%global _empty_manifest_terminate_build 0 Name: python-torchjpeg Version: 0.9.29 Release: 1 Summary: Utilities for JPEG data access and manipulation in pytorch License: MIT URL: https://queuecumber.gitlab.io/torchjpeg Source0: https://mirrors.nju.edu.cn/pypi/web/packages/f6/d3/9155bef69a398ddf63902321fb236d0195491ceb23f075b353008c87971d/torchjpeg-0.9.29.tar.gz BuildArch: noarch Requires: python3-torch Requires: python3-torchvision Requires: python3-Pillow %description # TorchJPEG [![pipeline status](https://gitlab.com/Queuecumber/torchjpeg/badges/master/pipeline.svg)](https://gitlab.com/Queuecumber/torchjpeg/-/pipelines/latest) [![coverage report](https://gitlab.com/Queuecumber/torchjpeg/badges/master/coverage.svg)](https://gitlab.com/Queuecumber/torchjpeg/-/pipelines/latest) [![PyPI](https://img.shields.io/pypi/v/torchjpeg)](https://pypi.org/project/torchjpeg/) [![License](https://img.shields.io/badge/license-MIT-blue)](https://gitlab.com/Queuecumber/torchjpeg/-/blob/master/LICENSE) This package contains a C++ extension for pytorch that interfaces with libjpeg to allow for manipulation of low-level JPEG data. By using libjpeg, quantization results are guaranteed to be consistent with other applications, like image viewers or MATLAB, which use libjpeg to compress and decompress images. This is useful because JPEG images can be effected by round-off errors or slight differences in the decompression procedure. Besides this, this library can be used to read and write DCT coefficients, functionality which is not available from other python interfaces. Besides this, the library includes many utilities related to JPEG compression, many of which are written using native pytorch code meaning they can be differentiated or GPU accelerated. The library currently includes packages related to the DCT, quantization, metrics, and dataset transformations. ## LIBJPEG Currently builds against: `libjpeg-9d`. libjpeg is statically linked during the build process. See [http://www.ijg.org/files/](http://www.ijg.org/files/) for libjpeg source. The full libjpeg source is included with the torchjpeg source code and will be built during the install process (for a source or sdist install). ## Install Packages are hosted on [pypi](https://pypi.org/project/torchjpeg/). Install using pip, note that only Linux builds are supported at the moment. ``` pip install torchjpeg ``` If there is demand for builds on other platforms it may happen in the future. Also note that the wheel is intended to be compatible with manylinux2014 which means it should work on modern Linux systems, however, because of they way pytorch works, we can't actually build it using all of the manylinux2014 tools. So compliance is not guaranteed and YMMV. ```{warning} torchjpeg is currently in pre-beta development and consists mostly of converted research code. The public facing API, including any and all names of parameters and functions, is subject to change at any time. We follow semver for versioning and will adhere to that before making and breaking changes. ``` ## Citation If you use our code in a publication, we ask that you cite the following paper ([bibtex](http://maxehr.umiacs.io/bibtex/ehrlich2020quantization.txt)): > Max Ehrlich, Larry Davis, Ser-Nam Lim, and Abhinav Shrivastava. "Quantization Guided JPEG Artifact Correction." In Proceedings of the European Conference on Computer Vision, 2020 %package -n python3-torchjpeg Summary: Utilities for JPEG data access and manipulation in pytorch Provides: python-torchjpeg BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-torchjpeg # TorchJPEG [![pipeline status](https://gitlab.com/Queuecumber/torchjpeg/badges/master/pipeline.svg)](https://gitlab.com/Queuecumber/torchjpeg/-/pipelines/latest) [![coverage report](https://gitlab.com/Queuecumber/torchjpeg/badges/master/coverage.svg)](https://gitlab.com/Queuecumber/torchjpeg/-/pipelines/latest) [![PyPI](https://img.shields.io/pypi/v/torchjpeg)](https://pypi.org/project/torchjpeg/) [![License](https://img.shields.io/badge/license-MIT-blue)](https://gitlab.com/Queuecumber/torchjpeg/-/blob/master/LICENSE) This package contains a C++ extension for pytorch that interfaces with libjpeg to allow for manipulation of low-level JPEG data. By using libjpeg, quantization results are guaranteed to be consistent with other applications, like image viewers or MATLAB, which use libjpeg to compress and decompress images. This is useful because JPEG images can be effected by round-off errors or slight differences in the decompression procedure. Besides this, this library can be used to read and write DCT coefficients, functionality which is not available from other python interfaces. Besides this, the library includes many utilities related to JPEG compression, many of which are written using native pytorch code meaning they can be differentiated or GPU accelerated. The