Séquence d'images

j en comprend le principe ,mais je ne peux t aider.desolé
slts

merci
maintenant j'ai passé au 2éme code il est pour la compensation de mouvement, il me signale des erreurs au niveau des fonctions.
voila le code:

% Computes the motion vectors and motion compensated
% prediction error image corresponding to two
% temporally adjacent image frames using the
% Sum of Absolute Difference (SAD) metric and
% full search procedure.
% It can estimate the motion to integer pel, half pel, or
% quarter pel accuracy.
% Motion blocks are of size 8 x 8 pixels and the
% search window size is 16 x 16 pixels.
clear

fid = fopen('foreman.qcif.yuv', 'r');
foreman=fread(fid);
%lecture de l'image i en Y
X=6*72*88;
i=10;
Y10=foreman((i-1)*X+1:(i-1)*X+176*144);
Y10=reshape(Y10,176,144);
Y10=Y10';
%figure; imshow(Y,gray(256))
i=12;
Y12=foreman((i-1)*X+1:(i-1)*X+176*144);
Y12=reshape(Y12,176,144);
Y12=Y12';

A = Y10; % frame #1
B = Y12; % frame #2
N = 8;% block size is N x N pixels
W = 16; % search window size is W x W pixels
PredictionType = 'full'; % Options: "full", "half" and "quarter"
% Make image size divisible by 8
[X,Y,Z] = size(A);
if mod(X,8)~=0
Height = floor(X/8)*8;
else
Height = X;
end
if mod(Y,8)~=0
Width = floor(Y/8)*8;
else
Width = Y;
end
Depth = Z;
clear X Y Z
%
t1 = cputime; % start CPU time
% call appropriate motion estimation routine
switch PredictionType
case 'full'
[x,y,pf] = MCpredict_Full(A,B,N,W);
case 'half'
[x,y,pf] = MCpredict_Half(A,B,N,W);
case 'quarter'
[x,y,pf] = MCpredict_Quarter(A,B,N,W);
end
t2 = cputime; % end CPU time (t2-t1) is the time for motion estimation
figure,quiver(x,y,'k'),title('Motion Vector')
sprintf('MSE with MC = %3.3f',std2(pf)*std2(pf))
sprintf('MSE without MC = %3.3f', std2(single(A)-single(B))*std2(single(A)-single(B)))
figure,imshow(pf,[]),title('MC prediction')
figure,imshow(single(A)-single(B),[]),title('prediction without MC')
[hNoMC,BinNoMC] = hist(single(A)-single(B),256);
[hMC,BinMC] = hist(pf,256);
figure,plot(BinMC,hMC,'k'),title('histogram of p frame with MC')
figure,plot(BinNoMC,hNoMC,'k')
title('histogram of p frame with no MC')

function [x,y,pf] = MCpredict_full(A,B,N,W)
% [x,y,pf] = MCpredict_Full(A,B,N,W)
% Computes motion vectors of N x N moving blocks
% in an intensity image using full search and
% does a motion compensated prediction to a single pel accuracy
% Input:
% A = reference frame
% B = current frame
% N = block size (nominal value is 8, assumed square)
% W = search window (2N x 2N)
% Output:
% x = horizontal component of motion vector
% y = Vertical component of motion vector
% pf = motion compensated prediction image, same size as input image

[Height,Width] = size(A);
% pad input images on left, right, top, and bottom
% padding by replicating works better than padding w/ zeros, which is
% better than symmetric which is better than circular
A1 = double(padarray(A,[W/2 W/2],'replicate'));
B1 = double(padarray(B,[W/2 W/2],'replicate'));
x = int16(zeros(Height/N,Width/N));% x-component of motion vector
y = int16(zeros(Height/N,Width/N));% y-component of motion vector
% Find motion vector by exhaustive search to a single pel accuracy
figure,imshow(B), title('Superimposed motion vectors')
hold on % display image & superimpose motion vectors
for r = N:N:Height
rblk = floor(r/N);
for c = N:N:Width
cblk = floor(c/N);
D = 1.0e+10;% initial city block distance
for u = -N:N
for v = -N:N
d = B1(r+1:r+N,c+1:c+N)-A1(r+u+1:r+u+N,c+v+1:c+v+N);
d = sum(abs(d(:)));% city block distance between pixels
if d < D
D = d;
x(rblk,cblk) = v; y(rblk,cblk) = u;
end
end
end
quiver(c+y(rblk,cblk),r+x(rblk,cblk),x(rblk,cblk),y(rblk,cblk),'k','LineWidth',1)
end
end
hold off
% Reconstruct current frame using prediction error & reference frame
N2 = 2*N;
pf = double(zeros(Height,Width)); % prediction frame
Br = double(zeros(Height,Width)); % reconstructed frame
for r = 1:N:Height
rblk = floor(r/N) + 1;
for c = 1:N:Width
cblk = floor(c/N) + 1;
x1 = x(rblk,cblk); y1 = y(rblk,cblk);
pf(r:r+N-1,c:c+N-1) = B1(r+N:r+N2-1,c+N:c+N2-1-A1(r+N+y1:r+y1+N2-1,c+N+x1:c+x1+N2-1);
Br(r:r+N-1,c:c+N-1) = A1(r+N+y1:r+y1+N2-1,c+N+x1:c+x1+N2-1)+ pf(r:r+N-1,c:c+N-1);
end
end
%
figure,imshow(uint8(round(Br))),title('Reconstructed image')

