entire.txt

a code for cutting only the vcentre of the face

%Given an array of images, will allow the user to select the center of the face, and will cut it out. This allows all the images to be the same size, and to be focused on the correct area of interest. 


%F = function centerfaces(F, windowX, windowY)
function F = centerfaces(F, windowX, windowY)

if( nargin < 2 )
    windowX = 80;
    windowY = 80;
end;

fields = fieldnames(F);

for i = 1:length(F),
    for j = 1:length(fields);
        X = F(i).(fields{j});
        
        figure(1);
        truesize;
        imagesc(X);
        colormap(Gray);
        
        while(1),
            [y x ] = ginput(1);
            if( x > windowX && x < size(X,1) - windowX && ...
                y > windowY && y < size(X,2) - windowY )
                break;
            else
                fprintf('Error, point (%d %d) with window size %d too close to edge', x, y, windowX);
            end;
        end;
        
        x = round(x);
        y = round(y);
        
        Y = X( x - windowX + 1: x + windowX - 1, y - windowY + 1 : y + windowY - 1 );        
        figure(2);
        imagesc(Y);
        truesize;
        drawnow;
        
        F(i).(fields{j}) = Y;
    end;
end;


%Main routine that performs Eigenface analysis and shows a bunch of figures, as presented above. 


allextensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' };
bestextensions = {'happy', 'noglasses', 'normal', 'sad', 'sleepy', 'surprised', 'wink' };

noglassesextensions = {'centerlight', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' };

lightextensions = {'centerlight', 'leftlight', 'normal', 'rightlight' };
expressionextensions = {'happy', 'normal', 'sad', 'sleepy', 'surprised', 'wink' };

extensions = allextensions;

clear train;
clear test;
clear M;

%First, extract the faces into training and test faces
trainidx = 1;
testidx = 1;

for i = 1:length(Faces),
    for j = 1:length(extensions);
        X = double(Faces(i).(extensions{j}));

        if( rand > 0.3 )
            train(trainidx).data = X;
            trainidx = trainidx + 1;
        else
            test(testidx).data = X;
            testidx = testidx + 1;
        end;
        
    end;
end;

%Take the average of the training faces
avg = meanface(train);

%Put all of the training faces into one big matrix and do svd
idx = 1;
for i = 1:length(train),
    X = double(train(i).data);
    W = X - avg;

    M(:, idx) = W(:);
    idx = idx + 1;
end;

[U, W, V] = svd(M,0);

Result = U * W * V';

%Show the variance produced by the top eigenvectors
cvalues = zeros(1,size(W,1)-3);
cvalues(1) = W(1,1);
valsum = W(1,1);
for i = 2:length(cvalues);
    cvalues(i) = cvalues(i-1) + W(i,i);
    valsum = valsum + W(i,i);
end;
cvalues = cvalues / valsum;
figure;
set(gcf, 'Name', 'Eigenvalue Variance');
plot(cvalues);
fprintf('10 PC Variance: %f\n25 PC Variance %f\n', cvalues(10), cvalues(25));
    
    
%Show the top k eigenfaces
k = 25;
dim = ceil(sqrt(k));
eigenfaceFigure = figure;
axis off;
for i = 1:k,
    tightsubplot(dim, i, reshape(U(:,i), size(avg)));
end;
colormap(gray);
set(gcf, 'Name', 'Top 25 Eigenfaces');

%Pick a random sample of the training input and reconstruct
reconstructFigure = figure;
axis off;
set(gcf, 'Name', 'Reconstruction using 25 eigenfaces');
idx = 1;
reconstruct = zeros(size(avg));
for i = 1:25
    
    %reconstruct = reconstruct + reshape( V(idx, i) * W(i,i) * U(:,idx), size(avg) );

%     WRecon = zeros(size(W));
%     WRecon(1:i, 1:i) = W(1:i, 1:i);
%     reconstruct = U * WRecon * V';

    reconstruct = U(:,1:i) * W(1:i,1:i) * V(:,1:i)';
    
    recon = reshape( reconstruct(:,1), size(avg) ) + avg;

    tightsubplot( 5, idx, recon );
    idx = idx + 1;
    
    imagesc(recon);
    colormap(gray);
    axis off;
    drawnow;
    
end;


%Put all of the training faces into one big matrix and project
idx = 1;
for i = 1:length(test),
    X = double(test(i).data);
    Wh = X - avg;

    T(:, idx) = Wh(:);
    idx = idx + 1;
end;

