lbp.rar_LBP 纹理_LBP值计算_lbp_lbp纹理_图像LBP

%LBP returns the local binary pattern image or LBP histogram of an image. % J = LBP(I,R,N,MAPPING,MODE) returns either a local binary pattern % coded image or the local binary pattern histogram of an intensity % image I. The LBP codes are computed using N sampling points on a % circle of radius R and using mapping table defined by MAPPING. % See the getmapping function for different mappings and use 0 for % no mapping. Possible values for MODE are % 'h' or 'hist' to get a histogram of LBP codes % 'nh' to get a normalized histogram % Otherwise an LBP code image is returned. % % J = LBP(I) returns the original (basic) LBP histogram of image I % % J = LBP(I,SP,MAPPING,MODE) computes the LBP codes using n sampling % points defined in (n * 2) matrix SP. The sampling points should be % defined around the origin (coordinates (0,0)). % % Examples % -------- % I=imread('test1.bmp'); % mapping=getmapping(8,'u2'); % H1=lbp(I,1,8,mapping,'h'); %LBP histogram in (8,1) neighborhood % %using uniform patterns % subplot(2,1,1),stem(H1); % % H2=lbp(I); % subplot(2,1,2),stem(H2); % % SP=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; 1 0; 1 1]; % I2=lbp(I,SP,0,'i'); %LBP code image using sampling points in SP % %and no mapping. Now H2 is equal to histogram % %of I2. function result = lbp(varargin) % image,radius,neighbors,mapping,mode) % Version 0.3.2 % Authors: Marko Heikkil?and Timo Ahonen % Changelog % Version 0.3.2: A bug fix to enable using mappings together with a % predefined spoints array % Version 0.3.1: Changed MAPPING input to be a struct containing the mapping % table and the number of bins to make the function run faster with high number % of sampling points. Lauge Sorensen is acknowledged for spotting this problem. % Check number of input arguments.% %% 检查参数的个数nargin，使其大于1小于5。如果不在此区间，就报错 error(nargchk(1,5,nargin)); image=varargin{1};% % %% 把第一个参数赋值给image d_image=double(image);% % % 把图像从uint8转成double类型，以便以后计算 % % % 只有给出待处理的图像（一个参数）时，使用默认的设置。 % % % sp定义了中心点与它的近邻的相对位置 % % % neighbors定义近邻个数 % % % mapping定义的映射 % % % mode区别直方图的类型，'h' or 'hist'是直方图，nh是规一化的直方图 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 % % % 给出两个参数，并且第二个参数（代表近邻半径）的长度为1时 % % % 只给出了近邻的半径，没给出近邻的个数，报错 if (nargin == 2) && (length(varargin{2}) == 1) error('Input arguments'); end % % % 如果给出两个以上的参数，并且第二个参数（代表近邻半径）的长度为1 % % % 半径设为第二个参数 % % % 近邻个数设为第三个参数 if (nargin > 2) && (length(varargin{2}) == 1) radius=varargin{2}; neighbors=varargin{3}; spoints=zeros(neighbors,2); % % % 把360度均匀分成neighbors分，以计算近邻点与中心的相对坐标 % Angle step. a = 2*pi/neighbors; % % % 计算坐标，每一维代表y,第二维代表x % % % spoints的第i行代表第i个近邻 for i = 1:neighbors spoints(i,1) = -radius*sin((i-1)*a); spoints(i,2) = radius*cos((i-1)*a); end % % % 如果参数个数大于等于4，第四个参数赋值给映射mapping；否则，无映射。 if(nargin >= 4) mapping=varargin{4}; if(isstruct(mapping) && mapping.samples ~= neighbors) error('Incompatible mapping'); end else mapping=0; end % % % 第五个参数确定直方图的属性 if(nargin >= 5) mode=varargin{5}; else mode='h'; end end % % % 如果参数个数大于1，并且第二个参数的长度大于1。则第二个参数给出近邻点与中心点的相对位置 if (nargin > 1) && (length(varargin{2}) > 1) spoints=varargin{2}; neighbors=size(spoints,1); % % % 如果还有第三个参数，把它赋值给映射mapping 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.图像的大小，第一维是y，第二维是x [ysize xsize] = size(image); % % % 确定block的左上和右下两个点 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 % % % block的大小 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 % % % 在block里中心点的坐标 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. % % % 检查block和img的大小 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. % % % 所有可以作为模板中心点的像素集合 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); % % % 初始化结果矩阵 %Compute the LBP code image 这一段写得很漂亮！！！！ % % % 对于每一个neighbor，先使要比较的点与中心点对齐，然后利用D = N >= C比较它们的大小。 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. % % % 更新结果矩阵 v = 2^(i-1); result = result + v*D; end %Apply mapping if it is defined % % % 如果mapping已经存在，那么利用这个mapping. if isstruct(mapping) bins = mapping.num; for i = 1:size(result,1) for j = 1:size(result,2) result(i,j) = mapping.table(result(i,j)+1); end end end % % % 如果要参数列表指定了直方图的属性，计算直方图 if (strcmp(mode,'h') || strcmp(mode,'hist') || strcmp(mode,'nh')) % Return with LBP histogram if mode equals 'hist'. result=hist(result(:),0:(bins-1)); if (strcmp(mode,'nh')) result=result/sum(result); end else %Otherwise return a matrix of unsigned integers % % % 如果没有指定直方图的属性，返回数值方阵 if ((bins-1)<=intmax('uint8')) result=uint8(result); elseif ((bins-1)<=intmax('uint16')) result=uint16(result); else result=uint32(result); end