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利用opencv源码和vs编程序训练分类器haartraining.cpp

2019-07-13 18:32发布生成海报

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如需转载请注明本博网址：http://blog.csdn.net/ding977921830/article/details/47733363。

一训练框架

训练人脸检测分类器需要三个步骤： (1) 准备正负样本集，分别放到两个文件夹里。我使用的是麻省理工的那个人脸库，大家可以网上搜一下。（2）把正样本集生成正样本描述文件（*.vec），把负样本集生成负样本集合文件。具体怎么操作请参考我博客中的另外两篇文章，分别是http://blog.csdn.net/ding977921830/article/details/45913789和http://blog.csdn.net/ding977921830/article/details/45914137。（3）利用........opencvsourcesappshaartraininghaartraining.cpp训练分类器。

二建立工程

我使用的是vs2012和opencv2.4.9，其实，使用其他的版本也差别不多大。 1 配置opencv2.4.9和vs2012，这个网上有很多资料，我就不啰嗦了哈； 2 在vs中新建工程，把opencv库中的下面文件........opencvsourcesappshaartraining添加到工程中，在解决方案资源管理器中，分别添加头文件和源文件，添加好后，内容如下：

三程序

上面main.cpp的内容也就是haartraining.cpp中的程序，具体内容如下：

//M*/

/*
 * haartraining.cpp
 *里面有部分参数我是稍作修改
 *http://blog.csdn.net/ding977921830/article/details/47733363
 * Train cascade classifier
 */

#include 
#include 
#include 

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之间比较好
    int nneg    = 4000;
    int nstages = 3;      //为了节约时间可以把把设置为1，或2或3，当然也可以设置十几或二十几，不过，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DAB
    const char* stumperrors[] = { "misclass", "gini", "entropy" };
    int stumperror = 0;   //选用misclass
    int maxtreesplits = 0;
    int minpos = 500;

    if( argc == 1 )
    {
        printf( "Usage: %s
  -data 
"
                "  -vec 
"
                "  -bg 
"
                "  [-bg-vecfile]
"
                "  [-npos ]
"
                "  [-nneg ]
"
                "  [-nstages ]
"
                "  [-nsplits ]
"
                "  [-mem ]
"
                "  [-sym (default)] [-nonsym]
"
                "  [-minhitrate ]
"
                "  [-maxfalsealarm ]
"
                "  [-weighttrimming ]
"
                "  [-eqw]
"
                "  [-mode ]
"
                "  [-w ]
"
                "  [-h ]
"
                "  [-bt ]
"
                "  [-err ]
"
                "  [-maxtreesplits ]
"
                "  [-minpos ]
",
                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修改
        {
            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
", ((dirname == NULL) ? nullname : dirname ) );
    printf( "Vec file name: %s
", ((vecname == NULL) ? nullname : vecname ) );
    printf( "BG  file name: %s, is a vecfile: %s
", ((bgname == NULL) ? nullname : bgname ), bg_vecfile ? "yes" : "no" );
    printf( "Num pos: %d
", npos );
    printf( "Num neg: %d
", nneg );
    printf( "Num stages: %d
", nstages );
    printf( "Num splits: %d (%s as weak classifier)
", nsplits,
        (nsplits == 1) ? "stump" : "tree" );
    printf( "Mem: %d MB
", mem );
    printf( "Symmetric: %s
", (symmetric) ? "TRUE" : "FALSE" );
    printf( "Min hit rate: %f
", minhitrate );
    printf( "Max false alarm rate: %f
", maxfalsealarm );
    printf( "Weight trimming: %f
", weightfraction );
    printf( "Equal weights: %s
", (equalweights) ? "TRUE" : "FALSE" );
    printf( "Mode: %s
", ( (mode == 0) ? "BASIC" : ( (mode == 1) ? "CORE" : "ALL") ) );
    printf( "Width: %d
", width );
    printf( "Height: %d
", height );
    //printf( "Max num of precalculated features: %d
", numprecalculated );
    printf( "Applied boosting algorithm: %s
", boosttypes[boosttype] );
    printf( "Error (valid only for Discrete and Real AdaBoost): %s
",
            stumperrors[stumperror] );

    printf( "Max number of splits in tree cascade: %d
", maxtreesplits );
    printf( "Min number of positive samples per cluster: %d
", 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;
}

我的命令行参数为："D:vs2012projects rain_opencv_main rain_cascadeDebug est.exe" "-data" "-vec.vec" "-bg.txt" ，具体设置方法是调试----属性----配置属性----调试---命令参数

1 注意命令行参数中间要有空格的。 2 其中第一个你要修改为你自己电脑上工程的绝对路径； 3 "-data" 是存放训练好的分类器，需要预先建立好一个的空文件夹； 4 "-vec.vec" 是我的正样本描述文件； 5 "-bg.txt"是我的负样本集合文件。

四训练结果

1 dos操作窗口

2 data文件夹的内容为：

我的0文件中训练了6个弱文类器，1文件中含有9个弱分类器，2文件夹下有17个弱分类器，每一个文件夹就是一个级联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终于出来的，XML文件可以在在vs中直接调用了，xml文件的内容你看是跟上面data文件中的内容是严格一一对应的，我摘录其中部分内容（也就是0文件夹部分）如下：

<_-data type_id="opencv-haar-classifier">

    20 20

    <_>


        <_>

          <_>



                <_>
                  7 1 6 10 -1.
                <_>
                  9 1 2 10 3.
              0
            4.7923330217599869e-002
            -1.8457030057907104e+000
            1.8457030057907104e+000
        <_>

          <_>



                <_>
                  1 3 18 12 -1.
                <_>
                  1 7 18 4 3.
              0
            2.3897969722747803e-001
            -1.3966230154037476e+000
            1.3966230154037476e+000
        <_>

          <_>



                <_>
                  2 16 6 4 -1.
                <_>
                  2 16 3 2 2.
                <_>
                  5 18 3 2 2.
              0
            6.9004269316792488e-003
            -9.7984451055526733e-001
            9.7984451055526733e-001
        <_>

          <_>



                <_>
                  10 0 10 1 -1.
                <_>
                  10 0 5 1 2.
              0
            1.2191389687359333e-002
            -5.1561182737350464e-001
            5.1561182737350464e-001
        <_>

          <_>



                <_>
                  0 0 10 1 -1.
                <_>
                  5 0 5 1 2.
              0
            1.0146640241146088e-002
            -7.3657321929931641e-001
            7.3657321929931641e-001
        <_>

          <_>



                <_>
                  9 14 5 3 -1.
                <_>
                  9 15 5 1 3.
              0
            -6.5789339132606983e-003
            7.8852808475494385e-001
            -7.8852808475494385e-001
      -3.7585139274597168e+000
      -1
      -1
    <_>

利用opencv源码和vs编程序训练分类器haartraining.cpp

一训练框架

二建立工程

三程序

四训练结果

Ta的文章更多 >>

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利用opencv源码和vs编程序训练分类器haartraining.cpp

一 训练框架

二 建立工程

三 程序

四 训练结果

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一训练框架

二建立工程

三程序

四训练结果

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