VWDictionary.cpp

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/*
Copyright (c) 2010-2014, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.

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      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

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#include "rtabmap/core/VWDictionary.h"
#include "VisualWord.h"

#include "rtabmap/core/Signature.h"
#include "rtabmap/core/DBDriver.h"
#include "rtabmap/core/Parameters.h"

#include "rtabmap/utilite/UtiLite.h"

#include <opencv2/gpu/gpu.hpp>

#include <fstream>
#include <string>

namespace rtabmap
{

const int VWDictionary::ID_START = 1;
const int VWDictionary::ID_INVALID = 0;

VWDictionary::VWDictionary(const ParametersMap & parameters) :
        _totalActiveReferences(0),
        _incrementalDictionary(Parameters::defaultKpIncrementalDictionary()),
        _nndrRatio(Parameters::defaultKpNndrRatio()),
        _dictionaryPath(Parameters::defaultKpDictionaryPath()),
        _newWordsComparedTogether(Parameters::defaultKpNewWordsComparedTogether()),
        _lastWordId(0),
        _flannIndex(new cv::flann::Index()),
        _strategy(kNNBruteForce)
{
        this->setNNStrategy((NNStrategy)Parameters::defaultKpNNStrategy());
        this->parseParameters(parameters);
}

VWDictionary::~VWDictionary()
{
        this->clear();
        delete _flannIndex;
}

void VWDictionary::parseParameters(const ParametersMap & parameters)
{
        ParametersMap::const_iterator iter;
        Parameters::parse(parameters, Parameters::kKpNndrRatio(), _nndrRatio);
        Parameters::parse(parameters, Parameters::kKpNewWordsComparedTogether(), _newWordsComparedTogether);

        UASSERT_MSG(_nndrRatio > 0.0f, uFormat("String=%s value=%f", uContains(parameters, Parameters::kKpNndrRatio())?parameters.at(Parameters::kKpNndrRatio()).c_str():"", _nndrRatio).c_str());

        std::string dictionaryPath = _dictionaryPath;
        bool incrementalDictionary = _incrementalDictionary;
        if((iter=parameters.find(Parameters::kKpDictionaryPath())) != parameters.end())
        {
                dictionaryPath = (*iter).second.c_str();
        }
        if((iter=parameters.find(Parameters::kKpIncrementalDictionary())) != parameters.end())
        {
                incrementalDictionary = uStr2Bool((*iter).second.c_str());
        }

        // Verifying hypotheses strategy
        if((iter=parameters.find(Parameters::kKpNNStrategy())) != parameters.end())
        {
                NNStrategy nnStrategy = (NNStrategy)std::atoi((*iter).second.c_str());
                this->setNNStrategy(nnStrategy);
        }

        if(incrementalDictionary)
        {
                this->setIncrementalDictionary();
        }
        else
        {
                this->setFixedDictionary(dictionaryPath);
        }

}

void VWDictionary::setIncrementalDictionary()
{
        if(!_incrementalDictionary)
        {
                _incrementalDictionary = true;
                if(_visualWords.size())
                {
                        UWARN("Incremental dictionary set: already loaded visual words (%d) from the fixed dictionary will be included in the incremental one.", _visualWords.size());
                }
        }
        _dictionaryPath = "";
}

void VWDictionary::setFixedDictionary(const std::string & dictionaryPath)
{
        if(!dictionaryPath.empty())
        {
                if((!_incrementalDictionary && _dictionaryPath.compare(dictionaryPath) != 0) ||
                   _visualWords.size() == 0)
                {
                        std::ifstream file;
                        file.open(dictionaryPath.c_str(), std::ifstream::in);
                        if(file.good())
                        {
                                UDEBUG("Deleting old dictionary and loading the new one from \"%s\"", dictionaryPath.c_str());
                                UTimer timer;

                                // first line is the header
                                std::string str;
                                std::list<std::string> strList;
                                std::getline(file, str);
                                strList = uSplitNumChar(str);
                                unsigned int dimension  = 0;
                                for(std::list<std::string>::iterator iter = strList.begin(); iter != strList.end(); ++iter)
                                {
                                        if(uIsDigit(iter->at(0)))
                                        {
                                                dimension = std::atoi(iter->c_str());
                                                break;
                                        }
                                }

