first commit with current lessons

09-29/eigenfaces/eigenfaces_classify.m

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+function [matched_individual,bestmatchdistance]=eigenfaces_classify(test,training,n);
+%classifies using n principal components, closest match
+[w, h, nExpressions, nIndividuals]=size(training);
+X=reshape(training,[w*h,nIndividuals*nExpressions]);
+avg=mean(X,2);
+Xd=bsxfun(@minus,X,avg);
+[U,S,V]=svd(Xd,0);
+Xt=reshape(test,w*h,numel(test)/(w*h));
+Xtd=bsxfun(@minus,Xt,avg);
+scores=U(:,1:n)'*Xtd;
+trainingscores=U(:,1:n)'*Xd;
+%normalize scores and samples
+%scores=bsxfun(@rdivide,scores,sqrt(sum(abs(scores).^2)));
+%trainingscores=bsxfun(@rdivide,trainingscores,sqrt(sum(abs(trainingscores).^2)));
+%cosine similarity
+%C=scores'*trainingscores;
+%[bestmatchdistance bestmatchindex]=max(C,[],2);
+%matched_individual=ceil(bestmatchindex/nExpressions);
+
+%Euclidean distance
+distanceMatrix=nan(size(scores,2),size(trainingscores,2));
+for i=1:size(scores,2)
+    for j=1:size(trainingscores,2)
+        distanceMatrix(i,j)=norm(scores(:,i)-trainingscores(:,j));
+    end
+end
+[bestmatchdistance bestmatchindex]=min(distanceMatrix,[],2);
+matched_individual=ceil(bestmatchindex/nExpressions);
+if numel(test)==w*h
+   subplot(1,2,1);
+   imagesc(test);
+   colormap(gray);
+   subplot(1,2,2);
+   imagesc(reshape(X(:,bestmatchindex),[w,h]));
+   colormap(gray);
+   disp('best match distance=');
+   disp(bestmatchdistance);
+end

09-29/eigenfaces/eigenfaces_scatter.m

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+function eigenfaces_scatter(images, indices);
+
+[w, h, nExpressions, nIndividuals]=size(images);
+X=reshape(images,[w*h,nIndividuals*nExpressions]);
+avg=mean(X,2);
+Xd=bsxfun(@minus,X,avg);
+[U,S,V]=svd(Xd,0);
+scores=U(:,indices)'*Xd;
+%normalize scores and samples
+%scores=bsxfun(@rdivide,scores,sqrt(sum(abs(scores).^2)));
+
+if length(indices) == 3
+    scatter3(scores(1,:),scores(2,:),scores(3,:),50*ones(size(scores(1,:))),kron(1:nIndividuals,ones(1,nExpressions)));
+elseif length(indices) == 2
+    scatter(scores(1,:),scores(2,:),50*ones(size(scores(1,:))),kron(1:nIndividuals,ones(1,nExpressions)));
+else
+    error('wrong indices size');
+end

09-29/eigenfaces/interactiverec.m

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+function interactiverec(F)
+% given a 243x320 image F, displays it as sum of components
+stdsize=[243,320];
+
+F = F(:);
+
+if not(numel(F) == prod(stdsize))
+    error('The first argument must be the picture to reconstruct');
+end
+
+X=readyalefaces_to_tensor;
+X=reshape(X,[prod(stdsize),numel(X)/prod(stdsize)]);
+avg=mean(X,2);
+Xs=bsxfun(@minus,X,avg);
+[U,S,V]=svd(X,0);
+colormap(gray);
+ncolors=size(gray,1);
+h = image(reshape(avg,stdsize)*ncolors);
+
+% Add a slider
+uicontrol('Style', 'slider', 'Min', 0, 'Max',  size(U,2), ...
+    'Callback', @callback,'Position',[10 0 300 20]);
+
+function callback(src,evt)
+d=round(get(src, 'Value'))
+set(h, 'CData', reshape(ncolors*(avg+U(:,1:d)*(U(:,1:d)'*F)),stdsize));
+end
+
+end

09-29/eigenfaces/readyalefaces_to_tensor.m

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+function [F,descr] = readyalefaces_to_tensor(str)
+
+if not(exist('str','var'))
+    str='all';
+end
+switch str
+    case 'easy'
+        extensions = {'happy', 'normal', 'sad', 'sleepy', 'surprised', 'wink' };
+    case 'easy-nowink'
+        extensions = {'happy', 'normal', 'sad', 'sleepy', 'surprised' };
+    case 'nowink'
+        extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised' };
+    case {'hard','all'}
+        extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' };
+    otherwise
+        error 'unknown selector';
+end
+
+for i = 1 : 15,
+    basename = 'yalefaces/subject';
+    if( i < 10 )
+        basename = [basename, '0', num2str(i)];
+    else
+        basename = [basename, num2str(i)];
+    end;
+
+    for j = 1:length(extensions),
+        fullname = [basename, '.', extensions{j}, '.gif'];
+        X = imread(fullname);
+        F(:,:,j,i) = double(X)/255;
+    end;
+
+end;
+descr=extensions;

09-29/eigenfaces/showyalefaces.m

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+X=readyalefaces_to_tensor;
+%X = X(:,:,1:4,[12,9,6,1]); %filters out only some faces
+[w h expr ind]=size(X);
+clf;
+for i=1:expr
+    for j=1:ind
+        subplot('position', [(i-1)/expr, (j-1)/ind,1/expr,1/ind]); 
+%        subplot(expr,ind,j+(i-1)*ind);
+        imagesc(X(:,:,i,j));
+        colormap(gray);
+    end
+end
+set(findobj(gcf, 'type','axes'), 'Visible','off')

09-29/eigenfaces/tightsubplot.m

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+function tightsubplot(dim, i, data)
+
+row = mod(i-1, dim);
+col = floor((i-1) / dim);    
+subplot('position', [row*(1/dim), (dim-col-1)*(1/dim), 1/dim-.001, 1/dim-0.001 ]); 
+imagesc(data);
+axis off;

09-29/eigenfaces/yalefaces/README

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+			The Yale Face Database
+			----------------------
+
+The database contains 165 GIF images of 15 subjects (subject01, 
+subject02, etc.).  There are 11 images per subject, one  for each 
+of the following facial expressions or configurations: center-light, 
+w/glasses, happy, left-light, w/no glasses, normal, right-light, 
+sad, sleepy, surprised, and wink.  Note that the image "subject04.sad" 
+has been corrupted and has been substituted by "subject04.normal".
+
+All the images including this readme file are contained in the file 
+"yalefaces.tar". It can be unpacked with:
+
+    tar xvf yalefaces.tar
+
+A directory called "yalefaces" will be created containing all the images.
+These can be viewed using "xv" or ported with any software package that 
+can understand the GIF format.
+
+You are free to use the Yale Face Database for research purposes. 
+If experimental results are obtained that use images from within the
+database, all publications of these results should acknowledge the use
+of the "Yale Face Database."  Without permission from Yale, images
+from within the database cannot be incorporated into a larger database
+which is then publicly distributed.
+
+If you have any trouble or questions please email Prof. David Kriegman
+(kriegman@yale.edu) or Prof. Peter Belhumeur (Belhumeur@ledoux.eng.yale.edu).
+