automatic import of python-imagecorruptions

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+/imagecorruptions-1.1.2.tar.gz
diff --git a/python-imagecorruptions.spec b/python-imagecorruptions.spec
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+%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).
+
+![image corruptions](https://raw.githubusercontent.com/bethgelab/imagecorruptions/master/assets/corruptions_sev_3.png?token=ACY4L7YQWNOLTMRRO53U6FS5G3UF6)
+
+## 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).
+
+![image corruptions](https://raw.githubusercontent.com/bethgelab/imagecorruptions/master/assets/corruptions_sev_3.png?token=ACY4L7YQWNOLTMRRO53U6FS5G3UF6)
+
+## 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).
+
+![image corruptions](https://raw.githubusercontent.com/bethgelab/imagecorruptions/master/assets/corruptions_sev_3.png?token=ACY4L7YQWNOLTMRRO53U6FS5G3UF6)
+
+## 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
+* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..96c09af
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+f8b0c29ec4f791b8857f84f25a13019f  imagecorruptions-1.1.2.tar.gz