debug libextra and remove matlab

1 files changed, 0 insertions, 436 deletions

diff --git a/SD-VBS/benchmarks/texture_synthesis/src/matlab/textureSynthesis.m b/SD-VBS/benchmarks/texture_synthesis/src/matlab/textureSynthesis.m
deleted file mode 100755
index 38fce5a..0000000
--- a/SD-VBS/benchmarks/texture_synthesis/src/matlab/textureSynthesis.m
+++ /dev/null
@@ -1,436 +0,0 @@
-function [im,snrP,imS] = textureSynthesis(params, im0, Niter, cmask, imask)
-
-% [res,snrP,imS] = textureSynthesis(params, initialIm, Niter, cmask, imask)
-%
-% Synthesize texture applying Portilla-Simoncelli model/algorithm.
-%       
-% params: structure containing texture parameters (as returned by textureAnalysis).
-%
-% im0: initial image, OR a vector (Ydim, Xdim, [SEED]) containing
-% dimensions of desired image and an optional seed for the random
-% number generator.  If dimensions are passed, initial image is
-% Gaussian white noise.
-%
-% Niter (optional): Number of iterations.  Default = 50.
-%
-% cmask (optional): binary column vector (4x1) indicating which sets of
-% constraints we want to apply in the synthesis. The four sets are:
-%               1) Marginal statistics (mean, var, skew, kurt, range)
-%               2) Correlation of subbands (space, orientation, scale)
-%               3) Correlation of magnitude responses (sp, or, sc)
-%               4) Relative local phase
-%
-% imask (optional): imsizex2 matrix.  First column is a mask, second
-%       column contains the image values to be imposed. If only one column is
-%       provided, it assumes it corresponds to the image values, and it uses
-%       a raised cosine square for the mask.
-% snrP (optional):      Set of adjustment values (in dB) of the parameters.
-% imS (optional):       Sequence of synthetic images, from niter = 1 to 2^n, being
-%                       n = floor(log2(Niter)).
-
-% Javier Portilla and Eero Simoncelli.
-% Work described in:
-%  "A Parametric Texture Model based on Joint Statistics of Complex Wavelet Coefficients".
-%  J Portilla and E P Simoncelli. Int'l Journal of Computer Vision,
-%  vol.40(1), pp. 49-71, Dec 2000.   
-%
-% Please refer to this publication if you use the program for research or
-% for technical applications. Thank you.
-%
-% Copyright, Center for Neural Science, New York University, January 2001.
-% All rights reserved.
-
-Warn = 0;  % Set to 1 if you want to see warning messages
-
-%% Check required args are passed:
-if (nargin < 2)
-  error('Function called with too few input arguments');
-end
-
-if ( ~exist('Niter') | isempty(Niter) )
-  Niter = 50;
-end
-
-if (exist('cmask') & ~isempty(cmask) )
-  cmask = (cmask > 0.5);  % indices of ones in mask
-else
-  cmask = ones(4,1);
-end
-
-%% Extract parameters  
-statg0 = params.pixelStats;
-mean0 = statg0(1); var0 =  statg0(2);
-skew0 =  statg0(3); kurt0 =  statg0(4);
-mn0 =  statg0(5);  mx0 = statg0(6);
-statsLPim = params.pixelLPStats;
-skew0p = statsLPim(:,1);
-kurt0p = statsLPim(:,2);
-vHPR0 = params.varianceHPR;
-acr0 = params.autoCorrReal;
-ace0 = params.autoCorrMag;
-magMeans0 = params.magMeans;
-C0 = params.cousinMagCorr;
-Cx0 = params.parentMagCorr;
-Crx0 = params.parentRealCorr;
-
-%% Extract {Nsc, Nor, Na} from params
-tmp = size(params.autoCorrMag);
-Na = tmp(1); Nsc = tmp(3);
-Nor = tmp(length(tmp))*(length(tmp)==4) + (length(tmp)<4);
-la = (Na-1)/2;
-
-%% If im0 is a vector of length 2, create Gaussian white noise image of this
-%% size, with desired pixel mean and variance.  If vector length is
-%% 3,  use the 3rd element to seed the random number generator.
