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ORB-SLAM3-UESTC/Workspace/Thirdparty/DBoW2/DBoW2/TemplatedVocabulary.h

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2023-11-28 19:20:25 +08:00
/**
* This is a modified version of TemplatedVocabulary.h from DBoW2 (see below).
* Added functions: Save and Load from text files without using cv::FileStorage.
* Date: August 2015
* Raúl Mur-Artal
*/
/**
* File: TemplatedVocabulary.h
* Date: February 2011
* Author: Dorian Galvez-Lopez
* Description: templated vocabulary
* License: see the LICENSE.txt file
*
*/
#ifndef __D_T_TEMPLATED_VOCABULARY__
#define __D_T_TEMPLATED_VOCABULARY__
#include <cassert>
#include <vector>
#include <numeric>
#include <fstream>
#include <string>
#include <algorithm>
#include <opencv2/core/core.hpp>
#include <limits>
#include "FeatureVector.h"
#include "BowVector.h"
#include "ScoringObject.h"
#include "../DUtils/Random.h"
using namespace std;
namespace DBoW2 {
/// @param TDescriptor class of descriptor
/// @param F class of descriptor functions
template<class TDescriptor, class F>
/// Generic Vocabulary
class TemplatedVocabulary
{
public:
/**
* Initiates an empty vocabulary
* @param k branching factor
* @param L depth levels
* @param weighting weighting type
* @param scoring scoring type
*/
TemplatedVocabulary(int k = 10, int L = 5,
WeightingType weighting = TF_IDF, ScoringType scoring = L1_NORM);
/**
* Creates the vocabulary by loading a file
* @param filename
*/
TemplatedVocabulary(const std::string &filename);
/**
* Creates the vocabulary by loading a file
* @param filename
*/
TemplatedVocabulary(const char *filename);
/**
* Copy constructor
* @param voc
*/
TemplatedVocabulary(const TemplatedVocabulary<TDescriptor, F> &voc);
/**
* Destructor
*/
virtual ~TemplatedVocabulary();
/**
* Assigns the given vocabulary to this by copying its data and removing
* all the data contained by this vocabulary before
* @param voc
* @return reference to this vocabulary
*/
TemplatedVocabulary<TDescriptor, F>& operator=(
const TemplatedVocabulary<TDescriptor, F> &voc);
/**
* Creates a vocabulary from the training features with the already
* defined parameters
* @param training_features
*/
virtual void create
(const std::vector<std::vector<TDescriptor> > &training_features);
/**
* Creates a vocabulary from the training features, setting the branching
* factor and the depth levels of the tree
* @param training_features
* @param k branching factor
* @param L depth levels
*/
virtual void create
(const std::vector<std::vector<TDescriptor> > &training_features,
int k, int L);
/**
* Creates a vocabulary from the training features, setting the branching
* factor nad the depth levels of the tree, and the weighting and scoring
* schemes
*/
virtual void create
(const std::vector<std::vector<TDescriptor> > &training_features,
int k, int L, WeightingType weighting, ScoringType scoring);
/**
* Returns the number of words in the vocabulary
* @return number of words
*/
virtual inline unsigned int size() const;
/**
* Returns whether the vocabulary is empty (i.e. it has not been trained)
* @return true iff the vocabulary is empty
*/
virtual inline bool empty() const;
/**
* Transforms a set of descriptores into a bow vector
* @param features
* @param v (out) bow vector of weighted words
*/
virtual void transform(const std::vector<TDescriptor>& features, BowVector &v)
const;
/**
* Transform a set of descriptors into a bow vector and a feature vector
* @param features
* @param v (out) bow vector
* @param fv (out) feature vector of nodes and feature indexes
* @param levelsup levels to go up the vocabulary tree to get the node index
*/
virtual void transform(const std::vector<TDescriptor>& features,
BowVector &v, FeatureVector &fv, int levelsup) const;
/**
* Transforms a single feature into a word (without weight)
* @param feature
* @return word id
*/
virtual WordId transform(const TDescriptor& feature) const;
/**
* Returns the score of two vectors
* @param a vector
* @param b vector
* @return score between vectors
* @note the vectors must be already sorted and normalized if necessary
*/
inline double score(const BowVector &a, const BowVector &b) const;
/**
* Returns the id of the node that is "levelsup" levels from the word given
* @param wid word id
* @param levelsup 0..L
* @return node id. if levelsup is 0, returns the node id associated to the
* word id
*/
virtual NodeId getParentNode(WordId wid, int levelsup) const;
/**
* Returns the ids of all the words that are under the given node id,
* by traversing any of the branches that goes down from the node
* @param nid starting node id
* @param words ids of words
*/
void getWordsFromNode(NodeId nid, std::vector<WordId> &words) const;
