Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
Serving
提交
2328bb7b
S
Serving
项目概览
PaddlePaddle
/
Serving
接近 2 年 前同步成功
通知
186
Star
833
Fork
253
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
105
列表
看板
标记
里程碑
合并请求
10
Wiki
2
Wiki
分析
仓库
DevOps
项目成员
Pages
S
Serving
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
105
Issue
105
列表
看板
标记
里程碑
合并请求
10
合并请求
10
Pages
分析
分析
仓库分析
DevOps
Wiki
2
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
提交
2328bb7b
编写于
5月 01, 2020
作者:
D
dongdaxiang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add pytorch style image preprocessing class and functions
上级
f5fd7c9d
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
240 addition
and
1 deletion
+240
-1
python/paddle_serving_app/reader/daisy.jpg
python/paddle_serving_app/reader/daisy.jpg
+0
-0
python/paddle_serving_app/reader/functional.py
python/paddle_serving_app/reader/functional.py
+59
-0
python/paddle_serving_app/reader/image_reader.py
python/paddle_serving_app/reader/image_reader.py
+151
-1
python/paddle_serving_app/reader/test_image_reader.py
python/paddle_serving_app/reader/test_image_reader.py
+30
-0
未找到文件。
python/paddle_serving_app/reader/daisy.jpg
0 → 100644
浏览文件 @
2328bb7b
38.8 KB
python/paddle_serving_app/reader/functional.py
0 → 100644
浏览文件 @
2328bb7b
# 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
cv2
import
numpy
as
np
def
normalize
(
img
,
mean
,
std
):
# need to optimize here
img
=
img
.
astype
(
'float32'
).
transpose
((
2
,
0
,
1
))
/
255
img_mean
=
np
.
array
(
mean
).
reshape
((
3
,
1
,
1
))
img_std
=
np
.
array
(
std
).
reshape
((
3
,
1
,
1
))
img
-=
img_mean
img
/=
img_std
img
=
img
.
transpose
((
1
,
2
,
0
))
return
img
def
crop
(
img
,
target_size
,
center
):
height
,
width
=
img
.
shape
[:
2
]
size
=
target_size
if
center
==
True
:
w_start
=
(
width
-
size
)
//
2
h_start
=
(
height
-
size
)
//
2
else
:
w_start
=
np
.
random
.
randint
(
0
,
width
-
size
+
1
)
h_start
=
np
.
random
.
randint
(
0
,
height
-
size
+
1
)
w_end
=
w_start
+
size
h_end
=
h_start
+
size
img
=
img
[
h_start
:
h_end
,
w_start
:
w_end
,
:]
return
img
def
resize
(
img
,
target_size
,
interpolation
):
if
isinstance
(
target_size
,
tuple
):
resized_width
=
target_size
[
0
]
resized_height
=
target_size
[
1
]
else
:
percent
=
float
(
target_size
)
/
min
(
img
.
shape
[
1
],
img
.
shape
[
2
])
resized_width
=
int
(
round
(
img
.
shape
[
1
]
*
percent
))
resized_height
=
int
(
round
(
img
.
shape
[
0
]
*
percent
))
if
interpolation
:
resized
=
cv2
.
resize
(
img
,
(
resized_width
,
resized_height
),
interpolation
=
interpolation
)
else
:
resized
=
cv2
.
resize
(
img
,
(
resized_width
,
resized_height
))
print
(
resized
.
shape
)
return
resized
python/paddle_serving_app/reader/image_reader.py
浏览文件 @
2328bb7b
...
@@ -11,9 +11,159 @@
...
@@ -11,9 +11,159 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# See the License for the specific language governing permissions and
# limitations under the License.
# limitations under the License.
import
cv2
import
cv2
import
urllib
import
numpy
as
np
import
numpy
as
np
import
base64
import
functional
as
F
_cv2_interpolation_to_str
=
{
cv2
.
INTER_LINEAR
:
"cv2.INTER_LINEAR"
}
class
Sequential
(
object
):
"""
Args:
sequence (sequence of ``Transform`` objects): list of transforms to chain.
This API references some of the design pattern of torchvision
Users can simply use this API in training as well
Example:
>>> image_reader.Sequnece([
>>> transforms.CenterCrop(10),
>>> ])
"""
def
__init__
(
self
,
transforms
):
self
.
transforms
=
transforms
def
__call__
(
self
,
img
):
for
t
in
self
.
transforms
:
img
=
t
(
img
)
return
img
def
__repr__
(
self
):
format_string_
=
self
.
