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4af18109
编写于
5月 27, 2020
作者:
L
LDOUBLEV
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
opt visualized func and add docker usage in cpu
上级
fcfdd0a3
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
128 addition
and
23 deletion
+128
-23
doc/installation.md
doc/installation.md
+4
-1
tools/infer/utility.py
tools/infer/utility.py
+124
-22
未找到文件。
doc/installation.md
浏览文件 @
4af18109
...
...
@@ -15,6 +15,9 @@ cd /home/Projects
# 首次运行需创建一个docker容器,再次运行时不需要运行当前命令
# 创建一个名字为ppocr的docker容器,并将当前目录映射到容器的/paddle目录下
如果您希望在CPU环境下使用docker,使用docker而不是nvidia-docker创建docker
sudo docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
如果您的机器安装的是CUDA9,请运行以下命令创建容器
sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidubce.com/paddlepaddle/paddle:latest-gpu-cuda9.0-cudnn7-dev /bin/bash
...
...
@@ -24,7 +27,7 @@ sudo nvidia-docker run --name ppocr -v $PWD:/paddle --network=host -it hub.baidu
您也可以访问[DockerHub](https://hub.docker.com/r/paddlepaddle/paddle/tags/)获取与您机器适配的镜像。
# ctrl+P+Q可退出docker,重新进入docker使用如下命令
sudo
nvidia-
docker container exec -it ppocr /bin/bash
sudo docker container exec -it ppocr /bin/bash
```
注意:如果docker pull过慢,可以按照如下步骤手动下载后加载docker,以cuda9 docker为例,使用cuda10 docker只需要将cuda9改为cuda10即可。
...
...
tools/infer/utility.py
浏览文件 @
4af18109
...
...
@@ -23,6 +23,7 @@ import cv2
import
numpy
as
np
import
json
from
PIL
import
Image
,
ImageDraw
,
ImageFont
import
math
def
parse_args
():
...
...
@@ -127,6 +128,18 @@ def resize_img(img, input_size=600):
def
draw_ocr
(
image
,
boxes
,
txts
,
scores
,
draw_txt
=
True
,
drop_score
=
0.5
):
"""
Visualize the results of OCR detection and recognition
args:
image(Image): image from Image.open
boxes(list): boxes with shape(N, 4, 2)
txts(list): the texts
scores(list): txxs corresponding scores
draw_txt(bool): whether draw text or not
drop_score(float): only scores greater than drop_threshold will be visualized
return(array):
the visualized img
"""
from
PIL
import
Image
,
ImageDraw
,
ImageFont
img
=
image
.
copy
()
...
...
@@ -154,35 +167,123 @@ def draw_ocr(image, boxes, txts, scores, draw_txt=True, drop_score=0.5):
fill
=
'red'
)
if
draw_txt
:
txt_color
=
(
0
,
0
,
0
)
img
=
np
.
array
(
resize_img
(
img
))
_h
=
img
.
shape
[
0
]
blank_img
=
np
.
ones
(
shape
=
[
_h
,
600
],
dtype
=
np
.
int8
)
*
255
img
=
np
.
array
(
resize_img
(
img
,
input_size
=
600
))
txt_img
=
text_visual
(
txts
,
scores
,
img_h
=
img
.
shape
[
0
],
img_w
=
600
,
threshold
=
drop_score
)
img
=
np
.
concatenate
([
np
.
array
(
img
),
np
.
array
(
txt_img
)],
axis
=
1
)
return
img
def
str_count
(
s
):
"""
Count the number of Chinese characters,
a single English character and a single number
equal to half the length of Chinese characters.
args:
s(string): the input of string
return(int):
the number of Chinese characters
"""
import
string
count_zh
=
count_pu
=
0
s_len
=
len
(
s
)
en_dg_count
=
0
for
c
in
s
:
if
c
in
string
.
ascii_letters
or
c
.
isdigit
()
or
c
.
isspace
():
en_dg_count
+=
1
elif
c
.
isalpha
():
count_zh
+=
1
else
:
count_pu
+=
1
return
s_len
-
math
.
ceil
(
en_dg_count
/
2
)
def
text_visual
(
texts
,
scores
,
img_h
=
400
,
img_w
=
600
,
threshold
=
0.
):
"""
create new blank img and draw txt on it
args:
texts(list): the text will be draw
scores(list|None): corresponding score of each txt
img_h(int): the height of blank img
img_w(int): the width of blank img
return(array):
"""
if
scores
is
not
None
:
assert
len
(
texts
)
==
len
(
scores
),
"The number of txts and corresponding scores must match"
def
create_blank_img
():
blank_img
=
np
.
ones
(
shape
=
[
img_h
,
img_w
],
dtype
=
np
.
int8
)
*
255
blank_img
[:,
img_w
-
1
:]
=
0
blank_img
=
Image
.
fromarray
(
blank_img
).
convert
(
"RGB"
)
draw_txt
=
ImageDraw
.
