samples/python/snippets/kmeans.py

An example on k-means clustering in python

#!/usr/bin/env python
 
'''
K-means clusterization sample.
Usage:
   kmeans.py
 
Keyboard shortcuts:
   ESC   - exit
   space - generate new distribution
'''
 
# Python 2/3 compatibility
from __future__ import print_function
 
import numpy as np
import cv2 as cv
 
from gaussian_mix import make_gaussians
 
def main():
    cluster_n = 5
    img_size = 512
 
    # 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 = cv.cvtColor(colors, cv.COLOR_HSV2BGR)[0]
 
    while True:
        print('sampling distributions...')
        points, _ = make_gaussians(cluster_n, img_size)
 
        term_crit = (cv.TERM_CRITERIA_EPS, 30, 0.1)
        _ret, labels, _centers = cv.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 = list(map(int, colors[label]))
 
            cv.circle(img, (x, y), 1, c, -1)
 
        cv.imshow('kmeans', img)
        ch = cv.waitKey(0)
        if ch == 27:
            break
 
    print('Done')
 
 
if __name__ == '__main__':
    print(__doc__)
    main()
    cv.destroyAllWindows()