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454e4c8d
编写于
5月 20, 2021
作者:
B
bjjwwang
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deploy/pdserving/win/ocr_reader.py
deploy/pdserving/win/ocr_reader.py
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deploy/pdserving/win/ocr_web_client.py
deploy/pdserving/win/ocr_web_client.py
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deploy/pdserving/win/ocr_web_server.py
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deploy/pdserving/win/ocr_reader.py
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454e4c8d
# 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.
import
cv2
import
copy
import
numpy
as
np
import
math
import
re
import
sys
import
argparse
import
string
from
copy
import
deepcopy
class
DetResizeForTest
(
object
):
def
__init__
(
self
,
**
kwargs
):
super
(
DetResizeForTest
,
self
).
__init__
()
self
.
resize_type
=
0
if
'image_shape'
in
kwargs
:
self
.
image_shape
=
kwargs
[
'image_shape'
]
self
.
resize_type
=
1
elif
'limit_side_len'
in
kwargs
:
self
.
limit_side_len
=
kwargs
[
'limit_side_len'
]
self
.
limit_type
=
kwargs
.
get
(
'limit_type'
,
'min'
)
elif
'resize_short'
in
kwargs
:
self
.
limit_side_len
=
736
self
.
limit_type
=
'min'
else
:
self
.
resize_type
=
2
self
.
resize_long
=
kwargs
.
get
(
'resize_long'
,
960
)
def
__call__
(
self
,
data
):
img
=
deepcopy
(
data
)
src_h
,
src_w
,
_
=
img
.
shape
if
self
.
resize_type
==
0
:
img
,
[
ratio_h
,
ratio_w
]
=
self
.
resize_image_type0
(
img
)
elif
self
.
resize_type
==
2
:
img
,
[
ratio_h
,
ratio_w
]
=
self
.
resize_image_type2
(
img
)
else
:
img
,
[
ratio_h
,
ratio_w
]
=
self
.
resize_image_type1
(
img
)
return
img
def
resize_image_type1
(
self
,
img
):
resize_h
,
resize_w
=
self
.
image_shape
ori_h
,
ori_w
=
img
.
shape
[:
2
]
# (h, w, c)
ratio_h
=
float
(
resize_h
)
/
ori_h
ratio_w
=
float
(
resize_w
)
/
ori_w
img
=
cv2
.
resize
(
img
,
(
int
(
resize_w
),
int
(
resize_h
)))
return
img
,
[
ratio_h
,
ratio_w
]
def
resize_image_type0
(
self
,
img
):
"""
resize image to a size multiple of 32 which is required by the network
args:
img(array): array with shape [h, w, c]
return(tuple):
img, (ratio_h, ratio_w)
"""
limit_side_len
=
self
.
limit_side_len
h
,
w
,
_
=
img
.
shape
# limit the max side
if
self
.
limit_type
==
'max'
:
if
max
(
h
,
w
)
>
limit_side_len
:
if
h
>
w
:
ratio
=
float
(
limit_side_len
)
/
h
else
:
ratio
=
float
(
limit_side_len
)
/
w
else
:
ratio
=
1.
else
:
if
min
(
h
,
w
)
<
limit_side_len
:
if
h
<
w
:
ratio
=
float
(
limit_side_len
)
/
h
else
:
ratio
=
float
(
limit_side_len
)
/
w
else
:
ratio
=
1.
resize_h
=
int
(
h
*
ratio
)
resize_w
=
int
(
w
*
ratio
)
resize_h
=
int
(
round
(
resize_h
/
32
)
*
32
)
resize_w
=
int
(
round
(
resize_w
/
32
)
*
32
)
try
:
if
int
(
resize_w
)
<=
0
or
int
(
resize_h
)
<=
0
:
return
None
,
(
None
,
None
)
img
=
cv2
.
resize
(
img
,
(
int
(
resize_w
),
int
(
resize_h
)))
except
:
print
(
img
.
shape
,
resize_w
,
resize_h
)
sys
.
exit
(
0
)
ratio_h
=
resize_h
/
float
(
h
)
ratio_w
=
resize_w
/
float
(
w
)
# return img, np.array([h, w])
return
img
,
[
ratio_h
,
ratio_w
]
def
resize_image_type2
(
self
,
img
):
h
,
w
,
_
=
img
.
