GAAS

#include "../src/Database.h"

namespace DBoW3{

// --------------------------------------------------------------------------


Database::Database
  (bool use_di, int di_levels)
  : m_voc(NULL), m_use_di(use_di), m_dilevels(di_levels), m_nentries(0)
{
}

// --------------------------------------------------------------------------

Database::Database
  (const Vocabulary &voc, bool use_di, int di_levels)
  : m_voc(NULL), m_use_di(use_di), m_dilevels(di_levels)
{
  setVocabulary(voc);
  clear();
}

// --------------------------------------------------------------------------


Database::Database
  (const Database &db)
  : m_voc(NULL)
{
  *this = db;
}

// --------------------------------------------------------------------------


Database::Database
  (const std::string &filename)
  : m_voc(NULL)
{
  load(filename);
}

// --------------------------------------------------------------------------


Database::Database
  (const char *filename)
  : m_voc(NULL)
{
  load(filename);
}

// --------------------------------------------------------------------------


Database::~Database(void)
{
  delete m_voc;
}

// --------------------------------------------------------------------------


Database& Database::operator=
  (const Database &db)
{
  if(this != &db)
  {
    m_dfile = db.m_dfile;
    m_dilevels = db.m_dilevels;
    m_ifile = db.m_ifile;
    m_nentries = db.m_nentries;
    m_use_di = db.m_use_di;
    if (db.m_voc!=0) setVocabulary(*db.m_voc);
  }
  return *this;
}

// --------------------------------------------------------------------------

EntryId Database::add(
  const  cv::Mat &features,
  BowVector *bowvec, FeatureVector *fvec)
{
    std::vector<cv::Mat> vf(features.rows);
    for(int r=0;r<features.rows;r++) vf[r]=features.rowRange(r,r+1);
    return add(vf,bowvec,fvec);
}

EntryId Database::add(
  const std::vector<cv::Mat> &features,
  BowVector *bowvec, FeatureVector *fvec)
{
  BowVector aux;
  BowVector& v = (bowvec ? *bowvec : aux);

  if(m_use_di && fvec != NULL)
  {
    m_voc->transform(features, v, *fvec, m_dilevels); // with features
    return add(v, *fvec);
  }
  else if(m_use_di)
  {
    FeatureVector fv;
    m_voc->transform(features, v, fv, m_dilevels); // with features
    return add(v, fv);
  }
  else if(fvec != NULL)
  {
    m_voc->transform(features, v, *fvec, m_dilevels); // with features
    return add(v);
  }
  else
  {
    m_voc->transform(features, v); // with features
    return add(v);
  }
}

// ---------------------------------------------------------------------------


EntryId Database::add(const BowVector &v,
  const FeatureVector &fv)
{
  EntryId entry_id = m_nentries++;

  BowVector::const_iterator vit;
  std::vector<unsigned int>::const_iterator iit;

  if(m_use_di)
  {
    // update direct file
    if(entry_id == m_dfile.size())
    {
      m_dfile.push_back(fv);
    }
    else
    {
      m_dfile[entry_id] = fv;
    }
  }

  // update inverted file
  for(vit = v.begin(); vit != v.end(); ++vit)
  {
    const WordId& word_id = vit->first;
    const WordValue& word_weight = vit->second;

    IFRow& ifrow = m_ifile[word_id];
    ifrow.push_back(IFPair(entry_id, word_weight));
  }

  return entry_id;
}

// --------------------------------------------------------------------------


  void Database::setVocabulary
  (const Vocabulary& voc)
{
  delete m_voc;
  m_voc = new Vocabulary(voc);
  clear();
}

// --------------------------------------------------------------------------


  void Database::setVocabulary
  (const Vocabulary& voc, bool use_di, int di_levels)
{
  m_use_di = use_di;
  m_dilevels = di_levels;
  delete m_voc;
  m_voc = new Vocabulary(voc);
  clear();
}

// --------------------------------------------------------------------------


 const Vocabulary*
Database::getVocabulary() const
{
  return m_voc;
}

// --------------------------------------------------------------------------


 void Database::clear()
{
  // resize vectors
  m_ifile.resize(0);
  m_ifile.resize(m_voc->size());
  m_dfile.resize(0);
  m_nentries = 0;
}

