traincascade.cpp

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int main( int argc, char* argv[] ){    CvCascadeClassifier classifier;    string cascadeDirName, vecName, bgName;    int numPos    = 2000;    int numNeg    = 1000;    int numStages = 20;    int precalcValBufSize = 256,        precalcIdxBufSize = 256;    bool baseFormatSave = false;    CvCascadeParams cascadeParams;//stage type,featuretype,windowsize    CvCascadeBoostParams stageParams;//minHitRate,maxFalseAlarm,boost-type(real,gentle),weak_count,split_criteria,weight_trim_rate,cv_folds,max_depth    Ptr<CvFeatureParams> featureParams[] = { Ptr<CvFeatureParams>(new CvHaarFeatureParams),                                             Ptr<CvFeatureParams>(new CvLBPFeatureParams),                                             Ptr<CvFeatureParams>(new CvHOGFeatureParams)                                           };    int fc = sizeof(featureParams)/sizeof(featureParams[0]);    if( argc == 1 )    {        cout << "Usage: " << argv[0] << endl;        cout << "  -data <cascade_dir_name>" << endl;        cout << "  -vec <vec_file_name>" << endl;        cout << "  -bg <background_file_name>" << endl;        cout << "  [-numPos <number_of_positive_samples = " << numPos << ">]" << endl;        cout << "  [-numNeg <number_of_negative_samples = " << numNeg << ">]" << endl;        cout << "  [-numStages <number_of_stages = " << numStages << ">]" << endl;        cout << "  [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = " << precalcValBufSize << ">]" << endl;        cout << "  [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = " << precalcIdxBufSize << ">]" << endl;        cout << "  [-baseFormatSave]" << endl;        cascadeParams.printDefaults();        stageParams.printDefaults();        for( int fi = 0; fi < fc; fi++ )            featureParams[fi]->printDefaults();        return 0;    }    for( int i = 1; i < argc; i++ )    {        bool set = false;        if( !strcmp( argv[i], "-data" ) )        {            cascadeDirName = argv[++i];        }        else if( !strcmp( argv[i], "-vec" ) )        {            vecName = argv[++i];        }        else if( !strcmp( argv[i], "-bg" ) )        {            bgName = argv[++i];        }        else if( !strcmp( argv[i], "-numPos" ) )        {            numPos = atoi( argv[++i] );        }        else if( !strcmp( argv[i], "-numNeg" ) )        {            numNeg = atoi( argv[++i] );        }        else if( !strcmp( argv[i], "-numStages" ) )        {            numStages = atoi( argv[++i] );        }        else if( !strcmp( argv[i], "-precalcValBufSize" ) )        {            precalcValBufSize = atoi( argv[++i] );        }        else if( !strcmp( argv[i], "-precalcIdxBufSize" ) )        {            precalcIdxBufSize = atoi( argv[++i] );        }        else if( !strcmp( argv[i], "-baseFormatSave" ) )        {            baseFormatSave = true;        }        else if ( cascadeParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }        else if ( stageParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }        else if ( !set )        {            for( int fi = 0; fi < fc; fi++ )            {                set = featureParams[fi]->scanAttr(argv[i], argv[i+1]);                if ( !set )                {                    i++;                    break;                }            }        }    }    classifier.train( cascadeDirName,                      vecName,                      bgName,                      numPos, numNeg,                      precalcValBufSize, precalcIdxBufSize,                      numStages,                      cascadeParams,                      *featureParams[cascadeParams.featureType],                      stageParams,                      baseFormatSave );    return 0;}
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