K均值算法

来源：互联网发布：淘宝宝贝描述用什么做编辑：程序博客网时间：2024/05/21 21:37
1. K均值算法流程图
2. 模拟效果图
3. Learning Opencv的源码
#include <stdio.h>#include <stdlib.h>#include <ctype.h>#include "opencv2/opencv.hpp"#include <iostream>#include <opencv/cv.h>#include <opencv/cvaux.h>#include <opencv/cxcore.h>#include <opencv/highgui.h>int main( int argc, char** argv ){//类别个数的上界    #define MAX_CLUSTERS 5    CvScalar color_tab[MAX_CLUSTERS];    IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );    CvRNG rng = cvRNG(0xffffffff);    color_tab[0] = CV_RGB(255,0,0);    color_tab[1] = CV_RGB(0,255,0);    color_tab[2] = CV_RGB(100,100,255);    color_tab[3] = CV_RGB(255,0,255);    color_tab[4] = CV_RGB(255,255,0);    cvNamedWindow( "clusters", 1 );    for(;;)    {    //产生随机数        int k, cluster_count = cvRandInt(&rng)%MAX_CLUSTERS + 1;        int i, sample_count = cvRandInt(&rng)%1000 + 1;        CvMat* points = cvCreateMat( sample_count, 1, CV_32FC2 );        CvMat* clusters = cvCreateMat( sample_count, 1, CV_32SC1 );        /* generate random sample from multivariate           Gaussian distribution */        for( k = 0; k < cluster_count; k++ )        {            CvPoint center;            CvMat point_chunk;            center.x = cvRandInt(&rng)%img->width;            center.y = cvRandInt(&rng)%img->height;            /* Selects row span of the input array: arr(start_row:delta_row:end_row,:)                (end_row is not included into the span). */            //CVAPI(CvMat*) cvGetRows( const CvArr* arr, CvMat* submat,            //                        int start_row, int end_row,            //                         int delta_row CV_DEFAULT(1));            //得到某一行的值            cvGetRows( points, &point_chunk,                       k*sample_count/cluster_count,                       k == cluster_count - 1 ? sample_count :                       (k+1)*sample_count/cluster_count );            cvRandArr( &rng, &point_chunk, CV_RAND_NORMAL,                       cvScalar(center.x,center.y,0,0),                       cvScalar(img->width/6, img->height/6,0,0) );        }        /* shuffle samples */        for( i = 0; i < sample_count/2; i++ )        {            CvPoint2D32f* pt1 = (CvPoint2D32f*)points->data.fl +                                 cvRandInt(&rng)%sample_count;            CvPoint2D32f* pt2 = (CvPoint2D32f*)points->data.fl +                                 cvRandInt(&rng)%sample_count;            CvPoint2D32f temp;            CV_SWAP( *pt1, *pt2, temp );        }        cvKMeans2( points, cluster_count, clusters,                   cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,                                   10, 1.0 ));        cvZero( img );        for( i = 0; i < sample_count; i++ )        {            CvPoint2D32f pt = ((CvPoint2D32f*)points->data.fl)[i];            int cluster_idx = clusters->data.i[i];            cvCircle( img, cvPointFrom32f(pt), 2,                      color_tab[cluster_idx], CV_FILLED );        }        cvReleaseMat( &points );        cvReleaseMat( &clusters );        cvShowImage( "clusters", img );        int key = cvWaitKey(0);        if( key == 27 ) // 'ESC'            break;    }}
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