function [x,y,pf] = MCpredict_Half(A,B,N,W)
% [x,y,pf] = MCpredict_Full(A,B,N,W)
% Computes motion vectors of N x N moving blocks
% in an intensity image using full search and
% does a motion compensated prediction to a half pel accuracy
% Input:
% A = reference frame
% B = current frame
% N = block size (nominal value is 8, assumed square)
% W = search window (2N x 2N)
% Output:
% x = horizontal component of motion vector
% y = Vertical component of motion vector
% pf = motion compensated prediction image, same size as input image
[Height,Width] = size(A);
% pad input images on left, right, top, and bottom
A1 = double(padarray(A,[W/2 W/2],'symmetric')); % reference block
B1 = double(padarray(B,[W/2 W/2],'symmetric')); % current block
NumRblk = Height/N;
NumCblk = Width/N;
x = zeros(NumRblk,NumCblk);% x-component of motion vector
y = zeros(NumRblk,NumCblk);% %y-component of motion vector
pf = double(zeros(Height,Width)); % prediction frame
% Find motion vectors by exhaustive search to 1/2 pel accuracy
figure,imshow(B), title('Superimposed motion vectors')
hold on % display image & superimpose motion vectors
for r = N:N:Height
rblk = floor(r/N);
for c = N:N:Width
cblk = floor(c/N);
D = 1.0e+10;% initial city block distance
for u = -N:N
for v = -N:N
%
RefBlk = A1(r+u+1:r+u+N,c+v+1:c+v+N);
CurrentBlk = B1(r+1:r+N,c+1:c+N);
[x2,y2] = meshgrid(r+u+1:r+u+N,c+v+1:c+v+N);
[x3,y3] = meshgrid(r+u-0.5:r+u+N-1,c+v-0.5:c+v+N-1);
% interpolate at 1/2 pel accuracy
z1 = interp2(x2,y2,RefBlk,x3,y3,'*linear');
Indx = isnan(z1);
z1(Indx == 1) = CurrentBlk(Indx==1);
%
dd = CurrentBlk - round(z1);
d = sum(abs(dd(:)));
if d < D
D = d;
U = u+0.5; L = v+0.5;
pf(r-N+1:r,c-N+1:c) = dd;
end
end
end
x(rblk,cblk) = L; % Motion in the vertical direction
y(rblk,cblk) = U; % Motion in the horizontal direction
quiver(c+y(rblk,cblk),r+x(rblk,cblk),x(rblk,cblk),y(rblk,cblk),'k','LineWidth',1)
end
end
hold off
% Reconstruct current frame using prediction error & reference frame
N2 = 2*N;
Br = double(zeros(Height,Width));
for r = N:N:Height
rblk = floor(r/N);
for c = N:N:Width
cblk = floor(c/N);
x1 = x(rblk,cblk); y1 = y(rblk,cblk);
Indr1 = floor(r+y1+1); Indr2 = floor(r+y1+N);
if Indr1 <= 0
Indr1 = 1;
end
if Indr2 > Height +N2
Indr2 = Height + N2;
end
Indc1 = floor(c+x1+1); Indc2 = floor(c+x1+N);
if Indc1 <= 0
Indc1 = 1;
end
if Indc2 > Width +N2
Indc2 = Width + N2;
end
RefBlk = A1(Indr1:Indr2,Indc1:Indc2);
%
[x2,y2] = meshgrid(Indr1:Indr2,Indc1:Indc2);
[x3,y3] = meshgrid(r-N+1:r,c-N+1:c);
z1 = interp2(x2,y2,RefBlk,x3,y3,'*linear');
Indx = isnan(z1);
z1(Indx==1) = RefBlk(Indx == 1);
Br(r-N+1:r,c-N+1:c)= round(pf(r-N+1:r,c-N+1:c) + z1);
end
end
figure,imshow(uint8(round(Br))),title('Reconstructed image')

