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。