from __future__ import print_function
import binascii
from PIL import Image
import numpy as np
import scipy
import scipy.misc
import scipy.cluster
import sys

NUM_CLUSTERS = 5

# print('reading image')
im = Image.open(sys.argv[1])
im = im.resize((150, 150))      # optional, to reduce time
ar = np.asarray(im)
shape = ar.shape
ar = ar.reshape(np.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 = np.histogram(vecs, len(codes))    # count occurrences

index_max = np.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))
print(colour)