www.pudn.com > lbp.rar > lbp.m, change:2014-08-03,size:6050b

```%  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
%  --------
%       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 Heikkilä 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.
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)
neighbors=varargin{3};

spoints=zeros(neighbors,2);

% Angle step.
a = 2*pi/neighbors;

for i = 1:neighbors
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);

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

%Apply mapping if it is defined
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

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
if ((bins-1)<=intmax('uint8'))
result=uint8(result);
elseif ((bins-1)<=intmax('uint16'))
result=uint16(result);
else
result=uint32(result);
end
end

end

```