%   This Research is funded by IIIT Sri City, INDIA through Institute Research Grant.
%	LDRP returns the Local Directional Relation Pattern histogram for an input image.
%   The original code of LBP is used and updated to the LDRP by Dr. Shiv Ram Dubey, IIIT Sri City.
%   This code can be used only for the academic and research purposes and can not be used for any commercial purposes.
%   Cite the paper 
%		'S.R. Dubey, Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval. arXiv:1709.09518, 2017.' 
%	In case you are using this code.


function h=LDRP(path_image)
% path_image='datasets\AT&T\s1\1.pgm';
img=imresize(imread(path_image),[64 64]);	% Reading the image and resizing to 64 * 64
if length(size(img))==3		% Convert into gray in case of color image
    img=rgb2gray(img);
end

R=6; % Max Radius
S=8; % Number of neighbors per radius
h=[]; % Variable to store the final LDRP histogram
for r=3:R
    C=double(img(r+1:end-r,r+1:end-r));
    k=0;N_dec=zeros(size(C,1),size(C,2),S);Ne=[];N=[];
    for i=1:r
        Ne(:,:,:,i+1)=neighbors(img(r-i+1:end-r+i,r-i+1:end-r+i),i,S); % Returns the neighbors
    
        for j=1:i-1
            k=k+1;
            N(:,:,:,k)=Ne(:,:,:,i+1)>=Ne(:,:,:,j);
            N_dec=N_dec+N(:,:,:,k)*2^(k-1);
        end
    end
    C_mod=((2^k)-1)*C/255;	% Range matching
    result=zeros(size(Ne,1),size(Ne,2));
    for i=1:S
        result=result+(N_dec(:,:,i)>=C_mod)*2^(i-1);
    end
    result=hist(result(:),0:255);result=result/sum(result);
    h=[h result];
end
h=h/sum(h);		% Final Feature Normalization
end







function N = neighbors(varargin) % image,radius,neighbors,mapping,mode)
% error(nargchk(1,5,nargin));

image=varargin{1};
d_image=double(image);

if nargin==1
    spoints=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; 1 0; 1 1];
    neighbors=8;
    mapping=0;
    mode='h';
end

if (nargin == 2) && (length(varargin{2}) == 1)
    error('Input arguments');
end

if (nargin > 2) && (length(varargin{2}) == 1)
    radius=varargin{2};
    neighbors=varargin{3};
    
    spoints=zeros(neighbors,2);

    % Angle step.
    a = 2*pi/neighbors;
    
    for i = 1:neighbors
        spoints(i,1) = -radius*sin((i-1)*a);
        spoints(i,2) = radius*cos((i-1)*a);
    end
    
    if(nargin >= 4)
        mapping=varargin{4};
        if(isstruct(mapping) && mapping.samples ~= neighbors)
            error('Incompatible mapping');
        end
    else
        mapping=0;
    end
    
    if(nargin >= 5)
        mode=varargin{5};
    else
        mode='h';
    end
end

if (nargin > 1) && (length(varargin{2}) > 1)
    spoints=varargin{2};
    neighbors=size(spoints,1);
    
    if(nargin >= 3)
        mapping=varargin{3};
        if(isstruct(mapping) && mapping.samples ~= neighbors)
            error('Incompatible mapping');
        end
    else
        mapping=0;
    end
    
    if(nargin >= 4)
        mode=varargin{4};
    else
        mode='h';
    end   
end

% Determine the dimensions of the input image.
[ysize xsize] = size(image);



miny=min(spoints(:,1));
maxy=max(spoints(:,1));
minx=min(spoints(:,2));
maxx=max(spoints(:,2));

% Block size, each LBP code is computed within a block of size bsizey*bsizex
bsizey=ceil(max(maxy,0))-floor(min(miny,0))+1;
bsizex=ceil(max(maxx,0))-floor(min(minx,0))+1;

% Coordinates of origin (0,0) in the block
origy=1-floor(min(miny,0));
origx=1-floor(min(minx,0));

% Minimum allowed size for the input image depends
% on the radius of the used LBP operator.
if(xsize < bsizex || ysize < bsizey)
  error('Too small input image. Should be at least (2*radius+1) x (2*radius+1)');
end

% Calculate dx and dy;
dx = xsize - bsizex;
dy = ysize - bsizey;

% Fill the center pixel matrix C.
C = image(origy:origy+dy,origx:origx+dx);
d_C = double(C);

for i = 1:neighbors
  y = spoints(i,1)+origy;
  x = spoints(i,2)+origx;
  % Calculate floors, ceils and rounds for the x and y.
  fy = floor(y); cy = ceil(y); ry = round(y);
  fx = floor(x); cx = ceil(x); rx = round(x);
  % Check if interpolation is needed.
  if (abs(x - rx) < 1e-6) && (abs(y - ry) < 1e-6)
    % Interpolation is not needed, use original datatypes
    N(:,:,i) = d_image(ry:ry+dy,rx:rx+dx);
  else
    % Interpolation needed, use double type images 
    ty = y - fy;
    tx = x - fx;

    % Calculate the interpolation weights.
    w1 = (1 - tx) * (1 - ty);
    w2 =      tx  * (1 - ty);
    w3 = (1 - tx) *      ty ;
    w4 =      tx  *      ty ;
    % Compute interpolated pixel values
    N(:,:,i) = w1*d_image(fy:fy+dy,fx:fx+dx) + w2*d_image(fy:fy+dy,cx:cx+dx) + ...
        w3*d_image(cy:cy+dy,fx:fx+dx) + w4*d_image(cy:cy+dy,cx:cx+dx);
  end  
end
end