/**
* Returns the branching factor of the tree (k)
* @return k
*/
inline int getBranchingFactor() const { return m_k; }
/**
* Returns the depth levels of the tree (L)
* @return L
*/
inline int getDepthLevels() const { return m_L; }
/**
* Returns the real depth levels of the tree on average
* @return average of depth levels of leaves
*/
float getEffectiveLevels() const;
/**
* Returns the descriptor of a word
* @param wid word id
* @return descriptor
*/
virtual inline TDescriptor getWord(WordId wid) const;
/**
* Returns the weight of a word
* @param wid word id
* @return weight
*/
virtual inline WordValue getWordWeight(WordId wid) const;
/**
* Returns the weighting method
* @return weighting method
*/
inline WeightingType getWeightingType() const { return m_weighting; }
/**
* Returns the scoring method
* @return scoring method
*/
inline ScoringType getScoringType() const { return m_scoring; }
/**
* Changes the weighting method
* @param type new weighting type
*/
inline void setWeightingType(WeightingType type);
/**
* Changes the scoring method
* @param type new scoring type
*/
void setScoringType(ScoringType type);
/**
* Loads the vocabulary from a text file
* @param filename
*/
bool loadFromTextFile(const std::string &filename);
/**
* Saves the vocabulary into a text file
* @param filename
*/
void saveToTextFile(const std::string &filename) const;
/**
* Saves the vocabulary into a file
* @param filename
*/
void save(const std::string &filename) const;
/**
* Loads the vocabulary from a file
* @param filename
*/
void load(const std::string &filename);
/**
* Saves the vocabulary to a file storage structure
* @param fn node in file storage
*/
virtual void save(cv::FileStorage &fs,
const std::string &name = "vocabulary") const;
/**
* Loads the vocabulary from a file storage node
* @param fn first node
* @param subname name of the child node of fn where the tree is stored.
* If not given, the fn node is used instead
*/
virtual void load(const cv::FileStorage &fs,
const std::string &name = "vocabulary");
/**
* Stops those words whose weight is below minWeight.
* Words are stopped by setting their weight to 0. There are not returned
* later when transforming image features into vectors.
* Note that when using IDF or TF_IDF, the weight is the idf part, which
* is equivalent to -log(f), where f is the frequency of the word
* (f = Ni/N, Ni: number of training images where the word is present,
* N: number of training images).
* Note that the old weight is forgotten, and subsequent calls to this
* function with a lower minWeight have no effect.
* @return number of words stopped now
*/
virtual int stopWords(double minWeight);
protected:
/// Pointer to descriptor
typedef const TDescriptor *pDescriptor;
/// Tree node
struct Node
{
/// Node id
NodeId id;
/// Weight if the node is a word
WordValue weight;
/// Children
vector<NodeId> children;
/// Parent node (undefined in case of root)
NodeId parent;
/// Node descriptor
TDescriptor descriptor;
/// Word id if the node is a word
WordId word_id;
/**
* Empty constructor
*/
Node(): id(0), weight(0), parent(0), word_id(0){}
/**
* Constructor
* @param _id node id
*/
Node(NodeId _id): id(_id), weight(0), parent(0), word_id(0){}
/**
* Returns whether the node is a leaf node
* @return true iff the node is a leaf
*/
inline bool isLeaf() const { return children.empty(); }
};
protected:
/**
* Creates an instance of the scoring object accoring to m_scoring
*/
void createScoringObject();
/**
* Returns a set of pointers to descriptores
* @param training_features all the features
* @param features (out) pointers to the training features
*/
void getFeatures(
const vector<vector<TDescriptor> > &training_features,
vector<pDescriptor> &features) const;
/**
* Returns the word id associated to a feature
* @param feature
* @param id (out) word id
* @param weight (out) word weight
* @param nid (out) if given, id of the node "levelsup" levels up
* @param levelsup
*/
virtual void transform(const TDescriptor &feature,
WordId &id, WordValue &weight, NodeId* nid = NULL, int levelsup = 0) const;
/**
* Returns the word id associated to a feature
* @param feature
* @param id (out) word id
*/
virtual void transform(const TDescriptor &feature, WordId &id) const;
/**
* Creates a level in the tree, under the parent, by running kmeans with
* a descriptor set, and recursively creates the subsequent levels too
* @param parent_id id of parent node
* @param descriptors descriptors to run the kmeans on
* @param current_level current level in the tree
*/
void HKmeansStep(NodeId parent_id, const vector<pDescriptor> &descriptors,
int current_level);
/**
* Creates k clusters from the given descriptors with some seeding algorithm.
