cCIEDE2000

Python package for computing the CIE Delta Empfindung with a C submodule.

What this Code does

This Code is a Python wrapper around a C implementation of the CIEDE2000 algorithm.

Documentation

Installation

This project is not on PyPI.

pip install git+https://github.com/jkschluesener/cCIEDE2000.git@main

Import

from cCIEDE2000 import deltaE_min, deltaE_matrix

deltaE_min

# both gamut and pixel should be in CIELAB colorspace.
gamut = np.random.rand(2**24, 3) # pseudo screen gamut
pixel = np.random.rand(3) # pseudo pixel

lowest_deltaE: int = deltaE_min(gamut, pixel)

deltaE_matrix

from numpy.typing import ArrayLike # only for typing

# both gamut and pixel should be in CIELAB colorspace.
gamut = np.random.rand(2**24, 3) # pseudo screen gamut
pixel = np.random.rand(3) # pseudo pixel

deltaE_px_gamut: ArrayLike = deltaE_min(gamut, pixel)

Motivation

Motivation for this small project:

• Exploring the possibility of a faster implementation of the CIEDE2000 algorithm for another project
• Comparing the execution speed and memory consumption of a Python wrapper around C, compared to a vectorized Numpy function
• Create a blueprint for how to wrap a C function in Python, handing over pointers to arrays and compiling C through python on pip install

🤔 Check benchmarking.ipynb for benchmarking results.
Short Summary: Non-exhaustive evaluation showed that the C implementation is faster and more memory efficient than the vectorized numpy function deltaE_ciede2000 from scikit-image, at least for very large color gamuts.

Attribution

The C functions herein were taken from: https://github.com/gfiumara/CIEDE2000/

MIT License

Commit Hash af1de42515f3916c16e75980a4635962489af56a from 2017-Oct-26, accessed 2023-Sep-26.

I did some minor modifications of the code and included some C wrapper functions.