%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 [![Build Status](https://github.com/imagingdatacommons/highdicom/actions/workflows/run_unit_tests.yml/badge.svg)](https://github.com/imagingdatacommons/highdicom/actions) [![PyPi Distribution](https://img.shields.io/pypi/v/highdicom.svg)](https://pypi.python.org/pypi/highdicom/) [![Python Versions](https://img.shields.io/pypi/pyversions/highdicom.svg)](https://pypi.org/project/highdicom/) [![Downloads](https://pepy.tech/badge/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 [![Build Status](https://github.com/imagingdatacommons/highdicom/actions/workflows/run_unit_tests.yml/badge.svg)](https://github.com/imagingdatacommons/highdicom/actions) [![PyPi Distribution](https://img.shields.io/pypi/v/highdicom.svg)](https://pypi.python.org/pypi/highdicom/) [![Python Versions](https://img.shields.io/pypi/pyversions/highdicom.svg)](https://pypi.org/project/highdicom/) [![Downloads](https://pepy.tech/badge/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 [![Build Status](https://github.com/imagingdatacommons/highdicom/actions/workflows/run_unit_tests.yml/badge.svg)](https://github.com/imagingdatacommons/highdicom/actions) [![PyPi Distribution](https://img.shields.io/pypi/v/highdicom.svg)](https://pypi.python.org/pypi/highdicom/) [![Python Versions](https://img.shields.io/pypi/pyversions/highdicom.svg)](https://pypi.org/project/highdicom/) [![Downloads](https://pepy.tech/badge/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 - 0.21.1-1 - Package Spec generated