%圖像塊計算灰度共生矩陣
clc;
close?all;
clear?all;
???
gray=256;
I?=?imread（‘lena.bmp‘）;
[mn]=size（I）;

Energy?=?zeros（mn）;
Entropy?=?zeros（mn）;
entropy?=?[];
R?=?8;
C?=?8;

%劃分成8*8圖像塊
for?u?=?1:8:m-7
????for?v?=?1:8:n-7
????????IN?=?I（u:u+7v:v+7）;
????????
????????%采用平方求和計算梯度矩陣
????????GM=zeros（RC）;
????????for?i=1:R-1
????????????for?j=1:C-1
%?????????????????n_GM=（IN（ij+1）-IN（ij））^2+（IN（i+1j）-IN（ij））^2+（IN（ij）-IN（i+1j+1））^2;
?????????????????n_GM=（IN（ij+1）-IN（ij））^2+（IN（i+1j）-IN（ij））^2;
????????????????GM（ij）=sqrt（double（n_GM））;
????????????end
????????end
????????GM（1:end-1C）?=?GM（1:end-1C-1）;
????????GM（R1:end-1）?=?GM（R-11:end-1）;
????????GM（endend）?=?GM（end-1end-1）;

????????%找出最大值最小值
????????n_min=min（GM（:））;
????????n_max=max（GM（:））;
????????%把梯度圖象灰度級離散化
????????%設置新的灰度級為new_gray
????????new_gray=128;
????????
????????%新的梯度矩陣為new_GM
????????new_GM=zeros（RC）;
????????new_GM=uint8（（GM-n_min）/（n_max-n_min）*（new_gray-1））;

????????%?計算灰度梯度共生矩陣
????????H=zeros（graynew_gray）;
????????for?i?=?1:R
????????????for?j?=1:C
????????????????x?=?IN（ij）;
????????????????y?=?new_GM（ij）;
????????????????H（x+1y+1）?=?H（x+1y+1）+1;
????????????end
????????end


????????%歸一化灰度梯度矩陣?H_basic
????????H_basic=H/（R*C）;
????????
????????%計算能量?T5
????????T5=0;
????????for?i=1:gray
????????????for?j=1:new_gray
????????????????T5=T5+H_basic（ij）^2;
????????????end
????????end
????????
????????%計算混合熵?T13
????????T13=0;
????????for?i=1:gray
????????????for?j=1:new_gray
????????????????if（H_basic（ij）~=0）
????????????????????T13=T13-H_basic（ij）*log2（H_basic（ij））;
????????????????end
????????????end
????????end
???????
????????Energy（u:u+7v:v+7）?=?T5;
????????Entropy（u:u+7v:v+7）?=?T13;
????????entropy?=?[entropy?T13];
????end
end
figure;imshow（I）;
%figure;imagesc（Energy）;colorbar;
figure;imagesc（Entropy）;colorbar;

%分為c類，每類數量平均
en?=?sort（entropy）;
classpic?=?ones（mn）;

%分為2類
%?c?=?2;
%?fenwei_loc?=?round（length（en）*0.5）;
%?thre?=?en（fenwei_loc）;
%?%小于門限值的圖像塊標記為1，大于門限值的圖像塊標記為2
%?loc?=?find（Entropy>thre）;
%?classpic（loc）?=?2;

%分為c類
c?=?4;
%分位點
fp?=?（1:（c-1））*（1/c）;
floc?=?length（en）*fp;
thre?=?[min（en）?en（floc）?max（en）];
for?i?=?1:length（thre）-1
????mint?=?thre（i）;
????maxt?=?thre（i+1）;
????loc?=?find（Entropy>=mint&Entropy<=maxt）;
????classpic（loc）?=?i;
end
figure;imagesc（classpic）;

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????2560??2015-12-16?19:51??test_gray_gradient.m