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colorspacious·PyPI

Project 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 <njs@pobox.com>

Dependencies:
Developer dependencies (only needed for hacking on source):
License:

MIT, see LICENSE.txt for details.

References for algorithms we implement:

Other Python packages with similar functionality that you might want to check out as well or instead:

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