Kmeans算法实现

转载地址：http://www.cnblogs.com/tornadomeet/archive/2012/11/23/2783709.html

#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[] =     //因为最多只有5类，所以最多也就给5个颜色    {        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); //随机数产生器    for(;;)    {        int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);        int i, sampleCount = rng.uniform(1, 1001);        Mat points(sampleCount, 1, CV_32FC2), labels;   //产生的样本数，实际上为2通道的列向量，元素类型为Point2f        clusterCount = MIN(clusterCount, sampleCount);        Mat centers(clusterCount, 1, points.type());    //用来存储聚类后的中心点        /* generate random sample from multigaussian distribution */        for( k = 0; k < clusterCount; k++ ) //产生随机数        {            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);   //最后一个类的样本数不一定是平分的，                                                                                //剩下的一份都给最后一类            //每一类都是同样的方差，只是均值不同而已            rng.fill(pointChunk, CV_RAND_NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));        }        randShuffle(points, 1, &rng);   //因为要聚类，所以先随机打乱points里面的点，注意points和pointChunk是共用数据的。        kmeans(points, clusterCount, labels,               TermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0),               3, KMEANS_PP_CENTERS, centers);  //聚类3次，取结果最好的那次，聚类的初始化采用PP特定的随机算法。        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();     //无限等待        if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'            break;    }    return 0;}