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8e05ffed
编写于
7月 13, 2020
作者:
D
dyning
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
move out visulization from hubserving
上级
a5c095e0
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
169 addition
and
121 deletion
+169
-121
deploy/hubserving/ocr_det/config.json
deploy/hubserving/ocr_det/config.json
+0
-1
deploy/hubserving/ocr_det/module.py
deploy/hubserving/ocr_det/module.py
+13
-25
deploy/hubserving/ocr_rec/module.py
deploy/hubserving/ocr_rec/module.py
+14
-2
deploy/hubserving/ocr_system/config.json
deploy/hubserving/ocr_system/config.json
+0
-1
deploy/hubserving/ocr_system/module.py
deploy/hubserving/ocr_system/module.py
+15
-44
doc/doc_ch/serving.md
doc/doc_ch/serving.md
+20
-26
tools/infer/predict_system.py
tools/infer/predict_system.py
+0
-4
tools/infer/utility.py
tools/infer/utility.py
+1
-1
tools/test_hubserving.py
tools/test_hubserving.py
+106
-17
未找到文件。
deploy/hubserving/ocr_det/config.json
浏览文件 @
8e05ffed
...
...
@@ -6,7 +6,6 @@
"use_gpu"
:
true
},
"predict_args"
:
{
"visualization"
:
false
}
}
},
...
...
deploy/hubserving/ocr_det/module.py
浏览文件 @
8e05ffed
...
...
@@ -19,7 +19,7 @@ import numpy as np
import
paddle.fluid
as
fluid
import
paddlehub
as
hub
from
tools.infer.utility
import
draw_boxes
,
base64_to_cv2
from
tools.infer.utility
import
base64_to_cv2
from
tools.infer.predict_det
import
TextDetector
...
...
@@ -68,16 +68,12 @@ class OCRDet(hub.Module):
def
predict
(
self
,
images
=
[],
paths
=
[],
draw_img_save
=
'ocr_det_result'
,
visualization
=
False
):
paths
=
[]):
"""
Get the text box in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
draw_img_save (str): The directory to store output images.
visualization (bool): Whether to save image or not.
Returns:
res (list): The result of text detection box and save path of images.
"""
...
...
@@ -93,29 +89,21 @@ class OCRDet(hub.Module):
all_results
=
[]
for
img
in
predicted_data
:
result
=
{
'save_path'
:
''
}
if
img
is
None
:
logger
.
info
(
"error in loading image"
)
result
[
'data'
]
=
[]
all_results
.
append
(
result
)
all_results
.
append
([])
continue
dt_boxes
,
elapse
=
self
.
text_detector
(
img
)
print
(
"Predict time : "
,
elapse
)
result
[
'data'
]
=
dt_boxes
.
astype
(
np
.
int
).
tolist
()
logger
.
info
(
"Predict time : {}"
.
format
(
elapse
))
if
visualization
:
image
=
Image
.
fromarray
(
cv2
.
cvtColor
(
img
,
cv2
.
COLOR_BGR2RGB
))
draw_img
=
draw_boxes
(
image
,
dt_boxes
)
draw_img
=
np
.
array
(
draw_img
)
if
not
os
.
path
.
exists
(
draw_img_save
):
os
.
makedirs
(
draw_img_save
)
saved_name
=
'ndarray_{}.jpg'
.
format
(
time
.
time
())
save_file_path
=
os
.
path
.
join
(
draw_img_save
,
saved_name
)
cv2
.
imwrite
(
save_file_path
,
draw_img
[:,
:,
::
-
1
])
print
(
"The visualized image saved in {}"
.
format
(
save_file_path
))
result
[
'save_path'
]
=
save_file_path
all_results
.
append
(
result
)
rec_res_final
=
[]
for
dno
in
range
(
len
(
dt_boxes
)):
rec_res_final
.
append
(
{
'text_region'
:
dt_boxes
[
dno
].
astype
(
np
.
int
).
tolist
()
}
)
all_results
.
append
(
rec_res_final
)
return
all_results
@
serving
...