* @note In this class, kmeans++ is used, but this function should be
* overriden by inherited classes.
*/
virtual void initiateClusters(const vector<pDescriptor> &descriptors,
vector<TDescriptor> &clusters) const;
/**
* Creates k clusters from the given descriptor sets by running the
* initial step of kmeans++
* @param descriptors
* @param clusters resulting clusters
*/
void initiateClustersKMpp(const vector<pDescriptor> &descriptors,
vector<TDescriptor> &clusters) const;
/**
* Create the words of the vocabulary once the tree has been built
*/
void createWords();
/**
* Sets the weights of the nodes of tree according to the given features.
* Before calling this function, the nodes and the words must be already
* created (by calling HKmeansStep and createWords)
* @param features
*/
void setNodeWeights(const vector<vector<TDescriptor> > &features);
protected:
/// Branching factor
int m_k;
/// Depth levels
int m_L;
/// Weighting method
WeightingType m_weighting;
/// Scoring method
ScoringType m_scoring;
/// Object for computing scores
GeneralScoring* m_scoring_object;
/// Tree nodes
std::vector<Node> m_nodes;
/// Words of the vocabulary (tree leaves)
/// this condition holds: m_words[wid]->word_id == wid
std::vector<Node*> m_words;
};
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TemplatedVocabulary<TDescriptor,F>::TemplatedVocabulary
(int k, int L, WeightingType weighting, ScoringType scoring)
: m_k(k), m_L(L), m_weighting(weighting), m_scoring(scoring),
m_scoring_object(NULL)
{
createScoringObject();
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TemplatedVocabulary<TDescriptor,F>::TemplatedVocabulary
(const std::string &filename): m_scoring_object(NULL)
{
load(filename);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TemplatedVocabulary<TDescriptor,F>::TemplatedVocabulary
(const char *filename): m_scoring_object(NULL)
{
load(filename);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::createScoringObject()
{
delete m_scoring_object;
m_scoring_object = NULL;
switch(m_scoring)
{
case L1_NORM:
m_scoring_object = new L1Scoring;
break;
case L2_NORM:
m_scoring_object = new L2Scoring;
break;
case CHI_SQUARE:
m_scoring_object = new ChiSquareScoring;
break;
case KL:
m_scoring_object = new KLScoring;
break;
case BHATTACHARYYA:
m_scoring_object = new BhattacharyyaScoring;
break;
case DOT_PRODUCT:
m_scoring_object = new DotProductScoring;
break;
}
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::setScoringType(ScoringType type)
{
m_scoring = type;
createScoringObject();
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::setWeightingType(WeightingType type)
{
this->m_weighting = type;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TemplatedVocabulary<TDescriptor,F>::TemplatedVocabulary(
const TemplatedVocabulary<TDescriptor, F> &voc)
: m_scoring_object(NULL)
{
*this = voc;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TemplatedVocabulary<TDescriptor,F>::~TemplatedVocabulary()
{
delete m_scoring_object;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TemplatedVocabulary<TDescriptor, F>&
TemplatedVocabulary<TDescriptor,F>::operator=
(const TemplatedVocabulary<TDescriptor, F> &voc)
{
this->m_k = voc.m_k;
this->m_L = voc.m_L;
this->m_scoring = voc.m_scoring;
this->m_weighting = voc.m_weighting;
this->createScoringObject();
this->m_nodes.clear();
this->m_words.clear();
this->m_nodes = voc.m_nodes;
this->createWords();
return *this;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::create(
const std::vector<std::vector<TDescriptor> > &training_features)
{
m_nodes.clear();
m_words.clear();
// expected_nodes = Sum_{i=0..L} ( k^i )
int expected_nodes =
(int)((pow((double)m_k, (double)m_L + 1) - 1)/(m_k - 1));
m_nodes.reserve(expected_nodes); // avoid allocations when creating the tree
vector<pDescriptor> features;
getFeatures(training_features, features);
// create root
m_nodes.push_back(Node(0)); // root
// create the tree
HKmeansStep(0, features, 1);
// create the words
createWords();