                                if(dimension == 0 || dimension > 1000)
                                {
                                        UERROR("Invalid dictionary file, visual word dimension (%d) is not valid, \"%s\"", dimension, dictionaryPath.c_str());
                                }
                                else
                                {
                                        // Process all words
                                        while(file.good())
                                        {
                                                std::getline(file, str);
                                                strList = uSplit(str);
                                                if(strList.size() == dimension+1)
                                                {
                                                        //first one is the visual word id
                                                        std::list<std::string>::iterator iter = strList.begin();
                                                        int id = std::atoi(iter->c_str());
                                                        cv::Mat descriptor(1, dimension, CV_32F);
                                                        ++iter;
                                                        unsigned int i=0;

                                                        //get descriptor
                                                        for(;i<dimension && iter != strList.end(); ++i, ++iter)
                                                        {
                                                                descriptor.at<float>(i) = uStr2Float(*iter);
                                                        }
                                                        if(i != dimension)
                                                        {
                                                                UERROR("");
                                                        }

                                                        VisualWord * vw = new VisualWord(id, descriptor, 0);
                                                        _visualWords.insert(_visualWords.end(), std::pair<int, VisualWord*>(id, vw));
                                                        _notIndexedWords.insert(_notIndexedWords.end(), id);
                                                }
                                                else
                                                {
                                                        UWARN("Cannot parse line \"%s\"", str.c_str());
                                                }
                                        }
                                        this->update();
                                        _incrementalDictionary = false;
                                }


                                UDEBUG("Time changing dictionary = %fs", timer.ticks());
                        }
                        else
                        {
                                UERROR("Cannot open dictionary file \"%s\"", dictionaryPath.c_str());
                        }
                        file.close();
                }
                else if(!_incrementalDictionary)
                {
                        UDEBUG("Dictionary \"%s\" already loaded...", dictionaryPath.c_str());
                }
                else
                {
                        UERROR("Cannot change to a fixed dictionary if there are already words (%d) in the incremental one.", _visualWords.size());
                }
        }
        else if(_visualWords.size() == 0)
        {
                _incrementalDictionary = false;
        }
        else if(_incrementalDictionary)
        {
                UWARN("Cannot change to fixed dictionary, %d words already loaded as incremental", (int)_visualWords.size());
        }
        _dictionaryPath = dictionaryPath;
}

void VWDictionary::setNNStrategy(NNStrategy strategy)
{
        if(strategy!=kNNUndef)
        {
                if(strategy == kNNBruteForceGPU && !cv::gpu::getCudaEnabledDeviceCount())
                {
                        UERROR("Nearest neighobr strategy \"kNNBruteForceGPU\" chosen but no CUDA devices found! Doing \"kNNBruteForce\" instead.");
                        strategy = kNNBruteForce;
                }

                if(RTABMAP_NONFREE == 0 && strategy == kNNFlannKdTree)
                {
                        UWARN("KdTree (%d) nearest neighbor is not available because RTAB-Map isn't built "
                                  "with OpenCV nonfree module (KdTree only used for SURF/SIFT features). "
                                  "NN strategy is not modified (current=%d).", (int)kNNFlannKdTree, (int)_strategy);
                }
                else
                {
                        _strategy = strategy;
                }
        }
}

int VWDictionary::getLastIndexedWordId() const
{
        if(_mapIndexId.size())
        {
                return _mapIndexId.rbegin()->second;
        }
        else
        {
                return 0;
        }
}

void VWDictionary::update()
{
        ULOGGER_DEBUG("");
        if(!_incrementalDictionary && !_notIndexedWords.size())
        {
                // No need to update the search index if we
                // use a fixed dictionary and the index is
                // already built
                return;
        }

        if(_notIndexedWords.size() || _visualWords.size() == 0 || _removedIndexedWords.size())
        {
                _mapIndexId.clear();
                int oldSize = _dataTree.rows;
                _dataTree = cv::Mat();
                _flannIndex->release();

                if(_visualWords.size())
                {
                        UTimer timer;
                        timer.start();

