["python","image","image-processing","colors"]
How to find the dominant/most common color in an image?

I'm looking for a way to find the most dominant color/tone in an image using python. Either the average shade or the most common out of RGB will do. I've looked at the Python Imaging library, and could not find anything relating to what I was looking for in their manual, and also briefly at VTK. I did however find a PHP script which does what I need, here (login required to download). The script seems to resize the image to 150*150, to bring out the dominant colors. However, after that, I am fairly lost. I did consider writing something that would resize the image to a small size then check every other pixel or so for it's image, though I imagine this would be very inefficient (though implementing this idea as a C python module might be an idea). However, after all of that, I am still stumped. Is there an easy, efficient way to find the dominant color in an image?
Here's code making use of Pillow and Scipy's cluster package. For simplicity I've hardcoded the filename as "image.jpg". Resizing the image is for speed: if you don't mind the wait, comment out the resize call. When run on this sample image, it usually says the dominant colour is #d8c865, which corresponds roughly to the bright yellowish area to the lower left of the two peppers. I say "usually" because the clustering algorithm used has a degree of randomness to it. There are various ways you could change this, but for your purposes it may suit well. (Check out the options on the kmeans2() variant if you need deterministic results.) from __future__ import print_function import binascii import struct from PIL import Image import numpy as np import scipy import scipy.misc import scipy.cluster NUM_CLUSTERS = 5 print('reading image') im = Image.open('image.jpg') im = im.resize((150, 150)) # optional, to reduce time ar = np.asarray(im) shape = ar.shape ar = ar.reshape(scipy.product(shape[:2]), shape[2]).astype(float) print('finding clusters') codes, dist = scipy.cluster.vq.kmeans(ar, NUM_CLUSTERS) print('cluster centres:\n', codes) vecs, dist = scipy.cluster.vq.vq(ar, codes) # assign codes counts, bins = scipy.histogram(vecs, len(codes)) # count occurrences index_max = scipy.argmax(counts) # find most frequent peak = codes[index_max] colour = binascii.hexlify(bytearray(int(c) for c in peak)).decode('ascii') print('most frequent is %s (#%s)' % (peak, colour)) Note: when I expand the number of clusters to find from 5 to 10 or 15, it frequently gave results that were greenish or bluish. Given the input image, those are reasonable results too... I can't tell which colour is really dominant in that image either, so I don't fault the algorithm! Also a small bonus: save the reduced-size image with only the N most-frequent colours: # bonus: save image using only the N most common colours import imageio c = ar.copy() for i, code in enumerate(codes): c[scipy.r_[scipy.where(vecs==i)],:] = code imageio.imwrite('clusters.png', c.reshape(*shape).astype(np.uint8)) print('saved clustered image')