OpenCV Adaboost 분석(2)

중요 코드

//가중치 갱신, 및 약분류기 신뢰도 계산 함수

float icvBoostNextWeakClassifierGAB( CvMat* weakEvalVals,
                                     CvMat* trainClasses,
                                     CvMat* /*weakTrainVals*/,
                                     CvMat* weights,
                                     CvBoostTrainer* trainer )
{
    uchar* evaldata;
    int evalstep;
    int m;
    uchar* ydata;
    int ystep;
    int ynum;
    uchar* wdata;
    int wstep;
    int wnum;

    int i, idx;
    float sumw;

    CV_Assert( weakEvalVals != NULL );
    CV_Assert( CV_MAT_TYPE( weakEvalVals->type ) == CV_32FC1 );
    CV_Assert( trainClasses != NULL );
    CV_Assert( CV_MAT_TYPE( trainClasses->type ) == CV_32FC1 );
    CV_Assert( weights != NULL );
    CV_Assert( CV_MAT_TYPE( weights->type ) == CV_32FC1 );

    CV_MAT2VEC( *weakEvalVals, evaldata, evalstep, m );
    CV_MAT2VEC( *trainClasses, ydata, ystep, ynum );
    CV_MAT2VEC( *weights, wdata, wstep, wnum );

    assert( m == ynum );
    assert( m == wnum );

    sumw = 0.0F;
    for( i = 0; i < trainer->count; i++ )
    {
        idx = (trainer->idx) ? trainer->idx[i] : i;

        *((float*) (wdata + idx*wstep)) *= 
            expf( -(*((float*) (evaldata + idx*evalstep)))
                  * ( 2.0F * (*((float*) (ydata + idx*ystep))) - 1.0F ) );
        sumw += *((float*) (wdata + idx*wstep));
    }
    
    for( i = 0; i < trainer->count; i++ )
    {
        idx = (trainer->idx) ? trainer->idx[i] : i;

        *((float*) (wdata + idx*wstep)) /= sumw;
    }

    return 1.0F;
}

//평가 함수

float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
                                 sum_type* sum, sum_type* tilted, float normfactor )
{
    int idx = 0;

    do
    {
        if( cvEvalFastHaarFeature(
                ((CvCARTHaarClassifier*) classifier)->fastfeature + idx, sum, tilted )
              < (((CvCARTHaarClassifier*) classifier)->threshold[idx] * normfactor) )
        {
            idx = ((CvCARTHaarClassifier*) classifier)->left[idx];
        }
        else
        {
            idx = ((CvCARTHaarClassifier*) classifier)->right[idx];
        }
    } while( idx > 0 );

    return ((CvCARTHaarClassifier*) classifier)->val[-idx];
}