Lesson 3/11 and moved eigenfaces folder from 29/9 lesson folder

09-29/eigenfaces/eigenfaces_classify.m

@@ -1,38 +0,0 @@
-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

@@ -1,18 +0,0 @@
-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

@@ -1,29 +0,0 @@
-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

@@ -1,34 +0,0 @@
-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

@@ -1,13 +0,0 @@
-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

@@ -1,7 +0,0 @@
-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

@@ -1,29 +0,0 @@
-			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).
-