third_party/opencv
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add the possibility to add traincascade accuracy

Browse Source
This commit is contained in:
StevenPuttemans
2015-04-03 09:39:23 +02:00
parent ba0a8dc723
commit e30139edc3
4 changed files with 24 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
apps/traincascade/cascadeclassifier.cpp
+10 -3
View File
@@ -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,