...
@@ -134,5 +122,5 @@ if __name__ == '__main__':
'./doc/imgs/11.jpg'
,
'./doc/imgs/12.jpg'
,
]
res
=
ocr
.
predict
(
paths
=
image_path
,
visualization
=
True
)
res
=
ocr
.
predict
(
paths
=
image_path
)
print
(
res
)
\ No newline at end of file
deploy/hubserving/ocr_rec/module.py
浏览文件 @
8e05ffed
...
...
@@ -92,12 +92,24 @@ class OCRRec(hub.Module):
if
img
is
None
:
continue
img_list
.
append
(
img
)
rec_res_final
=
[]
try
:
rec_res
,
predict_time
=
self
.
text_recognizer
(
img_list
)
for
dno
in
range
(
len
(
rec_res
)):
text
,
score
=
rec_res
[
dno
]
rec_res_final
.
append
(
{
'text'
:
text
,
'confidence'
:
float
(
score
),
}
)
except
Exception
as
e
:
print
(
e
)
return
[]
return
rec_res
return
[[]]
return
[
rec_res_final
]
@
serving
def
serving_method
(
self
,
images
,
**
kwargs
):
...
...
deploy/hubserving/ocr_system/config.json
浏览文件 @
8e05ffed
...
...
@@ -6,7 +6,6 @@
"use_gpu"
:
true
},
"predict_args"
:
{
"visualization"
:
false
}
}
},
...
...
deploy/hubserving/ocr_system/module.py
浏览文件 @
8e05ffed
...
...
@@ -19,7 +19,7 @@ import numpy as np
import
paddle.fluid
as
fluid
import
paddlehub
as
hub
from
tools.infer.utility
import
draw_ocr
,
base64_to_cv2
from
tools.infer.utility
import
base64_to_cv2
from
tools.infer.predict_system
import
TextSystem
...
...
@@ -68,18 +68,12 @@ class OCRSystem(hub.Module):
def
predict
(
self
,
images
=
[],
paths
=
[],
draw_img_save
=
'ocr_result'
,
visualization
=
False
,
text_thresh
=
0.5
):
paths
=
[]):
"""
Get the chinese texts in the predicted images.
Args:
images (list(numpy.ndarray)): images data, shape of each is [H, W, C]. If images not paths
paths (list[str]): The paths of images. If paths not images
draw_img_save (str): The directory to store output images.
visualization (bool): Whether to save image or not.
text_thresh(float): the threshold of the recognize chinese texts' confidence
Returns:
res (list): The result of chinese texts and save path of images.
"""
...
...
@@ -93,53 +87,30 @@ class OCRSystem(hub.Module):
assert
predicted_data
!=
[],
"There is not any image to be predicted. Please check the input data."
cnt
=
0
all_results
=
[]
for
img
in
predicted_data
:
result
=
{
'save_path'
:
''
}
if
img
is
None
:
logger
.
info
(
"error in loading image"
)
result
[
'data'
]
=
[]
all_results
.
append
(
result
)
all_results
.
append
([])
continue
starttime
=
time
.
time
()
dt_boxes
,
rec_res
=
self
.
text_sys
(
img
)
elapse
=
time
.
time
()
-
starttime
cnt
+=
1
print
(
"Predict time of image %d: %.3fs"
%
(
cnt
,
elapse
))
logger
.
info
(
"Predict time: {}"
.
format
(
elapse
))
dt_num
=
len
(
dt_boxes
)
rec_res_final
=
[]
for
dno
in
range
(
dt_num
):
text
,
score
=
rec_res
[
dno
]
# if the recognized text confidence score is lower than text_thresh, then drop it
if
score
>=
text_thresh
:
# text_str = "%s, %.3f" % (text, score)
# print(text_str)
rec_res_final
.
append
(
{
'text'
:
text
,
'confidence'
:
float
(
score
),
'text_box_position'
:
dt_boxes
[
dno
].
astype
(
np
.
int
).
tolist
()
}
)
result
[
'data'
]
=
rec_res_final
if
visualization
:
image
=
Image
.
fromarray
(
cv2
.
cvtColor
(
img
,
cv2
.
