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%%%利用二维最佳直方图熵法（KSW熵法）及穷举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 
t0=clock; 
 
    for s=0:255 
        for t=0:255 
            adapt_value(s+1,t+1)=ksw_2d(s,t,0,255,hist_2d_1,Hst); 
        end 
    end 
         
     
    [max_value1,index1]=max(adapt_value); 
    [max_value2,index2]=max(max_value1); 
    t_opt=index2-1; 
     
     
    number=find(adapt_value==max_value); 
    s_opt=number(1)-1; 
    t_opt=number(2)-1; 
 
t1=clock; 
search_time=etime(t1,t0); 
     
%%阈值分割及显示部分 
 
threshold_opt=s_opt/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穷举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穷举法阈值分割后的图像'); 
 
 
disp('二维最佳直方图熵法及穷举法阈值为：'); 
disp(number); 
 
disp('二维最佳直方图熵法及穷举法阈值搜索所用时间（s）：'); 
disp(search_time); 
 
%%程序结束