lbp.rar_lbp face_lbp 识别率_lbp人脸_lbp人脸识别_人脸识别 LBP

function [result,bins] = lbp(varargin) 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]; 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}; 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}; if(nargin >= 3) mapping=varargin{3}; 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); neighbors=size(spoints,1); 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); bins = 2^neighbors; % Initialize the result matrix with zeros. result=zeros(dy+1,dx+1); %Compute the LBP code image 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 = image(ry:ry+dy,rx:rx+dx); D = N >= C; 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 = 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); D = N >= d_C; end % Update the result matrix. v = 2^(i-1); result = result + v*D; end if length(mapping) > 1 bins = max(max(mapping)) + 1; for i = 1:size(result,1) for j = 1:size(result,2) result(i,j) = mapping(result(i,j)+1); end end end