extractRandC1Patches.m

function cPatches = extractRandC1Patches(cItrainingOnly, numPatchSizes, numPatchesPerSize, patchSizes);
%extracts random prototypes as part of the training of the C2 classification
%system.
%Note: we extract only from BAND 2. Extracting from all bands might help
%cPatches the returned prototypes
%cItrainingOnly the training images
%numPatchesSize is the number of sizes in which the prototypes come
%numPatchesPerSize is the number of prototypes extracted for each size
%patchSizes is the vector of the patche sizes

if nargin<2
    numPatchSizes = 4;
    numPatchesPerSize = 250;
    patchSizes = 4:4:16;
end

nImages = length(cItrainingOnly);
%----Settings for Training the random patches--------%
rot = [0 90];
c1ScaleSS = [1 3];
RF_siz    = [11 13];
c1SpaceSS = 10;
div = 4:-.05:3.2;
Div       = div(3:4);
%--- END Settings for Training the random patches--------%


fprintf(1,'Initializing gabor filters -- partial set...');%initializing gabor filters -- partial set...K
[fSiz,filters,c1OL] = init_gabor(rot, RF_siz, Div);
fprintf(1,'done\n');


cPatches = cell(numPatchSizes,1);
bsize = [0 0];

pind = zeros(numPatchSizes,1);
for j = 1:numPatchSizes
    cPatches{j} = zeros(patchSizes(j)^2*length(rot),numPatchesPerSize);
end


for i = 1:numPatchesPerSize,
    ii = floor(rand*nImages) + 1;
    fprintf(1,'.');
    stim = cItrainingOnly{ii};
    [~,~,dim] = size(stim);
    if dim ~= 1;
        stim = rgb2gray(stim);
    end
    
    c1source = C1(stim, filters, fSiz, c1SpaceSS, ...
        c1ScaleSS, c1OL);
    b = c1source{1}; %new C1 interface;
    bsize(1) = size(b,1);
    bsize(2) = size(b,2);
    for j = 1:numPatchSizes,
        xy = floor(rand(1,2).*(bsize-patchSizes(j)))+1;
        tmp = b(xy(1):xy(1)+patchSizes(j)-1,xy(2):xy(2)+patchSizes(j)-1,:,:);
        pind(j) = pind(j) + 1; % counting how many patches per size
        cPatches{j}(:,pind(j)) = tmp(:);
    end
    
end

fprintf('\n');