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+%global _empty_manifest_terminate_build 0
+Name:		python-miscnn
+Version:	1.4.0
+Release:	1
+Summary:	Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
+License:	GPLv3
+URL:		https://github.com/frankkramer-lab/MIScnn
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3e/c6/3368ea5168d440a809264a0815beb9b26516c137a1fb106c3596e0f5125e/miscnn-1.4.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-tensorflow
+Requires:	python3-tensorflow-addons
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-tqdm
+Requires:	python3-nibabel
+Requires:	python3-matplotlib
+Requires:	python3-pillow
+Requires:	python3-batchgenerators
+Requires:	python3-pydicom
+Requires:	python3-SimpleITK
+Requires:	python3-scikit-image
+
+%description
+![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png)
+
+[![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/)
+[![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn)
+[![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn)
+[![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/)
+[![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn)
+[![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](https://www.gnu.org/licenses/gpl-3.0.en.html)
+
+
+The open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.
+
+**MIScnn provides several core features:**
+- 2D/3D medical image segmentation for binary and multi-class problems
+- Data I/O, preprocessing and data augmentation for biomedical images
+- Patch-wise and full image analysis
+- State-of-the-art deep learning model and metric library
+- Intuitive and fast model utilization (training, prediction)
+- Multiple automatic evaluation techniques (e.g. cross-validation)
+- Custom model, data I/O, pre-/postprocessing and metric support
+- Based on Keras with Tensorflow as backend
+
+![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)
+
+## Resources
+
+- MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)
+- MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)
+- MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)
+- MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)
+- MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)
+- MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)
+
+## Author
+
+Dominik Müller  
+Email: dominik.mueller@informatik.uni-augsburg.de  
+IT-Infrastructure for Translational Medical Research  
+University Augsburg  
+Augsburg, Bavaria, Germany
+
+## How to cite / More information
+
+Dominik Müller and Frank Kramer. (2019)  
+MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.  
+arXiv e-print: [https://arxiv.org/abs/1910.09308](https://arxiv.org/abs/1910.09308)
+
+```
+Article{miscnn,
+  title={MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},
+  author={Dominik Müller and Frank Kramer},
+  year={2019},
+  eprint={1910.09308},
+  archivePrefix={arXiv},
+  primaryClass={eess.IV}
+}
+```
+
+Thank you for citing our work.
+
+## License
+
+This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
+See the LICENSE.md file for license rights and limitations.
+
+
+
+
+%package -n python3-miscnn
+Summary:	Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
+Provides:	python-miscnn
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-miscnn
+![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png)
+
+[![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/)
+[![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn)
+[![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn)
+[![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/)
+[![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn)
+[![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](https://www.gnu.org/licenses/gpl-3.0.en.html)
+
+
+The open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.
+
+**MIScnn provides several core features:**
+- 2D/3D medical image segmentation for binary and multi-class problems
+- Data I/O, preprocessing and data augmentation for biomedical images
+- Patch-wise and full image analysis
+- State-of-the-art deep learning model and metric library
+- Intuitive and fast model utilization (training, prediction)
+- Multiple automatic evaluation techniques (e.g. cross-validation)
+- Custom model, data I/O, pre-/postprocessing and metric support
+- Based on Keras with Tensorflow as backend
+
+![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)
+
+## Resources
+
+- MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)
+- MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)
+- MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)
+- MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)
+- MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)
+- MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)
+
+## Author
+
+Dominik Müller  
+Email: dominik.mueller@informatik.uni-augsburg.de  
+IT-Infrastructure for Translational Medical Research  
+University Augsburg  
+Augsburg, Bavaria, Germany
+
+## How to cite / More information
+
+Dominik Müller and Frank Kramer. (2019)  
+MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.  
+arXiv e-print: [https://arxiv.org/abs/1910.09308](https://arxiv.org/abs/1910.09308)
+
+```
+Article{miscnn,
+  title={MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},
+  author={Dominik Müller and Frank Kramer},
+  year={2019},
+  eprint={1910.09308},
+  archivePrefix={arXiv},
+  primaryClass={eess.IV}
+}
+```
+
+Thank you for citing our work.