// and set the weight of each node of the tree
setNodeWeights(training_features);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::create(
const std::vector<std::vector<TDescriptor> > &training_features,
int k, int L)
{
m_k = k;
m_L = L;
create(training_features);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::create(
const std::vector<std::vector<TDescriptor> > &training_features,
int k, int L, WeightingType weighting, ScoringType scoring)
{
m_k = k;
m_L = L;
m_weighting = weighting;
m_scoring = scoring;
createScoringObject();
create(training_features);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::getFeatures(
const vector<vector<TDescriptor> > &training_features,
vector<pDescriptor> &features) const
{
features.resize(0);
typename vector<vector<TDescriptor> >::const_iterator vvit;
typename vector<TDescriptor>::const_iterator vit;
for(vvit = training_features.begin(); vvit != training_features.end(); ++vvit)
{
features.reserve(features.size() + vvit->size());
for(vit = vvit->begin(); vit != vvit->end(); ++vit)
{
features.push_back(&(*vit));
}
}
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::HKmeansStep(NodeId parent_id,
const vector<pDescriptor> &descriptors, int current_level)
{
if(descriptors.empty()) return;
// features associated to each cluster
vector<TDescriptor> clusters;
vector<vector<unsigned int> > groups; // groups[i] = [j1, j2, ...]
// j1, j2, ... indices of descriptors associated to cluster i
clusters.reserve(m_k);
groups.reserve(m_k);
//const int msizes[] = { m_k, descriptors.size() };
//cv::SparseMat assoc(2, msizes, CV_8U);
//cv::SparseMat last_assoc(2, msizes, CV_8U);
//// assoc.row(cluster_idx).col(descriptor_idx) = 1 iif associated
if((int)descriptors.size() <= m_k)
{
// trivial case: one cluster per feature
groups.resize(descriptors.size());
for(unsigned int i = 0; i < descriptors.size(); i++)
{
groups[i].push_back(i);
clusters.push_back(*descriptors[i]);
}
}
else
{
// select clusters and groups with kmeans
bool first_time = true;
bool goon = true;
// to check if clusters move after iterations
vector<int> last_association, current_association;
while(goon)
{
// 1. Calculate clusters
if(first_time)
{
// random sample
initiateClusters(descriptors, clusters);
}
else
{
// calculate cluster centres
for(unsigned int c = 0; c < clusters.size(); ++c)
{
vector<pDescriptor> cluster_descriptors;
cluster_descriptors.reserve(groups[c].size());
/*
for(unsigned int d = 0; d < descriptors.size(); ++d)
{
if( assoc.find<unsigned char>(c, d) )
{
cluster_descriptors.push_back(descriptors[d]);
}
}
*/
vector<unsigned int>::const_iterator vit;
for(vit = groups[c].begin(); vit != groups[c].end(); ++vit)
{
cluster_descriptors.push_back(descriptors[*vit]);
}
F::meanValue(cluster_descriptors, clusters[c]);
}
} // if(!first_time)
// 2. Associate features with clusters
// calculate distances to cluster centers
groups.clear();
groups.resize(clusters.size(), vector<unsigned int>());
current_association.resize(descriptors.size());
//assoc.clear();
typename vector<pDescriptor>::const_iterator fit;
//unsigned int d = 0;
for(fit = descriptors.begin(); fit != descriptors.end(); ++fit)//, ++d)
{
double best_dist = F::distance(*(*fit), clusters[0]);
unsigned int icluster = 0;
for(unsigned int c = 1; c < clusters.size(); ++c)
{
double dist = F::distance(*(*fit), clusters[c]);
if(dist < best_dist)
{
best_dist = dist;
icluster = c;
}
}
//assoc.ref<unsigned char>(icluster, d) = 1;
groups[icluster].push_back(fit - descriptors.begin());
current_association[ fit - descriptors.begin() ] = icluster;
}
// kmeans++ ensures all the clusters has any feature associated with them
// 3. check convergence
if(first_time)
{
first_time = false;
}
else
{
//goon = !eqUChar(last_assoc, assoc);
goon = false;
for(unsigned int i = 0; i < current_association.size(); i++)
{
if(current_association[i] != last_association[i]){
goon = true;
break;
}
}
}
if(goon)
{
// copy last feature-cluster association
last_association = current_association;
