function varargout = Main(varargin)
% MAIN MATLAB code for Main.fig
%      MAIN, by itself, creates a new MAIN or raises the existing
%      singleton*.
%
%      H = MAIN returns the handle to a new MAIN or the handle to
%      the existing singleton*.
%
%      MAIN('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in MAIN.M with the given input arguments.
%
%      MAIN('Property','Value',...) creates a new MAIN or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before Main_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to Main_OpeningFcn via varargin.
%
%      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
%      instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES

% Edit the above text to modify the response to help Main

% Last Modified by GUIDE v2.5 02-Feb-2022 18:26:26

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @Main_OpeningFcn, ...
                   'gui_OutputFcn',  @Main_OutputFcn, ...
                   'gui_LayoutFcn',  [] , ...
                   'gui_Callback',   []);
if nargin && ischar(varargin{1})
    gui_State.gui_Callback = str2func(varargin{1});
end

if nargout
    [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
    gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT


% --- Executes just before Main is made visible.
function Main_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
% varargin   command line arguments to Main (see VARARGIN)

% Choose default command line output for Main
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);

% UIWAIT makes Main wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = Main_OutputFcn(hObject, eventdata, handles) 
% varargout  cell array for returning output args (see VARARGOUT);
% hObject    handle to figure
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)

% Get default command line output from handles structure
varargout{1} = handles.output;


% --- Executes on button press in pushbutton6.
function pushbutton6_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton6 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
run('SVM_RF.m')
run('FeatureReduction\Check_dta.m')


% --- Executes on button press in pushbutton7.
function pushbutton7_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton7 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
cd('FeatureReduction')
load out.mat
if isequal(k,[])
    uiwait(msgbox('Input_image was real image'))
else
    uiwait(msgbox('Input_image was fake image'))
end

% --- Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton4 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
load DTA.mat
augmenter = imageDataAugmenter( ...
    'RandRotation',[0 360], ...
    'RandScale',[0.5 1])
imageSize = [56 56 1];
auimds = augmentedImageDatastore(imageSize,XTrain,YTrain,'DataAugmentation',augmenter)
basicwaitbar
uiwait(msgbox('Image resized succuesfully'))

% --- Executes on button press in pushbutton5.
function pushbutton5_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton5 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
run('FeatureReduction\feature_extraction.m')
pause(10)
uiwait(msgbox('feature extracted succuesfully'))


% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton1 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
path_folder= uigetdir('Please enter your respective path','fingerprint_data');
try
    data_dir_Fake = 'fake data path';
    data_dir_real = 'real data path';

    images_fake = dir([data_dir_Fake '/*.jpg']);
    images_real = dir([data_dir_real '/*.jpg']);


    score_artifacts = [];       
    score_authentic = [];      

    for i = 1:N
       IMAGE_real = imread(['real data path' images_fake(i).name]);
       PORE_NUMBER = BIO_PAD_FG_HOWTO(IMAGE_real);
       score_artifacts = [score_artifacts PORE_NUMBER];
       score_all = [score_all PORE_NUMBER];
    end

    for i = 1:N
       IMAGE_fake = imread(['fake data path' images_silicone(i).name]);
       PORE_NUMBER = Finer_Sect(IMAGE_fake);
       score_artifacts = [score_artifacts PORE_NUMBER];
       score_all = [score_all PORE_NUMBER];
    end

    APCER_score = [];
    NPCER_score = [];
    for n = 50:100
        fault_negative = sum(score_artifacts >= prctile(score_all, n));
        fault_positive = sum(score_authentic <= prctile(score_all, n)); 
        APCER = fault_negative / length(score_artifacts);
        NPCER = fault_positive / length(score_authentic);
        APCER_score = [APCER_score APCER]; 
        NPCER_score = [NPCER_score NPCER];
    end 

    index = find(NPCER_score == min(NPCER_score));

    APCER_min = [];
    for i = 1 : length(index)
        APCER_min = [APCER_min APCER_score(i)];
    end 
    APCER = min(APCER_min);
    NPCER = min(NPCER_score);
    
    train_authentic = score_authentic(1:6,:);


    test_authentic = score_authentic(7:12,:);
    test_artifacts = score_artifacts; 

    % Calculate threshold using authentic reference. 
    authentic_ref = [];        
    for i = 1 : length(train_authentic)
       authentic_score = [];
       for j = 1 :length(train_authentic)
           authentic_score = [authentic_score ...
               abs(train_authentic(i) - train_authentic(j))]; 
       end
       authentic_ref = [authentic_ref authentic_score];
    end

    threshold = max(authentic_ref); 

