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#!/usr/bin/env python
'''
K-means clusterization sample.
Usage:
kmeans.py
Keyboard shortcuts:
ESC - exit
space - generate new distribution
'''
import numpy as np
import cv2
from gaussian_mix import make_gaussians
if __name__ == '__main__':
cluster_n = 5
img_size = 512
print __doc__
# generating bright palette
colors = np.zeros((1, cluster_n, 3), np.uint8)
colors[0,:] = 255
colors[0,:,0] = np.arange(0, 180, 180.0/cluster_n)
colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
while True:
print 'sampling distributions...'
points, _ = make_gaussians(cluster_n, img_size)
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
ret, labels, centers = cv2.kmeans(points, cluster_n, None, term_crit, 10, 0)
img = np.zeros((img_size, img_size, 3), np.uint8)
for (x, y), label in zip(np.int32(points), labels.ravel()):
c = map(int, colors[label])
cv2.circle(img, (x, y), 1, c, -1)
cv2.imshow('gaussian mixture', img)
ch = 0xFF & cv2.waitKey(0)
if ch == 27:
break
cv2.destroyAllWindows()