%global _empty_manifest_terminate_build 0 Name: python-colorspacious Version: 1.1.2 Release: 1 Summary: A powerful, accurate, and easy-to-use Python library for doing colorspace conversions License: MIT URL: https://github.com/njsmith/colorspacious Source0: https://mirrors.nju.edu.cn/pypi/web/packages/75/e4/aa41ae14c5c061205715006c8834496d86ec7500f1edda5981f0f0190cc6/colorspacious-1.1.2.tar.gz BuildArch: noarch Requires: python3-numpy %description Colorspacious is a powerful, accurate, and easy-to-use library for performing colorspace conversions. In addition to the most common standard colorspaces (sRGB, XYZ, xyY, CIELab, CIELCh), we also include: color vision deficiency ("color blindness") simulations using the approach of Machado et al (2009); a complete implementation of `CIECAM02 `_; and the perceptually uniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al (2006). To get started, simply write:: from colorspacious import cspace_convert Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS") This converts an sRGB value (represented as integers between 0-255) to CAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing conditions by default). This requires passing through 4 intermediate colorspaces; ``cspace_convert`` automatically finds the optimal route and applies all conversions in sequence: This function also of course accepts arbitrary NumPy arrays, so converting a whole image is just as easy as converting a single value. Documentation: http://colorspacious.readthedocs.org/ Installation: ``pip install colorspacious`` Downloads: https://pypi.python.org/pypi/colorspacious/ Code and bug tracker: https://github.com/njsmith/colorspacious Contact: Nathaniel J. Smith Dependencies: * Python 2.6+, or 3.3+ * NumPy Developer dependencies (only needed for hacking on source): * nose: needed to run tests License: MIT, see LICENSE.txt for details. References for algorithms we implement: * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on CIECAM02 colour appearance model. Color Research & Application, 31(4), 320–330. doi:10.1002/col.20227 * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A physiologically-based model for simulation of color vision deficiency. Visualization and Computer Graphics, IEEE Transactions on, 15(6), 1291–1298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html Other Python packages with similar functionality that you might want to check out as well or instead: * ``colour``: http://colour-science.org/ * ``colormath``: http://python-colormath.readthedocs.org/ * ``ciecam02``: https://pypi.python.org/pypi/ciecam02/ * ``ColorPy``: http://markkness.net/colorpy/ColorPy.html %package -n python3-colorspacious Summary: A powerful, accurate, and easy-to-use Python library for doing colorspace conversions Provides: python-colorspacious BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-colorspacious Colorspacious is a powerful, accurate, and easy-to-use library for performing colorspace conversions. In addition to the most common standard colorspaces (sRGB, XYZ, xyY, CIELab, CIELCh), we also include: color vision deficiency ("color blindness") simulations using the approach of Machado et al (2009); a complete implementation of `CIECAM02 `_; and the perceptually uniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al (2006). To get started, simply write:: from colorspacious import cspace_convert Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS") This converts an sRGB value (represented as integers between 0-255) to CAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing conditions by default). This requires passing through 4 intermediate colorspaces; ``cspace_convert`` automatically finds the optimal route and applies all conversions in sequence: This function also of course accepts arbitrary NumPy arrays, so converting a whole image is just as easy as converting a single value. Documentation: http://colorspacious.readthedocs.org/ Installation: ``pip install colorspacious`` Downloads: https://pypi.python.org/pypi/colorspacious/ Code and bug tracker: https://github.com/njsmith/colorspacious Contact: Nathaniel J. Smith Dependencies: * Python 2.6+, or 3.3+ * NumPy Developer dependencies (only needed for hacking on source): * nose: needed to run tests License: MIT, see LICENSE.txt for details. References for algorithms we implement: * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on CIECAM02 colour appearance model. Color Research & Application, 31(4), 320–330. doi:10.1002/col.20227 * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A physiologically-based model for simulation of color vision deficiency. Visualization and Computer Graphics, IEEE Transactions on, 15(6), 1291–1298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html Other Python packages with similar functionality that you might want to check out as well or instead: * ``colour``: http://colour-science.org/ * ``colormath``: http://python-colormath.readthedocs.org/ * ``ciecam02``: https://pypi.python.org/pypi/ciecam02/ * ``ColorPy``: http://markkness.net/colorpy/ColorPy.html %package help Summary: Development documents and examples for colorspacious Provides: python3-colorspacious-doc %description help Colorspacious is a powerful, accurate, and easy-to-use library for performing colorspace conversions. In addition to the most common standard colorspaces (sRGB, XYZ, xyY, CIELab, CIELCh), we also include: color vision deficiency ("color blindness") simulations using the approach of Machado et al (2009); a complete implementation of `CIECAM02 `_; and the perceptually uniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al (2006). To get started, simply write:: from colorspacious import cspace_convert Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS") This converts an sRGB value (represented as integers between 0-255) to CAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing conditions by default). This requires passing through 4 intermediate colorspaces; ``cspace_convert`` automatically finds the optimal route and applies all conversions in sequence: This function also of course accepts arbitrary NumPy arrays, so converting a whole image is just as easy as converting a single value. Documentation: http://colorspacious.readthedocs.org/ Installation: ``pip install colorspacious`` Downloads: https://pypi.python.org/pypi/colorspacious/ Code and bug tracker: https://github.com/njsmith/colorspacious Contact: Nathaniel J. Smith Dependencies: * Python 2.6+, or 3.3+ * NumPy Developer dependencies (only needed for hacking on source): * nose: needed to run tests License: MIT, see LICENSE.txt for details. References for algorithms we implement: * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on CIECAM02 colour appearance model. Color Research & Application, 31(4), 320–330. doi:10.1002/col.20227 * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A physiologically-based model for simulation of color vision deficiency. Visualization and Computer Graphics, IEEE Transactions on, 15(6), 1291–1298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html Other Python packages with similar functionality that you might want to check out as well or instead: * ``colour``: http://colour-science.org/ * ``colormath``: http://python-colormath.readthedocs.org/ * ``ciecam02``: https://pypi.python.org/pypi/ciecam02/ * ``ColorPy``: http://markkness.net/colorpy/ColorPy.html %prep %autosetup -n colorspacious-1.1.2 %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-colorspacious -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 21 2023 Python_Bot - 1.1.2-1 - Package Spec generated