COLOR_BGR2RGB
))
boxes
=
dt_boxes
txts
=
[
rec_res
[
i
][
0
]
for
i
in
range
(
len
(
rec_res
))]
scores
=
[
rec_res
[
i
][
1
]
for
i
in
range
(
len
(
rec_res
))]
draw_img
=
draw_ocr
(
image
,
boxes
,
txts
,
scores
,
draw_txt
=
True
,
drop_score
=
0.5
)
if
not
os
.
path
.
exists
(
draw_img_save
):
os
.
makedirs
(
draw_img_save
)
saved_name
=
'ndarray_{}.jpg'
.
format
(
time
.
time
())
save_file_path
=
os
.
path
.
join
(
draw_img_save
,
saved_name
)
cv2
.
imwrite
(
save_file_path
,
draw_img
[:,
:,
::
-
1
])
print
(
"The visualized image saved in {}"
.
format
(
save_file_path
))
result
[
'save_path'
]
=
save_file_path
all_results
.
append
(
result
)
rec_res_final
.
append
(
{
'text'
:
text
,
'confidence'
:
float
(
score
),
'text_region'
:
dt_boxes
[
dno
].
astype
(
np
.
int
).
tolist
()
}
)
all_results
.
append
(
rec_res_final
)
return
all_results
@
serving
...
...
@@ -158,5 +129,5 @@ if __name__ == '__main__':
'./doc/imgs/11.jpg'
,
'./doc/imgs/12.jpg'
,
]
res
=
ocr
.
predict
(
paths
=
image_path
,
visualization
=
False
)
res
=
ocr
.
predict
(
paths
=
image_path
)
print
(
res
)
\ No newline at end of file
doc/doc_ch/serving.md
浏览文件 @
8e05ffed
...
...
@@ -23,8 +23,14 @@ deploy/hubserving/ocr_system/
## 快速启动服务
以下步骤以检测+识别2阶段串联服务为例,如果只需要检测服务或识别服务,替换相应文件路径即可。
### 1. 安装paddlehub
```
pip3 install paddlehub --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple```
### 1. 准备环境
```
shell
# 安装paddlehub
pip3
install
paddlehub
--upgrade
-i
https://pypi.tuna.tsinghua.edu.cn/simple
# 设置环境变量
export
PYTHONPATH
=
.
```
### 2. 安装服务模块
PaddleOCR提供3种服务模块,根据需要安装所需模块。如:
...
...
@@ -75,7 +81,6 @@ $ hub serving start --modules [Module1==Version1, Module2==Version2, ...] \
"use_gpu": true
},
"predict_args": {
"visualization": false
}
}
},
...
...