function [x,y,pf] = MCpredict_Quarter(A,B,N,W)
% [x,y,pf] = MCpredict_Quarter(A,B,N,W)
% Computes motion vectors of N x N moving blocks
% in an intensity image using full search and
% does a motion compensated prediction to a quarter pel accuracy
% Input:
% A = reference frame
% B = current frame
% N = block size (nominal value is 8, assumed square)
% W = search window (2N x 2N)
% Output:
% x = horizontal component of motion vector
% y = Vertical component of motion vector
% pf = motion compensated prediction image, same size as input image

[Height,Width] = size(A);
% pad input images on left, right, top, and bottom
A1 = single(padarray(A,[W/2 W/2],'symmetric')); % reference block
B1 = single(padarray(B,[W/2 W/2],'symmetric')); % current block
NumRblk = Height/N;
NumCblk = Width/N;
x = zeros(NumRblk,NumCblk);% x-component of motion vector
y = zeros(NumRblk,NumCblk);% %y-component of motion vector
pf = single(zeros(Height,Width)); % predicted frame
% Find motion vectors to 1/4 pel accuracy
figure,imshow(B), title('Superimposed motion vectors')
hold on % display image & superimpose motion vectros
for r = N:N:Height
rblk = floor(r/N);
for c = N:N:Width
cblk = floor(c/N);
D = 1.0e+10;% initial city block distance
for u = -N:N
for l = -N:N
RefBlk = A1(r+u+1:r+u+N,c+l+1:c+l+N);
CurrentBlk = B1(r+1:r+N,c+1:c+N);
[x2,y2] = meshgrid(r+u+1:r+u+N,c+l+1:c+l+N);
[x3,y3] = meshgrid(r+u-0.25:r+u+N-1,c+l-0.25:c+l+N-1);
% interpolate at 1/4 pel
z1 = interp2(x2,y2,RefBlk,x3,y3,'*linear');
Indx = isnan(z1);
z1(Indx == 1) = CurrentBlk(Indx==1);
dd = CurrentBlk - round(z1);
d = sum(abs(dd(:)));
if d < D
D = d;
U = u+0.25; L = l+0.25;
pf(r:r+N-1,c:c+N-1) = dd;
end
end
end
x(rblk,cblk) = L; % Motion in the vertical direction
y(rblk,cblk) = U; % Motion in the horizontal direction
quiver(c+y(rblk,cblk),r+x(rblk,cblk),x(rblk,cblk),y(rblk,cblk),'k','LineWidth',1)
end
end
hold off
% Reconstruct current frame using prediction error &
reference frame
%
N2 = 2*N;
Br = single(zeros(Height,Width));
for r = N:N:Height
rblk = floor(r/N);
for c = N:N:Width
cblk = floor(c/N);
x1 = x(rblk,cblk); y1 = y(rblk,cblk);
Indr1 = floor(r+y1+1); Indr2 = floor(r+y1+N);
if Indr1 <= 0
Indr1 = 1;
end
if Indr2 > Height +N2
Indr2 = Height + N2;
end
Indc1 = floor(c+x1+1); Indc2 = floor(c+x1+N);
if Indc1 <= 0
Indc1 = 1;
end
if Indc2 > Width +N2
Indc2 = Width + N2;
end
RefBlk = A1(Indr1:Indr2,Indc1:Indc2);
[x2,y2] = meshgrid(Indr1:Indr2,Indc1:Indc2);
[x3,y3] = meshgrid(r-N+1:r,c-N+1:c);
z1 = interp2(x2,y2,RefBlk,x3,y3,'*linear');
Indx = isnan(z1);
z1(Indx==1) = RefBlk(Indx == 1);
Br(r-N+1:r,c-N+1:c)= round(pf(r-N+1:r,c-N+1:c) + z1);
end
end
figure,imshow(uint8(round(Br))),title('Reconstructed image')

bravo
bonne continuation:-)
slts

salut merci bcq gej62 j'ai résolu le prob en ajoutant qlq lignes au programme ils m'ont permetté de lire des frames à partir d'une video avec extension .YUV

bonjour
tu peux essayer virtual dub ou avidemux selon le "format" de ta vidéo si tu veux extraire toute la sequence.ou redimensionner tes images via un soft photos .
slts