function SS = gaussianss(I,sigman,O,S,omin,smin,smax,sigma0)
% GAUSSIANSS
%   SS = GAUSSIANSS(I,SIGMAN,O,S,OMIN,SMIN,SMAX,SIGMA0) returns the
%   Gaussian scale space of image I. Image I is assumed to be
%   pre-smoothed at level SIGMAN. O,S,OMIN,SMIN,SMAX,SIGMA0 are the
%   parameters of the scale space as explained in PDF:SIFT.USER.SS.
%
%   See also DIFFSS(), PDF:SIFT.USER.SS.

% History
%  4-15-2006  Fixed some comments

% AUTORIGHTS
% Copyright (c) 2006 The Regents of the University of California.
% All Rights Reserved.
%
% Created by Andrea Vedaldi
% UCLA Vision Lab - Department of Computer Science
%
% Permission to use, copy, modify, and distribute this software and its
% documentation for educational, research and non-profit purposes,
% without fee, and without a written agreement is hereby granted,
% provided that the above copyright notice, this paragraph and the
% following three paragraphs appear in all copies.
%
% This software program and documentation are copyrighted by The Regents
% of the University of California. The software program and
% documentation are supplied "as is", without any accompanying services
% from The Regents. The Regents does not warrant that the operation of
% the program will be uninterrupted or error-free. The end-user
% understands that the program was developed for research purposes and
% is advised not to rely exclusively on the program for any reason.
%
% This software embodies a method for which the following patent has
% been issued: "Method and apparatus for identifying scale invariant
% features in an image and use of same for locating an object in an
% image," David G. Lowe, US Patent 6,711,293 (March 23,
% 2004). Provisional application filed March 8, 1999. Asignee: The
% University of British Columbia.
%
% IN NO EVENT SHALL THE UNIVERSITY OF CALIFORNIA BE LIABLE TO ANY PARTY
% FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES,
% INCLUDING LOST PROFITS, ARISING OUT OF THE USE OF THIS SOFTWARE AND
% ITS DOCUMENTATION, EVEN IF THE UNIVERSITY OF CALIFORNIA HAS BEEN
% ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. THE UNIVERSITY OF
% CALIFORNIA SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT
% LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
% A PARTICULAR PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS ON AN "AS IS"
% BASIS, AND THE UNIVERSITY OF CALIFORNIA HAS NO OBLIGATIONS TO PROVIDE
% MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR MODIFICATIONS.

% --------------------------------------------------------------------
%                                                  Check the arguments
% --------------------------------------------------------------------
if(nargin < 6)
  error('Six arguments are required.') ;
end

if(~isreal(I) || ndims(I) > 2)
  error('I must be a real two dimensional matrix') ;
end

if(smin >= smax)
  error('smin must be greather or equal to smax') ;
end

% --------------------------------------------------------------------
%                                                           Do the job
% --------------------------------------------------------------------

% Scale multiplicative step
k = 2^(1/S) ;

% Lowe's convention: the scale (o,s)=(0,-1) has standard deviation
% 1.6 (was it variance?)
if(nargin < 7)
  sigma0 = 1.6 * k ;
end

dsigma0 = sigma0 * sqrt(1 - 1/k^2) ; % Scale step factor
sigman  = 0.5 ;                      % Nominal smoothing of the image

% Scale space structure
SS.O          = O ;
SS.S          = S ;
SS.sigma0     = sigma0 ;
SS.omin       = omin ;
SS.smin       = smin ;
SS.smax       = smax ;

% If mino < 0, multiply the size of the image.
% (The rest of the code is consistent with this.)
if omin < 0
  for o=1:-omin
    I = doubleSize(I) ;
  end
elseif omin > 0
  for o=1:omin
    I = halveSize(I) ;
  end
end

[M,N] = size(I) ;

% Index offset
so = -smin+1 ;

% --------------------------------------------------------------------
%                                                         First octave
% --------------------------------------------------------------------
%
% The first level of the first octave has scale index (o,s) =
% (omin,smin) and scale coordinate
%
%    sigma(omin,smin) = sigma0 2^omin k^smin
%
% The input image I is at nominal scale sigman. Thus in order to get
% the first level of the pyramid we need to apply a smoothing of
%
%   sqrt( (sigma0 2^omin k^smin)^2 - sigman^2 ).
%
% As we have pre-scaled the image omin octaves (up or down,
% depending on the sign of omin), we need to correct this value
% by dividing by 2^omin, getting
%e
%   sqrt( (sigma0 k^smin)^2 - (sigman/2^omin)^2 )
%

if(sigma0 * 2^omin * k^smin < sigman)
  warning('The nominal smoothing exceeds the lowest level of the scale space.') ;
end

SS.octave{1} = zeros(M,N,smax-smin+1) ;
SS.octave{1}(:,:,1)  = imsmooth(I, ...
  sqrt((sigma0*k^smin)^2  - (sigman/2^omin)^2)) ;

for s=smin+1:smax
  % Here we go from (omin,s-1) to (omin,s). The extra smoothing
  % standard deviation is
  %
  %  (sigma0 2^omin 2^(s/S) )^2 - (simga0 2^omin 2^(s/S-1/S) )^2
  %
  % Aftred dividing by 2^omin (to take into account the fact
  % that the image has been pre-scaled omin octaves), the
  % standard deviation of the smoothing kernel is
  %
  %   dsigma = sigma0 k^s sqrt(1-1/k^2)
  %
  dsigma = k^s * dsigma0 ;
  SS.octave{1}(:,:,s +so) = ...
    imsmooth(squeeze(...
    SS.octave{1}(:,:,s-1 +so)...
    ), dsigma )  ;
end


% --------------------------------------------------------------------
%                                                        Other octaves
% --------------------------------------------------------------------

for o=2:O
  % We need to initialize the first level of octave (o,smin) from
  % the closest possible level of the previous octave. A level (o,s)
  % in this octave corrsponds to the level (o-1,s+S) in the previous
  % octave. In particular, the level (o,smin) correspnds to
  % (o-1,smin+S). However (o-1,smin+S) might not be among the levels
  % (o-1,smin), ..., (o-1,smax) that we have previously computed.
  % The closest pick is
  %
  %                       /  smin+S    if smin+S <= smax
  % (o-1,sbest) , sbest = |
  %                       \  smax      if smin+S > smax
  %
  % The amount of extra smoothing we need to apply is then given by
  %
  %  ( sigma0 2^o 2^(smin/S) )^2 - ( sigma0 2^o 2^(sbest/S - 1) )^2
  %
  % As usual, we divide by 2^o to cancel out the effect of the
  % downsampling and we get
  %
  %  ( sigma 0 k^smin )^2 - ( sigma0 2^o k^(sbest - S) )^2
  %
  sbest = min(smin + S, smax) ;
  TMP = halveSize(squeeze(SS.octave{o-1}(:,:,sbest+so))) ;
  target_sigma = sigma0 * k^smin ;
    prev_sigma = sigma0 * k^(sbest - S) ;

  if(target_sigma > prev_sigma)
          TMP = imsmooth(TMP, sqrt(target_sigma^2 - prev_sigma^2) ) ;
  end
  [M,N] = size(TMP) ;

  SS.octave{o} = zeros(M,N,smax-smin+1) ;
  SS.octave{o}(:,:,1) = TMP ;

  for s=smin+1:smax
    % The other levels are determined as above for the first octave.
    dsigma = k^s * dsigma0 ;
    SS.octave{o}(:,:,s +so) = ...
      imsmooth(squeeze(...
      SS.octave{o}(:,:,s-1 +so)...
      ), dsigma)  ;
  end

end

% -------------------------------------------------------------------------
%                                                       Auxiliary functions
% -------------------------------------------------------------------------
function J = doubleSize(I)
[M,N]=size(I) ;
J = zeros(2*M,2*N) ;
J(1:2:end,1:2:end) = I ;
J(2:2:end-1,2:2:end-1) = ...
  0.25*I(1:end-1,1:end-1) + ...
  0.25*I(2:end,1:end-1) + ...
  0.25*I(1:end-1,2:end) + ...
  0.25*I(2:end,2:end) ;
J(2:2:end-1,1:2:end) = ...
  0.5*I(1:end-1,:) + ...
    0.5*I(2:end,:) ;
J(1:2:end,2:2:end-1) = ...
  0.5*I(:,1:end-1) + ...
    0.5*I(:,2:end) ;

function J = halveSize(I)
J=I(1:2:end,1:2:end) ;
%[M,N] = size(I) ;
%m=floor((M+1)/2) ;
%n=floor((N+1)/2) ;
%J = I(:,1:2:2*n) + I(:,2:2:2*n+1) ;
%J = 0.25*(J(1:2:2*m,:)+J(2:2:2*m+1,:)) ;
