基于稀疏和HOG特征的人耳识别（代码）

毕业两年了，这是研三上学期写的一个实验的代码，刚从云盘下载再来，贴在博客里，不足之处有时间再改进吧，工作两年心态也变了很多。

function result=srchog(k,n)
%n为选取的特征数（hog）
%k=1,3,5,7，9,对应0度，5，10，15，20度test
I=cell(8,78);
for j=1:78
for i=1:k-1imageName=strcat('F:\USTB人耳图像3（对外版79乘10）\',num2str(j),'-',num2str(i),'.bmp');
I{i,j}=imread(imageName);
end
end
for j=1:78
for i=k+2:10imageName=strcat('F:\USTB人耳图像3（对外版79乘10）\',num2str(j),'-',num2str(i),'.bmp');
I{i-2,j}=imread(imageName);
end
end
II=cell(8,78);
for i=1:78for j=1:8II{j,i}=imresize(I{j,i},[240 320],'nearest');end
endfor i=1:78for j=1:8
temp=hogcalculator(II{j,i});
S(i*8+j-8,:)=temp(:);
end
end
SS=S;
[coeff,score,latent,tsquare] = princomp(SS,'econ');
size(score)
sss=score(:,1:n);
%sss=score;
TestI=cell(78);for l=1:78
testimageName=strcat('F:\USTB人耳图像3（对外版79乘10）\',num2str(l),'-',num2str(k),'.bmp');
TestI{l}=imread(testimageName);
picture=TestI{l};
test=hogcalculator(imresize(picture,[240 320],'nearest'));test_mean=test-mean(SS,1);
w=test_mean*coeff;
ww=w(:,1:n);y_double=double(ww');
D=double(sss');
cvx_setup;
cvx_beginvariable u(624);minimize(norm(u,1));subject toD*u == y_double;cvx_end
rri=zeros(78,1);
for i=1:78ti=zeros(624,1);for j=1:8ti(8*i+j-8)=u(8*i-8+j);endyyi=D*ti;rri(i)=norm(yyi-y_double,2);
end
[identityy,qy]=find(rri==min(min(rri)));result(l)=identityy;
end

下面这个hog提取，非原创，一起补充上吧，运行的自己创建个加入库里

function F = hogcalculator(img, cellpw, cellph, nblockw, nblockh,...
nthet, overlap, isglobalinterpolate, issigned, normmethod)
% HOGCALCULATOR calculate R-HOG feature vector of an input image using the
% procedure presented in Dalal and Triggs's paper in CVPR 2005.
%
% Author: timeHandle
% Time: March 24, 2010
% May 12，2010 update.
%
% this copy of code is written for my personal interest, which is an
% original and inornate realization of [Dalal CVPR2005]'s algorithm
% without any optimization. I just want to check whether I understand
% the algorithm really or not, and also do some practices for knowing
% matlab programming more well because I could be called as 'novice'.
% OpenCV 2.0 has realized Dalal's HOG algorithm which runs faster
% than mine without any doubt, ╮(╯▽╰)╭ . Ronan pointed a error in
% the code，thanks for his correction. Note that at the end of this
% code, there are some demonstration code，please remove in your work.  %
% F = hogcalculator(img, cellpw, cellph, nblockw, nblockh,
% nthet, overlap, isglobalinterpolate, issigned, normmethod)
%
% IMG:
% IMG is the input image.
%
% CELLPW, CELLPH:
% CELLPW and CELLPH are cell's pixel width and height respectively.
%
% NBLOCKW, NBLCOKH:
% NBLOCKW and NBLCOKH are block size counted by cells number in x and
% y directions respectively.
%
% NTHET, ISSIGNED:
% NTHET is the number of the bins of the histogram of oriented
% gradient. The histogram of oriented gradient ranges from 0 to pi in
% 'unsigned' condition while to 2*pi in 'signed' condition, which can
% be specified through setting the value of the variable ISSIGNED by
% the string 'unsigned' or 'signed'.