//last_assoc = assoc.clone();
}
} // while(goon)
} // if must run kmeans
// create nodes
for(unsigned int i = 0; i < clusters.size(); ++i)
{
NodeId id = m_nodes.size();
m_nodes.push_back(Node(id));
m_nodes.back().descriptor = clusters[i];
m_nodes.back().parent = parent_id;
m_nodes[parent_id].children.push_back(id);
}
// go on with the next level
if(current_level < m_L)
{
// iterate again with the resulting clusters
const vector<NodeId> &children_ids = m_nodes[parent_id].children;
for(unsigned int i = 0; i < clusters.size(); ++i)
{
NodeId id = children_ids[i];
vector<pDescriptor> child_features;
child_features.reserve(groups[i].size());
vector<unsigned int>::const_iterator vit;
for(vit = groups[i].begin(); vit != groups[i].end(); ++vit)
{
child_features.push_back(descriptors[*vit]);
}
if(child_features.size() > 1)
{
HKmeansStep(id, child_features, current_level + 1);
}
}
}
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor, F>::initiateClusters
(const vector<pDescriptor> &descriptors, vector<TDescriptor> &clusters) const
{
initiateClustersKMpp(descriptors, clusters);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::initiateClustersKMpp(
const vector<pDescriptor> &pfeatures, vector<TDescriptor> &clusters) const
{
// Implements kmeans++ seeding algorithm
// Algorithm:
// 1. Choose one center uniformly at random from among the data points.
// 2. For each data point x, compute D(x), the distance between x and the nearest
// center that has already been chosen.
// 3. Add one new data point as a center. Each point x is chosen with probability
// proportional to D(x)^2.
// 4. Repeat Steps 2 and 3 until k centers have been chosen.
// 5. Now that the initial centers have been chosen, proceed using standard k-means
// clustering.
DUtils::Random::SeedRandOnce();
clusters.resize(0);
clusters.reserve(m_k);
vector<double> min_dists(pfeatures.size(), std::numeric_limits<double>::max());
// 1.
int ifeature = DUtils::Random::RandomInt(0, pfeatures.size()-1);
// create first cluster
clusters.push_back(*pfeatures[ifeature]);
// compute the initial distances
typename vector<pDescriptor>::const_iterator fit;
vector<double>::iterator dit;
dit = min_dists.begin();
for(fit = pfeatures.begin(); fit != pfeatures.end(); ++fit, ++dit)
{
*dit = F::distance(*(*fit), clusters.back());
}
while((int)clusters.size() < m_k)
{
// 2.
dit = min_dists.begin();
for(fit = pfeatures.begin(); fit != pfeatures.end(); ++fit, ++dit)
{
if(*dit > 0)
{
double dist = F::distance(*(*fit), clusters.back());
if(dist < *dit) *dit = dist;
}
}
// 3.
double dist_sum = std::accumulate(min_dists.begin(), min_dists.end(), 0.0);
if(dist_sum > 0)
{
double cut_d;
do
{
cut_d = DUtils::Random::RandomValue<double>(0, dist_sum);
} while(cut_d == 0.0);
double d_up_now = 0;
for(dit = min_dists.begin(); dit != min_dists.end(); ++dit)
{
d_up_now += *dit;
if(d_up_now >= cut_d) break;
}
if(dit == min_dists.end())
ifeature = pfeatures.size()-1;
else
ifeature = dit - min_dists.begin();
clusters.push_back(*pfeatures[ifeature]);
} // if dist_sum > 0
else
break;
} // while(used_clusters < m_k)
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::createWords()
{
m_words.resize(0);
if(!m_nodes.empty())
{
m_words.reserve( (int)pow((double)m_k, (double)m_L) );
typename vector<Node>::iterator nit;
nit = m_nodes.begin(); // ignore root
for(++nit; nit != m_nodes.end(); ++nit)
{
if(nit->isLeaf())
{
nit->word_id = m_words.size();
m_words.push_back( &(*nit) );
}
}
}
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::setNodeWeights
(const vector<vector<TDescriptor> > &training_features)
{
const unsigned int NWords = m_words.size();
const unsigned int NDocs = training_features.size();
if(m_weighting == TF || m_weighting == BINARY)
{
// idf part must be 1 always
for(unsigned int i = 0; i < NWords; i++)
m_words[i]->weight = 1;
}
else if(m_weighting == IDF || m_weighting == TF_IDF)
{
// IDF and TF-IDF: we calculte the idf path now
// Note: this actually calculates the idf part of the tf-idf score.