__class__
.
__name__
+
'('
for
t
in
self
.
transforms
:
format_string_
+=
'
\n
'
format_string_
+=
' {0}'
.
format
(
t
)
format_string_
+=
'
\n
)'
return
format_string_
class
File2Image
(
object
):
def
__init__
(
self
):
pass
def
__call__
(
self
,
img_path
):
fin
=
open
(
img_path
)
sample
=
fin
.
read
()
data
=
np
.
fromstring
(
sample
,
np
.
uint8
)
img
=
cv2
.
imdecode
(
data
,
cv2
.
IMREAD_COLOR
)
return
img
def
__repr__
(
self
):
return
self
.
__class__
.
__name__
+
"()"
class
URL2Image
(
object
):
def
__init__
(
self
):
pass
def
__call__
(
self
,
img_url
):
resp
=
urllib
.
urlopen
(
img_url
)
sample
=
resp
.
read
()
data
=
np
.
fromstring
(
sample
,
np
.
uint8
)
img
=
cv2
.
imdecode
(
data
,
cv2
.
IMREAD_COLOR
)
return
img
def
__repr__
(
self
):
return
self
.
__class__
.
__name__
+
"()"
class
Normalize
(
object
):
"""Normalize a tensor image with mean and standard deviation.
Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
will normalize each channel of the input ``torch.*Tensor`` i.e.
``output[channel] = (input[channel] - mean[channel]) / std[channel]``
.. note::
This transform acts out of place, i.e., it does not mutate the input tensor.
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
"""
def
__init__
(
self
,
mean
,
std
):
self
.
mean
=
mean
self
.
std
=
std
def
__call__
(
self
,
img
):
"""
Args:
img (numpy array): (C, H, W) to be normalized.
Returns:
Tensor: Normalized Tensor image.
"""
return
F
.
normalize
(
img
,
self
.
mean
,
self
.
std
)
def
__repr__
(
self
):
return
self
.
__class__
.
__name__
+
'(mean={0}, std={1})'
.
format
(
self
.
mean
,
self
.
std
)
class
CenterCrop
(
object
):
"""Crops the given Image at the center.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def
__init__
(
self
,
size
):
self
.
size
=
size
def
__call__
(
self
,
img
):
"""
Args:
img (numpy array): Image to be cropped.
Returns:
numpy array Image: Cropped image.
"""
return
F
.
crop
(
img
,
self
.
size
,
True
)
def
__repr__
(
self
):
return
self
.
__class__
.
__name__
+
'(size={0})'
.
format
(
self
.
size
)
class
Resize
(
object
):
"""Resize the input numpy array Image to the given size.
Args:
size (sequence or int): Desired output size. If size is a sequence like
(h, w), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size)
interpolation (int, optional): Desired interpolation. Default is
``PIL.Image.BILINEAR``
"""
def
__init__
(
self
,
size
,
interpolation
=
cv2
.
INTER_LINEAR
):
self
.
size
=
size
self
.
interpolation
=
interpolation
def
__call__
(
self
,
img
):
return
F
.
resize
(
img
,
self
.
size
,
self
.
interpolation
)
def
__repr__
(
self
,
img
):
return
self
.
__class__
.
__name__
+
'(size={0}, interpolation={1})'
.
format
(
self
.
size
,
_cv2_interpolation_to_str
[
self
.
interpolation
])
class
ImageReader
():
class
ImageReader
():
...
...
python/paddle_serving_app/reader/test_image_reader.py
0 → 100644
浏览文件 @
2328bb7b
# 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
image_reader
import
File2Image
from
image_reader
import
URL2Image
from
image_reader
import
Sequential
from
image_reader
import
Normalize
from
image_reader
import
CenterCrop
from
image_reader
import
Resize
seq
=
Sequential
([
File2Image
(),
CenterCrop
(
30
),
Normalize
([
0.485
,
0.456
,
0.406
],
[
0.229
,
0.224
,
0.225
]),
Resize
((
5
,
5
))
])
url
=
"daisy.jpg"
for
x
in
range
(
100
):
img
=
seq
(
url
)
print
(
img
.
shape
)
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录