基于PCA的人脸识别opencv代码

1、基于pca的人脸识别opencv代码# inelude stdafx. h# include < stdio, h ># inelude < string h ># include "cv h # include cvaux h # inelude "highgui. hh # inelude "time.# include "iostreantusing the namespace cv;using namespace std./ / define several important global variablesiplima

2、ge * * fdcelmgarr = 0; / / pointers to training faces and test faces (points to different learning and recognition stages)cvmat * personnumtruthmat = 0; / / the id of the face imageint ntrainfaces = 0; / / the number of training imagesint neigens 二 0; / / the number of major eigenvalues that you tak

3、eip1image * pavgtrainlmg 二 0; / / the average of training face dataip1image * eigenvectarr = 0; / / projection matrix, which is the main eigenvectorcvmat * eigenvalmat 二 0; / / eigenvaluecvmat * projectedtrainfacemat 二 0; / / the projection of the training image/ / function prototypevoid learn ();vo

4、id recognize ();void dopca ();void storetrainingdata ();int loadtrainingdata (cvmat * ptrainpersonnummat);a float * projectedtestface;int loadfacelmgarray (char * filename);void printusage ();/ / the main function, which consists of learning andidentifying two phases, needs to be run twice, separate

5、d by the parameters passed by the command lineint main ()/ / learn ();recognize ();/ / the learning phase codevoid learn ()cout <<<<<<<<<<<<<">>/ / start timeclock_t start and finish;double duration;start 二 clock ();int i, offset;/ / load training im

6、agesntrainfaces 二 loadfacelmgarray (“ train. txt “);if (ntrainfaces < 2)fprintf (stderr,"need 2 or more training faces n"input file contains only % d rt, ntrainfaces);the return;/ / the main component analysisdopca ();/ / the training atlas is projected into the subspaceprojectedtrainfa

7、cemat 二 cvcreatemat (ntrainfaces, neigens, cv_32fc1);offset 二 projectedtrainfacemat-> step/sizeof (float);for (i = 0; i < ntrainfaces; i + +)/ / int offset 二 i * neigenscveigendecomposite (facelmgarr ineigens,eigenvectarr,0, 0,pavgtrainlmg,/ / projectedtrainfacemat-> data f1 + i * neigenspr

8、ojectedtrainfacemat-> data fl./ / the eigenvalues of the training phase, the projection matrix,etcare stored as. xml files for the teststoretrainingdata ();/ / end timefinish 二 clock ();duration 二(double) (finish start)/clocks_per_sec;cout < < "duration <<<<<<<<

9、<<"<<<<<<<<<<< > < < < </ / recognition phase codevoid recognize ()cout < < "the beginning of the recognition process" < < < < < <<<<<<<<>>>/ / start timeclock_t start and finis

10、h;double duration;start 二 clock ();/ / test the number of facesint i, ntestfaces 二 0;/ / the number of faces in the training phasecvmat * trainpersonnummat 二 0;float * projectedtestface 二 0;/ / load test images and return the test face numberntestfaces 二 loadfacelmgarray ( test txt “);printf (“ d te

11、st faces loaded n "、 ntestfaces);/ / load saved in. xml fileif (! loadtrainingdata (& trainpersonnummat)the return;projectedtestface 二(float *) cvalloc (float);for (i = 0; i < ntestfaces; i + +)int inearest,/ / projected the test image into the subspacecveigendecomposite (facelmgarr 叮.ne

12、igens,eigenvectarr,0, 0,pavgtrainlmg,projectedtestface);inearest 二 findnearestneighbor (projectedtestface);truth 二 personnumtruthmat(1).printf ()ij/ / end timefinish 二 clock ();duration = (double) (finish start)/clocks per sec;cout <<<<<<<<<<<<<<<<<

13、<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<"><<<<< < </ / load saved training resultsint loadtrainingdata (cvmat * ptrainpersonnummat)cvfilestorage * filestorage;int i;fi

14、lestorage 二 cvopenf订estorage (“ facedata, xml "、0,cv storage read):if (! filestorage)fprintf (stderr, "can't open facedate xml n)；return 0:neigens 二 cvreadlntbyname (filestorage, 0, "neigens", 0);ntra in faces 二 cvreadlntbyname (filestorage, 0, ，ntrainfaces，0);* ptrainpersonn

15、ummat = (cvmat*) cvreadbyname (filestorage, 0, z，trainpersonnummat", 0)eigenvalmat 二(cvmat *) cvreadbyname (filestorage, 0, eigenvalmat， 0);projectedtrainfacemat 二(cvmat *) cvreadbyname (filestorage, 0, ，projectedtrainfacemat，/, 0);pavgtrainlmg 二(ipllmage *) cvreadbyname (filestorage, 0, ，avgtr

