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）