The author's approach is really good, and he hits on pretty much all the problems that arise from more naive approaches. In particular, using a perceptual colorspace, and how the most representative colour may not be the one that appears the most.

However, image processing makes my neck tingle because there are a lot of footguns. PNG bombs, anyone? I feel like any library needs to either be defensively programmed or explicit in its documentation.

The README says "Finding main colors of a reasonably sized image takes about 100ms" -- that's way too slow. I bet the operation takes a few hundred MB of RAM too.

For anyone that uses this, scale down your images substantially first, or only sample every N pixels. Avoid loading the whole thing into memory if possible, unless this handled serially by a job queue of some sort.

You can operate this kind of algorithm much faster and with less RAM usage on a small thumbnail than you would on a large input image. This makes performance concerns less of an issue. And prevents a whole class of OOM DoS vulnerabilities!

As a defensive step, I'd add something like this https://github.com/iamcalledrob/saferimg/blob/master/asset/p... to your test suite and see what happens.

This is a render from Second Life, in which all the texture images were shrunk down to one pixel, the lowest possible level of detail, producing a monocolor image. For distant objects, or for objects where the texture is still coming in from the net, there needs to be some default color. The existing system used grey for everything. I tried using an average of all the pixels, and, as the original poster points out, the result looks murky.[1] This new approach has real promise for big-world rendering.

[1] https://media.invisioncic.com/Mseclife/monthly_2023_05/monoc...

The SpotifyPlus HA integration [2] was near at hand and does a reasonably good job clustering with a version of ColorThief [3] under the hood. It has the same two problems you started with though: muddying when there's lots of gradation, even within a cluster; and no semantic understanding when the cover has something resembling a frame. A bit swapped from okmain's goal, but I can invert with the best of them and will give it a shot next time I fiddle. Thanks for posting!

[1] https://gist.github.com/kristjan/b305b83b0eb4455ee8455be108a... [2] https://github.com/thlucas1/homeassistantcomponent_spotifypl... [3] https://github.com/thlucas1/SpotifyWebApiPython/blob/master/...

[1] https://engineering.fb.com/2015/08/06/android/the-technology...

So, making a library that provides an alternative is a great service to the world, haha.

An additional feature that might be nice: the most prominent colors seem like they might be a bad pick in some cases, if you want the important part of the image to stand out. Maybe a color that is the close (in the color space) to the edges of your image, but far away (in the color space) from the center of your image could be interesting?

How is it "simple"? There are like a ton of different downscaling algorithms and each of them might produce a different result.

Cool article otherwise.

The blurred mirror is inoffensive to almost everyone, and yet it always strikes me as gauche. Easy to ignore and yet I feel that it adds a lot of useless visual noise.