                        int type = _visualWords.begin()->second->getDescriptor().type();
                        int dim = _visualWords.begin()->second->getDescriptor().cols;

                        UASSERT(type == CV_32F || type == CV_8U);
                        UASSERT(dim > 0);

                        // Create the data matrix
                        _dataTree = cv::Mat(_visualWords.size(), dim, type); // SURF descriptors are CV_32F
                        std::map<int, VisualWord*>::const_iterator iter = _visualWords.begin();
                        for(unsigned int i=0; i < _visualWords.size(); ++i, ++iter)
                        {
                                UASSERT(iter->second->getDescriptor().cols == dim);
                                UASSERT(iter->second->getDescriptor().type() == type);

                                iter->second->getDescriptor().copyTo(_dataTree.row(i));
                                _mapIndexId.insert(_mapIndexId.end(), std::pair<int, int>(i, iter->second->id()));
                        }

                        ULOGGER_DEBUG("_mapIndexId.size() = %d, words.size()=%d, _dim=%d",_mapIndexId.size(), _visualWords.size(), dim);
                        ULOGGER_DEBUG("copying data = %f s", timer.ticks());

                        switch(_strategy)
                        {
                        case kNNFlannNaive:
                                _flannIndex->build(_dataTree, cv::flann::LinearIndexParams(), type == CV_32F?cvflann::FLANN_DIST_L2:cvflann::FLANN_DIST_HAMMING);
                                break;
                        case kNNFlannKdTree:
                                UASSERT_MSG(type == CV_32F, "To use KdTree dictionary, float descriptors are required!");
                                _flannIndex->build(_dataTree, cv::flann::KDTreeIndexParams(), cvflann::FLANN_DIST_L2);
                                break;
                        case kNNFlannLSH:
                                UASSERT_MSG(type == CV_8U, "To use LSH dictionary, binary descriptors are required!");
                                _flannIndex->build(_dataTree, cv::flann::LshIndexParams(12, 20, 2), cvflann::FLANN_DIST_HAMMING);
                                break;
                        default:
                                break;
                        }

                        ULOGGER_DEBUG("Time to create kd tree = %f s", timer.ticks());
                }
                UDEBUG("Dictionary updated! (size=%d->%d added=%d removed=%d)",
                                oldSize, _dataTree.rows, _notIndexedWords.size(), _removedIndexedWords.size());
        }
        else
        {
                UDEBUG("Dictionary has not changed, so no need to update it! (size=%d)", _dataTree.rows);
        }
        _notIndexedWords.clear();
        _removedIndexedWords.clear();
}

void VWDictionary::clear()
{
        ULOGGER_DEBUG("");
        if(_visualWords.size() && _incrementalDictionary)
        {
                UWARN("Visual dictionary would be already empty here (%d words still in dictionary).", (int)_visualWords.size());
        }
        if(_notIndexedWords.size())
        {
                UWARN("Not indexed words should be empty here (%d words still not indexed)", (int)_notIndexedWords.size());
        }
        for(std::map<int, VisualWord *>::iterator i=_visualWords.begin(); i!=_visualWords.end(); ++i)
        {
                delete (*i).second;
        }
        _visualWords.clear();
        _notIndexedWords.clear();
        _removedIndexedWords.clear();
        _totalActiveReferences = 0;
        _lastWordId = 0;
        _dataTree = cv::Mat();
        _mapIndexId.clear();
        _unusedWords.clear();
        _flannIndex->release();
}

int VWDictionary::getNextId()
{
        return ++_lastWordId;
}

void VWDictionary::addWordRef(int wordId, int signatureId)
{
        if(signatureId > 0 && wordId > 0)
        {
                VisualWord * vw = 0;
                vw = uValue(_visualWords, wordId, vw);
                if(vw)
                {
                        vw->addRef(signatureId);
                        _totalActiveReferences += 1;

