--- /dev/null
+#!/usr/bin/python
+from opencv.cv import *
+from opencv.highgui import *
+from random import randint
+MAX_CLUSTERS = 5
+
+if __name__ == "__main__":
+
+ color_tab = [
+ CV_RGB(255,0,0),
+ CV_RGB(0,255,0),
+ CV_RGB(100,100,255),
+ CV_RGB(255,0,255),
+ CV_RGB(255,255,0)]
+ img = cvCreateImage( cvSize( 500, 500 ), 8, 3 )
+ rng = cvRNG(-1)
+
+ cvNamedWindow( "clusters", 1 )
+
+ while True:
+ cluster_count = randint(2, MAX_CLUSTERS)
+ sample_count = randint(1, 1000)
+ points = cvCreateMat( sample_count, 1, CV_32FC2 )
+ clusters = cvCreateMat( sample_count, 1, CV_32SC1 )
+
+ # generate random sample from multigaussian distribution
+ for k in range(cluster_count):
+ center = CvPoint()
+ center.x = cvRandInt(rng)%img.width
+ center.y = cvRandInt(rng)%img.height
+ first = k*sample_count/cluster_count
+ last = sample_count
+ if k != cluster_count:
+ last = (k+1)*sample_count/cluster_count
+
+ point_chunk = cvGetRows(points, first, last)
+
+ cvRandArr( rng, point_chunk, CV_RAND_NORMAL,
+ cvScalar(center.x,center.y,0,0),
+ cvScalar(img.width*0.1,img.height*0.1,0,0))
+
+
+ # shuffle samples
+ cvRandShuffle( points, rng )
+
+ cvKMeans2( points, cluster_count, clusters,
+ cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ))
+
+ cvZero( img )
+
+ for i in range(sample_count):
+ cluster_idx = clusters[i]
+ # a multi channel matrix access returns a scalar of
+ #dimension 4,0, which is not considerate a cvPoint
+ #we have to create a tuple with the first two elements
+ pt = (cvRound(points[i][0]), cvRound(points[i][1]))
+ cvCircle( img, pt, 2, color_tab[cluster_idx], CV_FILLED, CV_AA, 0 )
+
+ cvShowImage( "clusters", img )
+
+ key = cvWaitKey(0)
+ if( key == 27 or key == 'q' or key == 'Q' ): # 'ESC'
+ break
+
+
+ cvDestroyWindow( "clusters" )