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diff --git a/samples/swig_python/kmeans.py b/samples/swig_python/kmeans.py
new file mode 100755
index 0000000..75261c6
--- /dev/null
+++ b/samples/swig_python/kmeans.py
@@ -0,0 +1,66 @@
+#!/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" )