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一 训练框架
训练人脸检測分类器须要三个步骤：
(1) 准备正负样本集，分别放到两个目录里。
我使用的是麻省理工的那个人脸库。大家能够网上搜一下。
（2）把正样本集生成正样本描写叙述文件（*.vec），把负样本集生成负样本集合文件。详细怎么操作请參考我博客中的另外两篇文章，各自是http://blog.csdn.net/ding977921830/article/details/45913789和http://blog.csdn.net/ding977921830/article/details/45914137。
（3）利用........\opencv\sources\apps\haartraining\haartraining.cpp训练分类器。
二 建立project
我使用的是vs2012和opencv2.4.9，事实上，使用其它的版本号也区别不多大。
1 配置opencv2.4.9和vs2012，这个网上有非常多资料，我就不啰嗦了哈。
2 在vs中新建project，把opencv库中的以下文件........\opencv\sources\apps\haartraining加入到project中，在解决方式资源管理器中，分别加入头文件和源文件，加入好后，内容例如以下：
三 程序
上面main.cpp的内容也就是haartraining.cpp中的程序，详细内容例如以下：
//M*//** haartraining.cpp*里面有部分參数我是稍作改动*<a target=_blank href="http://blog.csdn.net/ding977921830/article/details/47733363">http://blog.csdn.net/ding977921830/article/details/47733363</a>* Train cascade classifier*/#include <cstdio>
#include <cstring>
#include <cstdlib>using namespace std;#include "cvhaartraining.h"int main( int argc, char* argv[] )
{int i = 0;char* nullname = (char*)"(NULL)";char* vecname = NULL;char* dirname = NULL;char* bgname = NULL;bool bg_vecfile = false;int npos = 2000; //保证npos与nneg的比例为1:2至1：：3之间比較好int nneg = 4000;int nstages = 3; //为了节约时间能够把把设置为1，或2或3。当然也能够设置十几或二十几。只是，我没有耐心实验int mem = 200;int nsplits = 1;float minhitrate = 0.995F;float maxfalsealarm = 0.5F;float weightfraction = 0.95F;int mode = 0;int symmetric = 1;int equalweights = 0;int width = 20;int height = 20;const char* boosttypes[] = { "DAB", "RAB", "LB", "GAB" };int boosttype = 0; //选用DABconst char* stumperrors[] = { "misclass", "gini", "entropy" };int stumperror = 0; //选用misclassint maxtreesplits = 0;int minpos = 500;if( argc == 1 ){printf( "Usage: %s\n -data <dir_name>\n"" -vec <vec_file_name>\n"" -bg <background_file_name>\n"" [-bg-vecfile]\n"" [-npos <number_of_positive_samples = %d>]\n"" [-nneg <number_of_negative_samples = %d>]\n"" [-nstages <number_of_stages = %d>]\n"" [-nsplits <number_of_splits = %d>]\n"" [-mem <memory_in_MB = %d>]\n"" [-sym (default)] [-nonsym]\n"" [-minhitrate <min_hit_rate = %f>]\n"" [-maxfalsealarm <max_false_alarm_rate = %f>]\n"" [-weighttrimming <weight_trimming = %f>]\n"" [-eqw]\n"" [-mode <BASIC (default) | CORE | ALL>]\n"" [-w <sample_width = %d>]\n"" [-h <sample_height = %d>]\n"" [-bt <DAB | RAB | LB | GAB (default)>]\n"" [-err <misclass (default) | gini | entropy>]\n"" [-maxtreesplits <max_number_of_splits_in_tree_cascade = %d>]\n"" [-minpos <min_number_of_positive_samples_per_cluster = %d>]\n",argv[0], npos, nneg, nstages, nsplits, mem,minhitrate, maxfalsealarm, weightfraction, width, height,maxtreesplits, minpos );return 0;}for( i = 1; i < argc; i++ ){/*if( !strcmp( argv[i], "-data" ) ){dirname = argv[++i];}else if( !strcmp( argv[i], "-vec" ) ){vecname = argv[++i];}else if( !strcmp( argv[i], "-bg" ) ){bgname = argv[++i];}*/if( !strcmp( argv[i], "-data" ) ) //前面这三个条件里面的内容我稍作改动{dirname = argv[i];}else if( !strcmp( argv[i], "-vec.vec" ) ){vecname = argv[i];}else if( !strcmp( argv[i], "-bg.txt" ) ){bgname = argv[i];}else if( !strcmp( argv[i], "-bg-vecfile" ) ){bg_vecfile = true;}else if( !strcmp( argv[i], "-npos" ) ){npos = atoi( argv[++i] );}else if( !strcmp( argv[i], "-nneg" ) ){nneg = atoi( argv[++i] );}else if( !strcmp( argv[i], "-nstages" ) ){nstages = atoi( argv[++i] );}else if( !strcmp( argv[i], "-nsplits" ) ){nsplits = atoi( argv[++i] );}else if( !strcmp( argv[i], "-mem" ) ){mem = atoi( argv[++i] );}else if( !strcmp( argv[i], "-sym" ) ){symmetric = 1;}else if( !strcmp( argv[i], "-nonsym" ) ){symmetric = 0;}else if( !strcmp( argv[i], "-minhitrate" ) ){minhitrate = (float) atof( argv[++i] );}else if( !strcmp( argv[i], "-maxfalsealarm" ) ){maxfalsealarm = (float) atof( argv[++i] );}else if( !strcmp( argv[i], "-weighttrimming" ) ){weightfraction = (float) atof( argv[++i] );}else if( !strcmp( argv[i], "-eqw" ) ){equalweights = 1;}else if( !strcmp( argv[i], "-mode" ) ){char* tmp = argv[++i];if( !strcmp( tmp, "CORE" ) ){mode = 1;}else if( !strcmp( tmp, "ALL" ) ){mode = 2;}else{mode = 0;}}else if( !strcmp( argv[i], "-w" ) ){width = atoi( argv[++i] );}else if( !strcmp( argv[i], "-h" ) ){height = atoi( argv[++i] );}else if( !strcmp( argv[i], "-bt" ) ){i++;if( !strcmp( argv[i], boosttypes[0] ) ){boosttype = 0;}else if( !strcmp( argv[i], boosttypes[1] ) ){boosttype = 1;}else if( !strcmp( argv[i], boosttypes[2] ) ){boosttype = 2;}else{boosttype = 3;}}else if( !strcmp( argv[i], "-err" ) ){i++;if( !strcmp( argv[i], stumperrors[0] ) ){stumperror = 0;}else if( !strcmp( argv[i], stumperrors[1] ) ){stumperror = 1;}else{stumperror = 2;}}else if( !strcmp( argv[i], "-maxtreesplits" ) ){maxtreesplits = atoi( argv[++i] );}else if( !strcmp( argv[i], "-minpos" ) ){minpos = atoi( argv[++i] );}}printf( "Data dir name: %s\n", ((dirname == NULL) ? nullname : dirname ) );printf( "Vec file name: %s\n", ((vecname == NULL) ? nullname : vecname ) );printf( "BG file name: %s, is a vecfile: %s\n", ((bgname == NULL) ? nullname : bgname ), bg_vecfile ? "yes" : "no" );printf( "Num pos: %d\n", npos );printf( "Num neg: %d\n", nneg );printf( "Num stages: %d\n", nstages );printf( "Num splits: %d (%s as weak classifier)\n", nsplits,(nsplits == 1) ?"stump" : "tree" ); printf( "Mem: %d MB\n", mem ); printf( "Symmetric: %s\n", (symmetric) ? "TRUE" : "FALSE" ); printf( "Min hit rate: %f\n", minhitrate ); printf( "Max false alarm rate: %f\n", maxfalsealarm ); printf( "Weight trimming: %f\n", weightfraction ); printf( "Equal weights: %s\n", (equalweights) ? "TRUE" : "FALSE" ); printf( "Mode: %s\n", ( (mode == 0) ? "BASIC" : ( (mode == 1) ? "CORE" : "ALL") ) ); printf( "Width: %d\n", width ); printf( "Height: %d\n", height ); //printf( "Max num of precalculated features: %d\n", numprecalculated ); printf( "Applied boosting algorithm: %s\n", boosttypes[boosttype] ); printf( "Error (valid only for Discrete and Real AdaBoost): %s\n", stumperrors[stumperror] ); printf( "Max number of splits in tree cascade: %d\n", maxtreesplits ); printf( "Min number of positive samples per cluster: %d\n", minpos ); cvCreateTreeCascadeClassifier( dirname, vecname, bgname, npos, nneg, nstages, mem, nsplits, minhitrate, maxfalsealarm, weightfraction, mode, symmetric, equalweights, width, height, boosttype, stumperror, maxtreesplits, minpos, bg_vecfile ); return 0; }