@@ -99,32 +104,21 @@ hub serving start -c deploy/hubserving/ocr_system/config.json
```
## 发送预测请求
配置好服务端,
以下数行代码即可实现发送预测请求,获取预测结果:
配置好服务端,
可使用以下命令发送预测请求,获取预测结果:
```
python
import requests
import json
import cv2
import base64
def cv2_to_base64(image):
return base64.b64encode(image).decode('utf8')
# 发送HTTP请求
data = {'images':[cv2_to_base64(open("./doc/imgs/11.jpg", 'rb').read())]}
headers = {"Content-type": "application/json"}
# url = "http://127.0.0.1:8866/predict/ocr_det"
# url = "http://127.0.0.1:8866/predict/ocr_rec"
url = "http://127.0.0.1:8866/predict/ocr_system"
r = requests.post(url=url, headers=headers, data=json.dumps(data))
# 打印预测结果
print(r.json()["results"])
```
```
python tools/test_hubserving.py server_url image_path
```
你可能需要根据实际情况修改`url`字符串中的端口号和服务模块名称。
需要给脚本传递2个参数:
- **server_url**:服务地址,格式为
`http://[ip_address]:[port]/predict/[module_name]`
例如,如果使用配置文件启动检测、识别、检测+识别2阶段服务,那么发送请求的url将分别是:
`http://127.0.0.1:8866/predict/ocr_det`
`http://127.0.0.1:8867/predict/ocr_rec`
`http://127.0.0.1:8868/predict/ocr_system`
- **image_path**:测试图像路径,可以是单张图片路径,也可以是图像集合目录路径
上面所示代码都已写入测试脚本,可直接运行命令:```python tools/test_hubserving.py```
访问示例:
```
python tools/test_hubserving.py http://127.0.0.1:8868/predict/ocr_system ./doc/imgs/
```
## 自定义修改服务模块
如果需要修改服务逻辑,你一般需要操作以下步骤(以修改`ocr_system`为例):
...
...
tools/infer/predict_system.py
浏览文件 @
8e05ffed
...
...
@@ -117,16 +117,12 @@ def main(args):
image_file_list
=
get_image_file_list
(
args
.
image_dir
)
text_sys
=
TextSystem
(
args
)
is_visualize
=
True
tackle_img_num
=
0
for
image_file
in
image_file_list
:
img
=
cv2
.
imread
(
image_file
)
if
img
is
None
:
logger
.
info
(
"error in loading image:{}"
.
format
(
image_file
))
continue
starttime
=
time
.
time
()
tackle_img_num
+=
1
if
not
args
.
use_gpu
and
tackle_img_num
%
30
==
0
:
text_sys
=
TextSystem
(
args
)
dt_boxes
,
rec_res
=
text_sys
(
img
)
elapse
=
time
.
time
()
-
starttime
print
(
"Predict time of %s: %.3fs"
%
(
image_file
,
elapse
))
...
...
tools/infer/utility.py
浏览文件 @
8e05ffed
...
...
@@ -91,7 +91,7 @@ def create_predictor(args, mode):
config
.
enable_use_gpu
(
args
.
gpu_mem
,
0
)
else
:
config
.
disable_gpu
()
config
.
enable_mkldnn
()
#
config.enable_mkldnn()
config
.
set_cpu_math_library_num_threads
(
4
)
#config.enable_memory_optim()
config
.
disable_glog_info
()
...
...
tools/test_hubserving.py
浏览文件 @
8e05ffed
#!usr/bin/python
# -*- coding: utf-8 -*-
# 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.
import
os
import
sys
__dir__
=
os
.
path
.
dirname
(
os
.
path
.
abspath
(
__file__
))
sys
.
path
.
append
(
__dir__
)
sys
.
path
.
append
(
os
.
path
.
abspath
(
os
.
path
.
join
(
__dir__
,
'..'
)))
from
ppocr.utils.utility
import
initial_logger
logger
=
initial_logger
()
import
cv2
import
numpy
as
np
import
time
from
PIL
import
Image
from
ppocr.utils.utility
import
get_image_file_list
from
tools.infer.utility
import
draw_ocr
,
draw_boxes
import
requests
import
json
import
cv2
import
base64
import
time
def
cv2_to_base64
(
image
):
return
base64
.
b64encode
(
image
).
decode
(
'utf8'
)
start
=
time
.
time
()
# 发送HTTP请求
data
=
{
'images'
:[
cv2_to_base64
(
open
(
"./doc/imgs/11.jpg"
,
'rb'
).
read
())]}
headers
=
{
"Content-type"
:
"application/json"
}
# url = "http://127.0.0.1:8866/predict/ocr_det"
# url = "http://127.0.0.1:8866/predict/ocr_rec"
url
=
"http://127.0.0.1:8866/predict/ocr_system"
r
=
requests
.
post
(
url
=
url
,
headers
=
headers
,
data
=
json
.
dumps
(
data
))
end
=
time
.
time
()
# 打印预测结果
print
(
r
.
json
()[
"results"
])
print
(
"time cost: "
,
end
-
start
)
def
draw_server_result
(
image_file
,
res
):
img
=
cv2
.
imread
(
image_file
)
image
=
Image
.
fromarray
(
cv2
.
cvtColor
(
img
,
cv2
.