shape
resize_w
=
w
resize_h
=
h
# Fix the longer side
if
resize_h
>
resize_w
:
ratio
=
float
(
self
.
resize_long
)
/
resize_h
else
:
ratio
=
float
(
self
.
resize_long
)
/
resize_w
resize_h
=
int
(
resize_h
*
ratio
)
resize_w
=
int
(
resize_w
*
ratio
)
max_stride
=
128
resize_h
=
(
resize_h
+
max_stride
-
1
)
//
max_stride
*
max_stride
resize_w
=
(
resize_w
+
max_stride
-
1
)
//
max_stride
*
max_stride
img
=
cv2
.
resize
(
img
,
(
int
(
resize_w
),
int
(
resize_h
)))
ratio_h
=
resize_h
/
float
(
h
)
ratio_w
=
resize_w
/
float
(
w
)
return
img
,
[
ratio_h
,
ratio_w
]
class
BaseRecLabelDecode
(
object
):
""" Convert between text-label and text-index """
def
__init__
(
self
,
config
):
support_character_type
=
[
'ch'
,
'en'
,
'EN_symbol'
,
'french'
,
'german'
,
'japan'
,
'korean'
,
'it'
,
'xi'
,
'pu'
,
'ru'
,
'ar'
,
'ta'
,
'ug'
,
'fa'
,
'ur'
,
'rs'
,
'oc'
,
'rsc'
,
'bg'
,
'uk'
,
'be'
,
'te'
,
'ka'
,
'chinese_cht'
,
'hi'
,
'mr'
,
'ne'
,
'EN'
]
character_type
=
config
[
'character_type'
]
character_dict_path
=
config
[
'character_dict_path'
]
use_space_char
=
True
assert
character_type
in
support_character_type
,
"Only {} are supported now but get {}"
.
format
(
support_character_type
,
character_type
)
self
.
beg_str
=
"sos"
self
.
end_str
=
"eos"
if
character_type
==
"en"
:
self
.
character_str
=
"0123456789abcdefghijklmnopqrstuvwxyz"
dict_character
=
list
(
self
.
character_str
)
elif
character_type
==
"EN_symbol"
:
# same with ASTER setting (use 94 char).
self
.
character_str
=
string
.
printable
[:
-
6
]
dict_character
=
list
(
self
.
character_str
)
elif
character_type
in
support_character_type
:
self
.
character_str
=
""
assert
character_dict_path
is
not
None
,
"character_dict_path should not be None when character_type is {}"
.
format
(
character_type
)
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
"
)
self
.
character_str
+=
line
if
use_space_char
:
self
.
character_str
+=
" "
dict_character
=
list
(
self
.
character_str
)
else
:
raise
NotImplementedError
self
.
character_type
=
character_type
dict_character
=
self
.
add_special_char
(
dict_character
)
self
.
dict
=
{}
for
i
,
char
in
enumerate
(
dict_character
):
self
.
dict
[
char
]
=
i
self
.
character
=
dict_character
def
add_special_char
(
self
,
dict_character
):
return
dict_character
def
decode
(
self
,
text_index
,
text_prob
=
None
,
is_remove_duplicate
=
False
):
""" convert text-index into text-label. """
result_list
=
[]
ignored_tokens
=
self
.
get_ignored_tokens
()
batch_size
=
len
(
text_index
)
for
batch_idx
in
range
(
batch_size
):
char_list
=
[]
conf_list
=
[]
for
idx
in
range
(
len
(
text_index
[
batch_idx
])):
if
text_index
[
batch_idx
][
idx
]
in
ignored_tokens
:
continue
if
is_remove_duplicate
:
# only for predict
if
idx
>
0
and
text_index
[
batch_idx
][
idx
-
1
]
==
text_index
[
batch_idx
][
idx
]:
continue
char_list
.
append
(
self
.
character
[
int
(
text_index
[
batch_idx
][
idx
])])
if
text_prob
is
not
None
:
conf_list
.
append
(
text_prob
[
batch_idx
][
idx
])
else
:
conf_list
.
append
(
1
)
text
=
''
.
join
(
char_list
)
result_list
.
append
((
text
,
np
.
mean
(
conf_list
)))
return
result_list
def
get_ignored_tokens
(
self
):
return
[
0
]
# for ctc blank
class
CTCLabelDecode
(
BaseRecLabelDecode
):
""" Convert between text-label and text-index """
def
__init__
(
self
,
config
,
#character_dict_path=None,
#character_type='ch',
#use_space_char=False,
**
kwargs
):
super
(
CTCLabelDecode
,
self
).