-if ( length(im0) <= 3 )
-  if ( length(im0) == 3)
-    randn('state', im0(3)); % Reset Seed
-    im0 = im0(1:2);
-  end
-  im = mean0 + sqrt(var0)*randn(im0);
-else
-  im = im0;
-end
-
-%% If the spatial neighborhood Na is too big for the lower scales,
-%% "modacor22.m" will make it as big as the spatial support at
-%% each scale:
-[Ny,Nx] = size(im);
-nth = log2(min(Ny,Nx)/Na);
-if nth<Nsc+1 & Warn,
-  fprintf(1,'Warning: Na will be cut off for levels above #%d !\n',floor(nth));
-end
-
-if  exist('imask') & ~isempty(imask),
-        if  size(imask,1) ~= prod(size(im))
-                error(sprintf('imask size %d does not match image dimensions [%d,%d]',...
-                size(imask,1), size(im,1), size(im,2)));
-        end
-        if size(imask,2) == 1,
-                mask = (cos(-pi/2:2*pi/Ny:pi*(1-2/Ny)/2)).'*cos(-pi/2:2*pi/Nx:pi*(1-2/Nx)/2);
-                mask = mask.^2;
-                aux = zeros(size(im));
-                aux(Ny/4+1:Ny/4+Ny/2,Nx/4+1:Nx/4+Nx/2)=mask;
-                mask = aux;
-        else
-                mask = reshape(imask(:,1),size(im));
-        end
-end
-
-imf = max(1,gcf-1); snrf = imf+1;
-figure(imf);  clf
-subplot(1,2,1); grayRange = showIm(im,'auto',1); title('Starting image');
-drawnow
-
-prev_im=im;
-
-imf = max(1,gcf-1);
-figure(imf);   
-clf;showIm(im,'auto',1); title(sprintf('iteration 0'));
-
-nq = 0;
-Nq = floor(log2(Niter));
-imS = zeros(Ny,Nx,Nq);
-
-%% MAIN LOOP
-for niter = 1:Niter
-
-%p = niter/Niter; 
-p = 1;
-
-  %% Build the steerable pyramid
-  [pyr,pind] = buildSCFpyr(im,Nsc,Nor-1);
-
-  if ( any(vector(mod(pind,4))) )
-    error('Algorithm will fail: band dimensions are not all multiples of 4!');
-  end
-
-  %% Subtract mean of lowBand:
-  nband = size(pind,1);
-  pyr(pyrBandIndices(pind,nband)) = ...
-      pyrBand(pyr,pind,nband) - mean2(pyrBand(pyr,pind,nband));
-
-  apyr = abs(pyr);
-
-  %% Adjust autoCorr of lowBand
-  nband = size(pind,1); 
-  ch = pyrBand(pyr,pind,nband);
-  Sch = min(size(ch)/2);
-  nz = sum(sum(~isnan(acr0(:,:,Nsc+1))));
-  lz = (sqrt(nz)-1)/2;
-  le = min(Sch/2-1,lz);
-  im = real(ch);  %Reconstructed image: initialize to lowband
-  [mpyr,mpind] = buildSFpyr(im,0,0);
-  im = pyrBand(mpyr,mpind,2);
-  vari =  acr0(la+1:la+1,la+1:la+1,Nsc+1);
-if cmask(2),
-  if vari/var0 > 1e-4,
-        [im, snr2(niter,Nsc+1)] = ...
