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

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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; 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.



pip install colorspacious


Code and bug tracker:


Nathaniel J. Smith <>

  • Python 2.6+, or 3.3+

  • NumPy

Developer dependencies (only needed for hacking on source):
  • nose: needed to run tests


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.

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

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