__init__
(
config
)
def
__call__
(
self
,
preds
,
label
=
None
,
*
args
,
**
kwargs
):
preds_idx
=
preds
.
argmax
(
axis
=
2
)
preds_prob
=
preds
.
max
(
axis
=
2
)
text
=
self
.
decode
(
preds_idx
,
preds_prob
,
is_remove_duplicate
=
True
)
if
label
is
None
:
return
text
label
=
self
.
decode
(
label
)
return
text
,
label
def
add_special_char
(
self
,
dict_character
):
dict_character
=
[
'blank'
]
+
dict_character
return
dict_character
class
CharacterOps
(
object
):
""" Convert between text-label and text-index """
def
__init__
(
self
,
config
):
self
.
character_type
=
config
[
'character_type'
]
self
.
loss_type
=
config
[
'loss_type'
]
if
self
.
character_type
==
"en"
:
self
.
character_str
=
"0123456789abcdefghijklmnopqrstuvwxyz"
dict_character
=
list
(
self
.
character_str
)
elif
self
.
character_type
==
"ch"
:
character_dict_path
=
config
[
'character_dict_path'
]
self
.
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
"
)
self
.
character_str
+=
line
dict_character
=
list
(
self
.
character_str
)
elif
self
.
character_type
==
"en_sensitive"
:
# same with ASTER setting (use 94 char).
self
.
character_str
=
string
.
printable
[:
-
6
]
dict_character
=
list
(
self
.
character_str
)
else
:
self
.
character_str
=
None
assert
self
.
character_str
is
not
None
,
\
"Nonsupport type of the character: {}"
.
format
(
self
.
character_str
)
self
.
beg_str
=
"sos"
self
.
end_str
=
"eos"
if
self
.
loss_type
==
"attention"
:
dict_character
=
[
self
.
beg_str
,
self
.
end_str
]
+
dict_character
self
.
dict
=
{}
for
i
,
char
in
enumerate
(
dict_character
):
self
.
dict
[
char
]
=
i
self
.
character
=
dict_character
def
encode
(
self
,
text
):
"""convert text-label into text-index.
input:
text: text labels of each image. [batch_size]
output:
text: concatenated text index for CTCLoss.
[sum(text_lengths)] = [text_index_0 + text_index_1 + ... + text_index_(n - 1)]
length: length of each text. [batch_size]
"""
if
self
.
character_type
==
"en"
:
text
=
text
.
lower
()
text_list
=
[]
for
char
in
text
:
if
char
not
in
self
.
dict
:
continue
text_list
.
append
(
self
.
dict
[
char
])
text
=
np
.
array
(
text_list
)
return
text
def
decode
(
self
,
text_index
,
is_remove_duplicate
=
False
):
""" convert text-index into text-label. """
char_list
=
[]
char_num
=
self
.
get_char_num
()
if
self
.
loss_type
==
"attention"
:
beg_idx
=
self
.
get_beg_end_flag_idx
(
"beg"
)
end_idx
=
self
.
get_beg_end_flag_idx
(
"end"
)
ignored_tokens
=
[
beg_idx
,
end_idx
]
else
:
ignored_tokens
=
[
char_num
]
for
idx
in
range
(
len
(
text_index
)):
if
text_index
[
idx
]
in
ignored_tokens
:
continue
if
is_remove_duplicate
:
if
idx
>
0
and
text_index
[
idx
-
1
]
==
text_index
[
idx
]:
continue
char_list
.
append
(
self
.
character
[
text_index
[
idx
]])
text
=
''
.
join
(
char_list
)
return
text
def
get_char_num
(
self
):
return
len
(
self
.
character
)
def
get_beg_end_flag_idx
(
self
,
beg_or_end
):
if
self
.
loss_type
==
"attention"
:
if
beg_or_end
==
"beg"
:
idx
=
np
.
array
(
self
.
dict
[
self
.
beg_str
])
elif
beg_or_end
==
"end"
:
idx
=
np
.
array
(
self
.