Draw
(
blank_img
)
return
blank_img
,
draw_txt
font_size
=
20
gap
=
20
title
=
"index text score"
font
=
ImageFont
.
truetype
(
"./doc/simfang.ttf"
,
font_size
,
encoding
=
"utf-8"
)
draw_txt
.
text
((
20
,
0
),
title
,
txt_color
,
font
=
font
)
count
=
0
for
idx
,
txt
in
enumerate
(
txts
):
if
scores
[
idx
]
<
drop_score
:
continue
font
=
ImageFont
.
truetype
(
"./doc/simfang.ttf"
,
font_size
,
encoding
=
"utf-8"
)
new_txt
=
str
(
idx
)
+
': '
+
txt
+
' '
+
'%.3f'
%
(
scores
[
idx
])
draw_txt
.
text
(
(
20
,
gap
*
(
count
+
1
)),
new_txt
,
txt_color
,
font
=
font
)
blank_img
,
draw_txt
=
create_blank_img
()
font_size
=
20
txt_color
=
(
0
,
0
,
0
)
font
=
ImageFont
.
truetype
(
"../../doc/simfang.ttf"
,
font_size
,
encoding
=
"utf-8"
)
gap
=
font_size
+
5
txt_img_list
=
[]
count
,
index
=
0
,
0
for
idx
,
txt
in
enumerate
(
texts
):
index
+=
1
if
scores
[
idx
]
<
threshold
:
index
-=
1
continue
first_line
=
True
while
str_count
(
txt
)
>=
img_w
//
font_size
-
4
:
tmp
=
txt
txt
=
tmp
[:
img_w
//
font_size
-
4
]
if
first_line
:
new_txt
=
str
(
index
)
+
': '
+
txt
first_line
=
False
else
:
new_txt
=
' '
+
txt
draw_txt
.
text
((
0
,
gap
*
(
count
+
1
)),
new_txt
,
txt_color
,
font
=
font
)
txt
=
tmp
[
img_w
//
font_size
-
4
:]
count
+=
1
img
=
np
.
concatenate
([
np
.
array
(
img
),
np
.
array
(
blank_img
)],
axis
=
1
)
return
img
if
count
>=
img_h
//
gap
-
1
:
txt_img_list
.
append
(
np
.
array
(
blank_img
))
blank_img
,
draw_txt
=
create_blank_img
()
count
=
0
if
first_line
:
new_txt
=
str
(
index
)
+
': '
+
txt
+
' '
+
'%.3f'
%
(
scores
[
idx
])
else
:
new_txt
=
" "
+
txt
+
" "
+
'%.3f'
%
(
scores
[
idx
])
draw_txt
.
text
((
0
,
gap
*
(
count
+
1
)),
new_txt
,
txt_color
,
font
=
font
)
count
+=
1
# whether add new blank img or not
if
count
>=
img_h
//
gap
-
1
and
idx
+
1
<
len
(
texts
):
txt_img_list
.
append
(
np
.
array
(
blank_img
))
blank_img
,
draw_txt
=
create_blank_img
()
count
=
0
txt_img_list
.
append
(
np
.
array
(
blank_img
))
if
len
(
txt_img_list
)
==
1
:
blank_img
=
np
.
array
(
txt_img_list
[
0
])
else
:
blank_img
=
np
.
concatenate
(
txt_img_list
,
axis
=
1
)
# cv2.imwrite("./draw_txt.jpg", np.array(blank_img))
return
np
.
array
(
blank_img
)
if
__name__
==
'__main__'
:
text
=
[
"旨在打造一套丰富领先、且实用的工具库助力使、用者训练出更好的模型,并应用落地"
,
"以下代码实现了文本检测、识别串联推理,在执行预测时,需要通过参数image_dir指定单张图像或者图像集合"
,
"上述DB模型的训练和评估,需设置后处理参数box_thresh=0.6,unclip_ratio=1.5,使用不同数据集"
]
img
=
text_visual
(
text
,
scores
=
[
0.999
,
0.999
,
0.999
],
img_h
=
100
)
cv2
.
imwrite
(
"./draw_txt.jpg"
,
np
.
array
(
img
))
"""
test_img = "./doc/test_v2"
predict_txt = "./doc/predict.txt"
f = open(predict_txt, 'r')
...
...
@@ -202,3 +303,4 @@ if __name__ == '__main__':
new_img = draw_ocr(image, boxes, txts, scores, draw_txt=True)
cv2.imwrite(img_name, new_img)
"""
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