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function g = gistGabor(img, w, G)
%
% Input:
% img = input image (it can be a block: [nrows, ncols, c, Nimages])
% w = number of windows (w*w)
% G = precomputed transfer functions
%
% Output:
% g: are the global features = [Nfeatures Nimages],
% Nfeatures = w*w*Nfilters*c
if ndims(img)==2
c = 1;
N = 1;
end
if ndims(img)==3
[nrows ncols c] = size(img);
N = c;
end
if ndims(img)==4
[nrows ncols c N] = size(img);
img = reshape(img, [nrows ncols c*N]);
N = c*N;
end
[n n Nfilters] = size(G);
W = w*w;
g = zeros([W*Nfilters N]);
img = single(fft2(img));
k=0;
for n = 1:Nfilters
ig = abs(ifft2(img.*repmat(G(:,:,n), [1 1 N])));
v = downN(ig, w);
g(k+1:k+W,:) = reshape(v, [W N]);
k = k + W;
drawnow
end
if c == 3
% If the input was a color image, then reshape 'g' so that one column
% is one images output:
g = reshape(g, [size(g,1)*3 size(g,2)/3]);
end
function y=downN(x, N)
%
% averaging over non-overlapping square image blocks
%
% Input
% x = [nrows ncols nchanels]
% Output
% y = [N N nchanels]
nx = fix(linspace(0,size(x,1),N+1));
ny = fix(linspace(0,size(x,2),N+1));
y = zeros(N, N, size(x,3));
for xx=1:N
for yy=1:N
v=mean(mean(x(nx(xx)+1:nx(xx+1), ny(yy)+1:ny(yy+1),:),1),2);
y(xx,yy,:)=v(:);
end
end
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