kmean之matlab

clc;close all;n=2;a=imread('3.jpg');a=rgb2hsv(a);M=size(a,1);N=size(a,2);for i=1:100b=double(a);b=b(:);b=reshape(b,M*N,3);[A,B]=kmeans(b,n);for j=1:nindex=find(A==j);b(index,:)=ones(length(index),1)*[rand rand rand];endb=reshape(b,M,N,3);imshow(mat2gray(b));pause(0.1);end

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clear all;clc;I_rgb=imread('dog.jpg');figure();imshow(I_rgb);title('原始图像');%去噪filter=ones(5,5);filter=filter/sum(filter(:));denoised_r=conv2(I_rgb(:,:,1),filter,'same');denoised_g=conv2(I_rgb(:,:,2),filter,'same');denoised_b=conv2(I_rgb(:,:,3),filter,'same');denoised_rgb=cat(3, denoised_r, denoised_g, denoised_b);D_rgb=uint8(denoised_rgb);figure();imshow(D_rgb);title('去噪后图像');%去噪后的结果%将彩色图像从RGB转化到lab彩色空间C =makecform('srgb2lab'); %设置转换格式I_lab= applycform(D_rgb, C);%进行K-mean聚类将图像分割成2个区域ab =double(I_lab(:,:,2:3)); %取出lab空间的a分量和b分量nrows= size(ab,1);ncols= size(ab,2);ab =reshape(ab,nrows*ncols,2);nColors= 4; %分割的区域个数为4[cluster_idx,cluster_center] =kmeans(ab,nColors,'distance','sqEuclidean','Replicates',2); %重复聚类2次pixel_labels= reshape(cluster_idx,nrows,ncols);%显示分割后的各个区域segmented_images= cell(1,4);rgb_label= repmat(pixel_labels,[1 1 3]);for k= 1:nColorscolor = I_rgb;color(rgb_label ~= k) = 0;segmented_images{k} = color;endfigure(),imshow(segmented_images{1}),title('分割结果——区域1');figure(),imshow(segmented_images{2}),title('分割结果——区域2');figure(),imshow(segmented_images{3}),title('分割结果——区域3');figure(),imshow(segmented_images{4}),title('分割结果——区域4');%使分割后的图像在一个图中显示m=uint8(rgb_label);for i=1:69       for j=1:97                 if m(i,j,1)==1                       m(i,j,1)=255;                     m(i,j,2)=0;                       m(i,j,3)=0;              end           if m(i,j,1)==2                m(i,j,1)=256;             m(i,j,2)=256;             m(i,j,3)=0;         end         if m(i,j,1)==3               m(i,j,1)=0;               m(i,j,2)=0;            m(i,j,3)=255;           end       if m(i,j,1)==4                 m(i,j,1)=0;            m(i,j,2)=128;          m(i,j,3)=0;       end endendfigure(),imshow(m)