// The complete tf-idf score is calculated in ::transform
vector<unsigned int> Ni(NWords, 0);
vector<bool> counted(NWords, false);
typename vector<vector<TDescriptor> >::const_iterator mit;
typename vector<TDescriptor>::const_iterator fit;
for(mit = training_features.begin(); mit != training_features.end(); ++mit)
{
fill(counted.begin(), counted.end(), false);
for(fit = mit->begin(); fit < mit->end(); ++fit)
{
WordId word_id;
transform(*fit, word_id);
if(!counted[word_id])
{
Ni[word_id]++;
counted[word_id] = true;
}
}
}
// set ln(N/Ni)
for(unsigned int i = 0; i < NWords; i++)
{
if(Ni[i] > 0)
{
m_words[i]->weight = log((double)NDocs / (double)Ni[i]);
}// else // This cannot occur if using kmeans++
}
}
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
inline unsigned int TemplatedVocabulary<TDescriptor,F>::size() const
{
return m_words.size();
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
inline bool TemplatedVocabulary<TDescriptor,F>::empty() const
{
return m_words.empty();
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
float TemplatedVocabulary<TDescriptor,F>::getEffectiveLevels() const
{
long sum = 0;
typename std::vector<Node*>::const_iterator wit;
for(wit = m_words.begin(); wit != m_words.end(); ++wit)
{
const Node *p = *wit;
for(; p->id != 0; sum++) p = &m_nodes[p->parent];
}
return (float)((double)sum / (double)m_words.size());
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
TDescriptor TemplatedVocabulary<TDescriptor,F>::getWord(WordId wid) const
{
return m_words[wid]->descriptor;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
WordValue TemplatedVocabulary<TDescriptor, F>::getWordWeight(WordId wid) const
{
return m_words[wid]->weight;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
WordId TemplatedVocabulary<TDescriptor, F>::transform
(const TDescriptor& feature) const
{
if(empty())
{
return 0;
}
WordId wid;
transform(feature, wid);
return wid;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::transform(
const std::vector<TDescriptor>& features, BowVector &v) const
{
v.clear();
if(empty())
{
return;
}
// normalize
LNorm norm;
bool must = m_scoring_object->mustNormalize(norm);
typename vector<TDescriptor>::const_iterator fit;
if(m_weighting == TF || m_weighting == TF_IDF)
{
for(fit = features.begin(); fit < features.end(); ++fit)
{
WordId id;
WordValue w;
// w is the idf value if TF_IDF, 1 if TF
transform(*fit, id, w);
// not stopped
if(w > 0) v.addWeight(id, w);
}
if(!v.empty() && !must)
{
// unnecessary when normalizing
const double nd = v.size();
for(BowVector::iterator vit = v.begin(); vit != v.end(); vit++)
vit->second /= nd;
}
}
else // IDF || BINARY
{
for(fit = features.begin(); fit < features.end(); ++fit)
{
WordId id;
WordValue w;
// w is idf if IDF, or 1 if BINARY
transform(*fit, id, w);
// not stopped
if(w > 0) v.addIfNotExist(id, w);
} // if add_features
} // if m_weighting == ...
