# parameters
# - the image
# - the number of vertical and horizontal divisions
function [data] = edgeOrientationHistogram(im, r)

% define the filters for the 5 types of edges
f2 = zeros(3,3,5);
f2(:,:,1) = [1 2 1;0 0 0;-1 -2 -1];
f2(:,:,2) = [-1 0 1;-2 0 2;-1 0 1];
f2(:,:,3) = [2 2 -1;2 -1 -1; -1 -1 -1];
f2(:,:,4) = [-1 2 2; -1 -1 2; -1 -1 -1];
f2(:,:,5) = [-1 0 1;0 0 0;1 0 -1];

% the size of the image
ys = size(im,1);
xs = size(im,2);

# The image has to be in gray scale (intensities)
if (isrgb(im))
    im = rgb2gray(im);
endif

# Build a new matrix of the same size of the image
# and 5 dimensions to save the gradients
im2 = zeros(ys,xs,5);

# iterate over the posible directions
for i = 1:5
    # apply the sobel mask
    im2(:,:,i) = filter2(f2(:,:,i), im);
end

# calculate the max sobel gradient
[mmax, maxp] = max(im2,[],3);
# save just the index (type) of the orientation
# and ignore the value of the gradient
im2 = maxp;

# detect the edges using the default Octave parameters
ime = edge(im, 'canny');

# multiply against the types of orientations detected
# by the Sobel masks
im2 = im2.*ime;

# produce a structur to save all the bins of the
# histogram of each region
eoh = zeros(r,r,6);
# for each region
for j = 1:r
    for i = 1:r
        # extract the subimage
        clip = im2(round((j-1)*ys/r+1):round(j*ys/r),round((i-1)*xs/r+1):round(i*xs/r));
        # calculate the histogram for the region
        eoh(j,i,:) = (hist(makelinear(clip), 0:5)*100)/numel(clip);
    end
end

# take out the zeros
eoh = eoh(:,:,2:6);

# represent all the histograms on one vector
data = zeros(1,numel(eoh));
data(:) = eoh(:);
endfunction
