% You need to change the following two lines to indicate where you put the Utility Toolbox and Machine Learning Toolbox
addpath /users/jang/matlab/toolbox/utility
addpath /users/jang/matlab/toolbox/machineLearning

close all;		% Close all windows
clear all;		% Cear all variables in the workspace
inImage = imread('vivian_orig.png');	% Read image file
[m, n]=size(inImage);	% For the size of the input image
blockSide=8;			% For computing compression ratio
centerCount=64;			% No. of centers/clusters
outImage=myImageCompress(inImage, centerCount);		% Perform image compression using k-means clustering
absDiff=abs(double(inImage)-outImage);	% You need to use double() to convert the image data back to double precision
distortion=sum(absDiff(:).^2);	% Total distortion
fprintf('distortion = %f\n', distortion);	% Print total distortion	
fprintf('Average deviation = %f\n', mean(absDiff(:)));	% Print average deviation for each pixel component
%% Plot the images
figure; 
subplot(211); image(inImage); axis image; title('Original image');
subplot(212); image(uint8(outImage)); axis image;		% Display the output image. You need to use uint8() to convert double precision back to unsigned 8-bit integers
compressionRatio=8*3*m*n/(log2(centerCount)*(m/blockSide)*(n/blockSide)+8*centerCount*3*blockSide*blockSide);	% Compute the compression ratio
titleStr=sprintf('Codebook size=%g, block=%dx%d, compression ratio=%g', centerCount, blockSide, blockSide, compressionRatio);	% Note that codebook size is equal to the number of centers/clusters
title(titleStr);