    % Calculate distance for authentic vs. authentic references.  
    authentic_dist = [];   
    score = 0; 
    for i = 1 : length(test_authentic)
        authentic_score = [];
        for j = 1 : length(train_authentic)
            authentic_score = [authentic_score ...
                abs(test_authentic(i) - train_authentic(j))];
        end
        authentic_dist = [authentic_dist mean(authentic_score)];
    end 

    for i = 1 : length(authentic_dist)
       if authentic_dist(i) > threshold
          score = score + 1;  
       end
    end

    NPCER = score / length(test_artifacts); 

    % Calculate distance for artifacts vs. authentic references. 
    artifact_dist = [];
    score = 0;
    for i = 1 : length(test_artifacts)
        artifact_score = [];
        for j = 1 : length(train_authentic)
            artifact_score = [artifact_score ...
                abs(test_artifacts(i) - train_authentic(j))];
        end
        artifact_dist = [artifact_dist mean(artifact_score)];
    end 

    for i = 1 : length(artifact_dist)
       if artifact_dist(i) < threshold
            score = score + 1; 
       end
    end
    [Data,~]=LOSIBF(artifact_dist,score,threshold);
    DATA_(2,:)=Data
    save('Data.mat','DATA_')
end
pause(5)
uiwait(msgbox('Finger_print data loaded successfully'))


% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton2 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
path_folder= uigetdir('Please enter your respective path','Face_data');
pause(5)
disp(path_folder)
%% Loading the database into matrix v
try
    w=load_database();
    %% Initializations
   
    ri=round(400*rand(1,1));            % Randomly pick an index.
    r=w(:,ri);                          % r contains the image we later on will use to test the algorithm
    v=w(:,[1:ri-1 ri+1:end]);            
    N=20;                               % Number of signatures used for each image.
    %% Subtracting the mean from v
    O=uint8(ones(1,size(v,2))); 
    m=uint8(mean(v,2));                 % m is the maen of all images.
    vzm=v-uint8(single(m)*single(O));   % vzm is v with the mean removed. 
    %% Calculating eignevectors of the correlation matrix
    % We are picking N of the 400 eigenfaces.
    L=single(vzm)'*single(vzm);
    [V,D]=eig(L);
    V=single(vzm)*V;
    V=V(:,end:-1:end-(N-1));            % Pick the eignevectors corresponding to the 10 largest eigenvalues. 
    %% Calculating the signature for each image
    cv=zeros(size(v,2),N);
    for i=1:size(v,2)
        cv(i,:)=single(vzm(:,i))'*V;    % Each row in cv is the signature for one image.
    end
    [Data,~]=LOSIBF(cv,V,D);
    DATA_(1,:)=Data
    save('Data.mat','DATA_')

end

uiwait(msgbox('Face data loaded successfully'))


% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject    handle to pushbutton3 (see GCBO)
% eventdata  reserved - to be defined in a future version of MATLAB
% handles    structure with handles and user data (see GUIDATA)
path_folder= uigetdir('Please enter your respective path','Iris_data');
% Fix the size of all images to 256x256
try
    subarea_size = 256;

    % Load iris images
    iris_fake_dir = dir(['../data-raw/LivDet-Iris-2013-Warsaw-Subset/fake*.png']);
    iris_real_dir = dir(['../data-raw/LivDet-Iris-2013-Warsaw-Subset/real*.png']);

 
    seg =[iris_fake_dir iris_real_dir]

    x = seg(:,1);
    y = seg(:,2);
    r =max(randi([-10 10],1,10));

    % Crop the iris portion of the image 
    subimage = image(y-subarea_size/2+1 : y+subarea_size/2, x-subarea_size/2+1 : x+subarea_size/2);

  
    %% Choose paramaters f0, f1, df
    f0 = 5;
    f1 = 40;
    df = 15; 

    

    [rr cc] = meshgrid(1:256);
    C1 = sqrt((rr-256/2).^2+(cc-256/2).^2)>=f0;         % start point f0 
    C2 = sqrt((rr-256/2).^2+(cc-256/2).^2)<=(f0+df);    % end point f0+df
    mask1 = C1 .* C2; 

    masked_image1 = double(subimage).* double(mask1); 
    numerator = max(masked_image1(:));

    % Create the second mask which starts at f1 and ends at f1+df
    C3 = sqrt((rr-256/2).^2+(cc-256/2).^2)>=f1;         % start point f1
    C4 = sqrt((rr-256/2).^2+(cc-256/2).^2)<=(f1+df);    % end point f1+df
    mask2 = C3 .* C4; 

    masked_image2 = double(subimage) .* double(mask2);
    denominator = max(masked_image2(:)); 

    %% Calculate  score
    liveness_score = numerator/denominator;
    [Data,~]=LOSIBF(masked_image2,df,liveness_score);
    DATA_(3,:)=Data
    save('Data.mat','DATA_')
end
pause(5)
uiwait(msgbox('Iris data loaded successfully'))