library currently includes packages related to the DCT, quantization, metrics, and dataset transformations. ## LIBJPEG Currently builds against: `libjpeg-9d`. libjpeg is statically linked during the build process. See [http://www.ijg.org/files/](http://www.ijg.org/files/) for libjpeg source. The full libjpeg source is included with the torchjpeg source code and will be built during the install process (for a source or sdist install). ## Install Packages are hosted on [pypi](https://pypi.org/project/torchjpeg/). Install using pip, note that only Linux builds are supported at the moment. ``` pip install torchjpeg ``` If there is demand for builds on other platforms it may happen in the future. Also note that the wheel is intended to be compatible with manylinux2014 which means it should work on modern Linux systems, however, because of they way pytorch works, we can't actually build it using all of the manylinux2014 tools. So compliance is not guaranteed and YMMV. ```{warning} torchjpeg is currently in pre-beta development and consists mostly of converted research code. The public facing API, including any and all names of parameters and functions, is subject to change at any time. We follow semver for versioning and will adhere to that before making and breaking changes. ``` ## Citation If you use our code in a publication, we ask that you cite the following paper ([bibtex](http://maxehr.umiacs.io/bibtex/ehrlich2020quantization.txt)): > Max Ehrlich, Larry Davis, Ser-Nam Lim, and Abhinav Shrivastava. "Quantization Guided JPEG Artifact Correction." In Proceedings of the European Conference on Computer Vision, 2020 %package help Summary: Development documents and examples for torchjpeg Provides: python3-torchjpeg-doc %description help # TorchJPEG [![pipeline status](https://gitlab.com/Queuecumber/torchjpeg/badges/master/pipeline.svg)](https://gitlab.com/Queuecumber/torchjpeg/-/pipelines/latest) [![coverage report](https://gitlab.com/Queuecumber/torchjpeg/badges/master/coverage.svg)](https://gitlab.com/Queuecumber/torchjpeg/-/pipelines/latest) [![PyPI](https://img.shields.io/pypi/v/torchjpeg)](https://pypi.org/project/torchjpeg/) [![License](https://img.shields.io/badge/license-MIT-blue)](https://gitlab.com/Queuecumber/torchjpeg/-/blob/master/LICENSE) This package contains a C++ extension for pytorch that interfaces with libjpeg to allow for manipulation of low-level JPEG data. By using libjpeg, quantization results are guaranteed to be consistent with other applications, like image viewers or MATLAB, which use libjpeg to compress and decompress images. This is useful because JPEG images can be effected by round-off errors or slight differences in the decompression procedure. Besides this, this library can be used to read and write DCT coefficients, functionality which is not available from other python interfaces. Besides this, the library includes many utilities related to JPEG compression, many of which are written using native pytorch code meaning they can be differentiated or GPU accelerated. The library currently includes packages related to the DCT, quantization, metrics, and dataset transformations. ## LIBJPEG Currently builds against: `libjpeg-9d`. libjpeg is statically linked during the build process. See [http://www.ijg.org/files/](http://www.ijg.org/files/) for libjpeg source. The full libjpeg source is included with the torchjpeg source code and will be built during the install process (for a source or sdist install). ## Install Packages are hosted on [pypi](https://pypi.org/project/torchjpeg/). Install using pip, note that only Linux builds are supported at the moment. ``` pip install torchjpeg ``` If there is demand for builds on other platforms it may happen in the future. Also note that the wheel is intended to be compatible with manylinux2014 which means it should work on modern Linux systems, however, because of they way pytorch works, we can't actually build it using all of the manylinux2014 tools. So compliance is not guaranteed and YMMV. ```{warning} torchjpeg is currently in pre-beta development and consists mostly of converted research code. The public facing API, including any and all names of parameters and functions, is subject to change at any time. We follow semver for versioning and will adhere to that before making and breaking changes. ``` ## Citation If you use our code in a publication, we ask that you cite the following paper ([bibtex](http://maxehr.umiacs.io/bibtex/ehrlich2020quantization.txt)): > Max Ehrlich, Larry Davis, Ser-Nam Lim, and Abhinav Shrivastava. "Quantization Guided JPEG Artifact Correction." In Proceedings of the European Conference on Computer Vision, 2020 %prep %autosetup -n torchjpeg-0.9.29 %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-torchjpeg -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 0.9.29-1 - Package Spec generated