colorspacious

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

Converts colors between standard and specialized colorspaces (sRGB, XYZ, CIELab, CIECAM02, CAM02-UCS) and simulates color vision deficiency.

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

pip install colorspacious

uv add colorspacious

poetry add colorspacious

Installing colorspacious

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

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

Tags

More Graphics packages