-        modacor22(im, acr0(la-le+1:la+le+1,la-le+1:la+le+1,Nsc+1),p);
-  else
-        im = im*sqrt(vari/var2(im));
-  end
-  if (var2(imag(ch))/var2(real(ch)) > 1e-6)
-    fprintf(1,'Discarding non-trivial imaginary part, lowPass autoCorr!');
-  end
-  im = real(im);
-end % cmask(2)
-if cmask(1),
-  if vari/var0 > 1e-4,
-        [im,snr7(niter,2*(Nsc+1)-1)] = modskew(im,skew0p(Nsc+1),p);     % Adjusts skewness 
-        [im,snr7(niter,2*(Nsc+1))] = modkurt(im,kurt0p(Nsc+1),p);       % Adjusts kurtosis 
-  end
-end     % cmask(2)
- 
-  %% Subtract mean of magnitude
-if cmask(3),
-  magMeans = zeros(size(pind,1), 1);
-  for nband = 1:size(pind,1)
-    indices = pyrBandIndices(pind,nband);
-    magMeans(nband) = mean2(apyr(indices));
-    apyr(indices) = apyr(indices) - magMeans(nband);
-  end
-end     % cmask(3)
-
-  %% Coarse-to-fine loop:
-  for nsc = Nsc:-1:1
-    
-    firstBnum = (nsc-1)*Nor+2;
-    cousinSz = prod(pind(firstBnum,:));
-    ind = pyrBandIndices(pind,firstBnum);
-    cousinInd = ind(1) + [0:Nor*cousinSz-1];
-
-    %% Interpolate parents
-if (cmask(3) | cmask(4)),
-    if (nsc<Nsc)
-      parents = zeros(cousinSz,Nor);
-      rparents = zeros(cousinSz,Nor*2);
-      for nor = 1:Nor
-        nband = (nsc+1-1)*Nor+nor+1; 
-
-        tmp = expand(pyrBand(pyr, pind, nband),2)/4;
-        rtmp = real(tmp); itmp = imag(tmp);
-        tmp = sqrt(rtmp.^2 + itmp.^2) .* exp(2 * sqrt(-1) * atan2(rtmp,itmp));
-        rparents(:,nor) = vector(real(tmp));
-        rparents(:,Nor+nor) = vector(imag(tmp));
-
-        tmp = abs(tmp);
-        parents(:,nor) = vector(tmp - mean2(tmp));
-      end
-    else
-      rparents = [];
-      parents = [];
-    end
-end % if (cmask(3) | cmask(4))
-
-if cmask(3),
-    %% Adjust cross-correlation with MAGNITUDES at other orientations/scales:
-    cousins = reshape(apyr(cousinInd), [cousinSz Nor]);
-    nc = size(cousins,2);   np = size(parents,2);
-    if (np == 0)    
-      [cousins, snr3(niter,nsc)] = adjustCorr1s(cousins, C0(1:nc,1:nc,nsc), 2, p);
-    else
-      [cousins, snr3(niter,nsc), snr4(niter,nsc)] = ...
-          adjustCorr2s(cousins, C0(1:nc,1:nc,nsc), parents, Cx0(1:nc,1:np,nsc), 3, p);
-    end
-    if (var2(imag(cousins))/var2(real(cousins)) > 1e-6)
-      fprintf(1,'Non-trivial imaginary part, mag crossCorr, lev=%d!\n',nsc);
-    else
-      cousins = real(cousins);
-      ind = cousinInd;
-      apyr(ind) = vector(cousins);
-    end
-
-    %% Adjust autoCorr of mag responses 
-    nband = (nsc-1)*Nor+2; 
-    Sch = min(pind(nband,:)/2);
-    nz = sum(sum(~isnan(ace0(:,:,nsc,1))));
-    lz = (sqrt(nz)-1)/2;
-    le = min(Sch/2-1,lz);
-    for nor = 1:Nor,
-      nband = (nsc-1)*Nor+nor+1; 
-      ch = pyrBand(apyr,pind,nband);
-      [ch, snr1(niter,nband-1)] = modacor22(ch,...
-        ace0(la-le+1:la+le+1,la-le+1:la+le+1,nsc,nor), p);
-      ch = real(ch);
-      ind = pyrBandIndices(pind,nband);
-      apyr(ind) = ch;
-      %% Impose magnitude:
-      mag = apyr(ind) + magMeans0(nband);
-      mag = mag .* (mag>0);
-      pyr(ind) = pyr(ind) .* (mag./(abs(pyr(ind))+(abs(pyr(ind))<eps)));
-    end
-end   % cmask(3)
-
-    %% Adjust cross-correlation of REAL PARTS at other orientations/scales:
-    cousins = reshape(real(pyr(cousinInd)), [cousinSz Nor]);
-    Nrc = size(cousins,2);  Nrp = size(rparents,2);
-    if  cmask(4) & (Nrp ~= 0)
-        a3 = 0; a4 = 0;
-        for nrc = 1:Nrc,
-          cou = cousins(:,nrc);
-          [cou, s3, s4] = ...
-            adjustCorr2s(cou,mean(cou.^2),rparents,Crx0(nrc,1:Nrp,nsc), 3);
-          a3 = s3 + a3;
-          a4 = s4 + a4;
-          cousins(:,nrc) = cou;
-        end
-        snr4r(niter,nsc) = a4/Nrc;
-    end
-    if (var2(imag(cousins))/var2(real(cousins)) > 1e-6)
-      fprintf(1,'Non-trivial imaginary part, real crossCorr, lev=%d!\n',nsc);
-    else
-      %%% NOTE: THIS SETS REAL PART ONLY - signal is now NONANALYTIC!
-      pyr(cousinInd) = vector(cousins(1:Nor*cousinSz));
-    end
-
-    %% Re-create analytic subbands
-    dims = pind(firstBnum,:);
-    ctr = ceil((dims+0.5)/2);
-    ang = mkAngle(dims, 0, ctr);
-    ang(ctr(1),ctr(2)) = -pi/2;
-    for nor = 1:Nor,
-      nband = (nsc-1)*Nor+nor+1; 
-      ind = pyrBandIndices(pind,nband); 
-      ch = pyrBand(pyr, pind, nband);
-      ang0 = pi*(nor-1)/Nor;
-      xang = mod(ang-ang0+pi, 2*pi) - pi;
-      amask = 2*(abs(xang) < pi/2) + (abs(xang) == pi/2);
-      amask(ctr(1),ctr(2)) = 1;
-      amask(:,1) = 1;
-      amask(1,:) = 1; 
-      amask = fftshift(amask);
-      ch = ifft2(amask.*fft2(ch));      % "Analytic" version
-      pyr(ind) = ch;
-    end
-
-    %% Combine ori bands
-    bandNums = [1:Nor] + (nsc-1)*Nor+1;  %ori bands only
-    ind1 = pyrBandIndices(pind, bandNums(1));
-    indN = pyrBandIndices(pind, bandNums(Nor));
-    bandInds = [ind1(1):indN(length(indN))];
-    %% Make fake pyramid, containing dummy hi, ori, lo
-    fakePind = pind([bandNums(1), bandNums, bandNums(Nor)+1],:);
-    fakePyr = [zeros(prod(fakePind(1,:)),1);...
-         real(pyr(bandInds)); zeros(prod(fakePind(size(fakePind,1),:)),1)]; 
-    ch = reconSFpyr(fakePyr, fakePind, [1]);     % recon ori bands only
-    im = real(expand(im,2))/4;
-    im = im + ch;
-    vari =  acr0(la+1:la+1,la+1:la+1,nsc);
-if cmask(2),
-    if vari/var0 > 1e-4,
-        [im, snr2(niter,nsc)] = ...
