Recognition(TestImage, m, A, Eigenfaces,varargin)

function OutputName = Recognition(TestImage, m, A, Eigenfaces,varargin)
% Recognizing step....
%
% Description: This function compares two faces by projecting the images into facespace and 
% measuring the Euclidean distance between them.
%
% Argument:      TestImage              - Path of the input test image
%
%                m                      - (M*Nx1) Mean of the training
%                                         database, which is output of 'EigenfaceCore' function.
%
%                Eigenfaces             - (M*Nx(P-1)) Eigen vectors of the
%                                         covariance matrix of the training
%                                         database, which is output of 'EigenfaceCore' function.
%
%                A                      - (M*NxP) Matrix of centered image
%                                         vectors, which is output of 'EigenfaceCore' function.
% 
% Returns:       OutputName             - Name of the recognized image in the training database.
%
% See also: RESHAPE, STRCAT

% Original version by Amir Hossein Omidvarnia, October 2007
%                     Email: [e-mail]                  

%%%%%%%%%%%%%%%%%%%%%%%% Projecting centered image vectors into facespace
% All centered images are projected into facespace by multiplying in
% Eigenface basis's. Projected vector of each face will be its corresponding
% feature vector.
% varargin
ProjectedImages = [];
Train_Number = size(Eigenfaces,2);
for i = 1 : Train_Number
    temp = Eigenfaces'*A(:,i); % Projection of centered images into facespace
    ProjectedImages = [ProjectedImages temp]; 
end

%%%%%%%%%%%%%%%%%%%%%%%% Extracting the PCA features from test image

    
InputImage = imread(TestImage);
if isempty(varargin)~=1
    if fix(varargin{1}/10)==1
%% 11     

        
        x = InputImage;
if (size(x,3)>1)%if RGB image make gray scale
    try
        x=rgb2gray(x);%image toolbox dependent
    catch
        x=sum(double(x),3)/3;%if no image toolbox do simple sum
    end
end
x=double(x);%make sure the input is double format

[output,count,mm,svec]=facefind(x,32,inf,5,3,1);%full scan 
b=[output(1)-(output(2)-output(1))*0.3 ...
   output(2)+(output(2)-output(1))*0.3...
   output(3)-(output(4)-output(3))*0.6...
   output(4)+(output(4)-output(3))*0.3];

syms x0 y0 r x y
F=(x-x0)^2+(y-y0)^2-r^2;
g=solve(subs(F,[x,y],[(b(2)+b(1))/2 b(4)]),...
        subs(F,[x,y],[b(1)          b(3)-(b(3)-b(4))*3/4]),...
        subs(F,[x,y],[b(2)          b(3)-(b(3)-b(4))*3/4]));

im1=InputImage;
im1(1   :b(3),:        ,:)=0;
im1(b(4):end ,:        ,:)=0;
im1(:        ,1 :b(1)  ,:)=0;
im1(:        ,b(2):end ,:)=0;


InputImage=im1;

  if varargin{1}-10==3
      InputImage=InputImage;
  end
  
  imshow(InputImage)
    elseif fix(varargin{1}/10)==2
%% 2222     
222

if varargin{1}-20==3
      InputImage=InputImage;
  end

    
 imshow(InputImage)       
    elseif fix(varargin{1}/10)==3
%% 333   
333

        x = InputImage;
if (size(x,3)>1)%if RGB image make gray scale
    try
        x=rgb2gray(x);%image toolbox dependent
    catch
        x=sum(double(x),3)/3;%if no image toolbox do simple sum
    end
end
x=double(x);%make sure the input is double format

[output,count,mm,svec]=facefind(x,32,inf,5,5,1);%full scan 
b=[output(1)-(output(2)-output(1))*0.3 ...
   output(2)+(output(2)-output(1))*0.3...
   output(3)-(output(4)-output(3))*0.6...
   output(4)+(output(4)-output(3))*0.3];

syms x0 y0 r x y
F=(x-x0)^2+(y-y0)^2-r^2;
g=solve(subs(F,[x,y],[(b(2)+b(1))/2 b(4)]),...
        subs(F,[x,y],[b(1)          b(3)-(b(3)-b(4))*3/4]),...
        subs(F,[x,y],[b(2)          b(3)-(b(3)-b(4))*3/4]));

im1=InputImage;
im1(1   :b(3),:        ,:)=0;
im1(b(4):end ,:        ,:)=0;
im1(:        ,1 :b(1)  ,:)=0;
im1(:        ,b(2):end ,:)=0;

InputImage=imresize(imcrop(im1,[b(1) b(3)  b(2)-b(1) b(4)-b(3)]),[240 320]);
% if varargin{1}-30==1
%     InputImage=InputImage;      
% elseif varargin{1}-30==2
%     InputImage=InputImage;  
% elseif varargin{1}-30==3
%    
%  end
imshow(InputImage)

    end
    
end
temp = InputImage(:,:,1);

[irow icol] = size(temp);
InImage = reshape(temp',irow*icol,1);
size(InImage)
size(m)
Difference = double(InImage)-m; % Centered test image
ProjectedTestImage = Eigenfaces'*Difference; % Test image feature vector

%%%%%%%%%%%%%%%%%%%%%%%% Calculating Euclidean distances 
% Euclidean distances between the projected test image and the projection
% of all centered training images are calculated. Test image is
% supposed to have minimum distance with its corresponding image in the
% training database.

Euc_dist = [];
for i = 1 : Train_Number
    q = ProjectedImages(:,i);
    temp = ( norm( ProjectedTestImage - q ) )^2;
    Euc_dist = [Euc_dist temp];
end

[Euc_dist_min , Recognized_index] = min(Euc_dist);
OutputName = strcat(int2str(Recognized_index),'.jpg');