%
% OVERLAP:
% OVERLAP is the overlap proportion of two neighboring block.
%
% ISGLOBALINTERPOLATE:
% ISGLOBALINTERPOLATE specifies whether the trilinear interpolation
% is done in a single global 3d histogram of the whole detecting
% window by the string 'globalinterpolate' or in each local 3d
% histogram corresponding to respective blocks by the string
% 'localinterpolate' which is in strict accordance with the procedure
% proposed in Dalal's paper. Interpolating in the whole detecting
% window requires the block's sliding step to be an integral multiple
% of cell's width and height because the histogram is fixing before
% interpolate. In fact here the so called 'global interpolation' is
% a notation given by myself. at first the spatial interpolation is
% done without any relevant to block's slide position, but when I was
% doing calculation while OVERLAP is 0.75, something occurred and
% confused me o__O"… . This let me find that the operation I firstly
% did is different from which mentioned in Dalal's paper. But this
% does not mean it is incorrect ^◎^, so I reserve this. As for name,
% besides 'global interpolate', any others would be all ok, like 'Lady GaGa'
% or what else, :-).
%
% NORMMETHOD：
% NORMMETHOD is the block histogram normalized method which can be
% set as one of the following strings:
% 'none', which means non-normalization;
% 'l1', which means L1-norm normalization;
% 'l2', which means L2-norm normalization;
% 'l1sqrt', which means L1-sqrt-norm normalization;
% 'l2hys', which means L2-hys-norm normalization.
% F：
% F is a row vector storing the final histogram of all of the blocks
% one by one in a top-left to bottom-right image scan manner, the
% cells histogram are stored in the same manner in each block's
% section of F.
%
% Note that CELLPW*NBLOCKW and CELLPH*NBLOCKH should be equal to IMG's
% width and height respectively.
%
% Here is a demonstration, which all of parameters are set as the
% best value mentioned in Dalal's paper when the window detected is 128*64
% size(128 rows, 64 columns):
% F = hogcalculator(window, 8, 8, 2, 2, 9, 0.5,
% 'localinterpolate', 'unsigned', 'l2hys');
% Also the function can be called like:
% F = hogcalculator(window);
% the other parameters are all set by using the above-mentioned "dalal's
% best value" as default.
%
if nargin < 2
% set default parameters value.
cellpw = 8;
cellph = 8;
nblockw = 2;
nblockh = 2;
nthet = 9;
overlap = 0.5;
isglobalinterpolate = 'localinterpolate';
issigned = 'unsigned';
normmethod = 'l2hys';
else
if nargin < 10
error('Input parameters are not enough.');
end
end
% check parameters's validity.
[M, N, K] = size(img);
if mod(M,cellph*nblockh) ~= 0
error('IMG''s height should be an integral multiple of CELLPH*NBLOCKH.');
end
if mod(N,cellpw*nblockw) ~= 0
error('IMG''s width should be an integral multiple of CELLPW*NBLOCKW.');
end
if mod((1-overlap)*cellpw*nblockw, cellpw) ~= 0 ||...
mod((1-overlap)*cellph*nblockh, cellph) ~= 0
str1 = 'Incorrect OVERLAP or ISGLOBALINTERPOLATE parameter';
str2 = ', slide step should be an intergral multiple of cell size';
error([str1, str2]);
end
% set the standard deviation of gaussian spatial weight window.
delta = cellpw*nblockw * 0.5;
% calculate gradient scale matrix.
