generic-library/opencv
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Merge pull request #3899 from StevenPuttemans:add_breakrule_traincascade_2.4

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This commit is contained in:
Vadim Pisarevsky 2015-04-03 11:44:55 +00:00
commit cc63452f65
4 changed files with 24 additions and 5 deletions
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13
apps/traincascade/cascadeclassifier.cpp
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@ -136,7 +136,8 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
const CvCascadeParams& _cascadeParams,
const CvFeatureParams& _featureParams,
const CvCascadeBoostParams& _stageParams,
bool baseFormatSave )
bool baseFormatSave,
double acceptanceRatioBreakValue)
{
// Start recording clock ticks for training time output
const clock_t begin_time = clock();
@ -186,6 +187,7 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
cout << "numStages: " << numStages << endl;
cout << "precalcValBufSize[Mb] : " << _precalcValBufSize << endl;
cout << "precalcIdxBufSize[Mb] : " << _precalcIdxBufSize << endl;
cout << "acceptanceRatioBreakValue : " << acceptanceRatioBreakValue << endl;
cascadeParams.printAttrs();
stageParams->printAttrs();
featureParams->printAttrs();
@ -208,15 +210,20 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
if ( !updateTrainingSet( requiredLeafFARate, tempLeafFARate ) )
{
cout << "Train dataset for temp stage can not be filled. "
"Branch training terminated." << endl;
"Branch training terminated." << endl;
break;
}
if( tempLeafFARate <= requiredLeafFARate )
{
cout << "Required leaf false alarm rate achieved. "
"Branch training terminated." << endl;
"Branch training terminated." << endl;
break;
}
if( (tempLeafFARate <= acceptanceRatioBreakValue) && (acceptanceRatioBreakValue >= 0) ){
cout << "The required acceptanceRatio for the model has been reached to avoid overfitting of trainingdata. "
"Branch training terminated." << endl;
break;
}
CvCascadeBoost* tempStage = new CvCascadeBoost;
bool isStageTrained = tempStage->train( (CvFeatureEvaluator*)featureEvaluator,

3
apps/traincascade/cascadeclassifier.h
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@ -96,7 +96,8 @@ public:
const CvCascadeParams& _cascadeParams,
const CvFeatureParams& _featureParams,
const CvCascadeBoostParams& _stageParams,
bool baseFormatSave = false );
bool baseFormatSave = false,
double acceptanceRatioBreakValue = -1.0 );
private:
int predict( int sampleIdx );
void save( const std::string cascadeDirName, bool baseFormat = false );

9
apps/traincascade/traincascade.cpp
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@ -17,6 +17,7 @@ int main( int argc, char* argv[] )
int precalcValBufSize = 1024,
precalcIdxBufSize = 1024;
bool baseFormatSave = false;
double acceptanceRatioBreakValue = -1.0;
CvCascadeParams cascadeParams;
CvCascadeBoostParams stageParams;
@ -37,6 +38,7 @@ int main( int argc, char* argv[] )
cout << " [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = " << precalcValBufSize << ">]" << endl;
cout << " [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = " << precalcIdxBufSize << ">]" << endl;
cout << " [-baseFormatSave]" << endl;
cout << " [-acceptanceRatioBreakValue <value> = " << acceptanceRatioBreakValue << ">]" << endl;
cascadeParams.printDefaults();
stageParams.printDefaults();
for( int fi = 0; fi < fc; fi++ )
@ -83,6 +85,10 @@ int main( int argc, char* argv[] )
{
baseFormatSave = true;
}
else if( !strcmp( argv[i], "-acceptanceRatioBreakValue" ) )
{
acceptanceRatioBreakValue = atof(argv[++i]);
}
else if ( cascadeParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
else if ( stageParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
else if ( !set )
@ -108,6 +114,7 @@ int main( int argc, char* argv[] )
cascadeParams,
*featureParams[cascadeParams.featureType],
stageParams,
baseFormatSave );
baseFormatSave,
acceptanceRatioBreakValue );
return 0;
}

4
doc/user_guide/ug_traincascade.rst
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@ -294,6 +294,10 @@ Command line arguments of ``opencv_traincascade`` application grouped by purpose
This argument is actual in case of Haar-like features. If it is specified, the cascade will be saved in the old format.
* ``-acceptanceRatioBreakValue``
This argument is used to determine how precise your model should keep learning and when to stop. A good guideline is to train not further than 10e-5, to ensure the model does not overtrain on your training data. By default this value is set to -1 to disable this feature.
#.
Cascade parameters: