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%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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.21.1-1
- Package Spec generated
|
