get_match.m

this is a very very very nice code

function p = get_match(target, loc_mask, param)
thresh = param.thresh;
gauss = param.gauss(:)';
patches = param.patches;

loc_mask = loc_mask(:)';
target = target(:)';

%Compute the distance between each candidate patch and the target patch,
%weighting pixels using the gaussian, and only considering pixels used
%under a valid mask.
vals = [];
target = target .* loc_mask;
for i = 1:size(patches, 1)
    cmp = sum( ((patches(i, :) - target) .* loc_mask).^2 .* gauss );
    vals = [vals; [cmp, i]];
end
%Sort the values in order
vals = sortrows(vals, 1);

%Now, iterate over all the candidates, keeping any whose distance is less
%than the threshold, building a distribution on the intensity of the center
%pixel in the candidates
target = patches(vals(1, 2), :);
szh = 1 + (size(patches,2)-1) / 2;
distr = [patches(vals(1, 2), szh)];
for i = 2:size(vals, 1)
    if param.fast == 0
        distance = sum( ((patches(vals(i, 2), :) - target) .* loc_mask).^2 .* gauss );
    else
        distance = vals(i,1);
    end
    if distance <= thresh
        distr = [distr, patches(vals(i, 2), szh)];
    end
end

%Make sure we have a column vector...
if size(distr,2) > size(distr,1)
    distr = distr';
end

%Sample from the generated distribution
ind = ceil(rand(1) * size(distr,1));
p = distr(ind);