hx = [-1,0,1];
hy = -hx';
gradscalx = imfilter(double(img),hx);
gradscaly = imfilter(double(img),hy);
%
if K > 1
% gradscalx = max(max(gradscalx(:,:,1),gradscalx(:,:,2)), gradscalx(:,:,3));
% gradscaly = max(max(gradscaly(:,:,1),gradscaly(:,:,2)), gradscaly(:,:,3));
maxgrad = sqrt(double(gradscalx.*gradscalx + gradscaly.*gradscaly));
[gradscal, gidx] = max(maxgrad,[],3);
gxtemp = zeros(M,N);
gytemp = gxtemp;
for kn = 1:K
[rowidx, colidx] = ind2sub(size(gidx),find(gidx==kn));
gxtemp(rowidx, colidx) = gradscalx(rowidx,colidx,kn);
gytemp(rowidx, colidx) =gradscaly(rowidx,colidx,kn);
end
gradscalx = gxtemp;
gradscaly = gytemp;
else
gradscal = sqrt(double(gradscalx.*gradscalx + gradscaly.*gradscaly));
end  % calculate gradient orientation matrix.
% plus small number for avoiding dividing zero.
gradscalxplus = gradscalx+ones(size(gradscalx))*0.0001;
gradorient = zeros(M,N);
% unsigned situation: orientation region is 0 to pi.
if strcmp(issigned, 'unsigned') == 1
gradorient =...
atan(gradscaly./gradscalxplus) + pi/2;
or = 1;
else
% signed situation: orientation region is 0 to 2*pi.
if strcmp(issigned, 'signed') == 1
idx = find(gradscalx >= 0 & gradscaly >= 0);
gradorient(idx) = atan(gradscaly(idx)./gradscalxplus(idx));
idx = find(gradscalx < 0);
gradorient(idx) = atan(gradscaly(idx)./gradscalxplus(idx)) + pi;
idx = find(gradscalx >= 0 & gradscaly < 0);
gradorient(idx) = atan(gradscaly(idx)./gradscalxplus(idx)) + 2*pi;
or = 2;
else
error('Incorrect ISSIGNED parameter.');
end
end
% calculate block slide step.
xbstride = cellpw*nblockw*(1-overlap);
ybstride = cellph*nblockh*(1-overlap);
xbstridend = N - cellpw*nblockw + 1;
ybstridend = M - cellph*nblockh + 1;
% calculate the total blocks number in the window detected, which is
% ntotalbh*ntotalbw.
ntotalbh = ((M-cellph*nblockh)/ybstride)+1;
ntotalbw = ((N-cellpw*nblockw)/xbstride)+1;
% generate the matrix hist3dbig for storing the 3-dimensions histogram. the
% matrix covers the whole image in the 'globalinterpolate' condition or
% covers the local block in the 'localinterpolate' condition. The matrix is
% bigger than the area where it covers by adding additional elements
% (corresponding to the cells) to the surround for calculation convenience.
if strcmp(isglobalinterpolate, 'globalinterpolate') == 1
ncellx = N / cellpw;
ncelly = M / cellph;
hist3dbig = zeros(ncelly+2, ncellx+2, nthet+2);
F = zeros(1, (M/cellph-1)*(N/cellpw-1)*nblockw*nblockh*nthet);
glbalinter = 1;
else
if strcmp(isglobalinterpolate, 'localinterpolate') == 1
hist3dbig = zeros(nblockh+2, nblockw+2, nthet+2);
F = zeros(1, ntotalbh*ntotalbw*nblockw*nblockh*nthet);
glbalinter = 0;
else
error('Incorrect ISGLOBALINTERPOLATE parameter.')
end
end
% generate the matrix for storing histogram of one block;
sF = zeros(1, nblockw*nblockh*nthet);
% generate gaussian spatial weight.
[gaussx, gaussy] = meshgrid(0:(cellpw*nblockw-1), 0:(cellph*nblockh-1));
weight = exp(-((gaussx-(cellpw*nblockw-1)/2)...
.*(gaussx-(cellpw*nblockw-1)/2)+(gaussy-(cellph*nblockh-1)/2)...