                        _unusedWords.erase(vw->id());
                }
                else
                {
                        UERROR("Not found word %d", wordId);
                }
        }
}

void VWDictionary::removeAllWordRef(int wordId, int signatureId)
{
        VisualWord * vw = 0;
        vw = uValue(_visualWords, wordId, vw);
        if(vw)
        {
                _totalActiveReferences -= vw->removeAllRef(signatureId);
                if(vw->getReferences().size() == 0)
                {
                        _unusedWords.insert(std::pair<int, VisualWord*>(vw->id(), vw));
                }
        }
}

std::list<int> VWDictionary::addNewWords(const cv::Mat & descriptors,
                                                           int signatureId)
{
        UASSERT(signatureId > 0);

        UDEBUG("id=%d descriptors=%d", signatureId, descriptors.rows);
        UTimer timer;
        std::list<int> wordIds;
        if(descriptors.rows == 0 || descriptors.cols == 0)
        {
                UERROR("Descriptors size is null!");
                return wordIds;
        }
        int dim = 0;
        int type = -1;
        if(_visualWords.size())
        {
                dim = _visualWords.begin()->second->getDescriptor().cols;
                type = _visualWords.begin()->second->getDescriptor().type();
                UASSERT(type == CV_32F || type == CV_8U);
        }

        if(dim && dim != descriptors.cols)
        {
                UERROR("Descriptors (size=%d) are not the same size as already added words in dictionary(size=%d)", descriptors.cols, dim);
                return wordIds;
        }
        dim = descriptors.cols;

        if(type>=0 && type != descriptors.type())
        {
                UERROR("Descriptors (type=%d) are not the same type as already added words in dictionary(type=%d)", descriptors.type(), type);
                return wordIds;
        }
        type = descriptors.type();

        if(!_incrementalDictionary && _visualWords.empty())
        {
                UERROR("Dictionary mode is set to fixed but no words are in it!");
                return wordIds;
        }

        int dupWordsCountFromDict= 0;
        int dupWordsCountFromLast= 0;

        unsigned int k=2; // k nearest neighbors

        cv::Mat newWords;
        std::vector<int> newWordsId;

        cv::Mat results;
        cv::Mat dists;
        std::vector<std::vector<cv::DMatch> > matches;
        bool bruteForce = false;

        UTimer timerLocal;
        timerLocal.start();

        if(!_dataTree.empty() && _dataTree.rows >= (int)k)
        {
                //Find nearest neighbors
                UDEBUG("newPts.total()=%d ", descriptors.rows);

                if(_strategy == kNNFlannNaive || _strategy == kNNFlannKdTree || _strategy == kNNFlannLSH)
                {
                        _flannIndex->knnSearch(descriptors, results, dists, k);
                }
                else if(_strategy == kNNBruteForce)
                {
                        bruteForce = true;
                        cv::BFMatcher matcher(type==CV_8U?cv::NORM_HAMMING:cv::NORM_L2SQR);
                        matcher.knnMatch(descriptors, _dataTree, matches, k);
                }
                else if(_strategy == kNNBruteForceGPU)
                {
                        bruteForce = true;
                        cv::gpu::GpuMat newDescriptorsGpu(descriptors);
                        cv::gpu::GpuMat lastDescriptorsGpu(_dataTree);
                        if(type==CV_8U)
                        {
                                cv::gpu::BruteForceMatcher_GPU<cv::Hamming> gpuMatcher;
                                gpuMatcher.knnMatch(newDescriptorsGpu, lastDescriptorsGpu, matches, k);
                        }
                        else
                        {
                                cv::gpu::BruteForceMatcher_GPU<cv::L2<float> > gpuMatcher;
                                gpuMatcher.knnMatch(newDescriptorsGpu, lastDescriptorsGpu, matches, k);
                        }
                }
                else
                {
                        UFATAL("");
                }

                // In case of binary descriptors
                if(dists.type() == CV_32S)
                {
                        cv::Mat temp;
                        dists.convertTo(temp, CV_32F);
                        dists = temp;
                }

