opencv/modules/features2d/src/bagofwords.cpp

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#include "precomp.hpp"

using namespace std;

namespace cv
{

BOWTrainer::BOWTrainer()
{}

BOWTrainer::~BOWTrainer()
{}

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);
}

const vector<Mat>& BOWTrainer::getDescriptors() const
{
    return descriptors;
}

int BOWTrainer::descripotorsCount() const
{
    return descriptors.empty() ? 0 : size;
}

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((int)start, (int)(start + descriptors[i].rows));
        descriptors[i].copyTo(submut);
        start += descriptors[i].rows;
    }
    return cluster( mergedDescriptors );
}

BOWKMeansTrainer::~BOWKMeansTrainer()
{}

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)
{}

BOWImgDescriptorExtractor::~BOWImgDescriptorExtractor()
{}

void BOWImgDescriptorExtractor::setVocabulary( const Mat& _vocabulary )
{
    dmatcher->clear();
    vocabulary = _vocabulary;
    dmatcher->add( vector<Mat>(1, vocabulary) );
}

const Mat& BOWImgDescriptorExtractor::getVocabulary() const
{
    return vocabulary;
}

void BOWImgDescriptorExtractor::compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
                                         vector<vector<int> >* pointIdxsOfClusters, Mat* _descriptors )
{
    imgDescriptor.release();

    if( keypoints.empty() )
        return;

    int clusterCount = descriptorSize(); // = vocabulary.rows

    // Compute descriptors for the image.
    Mat descriptors = _descriptors ? *_descriptors : Mat();
    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].queryIdx;
        int trainIdx = matches[i].trainIdx; // 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;
}

int BOWImgDescriptorExtractor::descriptorSize() const
{
    return vocabulary.empty() ? 0 : vocabulary.rows;
}

int BOWImgDescriptorExtractor::descriptorType() const
{
    return CV_32FC1;
}

}