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colorspacious

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

With conditionsPyPI GraphicsReleased Apr 2018142.3K downloads / moMITPure Python

Decision gist · record as of 2026-08-14

pure-Python wheel — colorspacious-1.1.2-py2.py3-none-any.whl
v1.1.2 · released 2018-04-08 · 1 runtime deps: numpy

Yes, if you need colorspace conversion and color vision deficiency simulation and can accept an unmaintained package. The library is stable and has no known vulnerabilities, but verify compatibility with your current NumPy and Python versions before relying on it in production. For new projects, consider alternatives like the colour package if you need ongoing maintenance.AI-flagged interpretation of the facts on this page — verify before relying

Before you install

  • Low install friction with a single NumPy dependency.
  • However, the package is abandoned—last release was 2018-04-08 and last commit 2019-11-12—so no maintenance or bug fixes should be expected going forward.

License · maintenance · safety

MIT (permissive) — MIT license is permissive, allowing commercial and private use with minimal restrictions.

last release 2018-04-08 (3050 days) · last repo commit 2019-11-12 · 186 stars

0 known vulnerabilities (OSV.dev, 2026-08-14) · 142,331 downloads/mo, #11,211 on PyPI

Verify before relying

pip install colorspacious

from colorspacious import cspace_convert
Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS")
  • Whether the package works reliably with current NumPy versions (last tested in 2019)
  • Whether Python 2 support is still relevant or if Python 3 is the practical baseline
  • Whether color vision deficiency simulations match current research standards
Same gist for agents: .md · .json

What it is and what it does

Colorspacious converts colors between a range of colorspaces, from common standards like sRGB and XYZ to specialized perceptually uniform spaces like CIECAM02 and CAM02-UCS. It also includes simulations of color vision deficiency based on the Machado et al approach. The library automatically finds the optimal conversion path through intermediate colorspaces and applies all transformations in sequence, working with both single values and NumPy arrays.

The package depends only on NumPy and installs with low friction. However, it is abandoned—the last release was in 2018 and the last commit in 2019—so it will not receive updates, bug fixes, or compatibility maintenance. It remains functional for its core use case but carries the risk of incompatibility with future Python or NumPy versions.

Use it for

  • Convert RGB image pixels to perceptually uniform spaces for color analysis or image processing
  • Simulate how colors appear to people with color vision deficiency for accessibility testing
  • Transform colors between standard colorspaces (sRGB, XYZ, LAB) for color science workflows
  • Batch-convert large image arrays across multiple colorspaces with automatic routing

Worth the install?

AI-flagged interpretation of the facts on this page. Verify before relying on it.

With conditions

Yes, if you need colorspace conversion and color vision deficiency simulation and can accept an unmaintained package.

The library is stable and has no known vulnerabilities, but verify compatibility with your current NumPy and Python versions before relying on it in production. For new projects, consider alternatives like the colour package if you need ongoing maintenance.

Install

colorspacious on PyPI

Before you install

Low install friction with a single NumPy dependency. However, the package is abandoned—last release was 2018-04-08 and last commit 2019-11-12—so no maintenance or bug fixes should be expected going forward.

License in practice

MIT license is permissive, allowing commercial and private use with minimal restrictions.

Quickstart

pip install colorspacious

from colorspacious import cspace_convert
Jp, ap, bp = cspace_convert([64, 128, 255], "sRGB255", "CAM02-UCS")

Verify before relying

  • Whether the package works reliably with current NumPy versions (last tested in 2019)
  • Whether Python 2 support is still relevant or if Python 3 is the practical baseline
  • Whether color vision deficiency simulations match current research standards

Package facts

LicenseMIT permissive
Python supportNot specified
Install frictionLow. Pure-Python wheel
Runtime dependencies
1 package
numpy
MaintenanceAbandoned 3,050 days since the last release
Last repo commit
First released
Downloads142,331 / month, #11,211 on PyPI 30-day window, as of 2026-08-14
Known vulnerabilitiesNone known OSV.dev, checked 2026-08-14
Classifiers
Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 2Programming Language :: Python :: 3

Evidence: colorspacious-1.1.2-py2.py3-none-any.whl

Tags

Capabilities
colorspace conversioncolor space transformationsRGB to LAB conversionCIECAM02 color appearancecolor blindness simulationcolor vision deficiencyperceptually uniform color spaces
Topics
color-scienceimage-processingabandoned

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See also colour-science · coloraide · colormath · cmcrameri · cmweather · colour · colorcet · colorzero · color-matcher · distinctipy