mainprog.m

用遗传算法做的图象融合

%遗传算法主程序
%Name:genmain05.m
clear
q=imread('pic.bmp');
q2=imread('pic2.bmp');
figure,imshow(q);
figure,imshow(q2);
[q1,rect]=imcrop(q2);
imwrite(q1,'pic3.bmp','bmp');
imwrite(q,'pic1.bmp','bmp');
popsize=100; %设置初始参数，群体大小
chromlength=18; %字符串长度（个体长度），染色体长度
pc=0.6; %设置交叉概率
pm=0.6; %设置变异概率
pop=initpop(popsize,chromlength); %运行初始化函数，随机产生初始群体
dai=200;
for i=1:dai%20为迭代次数
[objvalue]=calobjvalue(pop);%计算目标函数
fitvalue=calfitvalue(objvalue); %计算群体中每个个体的适应度
[newpop]=selection(pop,fitvalue); %复制
[newpop]=crossover(newpop,pc); %交叉
[newpop]=mutation(newpop,pm);%变异
[bestindividual,bestfit]=best(pop,fitvalue);%求出群体中适应值最大的个体及其适应值
c(i)=max(bestfit);
n(i)=i;
pop5=bestindividual;
x1(i)=decodechrom(pop5,1,9);
y1(i)=decodechrom(pop5,10,9);
pop=newpop;
end
z=[c
   x1
   y1];
x=z(2,1);
y=z(3,1);
r=z(1,1);
for i=2:dai
    if z(1,i)>r
        x=z(2,i);
        y=z(3,i);
        r=z(1,i);
    end
end
q3=double(q)/255;
Q=q3;
[px,py]=size(q);
for i=1:px
    for j=x+1:py
        Q(i,j)=0;
    end
end
figure,imshow(Q);
Q2=double(q2)/255;
[px1,py1]=size(q2);
for i=1:px1
    for j=1:rect(1)-1
        Q2(i,j)=0;
    end
end
figure,imshow(Q2);
for i=1:700
    for j=1:1000
        backdrop(i,j)=0;
    end
end
for i=1:px1
    for j=1:x
        backdrop(i,j)=Q(i,j);
    end
    t=x+py1-rect(1)+1;
    l=round(rect(1));
    for k=(x+1):t
        backdrop(i,k)=Q2(i,l);
        l=l+1;
    end
end
figure,imshow(backdrop);