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

11-3/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

11-3/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

11-3/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

11-3/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;

11-3/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')

11-3/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;

11-3/readyjalefaces_to_tensor.jl

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+using Images, GIFImages
+
+function readyalefaces_to_tensor(str::String="all")::Tuple{Array, Array{String}}
+    # output (F, descr)
+
+    nindividuals = 15
+
+    if str == "easy"
+        extensions = ["happy", "normal", "sad", "sleepy", "surprised", "wink"]
+    elseif str == "easy-nowink"
+        extensions = ["happy", "normal", "sad", "sleepy", "surprised"]
+    elseif str == "nowink"
+        extensions = ["centerlight", "glasses", "happy", "leftlight", "noglasses", "normal", "rightlight", "sad", "sleepy", "surprised"]
+    elseif str == "hard" || str == "all"
+        extensions = ["centerlight", "glasses", "happy", "leftlight", "noglasses", "normal", "rightlight", "sad", "sleepy", "surprised", "wink"]
+    else
+        throw(ArgumentError("unknown selector"))
+    end
+
+    F = Array{Float32}(undef, (243, 320, length(extensions), nindividuals))
+    
+    for i ∈ 1:nindividuals
+        basename = "yalefaces/subject"
+        if i < 10
+            basename *= "0" * string(i)
+        else
+            basename *= string(i)
+        end
+    
+        for j = 1:length(extensions)
+            fullname = basename * "." * extensions[j] * ".gif"
+            X = Gray.(gif_decode(fullname))
+            F[:, :, j, i] = X[:,:,1] # just the first frame of the gif (should have only one in total)
+        end
+    end
+    descr = extensions
+
+    (F, descr)
+end

11-3/showyalefaces.jl

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+include("readyjalefaces_to_tensor.jl")
+using Plots, Plots.PlotMeasures, ColorSchemes
+
+(X, _) = readyalefaces_to_tensor()
+# X = X[:,:,1:4,[12,9,6,1]]; # filters out only some faces
+(w, h, expr, ind) = size(X)
+
+function showyalefaces()::Plots.Plot
+    plts = Array{Plots.Plot}(undef, expr, ind)
+
+    for i ∈ 1:expr
+        for j ∈ 1:ind
+            plts[i, j] = heatmap(X[:, :, i, j],
+                leg = false,
+                ticks = nothing,
+                yflip=true,
+                c = cgrad(:dense, rev = true)
+            )
+        end
+    end
+
+    l = @layout [
+        grid(ind, expr)
+    ]
+
+    plot(plts..., layout = l, margin = -2mm)
+end

11-3/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).
+