand
=
shrink_quad_along_width
(
left_quad
,
left_ratio
,
1.0
)
right_quad
=
np
.
array
(
[
poly
[
point_num
//
2
-
2
],
poly
[
point_num
//
2
-
1
],
poly
[
point_num
//
2
],
poly
[
point_num
//
2
+
1
]
],
dtype
=
np
.
float32
)
right_ratio
=
1.0
+
shrink_ratio_of_width
*
np
.
linalg
.
norm
(
right_quad
[
0
]
-
right_quad
[
3
])
/
\
(
np
.
linalg
.
norm
(
right_quad
[
0
]
-
right_quad
[
1
])
+
1e-6
)
right_quad_expand
=
shrink_quad_along_width
(
right_quad
,
0.0
,
right_ratio
)
poly
[
0
]
=
left_quad_expand
[
0
]
poly
[
-
1
]
=
left_quad_expand
[
-
1
]
poly
[
point_num
//
2
-
1
]
=
right_quad_expand
[
1
]
poly
[
point_num
//
2
]
=
right_quad_expand
[
2
]
return
poly
def
restore_poly
(
instance_yxs_list
,
seq_strs
,
p_border
,
ratio_w
,
ratio_h
,
src_w
,
src_h
,
valid_set
):
poly_list
=
[]
keep_str_list
=
[]
for
yx_center_line
,
keep_str
in
zip
(
instance_yxs_list
,
seq_strs
):
if
len
(
keep_str
)
<
2
:
print
(
'--> too short, {}'
.
format
(
keep_str
))
continue
offset_expand
=
1.0
if
valid_set
==
'totaltext'
:
offset_expand
=
1.2
point_pair_list
=
[]
for
y
,
x
in
yx_center_line
:
offset
=
p_border
[:,
y
,
x
].
reshape
(
2
,
2
)
*
offset_expand
ori_yx
=
np
.
array
([
y
,
x
],
dtype
=
np
.
float32
)
point_pair
=
(
ori_yx
+
offset
)[:,
::
-
1
]
*
4.0
/
np
.
array
(
[
ratio_w
,
ratio_h
]).
reshape
(
-
1
,
2
)
point_pair_list
.
append
(
point_pair
)
detected_poly
=
point_pair2poly
(
point_pair_list
)
detected_poly
=
expand_poly_along_width
(
detected_poly
,
shrink_ratio_of_width
=
0.2
)
detected_poly
[:,
0
]
=
np
.
clip
(
detected_poly
[:,
0
],
a_min
=
0
,
a_max
=
src_w
)
detected_poly
[:,
1
]
=
np
.
clip
(
detected_poly
[:,
1
],
a_min
=
0
,
a_max
=
src_h
)
keep_str_list
.
append
(
keep_str
)
if
valid_set
==
'partvgg'
:
middle_point
=
len
(
detected_poly
)
//
2
detected_poly
=
detected_poly
[
[
0
,
middle_point
-
1
,
middle_point
,
-
1
],
:]
poly_list
.
append
(
detected_poly
)
elif
valid_set
==
'totaltext'
:
poly_list
.
append
(
detected_poly
)
else
:
print
(
'--> Not supported format.'
)
exit
(
-
1
)
return
poly_list
,
keep_str_list
def
generate_pivot_list_fast
(
p_score
,
p_char_maps
,
f_direction
,
Lexicon_Table
,
score_thresh
=
0.5
):
"""
return center point and end point of TCL instance; filter with the char maps;
"""
p_score
=
p_score
[
0
]
f_direction
=
f_direction
.
transpose
(
1
,
2
,
0
)
ret
,
p_tcl_map
=
cv2
.
threshold
(
p_score
,
score_thresh
,
255
,
cv2
.
THRESH_BINARY
)
skeleton_map
=
thin
(
p_tcl_map
.
astype
(
'uint8'
))
instance_count
,
instance_label_map
=
cv2
.
connectedComponents
(
skeleton_map
,
connectivity
=
8
)
# get TCL Instance
all_pos_yxs
=
[]
if
instance_count
>
0
:
for
instance_id
in
range
(
1
,
instance_count
):
pos_list
=
[]
ys
,
xs
=
np
.
where
(
instance_label_map
==
instance_id
)
pos_list
=
list
(
zip
(
ys
,
xs
))
if
len
(
pos_list
)
<
3
:
continue
pos_list_sorted
=
sort_and_expand_with_direction_v2
(
pos_list
,
f_direction
,
p_tcl_map
)
all_pos_yxs
.
