% sampleIMAGES.m

% sampling patches for learning

function patches = sampleIMAGES(numpatches)

% 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

counter = 1;

ranimg = ceil(rand(1, numpatches) * 10);

ranpix = ceil(rand(2, numpatches) * (512 - patchsize));

ranpixm = ranpix + patchsize - 1;

while(counter <= numpatches)

whichimg = ranimg(1, counter);

whichpix = ranpix(:, counter);

whichpixm = ranpixm(:, counter);

patch = IMAGES(whichpix(1):whichpixm(1), whichpix(2):whichpixm(2), whichimg);

repatch = reshape(patch, patchsize * patchsize, 1);

patches(:, counter) = repatch;

counter = counter + 1;

end

%% ---------------------------------------------------------------

% 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