+
+## License
+
+This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
+See the LICENSE.md file for license rights and limitations.
+
+
+
+
+%package help
+Summary:	Development documents and examples for miscnn
+Provides:	python3-miscnn-doc
+%description help
+![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/logo_long.png)
+
+[![shield_python](https://img.shields.io/pypi/pyversions/miscnn?style=flat-square)](https://www.python.org/)
+[![shield_build](https://img.shields.io/travis/frankkramer-lab/miscnn/master?style=flat-square)](https://travis-ci.org/github/frankkramer-lab/MIScnn)
+[![shield_coverage](https://img.shields.io/codecov/c/gh/frankkramer-lab/miscnn?style=flat-square)](https://codecov.io/gh/frankkramer-lab/miscnn)
+[![shield_pypi_version](https://img.shields.io/pypi/v/miscnn?style=flat-square)](https://pypi.org/project/miscnn/)
+[![shield_pypi_downloads](https://img.shields.io/pypi/dm/miscnn?style=flat-square)](https://pypistats.org/packages/miscnn)
+[![shield_license](https://img.shields.io/github/license/frankkramer-lab/miscnn?style=flat-square)](https://www.gnu.org/licenses/gpl-3.0.en.html)
+
+
+The open-source Python library MIScnn is an intuitive API allowing fast setup of medical image segmentation pipelines with state-of-the-art convolutional neural network and deep learning models in just a few lines of code.
+
+**MIScnn provides several core features:**
+- 2D/3D medical image segmentation for binary and multi-class problems
+- Data I/O, preprocessing and data augmentation for biomedical images
+- Patch-wise and full image analysis
+- State-of-the-art deep learning model and metric library
+- Intuitive and fast model utilization (training, prediction)
+- Multiple automatic evaluation techniques (e.g. cross-validation)
+- Custom model, data I/O, pre-/postprocessing and metric support
+- Based on Keras with Tensorflow as backend
+
+![MIScnn workflow](https://raw.githubusercontent.com/frankkramer-lab/MIScnn/master/docs/MIScnn.pipeline.png)
+
+## Resources
+
+- MIScnn Documentation: [GitHub wiki - Home](https://github.com/frankkramer-lab/MIScnn/wiki)
+- MIScnn Tutorials: [Overview of Tutorials](https://github.com/frankkramer-lab/MIScnn/wiki/Tutorials)
+- MIScnn Examples: [Overview of Use Cases and Examples](https://github.com/frankkramer-lab/MIScnn/wiki/Examples)
+- MIScnn Development Tracker: [GitHub project - MIScnn Development](https://github.com/frankkramer-lab/MIScnn/projects/1)
+- MIScnn on GitHub: [GitHub - frankkramer-lab/MIScnn](https://github.com/frankkramer-lab/MIScnn)
+- MIScnn on PyPI: [PyPI - miscnn](https://pypi.org/project/miscnn/)
+
+## Author
+
+Dominik Müller  
+Email: dominik.mueller@informatik.uni-augsburg.de  
+IT-Infrastructure for Translational Medical Research  
+University Augsburg  
+Augsburg, Bavaria, Germany
+
+## How to cite / More information
+
+Dominik Müller and Frank Kramer. (2019)  
+MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning.  
+arXiv e-print: [https://arxiv.org/abs/1910.09308](https://arxiv.org/abs/1910.09308)
+
+```
+Article{miscnn,
+  title={MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning},
+  author={Dominik Müller and Frank Kramer},
+  year={2019},
+  eprint={1910.09308},
+  archivePrefix={arXiv},
+  primaryClass={eess.IV}
+}
+```
+
+Thank you for citing our work.
+
+## License
+
+This project is licensed under the GNU GENERAL PUBLIC LICENSE Version 3.\
+See the LICENSE.md file for license rights and limitations.
+
+
+
+
+%prep
+%autosetup -n miscnn-1.4.0
+
+%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-miscnn -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.4.0-1
+- Package Spec generated