// --------------------------------------------------------------------------


void Database::allocate(int nd, int ni)
{
  // m_ifile already contains |words| items
  if(ni > 0)
  {
    for(auto rit = m_ifile.begin(); rit != m_ifile.end(); ++rit)
    {
      int n = (int)rit->size();
      if(ni > n)
      {
        rit->resize(ni);
        rit->resize(n);
      }
    }
  }

  if(m_use_di && (int)m_dfile.size() < nd)
  {
    m_dfile.resize(nd);
  }
}



// --------------------------------------------------------------------------

void Database::query(
  const  cv::Mat &features,
  QueryResults &ret, int max_results, int max_id) const
{

    std::vector<cv::Mat> vf(features.rows);
    for(int r=0;r<features.rows;r++) vf[r]=features.rowRange(r,r+1);
    query(vf, ret, max_results, max_id);
}



void Database::query(
  const std::vector<cv::Mat> &features,
  QueryResults &ret, int max_results, int max_id) const
{
  BowVector vec;
  m_voc->transform(features, vec);
  query(vec, ret, max_results, max_id);
}

// --------------------------------------------------------------------------


void Database::query(
  const BowVector &vec,
  QueryResults &ret, int max_results, int max_id) const
{
  ret.resize(0);

  switch(m_voc->getScoringType())
  {
    case L1_NORM:
      queryL1(vec, ret, max_results, max_id);
      break;

    case L2_NORM:
      queryL2(vec, ret, max_results, max_id);
      break;

    case CHI_SQUARE:
      queryChiSquare(vec, ret, max_results, max_id);
      break;

    case KL:
      queryKL(vec, ret, max_results, max_id);
      break;

    case BHATTACHARYYA:
      queryBhattacharyya(vec, ret, max_results, max_id);
      break;

    case DOT_PRODUCT:
      queryDotProduct(vec, ret, max_results, max_id);
      break;
  }
}

// --------------------------------------------------------------------------


void Database::queryL1(const BowVector &vec,
  QueryResults &ret, int max_results, int max_id) const
{
  BowVector::const_iterator vit;

  std::map<EntryId, double> pairs;
  std::map<EntryId, double>::iterator pit;

  for(vit = vec.begin(); vit != vec.end(); ++vit)
  {
    const WordId word_id = vit->first;
    const WordValue& qvalue = vit->second;

    const IFRow& row = m_ifile[word_id];

    // IFRows are sorted in ascending entry_id order

    for(auto rit = row.begin(); rit != row.end(); ++rit)
    {
      const EntryId entry_id = rit->entry_id;
      const WordValue& dvalue = rit->word_weight;

      if((int)entry_id < max_id || max_id == -1)
      {
        double value = fabs(qvalue - dvalue) - fabs(qvalue) - fabs(dvalue);

        pit = pairs.lower_bound(entry_id);
        if(pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first)))
        {
          pit->second += value;
        }
        else
        {
          pairs.insert(pit,
            std::map<EntryId, double>::value_type(entry_id, value));
        }
      }

    } // for each inverted row
  } // for each query word

  // move to vector
  ret.reserve(pairs.size());
  for(pit = pairs.begin(); pit != pairs.end(); ++pit)
  {
    ret.push_back(Result(pit->first, pit->second));
  }

  // resulting "scores" are now in [-2 best .. 0 worst]

  // sort vector in ascending order of score
  std::sort(ret.begin(), ret.end());
  // (ret is inverted now --the lower the better--)

  // cut vector
  if(max_results > 0 && (int)ret.size() > max_results)
    ret.resize(max_results);

  // complete and scale score to [0 worst .. 1 best]
  // ||v - w||_{L1} = 2 + Sum(|v_i - w_i| - |v_i| - |w_i|)
  //		for all i | v_i != 0 and w_i != 0
  // (Nister, 2006)
  // scaled_||v - w||_{L1} = 1 - 0.5 * ||v - w||_{L1}
  QueryResults::iterator qit;
  for(qit = ret.begin(); qit != ret.end(); qit++)
    qit->Score = -qit->Score/2.0;
}

// --------------------------------------------------------------------------


void Database::queryL2(const BowVector &vec,
  QueryResults &ret, int max_results, int max_id) const
{
  BowVector::const_iterator vit;

  std::map<EntryId, double> pairs;
  std::map<EntryId, double>::iterator pit;