                UDEBUG("Time to find nn = %f s", timerLocal.ticks());
        }

        // Process results
        for(int i = 0; i < descriptors.rows; ++i)
        {
                std::multimap<float, int> fullResults; // Contains results from the kd-tree search and the naive search in new words
                if(!bruteForce && dists.cols)
                {
                        for(int j=0; j<dists.cols; ++j)
                        {
                                if(results.at<int>(i,j) >= 0)
                                {
                                        float d = dists.at<float>(i,j);
                                        fullResults.insert(std::pair<float, int>(d, uValue(_mapIndexId, results.at<int>(i,j))));
                                }
                        }
                }
                else if(bruteForce && matches.size())
                {
                        for(unsigned int j=0; j<matches.at(i).size(); ++j)
                        {
                                if(matches.at(i).at(j).trainIdx >= 0)
                                {
                                        float d = matches.at(i).at(j).distance;
                                        fullResults.insert(std::pair<float, int>(d, uValue(_mapIndexId, matches.at(i).at(j).trainIdx)));
                                }
                        }
                }

                // Check if this descriptor matches with a word from the last signature (a word not already added to the tree)
                if(_newWordsComparedTogether && newWords.rows)
                {
                        cv::flann::Index linearSeach;
                        linearSeach.build(newWords, cv::flann::LinearIndexParams(), type == CV_32F?cvflann::FLANN_DIST_L2:cvflann::FLANN_DIST_HAMMING);
                        cv::Mat resultsLinear;
                        cv::Mat distsLinear;
                        linearSeach.knnSearch(descriptors.row(i), resultsLinear, distsLinear, newWords.rows>1?2:1);
                        // In case of binary descriptors
                        if(distsLinear.type() == CV_32S)
                        {
                                cv::Mat temp;
                                distsLinear.convertTo(temp, CV_32F);
                                distsLinear = temp;
                        }
                        if(resultsLinear.cols)
                        {
                                for(int j=0; j<resultsLinear.cols; ++j)
                                {
                                        if(resultsLinear.at<int>(0,j) >= 0)
                                        {
                                                 float d = distsLinear.at<float>(0,j);
                                                 fullResults.insert(std::pair<float, int>(d, newWordsId[resultsLinear.at<int>(0,j)]));
                                        }
                                }
                        }
                }

                if(_incrementalDictionary)
                {
                        bool badDist = false;
                        if(fullResults.size() == 0)
                        {
                                badDist = true;
                        }
                        if(!badDist)
                        {
                                if(fullResults.size() >= 2)
                                {
                                        // Apply NNDR
                                        if(fullResults.begin()->first > _nndrRatio * (++fullResults.begin())->first)
                                        {
                                                badDist = true; // Rejected
                                        }
                                }
                                else
                                {
                                        badDist = true; // Rejected
                                }
                        }

                        if(badDist)
                        {
                                VisualWord * vw = new VisualWord(getNextId(), descriptors.row(i), signatureId);
                                _visualWords.insert(_visualWords.end(), std::pair<int, VisualWord *>(vw->id(), vw));
                                _notIndexedWords.insert(_notIndexedWords.end(), vw->id());
                                newWords.push_back(vw->getDescriptor());
                                newWordsId.push_back(vw->id());
                                wordIds.push_back(vw->id());
                                UASSERT(vw->id()>0);
                        }
                        else
                        {
                                if(_notIndexedWords.find(fullResults.begin()->second) != _notIndexedWords.end())
                                {
                                        ++dupWordsCountFromLast;
                                }
                                else
                                {
                                        ++dupWordsCountFromDict;
                                }

                                this->addWordRef(fullResults.begin()->second, signatureId);
                                wordIds.push_back(fullResults.begin()->second);
                                UASSERT(fullResults.begin()->second>0);
                        }
                }
                else if(fullResults.size())
                {
                        // If the dictionary is not incremental, just take the nearest word
                        ++dupWordsCountFromDict;
                        this->addWordRef(fullResults.begin()->second, signatureId);
                        wordIds.push_back(fullResults.begin()->second);
                        UASSERT(fullResults.begin()->second>0);
                }
        }
        ULOGGER_DEBUG("naive search and add ref/words time = %f s", timerLocal.ticks());

