colorspacious

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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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