Algorithm to find the dominant color in an image.

image_dominant_color.py

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

image = Image.open('logo_newsblur_512.png')
NUM_CLUSTERS = 15

# 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] < low and code[1] < low and code[2] < low) or
                                 (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')

Nice algorithm. Do you have any suggestions on how to return the second most frequent color? This is for the case when black is the most frequent and you wish to ignore it.
Thank you for sharing!