        ULOGGER_DEBUG("%d new words added...", _notIndexedWords.size());
        ULOGGER_DEBUG("%d duplicated words added (from current image = %d)...",
                        dupWordsCountFromDict+dupWordsCountFromLast, dupWordsCountFromLast);
        UDEBUG("total time %fs", timer.ticks());

        _totalActiveReferences += _notIndexedWords.size();
        return wordIds;
}

std::vector<int> VWDictionary::findNN(const std::list<VisualWord *> & vws) const
{
        UTimer timer;
        timer.start();
        std::vector<int> resultIds(vws.size(), 0);
        unsigned int k=2; // k nearest neighbor

        if(_visualWords.size() && vws.size())
        {
                int dim = _visualWords.begin()->second->getDescriptor().cols;
                int type = _visualWords.begin()->second->getDescriptor().type();

                if(dim != (*vws.begin())->getDescriptor().cols)
                {
                        UERROR("Descriptors (size=%d) are not the same size as already added words in dictionary(size=%d)", (*vws.begin())->getDescriptor().cols, dim);
                        return resultIds;
                }

                if(type != (*vws.begin())->getDescriptor().type())
                {
                        UERROR("Descriptors (type=%d) are not the same type as already added words in dictionary(type=%d)", (*vws.begin())->getDescriptor().type(), type);
                        return resultIds;
                }

                std::vector<std::vector<cv::DMatch> > matches;
                bool bruteForce = false;
                cv::Mat results;
                cv::Mat dists;

                // fill the request matrix
                int index = 0;
                VisualWord * vw;
                cv::Mat query(vws.size(), dim, type);
                for(std::list<VisualWord *>::const_iterator iter=vws.begin(); iter!=vws.end(); ++iter, ++index)
                {
                        vw = *iter;
                        UASSERT(vw);
                        UASSERT(vw->getDescriptor().cols == dim);
                        UASSERT(vw->getDescriptor().type() == type);

                        vw->getDescriptor().copyTo(query.row(index));
                }
                ULOGGER_DEBUG("Preparation time = %fs", timer.ticks());

                if(!_dataTree.empty() && _dataTree.rows >= (int)k)
                {
                        //Find nearest neighbors
                        UDEBUG("newPts.total()=%d ", query.total());

                        if(_strategy == kNNFlannNaive || _strategy == kNNFlannKdTree || _strategy == kNNFlannLSH)
                        {
                                _flannIndex->knnSearch(query, results, dists, k);
                        }
                        else if(_strategy == kNNBruteForce)
                        {
                                bruteForce = true;
                                cv::BFMatcher matcher(type==CV_8U?cv::NORM_HAMMING:cv::NORM_L2SQR);
                                matcher.knnMatch(query, _dataTree, matches, k);
                        }
                        else if(_strategy == kNNBruteForceGPU)
                        {
                                bruteForce = true;
                                cv::gpu::GpuMat newDescriptorsGpu(query);
                                cv::gpu::GpuMat lastDescriptorsGpu(_dataTree);
                                if(type==CV_8U)
                                {
                                        cv::gpu::BruteForceMatcher_GPU<cv::Hamming> gpuMatcher;
                                        gpuMatcher.knnMatch(newDescriptorsGpu, lastDescriptorsGpu, matches, k);
                                }
                                else
                                {
                                        cv::gpu::BruteForceMatcher_GPU<cv::L2<float> > gpuMatcher;
                                        gpuMatcher.knnMatch(newDescriptorsGpu, lastDescriptorsGpu, matches, k);
                                }
                        }
                        else
                        {
                                UFATAL("");
                        }

