---
id: colorspacious
version: "1.1.2"
license: MIT
license_treatment: permissive
maintenance: abandoned
---

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

License: permissive · Maintenance: abandoned · Downloads: 142.3K/mo

## 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 above — 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

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

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: abandoned
- Downloads: 142.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

colorspace conversion, color space transformation, sRGB to LAB conversion, CIECAM02 color appearance, color blindness simulation, color vision deficiency, perceptually uniform color spaces, color-science, image-processing, abandoned

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