% ORIENTATIONFILTER Generate orientation selective filterings of an image
%
% Usage: oim = orientationfilter(im, norient, angoverlap, boost, cutoff, histcut)
%
% Arguments: im - Image to be filtered.
% norient - Number of orientations, try 8.
% angoverlap - Angular bandwidth overlap factor. A value of 1 gives a
% minimum overlap between adjacent filter orientations
% while still providing full coverage of the spectrum.
% However, it is often useful to provide greater overlap
% between adjacent filter orientations, especially if the
% output is to be viewed using the CYCLEMIX image
% blender. This gives smoother transitions between
% images. Try values 1.5 to 2.0
% boost - The ratio that high frequency values are boosted
% (or supressed) relative to the low frequency values.
% Try values in the range 2 - 4 to start with. Use a
% value of 1 for no boost.
% cutoff - Cutoff frequency of the highboost filter 0 - 0.5, try 0.2
% histcut - Percentage of the histogram extremes to truncate. This
% is useful in preventing outlying values in the data
% from dominating in the image rescaling for display. Try
% a small amount, say, 0.01%
%
% Returns: oim - Cell array of length 'norient' containing the
% orientation filtered images.
%
%
% This function is designed to help identify oriented structures within an image
% by applying orientation selective filters. If one is looking for lineaments
% it is typically useful to boost the high frequency components in the image as
% well, hence the combination of the two filters
%
% Example: Generate 8 orientation filterings of an image with an angular
% bandwidth overlap factor of 1.5 and amplifying spatial frequencies greater
% than 0.1 (wavelenths < 10 pixels) by a factor of 2. Also truncate the image
% histograms so that 0.01% of image pixels are saturated. This helps ensure
% that outlying data values do not dominate when the image is scaled for
% display.
%
% >> oim = orienationfilter(im, 8, 1.5, 2, 0.1, 0.01);
%
% An effective way to view all the output images is to use the cyclic image
% blending tool CYCLEMIX.m
%
% >> cyclemix(oim);
%
% Note that the cyclemix control wheel varies from 0 - 2pi and these angles are
% mapped to the orientation filtered images which vary from 0 - pi in angle.
% This makes the interface a bit counterintuitive to use. To get around this
% one can replicate the the cell array of orientation filtered images giving an
% array that corresponds to the angles 0 - pi, 0 - pi. When this is passed to
% cyclemix you get a much more intuitive interface
%
% >> cyclemix({oim{:} oim{:}})
%
% See also: CYCLEMIX, HIGHBOOSTFILTER, HISTTRUNCATE
% Copyright (c) 2012-2017 Peter Kovesi
% Centre for Exploration Targeting
% The University of Western Australia
% peter.kovesi at uwa edu au
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, subject to the following conditions:
%
% The above copyright notice and this permission notice shall be included in
% all copies or substantial portions of the Software.
%
% The Software is provided "as is", without warranty of any kind.
% August 2012 Original version
% July 2014 Revised to incorporate highboost filtering and histogram
% truncation.
% December 2017 Eliminated use of fliplr etc to create symmetric filter as
% this was slightly incorrect. Also ensured that filters have
% 0 DC value.
function oim = orientationfilter(im, norient, angoverlap, boost, cutoff, histcut)
IM = fft2(im);
[rows,cols] = size(im);
if ~exist('angoverlap','var')
angScale = norient/2;
else
angScale = norient/2 / angoverlap;
end
% If a highboost filter has been specified apply it to the fourier
% transform of the image.
if exist('cutoff', 'var')
IM = IM .* highboostfilter([rows,cols], cutoff, 1, boost);
end
if ~exist('histcut','var'), histcut = 0; end
% Construct frequency domain filter matrices
[ ~ , u1, u2] = filtergrid(rows,cols);
theta = atan2(-u2,u1); % Matrix values contain polar angle.
% (note -ve y is used to give +ve
% anti-clockwise angles)
sintheta = sin(theta);
costheta = cos(theta);
for o = 1:norient % For each orientation...
angl = (o-1)*pi/norient; % Filter angle.
% For each point in the filter matrix calculate the angular distance from
% the specified filter orientation. To overcome the angular wrap-around
% problem sine difference and cosine difference values are first computed
% and then the atan2 function is used to determine angular distance.
ds = sintheta * cos(angl) - costheta * sin(angl); % Difference in sine.
dc = costheta * cos(angl) + sintheta * sin(angl); % Difference in cosine.
dtheta = abs(atan2(ds,dc)); % Absolute angular distance.
% Make the filter symmetric so that the angle deviation from angl =
% deviation from angle + pi.
dtheta(dtheta>pi/2) = pi - dtheta(dtheta>pi/2);
% Scale theta so that cosine spread function has the right wavelength
% and clamp to pi
dtheta = min(dtheta*angScale, pi);
% The orientation filter haa a cosine cross section in the frequency domain
% The spread function is cos(dtheta) between -pi and pi. We add 1,
% and then divide by 2 so that the value ranges 0-1
spread = (cos(dtheta)+1)/2;
spread(1,1) = 0; % Ensure 0 DC
% show(fftshift(spread),o);
% Apply orientation filter
oim{o} = real(ifft2(IM.*spread));
% Truncate extremes of image histogram
if histcut
oim{o} = histtruncate(oim{o}, histcut, histcut);
end
end