@ -751,11 +751,11 @@ void LBPH::train(InputArray _src, InputArray _lbls) {
CV_Error ( CV_StsBadArg , error_message ) ;
}
// append labels to _labels matrix
for ( in t labelIdx = 0 ; labelIdx < labels . total ( ) ; labelIdx + + ) {
_labels . push_back ( labels . at < int > ( labelIdx ) ) ;
for ( size_ t labelIdx = 0 ; labelIdx < labels . total ( ) ; labelIdx + + ) {
_labels . push_back ( labels . at < int > ( ( int ) labelIdx ) ) ;
}
// store the spatial histograms of the original data
for ( in t sampleIdx = 0 ; sampleIdx < src . size ( ) ; sampleIdx + + ) {
for ( size_ t sampleIdx = 0 ; sampleIdx < src . size ( ) ; sampleIdx + + ) {
// calculate lbp image
Mat lbp_image = elbp ( src [ sampleIdx ] , _radius , _neighbors ) ;
// get spatial histogram from this lbp image
@ -788,11 +788,11 @@ void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
// find 1-nearest neighbor
minDist = DBL_MAX ;
minClass = - 1 ;
for ( in t sampleIdx = 0 ; sampleIdx < _histograms . size ( ) ; sampleIdx + + ) {
for ( size_ t sampleIdx = 0 ; sampleIdx < _histograms . size ( ) ; sampleIdx + + ) {
double dist = compareHist ( _histograms [ sampleIdx ] , query , CV_COMP_CHISQR ) ;
if ( ( dist < minDist ) & & ( dist < _threshold ) ) {
minDist = dist ;
minClass = _labels . at < int > ( sampleIdx ) ;
minClass = _labels . at < int > ( ( int ) sampleIdx ) ;
}
}
}