Mat BOWKMeansTrainer::cluster() const
{
CV_Assert( !descriptors.empty() );
int descCount = 0;
for( size_t i = 0; i < descriptors.size(); i++ )
descCount += descriptors[i].rows;
Mat mergedDescriptors( descCount, descriptors[0].cols, descriptors[0].type() );
for( size_t i = 0, start = 0; i < descriptors.size(); i++ )
{
Mat submut = mergedDescriptors.rowRange((int)start, (int)(start + descriptors[i].rows));
descriptors[i].copyTo(submut);
start += descriptors[i].rows;
}
return cluster( mergedDescriptors );
}
void OpponentColorDescriptorExtractor::computeImpl( InputArray _bgrImage, std::vector<KeyPoint>& keypoints, OutputArray descriptors ) const
{
Mat bgrImage = _bgrImage.getMat();
std::vector<Mat> opponentChannels;
convertBGRImageToOpponentColorSpace( bgrImage, opponentChannels );
const int N = 3; // channels count
std::vector<KeyPoint> channelKeypoints[N];
Mat channelDescriptors[N];
std::vector<int> idxs[N];
// Compute descriptors three times, once for each Opponent channel to concatenate into a single color descriptor
int maxKeypointsCount = 0;
for( int ci = 0; ci < N; ci++ )
{
channelKeypoints[ci].insert( channelKeypoints[ci].begin(), keypoints.begin(), keypoints.end() );
// Use class_id member to get indices into initial keypoints vector
for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ )
channelKeypoints[ci][ki].class_id = (int)ki;
descriptorExtractor->compute( opponentChannels[ci], channelKeypoints[ci], channelDescriptors[ci] );
idxs[ci].resize( channelKeypoints[ci].size() );
for( size_t ki = 0; ki < channelKeypoints[ci].size(); ki++ )
{
idxs[ci][ki] = (int)ki;
}
std::sort( idxs[ci].begin(), idxs[ci].end(), KP_LessThan(channelKeypoints[ci]) );
maxKeypointsCount = std::max( maxKeypointsCount, (int)channelKeypoints[ci].size());
}
std::vector<KeyPoint> outKeypoints;
outKeypoints.reserve( keypoints.size() );
int dSize = descriptorExtractor->descriptorSize();
Mat mergedDescriptors( maxKeypointsCount, 3*dSize, descriptorExtractor->descriptorType() );
int mergedCount = 0;
// cp - current channel position
size_t cp[] = {0, 0, 0};
while( cp[0] < channelKeypoints[0].size() &&
cp[1] < channelKeypoints[1].size() &&
cp[2] < channelKeypoints[2].size() )
{
const int maxInitIdx = std::max( 0, std::max( channelKeypoints[0][idxs[0][cp[0]]].class_id,
std::max( channelKeypoints[1][idxs[1][cp[1]]].class_id,
channelKeypoints[2][idxs[2][cp[2]]].class_id ) ) );
while( channelKeypoints[0][idxs[0][cp[0]]].class_id < maxInitIdx && cp[0] < channelKeypoints[0].size() ) { cp[0]++; }
while( channelKeypoints[1][idxs[1][cp[1]]].class_id < maxInitIdx && cp[1] < channelKeypoints[1].size() ) { cp[1]++; }
while( channelKeypoints[2][idxs[2][cp[2]]].class_id < maxInitIdx && cp[2] < channelKeypoints[2].size() ) { cp[2]++; }
if( cp[0] >= channelKeypoints[0].size() || cp[1] >= channelKeypoints[1].size() || cp[2] >= channelKeypoints[2].size() )
break;
if( channelKeypoints[0][idxs[0][cp[0]]].class_id == maxInitIdx &&
channelKeypoints[1][idxs[1][cp[1]]].class_id == maxInitIdx &&
channelKeypoints[2][idxs[2][cp[2]]].class_id == maxInitIdx )
{
outKeypoints.push_back( keypoints[maxInitIdx] );
// merge descriptors
for( int ci = 0; ci < N; ci++ )
{
Mat dst = mergedDescriptors(Range(mergedCount, mergedCount+1), Range(ci*dSize, (ci+1)*dSize));
channelDescriptors[ci].row( idxs[ci][cp[ci]] ).copyTo( dst );
cp[ci]++;
}
mergedCount++;
}
}
mergedDescriptors.rowRange(0, mergedCount).copyTo( descriptors );
std::swap( outKeypoints, keypoints );
}
