colorspacious
=============
.. image:: https://travis-ci.org/njsmith/colorspacious.svg?branch=master
:target: https://travis-ci.org/njsmith/colorspacious
:alt: Automated test status
.. image:: https://codecov.io/gh/njsmith/colorspacious/branch/master/graph/badge.svg
:target: https://codecov.io/gh/njsmith/colorspacious
:alt: Test coverage
.. image:: https://readthedocs.org/projects/colorspacious/badge/?version=latest
:target: http://colorspacious.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://zenodo.org/badge/38525000.svg
:target: https://zenodo.org/badge/latestdoi/38525000
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
<https://en.wikipedia.org/wiki/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 <njs@pobox.com>
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
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"description": "colorspacious\n=============\n\n.. image:: https://travis-ci.org/njsmith/colorspacious.svg?branch=master\n :target: https://travis-ci.org/njsmith/colorspacious\n :alt: Automated test status\n\n.. image:: https://codecov.io/gh/njsmith/colorspacious/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/njsmith/colorspacious\n :alt: Test coverage\n\n.. image:: https://readthedocs.org/projects/colorspacious/badge/?version=latest\n :target: http://colorspacious.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\n.. image:: https://zenodo.org/badge/38525000.svg\n :target: https://zenodo.org/badge/latestdoi/38525000\n\nColorspacious is a powerful, accurate, and easy-to-use library for\nperforming colorspace conversions.\n\nIn addition to the most common standard colorspaces (sRGB, XYZ, xyY,\nCIELab, CIELCh), we also include: color vision deficiency (\"color\nblindness\") simulations using the approach of Machado et al (2009); a\ncomplete implementation of `CIECAM02\n<https://en.wikipedia.org/wiki/CIECAM02>`_; and the perceptually\nuniform CAM02-UCS / CAM02-LCD / CAM02-SCD spaces proposed by Luo et al\n(2006).\n\nTo get started, simply write::\n\n from colorspacious import cspace_convert\n\n Jp, ap, bp = cspace_convert([64, 128, 255], \"sRGB255\", \"CAM02-UCS\")\n\nThis converts an sRGB value (represented as integers between 0-255) to\nCAM02-UCS `J'a'b'` coordinates (assuming standard sRGB viewing\nconditions by default). This requires passing through 4 intermediate\ncolorspaces; ``cspace_convert`` automatically finds the optimal route\nand applies all conversions in sequence:\n\nThis function also of course accepts arbitrary NumPy arrays, so\nconverting a whole image is just as easy as converting a single value.\n\nDocumentation:\n http://colorspacious.readthedocs.org/\n\nInstallation:\n ``pip install colorspacious``\n\nDownloads:\n https://pypi.python.org/pypi/colorspacious/\n\nCode and bug tracker:\n https://github.com/njsmith/colorspacious\n\nContact:\n Nathaniel J. Smith <njs@pobox.com>\n\nDependencies:\n * Python 2.6+, or 3.3+\n * NumPy\n\nDeveloper dependencies (only needed for hacking on source):\n * nose: needed to run tests\n\nLicense:\n MIT, see LICENSE.txt for details.\n\nReferences for algorithms we implement:\n * Luo, M. R., Cui, G., & Li, C. (2006). Uniform colour spaces based on\n CIECAM02 colour appearance model. Color Research & Application, 31(4),\n 320\u2013330. doi:10.1002/col.20227\n * Machado, G. M., Oliveira, M. M., & Fernandes, L. A. (2009). A\n physiologically-based model for simulation of color vision\n deficiency. Visualization and Computer Graphics, IEEE Transactions on,\n 15(6), 1291\u20131298. http://www.inf.ufrgs.br/~oliveira/pubs_files/CVD_Simulation/CVD_Simulation.html\n\nOther Python packages with similar functionality that you might want\nto check out as well or instead:\n\n* ``colour``: http://colour-science.org/\n* ``colormath``: http://python-colormath.readthedocs.org/\n* ``ciecam02``: https://pypi.python.org/pypi/ciecam02/\n* ``ColorPy``: http://markkness.net/colorpy/ColorPy.html\n\n\n",
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