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All Projects → njsmith → Colorspacious

njsmith / Colorspacious

Licence: mit
A powerful, accurate, and easy-to-use Python library for doing colorspace conversions

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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 [email protected]

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:

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