%For each test example, find best match in training data
%trainweights = W(1:k,:)*V(:, 1:k)';
clear trainweights;
clear distances;
for i = 1:length(train)
    trainweights(:,i) = U(:,1:k)' * M(:,i);
end;

recognitionFigure = figure;
axis off;
colormap(gray);
set(gcf, 'Name', 'Recognition Results');

idx = 1;
for i = 1:length(test)

    testweights = U(:,1:k)' * T(:,i);
    
    for j = 1:length(train),
        distances(j) = sum((trainweights(:,j) - testweights(:)).^2);
    end;
        
    [val, best] = min(distances);
    
    tightsubplot(8, idx, test(i).data ); 
    
    axis off;
    tightsubplot(8, idx+1, train(best).data ); axis off;
    idx = idx + 2;
    
    %subplot(1,2,1); imagesc(test(i).data);
    %subplot(1,2,2); imagesc(train(best).data);
    drawnow;
end;

%Now load three images and project them to both the eigenfaces,
% and to the orthogonal complement
 nonfaces = {'face.gif', 'face2.gif', 'nonface1.jpg', 'nonface2.jpg', 'nonface3.jpg'};
for j=1:length(nonfaces),
    face = double(imread(nonfaces{j}));
    faceweights = U(:,1:k)' * face(:);
    
    reconstruct = U(:,1:k) * faceweights(:) + avg(:);
    reconstruct = reshape(reconstruct, [199, 199]);
    
    figure;
    imagesc(reconstruct);
    
    recon2 = face - reconstruct;
    
    normA = norm(face - reconstruct)
    normB = norm(face - recon2);
    
    ratio = normA / normB;
    
    %diff1 = sqrt((face(:) - reconstruct(:)) .^2);
    %diff2 = sqrt((face(:) - (face(:) - reconstruct(:))) .^ 2);
    fprintf('Ratio: %d\n' , ratio);
%    fprintf('Distance to reconstruction: %f, Distance to complement: %f \n', diff1, diff2);
    
end;

%Reads the Yale face dataset from a directory, and puts then into a structure labeled by their types. 



function F = readyalefaces()

extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' };

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}];
        X = imread(fullname);
        F(i).(extensions{j}) = X;
    end;

end;



% A simple image viewer that allows scrolling through the Yale dataset in 2 dimensions 


function viewfaces(faces)
extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' };
if( nargin < 1 )
    faces = Faces;
end;

faceviewer = figure;
imagesc(faces(1).(extensions{1}));
drawnow;

row = 1;
col = 1;

while(1)
    waitforbuttonpress;
    key = double(get(gcf, 'CurrentCharacter'));
    if( key == 30 ) %up
        if( row > 1 )
            row = row - 1;
        else
            continue;
        end;
        
    elseif( key == 31 ) %down
        if( row < length(faces) )
            row = row + 1;
        else
            continue;
        end;
        
    elseif( key == 28 ) %left
        if( col > 1 )
            col = col - 1;
        else
            continue;
        end;      
        
    elseif( key == 29 ) %right
        if( col < length(extensions) )
            col = col + 1;
        else
            continue;
        end;      
                
    elseif( key == 27 ) %esc
        break;
        
    else
        continue;
    end;
    
    imagesc(faces(row).(extensions{col}));
    set(gcf, 'Name', sprintf('Person %d/%d, Pose %d/%d (%s)', ...
        row, length(faces), col, length(extensions), extensions{col}));
    drawnow;
end;




%function viewfaces(faces)
extensions = {'centerlight', 'glasses', 'happy', 'leftlight', 'noglasses', 'normal', 'rightlight', 'sad', 'sleepy', 'surprised', 'wink' };
if( nargin < 1 )
    faces = Faces;
end;

faceviewer = figure;
imagesc(faces(1).(extensions{1}));
drawnow;

row = 1;
col = 1;

while(1)
    waitforbuttonpress;
    key = double(get(gcf, 'CurrentCharacter'));
    if( key == 30 ) %up
        if( row > 1 )
            row = row - 1;
        else
            continue;
        end;
        
    elseif( key == 31 ) %down
        if( row < length(faces) )
            row = row + 1;
        else
            continue;
        end;
        
    elseif( key == 28 ) %left
        if( col > 1 )
            col = col - 1;
        else
            continue;
        end;      
        
    elseif( key == 29 ) %right
        if( col < length(extensions) )
            col = col + 1;
        else
            continue;
        end;      
                
    elseif( key == 27 ) %esc
        break;
        
    else
        continue;
    end;
    
    imagesc(faces(row).(extensions{col}));
    set(gcf, 'Name', sprintf('Person %d/%d, Pose %d/%d (%s)', ...
        row, length(faces), col, length(extensions), extensions{col}));
    drawnow;
end;


%A helper routine that makes subplot lie immediatly next to each other, without borders. 


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;