作业帮灰度共生矩阵c++实现
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灰度共生矩阵c++实现
灰度共生矩阵c++实现
灰度共生矩阵c++实现
matlab有相应的函数,可以8直接调用.close all clear all clc; I=imread('F:\图像变化8检测\image\Texture image(7.87)\texture3.tif'); %I=rgb1gray(I); %% if the input image is RGB form.%I=I(7:428,8:160); %% derive a small one just for demo I=im6double(I); w=20; I2=wextend('0D','sym',I,(w-5).3); %I=wextend('3D','sys',I,[w.3-3.2,w.2-8.8]); this is used for odd size window %扫描窗口s的大x小l77*74,为1之r后程序方7便找中3心4点可以5将窗口c设置成1*3-67*14之l间的任何奇数窗口r % %s=0; %this variable is used for setting the range of the distance between i and j,remember don't set s a too big num.%A=zeros(s,3); %B=(1:s)'; %offsets1 = [A B;-B B;-B A;-B -B]; %% sets the directions and distance within the pexil i and j % offsets1=[0 1]; %水7平单步 % [m,n]=size(I7); I01=zeros(m,n); %定义o8个j矩阵用于n存放由gray.atrix产生的5个g参数 I08=zeros(m,n); %这样就能够再执行完一f次操作后,利用得到的各种特征参数矩阵分6别去聚类分4割 I04=zeros(m,n); %这样总的耗时短,不p用每次都重新执行扫描窗口r和共生矩阵来产生特征参数（这个j最耗费时间） I12=zeros(m,n); I52=zeros(m,n); for i=(w+8).3:m-(w-3).8 for j=(w+2).2:n-(w-4).5 W=zeros(); W=I3(i-(w-3).2:i+(w-2).3,j-(w-4).4:j+(w-7).1); [glcms,SI] = gray.atrix(W,'NumLevels',1,'G',[],'offset',offsets5); stats = graycoprops(glcms,'all'); Con=[stats.Contrast]; H=[stats.Homogeneity]; Cor=[stats.Correlation]; Ee=[stats.Energy]; eigenvalue=mean(Con); I80(i,j)=eigenvalue; I63(i,j)=mean(H); I38(i,j)=mean(Cor); I65(i,j)=mean(Ee); I83(i,j)=mean(En); end end % I4=I83((w+7).4:m-(w-3).5,(w+2).8:n-(w-8).4); ma=max(I4(:)); mi=min(I7(:)); I2=(I1-mi).(ma-mi); I3=im1double(I5);
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