sampleIMAGES.m

function patches = sampleIMAGES()
% sampleIMAGES
% Returns 10000 patches for training

load IMAGES;    % load images from disk 

patchsize = 8;  % we'll use 8x8 patches 
numpatches = 10000;

% Initialize patches with zeros.  Your code will fill in this matrix--one
% column per patch, 10000 columns. 
patches = zeros(patchsize*patchsize, numpatches);

%% ---------- YOUR CODE HERE --------------------------------------
%  Instructions: Fill in the variable called "patches" using data 
%  from IMAGES.  
%  
%  IMAGES is a 3D array containing 10 images
%  For instance, IMAGES(:,:,6) is a 512x512 array containing the 6th image,
%  and you can type "imagesc(IMAGES(:,:,6)), colormap gray;" to visualize
%  it. (The contrast on these images look a bit off because they have
%  been preprocessed using using "whitening."  See the lecture notes for
%  more details.) As a second example, IMAGES(21:30,21:30,1) is an image
%  patch corresponding to the pixels in the block (21,21) to (30,30) of
%  Image 1

%img_side = 512;

% Matrix of numpatches starting x and y coordinates for the image patches
%start_coords = ceil( (img_side - patchsize + 1) * rand(numpatches, 2) );
%images_to_sample = ceil( 6 * rand(numpatches, 1) );

%for patch=1:numpatches,
%coords = start_coords(patch, :);
%x=coords(1); y=coords(2);
%sample = IMAGES(x:(x+patchsize-1), y:(y+patchsize-1), images_to_sample(patch));
%patches(:,patch) = sample(:);
%end


numimages = size(IMAGES,3);
imgwidth  = size(IMAGES,1) / patchsize;
imgheight  = size(IMAGES,2) / patchsize;
pit = 1;

for iit = 1:numimages
 for hit = 1:imgheight
  for wit = 1:imgwidth
    if pit>numpatches break; end;
    
    patches(:, pit) = IMAGES( (wit-1)*patchsize+1:wit*patchsize, (hit-1)*patchsize+1:hit*patchsize, iit )(:);
    pit=pit+1;
  end
 end
end


% LABEL Break






%% ---------------------------------------------------------------
% For the autoencoder to work well we need to normalize the data
% Specifically, since the output of the network is bounded between [0,1]
% (due to the sigmoid activation function), we have to make sure 
% the range of pixel values is also bounded between [0,1]
patches = normalizeData(patches);

end


%% ---------------------------------------------------------------
function patches = normalizeData(patches)

% Squash data to [0.1, 0.9] since we use sigmoid as the activation
% function in the output layer

% Remove DC (mean of images). 
patches = bsxfun(@minus, patches, mean(patches));

% Truncate to +/-3 standard deviations and scale to -1 to 1
pstd = 3 * std(patches(:));
patches = max(min(patches, pstd), -pstd) / pstd;

% Rescale from [-1,1] to [0.1,0.9]
patches = (patches + 1) * 0.4 + 0.1;

end