.*(gaussy-(cellph*nblockh-1)/2))/(delta*delta));
% vote for histogram. there are two situations according to the interpolate
% condition('global' interpolate or local interpolate). The hist3d which is
% generated from the 'bigger' matrix hist3dbig is the final histogram.
if glbalinter == 1
xbstep = nblockw*cellpw;
ybstep = nblockh*cellph;
else
xbstep = xbstride;
ybstep = ybstride;
end
% block slide loop
for btly = 1:ybstep:ybstridend
for btlx = 1:xbstep:xbstridend
for bi = 1:(cellph*nblockh)
for bj = 1:(cellpw*nblockw)  i = btly + bi - 1;
j = btlx + bj - 1;
gaussweight = weight(bi,bj);  gs = gradscal(i,j);
go = gradorient(i,j);  if glbalinter == 1
jorbj = j;
iorbi = i;
else
jorbj = bj;
iorbi = bi;
end  % calculate bin index of hist3dbig
binx1 = floor((jorbj-1+cellpw/2)/cellpw) + 1;
biny1 = floor((iorbi-1+cellph/2)/cellph) + 1;
binz1 = floor((go+(or*pi/nthet)/2)/(or*pi/nthet)) + 1;  if gs < 1E-5
continue;
end  binx2 = binx1 + 1;
biny2 = biny1 + 1;
binz2 = binz1 + 1;  x1 = (binx1-1.5)*cellpw + 0.5;
y1 = (biny1-1.5)*cellph + 0.5;
z1 = (binz1-1.5)*(or*pi/nthet);  % trilinear interpolation.
hist3dbig(biny1,binx1,binz1) =...
hist3dbig(biny1,binx1,binz1) + gs*gaussweight...
* (1-(jorbj-x1)/cellpw)*(1-(iorbi-y1)/cellph)...
*(1-(go-z1)/(or*pi/nthet));
hist3dbig(biny1,binx1,binz2) =...
hist3dbig(biny1,binx1,binz2) + gs*gaussweight...
* (1-(jorbj-x1)/cellpw)*(1-(iorbi-y1)/cellph)...
*((go-z1)/(or*pi/nthet));
hist3dbig(biny2,binx1,binz1) =...
hist3dbig(biny2,binx1,binz1) + gs*gaussweight...
* (1-(jorbj-x1)/cellpw)*((iorbi-y1)/cellph)...
*(1-(go-z1)/(or*pi/nthet));
hist3dbig(biny2,binx1,binz2) =...
hist3dbig(biny2,binx1,binz2) + gs*gaussweight...
* (1-(jorbj-x1)/cellpw)*((iorbi-y1)/cellph)...
*((go-z1)/(or*pi/nthet));
hist3dbig(biny1,binx2,binz1) =...
hist3dbig(biny1,binx2,binz1) + gs*gaussweight...
* ((jorbj-x1)/cellpw)*(1-(iorbi-y1)/cellph)...
*(1-(go-z1)/(or*pi/nthet));
hist3dbig(biny1,binx2,binz2) =...
hist3dbig(biny1,binx2,binz2) + gs*gaussweight...
* ((jorbj-x1)/cellpw)*(1-(iorbi-y1)/cellph)...
*((go-z1)/(or*pi/nthet));
hist3dbig(biny2,binx2,binz1) =...
hist3dbig(biny2,binx2,binz1) + gs*gaussweight...
* ((jorbj-x1)/cellpw)*((iorbi-y1)/cellph)...
*(1-(go-z1)/(or*pi/nthet));
hist3dbig(biny2,binx2,binz2) =...
hist3dbig(biny2,binx2,binz2) + gs*gaussweight...
* ((jorbj-x1)/cellpw)*((iorbi-y1)/cellph)...
*((go-z1)/(or*pi/nthet));
end
end  % In the local interpolate condition. F is generated in this block
% slide loop. hist3dbig should be cleared in each loop.
if glbalinter == 0
if or == 2
hist3dbig(:,:,2) = hist3dbig(:,:,2)...