  //map<EntryId, int> counters;
  //map<EntryId, int>::iterator cit;

  for(vit = vec.begin(); vit != vec.end(); ++vit)
  {
    const WordId word_id = vit->first;
    const WordValue& qvalue = vit->second;

    const IFRow& row = m_ifile[word_id];

    // IFRows are sorted in ascending entry_id order

    for(auto rit = row.begin(); rit != row.end(); ++rit)
    {
      const EntryId entry_id = rit->entry_id;
      const WordValue& dvalue = rit->word_weight;

      if((int)entry_id < max_id || max_id == -1)
      {
        double value = - qvalue * dvalue; // minus sign for sorting trick

        pit = pairs.lower_bound(entry_id);
        //cit = counters.lower_bound(entry_id);
        if(pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first)))
        {
          pit->second += value;
          //cit->second += 1;
        }
        else
        {
          pairs.insert(pit,
            std::map<EntryId, double>::value_type(entry_id, value));

          //counters.insert(cit,
          //  map<EntryId, int>::value_type(entry_id, 1));
        }
      }

    } // for each inverted row
  } // for each query word

  // move to vector
  ret.reserve(pairs.size());
  //cit = counters.begin();
  for(pit = pairs.begin(); pit != pairs.end(); ++pit)//, ++cit)
  {
    ret.push_back(Result(pit->first, pit->second));// / cit->second));
  }

  // resulting "scores" are now in [-1 best .. 0 worst]

  // sort vector in ascending order of score
  std::sort(ret.begin(), ret.end());
  // (ret is inverted now --the lower the better--)

  // cut vector
  if(max_results > 0 && (int)ret.size() > max_results)
    ret.resize(max_results);

  // complete and scale score to [0 worst .. 1 best]
  // ||v - w||_{L2} = sqrt( 2 - 2 * Sum(v_i * w_i)
    //		for all i | v_i != 0 and w_i != 0 )
    // (Nister, 2006)
    QueryResults::iterator qit;
  for(qit = ret.begin(); qit != ret.end(); qit++)
  {
    if(qit->Score <= -1.0) // rounding error
      qit->Score = 1.0;
    else
      qit->Score = 1.0 - sqrt(1.0 + qit->Score); // [0..1]
      // the + sign is ok, it is due to - sign in
      // value = - qvalue * dvalue
  }

}

// --------------------------------------------------------------------------


void Database::queryChiSquare(const BowVector &vec,
  QueryResults &ret, int max_results, int max_id) const
{
  BowVector::const_iterator vit;

  std::map<EntryId, std::pair<double, int> > pairs;
  std::map<EntryId, std::pair<double, int> >::iterator pit;

  std::map<EntryId, std::pair<double, double> > sums; // < sum vi, sum wi >
  std::map<EntryId, std::pair<double, double> >::iterator sit;

  // In the current implementation, we suppose vec is not normalized

  //map<EntryId, double> expected;
  //map<EntryId, double>::iterator eit;

  for(vit = vec.begin(); vit != vec.end(); ++vit)
  {
    const WordId word_id = vit->first;
    const WordValue& qvalue = vit->second;

    const IFRow& row = m_ifile[word_id];

    // IFRows are sorted in ascending entry_id order

    for(auto rit = row.begin(); rit != row.end(); ++rit)
    {
      const EntryId entry_id = rit->entry_id;
      const WordValue& dvalue = rit->word_weight;

      if((int)entry_id < max_id || max_id == -1)
      {
        // (v-w)^2/(v+w) - v - w = -4 vw/(v+w)
        // we move the 4 out
        double value = 0;
        if(qvalue + dvalue != 0.0) // words may have weight zero
          value = - qvalue * dvalue / (qvalue + dvalue);

        pit = pairs.lower_bound(entry_id);
        sit = sums.lower_bound(entry_id);
        //eit = expected.lower_bound(entry_id);
        if(pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first)))
        {
          pit->second.first += value;
          pit->second.second += 1;
          //eit->second += dvalue;
          sit->second.first += qvalue;
          sit->second.second += dvalue;
        }
        else
        {
          pairs.insert(pit,
            std::map<EntryId, std::pair<double, int> >::value_type(entry_id,
              std::make_pair(value, 1) ));
          //expected.insert(eit,
          //  map<EntryId, double>::value_type(entry_id, dvalue));

          sums.insert(sit,
            std::map<EntryId, std::pair<double, double> >::value_type(entry_id,
              std::make_pair(qvalue, dvalue) ));
        }
      }

