【图像分割】遗传算法一维最大熵图像分割【含Matlab源码 2935期】.zip

% ----------------------- README ------------------------------------------ %% 清空环境 clc clear close all %% 载入图像及其计算 I = imread('KimSoHyun.jpg'); % I=imread('Barbara.tif'); % I=imread('lena.bmp'); [~,~,n] = size(I); if n==3 % 彩色灰度化 I = rgb2gray(I); end garynum = 255; % 灰度级为255 hist = imhist(I); total = 0; for i=0:255 total = total+hist(i+1); end hist1 = hist/total; HT = 0; for i=0:255 if hist1(i+1)==0 temp = 0; else temp = hist1(i+1)*log(1/hist1(i+1)); end HT = HT+temp; end tic %% 遗传算法参数 maxgen=500; %进化代数 sizepop=500; %种群规模 pcross=[0.9]; %交叉概率 pmutation=[0.2]; %变异概率 eps=1; % 二进制个数需要 bound=[0 255]; %变量范围 NC = size(bound,1); num=max(ceil(log2((bound(2)-bound(1))/eps + 1))); lenchrom=NC*num; %变量字串长度 trace=[]; %% 个体初始化 individuals=struct('fitness',zeros(1,sizepop), 'chrom',[]); %种群结构体 avgfitness=[]; %种群平均适应度 bestfitness=[]; %种群最佳适应度 bestchrom=[]; %适应度最好染色体 % 初始化种群 for i=1:sizepop individuals.chrom(i,:)=Code(lenchrom,bound); %随机产生个体 x=individuals.chrom(i,:); individuals.fitness(i)=fun(x,I,bound,lenchrom,hist1,HT); %个体适应度 end %找最好的染色体 [bestfitness bestindex]=max(individuals.fitness); bestchrom=individuals.chrom(bestindex,:); %最好的染色体 avgfitness=sum(individuals.fitness)/sizepop; %染色体的平均适应度 % 记录每一代进化中最好的适应度和平均适应度 chrom = []; %% 进化开始 for i=1:maxgen % 选择操作 individuals=Select(individuals,sizepop); avgfitness=sum(individuals.fitness)/sizepop; % 交叉操作 individuals.chrom=Cross(pcross,lenchrom,individuals.chrom,sizepop,bound); % 变异操作 individuals.chrom=Mutation(pmutation,lenchrom,individuals.chrom,sizepop,bound); % 计算适应度 for j=1:sizepop x=individuals.chrom(j,:); individuals.fitness(j)=fun(x,I,bound,lenchrom,hist1,HT); end %找到最小和最大适应度的染色体及它们在种群中的位置 [newbestfitness,newbestindex]=max(individuals.fitness); [worestfitness,worestindex]=min(individuals.fitness); % 代替上一次进化中最好的染色体 if bestfitness<newbestfitness bestfitness=newbestfitness; bestchrom=individuals.chrom(newbestindex,:); end individuals.chrom(worestindex,:)=bestchrom; individuals.fitness(worestindex)=bestfitness; avgfitness=sum(individuals.fitness)/sizepop; chrom = [chrom;bestchrom]; % trace=[trace;avgfitness bestfitness]; %记录每一代进化中最好的适应度和平均适应度 trace(i) = bestfitness; end %进化结束 toc %% 结果显示 [r ~]=size(trace); gen = 1:maxgen; plot(gen,trace(1,:),'Color','#1A3B30','LineWidth',3); % hold on % plot(gen,trace(2,:),'-.','Color','#509579','LineWidth',3); % plot(gen,trace(3,:),'--','Color','#99BDBD','LineWidth',3); % plot(gen,trace(4,:),':','Color','#C6E3D8','LineWidth',3); xlabel('Generation','FontName','Times New Roman','FontSize',12); ylabel('Fitness Value','FontName','Times New Roman','FontSize',12); title('SGA','FontName','Times New Roman','FontSize',12); % legend('1','2','3','4','FontName','Times New Roman','FontSize',11) [x,y] = size(I); img = zeros(x,y); bestchrom=Dec1(bestchrom,bound(1),bound(2),lenchrom); bestchrom for i=1:x for j=1:y if I(i,j)<bestchrom img(i,j) = 0; else img(i,j) = 255; end end end figure subplot(121) imshow(I); subplot(122) imshow(img)