opencv/modules/features2d/src/bagofwords.cpp

157 lines
5.2 KiB
C++
Executable File

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#include "precomp.hpp"
using namespace std;
namespace cv
{
void BOWTrainer::add( const Mat& _descriptors )
{
CV_Assert( !_descriptors.empty() );
if( !descriptors.empty() )
{
CV_Assert( descriptors[0].cols == _descriptors.cols );
CV_Assert( descriptors[0].type() == _descriptors.type() );
size += _descriptors.rows;
}
else
{
size = _descriptors.rows;
}
descriptors.push_back(_descriptors);
}
void BOWTrainer::clear()
{
descriptors.clear();
}
BOWKMeansTrainer::BOWKMeansTrainer( int _clusterCount, const TermCriteria& _termcrit,
int _attempts, int _flags ) :
clusterCount(_clusterCount), termcrit(_termcrit), attempts(_attempts), flags(_flags)
{}
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(start, start + descriptors[i].rows);
descriptors[i].copyTo(submut);
start += descriptors[i].rows;
}
return cluster( mergedDescriptors );
}
Mat BOWKMeansTrainer::cluster( const Mat& descriptors ) const
{
Mat labels, vocabulary;
kmeans( descriptors, clusterCount, labels, termcrit, attempts, flags, &vocabulary );
return vocabulary;
}
BOWImgDescriptorExtractor::BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& _dextractor,
const Ptr<DescriptorMatcher>& _dmatcher ) :
dextractor(_dextractor), dmatcher(_dmatcher)
{}
void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary )
{
dmatcher->clear();
vocabulary = _vocabulary;
dmatcher->add( vocabulary );
}
void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
vector<vector<int> >* pointIdxsOfClusters ) const
{
imgDescriptor.release();
if( keypoints.empty() )
return;
int clusterCount = descriptorSize(); // = vocabulary.rows
// Compute descriptors for the image.
Mat descriptors;
dextractor->compute( image, keypoints, descriptors );
// Match keypoint descriptors to cluster center (to vocabulary)
vector<DMatch> matches;
dmatcher->match( descriptors, matches );
// Compute image descriptor
if( pointIdxsOfClusters )
{
pointIdxsOfClusters->clear();
pointIdxsOfClusters->resize(clusterCount);
}
imgDescriptor = Mat( 1, clusterCount, descriptorType(), Scalar::all(0.0) );
float *dptr = (float*)imgDescriptor.data;
for( size_t i = 0; i < matches.size(); i++ )
{
int queryIdx = matches[i].indexQuery;
int trainIdx = matches[i].indexTrain; // cluster index
CV_Assert( queryIdx == (int)i );
dptr[trainIdx] = dptr[trainIdx] + 1.f;
if( pointIdxsOfClusters )
(*pointIdxsOfClusters)[trainIdx].push_back( queryIdx );
}
// Normalize image descriptor.
imgDescriptor /= descriptors.rows;
}
}