dict
[
self
.
end_str
])
else
:
assert
False
,
"Unsupport type %s in get_beg_end_flag_idx"
\
%
beg_or_end
return
idx
else
:
err
=
"error in get_beg_end_flag_idx when using the loss %s"
\
%
(
self
.
loss_type
)
assert
False
,
err
class
OCRReader
(
object
):
def
__init__
(
self
,
algorithm
=
"CRNN"
,
image_shape
=
[
3
,
32
,
320
],
char_type
=
"ch"
,
batch_num
=
1
,
char_dict_path
=
"./ppocr_keys_v1.txt"
):
self
.
rec_image_shape
=
image_shape
self
.
character_type
=
char_type
self
.
rec_batch_num
=
batch_num
char_ops_params
=
{}
char_ops_params
[
"character_type"
]
=
char_type
char_ops_params
[
"character_dict_path"
]
=
char_dict_path
char_ops_params
[
'loss_type'
]
=
'ctc'
self
.
char_ops
=
CharacterOps
(
char_ops_params
)
self
.
label_ops
=
CTCLabelDecode
(
char_ops_params
)
def
resize_norm_img
(
self
,
img
,
max_wh_ratio
):
imgC
,
imgH
,
imgW
=
self
.
rec_image_shape
if
self
.
character_type
==
"ch"
:
imgW
=
int
(
32
*
max_wh_ratio
)
h
=
img
.
shape
[
0
]
w
=
img
.
shape
[
1
]
ratio
=
w
/
float
(
h
)
if
math
.
ceil
(
imgH
*
ratio
)
>
imgW
:
resized_w
=
imgW
else
:
resized_w
=
int
(
math
.
ceil
(
imgH
*
ratio
))
resized_image
=
cv2
.
resize
(
img
,
(
resized_w
,
imgH
))
resized_image
=
resized_image
.
astype
(
'float32'
)
resized_image
=
resized_image
.
transpose
((
2
,
0
,
1
))
/
255
resized_image
-=
0.5
resized_image
/=
0.5
padding_im
=
np
.
zeros
((
imgC
,
imgH
,
imgW
),
dtype
=
np
.
float32
)
padding_im
[:,
:,
0
:
resized_w
]
=
resized_image
return
padding_im
def
preprocess
(
self
,
img_list
):
img_num
=
len
(
img_list
)
norm_img_batch
=
[]
max_wh_ratio
=
0
for
ino
in
range
(
img_num
):
h
,
w
=
img_list
[
ino
].
shape
[
0
:
2
]
wh_ratio
=
w
*
1.0
/
h
max_wh_ratio
=
max
(
max_wh_ratio
,
wh_ratio
)
for
ino
in
range
(
img_num
):
norm_img
=
self
.
resize_norm_img
(
img_list
[
ino
],
max_wh_ratio
)
norm_img
=
norm_img
[
np
.
newaxis
,
:]
norm_img_batch
.
append
(
norm_img
)
norm_img_batch
=
np
.
concatenate
(
norm_img_batch
)
norm_img_batch
=
norm_img_batch
.
copy
()
return
norm_img_batch
[
0
]
def
postprocess_old
(
self
,
outputs
,
with_score
=
False
):
rec_res
=
[]
rec_idx_lod
=
outputs
[
"ctc_greedy_decoder_0.tmp_0.lod"
]
rec_idx_batch
=
outputs
[
"ctc_greedy_decoder_0.tmp_0"
]
if
with_score
:
predict_lod
=
outputs
[
"softmax_0.tmp_0.lod"
]
for
rno
in
range
(
len
(
rec_idx_lod
)
-
1
):
beg
=
rec_idx_lod
[
rno
]
end
=
rec_idx_lod
[
rno
+
1
]
if
isinstance
(
rec_idx_batch
,
list
):
rec_idx_tmp
=
[
x
[
0
]
for
x
in
rec_idx_batch
[
beg
:
end
]]
else
:
#nd array
rec_idx_tmp
=
rec_idx_batch
[
beg
:
end
,
0
]
preds_text
=
self
.
char_ops
.
decode
(
rec_idx_tmp
)
if
with_score
:
beg
=
predict_lod
[
rno
]
end
=
predict_lod
[
rno
+
1
]
if
isinstance
(
outputs
[
"softmax_0.tmp_0"
],
list
):
outputs
[
"softmax_0.tmp_0"
]
=
np
.
