Today is the 10th anniversary of Colour and colour-science!
(colour-science-py3.12) Eris:colour kelsolaar$ git log --reverse commit 90bc42b9fedfc7291c7023247eab14b41d5c29af Author: Thomas Mansencal <thomas.mansencal@gmail.com> Date: Sat Apr 5 14:07:48 2014 +0200 Initial commit. Signed-off-by: Thomas Mansencal <thomas.mansencal@gmail.com>
It is time for a decade of work retrospective.
We have released a new version of Colour - Checker Detection that implements a new machine learning inference method to detect colour rendition charts, specifically the ColorChecker Classic 24 from X-Rite / Calibrite.
This post describes the journey to produce the model and package it with Colour - Checker Detection.
The colour-science Developers are pleased to announce the release of Colour 0.4.4!
The colour-science Developers are pleased to announce the release of Colour 0.4.3!
We are pleased to announce that Colour 0.4.2 has been released earlier this week!
We are pleased to announce that after over a year of work, Colour 0.4.0 has been released!
This post will be about colour rendition charts, e.g. X-Rite ColorChecker Classic, their usage and its dangers. It was a long time coming and some discussions accelerated the authoring. If you are in a hurry and do not want to read the entire post, the TLDR is as follows:
Unless you have all the relevant spectral measurements, a colour rendition chart should not be used to perform colour-correction of camera imagery but only for white balancing and relative exposure adjustments.
There is granularity in everything, and we will present, in the conclusion, some contexts when performing colour-correction is a good idea.
Colour 0.3.16 has finally been released!
Earlier this week, Colour received its 1000th Github star ⭐ and we are proud of the achievement!
We would not be here without the help of our users, sponsors, contributors and NumFOCUS, so thank you all!
Validation against ground truth data is an important step when implementing support for physical lighting quantities in a realtime or offline renderer.
In this post, a simple but effective method to assess that the physical camera model behaves as expected against ground truth data (or conversely) will be presented.