k均值

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 << "
This program demonstrates kmeans clustering.
"
//             "It generates an image with random points, then assigns a random number of cluster
"
//             "centers and uses kmeans to move those cluster centers to their representitive location
"
//             "Call
"
//             "./kmeans
" << 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 << "
This program demonstrates kmeans clustering.
"
//             "It generates an image with random points, then assigns a random number of cluster
"
//             "centers and uses kmeans to move those cluster centers to their representitive location
"
//             "Call
"
//             "./kmeans
" << 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;
}