    } // for each inverted row
  } // for each query word

  // move to vector
  ret.reserve(pairs.size());
  sit = sums.begin();
  for(pit = pairs.begin(); pit != pairs.end(); ++pit, ++sit)
  {
    if(pit->second.second >= MIN_COMMON_WORDS)
    {
      ret.push_back(Result(pit->first, pit->second.first));
      ret.back().nWords = pit->second.second;
      ret.back().sumCommonVi = sit->second.first;
      ret.back().sumCommonWi = sit->second.second;
      ret.back().expectedChiScore =
        2 * sit->second.second / (1 + sit->second.second);
    }

    //ret.push_back(Result(pit->first, pit->second));
  }

  // resulting "scores" are now in [-2 best .. 0 worst]
  // we have to add +2 to the scores to obtain the chi square score

  // sort vector in ascending order of score
  std::sort(ret.begin(), ret.end());
  // (ret is inverted now --the lower the better--)

  // cut vector
  if(max_results > 0 && (int)ret.size() > max_results)
    ret.resize(max_results);

  // complete and scale score to [0 worst .. 1 best]
  QueryResults::iterator qit;
  for(qit = ret.begin(); qit != ret.end(); qit++)
  {
    // this takes the 4 into account
    qit->Score = - 2. * qit->Score; // [0..1]

    qit->chiScore = qit->Score;
  }

}

// --------------------------------------------------------------------------


void Database::queryKL(const BowVector &vec,
  QueryResults &ret, int max_results, int max_id) const
{
  BowVector::const_iterator vit;

  std::map<EntryId, double> pairs;
  std::map<EntryId, double>::iterator pit;

  for(vit = vec.begin(); vit != vec.end(); ++vit)
  {
    const WordId word_id = vit->first;
    const WordValue& vi = vit->second;

    const IFRow& row = m_ifile[word_id];

    // IFRows are sorted in ascending entry_id order

    for(auto rit = row.begin(); rit != row.end(); ++rit)
    {
      const EntryId entry_id = rit->entry_id;
      const WordValue& wi = rit->word_weight;

      if((int)entry_id < max_id || max_id == -1)
      {
        double value = 0;
        if(vi != 0 && wi != 0) value = vi * log(vi/wi);

        pit = pairs.lower_bound(entry_id);
        if(pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first)))
        {
          pit->second += value;
        }
        else
        {
          pairs.insert(pit,
            std::map<EntryId, double>::value_type(entry_id, value));
        }
      }

    } // for each inverted row
  } // for each query word

  // resulting "scores" are now in [-X worst .. 0 best .. X worst]
  // but we cannot make sure which ones are better without calculating
  // the complete score

  // complete scores and move to vector
  ret.reserve(pairs.size());
  for(pit = pairs.begin(); pit != pairs.end(); ++pit)
  {
    EntryId eid = pit->first;
    double value = 0.0;

    for(vit = vec.begin(); vit != vec.end(); ++vit)
    {
      const WordValue &vi = vit->second;
      const IFRow& row = m_ifile[vit->first];

      if(vi != 0)
      {
        if(row.end() == find(row.begin(), row.end(), eid ))
        {
          value += vi * (log(vi) - GeneralScoring::LOG_EPS);
        }
      }
    }

    pit->second += value;

    // to vector
    ret.push_back(Result(pit->first, pit->second));
  }

  // real scores are now in [0 best .. X worst]

  // sort vector in ascending order
  // (scores are inverted now --the lower the better--)
  std::sort(ret.begin(), ret.end());

  // cut vector
  if(max_results > 0 && (int)ret.size() > max_results)
    ret.resize(max_results);

  // cannot scale scores

}

// --------------------------------------------------------------------------


void Database::queryBhattacharyya(
  const BowVector &vec, QueryResults &ret, int max_results, int max_id) const
{
  BowVector::const_iterator vit;

  //map<EntryId, double> pairs;
  //map<EntryId, double>::iterator pit;

  std::map<EntryId, std::pair<double, int> > pairs; // <eid, <score, counter> >
  std::map<EntryId, std::pair<double, int> >::iterator pit;

  for(vit = vec.begin(); vit != vec.end(); ++vit)
  {
    const WordId word_id = vit->first;
    const WordValue& qvalue = vit->second;

    const IFRow& row = m_ifile[word_id];