16、ainimg/，, 0);the eigenvectarr 二(ipllmage *) cvalloc (ipllmage *)for (i 二 0; i < neigens; i + +)char varname 200sprintf (varname,"eigenvect_ % d” ,我);特征向量i = (lpllmage*)cvreadbyname(filestorage, 0, varname, 0);cvre1eas ef i1estorage(&filestorage);返回1；/ /存储训练结果 空白 storetrainingdata ()cvfil

17、estorage * filestorage;int 我；filestorage 二 cvopenfilestorage( “facedata。xml” , 0, cv_storage_write);/ /存储特征值，投影矩阵,平均矩阵等训练结果cvwritelnt(filestorage, “neigens” , neigens);cvwritelnt(filestorage, untrainfaces,? , ntrainfaces);cvwrite(filestorage utrainpersonnummat” , personnumtruthmatcvattrlist(0, 0);cv

18、write(filestorage eigenvalmat,eigenvalmatcvattrlist(0, 0);cvwrite(filestorage uprojectedtrainfacemat, projectedtrainfacemat cvattrlist(0, 0);cvwrite(filestorage “avgtrainimg” , pavgtrainlmg cvattrlist(0, 0);(我二 0;< neigens; + +)char varname200;sprintf (varname, “特征 vect_ % d” , i);cvwrite(filesto

19、rage> varname eigenvectarr 我,cvattrlist (0, 0);cvnormalize(特征向量i,特征向量i, 255, 0, cv_l2, 0);cvnamedwindow( “演示” ,cvjvind0w_aut0size);cvshowimage( “演示” ,eigenvectarr我);cvwaitkey(100);cv 标准化(pavgtrainlmg, pavgtrainlmg, 255, 0, cv_l 1, 0);cvnamedwindow( “演示” ,cvjvindow_autosize);cvshowimage ( “演示” ,pa

20、vgtrainlmg);cvwaitkey(100);cvreleasefilestorage(&filestorage);/ /寻找最接近的图像int findneaxestneighbor (浮动* projectedtestface)/ /定义最小距离，并初始化为无穷大双 leastdistsq = dbl_max、准确性；i,it rain, inearest 二 0;双10;(itrain 二 0;itrain < ntrainfaces;itrain + +)双 distsq = 0;(我二 0;< neigens; + +)浮动d iprojectedtest

21、face 我projectedtrainfacemat - >数据。fl (itrain * neigens + i);/ / mahal anob is算法计算的距离/ / distsq +二d_i * dl;/ /欧儿里得算法计算的距离(我);(我)；itrain二 distsq;如果(distsq < leastdistsq)leastdistsq 二 distsq;inearest 二 itrain;/ /求阈值双max =0,阈值； int j;(j 二 1；< 1o；j + +)如果(max <j)max 二j;其他的max 二 max;jthreshold

22、 二 max / 2;/ / similarity ratioaccuracy 二 1 - leastdistsq/threshold;the cout <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<return inearest;/ / main component analysis

23、void dopca ()/ / terminate the algorithm criteriacvtermcriteria calclimit;/ / tectonic imagerycvsize facelmgsize;/ / set the main eigenvalues for yourselfneigens 二 ntrainfaces 一 1;/ / allocate the eigenvector storage spacefacelmgsize width 二 facelmgarr 0 - > width;faceimgs.height 二 facelmgarr 0-h

24、eight;/ / the number of assigned numbers is the number of eigenvalueseigenvectarr 二(ipllmage *) * neigensfor (i = 0; i < neigens; i + +)eigenvectarr i二 cvcreatelmage (facelmgsize, ipldepth32f,1)；/ / assign the main eigenvalue storage spaceeigenvalmat 二 cvcreatemat (1, neigens, cv_32fc1);/ / distr

25、ibute the average image storage spacepavgtrainlmg 二 cvcreatelmage (facelmgsize, ipl_depth_32f, 1);/ / set the pca analysis to finish the conditioncalclimit 二 cvtermcriteria (cv_termcrit_iter, neigens, 1) / / calculate average images, eigenvalues, eigenvectors cvcalceigenobjects (ntrainfaces,(void *)

26、 facelmgarr,(void *) eigenvectarr,cv_eigobj_no_callback,0,& calclimit,pavgtrainlmg,eigenvalmat - > data. fl);/ / normalized sizecv normalize (eigenvalmat, eigenvalmat, 1, 0, cv_l1, 0):/ / load the enumeration of the txt fileint loadfacelmgarray (char * filename)file * imglistfile 二 0;char imgfilename 512.int iface,