Cluster Menggunakan metode K-means dengan opencv

kali ini mencoba melakukan cluster menggunakan metode k-means. cluster sendiri bertujuan untuk mengngelompokan dari suatu data yang memiliki kemiripan fitur antar setiap data, banyak implemntasi dari cluster ini yaitu pada Inteleggent transportationn system dll, berikut hasil dan source codenya....selamat mencoba (ika butuh penjelasan lebih lanjut silahkan hub via email).....
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hasilnya (silahkan perbesar)

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#include "opencv2/highgui/highgui.hpp"
#include "opencv2/core/core.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);
        Mat 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, CV_RAND_NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));
}
      randShuffle(points, 1, &rng);
       kmeans(points, clusterCount, labels,
       TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 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], CV_FILLED, CV_AA );
}
       imshow("clusters", img);
       char key = (char)waitKey();
       if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'
       break;
    }
       return 0;
 }
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hasilnya (silahkan perbesar)

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