-        modacor22(im, acr0(la-le+1:la+le+1,la-le+1:la+le+1,nsc), p);
-    else
-        im = im*sqrt(vari/var2(im));
-    end
-end     % cmask(2)
-    im = real(im);  
-
-if cmask(1),
-  %% Fix marginal stats 
-  if vari/var0 > 1e-4,
-        [im,snr7(niter,2*nsc-1)] = modskew(im,skew0p(nsc),p);       % Adjusts skewness 
-        [im,snr7(niter,2*nsc)] = modkurt(im,kurt0p(nsc),p);         % Adjusts kurtosis
-  end
-end     % cmask(1)
-
-  end  %END Coarse-to-fine loop
-
-  %% Adjust variance in HP, if higher than desired
-if (cmask(2)|cmask(3)|cmask(4)),
-  ind = pyrBandIndices(pind,1);
-  ch = pyr(ind);
-  vHPR = mean2(ch.^2);
-  if vHPR > vHPR0,
-        ch = ch * sqrt(vHPR0/vHPR);
-        pyr(ind) = ch;
-  end
-end % cmask
-  im = im + reconSFpyr(real(pyr), pind, [0]);  %recon hi only
-
-  %% Pixel statistics
-  means = mean2(im);
-  vars = var2(im, means);
-  snr7(niter,2*(Nsc+1)+1) = snr(var0,var0-vars);
-  im = im-means;                        % Adjusts mean and variance
-  [mns mxs] = range2(im + mean0);
-  snr7(niter,2*(Nsc+1)+2) = snr(mx0-mn0,sqrt((mx0-mxs)^2+(mn0-mns)^2));
-if cmask(1),
-  im = im*sqrt(((1-p)*vars + p*var0)/vars);     
-end     % cmaks(1)
-  im = im+mean0;
-if cmask(1),
-  [im, snr7(niter,2*(Nsc+1)+3)] = modskew(im,skew0,p);  % Adjusts skewness (keep mean and variance)
-  [im, snr7(niter,2*(Nsc+1)+4)] = modkurt(im,kurt0,p);  % Adjusts kurtosis (keep mean and variance,
-                                        % but not skewness)
-  im = max(min(im,(1-p)*max(max(im))+p*mx0),...
-                  (1-p)*min(min(im))+p*mn0);            % Adjusts range (affects everything)    
-else 
-  snr7(niter,2*(Nsc+1)+3) = snr(skew0,skew0-skew2(im));
-  snr7(niter,2*(Nsc+1)+4) = snr(kurt0,kurt0-kurt2(im));
-end     % cmask(1)
-
-  %% Force pixels specified by image mask
-  if (exist('imask') & ~isempty(imask) )
-        im = mask.*reshape(imask(:,2 - (size(imask,2)==1)),size(im)) + ...
-                (1-mask).*im;
-  end
-
-  snr6(niter,1) = snr(im-mean0,im-prev_im);
-
-  if floor(log2(niter))==log2(niter),
-        nq = nq + 1;
-        imS(:,:,nq) = im;
-  end
-
-  tmp = prev_im;
-  prev_im=im;   
-
-  figure(imf);
-  subplot(1,2,1);
-  showIm(im-tmp,'auto',1); title('Change');
-  subplot(1,2,2);
-  showIm(im,'auto',1); title(sprintf('iteration %d/%d',niter,Niter));
-  drawnow
-  
-  % accelerator
-  alpha = 0.8;
-  im = im + alpha*(im - tmp);
-  
-commented = 1;  % set it to 0 for displaying convergence of parameters in SNR (dB)
-if ~commented,    
-   
-% The graphs that appear reflect
-% the relative distance of each parameter or group
-% of parametersi, to the original's, in decibels.
-% Note, however, that when the original parameters
-% are close to zero, this measurement is meaningless.
-% This is why in some cases it seems that some of
-% the parameters do not converge at all.
-
-figure(snrf);
-if cmask(1)
-  subplot(171); plot(snr7); title('Mrgl stats');
-end
-if cmask(2),
-  subplot(172); plot(snr2); title('Raw auto');
-end
-if cmask(3),
-  subplot(173); plot(snr1); title('Mag auto'); 
-  subplot(174); plot(snr3); title('Mag ori');
-  subplot(175); plot(snr4); title('Mag scale');
-end
-if (Nrp > 0) & cmask(4),
-  subplot(176); plot(snr4r); title('Phs scale');
-end
-  subplot(177); plot(snr6); title('Im change');
-  drawnow
-  
-end  % if ~commented
-
-end %END  MAIN LOOP
-
-im = prev_im;
-
-snrP = [snr7 snr2 snr1 snr3 snr4 snr4r snr6];