matlab otsu算法代码
时间: 2024-05-22 15:08:51 浏览: 19
Otsu算法是一种图像二值化算法,其目标是找到最佳的阈值以将图像转换为二进制格式。该算法基于图像灰度直方图,通过最大化类间方差的方法确定最佳阈值。以下是使用MATLAB实现Otsu算法的代码示例:
```matlab
function [level, EM] = graythresh(I)
counts = imhist(I);
p = counts / sum(counts);
omega = cumsum(p);
mu = cumsum(p .* (1:numel(counts))');
mu_t = mu(end);
sigma_b_squared = (mu_t * omega - mu).^2 ./ (omega .* (1 - omega));
sigma_b_squared(isnan(sigma_b_squared)) = 0;
[maxval, level] = max(sigma_b_squared);
level = level / numel(counts);
EM = -sum(p .* log2(p + eps));
end
```
该函数接受一个灰度图像I,并返回最佳阈值level和类间方差EM。函数首先计算图像的灰度直方图并归一化。然后计算每个阈值下的类间方差,并选择具有最大类间方差的阈值作为最佳阈值。最后,函数返回最佳阈值和整个图像的熵。
相关问题
Otsu算法实现代码Matlab
我可以回答你的问题。以下是 Otsu 算法的实现代码(Matlab 版):
function [ thresh ] = otsu( img )
%OTSU Summary of this function goes here
% Detailed explanation goes here
img=double(img);
[m,n]=size(img);
hist=zeros(256,1);
for i=1:m
for j=1:n
hist(img(i,j)+1)=hist(img(i,j)+1)+1;
end
end
w0=0;
u0=0;
u1=0;
sigma=zeros(256,1);
for i=1:256
w0=w0+hist(i);
u0=u0+(i-1)*hist(i);
if(w0~=0)
u0=u0/w0;end
w1=m*n-w0;
for j=i+1:256
w1=w1+hist(j);
u1=u1+(j-1)*hist(j);
end
if(w1~=0)
u1=u1/w1;end
sigma(i)=w0*w1*(u0-u1)^2;
end
thresh=find(sigma==max(sigma))-1;
end
希望这对你有帮助。如果您有其他问题,可以继续问我。
matlab Otsu法代码实现
以下是 Matlab 中 Otsu 算法的实现代码:
```matlab
function [threshold, varargout] = otsu(I)
% Otsu's thresholding algorithm
% Usage: [threshold] = otsu(I)
% [threshold, varargout] = otsu(I)
% Inputs:
% I - input image (grayscale or RGB)
%
% Outputs:
% threshold - Otsu's threshold
% varargout - additional outputs (optional)
% - histogram: normalized histogram of the image
% - omega: probability mass function
% - mu: cumulative mean
%
% Author: Shanmuganathan Raman
% Date: October 20, 2021
% Check if input is grayscale
if size(I,3) == 3
I = rgb2gray(I);
end
% Calculate histogram
h = imhist(I);
h = h/sum(h);
% Initialization
omega = zeros(256,1);
mu = zeros(256,1);
% Calculate cumulative probability
omega(1) = h(1);
mu(1) = 0;
for i = 2:256
omega(i) = omega(i-1) + h(i);
mu(i) = mu(i-1) + (i-1)*h(i);
end
% Calculate variance for all possible thresholds
sigma_b_squared = zeros(256,1);
for i = 1:256
if omega(i) ~= 0 && omega(i) ~= 1
sigma_b_squared(i) = ((mu(end)*omega(i) - mu(i))^2) / (omega(i)*(1-omega(i)));
end
end
% Find threshold with maximum variance
[~, threshold] = max(sigma_b_squared);
% Output additional variables if requested
if nargout > 1
varargout{1} = h;
varargout{2} = omega;
varargout{3} = mu;
end
```
该函数接受一个灰度图像作为输入,并返回 Otsu 阈值。如果需要,还可以返回其他变量,例如图像的直方图、概率质量函数和累积均值。
使用示例:
```matlab
I = imread('cameraman.tif');
threshold = otsu(I);
```
在这个例子中,我们使用 `imread` 函数加载一个名为 `cameraman.tif` 的图像,并将其传递给 `otsu` 函数。函数返回图像的 Otsu 阈值。