traincascade.cpp

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;
}