clear all;
I1=imread('F:\乱七八糟\car-avi-001.bmp'); %read the first frame
I2=imread('F:\乱七八糟\car-avi-002.bmp'); %read the second frame
dm=7;
%给图像扩边，每个边都扩dm大小。
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% |-----|-------------------------------|------|
% | B | C | D |
% |-----|-------------------------------|------|
% | | | |
% | | | |
% | E | A | F | ----> 图像
% | | | |
% | | | |
% | | | |
% |-----|-------------------------------|------|
% | G | H | I |
% |-----|-------------------------------|------|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
I1=double(I1); %I1 代表第一幅图像
I2=double(I2); %I2 代表第一幅图像，作为当前帧
[rownum colnum] = size(I1);
%给I1扩边，并赋值
% 学生自己添加。扩边后的图像矩阵用II表示。
% ................................
A=I1;
B=zeros(dm,dm);
for m=1:dm
for n=1:dm
B(m,n)=128;
end
end
D=B;G=B;I=B;
C=zeros(dm,colnum);
for n=1:colnum
C(:,n)=I1(1,n);
end
E=zeros(rownum,dm);
for m=1:rownum
E(m,:)=I1(m,1);
end
F=zeros(rownum,dm);
for m=1:rownum
F(m,:)=I1(m,colnum);
end
H=zeros(dm,colnum);
for n=1:colnum
H(:,n)=I1(rownum,n);
end
%imgrownumber = rownum + 2*dm;
%imgcolnumber = colnum + 2*dm;
II=[B,C,D; %拼接
E,A,F;
G,H,I];
%下面进行全搜索算法
blocksize=16;
rowblocks =rownum/blocksize;
colblocks =colnum/blocksize;
A=99999999999999999999; %为了找到最小的均方误差，A用于设定一个很大的初值
Eij=0;
xrecord=zeros(32,32); %xrecord，yrecord这两个数组的下标表示存放的匹配块的块号，而其值表示即运动矢量
yrecord=zeros(32,32);
%diff=zeros(256,256); %这幅图像的大小为256*256，diff用于存放像素差值
diff=zeros(rownum,colnum);
tic
count=0;
for x=0:(rowblocks-1) %x表示行中第几个子块
row=x*blocksize;
for y=0:(colblocks-1) %y表示列中第几个子块
col=y*blocksize;
for p=-dm:dm
for q=-dm:dm %（p，q）表示（x，y）对应子块在前一帧中所搜索的偏离距离，即运动矢量
Eij=0; %因为图像II是对图像I1扩边的结果，所以在计算Eij时，需要由dm的偏移。如果不扩边，Eij的计算应该为：
%Eij=sum(sum((I2(row+1:row+blocksize,col+1:col+blocksize)-II(row+p+1:row+dm+p+blocksize,col+q+1:col+dm+q+blocksize)).^2))/(blocksize^2);
Eij=sum(sum((I2(row+1:row+blocksize,col+1:col+blocksize)-II(row+dm+p+1:row+dm+p+blocksize,col+dm+q+1:col+dm+q+blocksize)).^2))/(blocksize^2);
count=count+1;
if Eij<A
A=Eij;
DFD(x*16+y+1)=A;
xrecord(x+1,y+1)=p;
yrecord(x+1,y+1)=q;
end
end
end
A=999999999999999999;
for mx=1:blocksize
for ny=1:blocksize
diff(row+mx,col+ny)=I2(row+mx,col+ny)-II(row+mx+dm+xrecord(x+1,y+1),col+ny+dm+yrecord(x+1,y+1));
end
end
end
end
toc
figure,imshow(I1,[]);
title('the first frame');
figure,imshow(I2,[]);
title('the second frame');
IIII=I2-I1;
figure,imshow(IIII,[]);
title('帧间差值');
figure,imshow(diff,[]);
title('利用全搜索算法块匹配后的帧间差');
for x=0:(rowblocks-1) %x表示行中第几个子块
row=x*blocksize;
for y=0:(colblocks-1) %y表示列中第几个子块
col=y*blocksize;
%由于没有量化与反量化，因此，恢复出的III跟I2的内容完全一样
III(row+1:row+blocksize,col+1:col+blocksize)=II(row+dm+xrecord(x+1,y+1)+1:row+dm+xrecord(x+1,y+1)+blocksize,col+dm+yrecord(x+1,y+1)+1:col+dm+yrecord(x+1,y+1)+blocksize)+diff(row+1:row+blocksize,col+1:col+blocksize);
end
end
figure,imshow(III,[]);
title('恢复后的第二帧图像');
ERR=diff;
figure,imshow(ERR,[]);
title('利用全搜索算法块匹配后的帧间差DFD');
% 学生自己编程统计DFD分布，并画出曲线
% ............................rownum colnum
figure,plot(DFD);
title('全搜索法帧间差DFD分布');
%figure,plot(DFD(1,:));
