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diff --git a/python-highdicom.spec b/python-highdicom.spec new file mode 100644 index 0000000..368cce3 --- /dev/null +++ b/python-highdicom.spec @@ -0,0 +1,196 @@ +%global _empty_manifest_terminate_build 0 +Name: python-highdicom +Version: 0.21.1 +Release: 1 +Summary: High-level DICOM abstractions. +License: MIT +URL: https://github.com/imagingdatacommons/highdicom +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/5f/fa/8de65491f282678c588a3059e3631c290fc53f55ff4ea2be5c0075392b57/highdicom-0.21.1.tar.gz +BuildArch: noarch + +Requires: python3-pydicom +Requires: python3-numpy +Requires: python3-pillow +Requires: python3-pillow-jpls +Requires: python3-pylibjpeg +Requires: python3-pylibjpeg-libjpeg +Requires: python3-pylibjpeg-openjpeg + +%description +[](https://github.com/imagingdatacommons/highdicom/actions) +[](https://pypi.python.org/pypi/highdicom/) +[](https://pypi.org/project/highdicom/) +[](https://pepy.tech/project/highdicom) + +# High DICOM + +A library that provides high-level DICOM abstractions for the Python programming language to facilitate the creation and handling of DICOM objects for image-derived information, including image annotations, and image analysis results. +It currently provides tools for creating and decoding the following DICOM information object definitions (IODs): +* Annotations +* Parametric Map images +* Segmentation images +* Structured Report documents +* Secondary Capture images +* Key Object Selection documents +* Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion) +* Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color) + +## Documentation + +Please refer to the online documentation at [highdicom.readthedocs.io](https://highdicom.readthedocs.io), which includes installation instructions, a user guide with examples, a developer guide, and complete documentation of the application programming interface of the `highdicom` package. + +## Citation + +For more information about the motivation of the library and the design of highdicom's API, please see the following article: + +> [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://arxiv.org/abs/2106.07806) +> C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann + +If you use highdicom in your research, please cite the above article. + +## Support + +The developers gratefully acknowledge their support: +* The [Alliance for Digital Pathology](https://digitalpathologyalliance.org/) +* The [MGH & BWH Center for Clinical Data Science](https://www.ccds.io/) +* [Quantitative Image Informatics for Cancer Research (QIICR)](http://qiicr.org) +* [Radiomics](http://radiomics.io) +* The [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/) + + +%package -n python3-highdicom +Summary: High-level DICOM abstractions. +Provides: python-highdicom +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-highdicom +[](https://github.com/imagingdatacommons/highdicom/actions) +[](https://pypi.python.org/pypi/highdicom/) +[](https://pypi.org/project/highdicom/) +[](https://pepy.tech/project/highdicom) + +# High DICOM + +A library that provides high-level DICOM abstractions for the Python programming language to facilitate the creation and handling of DICOM objects for image-derived information, including image annotations, and image analysis results. +It currently provides tools for creating and decoding the following DICOM information object definitions (IODs): +* Annotations +* Parametric Map images +* Segmentation images +* Structured Report documents +* Secondary Capture images +* Key Object Selection documents +* Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion) +* Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color) + +## Documentation + +Please refer to the online documentation at [highdicom.readthedocs.io](https://highdicom.readthedocs.io), which includes installation instructions, a user guide with examples, a developer guide, and complete documentation of the application programming interface of the `highdicom` package. + +## Citation + +For more information about the motivation of the library and the design of highdicom's API, please see the following article: + +> [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://arxiv.org/abs/2106.07806) +> C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann + +If you use highdicom in your research, please cite the above article. + +## Support + +The developers gratefully acknowledge their support: +* The [Alliance for Digital Pathology](https://digitalpathologyalliance.org/) +* The [MGH & BWH Center for Clinical Data Science](https://www.ccds.io/) +* [Quantitative Image Informatics for Cancer Research (QIICR)](http://qiicr.org) +* [Radiomics](http://radiomics.io) +* The [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/) + + +%package help +Summary: Development documents and examples for highdicom +Provides: python3-highdicom-doc +%description help +[](https://github.com/imagingdatacommons/highdicom/actions) +[](https://pypi.python.org/pypi/highdicom/) +[](https://pypi.org/project/highdicom/) +[](https://pepy.tech/project/highdicom) + +# High DICOM + +A library that provides high-level DICOM abstractions for the Python programming language to facilitate the creation and handling of DICOM objects for image-derived information, including image annotations, and image analysis results. +It currently provides tools for creating and decoding the following DICOM information object definitions (IODs): +* Annotations +* Parametric Map images +* Segmentation images +* Structured Report documents +* Secondary Capture images +* Key Object Selection documents +* Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion) +* Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color) + +## Documentation + +Please refer to the online documentation at [highdicom.readthedocs.io](https://highdicom.readthedocs.io), which includes installation instructions, a user guide with examples, a developer guide, and complete documentation of the application programming interface of the `highdicom` package. + +## Citation + +For more information about the motivation of the library and the design of highdicom's API, please see the following article: + +> [Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology](https://arxiv.org/abs/2106.07806) +> C.P. Bridge, C. Gorman, S. Pieper, S.W. Doyle, J.K. Lennerz, J. Kalpathy-Cramer, D.A. Clunie, A.Y. Fedorov, and M.D. Herrmann + +If you use highdicom in your research, please cite the above article. + +## Support + +The developers gratefully acknowledge their support: +* The [Alliance for Digital Pathology](https://digitalpathologyalliance.org/) +* The [MGH & BWH Center for Clinical Data Science](https://www.ccds.io/) +* [Quantitative Image Informatics for Cancer Research (QIICR)](http://qiicr.org) +* [Radiomics](http://radiomics.io) +* The [NCI Imaging Data Commons](https://imaging.datacommons.cancer.gov/) + + +%prep +%autosetup -n highdicom-0.21.1 + +%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-highdicom -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 0.21.1-1 +- Package Spec generated |