%%%
%
%  CNS/EE148
%
%  this code is used in Problem 1 of Homework 1
%
%  
%%%

% this is the threshold used for matched filtering
% how should this be chosen ? 

MF_THRESH = 6000000;  


% we assume the imagefiles are in the current directory.
% if not, adjust the filename.

inputImg = double(imread('img1.jpg'));  % we make a template from this image...
testImg  = double(imread('img2.jpg'));  % and do a correlation with this one. 


% Show the image in a figure;

figure(1);
imagesc(inputImg / max(inputImg(:)) ); 
colormap(gray);
axis('image');
title('Input Image');


% select a template by hand.
T = getTemplate(inputImg);

% show the template we just extracted. 
imagesc(T); 
axis('image');
title('Correlation Template');

% Note that the template is not normalized with respect 
% to brightness or contrast.


% !!! ADD CODE HERE TO NORMALIZE THE TEMPLATE !!!


% !!! ADD CODE THAT COMPUTES THE CORRELATION BETWEEN TEMPLATE AND TEST
% IMAGE -> CALCULATES R !!! 



% * for example, you can note that a *correlation* is equivalent to a *convolution*
% (function conv2 for images convolution) if the kernel (template) is
% mirrored in x and y direction (functions flipud and fliplr to mirror an
% image).
% 
% * another possibility is to consider both images as vectors (function
% reshape) and to do a scalar product of vectors ( <v1,v2> = v1 * v2'; )



% Show the results of the correlation

figure(3);
imagesc(R); axis('image');
title('Correlation Result');

% Use a nice colormap, and display legend.
colormap jet; colorbar;

% find local maxima in 3x3 neighborhoods.  this
% is important since the template will probably
% respond strongly with in a couple pixel neighborhood
% of the object to be detected
%
% There's a built-in localmax function in matlab, but it doesn't seem to
% do what we want (a bonus to whoever figures out how to get it working !) 
% You can download another 'localmax.m' from the
% homework web page.

lm = localmax(R);

% find local maxima that exceed threshold

detectionsIdx = find(lm & R > MF_THRESH);

% Since 'find' treats all matrices as 1D vectors, we need to do 
% some work to recover the (i, j) indices

detcJ = ceil(detectionsIdx / size(testImg, 1));
detcI = detectionsIdx - (detcJ - 1) * size(testImg, 1);
 

% Make nice plot by overlaying image with detected locations

figure(4); 
clf;
imagesc(testImg); 
colormap(gray);
axis('image'); title('Detection Results');
hold on;

plot(detcJ, detcI, 'r+');
hold off;


% Make a colorful PostScript file that can be send to the printer
% and attached to the homework.

print -depsc results.eps