append
(
pos_list_sorted
)
p_char_maps
=
p_char_maps
.
transpose
([
1
,
2
,
0
])
decoded_str
,
keep_yxs_list
=
ctc_decoder_for_image
(
all_pos_yxs
,
logits_map
=
p_char_maps
,
Lexicon_Table
=
Lexicon_Table
)
return
keep_yxs_list
,
decoded_str
def
extract_main_direction
(
pos_list
,
f_direction
):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
pos_list
=
np
.
array
(
pos_list
)
point_direction
=
f_direction
[
pos_list
[:,
0
],
pos_list
[:,
1
]]
point_direction
=
point_direction
[:,
::
-
1
]
# x, y -> y, x
average_direction
=
np
.
mean
(
point_direction
,
axis
=
0
,
keepdims
=
True
)
average_direction
=
average_direction
/
(
np
.
linalg
.
norm
(
average_direction
)
+
1e-6
)
return
average_direction
def
sort_by_direction_with_image_id_deprecated
(
pos_list
,
f_direction
):
"""
f_direction: h x w x 2
pos_list: [[id, y, x], [id, y, x], [id, y, x] ...]
"""
pos_list_full
=
np
.
array
(
pos_list
).
reshape
(
-
1
,
3
)
pos_list
=
pos_list_full
[:,
1
:]
point_direction
=
f_direction
[
pos_list
[:,
0
],
pos_list
[:,
1
]]
# x, y
point_direction
=
point_direction
[:,
::
-
1
]
# x, y -> y, x
average_direction
=
np
.
mean
(
point_direction
,
axis
=
0
,
keepdims
=
True
)
pos_proj_leng
=
np
.
sum
(
pos_list
*
average_direction
,
axis
=
1
)
sorted_list
=
pos_list_full
[
np
.
argsort
(
pos_proj_leng
)].
tolist
()
return
sorted_list
def
sort_by_direction_with_image_id
(
pos_list
,
f_direction
):
"""
f_direction: h x w x 2
pos_list: [[y, x], [y, x], [y, x] ...]
"""
def
sort_part_with_direction
(
pos_list_full
,
point_direction
):
pos_list_full
=
np
.
array
(
pos_list_full
).
reshape
(
-
1
,
3
)
pos_list
=
pos_list_full
[:,
1
:]
point_direction
=
np
.
array
(
point_direction
).
reshape
(
-
1
,
2
)
average_direction
=
np
.
mean
(
point_direction
,
axis
=
0
,
keepdims
=
True
)
pos_proj_leng
=
np
.
sum
(
pos_list
*
average_direction
,
axis
=
1
)
sorted_list
=
pos_list_full
[
np
.
argsort
(
pos_proj_leng
)].
tolist
()
sorted_direction
=
point_direction
[
np
.
argsort
(
pos_proj_leng
)].
tolist
()
return
sorted_list
,
sorted_direction
pos_list
=
np
.
array
(
pos_list
).
reshape
(
-
1
,
3
)
point_direction
=
f_direction
[
pos_list
[:,
1
],
pos_list
[:,
2
]]
# x, y
point_direction
=
point_direction
[:,
::
-
1
]
# x, y -> y, x
sorted_point
,
sorted_direction
=
sort_part_with_direction
(
pos_list
,
point_direction
)
point_num
=
len
(
sorted_point
)
if
point_num
>=
16
:
middle_num
=
point_num
//
2
first_part_point
=
sorted_point
[:
middle_num
]
first_point_direction
=
sorted_direction
[:
middle_num
]
sorted_fist_part_point
,
sorted_fist_part_direction
=
sort_part_with_direction
(
first_part_point
,
first_point_direction
)
last_part_point
=
sorted_point
[
middle_num
:]
last_point_direction
=
sorted_direction
[
middle_num
:]
sorted_last_part_point
,
sorted_last_part_direction
=
sort_part_with_direction
(
last_part_point
,
last_point_direction
)
sorted_point
=
sorted_fist_part_point
+
sorted_last_part_point
sorted_direction
=
sorted_fist_part_direction
+
sorted_last_part_direction
return
sorted_point
ppocr/utils/e2e_utils/extract_textpoint.py
→
ppocr/utils/e2e_utils/extract_textpoint
_slow
.py
浏览文件 @
d036c91a
...
@@ -21,7 +21,7 @@ import math
...
