%d=IterativeLKOpticalFlow(img1, img2, roi, dInit)%% Computes the Lucas-Kanade optical flow using the iterative method.% The function computes the flow, in each iteration the flow is refined.% The iteration is stopped if the flow magnitude falls beneath 0.01 pixels% or a maximum of 10 iterations occur.%% Inputs:% img1 - The source image.% img2 - The destination image.% roi  - A region in img1 in which the flow should be computed.% dInit - The initial estimate of the affine parameters%% Outpus:% d - The computed affine transformation parametersfunction d=IterativeLKOpticalFlow(img1, img2, roi, dInit)K=10; % Number of iterationsSTOP_THR = 0.01; % Stop if accuracy is better than 0.01 pixel% Copy inflated region of interest our of image (we need border for% derivative operation)img1 = img1(roi(1)-1:roi(3)+1,roi(2)-1:roi(4)+1);% Compute Ix and Iy, G and H[Ht,G]=ComputeLKFlowParms(img1);% Remove borderimg1=img1(2:end-1, 2:end-1);% Loop and perform iterations of optical flow computationd=dInit;k=1;dc=inf;while k<K && norm(dc)>STOP_THR  % Use current translation vector to resample img2 and compute  % temporal difference image  It=img1-ResampleImg(img2, roi, d);  % Find indexes which are not out of fov  I = ~isnan(It(:));    % Compute right side of optical flow equation: Gd=b  b=Ht(:,I)*It(I);  % Solve for current affine displacement parameters  dc = G\b;    % Add current displacement to d (This is actually concatinating the two  % affine matrixes)  d=d+reshape(dc, 2, 3)*(eye(3)+[d;0 0 0]);    k=k+1;enddummy=1;