As a designer, I've built variants of this several times throughout my career.

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.

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jaen an hour ago | parent | next [-]

I really wish people would read the article, the library does exactly this:

> Okmain downsamples the image by a power of two until the total number of pixels is below 250,000.

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iamcalledrob an hour ago | parent | next [-]

Somehow I missed that, oops. I see that the library samples a maximum of 250K pixels from the input buffer (I jumped over to the project readme)

That being said, this is sampling the fixed-size input buffer for the purposes of determining the right colour. You still have to load the bitmap into memory, with all the associated footguns that arise there. The library just isn't making it worse :) I suppose you could memmap it.

Makes me wonder if the sub-sampling is actually a bit of a red herring, as ideally you'd want to be operating on a small input buffer anyway. Or some sort of interface on top of the raw pixel data, so you can load what's needed on-demand.

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vasco an hour ago | parent | prev [-]

That's 500x500, I'm sure you can get good results at 32x32 or 64x64 but then part of your color choice is also getting done by the downsampling algorithm. I wonder if you could get away with just using a downsampling algorithm into a 1x1 and just use that as the main color.

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PaulHoule an hour ago | parent [-]

That last one is talked about in the article -- it sucks!

I think if you were going to "downsample" for the purpose of creating a color set you could just scan through the picture and randomly select 10% (or whatever) of the pixels and apply k-means to that and not do any averaging which costs resources and makes your colors muddy.

	▲	dgroshev 3 minutes ago \| parent [-]
		That seems sensible, but unfortunately doesn't work well. I just answered over at lobsters: https://lobste.rs/s/t43mh5/okmain_you_have_image_you_want_co... I should probably add this nuance to the post itself.

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latexr 2 hours ago | parent | prev [-]

> I've built variants of this several times throughout my career.

Got any to share? A self-contained command-line tool to get a good palette from an image is something I’d have a use for.

	▲	dylan604 2 minutes ago \| parent \| next [-]
		Fred's dominantcolor script for imagemagick might work for you: https://www.fmwconcepts.com/imagemagick/dominantcolor/index....
	▲	PaulHoule an hour ago \| parent \| prev [-]
		Back in the late 1980s people thought about color quantization a lot because a lot of computers of the time had 16 or 256 colors you could choose out of a larger palette and if you chose well you could do pretty well with photographic images.