[人工智能]MATLAB中利用灰度共生矩阵（GLCM）提取多张图片的纹理特征

MATLAB中利用灰度共生矩阵（GLCM）提取多张图片的纹理特征

查了好多资料，纹理特征提取都是单图的，自己拼拼凑凑搞出来了可以同时提取多图的纹理特征（利用灰度共生矩阵），并将最后特征矩阵保存在excel表中，这样在后续特征矩阵拼凑的时候会比较方便一点。

clear all;
clc;
%**************************************************************************
%                   图像检索——纹理特征
%基于共生矩阵纹理特征提取，d=1,θ=0°,45°,90°,135°共四个矩阵
%所用图像灰度级均为256
%function : T=Texture(Image)
%Image    : 输入图像数据
%T        : 返回15维纹理特征行向量
%**************************************************************************
% function T = Texture(Image)

niiname=dir('C:\Users\Mus\Desktop\处理后的图像\*.png');%输入你图片的地址
str1='C:\Users\Mus\Desktop\处理后的图像\';
for ii=1:8
    str=[str1 niiname(ii).name];
    A = imread(str);
    [xx,yy,zz]=size(A);
    for i=1:zz
       for j=1:yy
           for k=1:xx
               if(A(k,j,i) ~= 0)
                   a=i;
                   break;
               end
           end
       end
    end
     
    G=A(:,:,a);
    Gray=imresize(G,1/40);
    [M,N] = size(Gray);
    %M = 128;
    %N = 128;

    %--------------------------------------------------------------------------
    %1.将各颜色分量转化为灰度
    %--------------------------------------------------------------------------
    % Gray = double(0.3*Image(:,:,1)+0.59*Image(:,:,2)+0.11*Image(:,:,3));

    %--------------------------------------------------------------------------
    %2.为了减少计算量，对原始图像灰度级压缩，将Gray量化成16级
    %--------------------------------------------------------------------------
    for i = 1:M
        for j = 1:N
            for n = 1:256/16
                if (n-1)*16<=Gray(i,j)&&Gray(i,j)<=(n-1)*16+15
                    Gray(i,j) = n-1;
                end
            end
        end
    end

    %--------------------------------------------------------------------------
    %3.计算四个共生矩阵P,取距离为1，角度分别为0,45,90,135
    %--------------------------------------------------------------------------
    P = zeros(16,16,4);
    for m = 1:16
        for n = 1:16
            for i = 1:M
                for j = 1:N
                    if j<N&&Gray(i,j)==m-1&&Gray(i,j+1)==n-1
                        P(m,n,1) = P(m,n,1)+1;
                        P(n,m,1) = P(m,n,1);
                    end
                    if i>1&&j<N&&Gray(i,j)==m-1&&Gray(i-1,j+1)==n-1
                        P(m,n,2) = P(m,n,2)+1;
                        P(n,m,2) = P(m,n,2);
                    end
                    if i<M&&Gray(i,j)==m-1&&Gray(i+1,j)==n-1
                        P(m,n,3) = P(m,n,3)+1;
                        P(n,m,3) = P(m,n,3);
                    end
                    if i<M&&j<N&&Gray(i,j)==m-1&&Gray(i+1,j+1)==n-1
                        P(m,n,4) = P(m,n,4)+1;
                        P(n,m,4) = P(m,n,4);
                    end
                end
            end
            if m==n
                P(m,n,:) = P(m,n,:)*2;
            end
        end
    end

    %%---------------------------------------------------------
    % 对共生矩阵归一化
    %%---------------------------------------------------------
    for n = 1:4
        P(:,:,n) = P(:,:,n)/sum(sum(P(:,:,n)));
    end

    %--------------------------------------------------------------------------
    %4.对共生矩阵计算能量(角二阶矩)、熵、惯性矩（对比度）、相关及逆差距5个纹理参数
    %--------------------------------------------------------------------------
    H = zeros(1,4);
    I = H;
    Ux = H;      
    Uy = H;
    deltaX= H;  
    deltaY = H;
    C =H;
    L=H;
    for n = 1:4
        E(n) = sum(sum(P(:,:,n).^2)); %%能量
        for i = 1:16
            for j = 1:16
                if P(i,j,n)~=0
                    H(n) = -P(i,j,n)*log(P(i,j,n))+H(n); %%熵
                end
                I(n) = (i-j)^2*P(i,j,n)+I(n);  %%惯性矩

                Ux(n) = i*P(i,j,n)+Ux(n); %相关性中μx
                Uy(n) = j*P(i,j,n)+Uy(n); %相关性中μy
            end
        end
    end
    for n = 1:4
        for i = 1:16
            for j = 1:16
                deltaX(n) = (i-Ux(n))^2*P(i,j,n)+deltaX(n); %相关性中σx
                deltaY(n) = (j-Uy(n))^2*P(i,j,n)+deltaY(n); %相关性中σy
                C(n) = i*j*P(i,j,n)+C(n);  
                L(n)=P(i,j,n)^2/(1+(i-j)^2)+L(n);%逆差距
            end
        end
        C(n) = (C(n)-Ux(n)*Uy(n))/deltaX(n)/deltaY(n); %相关性  
    end

    %--------------------------------------------------------------------------
    %求 求能量、熵、惯性矩、相关的均值、标准差和方差作为15维纹理特征
    %--------------------------------------------------------------------------
    a1 = mean(E) ; 
    b1 = sqrt(cov(E));
    c1=var(E);

    a2 = mean(H);
    b2 = sqrt(cov(H));
    c2=var(H);

    a3 = mean(I) ;
    b3 = sqrt(cov(I));
    c3=var(I);

    a4 = mean(C);
    b4 = sqrt(cov(C));
    c4=var(C);
    
    a5=mean(L);
    b5=sqrt(cov(L));
    c5=var(L); 
    
    T(ii,:)={a1,b1,c1,a2,b2,c2,a3,b3,c3,a4,b4,c4,a5,b5,c5};
    
    %将矩阵写入ecxel中
    filename = 'GLCM纹理特征矩阵.xlsx';
    writecell(T,filename,'Sheet',1,'Range','A1');
end