COLOR_BGR2RGB
))
if
len
(
res
)
==
0
:
return
np
.
array
(
image
)
keys
=
res
[
0
].
keys
()
if
'text_region'
not
in
keys
:
# for ocr_rec, draw function is invalid
print
(
"draw function is invalid for ocr_rec!"
)
return
None
elif
'text'
not
in
keys
:
# for ocr_det
print
(
"draw text boxes only!"
)
boxes
=
[]
for
dno
in
range
(
len
(
res
)):
boxes
.
append
(
res
[
dno
][
'text_region'
])
boxes
=
np
.
array
(
boxes
)
draw_img
=
draw_boxes
(
image
,
boxes
)
return
draw_img
else
:
# for ocr_system
print
(
"draw boxes and texts!"
)
boxes
=
[]
texts
=
[]
scores
=
[]
for
dno
in
range
(
len
(
res
)):
boxes
.
append
(
res
[
dno
][
'text_region'
])
texts
.
append
(
res
[
dno
][
'text'
])
scores
.
append
(
res
[
dno
][
'confidence'
])
boxes
=
np
.
array
(
boxes
)
scores
=
np
.
array
(
scores
)
draw_img
=
draw_ocr
(
image
,
boxes
,
texts
,
scores
,
draw_txt
=
True
,
drop_score
=
0.5
)
return
draw_img
def
main
(
url
,
image_path
):
image_file_list
=
get_image_file_list
(
image_path
)
is_visualize
=
False
headers
=
{
"Content-type"
:
"application/json"
}
cnt
=
0
total_time
=
0
for
image_file
in
image_file_list
:
img
=
open
(
image_file
,
'rb'
).
read
()
if
img
is
None
:
logger
.
info
(
"error in loading image:{}"
.
format
(
image_file
))
continue
# 发送HTTP请求
starttime
=
time
.
time
()
data
=
{
'images'
:[
cv2_to_base64
(
img
)]}
r
=
requests
.
post
(
url
=
url
,
headers
=
headers
,
data
=
json
.
dumps
(
data
))
elapse
=
time
.
time
()
-
starttime
total_time
+=
elapse
print
(
"Predict time of %s: %.3fs"
%
(
image_file
,
elapse
))
res
=
r
.
json
()[
"results"
][
0
]
# print(res)
if
is_visualize
:
draw_img
=
draw_server_result
(
image_file
,
res
)
if
draw_img
is
not
None
:
draw_img_save
=
"./server_results/"
if
not
os
.
path
.
exists
(
draw_img_save
):
os
.
makedirs
(
draw_img_save
)
cv2
.
imwrite
(
os
.
path
.
join
(
draw_img_save
,
os
.
path
.
basename
(
image_file
)),
draw_img
[:,
:,
::
-
1
])
print
(
"The visualized image saved in {}"
.
format
(
os
.
path
.
join
(
draw_img_save
,
os
.
path
.
basename
(
image_file
))))
cnt
+=
1
if
cnt
%
100
==
0
:
print
(
cnt
,
"processed"
)
print
(
"avg time cost: "
,
float
(
total_time
)
/
cnt
)
if
__name__
==
'__main__'
:
if
len
(
sys
.
argv
)
!=
3
:
print
(
"Usage: %s server_url image_path"
%
sys
.
argv
[
0
])
else
:
server_url
=
sys
.
argv
[
1
]
image_path
=
sys
.
argv
[
2
]
main
(
server_url
,
image_path
)
\ No newline at end of file
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