"atomic bomb" commit. Reorganized OpenCV directory structure

samples/python/kmeans.py

@@ -0,0 +1,60 @@
+#!/usr/bin/python
+import urllib2
+import cv
+from random import randint
+MAX_CLUSTERS = 5
+
+if __name__ == "__main__":
+
+    color_tab = [
+        cv.CV_RGB(255, 0,0),
+        cv.CV_RGB(0, 255, 0),
+        cv.CV_RGB(100, 100, 255),
+        cv.CV_RGB(255, 0,255),
+        cv.CV_RGB(255, 255, 0)]
+    img = cv.CreateImage((500, 500), 8, 3)
+    rng = cv.RNG(-1)
+
+    cv.NamedWindow("clusters", 1)
+        
+    while True:
+        cluster_count = randint(2, MAX_CLUSTERS)
+        sample_count = randint(1, 1000)
+        points = cv.CreateMat(sample_count, 1, cv.CV_32FC2)
+        clusters = cv.CreateMat(sample_count, 1, cv.CV_32SC1)
+        
+        # generate random sample from multigaussian distribution
+        for k in range(cluster_count):
+            center = (cv.RandInt(rng)%img.width, cv.RandInt(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 = cv.GetRows(points, first, last)
+                        
+            cv.RandArr(rng, point_chunk, cv.CV_RAND_NORMAL,
+                       cv.Scalar(center[0], center[1], 0, 0),
+                       cv.Scalar(img.width*0.1, img.height*0.1, 0, 0))
+        
+
+        # shuffle samples 
+        cv.RandShuffle(points, rng)
+
+        cv.KMeans2(points, cluster_count, clusters,
+                   (cv.CV_TERMCRIT_EPS + cv.CV_TERMCRIT_ITER, 10, 1.0))
+
+        cv.Zero(img)
+
+        for i in range(sample_count):
+            cluster_idx = int(clusters[i, 0])
+            pt = (cv.Round(points[i, 0][0]), cv.Round(points[i, 0][1]))
+            cv.Circle(img, pt, 2, color_tab[cluster_idx], cv.CV_FILLED, cv.CV_AA, 0)
+
+        cv.ShowImage("clusters", img)
+
+        key = cv.WaitKey(0) % 0x100
+        if key in [27, ord('q'), ord('Q')]:
+            break
+    
+    cv.DestroyWindow("clusters")