matlab基于遗传算法的最大熵值法的双阈值图像分割

利用最佳直方图熵法（KSW熵法）及传统遗传算法实现灰度图像二阈值分割

matlab代码如下：
1、main.m(主函数)：

%%%利用最佳直方图熵法（KSW熵法）及传统遗传算法实现灰度图像二阈值分割%%%主程序%%  初始部分，读取图像及计算相关信息 clear; close all; clc;I=imread('D:\MATLAB\work\2.21.jpg');figurefigure(1),imshow(I); I=rgb2gray(I);% I=imread('Lenna.bmp');hist=imhist(I);     %显示图像数据柱状图total=0;for i=0:255    total=total+hist(i+1);endhist1=hist/total;   %求每点的归一化//求像素为i的概率Pi%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 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        if X0(i,1)>X0(i,2)            temp=X0(i,1);            X0(i,2)=temp;            X0(i,1)=temp;        else        end        adapt_value0(i)=ksw_2(X0(i,1),X0(i,2),0,255,hist1);    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_2(X2(i,1),X2(i,2),0,255,hist1);        end        adapt_average2=mean(adapt_value2);        if abs(adapt_average2-adapt_average1)<=0.03            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),:);    t1=clock;    search_time=etime(t1,t0);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%  阈值分割及显示部分I_temp1=I;[height,width]=size(I_temp1)for i=1:height    for j=1:width         if I_temp1(i,j)<opt(1);                 I_temp1(i,j)=0;        else if I_temp1(i,j)>opt(2);                 I_temp1(i,j)=255;            else I_temp1(i,j)=180;            end        end    endendI1= I_temp1;disp('灰度图像阈值分割的效果如图所示：');disp('源图为：Fifure No.1');disp('最佳直方图熵法及传统遗传算法阈二值分割后的图像为：Fifure No.2'); figure(2); imshow(I); title('源图');figure(3);imshow(I1);title('最佳直方图熵法及传统遗传算法阈二值分割后的图像');disp('最佳直方图熵法及传统遗传算法二阈值为(s,t)：');disp(opt(1));disp(opt(2));disp('最佳直方图熵法及传统遗传算法二阈值搜索所用时间（s）：');disp(search_time);%%  程序结束

2、子函数

function s1=select_2d(X1,adapt_value1)    %选择算子    population=20;    total_adapt_value1=0;    for i=1:population        total_adapt_value1=total_adapt_value1+adapt_value1(i);    end    adapt_value1_new=adapt_value1/total_adapt_value1;    r=rand(1,population);    for i=1:population        temp=0;        for j=1:population            temp=temp+adapt_value1_new(j);            if temp>=r(i)                s1(i,:)=X1(j,:);                break;            end        end    end

function [c10,c11]=cross_2d(s_code10,s_code11)   %交叉算子   pc=0.8;       %交叉概率取0.6   population=20;   %(1,2)/(3,4)/(5,6)进行交叉运算，(7,8)/(9,10)复制   ww0=s_code10;   ww1=s_code11;   for i=1:(pc*population/2)       r0=abs(round(rand(1)*10)-3);       r1=abs(round(rand(1)*10)-3);       for j=(r0+1):8           temp0=ww0(2*i-1,j);           ww0(2*i-1,j)=ww0(2*i,j);           ww0(2*i,j)=temp0;        end       for j=(r1+1):8           temp1=ww1(2*i-1,j);           ww1(2*i-1,j)=ww1(2*i,j);           ww1(2*i,j)=temp1;        end   end   c10=ww0;   c11=ww1;

function [v10,v11]=mutation_2d(c10,c11)    %变异算子    format long;    population=20;    pm=0.03;    for i=1:population        for j=1:8            r0=rand(1);            r1=rand(1);            if r0>pm                temp0(i,j)=c10(i,j);            else                tt=not(str2num(c10(i,j)));                temp0(i,j)=num2str(tt);            end            if r1>pm                temp1(i,j)=c11(i,j);            else                tt=not(str2num(c11(i,j)));                temp1(i,j)=num2str(tt);            end        end    end    v10=temp0;    v11=temp1;

function y=ksw_2(s,t,mingrayvalue,maxgrayvalue,hist1)   %计算最佳直方图熵（KSW熵）    Ps=0;%初始化    for i=mingrayvalue:s; %从0到s        Ps=Ps+hist1(i+1);%求和    end    Pt=0;%初始化    for i=s:t; %从s+1到t        Pt=Pt+hist1(i+1);%求和    end     Pn=0;%初始化    for i=t:maxgrayvalue; %从t+1到n        Pn=Pn+hist1(i+1);%求和    end    Hs=0;    for i=mingrayvalue:s        if hist1(i+1)==0%直方图值为零者赋零           temp=0;        else           temp=hist1(i+1)*log(1/hist1(i+1));%        end        Hs=Hs+temp;%    end         Ht=0;    for i=s:t        if hist1(i+1)==0%直方图值为零者赋零           temp=0;        else           temp=hist1(i+1)*log(1/hist1(i+1));%        end        Ht=Ht+temp;%    end       Hn=0;    for i=t:maxgrayvalue        if hist1(i+1)==0%直方图值为零者赋零           temp=0;        else           temp=hist1(i+1)*log(1/hist1(i+1));%        end        Hn=Hn+temp;%    end    if Ps==0 || Ps==1||Pt==0 || Pt==1||Pn==0 || Pn==1         %   or(Pt==0,Pt==1)        temp1=0;    else         temp1=log(Ps)+log(Pt)+log(Pn)+Hs/Ps+Ht/Pt+Hn/Pn;%图像总熵    end    if temp1 < 0        H=0;    else        H=temp1;    end    y=H;

运行结果如下：

原图：

灰度图像：

双阈值分割图像：