算法流程如下：
1.输入数据集合和类别数K（由用户指定）。
2.随机分配类别中心点的位置。
3.将每个店放入离它最近的类别中心点所在的集合。
4.移动类别中心点到他所在集合的中心。
5.转到第三步，直到收敛。
opencv里提供的实例代码如下：
#include "StdAfx.h"
#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 = 8; //类别个数上限
Scalar colorTab[] = //返回的类别显示的颜色
{
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);
for(;;)
{
int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);//类别个数随机产生
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;
}
opencv实例代码中随机数占用了太多篇幅，不利用更快理解k均值算法，可以自己写一组数多进行测试感受下，比如：
#include "StdAfx.h"
#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 = 8; //类别个数上限
Scalar colorTab[] = //返回的类别显示的颜色
{
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);
//for (;;)
//{
int k, clusterCount =3/* rng.uniform(2, MAX_CLUSTERS + 1)*/;//类别个数随机产生
int i, sampleCount = 6/*rng.uniform(1, 1001)*/;
Mat points(sampleCount, 1, CV_32FC2), labels;
//struct point_xy
//{
//};
Point2f point_xy[6], center;
center.x = 300;
center.y =300;
point_xy[0].x = 100 + center.x;
point_xy[0].y = 100 + center.y;
point_xy[1].x = 110 + center.x;
point_xy[1].y = 120 + center.y;
point_xy[2].x = 1 + center.x;
point_xy[2].y = 1 + center.y;
point_xy[3].x = 120 + center.x;
point_xy[3].y = 120 + center.y;
point_xy[4].x = 169 + center.x;
point_xy[4].y = 140 + center.y;
point_xy[5].x = 130 + center.x;
point_xy[5].y = 130 + center.y;
for (int j = 0; j < sampleCount;j++)
{
point_xy[j].x = point_xy[j].x;
point_xy[j].y = point_xy[j].y;
}
for (int j = 0; j < sampleCount; j++)
{
points.at<Point2f>(j).x = point_xy[j].x ;
points.at<Point2f>(j).y = point_xy[j].y ;
}
for (int j = 0; j < sampleCount; j++)
{
points.at<Point2f>(j) = point_xy[j];
}
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();
return 0;
}
原文：http://www.cnblogs.com/begoogatprogram/p/5728652.html