1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
|
%global _empty_manifest_terminate_build 0
Name: python-imagecorruptions
Version: 1.1.2
Release: 1
Summary: This package provides a set of image corruptions.
License: Apache Software License
URL: https://github.com/bethgelab/imagecorruptions
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/22/81/0e89d4524064b683cb3b0b5c5874e41307d730c473673051d3c68d52605f/imagecorruptions-1.1.2.tar.gz
BuildArch: noarch
Requires: python3-Pillow
Requires: python3-numpy
Requires: python3-opencv-python
Requires: python3-scikit-image
Requires: python3-scipy
%description
# imagecorruptions
This package provides a set of corruptions that can be applied to images in order to benchmark the robustness of neural networks. These corruptions are not meant to be used as training data augmentation but rather to test the networks against unseen perturbations. For more information have a look at the paper on the original corruption package by Hendrycks and Dietterich: [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://arxiv.org/abs/1807.01697).
## Installation and Usage
This package is pip installable via `pip3 install imagecorruptions`. An example of how to use the corruption function is given below:
```python
from imagecorruptions import corrupt
...
corrupted_image = corrupt(image, corruption_name='gaussian_blur', severity=1)
...
```
Looping over all available corruptions can be done either by name or by index:
```python
# via name
from imagecorruptions import get_corruption_names
for corruption in get_corruption_names():
for severity in range(5):
corrupted = corrupt(image, corruption_name=corruption, severity=severity+1)
...
# via number:
for i in range(15):
for severity in range(5):
corrupted = corrupt(image, corruption_number=i, severity=severity+1)
...
```
Note that the first 15 image corruptions are the common corruptions (the ones you get via `get_corruption_names()`). If you really wish to use these as data augmentation, there exist four additional validation corruptions which can be accessed via `get_corruption_names('validation')` which should then be used to test the corruption robustness of the trained model.
## Citation
If you use our code or the imagecorruptions package, please consider citing:
```
@article{michaelis2019dragon,
title={Benchmarking Robustness in Object Detection:
Autonomous Driving when Winter is Coming},
author={Michaelis, Claudio and Mitzkus, Benjamin and
Geirhos, Robert and Rusak, Evgenia and
Bringmann, Oliver and Ecker, Alexander S. and
Bethge, Matthias and Brendel, Wieland},
journal={arXiv preprint arXiv:1907.07484},
year={2019}
}
```
## Credit and Changelog
This package is an extension of the image corruption functions provided by Dan Hendrycks in the repository [corruptions](https://github.com/hendrycks/robustness). The image corruptions implemented by Hendrycks are generalized to work on images with arbitrary image dimensions and aspect ratios aswell as on grayscale images. We furthermore removed the dependency to `libmagickwand` and the python api `Wand` and reimplemented the `motion_blur` in python.
%package -n python3-imagecorruptions
Summary: This package provides a set of image corruptions.
Provides: python-imagecorruptions
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-imagecorruptions
# imagecorruptions
This package provides a set of corruptions that can be applied to images in order to benchmark the robustness of neural networks. These corruptions are not meant to be used as training data augmentation but rather to test the networks against unseen perturbations. For more information have a look at the paper on the original corruption package by Hendrycks and Dietterich: [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://arxiv.org/abs/1807.01697).
## Installation and Usage
This package is pip installable via `pip3 install imagecorruptions`. An example of how to use the corruption function is given below:
```python
from imagecorruptions import corrupt
...
corrupted_image = corrupt(image, corruption_name='gaussian_blur', severity=1)
...
```
Looping over all available corruptions can be done either by name or by index:
```python
# via name
from imagecorruptions import get_corruption_names
for corruption in get_corruption_names():
for severity in range(5):
corrupted = corrupt(image, corruption_name=corruption, severity=severity+1)
...
# via number:
for i in range(15):
for severity in range(5):
corrupted = corrupt(image, corruption_number=i, severity=severity+1)
...
```
Note that the first 15 image corruptions are the common corruptions (the ones you get via `get_corruption_names()`). If you really wish to use these as data augmentation, there exist four additional validation corruptions which can be accessed via `get_corruption_names('validation')` which should then be used to test the corruption robustness of the trained model.
## Citation
If you use our code or the imagecorruptions package, please consider citing:
```
@article{michaelis2019dragon,
title={Benchmarking Robustness in Object Detection:
Autonomous Driving when Winter is Coming},
author={Michaelis, Claudio and Mitzkus, Benjamin and
Geirhos, Robert and Rusak, Evgenia and
Bringmann, Oliver and Ecker, Alexander S. and
Bethge, Matthias and Brendel, Wieland},
journal={arXiv preprint arXiv:1907.07484},
year={2019}
}
```
## Credit and Changelog
This package is an extension of the image corruption functions provided by Dan Hendrycks in the repository [corruptions](https://github.com/hendrycks/robustness). The image corruptions implemented by Hendrycks are generalized to work on images with arbitrary image dimensions and aspect ratios aswell as on grayscale images. We furthermore removed the dependency to `libmagickwand` and the python api `Wand` and reimplemented the `motion_blur` in python.
%package help
Summary: Development documents and examples for imagecorruptions
Provides: python3-imagecorruptions-doc
%description help
# imagecorruptions
This package provides a set of corruptions that can be applied to images in order to benchmark the robustness of neural networks. These corruptions are not meant to be used as training data augmentation but rather to test the networks against unseen perturbations. For more information have a look at the paper on the original corruption package by Hendrycks and Dietterich: [Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations](https://arxiv.org/abs/1807.01697).
## Installation and Usage
This package is pip installable via `pip3 install imagecorruptions`. An example of how to use the corruption function is given below:
```python
from imagecorruptions import corrupt
...
corrupted_image = corrupt(image, corruption_name='gaussian_blur', severity=1)
...
```
Looping over all available corruptions can be done either by name or by index:
```python
# via name
from imagecorruptions import get_corruption_names
for corruption in get_corruption_names():
for severity in range(5):
corrupted = corrupt(image, corruption_name=corruption, severity=severity+1)
...
# via number:
for i in range(15):
for severity in range(5):
corrupted = corrupt(image, corruption_number=i, severity=severity+1)
...
```
Note that the first 15 image corruptions are the common corruptions (the ones you get via `get_corruption_names()`). If you really wish to use these as data augmentation, there exist four additional validation corruptions which can be accessed via `get_corruption_names('validation')` which should then be used to test the corruption robustness of the trained model.
## Citation
If you use our code or the imagecorruptions package, please consider citing:
```
@article{michaelis2019dragon,
title={Benchmarking Robustness in Object Detection:
Autonomous Driving when Winter is Coming},
author={Michaelis, Claudio and Mitzkus, Benjamin and
Geirhos, Robert and Rusak, Evgenia and
Bringmann, Oliver and Ecker, Alexander S. and
Bethge, Matthias and Brendel, Wieland},
journal={arXiv preprint arXiv:1907.07484},
year={2019}
}
```
## Credit and Changelog
This package is an extension of the image corruption functions provided by Dan Hendrycks in the repository [corruptions](https://github.com/hendrycks/robustness). The image corruptions implemented by Hendrycks are generalized to work on images with arbitrary image dimensions and aspect ratios aswell as on grayscale images. We furthermore removed the dependency to `libmagickwand` and the python api `Wand` and reimplemented the `motion_blur` in python.
%prep
%autosetup -n imagecorruptions-1.1.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-imagecorruptions -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1
- Package Spec generated
|