                        // In case of binary descriptors
                        if(dists.type() == CV_32S)
                        {
                                cv::Mat temp;
                                dists.convertTo(temp, CV_32F);
                                dists = temp;
                        }
                }
                ULOGGER_DEBUG("Search dictionary time = %fs", timer.ticks());

                cv::Mat resultsNotIndexed;
                cv::Mat distsNotIndexed;
                std::map<int, int> mapIndexIdNotIndexed;
                if(_notIndexedWords.size())
                {
                        cv::Mat dataNotIndexed = cv::Mat::zeros(_notIndexedWords.size(), dim, type);
                        unsigned int index = 0;
                        VisualWord * vw;
                        for(std::set<int>::iterator iter = _notIndexedWords.begin(); iter != _notIndexedWords.end(); ++iter, ++index)
                        {
                                vw = _visualWords.at(*iter);
                                UASSERT(vw != 0 && vw->getDescriptor().cols == dim && vw->getDescriptor().type() == type);
                                vw->getDescriptor().copyTo(dataNotIndexed.row(index));
                                mapIndexIdNotIndexed.insert(mapIndexIdNotIndexed.end(), std::pair<int,int>(index, vw->id()));
                        }

                        // Find nearest neighbor
                        ULOGGER_DEBUG("Searching in words not indexed...");
                        cv::flann::Index linearSeach;
                        linearSeach.build(dataNotIndexed, cv::flann::LinearIndexParams(), type == CV_32F?cvflann::FLANN_DIST_L2:cvflann::FLANN_DIST_HAMMING);
                        linearSeach.knnSearch(query, resultsNotIndexed, distsNotIndexed, _notIndexedWords.size()>1?2:1);
                        // In case of binary descriptors
                        if(distsNotIndexed.type() == CV_32S)
                        {
                                cv::Mat temp;
                                distsNotIndexed.convertTo(temp, CV_32F);
                                distsNotIndexed = temp;
                        }
                }
                ULOGGER_DEBUG("Search not yet indexed words time = %fs", timer.ticks());

                for(unsigned int i=0; i<vws.size(); ++i)
                {
                        std::multimap<float, int> fullResults; // Contains results from the kd-tree search [and the naive search in new words]
                        if(!bruteForce && dists.cols)
                        {
                                for(int j=0; j<dists.cols; ++j)
                                {
                                        if(results.at<int>(i,j) > 0)
                                        {
                                                float d = dists.at<float>(i,j);
                                                fullResults.insert(std::pair<float, int>(d, uValue(_mapIndexId, results.at<int>(i,j))));
                                        }
                                }
                        }
                        else if(bruteForce && matches.size())
                        {
                                for(unsigned int j=0; j<matches.at(i).size(); ++j)
                                {
                                        if(matches.at(i).at(j).trainIdx > 0)
                                        {
                                                float d = matches.at(i).at(j).distance;
                                                fullResults.insert(std::pair<float, int>(d, uValue(_mapIndexId, matches.at(i).at(j).trainIdx)));
                                        }
                                }
                        }

                        // not indexed..
                        for(int j=0; j<distsNotIndexed.cols; ++j)
                        {
                                if(resultsNotIndexed.at<int>(i,j) > 0)
                                {
                                        float d = distsNotIndexed.at<float>(i,j);
                                        fullResults.insert(std::pair<float, int>(d, uValue(mapIndexIdNotIndexed, resultsNotIndexed.at<int>(i,j))));
                                }
                        }

                        if(_incrementalDictionary)
                        {
                                bool badDist = false;
                                if(fullResults.size() == 0)
                                {
                                        badDist = true;
                                }
                                if(!badDist)
                                {
                                        if(fullResults.size() >= 2)
                                        {
                                                // Apply NNDR
                                                if(fullResults.begin()->first > _nndrRatio * (++fullResults.begin())->first)
                                                {
                                                        badDist = true; // Rejected
                                                }
                                        }
                                        else
                                        {
                                                badDist = true; // Rejected
                                        }
                                }

