initial sd-vbs

initial sd-vbs

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sd-vbs

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diff --git a/SD-VBS/benchmarks/texture_synthesis/src/matlabPyrTools/TUTORIALS/README b/SD-VBS/benchmarks/texture_synthesis/src/matlabPyrTools/TUTORIALS/README
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+
+This directory contains some Matlab script files that serve to give
+example usage of this code, and also to explain some of the 
+representations and algorithms.
+
+The files are NOT meant to be executed from the MatLab prompt (like many 
+of the MatLab demos).  You should instead read through the comments, 
+executing the subsequent pieces of code.  This gives you a chance to 
+explore as you go...
+
+matlabPyrTools.m - Example usage of the code in the distribution.
+
+pyramids.m - An introduction to multi-scale pyramid representations, 
+        covering Laplacian, QMF/Wavelet, and Steerable pyramids.  The
+        file assumes a knowledge of linear systems, matrix algebra, 
+        and 2D Fourier transforms.
+
+more to come....
diff --git a/SD-VBS/benchmarks/texture_synthesis/src/matlabPyrTools/TUTORIALS/matlabPyrTools.m b/SD-VBS/benchmarks/texture_synthesis/src/matlabPyrTools/TUTORIALS/matlabPyrTools.m
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+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%% Some examples using the tools in this distribution.
+%%% Eero Simoncelli, 2/97.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% Add directory to path (YOU'LL NEED TO ADJUST THIS):
+path('/lcv/matlab/lib/matlabPyrTools',path);
+
+%% Load an image, and downsample to a size appropriate for the machine speed.
+oim = pgmRead('einstein.pgm');
+tic; corrDn(oim,[1 1; 1 1]/4,'reflect1',[2 2]); time = toc;
+imSubSample = min(max(floor(log2(time)/2+3),0),2);
+im = blurDn(oim, imSubSample,'qmf9');
+clear oim;
+
+%%% ShowIm: 
+%% 3 types of automatic graylevel scaling, 2 types of automatic
+%% sizing, with or without title and Range information.
+help showIm
+clf; showIm(im,'auto1','auto','Al')
+clf; showIm('im','auto2')
+clf; showIm(im,'auto3',2)
+
+%%% Statistics:
+mean2(im)
+var2(im)
+skew2(im)
+kurt2(im)
+entropy2(im)
+imStats(im)
+
+%%% Synthetic images.  First pick some parameters:
+sz = 200;
+dir = 2*pi*rand(1)
+slope = 10*rand(1)-5
+int = 10*rand(1)-5;
+orig = round(1+(sz-1)*rand(2,1));
+expt = 0.8+rand(1)
+ampl = 1+5*rand(1)
+ph = 2*pi*rand(1)
+per = 20
+twidth = 7
+
+clf;
+showIm(mkRamp(sz,dir,slope,int,orig));
+showIm(mkImpulse(sz,orig,ampl));
+showIm(mkR(sz,expt,orig));
+showIm(mkAngle(sz,dir));
+showIm(mkDisc(sz,sz/4,orig,twidth));
+showIm(mkGaussian(sz,(sz/6)^2,orig,ampl));
+showIm(mkZonePlate(sz,ampl,ph));
+showIm(mkAngularSine(sz,3,ampl,ph,orig));
+showIm(mkSine(sz,per,dir,ampl,ph,orig));
+showIm(mkSquare(sz,per,dir,ampl,ph,orig,twidth));
+showIm(mkFract(sz,expt));
+
+
+%%% Point operations (lookup tables):
+[Xtbl,Ytbl] = rcosFn(20, 25, [-1 1]);
+plot(Xtbl,Ytbl);
+showIm(pointOp(mkR(100,1,[70,30]), Ytbl, Xtbl(1), Xtbl(2)-Xtbl(1), 0));
+
+
+%%% histogram Modification/matching:
+[N,X] = histo(im, 150);
+[mn, mx] = range2(im);
+matched = histoMatch(rand(size(im)), N, X);
+showIm(im + sqrt(-1)*matched);
+[Nm,Xm] = histo(matched,150);
+nextFig(2,1); 
+  subplot(1,2,1); plot(X,N); axis([mn mx 0 max(N)]);
+  subplot(1,2,2);  plot(Xm,Nm); axis([mn mx 0 max(N)]);
+nextFig(2,-1);
+
+%%% Convolution routines:
+
+%% Compare speed of convolution/downsampling routines:
+noise = rand(400); filt = rand(10);
+tic; res1 = corrDn(noise,filt(10:-1:1,10:-1:1),'reflect1',[2 2]); toc;
+tic; ires = rconv2(noise,filt); res2 = ires(1:2:400,1:2:400); toc;
+imStats(res1,res2)
+
+%% Display image and extension of left and top boundaries:
+fsz = [9 9];
+fmid = ceil((fsz+1)/2);
+imsz = [16 16];
+
+% pick one:
+im = eye(imsz);
+im = mkRamp(imsz,pi/6); 
+im = mkSquare(imsz,6,pi/6); 
+
+% pick one:
+edges='reflect1';
+edges='reflect2';
+edges='repeat';
+edges='extend';
+edges='zero';
+edges='circular';
+edges='dont-compute';
+
+filt = mkImpulse(fsz,[1 1]);
+showIm(corrDn(im,filt,edges));
+line([0,0,imsz(2),imsz(2),0]+fmid(2)-0.5, ...
+     [0,imsz(1),imsz(1),0,0]+fmid(1)-0.5);
+title(sprintf('Edges = %s',edges));
+
+%%% Multi-scale pyramids (see pyramids.m for more examples,
+%%% and explanations):
+
+%% A Laplacian pyramid:
+[pyr,pind] = buildLpyr(im);
+showLpyr(pyr,pind);
+
+res = reconLpyr(pyr, pind);             % full reconstruction
+imStats(im,res);                        % essentially perfect
+
+res = reconLpyr(pyr, pind, [2 3]);  %reconstruct 2nd and 3rd levels only  
+showIm(res);
+
+%% Wavelet/QMF pyramids:
+filt = 'qmf9'; edges = 'reflect1';
+filt = 'haar'; edges = 'qreflect2';
+filt = 'qmf12'; edges = 'qreflect2';
+filt = 'daub3'; edges = 'circular';
+
+[pyr,pind] = buildWpyr(im, 5-imSubSample, filt, edges);
+showWpyr(pyr,pind,'auto2');
+
+res = reconWpyr(pyr, pind, filt, edges);
+clf; showIm(im + i*res);
+imStats(im,res);
+
+res = reconWpyr(pyr, pind, filt, edges, 'all', [2]);  %vertical only
+clf; showIm(res);
+
+%% Steerable pyramid:
+[pyr,pind] = buildSpyr(im,4-imSubSample,'sp3Filters');  
+showSpyr(pyr,pind);
+
+%% Steerable pyramid, constructed in frequency domain:
+[pyr,pind] = buildSFpyr(im,5-imSubSample,4);  %5 orientation bands
+showSpyr(pyr,pind);
+res = reconSFpyr(pyr,pind);
+imStats(im,res);