opencv k-means

来源：互联网发布：手机监听监控定位软件编辑：程序博客网时间：2024/05/16 07:40

#include "opencv2/highgui.hpp"#include "opencv2/core.hpp"#include "opencv2/imgproc.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[] ={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;for (;;){int k, clusterCount = rng.uniform(2, MAX_CLUSTERS + 1);//聚类数int i, sampleCount = rng.uniform(100, 2000);//样点数Mat points(sampleCount, 1, CV_32FC2), labels;//建立一个2通道的一维数组clusterCount = MIN(clusterCount, sampleCount);//聚类数Mat centers;/* generate random sample from multigaussian distribution */for (k = 0; k < clusterCount; k++){Point center;center.x = rng.uniform(0, img.cols);//聚类中心的x坐标center.y = rng.uniform(0, img.rows);//聚类中心的y坐标Mat pointChunk = points.rowRange(k*sampleCount / clusterCount,//设置样本范围k == clusterCount - 1 ? sampleCount :(k + 1)*sampleCount / clusterCount);rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));//产生样本}randShuffle(points, 1, &rng);kmeans(points, clusterCount, labels,TermCriteria(TermCriteria::EPS + TermCriteria::COUNT, 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, 1, colorTab[clusterIdx], FILLED, LINE_AA);}imshow("clusters", img);char key = (char)waitKey();if (key == 27 || key == 'q' || key == 'Q') // 'ESC'break;}return 0;}

这个程序是opencv官方自带的源程序位置是opencv/sources/samples/cpp     kmeans.cpp定义了一个RNG 随机数产生器   Mat image用于显示分类结果  MAX_CLUSTERS 分类数目Scalar colorTab[] 因为分了5类多以用5种颜色表示uniform(a,b) 用于产生随机数,a,b表示产生随机数的范围sampleCount 产生的随机样点数

fill（）填充随机数，可以用高斯分布和均匀分布填充，如果采用均匀分布后边的参数表示范围，如果是高斯分布，后边的参数分别是均值和方差

<pre name="code" class="cpp">randShuffle（）将随机数打乱

kmeans（）第一个参数是样本数，第二个参数是聚类数，第三个参数是每个样本数的指数，例如样本中的第一个数属于第一类，对应的指数为1，第四个参数是终止条件，第五个参数是聚类次数，第六个参数是type这个可以查看opencv手册，第六个参数是聚类中心

0 0