%Code for leopard image courtesy of Neurodimension
%% Set up the data:
%******************************************
%
%  These initial lines set up the image, the
%     training set and the testing set.
%
%  (No need to change these)
%
%*******************************************

A = imread('leopard.jpg'); % Read Image into matrix
a = A(:,:,1); %We're only using the red channel
[r,c] = size(a);


%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Display the Leopard image
%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Code to position the figure on the screen
screenunits = get(0, 'Units');
set(0, 'Units', 'pixels');
sc = get(0, 'ScreenSize');  % screen co-ordinates

% Show figure
h1 = figure;
h2 = axes('Parent', h1);
image(A); 
set(h1, 'position', [20, sc(4)-(r+50), c, r], 'menubar', 'none', 'Name', 'Sub-sampled images as Training Data');
set(h2, 'Visible', 'Off');

pause(0.1)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Sub-Sample 5x5 images from the leopard image for Training
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Sample from the area of the image which consists of the leopard
count = 1;
for i=125:5:175
   for j = 200:5:400
      Temp=a(i:i+4,j:j+4);
      TrainLeopard(1:25,count)=Temp(:);
      %These set the pixels on the screen to red:
      h3 = rectangle ('Position', [j,i,5,5]);
      set(h3, 'FaceColor', [1 0 0])
      count = count + 1;         
   end
end

classes1 = ones(1,count-1); % Leopard "class" is set to 1

% Sample images from the area of the figure that consists of the background
count = 1;
for i=180:5:250
   for j = 1:5:100
      Temp=a(i:i+4,j:j+4); 
      TrainBackground(1:25,count)=Temp(:);
      %Sets the pixel colors to yellow
      h3 = rectangle ('Position', [j,i,5,5]);
      set(h3, 'FaceColor', [1 1 0]);
      count = count + 1;         
   end
end

pause(0.1);  %Used so the image gets rendered 
clear Temp
classes2 = -1*ones(1,count-1);  % Make the background "class" -1

% Combine the training data
X = double([TrainLeopard, TrainBackground]);
Targets =[classes1, classes2];

% Before training, get the whole image ready for testing:
count = 1;
for i=1:5:r
   for j = 1:5:c
       Temp=a(i:i+4,j:j+4);
       FullImage(1:25,count) = double(Temp(:));
       count = count + 1;
   end
end

%%
%****************************************************************
%
%  Neural network training:  Where you come in.
%
%****************************************************************

net=newff(X,Targets,10);

%  Here are some optional settings that come in useful:
%net.divideParam.trainRatio=0.8
%net.divideParam.valRatio=0.1;
%net.divideParam.testRatio=0.1;
%net.trainFcn='traingd';
%net.trainParam.epochs=1500;


net=train(net,X,Targets);
tst=sim(net,FullImage);

%***********************************************************************
%
% Display Testing results:  No need to change anything from here down
%
%***********************************************************************

%Just in case these were used in your code, we re-set them:
A = imread('leopard.jpg'); % Read Image into matrix
a = A(:,:,1); % We'll only use the red channel for this project.
[r,c] = size(a);

% set up figure
h1 = figure;
h2 = axes('Parent', h1);
image(A); 
set(h1, 'position', [40+c, sc(4)-(r+50), c, r], 'menubar', 'none', 'Name', 'Trained Neural Network Response');
set(h2, 'Visible', 'Off');

% Show results
count = 1;
for i=1:5:r
   for j = 1:5:c
      h3 = rectangle ('Position', [j,i,5,5]);
      if tst(count)>0
         set(h3, 'FaceColor', [1,0,0])
      end        
      count = count + 1;         
   end
end