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from PIL import Image
import scipy
import scipy.cluster
from pprint import pprint
image ='logo.png')
# 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]))
# 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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