+ hist3dbig(:,:,nthet+2);
hist3dbig(:,:,(nthet+1)) =...
hist3dbig(:,:,(nthet+1)) + hist3dbig(:,:,1);
end
hist3d = hist3dbig(2:(nblockh+1), 2:(nblockw+1), 2:(nthet+1));
for ibin = 1:nblockh
for jbin = 1:nblockw
idsF = nthet*((ibin-1)*nblockw+jbin-1)+1;
idsF = idsF:(idsF+nthet-1);
sF(idsF) = hist3d(ibin,jbin,:);
end
end
iblock = ((btly-1)/ybstride)*ntotalbw +...
((btlx-1)/xbstride) + 1;
idF = (iblock-1)*nblockw*nblockh*nthet+1;
idF = idF:(idF+nblockw*nblockh*nthet-1);
F(idF) = sF;
hist3dbig(:,:,:) = 0;
end
end
end
% In the global interpolate condition. F is generated here outside the
% block slide loop
if glbalinter == 1
ncellx = N / cellpw;
ncelly = M / cellph;
if or == 2
hist3dbig(:,:,2) = hist3dbig(:,:,2) + hist3dbig(:,:,nthet+2);
hist3dbig(:,:,(nthet+1)) = hist3dbig(:,:,(nthet+1)) + hist3dbig(:,:,1);
end
hist3d = hist3dbig(2:(ncelly+1), 2:(ncellx+1), 2:(nthet+1));  iblock = 1;
for btly = 1:ybstride:ybstridend
for btlx = 1:xbstride:xbstridend
binidx = floor((btlx-1)/cellpw)+1;
binidy = floor((btly-1)/cellph)+1;
idF = (iblock-1)*nblockw*nblockh*nthet+1;
idF = idF:(idF+nblockw*nblockh*nthet-1);
for ibin = 1:nblockh
for jbin = 1:nblockw
idsF = nthet*((ibin-1)*nblockw+jbin-1)+1;
idsF = idsF:(idsF+nthet-1);
sF(idsF) = hist3d(binidy+ibin-1, binidx+jbin-1, :);
end
end
F(idF) = sF;
iblock = iblock + 1;
end
end
end
% adjust the negative value caused by accuracy of floating-point
% operations.these value's scale is very small, usually at E-03 magnitude
% while others will be E+02 or E+03 before normalization.
F(F<0) = 0;
% block normalization.
e = 0.001;
l2hysthreshold = 0.2;
fslidestep = nblockw*nblockh*nthet;
switch normmethod
case 'none'
case 'l1'
for fi = 1:fslidestep:size(F,2)
div = sum(F(fi:(fi+fslidestep-1)));
F(fi:(fi+fslidestep-1)) = F(fi:(fi+fslidestep-1))/(div+e);
end
case 'l1sqrt'
for fi = 1:fslidestep:size(F,2)
div = sum(F(fi:(fi+fslidestep-1)));
F(fi:(fi+fslidestep-1)) = sqrt(F(fi:(fi+fslidestep-1))/(div+e));
end
case 'l2'
for fi = 1:fslidestep:size(F,2)
sF = F(fi:(fi+fslidestep-1)).*F(fi:(fi+fslidestep-1));
div = sqrt(sum(sF)+e*e);
F(fi:(fi+fslidestep-1)) = F(fi:(fi+fslidestep-1))/div;
end
case 'l2hys'
for fi = 1:fslidestep:size(F,2)
sF = F(fi:(fi+fslidestep-1)).*F(fi:(fi+fslidestep-1));
div = sqrt(sum(sF)+e*e);
sF = F(fi:(fi+fslidestep-1))/div;
sF(sF>l2hysthreshold) = l2hysthreshold;
div = sqrt(sum(sF.*sF)+e*e);
F(fi:(fi+fslidestep-1)) = sF/div;
end
otherwise
error('Incorrect NORMMETHOD parameter.');
end

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