基于遗传算法的图像分割matlab
时间: 2023-05-31 19:04:32 浏览: 125
代码
以下是一个基于遗传算法的图像分割MATLAB代码示例。该代码使用遗传算法来优化图像分割的阈值,并使用OTSU方法进行最终的图像分割。
清除所有变量和命令窗口:
clc;
clear all;
close all;
读取图像:
img=imread('cameraman.tif');
imshow(img);
将图像转换为灰度图像:
gray_img=rgb2gray(img);
定义图像分割的目标函数:
function [fitness]=fitness_func(threshold,img)
% 计算分割后的两个类别的方差
class1=img(img<=threshold);
class2=img(img>threshold);
var1=var(double(class1));
var2=var(double(class2));
% 计算适应度
fitness=1/(var1+var2);
end
定义遗传算法的参数:
pop_size=20; % 种群大小
num_vars=1; % 变量数量(阈值)
num_generations=50; % 迭代次数
mutation_rate=0.1; % 变异率
crossover_rate=0.8; % 交叉率
运行遗传算法来优化图像分割的阈值:
% 初始化种群
pop=round(rand(pop_size,num_vars)*255);
% 进化
for i=1:num_generations
% 计算适应度
fitness=zeros(pop_size,1);
for j=1:pop_size
fitness(j)=fitness_func(pop(j,:),gray_img);
end
% 选择
fitness_sum=sum(fitness);
fitness_norm=fitness/fitness_sum;
cum_fitness=cumsum(fitness_norm);
new_pop=zeros(pop_size,num_vars);
for j=1:pop_size
r=rand;
for k=1:pop_size
if r<=cum_fitness(k)
new_pop(j,:)=pop(k,:);
break;
end
end
end
% 交叉
for j=1:2:pop_size
r=rand;
if r<=crossover_rate
crossover_point=ceil(rand*(num_vars-1));
temp=new_pop(j,crossover_point+1:end);
new_pop(j,crossover_point+1:end)=new_pop(j+1,crossover_point+1:end);
new_pop(j+1,crossover_point+1:end)=temp;
end
end
% 变异
for j=1:pop_size
for k=1:num_vars
r=rand;
if r<=mutation_rate
new_pop(j,k)=round(rand*255);
end
end
end
% 更新种群
pop=new_pop;
end
找到最佳的阈值:
best_fitness=-Inf;
best_threshold=0;
for i=1:pop_size
fitness=fitness_func(pop(i,:),gray_img);
if fitness>best_fitness
best_fitness=fitness;
best_threshold=pop(i,:);
end
end
使用OTSU方法进行最终的图像分割:
threshold=graythresh(gray_img);
otsu_mask=gray_img>threshold*255;
ga_mask=gray_img>best_threshold;
figure;
subplot(1,3,1);
imshow(gray_img);
title('原始图像');
subplot(1,3,2);
imshow(otsu_mask);
title('OTSU方法分割');
subplot(1,3,3);
imshow(ga_mask);
title('遗传算法分割');
保存结果:
imwrite(ga_mask,'ga_segmentation.png');
阅读全文