k-means算法MATLAB和opencv代码

上一篇博客写了k-means聚类算法和改进的k-means算法。这篇博客就贴出相应的MATLAB和C++代码。

下面是MATLAB代码，实现用k-means进行切割：

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    功能：实现怎样利用Kmeans聚类实现图像的切割。

    时间：2015-07

    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

    function kmeans_segmentation()

    clear;close all;clc;

    %% 读取測试图像

    im = imread('city.jpg');

    imshow(im), title('Imput image');  %%转换图像的颜色空间得到样本

    cform = makecform('srgb2lab');

    lab = applycform(im,cform);

    ab = double(lab(:,:,2:3));

    nrows = size(lab,1); ncols = size(lab,2);

    X = reshape(ab,nrows*ncols,2)';

          figure, scatter(X(1,:)',X(2,:)',3,'filled'),title('image 2');  box on; %显示颜色空间转换后的二维样本空间分布

    %% 对样本空间进行Kmeans聚类

    k = 5; % 聚类个数

    max_iter = 100; %最大迭代次数

    [centroids, labels] = run_kmeans(X, k, max_iter);

    %% 显示聚类切割结果

    figure, scatter(X(1,:)',X(2,:)'3,labels,'filled'),title('image 3'); %显示二维样本空间聚类效果

    hold on; scatter(centroids(1,:),centroids(2,:), 60,'r','filled')

    hold on; scatter(centroids(1,:),centroids(2,:),30,'g','filled')

    box on; hold off;

    %print -dpdf 2D2.pdf

    pixel_labels = reshape(labels,nrows,ncols);

    rgb_labels = label2rgb(pixel_labels);

    figure, imshow(rgb_labels), title('Segmented Image');

    %print -dpdf Seg.pdf

    end

    function [centroids, labels] = run_kmeans(X, k, max_iter)

    % 该函数实现Kmeans聚类

    % 输入參数：

    %                   X为输入样本集，dxN

    %                   k为聚类中心个数

    %                   max_iter为kemans聚类的最大迭代的次数

    % 输出參数：

    %                   centroids为聚类中心 dxk

    %                   labels为样本的类别标记

    %% 採用K-means++算法初始化聚类中心

    centroids = X(:,1+round(rand*(size(X,2)-1)));

    labels = ones(1,size(X,2));

    for i = 2:k

        D = X-centroids(:,labels);

        D = cumsum(sqrt(dot(D,D,1)));

        if D(end) == 0, centroids(:,i:k) = X(:,ones(1,k-i+1)); return; end

        centroids(:,i) = X(:,find(rand < D/D(end),1));

        [~,labels] = max(bsxfun(@minus,2*real(centroids'*X),dot(centroids,centroids,1).'));

      end

    %% 标准Kmeans算法

     for iter = 1:max_iter

        for i = 1:k, l = labels==i; centroids(:,i) = sum(X(:,l),2)/sum(l); end

        [~,labels] = max(bsxfun(@minus,2*real(centroids'*X),dot(centroids,centroids,1).'),[],1);

    end

    end

实现效果例如以下：

上图图一是一张帅哥刘德华的JPG格式相片。图二是将RGB空间的图片转换为LAB空间的分布图；图三是对LAB空间的图像进行聚类，一共三类，图四是将聚类后的LAB图转换为原来的RGB图。

下面是VS+opencv实现:

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/core/core.hpp"

#include <iostream>

using namespace cv;

using namespace std;

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(12345);

    for(;;)

    {

        int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);

        int i, sampleCount = rng.uniform(1, 1001);

        Mat points(sampleCount, 2, CV_32F), 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' )

            break;

    }

    return 0;

}

这是产生随机的样本点，再用k-means进行聚类。

代码较为简陋。如有问题欢迎交流~

參考资料：

1、视觉机器学习20讲

2、opencv学习例程（在opencv安装路径的source目录下）