#include "opencv2/highgui/highgui.hpp" #include "opencv2/core/core.hpp" #include using namespace cv; using namespace std; 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(clusterCount, 1, points.type()); /* 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, ¢ers); img = Scalar::all(0); for( i = 0; i < sampleCount; i++ ) { int clusterIdx = labels.at(i); Point ipt = points.at(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; }