--- a
+++ b/combinedDeepLearningActiveContour/functions/sampleIMAGES.m
@@ -0,0 +1,47 @@
+function patches = sampleIMAGES(IMAGES,patchsize,norm_ena)
+% sampleIMAGES
+% inputs
+% numpatches: for example 1E4
+% patchsize% for example 8*8
+% IMAGES: images in a 3D matrix
+% output
+% patches % a vector of randomly chosen patches
+if nargin==2
+    norm_ena=1;
+end
+visibleSize = patchsize*patchsize;   % number of input units 
+
+% get size and number of images
+[xn, yn, zn]=size(IMAGES);
+
+scale=patchsize/xn;
+imgs = imresize(IMAGES, scale);
+patches=(reshape(imgs,visibleSize,zn));
+
+%% ---------------------------------------------------------------
+% 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]
+if norm_ena==1
+patches = normalizeData(patches);
+end
+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