How to process large video in Matlab with for loop and without memory error

Question

I\'m new to Matlab processing, and I would like to read and process a large video (more than 200k frames) inside a \"for loop\" (or without it). In particular, i would like

Answer 1

% create video handle and get number of frames
vidObj = VideoReader(video_file);
nFrames = get(vidObj, 'NumberOfFrames');

blocksize = 1000;
BlocksIDs = 1:blocksize:nFrames;
nBlocks = numel(BlocksIDs);

frame_step = 3;

% cell array with all correlations values grouped by block
xcorrs_all_blocks = cell(1, nBlocks);

for j = 1 : nBlocks

    % if this is the last block, process until the last frame
    if j == nBlocks
        last_frame = nFrames;
    % otherwise, read until next block
    else
        last_frame = BlocksIDs(j + 1);
    end
    % compute the frame numbers that we want to look at
    frame_indices = BlocksIDs(j) : frame_step : last_frame;
    nFrames = numel(frame_indices);

    first_frame = [];
    % pre-allocate array holding the in-block corr2 values.
    xcorrs_this_block = nan(1, nFrames-1);

    for k = 1 : nFrames

        % read-in raw frame from video file.
        raw_frame = read(vidObj, frame_indices(k));            
        % determine level for bw conversion - this might help.
        level = graythresh(raw_frame);
        raw_frame = im2bw(raw_frame, level);

        if k == 1
            % save current frame as first frame for later corr2 processing
            first_frame = raw_frame;
        else
            % calc the correlation between the first frame in the block and each successive frame
            xcorrs_this_block(k-1) = corr2(first_frame, raw_frame);
        end


    end

    % save all xcorr values into global cell array.
    xcorrs_all_blocks{j} = xcorrs_this_block;

end

Answer 2

Here is my version:

mov = VideoReader('movie.avi');
nFrames = mov.NumberOfFrames;

len = 1000;     %# epoch length
step = 3;       %# step size

%# indices of each epoch
indices = bsxfun(@plus, 1:step:len, (0:ceil(nFrames/len-1))'*len);   %#'
indices = num2cell(indices,2);
indices{end}(indices{end}>nFrames) = [];

%# loop over each epoch
corr_coef = cell(size(indices));
for e=1:numel(indices)
    %# read first image in epoch
    img1 = read(mov, indices{e}(1));
    img1 = rgb2gray(img1);            %# instead of im2bw(img1, graythresh(img1))

    %# read rest of images in epoch
    corr_coef{e} = zeros(1,numel(indices{e})-1);
    for f=2:numel(indices{e})
        img2 = read(mov, indices{e}(f));
        img2 = rgb2gray(img2);

        %# compute corr2 between the two images
        corr_coef{e}(f-1) = corr2(img1,img2);
    end
end

The cell array corr_coef contains the correlation coefficients in each epoch, where each cell contains a vector corr_coef{e}(i) of corr2 between the first frame and the (i+1)-th frame.

Note that if one of the frames is constant (all black for example), the 2D correlation coefficient is simply NaN (zero divided by zero in the formula‌​)