    // IFRows are sorted in ascending entry_id order

    for(auto rit = row.begin(); rit != row.end(); ++rit)
    {
      const EntryId entry_id = rit->entry_id;
      const WordValue& dvalue = rit->word_weight;

      if((int)entry_id < max_id || max_id == -1)
      {
        double value = sqrt(qvalue * dvalue);

        pit = pairs.lower_bound(entry_id);
        if(pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first)))
        {
          pit->second.first += value;
          pit->second.second += 1;
        }
        else
        {
          pairs.insert(pit,
            std::map<EntryId, std::pair<double, int> >::value_type(entry_id,
              std::make_pair(value, 1)));
        }
      }

    } // for each inverted row
  } // for each query word

  // move to vector
  ret.reserve(pairs.size());
  for(pit = pairs.begin(); pit != pairs.end(); ++pit)
  {
    if(pit->second.second >= MIN_COMMON_WORDS)
    {
      ret.push_back(Result(pit->first, pit->second.first));
      ret.back().nWords = pit->second.second;
      ret.back().bhatScore = pit->second.first;
    }
  }

  // scores are already in [0..1]

  // sort vector in descending order
  std::sort(ret.begin(), ret.end(), Result::gt);

  // cut vector
  if(max_results > 0 && (int)ret.size() > max_results)
    ret.resize(max_results);

}

// ---------------------------------------------------------------------------


void Database::queryDotProduct(
  const BowVector &vec, QueryResults &ret, int max_results, int max_id) const
{
  BowVector::const_iterator vit;

  std::map<EntryId, double> pairs;
  std::map<EntryId, double>::iterator pit;

  for(vit = vec.begin(); vit != vec.end(); ++vit)
  {
    const WordId word_id = vit->first;
    const WordValue& qvalue = vit->second;

    const IFRow& row = m_ifile[word_id];

    // IFRows are sorted in ascending entry_id order

    for(auto rit = row.begin(); rit != row.end(); ++rit)
    {
      const EntryId entry_id = rit->entry_id;
      const WordValue& dvalue = rit->word_weight;

      if((int)entry_id < max_id || max_id == -1)
      {
        double value;
        if(this->m_voc->getWeightingType() == BINARY)
          value = 1;
        else
          value = qvalue * dvalue;

        pit = pairs.lower_bound(entry_id);
        if(pit != pairs.end() && !(pairs.key_comp()(entry_id, pit->first)))
        {
          pit->second += value;
        }
        else
        {
          pairs.insert(pit,
            std::map<EntryId, double>::value_type(entry_id, value));
        }
      }

    } // for each inverted row
  } // for each query word

  // move to vector
  ret.reserve(pairs.size());
  for(pit = pairs.begin(); pit != pairs.end(); ++pit)
  {
    ret.push_back(Result(pit->first, pit->second));
  }

  // scores are the greater the better

  // sort vector in descending order
  std::sort(ret.begin(), ret.end(), Result::gt);

  // cut vector
  if(max_results > 0 && (int)ret.size() > max_results)
    ret.resize(max_results);

  // these scores cannot be scaled
}

// ---------------------------------------------------------------------------


const FeatureVector& Database::retrieveFeatures
  (EntryId id) const
{
  assert(id < size());
  return m_dfile[id];
}

// --------------------------------------------------------------------------


void Database::save(const std::string &filename) const
{
  cv::FileStorage fs(filename.c_str(), cv::FileStorage::WRITE);
  if(!fs.isOpened()) throw std::string("Could not open file ") + filename;

  save(fs);
}

// --------------------------------------------------------------------------


void Database::save(cv::FileStorage &fs,
  const std::string &name) const
{
  // Format YAML:
  // vocabulary { ... see TemplatedVocabulary::save }
  // database
  // {
  //   nEntries:
  //   usingDI:
  //   diLevels:
  //   invertedIndex
  //   [
  //     [
  //        {
  //          imageId:
  //          weight:
  //        }
  //     ]
  //   ]
  //   directIndex
  //   [
  //      [
  //        {
  //          nodeId:
  //          features: [ ]
  //        }
  //      ]
  //   ]

