#!/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 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 = list(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()