我的命令行參数为："D:\vs2012\projects\train_opencv_main\train_cascade\Debug\test.exe" "-data" "-vec.vec" "-bg.txt"。详细设置方法是 调试----属性----配置属性----调试---命令參数
1 注意命令行參数中间要有空格的。
2 当中第一个你要改动为你自己电脑上project的绝对路径；
3 "-data" 是存放训练好的分类器，须要预先建立好一个的空目录。
4 "-vec.vec" 是我的正样本描写叙述文件。
5 "-bg.txt"是我的负样本集合文件。
四 训练结果
1 dos操作窗体
2 data目录的内容为：
我的0文件里训练了6个弱文类器。1文件里含有9个弱分类器。2目录下有17个弱分类器，每个目录就是一个级联stage。显然是越来越复杂的哈。
3 以文件0为例，里面的内容为：
6
1
2
7 1 6 10 0 -1
9 1 2 10 0 3
haar_x3
4.792333e-002 0 -1
-1.845703e+000 1.845703e+000
1
2
1 3 18 12 0 -1
1 7 18 4 0 3
haar_y3
2.389797e-001 0 -1
-1.396623e+000 1.396623e+000
1
3
2 16 6 4 0 -1
2 16 3 2 0 2
5 18 3 2 0 2
haar_x2_y2
6.900427e-003 0 -1
-9.798445e-001 9.798445e-001
1
2
10 0 10 1 0 -1
10 0 5 1 0 2
haar_x2
1.219139e-002 0 -1
-5.156118e-001 5.156118e-001
1
2
0 0 10 1 0 -1
5 0 5 1 0 2
haar_x2
1.014664e-002 0 -1
-7.365732e-001 7.365732e-001
1
2
9 14 5 3 0 -1
9 15 5 1 0 3
haar_y3
-6.578934e-003 0 -1
7.885281e-001 -7.885281e-001
-3.758514e+000
-1
-1
4 xml文件
到这里我们的训练分类器最终出来的，XML文件能够在在vs中直接调用了。xml文件的内容你看是跟上面data文件里的内容是严格一一相应的，我摘录当中部分内容（也就是0目录部分）例如以下：
<?xml version="1.0"?>
<opencv_storage>
<_-data type_id="opencv-haar-classifier">
<size>
20 20</size>
<stages>
<_>
<!-- stage 0 -->
<trees>
<_>
<!-- tree 0 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>
7 1 6 10 -1.</_>
<_>
9 1 2 10 3.</_></rects>
<tilted>0</tilted></feature>
<threshold>4.7923330217599869e-002</threshold>
<left_val>-1.8457030057907104e+000</left_val>
<right_val>1.8457030057907104e+000</right_val></_></_>
<_>
<!-- tree 1 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>
1 3 18 12 -1.</_>
<_>
1 7 18 4 3.</_></rects>
<tilted>0</tilted></feature>
<threshold>2.3897969722747803e-001</threshold>
<left_val>-1.3966230154037476e+000</left_val>
<right_val>1.3966230154037476e+000</right_val></_></_>
<_>
<!-- tree 2 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>
2 16 6 4 -1.</_>
<_>
2 16 3 2 2.</_>
<_>
5 18 3 2 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>6.9004269316792488e-003</threshold>
<left_val>-9.7984451055526733e-001</left_val>
<right_val>9.7984451055526733e-001</right_val></_></_>
<_>
<!-- tree 3 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>
10 0 10 1 -1.</_>
<_>
10 0 5 1 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>1.2191389687359333e-002</threshold>
<left_val>-5.1561182737350464e-001</left_val>
<right_val>5.1561182737350464e-001</right_val></_></_>
<_>
<!-- tree 4 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>
0 0 10 1 -1.</_>
<_>
5 0 5 1 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>1.0146640241146088e-002</threshold>
<left_val>-7.3657321929931641e-001</left_val>
<right_val>7.3657321929931641e-001</right_val></_></_>
<_>
<!-- tree 5 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>
9 14 5 3 -1.</_>
<_>
9 15 5 1 3.</_></rects>
<tilted>0</tilted></feature>
<threshold>-6.5789339132606983e-003</threshold>
<left_val>7.8852808475494385e-001</left_val>
<right_val>-7.8852808475494385e-001</right_val></_></_></trees>
<stage_threshold>-3.7585139274597168e+000</stage_threshold>
<parent>-1</parent>
<next>-1</next></_>
<_>