@@ -21,7 +21,7 @@ import math
import
numpy
as
np
import
numpy
as
np
from
itertools
import
groupby
from
itertools
import
groupby
from
skimage.morphology._skeletonize
import
thin
from
cv2.ximgproc
import
thinning
as
thin
def
get_dict
(
character_dict_path
):
def
get_dict
(
character_dict_path
):
...
@@ -399,13 +399,13 @@ def generate_pivot_list_horizontal(p_score,
...
@@ -399,13 +399,13 @@ def generate_pivot_list_horizontal(p_score,
return
center_pos_yxs
,
end_points_yxs
return
center_pos_yxs
,
end_points_yxs
def
generate_pivot_list
(
p_score
,
def
generate_pivot_list
_slow
(
p_score
,
p_char_maps
,
p_char_maps
,
f_direction
,
f_direction
,
score_thresh
=
0.5
,
score_thresh
=
0.5
,
is_backbone
=
False
,
is_backbone
=
False
,
is_curved
=
True
,
is_curved
=
True
,
image_id
=
0
):
image_id
=
0
):
"""
"""
Warp all the function together.
Warp all the function together.
"""
"""
...
...
ppocr/utils/e2e_utils/pgnet_pp_utils.py
0 → 100644
浏览文件 @
d036c91a
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
paddle
from
extract_textpoint_slow
import
*
from
extract_textpoint_fast
import
*
class
PGNet_PostProcess
(
object
):
# two different post-process
def
__init__
(
self
,
character_dict_path
,
valid_set
,
score_thresh
,
outs_dict
,
shape_list
):
self
.
Lexicon_Table
=
get_dict
(
character_dict_path
)
self
.
valid_set
=
valid_set
self
.
score_thresh
=
score_thresh
self
.
outs_dict
=
outs_dict
self
.
shape_list
=
shape_list
def
pg_postprocess_fast
(
self
):
p_score
=
self
.
outs_dict
[
'f_score'
]
p_border
=
self
.
outs_dict
[
'f_border'
]
p_char
=
self
.
outs_dict
[
'f_char'
]
p_direction
=
self
.
outs_dict
[
'f_direction'
]
if
isinstance
(
p_score
,
paddle
.
Tensor
):
p_score
=
p_score
[
0
].
numpy
()
p_border
=
p_border
[
0
].
numpy
()
p_direction
=
p_direction
[
0
].
numpy
()
p_char
=
p_char
[
0
].
numpy
()
else
:
p_score
=
p_score
[
0
]
p_border
=
p_border
[
0
]
p_direction
=
p_direction
[
0
]
p_char
=
p_char
[
0
]
src_h
,
src_w
,
ratio_h
,
ratio_w
=
self
.
shape_list
[
0
]
instance_yxs_list
,
seq_strs
=
generate_pivot_list_fast
(
p_score
,
p_char
,
p_direction
,
self
.
Lexicon_Table
,
score_thresh
=
self
.
score_thresh
)
poly_list
,
keep_str_list
=
restore_poly
(
instance_yxs_list
,
seq_strs
,
p_border
,
ratio_w
,
ratio_h
,
src_w
,
src_h
,
self
.
valid_set
)
data
=
{
'points'
:
poly_list
,
'strs'
:
keep_str_list
,
}
return
data
def
pg_postprocess_slow
(
self
):
p_score
=
self
.
outs_dict
[
'f_score'
]
p_border
=
self
.
outs_dict
[
'f_border'
]
p_char
=
self
.
outs_dict
[
'f_char'
]
p_direction
=
self
.
outs_dict
[
'f_direction'
]
if
isinstance
(
p_score
,
paddle
.
Tensor
):
p_score
=
p_score
[
0
].
numpy
()
p_border
=
p_border
[
0
].
numpy
()
p_direction
=
p_direction
[
0
].
numpy
()
p_char
=
p_char
[
0
].
numpy
()
else
:
p_score
=
p_score
[
0
]
p_border
=
p_border
[
0
]
p_direction
=
p_direction
[
0
]
p_char
=
p_char
[
0
]
src_h
,
src_w
,
ratio_h
,
ratio_w
=
self
.
shape_list
[
0
]
is_curved
=
self
.
valid_set
==
"totaltext"
instance_yxs_list
=
generate_pivot_list_slow
(
p_score
,
p_char
,
p_direction
,
score_thresh
=
self
.
score_thresh
,
is_backbone
=
True
,
is_curved
=
is_curved
)
p_char
=
paddle
.
to_tensor
(
np
.
expand_dims
(
p_char
,
axis
=
0
))
char_seq_idx_set
=
[]
for
i
in
range
(
len
(
instance_yxs_list
)):
gather_info_lod
=
paddle
.
to_tensor
(
instance_yxs_list
[
i
])
f_char_map
=
paddle
.
transpose
(
p_char
,
[
0
,
2
,
3
,
1
])
feature_seq
=
paddle
.
gather_nd
(
f_char_map
,
gather_info_lod
)
feature_seq
=
np
.
expand_dims
(
feature_seq
.
numpy
(),
axis
=
0
)
feature_len
=
[
len
(
feature_seq
[
0
])]
featyre_seq
=
paddle
.
to_tensor
(
feature_seq
)
feature_len
=
np
.
array
([
feature_len
]).
astype
(
np
.
int64
)
length
=
paddle
.
to_tensor
(
feature_len
)
seq_pred
=
paddle
.
fluid
.
layers
.
ctc_greedy_decoder
(
input
=
featyre_seq
,
blank
=
36
,
input_length
=
length
)
seq_pred_str
=
seq_pred
[
0
].
numpy
().
tolist
()[
0
]
seq_len
=
seq_pred
[
1
].
numpy
()[
0
][
0
]
temp_t
=
[]
for
c
in
seq_pred_str
[:
seq_len
]:
temp_t
.
append
(
c
)
char_seq_idx_set
.
append
(
temp_t
)
seq_strs
=
[]
for
char_idx_set
in
char_seq_idx_set
:
pr_str
=
''
.
join
([
self
.
Lexicon_Table
[
pos
]
for
pos
in
char_idx_set
])
seq_strs
.
append
(
pr_str
)
poly_list
=
[]
keep_str_list
=
[]
all_point_list
=
[]
all_point_pair_list
=
[]
for
yx_center_line
,
keep_str
in
zip
(
instance_yxs_list
,
seq_strs
):
if
len
(
yx_center_line
)
==
1
:
yx_center_line
.
append
(
yx_center_line
[
-
1
])
offset_expand
=
1.0
if
self
.
valid_set
==
'totaltext'
:
offset_expand
=
1.2
point_pair_list
=
[]
for
batch_id
,
y
,
x
in
yx_center_line
:
offset
=
p_border
[:,
y
,
x
].
reshape
(
2
,
2
)
if
offset_expand
!=
1.0
:
offset_length
=
np
.
linalg
.
norm
(
offset
,
axis
=
1
,
keepdims
=
True
)
expand_length
=
np
.
clip
(
offset_length
*
(
offset_expand
-
1
),
a_min
=
0.5
,
a_max
=
3.0
)
offset_detal
=
offset
/
offset_length
*
expand_length
offset
=
offset
+
offset_detal
ori_yx
=
np
.
array
([
y
,
x
],
dtype
=
np
.
float32
)
point_pair
=
(
ori_yx
+
offset
)[:,
::
-
1
]
*
4.0
/
np
.
array
(
[
ratio_w
,
ratio_h
]).
reshape
(
-
1
,
2
)
point_pair_list
.
append
(
point_pair
)
all_point_list
.
append
([
int
(
round
(
x
*
4.0
/
ratio_w
)),
int
(
round
(
y
*
4.0
/
ratio_h
))
])
all_point_pair_list
.
append
(
point_pair
.
round
().
astype
(
np
.
int32
)
.
tolist
())
detected_poly
,
pair_length_info
=
point_pair2poly
(
point_pair_list
)
detected_poly
=
expand_poly_along_width
(
detected_poly
,
shrink_ratio_of_width
=
0.2
)
detected_poly
[:,
0
]
=
np
.
clip
(
detected_poly
[:,
0
],
a_min
=
0
,
a_max
=
src_w
)
detected_poly
[:,
1
]
=
np
.
clip
(
detected_poly
[:,
1
],
a_min
=
0
,
a_max
=
src_h
)
if
len
(
keep_str
)
<
2
:
continue
keep_str_list
.
append
(
keep_str
)
detected_poly
=
np
.
round
(
detected_poly
).
astype
(
'int32'
)
if
self
.
valid_set
==
'partvgg'
:
middle_point
=
len
(
detected_poly
)
//
2
detected_poly
=
detected_poly
[
[
0
,
middle_point
-
1
,
middle_point
,
-
1
],
:]
poly_list
.
append
(
detected_poly
)
elif
self
.
valid_set
==
'totaltext'
:
poly_list
.
append
(
detected_poly
)
else
:
print
(
'--> Not supported format.'
)
exit
(
-
1
)
data
=
{
'points'
:
poly_list
,
'strs'
:
keep_str_list
,
}
return
data
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