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Using python and k-means to find dominant colors in images

charlesleifer.com

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Re: Using python and k-means to find dominant colors in images

#3

Post by Niten »

Cool stuff... Andrew Ng's excellent Machine Learning course on Coursera also had a programming exercise that involved using k-means to reduce an image's palette (in Octave, though, rather than Python), so if you find this interesting you might consider signing up for his course the next time it comes around:

https://www.coursera.org/course/ml

Re: Using python and k-means to find dominant colors in images

#5

Post by coleifer »

I thought I'd share some good links that came up in the reddit discussion:

Color theme generator for Xresources and more: https://gist.github.com/3946121

Reference to similar features in scipy: http://docs.scipy.org/doc/scipy/reference/cluster.vq.html

Old-school computer graphics w/kmeans: http://pragprog.com/magazines/2011-12/revisiting-graphics-ha...

There was also some discussion on using different color spaces than RGB to get better results, lab*, HSL, YUV, etc. And recommending using something like Numpy to make this shit hum.

Re: Using python and k-means to find dominant colors in images

#6

Post by sprobertson »

Cool, I did something like this for an e-commerce store so that I could programmatically sort new products into color bins. Scipy handled the kmeans stuff for me though.

One problem I ran into but never solved was ignoring the background color. For example in the second picture it might be more interesting to bring out the oranges of the tail-lights and the light-blues of the street lights, rather than just the dark blues of the roads. You could ignore the largest cluster, but that's not always necessarily the background color. Have you thought about this at all?

Re: Using python and k-means to find dominant colors in images

#8

Post by coleifer »

sprobertson wrote: post #6

Cool, I did something like this for an e-commerce store so that I could programmatically sort new products into color bins. Scipy handled the kmeans stuff for me though. One problem I ran into but never solved was ignoring the background color. For example in the second picture it might be more interesting to bring out the oranges of the tail-lights and the light-blues of the street lights, rather than just the dark…

I myself did not think of this, but a thoughtful commenter on my site suggested that the cluster furthest from the center might be a good indicator of the background color, and the cluster nearest the center the foreground.

Re: Using python and k-means to find dominant colors in images

#9

Post by bduerst »

sprobertson wrote: post #6

Cool, I did something like this for an e-commerce store so that I could programmatically sort new products into color bins. Scipy handled the kmeans stuff for me though. One problem I ran into but never solved was ignoring the background color. For example in the second picture it might be more interesting to bring out the oranges of the tail-lights and the light-blues of the street lights, rather than just the dark…

Just a thought: If it's a photograph, you could use the areas of highest contrast to identify and outline the shape of the subject - assuming the photograph was focused. This then becomes the working area for your color selection.

Either that, or be sure that the background is consistent every time you take the picture for sorting into the color bins. It may be cheaper than trying to create a catch-all solution.

Re: Using python and k-means to find dominant colors in images

#10

Post by conesus »

Awesome, I used this technique to find the dominant color in favicons so I could create a gradient in NewsBlur.

See the effect here: http://cl.ly/KElb

Here's the Python I use to do it: https://github.com/samuelclay/NewsBlur/blob/master/utils/Ima...

    from PIL import Image
    import scipy
    import scipy.cluster
    from pprint import pprint

    image = Image.open('logo.png')
    NUM_CLUSTERS = 5

    # Convert image into array of values for each point.
    ar = scipy.misc.fromimage(image)
    shape = ar.shape

    # Reshape array of values to merge color bands.
    if len(shape) > 2:
        ar = ar.reshape(scipy.product(shape[:2]), shape[2])

    # Get NUM_CLUSTERS worth of centroids.
    codes, _ = scipy.cluster.vq.kmeans(ar, NUM_CLUSTERS)

    # Pare centroids, removing blacks and whites and shades of really dark and really light.
    original_codes = codes
    for low, hi in [(60, 200), (35, 230), (10, 250)]:
        codes = scipy.array([code for code in codes 
                             if not ((code[0]  hi and code[1] > hi and code[2] > hi))])
        if not len(codes): codes = original_codes
        else: break

    # Assign codes (vector quantization). Each vector is compared to the centroids
    # and assigned the nearest one.
    vecs, _ = scipy.cluster.vq.vq(ar, codes)

    # Count occurences of each clustered vector.
    counts, bins = scipy.histogram(vecs, len(codes))

    # Show colors for each code in its hex value.
    colors = [''.join(chr(c) for c in code).encode('hex') for code in codes]
    total = scipy.sum(counts)
    color_dist = dict(zip(colors, [count/float(total) for count in counts]))
    pprint(color_dist)

    # Find the most frequent color, based on the counts.
    index_max = scipy.argmax(counts)
    peak = codes[index_max]
    color = ''.join(chr(c) for c in peak).encode('hex')
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