added base files, updated readme

src/test-kmeans.cpp

@@ -0,0 +1,71 @@
+#include "opencv2/highgui.hpp"
+#include "opencv2/core.hpp"
+#include "opencv2/imgproc.hpp"
+#include <iostream>
+using namespace cv;
+using namespace std;
+// static void help()
+// {
+//     cout << "\nThis program demonstrates kmeans clustering.\n"
+//             "It generates an image with random points, then assigns a random number of cluster\n"
+//             "centers and uses kmeans to move those cluster centers to their representitive location\n"
+//             "Call\n"
+//             "./kmeans\n" << endl;
+// }
+int main( int /*argc*/, char** /*argv*/ )
+{
+    const int MAX_CLUSTERS = 5;
+    Scalar colorTab[] =
+    {
+        Scalar(0, 0, 255),
+        Scalar(0,255,0),
+        Scalar(255,100,100),
+        Scalar(255,0,255),
+        Scalar(0,255,255)
+    };
+    Mat img(500, 500, CV_8UC3);
+    RNG rng(12345);
+    for(;;)
+    {
+        int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
+        int i, sampleCount = rng.uniform(1, 1001);
+        Mat points(sampleCount, 1, CV_32FC2), labels;
+        clusterCount = MIN(clusterCount, sampleCount);
+        std::vector<Point2f> centers;
+        /* generate random sample from multigaussian distribution */
+        for( k = 0; k < clusterCount; k++ )
+        {
+            Point center;
+            center.x = rng.uniform(0, img.cols);
+            center.y = rng.uniform(0, img.rows);
+            Mat pointChunk = points.rowRange(k*sampleCount/clusterCount,
+                                             k == clusterCount - 1 ? sampleCount :
+                                             (k+1)*sampleCount/clusterCount);
+            rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
+        }
+        randShuffle(points, 1, &rng);
+        double compactness = kmeans(points, clusterCount, labels,
+            TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 10, 1.0),
+               3, KMEANS_PP_CENTERS, centers);
+        img = Scalar::all(0);
+        for( i = 0; i < sampleCount; i++ )
+        {
+            int clusterIdx = labels.at<int>(i);
+            Point ipt = points.at<Point2f>(i);
+            circle( img, ipt, 2, colorTab[clusterIdx], FILLED, LINE_AA );
+        }
+        for (i = 0; i < (int)centers.size(); ++i)
+        {
+            Point2f c = centers[i];
+            circle( img, c, 40, colorTab[i], 1, LINE_AA );
+        }
+        cout << "Compactness: " << compactness << endl;
+        cout << "Labels rows" << labels.rows;
+        cout << "Labels cols" << labels.cols;
+        imshow("clusters", img);
+        char key = (char)waitKey();
+        if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
+            break;
+    }
+    return 0;
+}