added kmeans.py sample

samples/python2/kmeans.py

+'''
+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, 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()