WeakClassifierHaarFeature::WeakClassifierHaarFeature(Size patchSize)
{
m_feature = new FeatureHaar(patchSize);
generateRandomClassifier();
m_feature->getInitialDistribution((EstimatedGaussDistribution*) m_classifier->getDistribution(-1));
m_feature->getInitialDistribution((EstimatedGaussDistribution*) m_classifier->getDistribution(1));
}
WeakClassifierHaarFeature::WeakClassifierHaarFeature()
{
sigma = 1;
mean = 0;
EstimatedGaussDistribution* m_posSamples = new EstimatedGaussDistribution();
EstimatedGaussDistribution* m_negSamples = new EstimatedGaussDistribution();
generateRandomClassifier( m_posSamples, m_negSamples );
getInitialDistribution( (EstimatedGaussDistribution*) m_classifier->getDistribution( -1 ) );
getInitialDistribution( (EstimatedGaussDistribution*) m_classifier->getDistribution( 1 ) );
}
BaseClassifier::BaseClassifier( int numWeakClassifier, int iterationInit )
{
this->m_numWeakClassifier = numWeakClassifier;
this->m_iterationInit = iterationInit;
weakClassifier = new WeakClassifierHaarFeature*[numWeakClassifier + iterationInit];
m_idxOfNewWeakClassifier = numWeakClassifier;
generateRandomClassifier();
m_referenceWeakClassifier = false;
m_selectedClassifier = 0;
m_wCorrect.assign( numWeakClassifier + iterationInit, 0 );
m_wWrong.assign( numWeakClassifier + iterationInit, 0 );
for ( int curWeakClassifier = 0; curWeakClassifier < numWeakClassifier + iterationInit; curWeakClassifier++ )
m_wWrong[curWeakClassifier] = m_wCorrect[curWeakClassifier] = 1;
}