array
(
outputs
[
"softmax_0.tmp_0"
]).
astype
(
np
.
float32
)
probs
=
outputs
[
"softmax_0.tmp_0"
][
beg
:
end
,
:]
ind
=
np
.
argmax
(
probs
,
axis
=
1
)
blank
=
probs
.
shape
[
1
]
valid_ind
=
np
.
where
(
ind
!=
(
blank
-
1
))[
0
]
score
=
np
.
mean
(
probs
[
valid_ind
,
ind
[
valid_ind
]])
rec_res
.
append
([
preds_text
,
score
])
else
:
rec_res
.
append
([
preds_text
])
return
rec_res
def
postprocess
(
self
,
outputs
,
with_score
=
False
):
preds
=
outputs
[
"save_infer_model/scale_0.tmp_1"
]
try
:
preds
=
preds
.
numpy
()
except
:
pass
preds_idx
=
preds
.
argmax
(
axis
=
2
)
preds_prob
=
preds
.
max
(
axis
=
2
)
text
=
self
.
label_ops
.
decode
(
preds_idx
,
preds_prob
,
is_remove_duplicate
=
True
)
return
text
deploy/pdserving/win/ocr_web_client.py
0 → 100644
浏览文件 @
454e4c8d
# Copyright (c) 2020 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.
# -*- coding: utf-8 -*-
import
requests
import
json
import
cv2
import
base64
import
os
,
sys
import
time
def
cv2_to_base64
(
image
):
#data = cv2.imencode('.jpg', image)[1]
return
base64
.
b64encode
(
image
).
decode
(
'utf8'
)
#data.tostring()).decode('utf8')
headers
=
{
"Content-type"
:
"application/json"
}
url
=
"http://127.0.0.1:9292/ocr/prediction"
test_img_dir
=
"../../../doc/imgs/"
for
idx
,
img_file
in
enumerate
(
os
.
listdir
(
test_img_dir
)):
with
open
(
os
.
path
.
join
(
test_img_dir
,
img_file
),
'rb'
)
as
file
:
image_data1
=
file
.
read
()
image
=
cv2_to_base64
(
image_data1
)
for
i
in
range
(
1
):
data
=
{
"feed"
:
[{
"image"
:
image
}],
"fetch"
:
[
"save_infer_model/scale_0.tmp_1"
]}
r
=
requests
.
post
(
url
=
url
,
headers
=
headers
,
data
=
json
.
dumps
(
data
))
print
(
r
.
json
())
test_img_dir
=
"../../../doc/imgs/"
print
(
"==> total number of test imgs: "
,
len
(
os
.
listdir
(
test_img_dir
)))
deploy/pdserving/win/ocr_web_server.py
0 → 100644
浏览文件 @
454e4c8d
# Copyright (c) 2020 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
paddle_serving_client
import
Client
import
cv2
import
sys
import
numpy
as
np
import
os
from
paddle_serving_client
import
Client
from
paddle_serving_app.reader
import
Sequential
,
URL2Image
,
ResizeByFactor
from
paddle_serving_app.reader
import
Div
,
Normalize
,
Transpose
from
paddle_serving_app.reader
import
DBPostProcess
,
FilterBoxes
,
GetRotateCropImage
,
SortedBoxes
from
ocr_reader
import
OCRReader
try
:
from
paddle_serving_server_gpu.web_service
import
WebService
except
ImportError
:
from
paddle_serving_server.web_service
import
WebService
from
paddle_serving_app.local_predict
import
LocalPredictor
import
time
import
re
import
base64
class
OCRService
(
WebService
):
def
init_det_debugger
(
self
,
det_model_config
):
self
.
det_preprocess
=
Sequential
([
ResizeByFactor
(
32
,
960
),
Div
(
255
),
Normalize
([
0.485
,
0.456
,
0.406
],
[
0.229
,
0.224
,
0.225
]),
Transpose
(
(
2
,
0
,
1
))
])
self
.
det_client
=
LocalPredictor
()
if
sys
.
argv
[
1
]
==
'gpu'
:
self
.
det_client
.
load_model_config
(
det_model_config
,
use_gpu
=
True
,
gpu_id
=
1
)
elif
sys
.
argv
[
1
]
==
'cpu'
:
self
.
det_client
.
load_model_config
(
det_model_config
)
self
.
ocr_reader
=
OCRReader
(
char_dict_path
=
"../../../ppocr/utils/ppocr_keys_v1.txt"
)
def
preprocess
(
self
,
feed
=
[],
fetch
=
[]):
data
=
base64
.
b64decode
(
feed
[
0
][
"image"
].
encode
(
'utf8'
))
data
=
np
.
fromstring
(
data
,
np
.
uint8
)
im
=
cv2
.
imdecode
(
data
,
cv2
.
