%   This Research is funded by IIIT Sri City, INDIA through Institute Research Grant.
%	LDOP returns the Local Directional Order Pattern histogram for an input image.
%   The original code of LBP is used and updated to the LDOP 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, S. Mukherjee, LDOP: Local Directional Order Pattern for Robust Face Retrieval. arXiv:1803.07441, 2018.' 
%	In case you are using this code.

function h=LDOP(path_image)
% path_image='datasets\AT&T\s1\1.pgm';
img=imresize(imread(path_image),[64 64]);
if length(size(img))==3
    img=rgb2gray(img);
end
R=4; % Set the number of radius (i.e. length of order)
S=8; % Set the number of directions
for i=1:R
    Ne(:,:,:,i)=neighbors(img(R-i+1:end-R+i,R-i+1:end-R+i),i,8);
end
h=[];
for r=2:R
    [Ne1 sin]=sort(Ne(:,:,:,1:r),4);
	pe=reshape((perms(1:r))',[1 1 1 r factorial(r)]);
	finv=zeros(size(sin,1),size(sin,2),S);
	for j=1:factorial(r)
		finv(sum(abs(repmat(pe(1,1,1,:,j),[size(sin,1) size(sin,2) S 1 1])-sin),4)==0)=j;
	end
	C=((factorial(r)-1)*(double(img(R+1:end-R,R+1:end-R)))/255)+1;

	result=zeros(size(finv,1), size(finv,2));
	for i=1:S
		result = result + (2^(i-1))*(finv(:,:,i)>=C);
	end
	result=hist(result(:),0:255);
	result=result/sum(result);
	h=[h result];
end
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
