%%%%%%%%%%%%%%%% MAIN PROGRAM BEGIN %%%%%%%%%%%%%%%%%%%
function D7_Final_NEPTUN(PIC_NUM)
	global mCNN;
	initCNN(PIC_NUM);
	
	for i=1:PIC_NUM
        % load images, initialize layers
        fname=sprintf('%sinput_%02d.bmp',mCNN.picdir,i); %point to the ith input picture
        mCNN.LAM_1 = lbmp2cnn(fname);
        
        % vizualize input
        subplot(PIC_NUM,3,3*i-2);
        cnnshow(mCNN.LAM_1);    
        title(['input ',num2str(i)]);        
        
        %% input: LAM_1
        %% output: LAM_2
        %% owerwrite: -
        preproc; %do the preprocessing
        
        % vizualize preproc subtask
        subplot(PIC_NUM,3,3*i-1);
        cnnshow(mCNN.LAM_2);    
        title('after prepoc');        

        %% input: LAM_2
        %% output: LAM_1
        %% owerwrite: -
        segment; %do the segmentation
        
        %% input: LAM_1
        %% output: -
        %% owerwrite: -
        cresult=classify; %classify the result
        
        % vizualize output
        subplot(PIC_NUM,3,3*i); 
        cnnshow(mCNN.LAM_1);
        title(['output ',num2str(i),' class: ',num2str(cresult)]);
        
        scnn2bmp(sprintf('output%02d_%d.bmp',i,cresult), mCNN.LAM_1); % write the file to disk (file name is important) 
	end

%%%%%%%%%%%%%%%%% MAIN PROGRAM END %%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%% INITIALIZATION BEGIN %%%%%%%%%%%%%%%%%%
function initCNN(PIC_NUM)
	global mCNN;
	% set CNN environment
	cnn_setenv; % default environment
	initTemplates;
	% define constants
	mCNN.BLACK=1;
	mCNN.WHITE=-1;
	mCNN.LAMsize=[144,176];
	mCNN.PIC_NUM=PIC_NUM;
	
	% analog memories
	mCNN.LAM_1=zeros(mCNN.LAMsize);
	mCNN.LAM_2=zeros(mCNN.LAMsize);
	mCNN.LAM_3=zeros(mCNN.LAMsize);
	mCNN.LAM_4=zeros(mCNN.LAMsize);
    mCNN.LAM_5=zeros(mCNN.LAMsize);
	mCNN.LAM_6=zeros(mCNN.LAMsize);
    
	
	% extra memories
%	mCNN.LAM_7=zeros(mCNN.LAMsize);
	
	%DO NOT TOUCH
	mCNN.picdir='';

%%%%%%%%%%%%%%%% INITIALIZATION  END %%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%% EXTRA TEMPLATES BEGIN %%%%%%%%%%%%%%%%%%
function initTemplates
    % DIFFUS
    mCNN.DIFFUS_A = [0.10 0.15 0.10;
            0.15 0.00 0.15;
            0.10 0.15 0.10]; 
    mCNN.DIFFUS_I = 0;
    
    % THRES
    mCNN.THRES_A = [0 0 0;
           0 2 0;
           0 0 0];
    mCNN.THRES_I = 0.0;     
%%%%%%%%%%%%%%%%% EXTRA TEMPLATES END %%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%% PREPROCESS BEGIN %%%%%%%%%%%%%%%%%%%
%% input: LAM_1
%% output: LAM_2
%% owerwrite: -

function preproc
	global mCNN;
    mCNN.INPUT1 = mCNN.LAM_1;
    mCNN.STATE = mCNN.LAM_1;    
    % set boundary condition
    mCNN.Boundary = 2; % zero flux boundary condition
    % run linear template
    loadtem('mCNN.DIFFUS'); % loads the DIFFUS template (linear)
    mCNN.TimeStep = 0.2;
    mCNN.IterNum = 50;
    mCNN.UseBiasMap=0;
    
    % run linear template        
    runtem; % runs the CNN simulation    
    mCNN.LAM_2=mCNN.OUTPUT; %save the results

%%%%%%%%%%%%%%%%% PREPROCESS END %%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%% SEGMENTATION BEGIN %%%%%%%%%%%%%%%%%%%
%% input: LAM_2
%% output: LAM_1
%% owerwrite: -

function segment
	global mCNN;
    mCNN.INPUT1 = mCNN.LAM_2;
    mCNN.STATE = mCNN.LAM_2;
    loadtem('mCNN.THRES'); % loads the THRES template (linear)        
    % set boundary condition
    mCNN.Boundary = 2; % zero flux boundary condition
    mCNN.TimeStep = 0.2;
    mCNN.IterNum = 50;
    
    mCNN.UseBiasMap=1;
    mCNN.BIAS=ones(mCNN.LAMsize)*-0.2;    
    
    % run linear template        
    runtem; % runs the CNN simulation    
    mCNN.LAM_1=mCNN.OUTPUT; %save the results


%%%%%%%%%%%%%%%%% SEGMENTATION END %%%%%%%%%%%%%%%%%%%%%

%%%%%%%%%%%%%%%%% CLASSIFICATION BEGIN %%%%%%%%%%%%%%%%%%%
%% input: LAM_1
%% output: -
%% owerwrite: -

function cresult=classify
	global mCNN;
    [x y]=find(mCNN.LAM_1==mCNN.BLACK);
    cresult=mod(length(x),3)+1;  

%%%%%%%%%%%%%%%%% CLASSIFICATION END %%%%%%%%%%%%%%%%%%%%%