                                if(!badDist)
                                {
                                        resultIds[i] = fullResults.begin()->second; // Accepted
                                }
                        }
                        else if(fullResults.size())
                        {
                                //Just take the nearest if the dictionary is not incremental
                                resultIds[i] = fullResults.begin()->second; // Accepted
                        }
                }
                ULOGGER_DEBUG("badDist check time = %fs", timer.ticks());
        }
        return resultIds;
}

void VWDictionary::addWord(VisualWord * vw)
{
        if(vw)
        {
                _visualWords.insert(std::pair<int, VisualWord *>(vw->id(), vw));
                _notIndexedWords.insert(vw->id());
                if(vw->getReferences().size())
                {
                        _totalActiveReferences += uSum(uValues(vw->getReferences()));
                }
                else
                {
                        _unusedWords.insert(std::pair<int, VisualWord *>(vw->id(), vw));
                }
        }
}

const VisualWord * VWDictionary::getWord(int id) const
{
        return uValue(_visualWords, id, (VisualWord *)0);
}

VisualWord * VWDictionary::getUnusedWord(int id) const
{
        return uValue(_unusedWords, id, (VisualWord *)0);
}

std::vector<VisualWord*> VWDictionary::getUnusedWords() const
{
        if(!_incrementalDictionary)
        {
                ULOGGER_WARN("This method does nothing on a fixed dictionary");
                return std::vector<VisualWord*>();
        }
        return uValues(_unusedWords);
}

std::vector<int> VWDictionary::getUnusedWordIds() const
{
        if(!_incrementalDictionary)
        {
                ULOGGER_WARN("This method does nothing on a fixed dictionary");
                return std::vector<int>();
        }
        return uKeys(_unusedWords);
}

void VWDictionary::removeWords(const std::vector<VisualWord*> & words)
{
        for(unsigned int i=0; i<words.size(); ++i)
        {
                _visualWords.erase(words[i]->id());
                _unusedWords.erase(words[i]->id());
                if(_notIndexedWords.erase(words[i]->id()) == 0)
                {
                        _removedIndexedWords.insert(words[i]->id());
                }
        }
}

void VWDictionary::deleteUnusedWords()
{
        std::vector<VisualWord*> unusedWords = uValues(_unusedWords);
        removeWords(unusedWords);
        for(unsigned int i=0; i<unusedWords.size(); ++i)
        {
                delete unusedWords[i];
        }
}

void VWDictionary::exportDictionary(const char * fileNameReferences, const char * fileNameDescriptors) const
{
    FILE* foutRef = 0;
    FILE* foutDesc = 0;
#ifdef _MSC_VER
    fopen_s(&foutRef, fileNameReferences, "w");
    fopen_s(&foutDesc, fileNameDescriptors, "w");
#else
    foutRef = fopen(fileNameReferences, "w");
    foutDesc = fopen(fileNameDescriptors, "w");
#endif

    if(foutRef)
    {
        fprintf(foutRef, "WordID SignaturesID...\n");
    }
        if(foutDesc)
        {
                if(_visualWords.begin() == _visualWords.end())
                {
                        fprintf(foutDesc, "WordID Descriptors...\n");
                }
                else
                {
                        fprintf(foutDesc, "WordID Descriptors...%d\n", (*_visualWords.begin()).second->getDescriptor().cols);
                }
        }

    for(std::map<int, VisualWord *>::const_iterator iter=_visualWords.begin(); iter!=_visualWords.end(); ++iter)
    {
        // References
        if(foutRef)
        {
                        fprintf(foutRef, "%d ", (*iter).first);
                        const std::map<int, int> ref = (*iter).second->getReferences();
                        for(std::map<int, int>::const_iterator jter=ref.begin(); jter!=ref.end(); ++jter)
                        {
                                for(int i=0; i<(*jter).second; ++i)
                                {
                                        fprintf(foutRef, "%d ", (*jter).first);
                                }
                        }
                        fprintf(foutRef, "\n");
        }

        //Descriptors
        if(foutDesc)
        {
                        fprintf(foutDesc, "%d ", (*iter).first);
                        const float * desc = (const float *)(*iter).second->getDescriptor().data;
                        int dim = (*iter).second->getDescriptor().cols;

                        for(int i=0; i<dim; i++)
                        {
                                fprintf(foutDesc, "%f ", desc[i]);
                        }
                        fprintf(foutDesc, "\n");
        }
    }

        if(foutRef)
                fclose(foutRef);
        if(foutDesc)
                fclose(foutDesc);
}

} // namespace rtabmap