  // invertedIndex[i] is for the i-th word
  // directIndex[i] is for the i-th entry
  // directIndex may be empty if not using direct index
  //
  // imageId's and nodeId's must be stored in ascending order
  // (according to the construction of the indexes)

  m_voc->save(fs);

  fs << name << "{";

  fs << "nEntries" << m_nentries;
  fs << "usingDI" << (m_use_di ? 1 : 0);
  fs << "diLevels" << m_dilevels;

  fs << "invertedIndex" << "[";

   for(auto iit = m_ifile.begin(); iit != m_ifile.end(); ++iit)
  {
    fs << "["; // word of IF
    for(auto irit = iit->begin(); irit != iit->end(); ++irit)
    {
      fs << "{:"
        << "imageId" << (int)irit->entry_id
        << "weight" << irit->word_weight
        << "}";
    }
    fs << "]"; // word of IF
  }

  fs << "]"; // invertedIndex

  fs << "directIndex" << "[";

   for(auto dit = m_dfile.begin(); dit != m_dfile.end(); ++dit)
  {
    fs << "["; // entry of DF

    for(auto drit = dit->begin(); drit != dit->end(); ++drit)
    {
      NodeId nid = drit->first;
      const std::vector<unsigned int>& features = drit->second;

      // save info of last_nid
      fs << "{";
      fs << "nodeId" << (int)nid;
      // msvc++ 2010 with opencv 2.3.1 does not allow FileStorage::operator<<
      // with vectors of unsigned int
      fs << "features" << "["
        << *(const std::vector<int>*)(&features) << "]";
      fs << "}";
    }

    fs << "]"; // entry of DF
  }

  fs << "]"; // directIndex

  fs << "}"; // database
}

// --------------------------------------------------------------------------


void Database::load(const std::string &filename)
{
  cv::FileStorage fs(filename.c_str(), cv::FileStorage::READ);
  if(!fs.isOpened()) throw std::string("Could not open file ") + filename;

  load(fs);
}

// --------------------------------------------------------------------------


void Database::load(const cv::FileStorage &fs,
  const std::string &name)
{
  // load voc first
  // subclasses must instantiate m_voc before calling this ::load
  if(!m_voc) m_voc = new Vocabulary;

  m_voc->load(fs);

  // load database now
  clear(); // resizes inverted file

  cv::FileNode fdb = fs[name];

  m_nentries = (int)fdb["nEntries"];
  m_use_di = (int)fdb["usingDI"] != 0;
  m_dilevels = (int)fdb["diLevels"];

  cv::FileNode fn = fdb["invertedIndex"];
  for(WordId wid = 0; wid < fn.size(); ++wid)
  {
    cv::FileNode fw = fn[wid];

    for(unsigned int i = 0; i < fw.size(); ++i)
    {
      EntryId eid = (int)fw[i]["imageId"];
      WordValue v = fw[i]["weight"];

      m_ifile[wid].push_back(IFPair(eid, v));
    }
  }

  if(m_use_di)
  {
    fn = fdb["directIndex"];

    m_dfile.resize(fn.size());
    assert(m_nentries == (int)fn.size());

    FeatureVector::iterator dit;
    for(EntryId eid = 0; eid < fn.size(); ++eid)
    {
      cv::FileNode fe = fn[eid];

      m_dfile[eid].clear();
      for(unsigned int i = 0; i < fe.size(); ++i)
      {
        NodeId nid = (int)fe[i]["nodeId"];

        dit = m_dfile[eid].insert(m_dfile[eid].end(),
          make_pair(nid, std::vector<unsigned int>() ));

        // this failed to compile with some opencv versions (2.3.1)
        //fe[i]["features"] >> dit->second;

        // this was ok until OpenCV 2.4.1
        //std::vector<int> aux;
        //fe[i]["features"] >> aux; // OpenCV < 2.4.1
        //dit->second.resize(aux.size());
        //std::copy(aux.begin(), aux.end(), dit->second.begin());

        cv::FileNode ff = fe[i]["features"][0];
        dit->second.reserve(ff.size());

        cv::FileNodeIterator ffit;
        for(ffit = ff.begin(); ffit != ff.end(); ++ffit)
        {
          dit->second.push_back((int)*ffit);
        }
      }
    } // for each entry
  } // if use_id

}


std::ostream& operator<<(std::ostream &os,
  const Database &db)
{
  os << "Database: Entries = " << db.size() << ", "
    "Using direct index = " << (db.usingDirectIndex() ? "yes" : "no");

  if(db.usingDirectIndex())
    os << ", Direct index levels = " << db.getDirectIndexLevels();

  os << ". " << *db.getVocabulary();
  return os;
}

}