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%%%利用最佳直方图熵法（KSW熵法）及遗传算法实现灰度图像阈值分割 
%%%主程序 
 
 
%%初始部分，读取图像及计算相关信息 
 
clear; 
close all; 
clc; 
 
filename=input('输入预进行阈值分割的灰度图像的文件名及路径：','s'); 
 
I=imread(filename); 
 
info=imfinfo(filename); 
 
mingrayvalue=info.MinSampleValue; 
maxgrayvalue=info.MaxSampleValue; 
graynum=maxgrayvalue-mingrayvalue+1; 
 
hist=imhist(I); 
total=0; 
for i=mingrayvalue:maxgrayvalue 
    total=total+hist(i+1); 
end 
hist1=hist/total; 
 
HT=0; 
for i=mingrayvalue:maxgrayvalue 
    if hist1(i+1)==0 
        temp=0; 
    else 
        temp=hist1(i+1)*log(1/hist1(i+1)); 
    end 
    HT=HT+temp; 
end 
 
 
%%程序主干部分 
 
    %种群随机初始化，种群数取10，染色体二进制编码取8位 
     
    population=10; 
   
    X0=round(rand(1,population)*graynum); 
     
    for i=1:population 
        adapt_value0(i)=ksw(X0(i),mingrayvalue,maxgrayvalue,hist1,HT); 
    end 
     
    adapt_average0=mean(adapt_value0); 
     
    X1=X0; 
    adapt_value1=adapt_value0; 
    adapt_average1=adapt_average0; 
     
    %循环搜索，搜索代数取50 
     
    generation=100; 
     
    for k=1:generation 
         
        s1=select(X1,adapt_value1); 
     
        s_code1=dec2bin(s1,8); 
     
        c1=cross(s_code1); 
     
        v1=mutation(c1); 
     
        X2=(bin2dec(v1))'; 
     
        for i=1:population 
            adapt_value2(i)=ksw(X2(i),mingrayvalue,maxgrayvalue,hist1,HT); 
        end 
     
        adapt_average2=mean(adapt_value2); 
     
        if abs(adapt_average2-adapt_average1)<=0.0001 
            break; 
        else 
            X1=X2; 
            adapt_value1=adapt_value2; 
            adapt_average1=adapt_average2;  
        end 
    end 
     
    max_value=max(adapt_value2); 
    number=find(adapt_value2==max_value); 
    t_opt=round(mean(X2(number))); 
 
     
%%阈值分割及显示部分 
 
threshold_opt=t_opt/graynum; 
 
I1=im2bw(I,threshold_opt); 
 
disp('灰度图像阈值分割的效果如图所示：'); 
disp('源图为：Fifure No.1'); 
disp('最佳直方图熵法及遗传算法阈值分割后的图像为：Fifure No.2'); 
disp('Otsu法阈值分割后的图像为：Fifure No.3'); 
 
figure(1); 
imshow(I); 
title('源图'); 
 
figure(2); 
imshow(I1); 
title('最佳直方图熵法及遗传算法阈值分割后的图像'); 
 
level=graythresh(I); 
level1=round(level*graynum); 
I2=im2bw(I,level); 
figure(3); 
imshow(I2); 
title('Otsu法阈值分割后的图像'); 
 
disp('最佳直方图熵法及遗传算法阈值为：'); 
disp(t_opt); 
disp('Otsu法阈值为：'); 
disp(level1); 
 
%%程序结束