IMREAD_COLOR
)
ori_h
,
ori_w
,
_
=
im
.
shape
det_img
=
self
.
det_preprocess
(
im
)
_
,
new_h
,
new_w
=
det_img
.
shape
det_img
=
det_img
[
np
.
newaxis
,
:]
det_img
=
det_img
.
copy
()
det_out
=
self
.
det_client
.
predict
(
feed
=
{
"x"
:
det_img
},
fetch
=
[
"save_infer_model/scale_0.tmp_1"
],
batch
=
True
)
filter_func
=
FilterBoxes
(
10
,
10
)
post_func
=
DBPostProcess
({
"thresh"
:
0.3
,
"box_thresh"
:
0.5
,
"max_candidates"
:
1000
,
"unclip_ratio"
:
1.5
,
"min_size"
:
3
})
sorted_boxes
=
SortedBoxes
()
ratio_list
=
[
float
(
new_h
)
/
ori_h
,
float
(
new_w
)
/
ori_w
]
dt_boxes_list
=
post_func
(
det_out
[
"save_infer_model/scale_0.tmp_1"
],
[
ratio_list
])
dt_boxes
=
filter_func
(
dt_boxes_list
[
0
],
[
ori_h
,
ori_w
])
dt_boxes
=
sorted_boxes
(
dt_boxes
)
get_rotate_crop_image
=
GetRotateCropImage
()
img_list
=
[]
max_wh_ratio
=
0
for
i
,
dtbox
in
enumerate
(
dt_boxes
):
boximg
=
get_rotate_crop_image
(
im
,
dt_boxes
[
i
])
img_list
.
append
(
boximg
)
h
,
w
=
boximg
.
shape
[
0
:
2
]
wh_ratio
=
w
*
1.0
/
h
max_wh_ratio
=
max
(
max_wh_ratio
,
wh_ratio
)
if
len
(
img_list
)
==
0
:
return
[],
[]
_
,
w
,
h
=
self
.
ocr_reader
.
resize_norm_img
(
img_list
[
0
],
max_wh_ratio
).
shape
imgs
=
np
.
zeros
((
len
(
img_list
),
3
,
w
,
h
)).
astype
(
'float32'
)
for
id
,
img
in
enumerate
(
img_list
):
norm_img
=
self
.
ocr_reader
.
resize_norm_img
(
img
,
max_wh_ratio
)
imgs
[
id
]
=
norm_img
feed
=
{
"x"
:
imgs
.
copy
()}
fetch
=
[
"save_infer_model/scale_0.tmp_1"
]
return
feed
,
fetch
,
True
def
postprocess
(
self
,
feed
=
{},
fetch
=
[],
fetch_map
=
None
):
rec_res
=
self
.
ocr_reader
.
postprocess
(
fetch_map
,
with_score
=
True
)
res_lst
=
[]
for
res
in
rec_res
:
res_lst
.
append
(
res
[
0
])
res
=
{
"res"
:
res_lst
}
return
res
ocr_service
=
OCRService
(
name
=
"ocr"
)
ocr_service
.
load_model_config
(
"../ppocr_rec_mobile_2.0_serving"
)
ocr_service
.
prepare_server
(
workdir
=
"workdir"
,
port
=
9292
)
ocr_service
.
init_det_debugger
(
det_model_config
=
"../ppocr_det_mobile_2.0_serving"
)
if
sys
.
argv
[
1
]
==
'gpu'
:
ocr_service
.
set_gpus
(
"0"
)
ocr_service
.
run_debugger_service
(
gpu
=
True
)
elif
sys
.
argv
[
1
]
==
'cpu'
:
ocr_service
.
run_debugger_service
()
ocr_service
.
run_web_service
()
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