lbp特征的matlab实现

matlab自带函数extractLBPFeatures可以很好地提取lbp特征，但是不好进行改进，因此在网上找了人家用maltab实现的lbp特征提取，但是性能比自带函数稍差，代码主要来自
http://www.cse.oulu.fi/wsgi/MVG/Downloads/LBPMatlab
针对lbp特征提取部分，有稍微进行修改，代码如下：
testmylbp.m

%lbp的参数是以下，直接在这改，就不用在函数里一个一个的改动了
lbpcellsize=48;
mapping=getmapping(8,'u2');%以统一模式lbp去映射 
radius=1;
neighbors=8;
mode='L2';%h仅仅只是直方图，没有归一化，‘nh’是L1归一化,'L2'是L2归一化

image=imread('rice.png');
imageSize=[256 256];
image = imresize(image,imageSize); %缩放
 % LBP
    [row,col,m]=size(image);
    if m>1
        image=rgb2gray(image);
    end   
    
lbpfeature=mylbp(image,row,col,lbpcellsize,lbpcellsize,radius,neighbors,mapping,mode);

mylbp.m(可以针对子块提取lbp特征，需要调用lbp函数)

function H2=mylbp(img,row,col,blocksizerow,blocksizecol,radius,neighbors,mapping,mode)
% [row,col,k]=size(img);
% if  k>1
%      img=rgb2gray(img);
% end
new_row = floor(row/blocksizerow) * blocksizerow;%ceil是向上取整,floor向下
new_col = floor(col/blocksizecol) * blocksizecol;
new_img = imresize(img, [new_row new_col], 'bilinear');%以双线性插值来缩放

[y_row y_col dim] = size(new_img);
row_blk_num = y_row/blocksizerow;  % 3
col_blk_num = y_col/blocksizecol;  % 6

blocks = 1;
for i = 1:row_blk_num
    for j = 1:col_blk_num
%         disp(blocks);
        block = new_img((i - 1) * blocksizerow + 1 : i * blocksizerow, (j - 1) * blocksizecol + 1 : j * blocksizecol, :);
        H1(blocks,:)=lbp(block,radius,neighbors,mapping,mode);
        
%         imshow(block);
        %imwrite(block, ['./' num2str(blocks) '.jpg']);
        blocks = blocks + 1;
    end
end
H2=reshape(H1,1,[]);%变成行向量

lbp.m 代码如下

%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('rice.png');
%       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锟? 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)
    radius=varargin{2};
    neighbors=varargin{3};
    
    spoints=zeros(neighbors,2);

    % Angle step. %由采样点个数计算出各个采样点的坐标，
    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);

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')||strcmp(mode,'L2'))
    % Return with LBP histogram if mode equals 'hist'.
    result=hist(result(:),0:(bins-1));
    if (strcmp(mode,'nh'))
        result=result/sum(result);
    elseif(strcmp(mode,'L2'))
         result=result/norm(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

getmapping.m 代码如下

%GETMAPPING returns a structure containing a mapping table for LBP codes.
%  MAPPING = GETMAPPING(SAMPLES,MAPPINGTYPE) returns a 
%  structure containing a mapping table for
%  LBP codes in a neighbourhood of SAMPLES sampling
%  points. Possible values for MAPPINGTYPE are
%       'u2'   for uniform LBP
%       'ri'   for rotation-invariant LBP
%       'riu2' for uniform rotation-invariant LBP.
%
%  Example:
%       I=imread('rice.tif');
%       MAPPING=getmapping(16,'riu2');
%       LBPHIST=lbp(I,2,16,MAPPING,'hist');
%  Now LBPHIST contains a rotation-invariant uniform LBP
%  histogram in a (16,2) neighbourhood.
%

function mapping = getmapping(samples,mappingtype)
% Version 0.1.1
% Authors: Marko Heikkil锟? and Timo Ahonen

% Changelog
% 0.1.1 Changed output to be a structure
% Fixed a bug causing out of memory errors when generating rotation 
% invariant mappings with high number of sampling points.
% Lauge Sorensen is acknowledged for spotting this problem.


samples2='uint8';
table = 0:2^samples-1;
newMax  = 0; %number of patterns in the resulting LBP code
index   = 0;

if strcmp(mappingtype,'u2') %Uniform 2
  newMax = samples*(samples-1) + 3; 
  for i = 0:2^samples-1
    j = bitset(bitshift(i,1,samples2),1,bitget(i,samples)); %rotate left
    numt = sum(bitget(bitxor(i,j),1:samples)); %number of 1->0 and
                                               %0->1 transitions
                                               %in binary string 
                                               %x is equal to the
                                               %number of 1-bits in
                                               %XOR(x,Rotate left(x)) 
    if numt <= 2
      table(i+1) = index;
      index = index + 1;
    else
      table(i+1) = newMax - 1;
    end
  end
end

if strcmp(mappingtype,'ri') %Rotation invariant
  tmpMap = zeros(2^samples,1) - 1;
  for i = 0:2^samples-1
    rm = i;
    r  = i;
    for j = 1:samples-1
      r = bitset(bitshift(r,1,samples),1,bitget(r,samples)); %rotate
                                                             %left
      if r < rm
        rm = r;
      end
    end
    if tmpMap(rm+1) < 0
      tmpMap(rm+1) = newMax;
      newMax = newMax + 1;
    end
    table(i+1) = tmpMap(rm+1);
  end
end

if strcmp(mappingtype,'riu2') %Uniform & Rotation invariant
  newMax = samples + 2;
  for i = 0:2^samples - 1
    j = bitset(bitshift(i,1,samples),1,bitget(i,samples)); %rotate left
    numt = sum(bitget(bitxor(i,j),1:samples));
    if numt <= 2
      table(i+1) = sum(bitget(i,1:samples));
    else
      table(i+1) = samples+1;
    end
  end
end

mapping.table=table;
mapping.samples=samples;
mapping.num=newMax;

把这几个函数放在一起，就可以运行tesmylbp文件了，testmylbp顾名思义就是测试muylbp函数能不能使用，mylbp其实就是加了一个子块的功能而已，lbp.m可以提取一整张图片的lbp特征，但是一般情况下是对图片划分子块，然后整个串联起来，所以只是把这些整合在一起罢了。