example_20-01.cpp

//Example 20-01. Using K-means
#include <opencv2/opencv.hpp>
#include <iostream>

using namespace std;

static void help(char* argv[]) {
    cout << "\nThis program demonstrates kmeans clustering.\n"
            " It generates an image with random points, then assigns a random number\n"
            " of cluster centers and uses kmeans to move those cluster centers to their\n"
            " representative location\n"
            "Usage:\n"
         << argv[0] << "\n\n" 
         << "ESC or 'q' or 'Q' to quit\n\n"<< endl;
}

int main(int argc, char** argv) {
	help(argv);
    const int MAX_CLUSTERS = 5;
    cv::Scalar colorTab[] = {
        cv::Scalar( 0, 0, 255 ),
        cv::Scalar( 0, 255, 0 ),
        cv::Scalar( 255, 100, 100 ),
        cv::Scalar( 255, 0, 255 ),
        cv::Scalar( 0, 255, 255 )
    };
    cv::Mat img(500, 500, CV_8UC3);
    cv::RNG rng(12345);
    for(;;) {
        int clusterCount = rng.uniform(2, MAX_CLUSTERS+1);
        int sampleCount = rng.uniform(1, 1001);
        cv::Mat points(sampleCount, 1, CV_32FC2), labels;
        clusterCount = MIN(clusterCount, sampleCount);
        cv::Mat centers(clusterCount, 1, points.type());
        /* generate random sample from multigaussian distribution */
        for(int k = 0; k < clusterCount; k++) {
            cv::Point center;
            center.x = rng.uniform(0, img.cols);
            center.y = rng.uniform(0, img.rows);
            cv::Mat pointChunk = points.rowRange(
                        k*sampleCount/clusterCount,
                        k == clusterCount - 1 ? sampleCount : (k+1)*sampleCount/clusterCount
                                                );
            rng.fill(
                        pointChunk,
                        cv::RNG::NORMAL,
                        cv::Scalar(center.x, center.y),
                        cv::Scalar(img.cols*0.05, img.rows*0.05)
                        );
        }
        randShuffle(points, 1, &rng);
        kmeans(
                    points,
                    clusterCount,
                    labels,
                    cv::TermCriteria(
                        cv::TermCriteria::EPS | cv::TermCriteria::COUNT,
                        10,
                        1.0
                        ),
                    3,
                    cv::KMEANS_PP_CENTERS,
                    centers
              );
        img = cv::Scalar::all(0);
        for(int i = 0; i < sampleCount; i++) {
            int clusterIdx = labels.at<int>(i);
            cv::Point ipt = points.at<cv::Point2f>(i);
            cv::circle(img, ipt, 2, colorTab[clusterIdx], cv::FILLED, cv::LINE_AA);
        }
        cv::imshow("Example 20-01", img);
        char key = (char)cv::waitKey();
        if(key == 27 || key == 'q' || key == 'Q') // 'ESC'
            break;
    }
    return 0;
}