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%%%利用二维最佳直方图熵法（KSW熵法）及传统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; 
 
%format long; 
 
I=imread('rice.tif'); 
 
 
windowsize=3; 
I_temp=I; 
for i=2:255 
    for j=2:255 
        I_temp(i,j)=round(mean2(I(i-1:i+1,j-1:j+1))); 
    end 
end 
I_average=I_temp; 
 
 
I_p=I; 
I_average_p=I_average; 
hist_2d(1:256,1:256)=zeros(256,256); 
for i=1:256 
    for j=1:256 
        hist_2d(I_p(i,j),I_average_p(i,j))=hist_2d(I_p(i,j),I_average_p(i,j))+1; 
    end 
end 
 
total=256*256; 
 
hist_2d_1=hist_2d/total; 
 
 
%%%%%% 
 
Hst=0; 
for i=0:255 
    for j=0:255 
        if hist_2d_1(i+1,j+1)==0 
            temp=0; 
        else 
            temp=hist_2d_1(i+1,j+1)*log(1/hist_2d_1(i+1,j+1)); 
        end 
        Hst=Hst+temp; 
    end 
end 
 
 
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
%%程序主干部分 
 
    %种群随机初始化，种群数取20，染色体二进制编码取16位 
    t0=clock; 
     
    population=20; 
   
    X00=round(rand(1,population)*255); 
    X01=round(rand(1,population)*255); 
     
    for i=1:population 
        X0(i,:)=[X00(i) X01(i)]; 
    end 
     
     
    for i=1:population 
        adapt_value0(i)=ksw_2d(X0(i,1),X0(i,2),0,255,hist_2d_1,Hst); 
    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100 
     
    generation=100; 
     
    for k=1:generation 
         
        s1=select_2d(X1,adapt_value1); 
     
        s_code10=dec2bin(s1(:,1),8); 
        s_code11=dec2bin(s1(:,2),8); 
     
        [c10,c11]=cross_2d(s_code10,s_code11); 
     
        [v10,v11]=mutation_2d(c10,c11); 
     
        X20=(bin2dec(v10))'; 
        X21=(bin2dec(v11))'; 
         
        for i=1:population 
            X2(i,:)=[X20(i) X21(i)]; 
        end 
     
        for i=1:population 
            adapt_value2(i)=ksw_2d(X2(i,1),X2(i,2),0,255,hist_2d_1,Hst); 
        end 
     
        adapt_average2=mean(adapt_value2); 
     
        if abs(adapt_average2-adapt_average1)<=0.01 
            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); 
    opt=X2(number(1),:); 
    s_opt=opt(1); 
    t_opt=opt(2); 
 
    t1=clock; 
    search_time=etime(t1,t0); 
 
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 
%%阈值分割及显示部分 
 
opt_tt=round((s_opt+t_opt)/2); 
 
threshold_opt=opt_tt/255; 
 
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('二维最佳直方图熵法及传统遗传算法阈值分割后的图像为：Fifure No.2'); 
 
figure(1); 
imshow(I); 
title('源图'); 
 
figure(2); 
imshow(I1); 
title('二维最佳直方图熵法及传统遗传算法阈值分割后的图像'); 
 
 
disp('二维最佳直方图熵法及传统遗传算法阈值为(s,t)：'); 
disp(s_opt); 
disp(t_opt); 
 
disp('二维最佳直方图熵法及传统遗传算法阈值搜索所用时间（s）：'); 
disp(search_time); 
 
%%程序结束