if(must) v.normalize(norm);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::transform(
const std::vector<TDescriptor>& features,
BowVector &v, FeatureVector &fv, int levelsup) const
{
v.clear();
fv.clear();
if(empty()) // safe for subclasses
{
return;
}
// normalize
LNorm norm;
bool must = m_scoring_object->mustNormalize(norm);
typename vector<TDescriptor>::const_iterator fit;
if(m_weighting == TF || m_weighting == TF_IDF)
{
unsigned int i_feature = 0;
for(fit = features.begin(); fit < features.end(); ++fit, ++i_feature)
{
WordId id;
NodeId nid;
WordValue w;
// w is the idf value if TF_IDF, 1 if TF
transform(*fit, id, w, &nid, levelsup);
if(w > 0) // not stopped
{
v.addWeight(id, w);
fv.addFeature(nid, i_feature);
}
}
if(!v.empty() && !must)
{
// unnecessary when normalizing
const double nd = v.size();
for(BowVector::iterator vit = v.begin(); vit != v.end(); vit++)
vit->second /= nd;
}
}
else // IDF || BINARY
{
unsigned int i_feature = 0;
for(fit = features.begin(); fit < features.end(); ++fit, ++i_feature)
{
WordId id;
NodeId nid;
WordValue w;
// w is idf if IDF, or 1 if BINARY
transform(*fit, id, w, &nid, levelsup);
if(w > 0) // not stopped
{
v.addIfNotExist(id, w);
fv.addFeature(nid, i_feature);
}
}
} // if m_weighting == ...
if(must) v.normalize(norm);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
inline double TemplatedVocabulary<TDescriptor,F>::score
(const BowVector &v1, const BowVector &v2) const
{
return m_scoring_object->score(v1, v2);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::transform
(const TDescriptor &feature, WordId &id) const
{
WordValue weight;
transform(feature, id, weight);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::transform(const TDescriptor &feature,
WordId &word_id, WordValue &weight, NodeId *nid, int levelsup) const
{
// propagate the feature down the tree
vector<NodeId> nodes;
typename vector<NodeId>::const_iterator nit;
// level at which the node must be stored in nid, if given
const int nid_level = m_L - levelsup;
if(nid_level <= 0 && nid != NULL) *nid = 0; // root
NodeId final_id = 0; // root
int current_level = 0;
do
{
++current_level;
nodes = m_nodes[final_id].children;
final_id = nodes[0];
double best_d = F::distance(feature, m_nodes[final_id].descriptor);
for(nit = nodes.begin() + 1; nit != nodes.end(); ++nit)
{
NodeId id = *nit;
double d = F::distance(feature, m_nodes[id].descriptor);
if(d < best_d)
{
best_d = d;
final_id = id;
}
}
if(nid != NULL && current_level == nid_level)
*nid = final_id;
} while( !m_nodes[final_id].isLeaf() );
// turn node id into word id
word_id = m_nodes[final_id].word_id;
weight = m_nodes[final_id].weight;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
NodeId TemplatedVocabulary<TDescriptor,F>::getParentNode
(WordId wid, int levelsup) const
{
NodeId ret = m_words[wid]->id; // node id
while(levelsup > 0 && ret != 0) // ret == 0 --> root
{
--levelsup;
ret = m_nodes[ret].parent;
}
return ret;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::getWordsFromNode
(NodeId nid, std::vector<WordId> &words) const
{
words.clear();
if(m_nodes[nid].isLeaf())
{
words.push_back(m_nodes[nid].word_id);
}
else
{
words.reserve(m_k); // ^1, ^2, ...
vector<NodeId> parents;
parents.push_back(nid);
while(!parents.empty())
{
NodeId parentid = parents.back();
parents.pop_back();
const vector<NodeId> &child_ids = m_nodes[parentid].children;
vector<NodeId>::const_iterator cit;
for(cit = child_ids.begin(); cit != child_ids.end(); ++cit)
{
const Node &child_node = m_nodes[*cit];
if(child_node.isLeaf())
words.push_back(child_node.word_id);
else
parents.push_back(*cit);
} // for each child
} // while !parents.empty
}
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
int TemplatedVocabulary<TDescriptor,F>::stopWords(double minWeight)
{
int c = 0;
typename vector<Node*>::iterator wit;
for(wit = m_words.begin(); wit != m_words.end(); ++wit)
{
if((*wit)->weight < minWeight)
{
++c;
(*wit)->weight = 0;
}
}
return c;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
bool TemplatedVocabulary<TDescriptor,F>::loadFromTextFile(const std::string &filename)
{
ifstream f;
f.open(filename.c_str());
if(f.eof())
return false;
m_words.clear();
m_nodes.clear();
string s;
getline(f,s);
stringstream ss;
ss << s;
ss >> m_k;
ss >> m_L;
int n1, n2;
ss >> n1;
ss >> n2;
if(m_k<0 || m_k>20 || m_L<1 || m_L>10 || n1<0 || n1>5 || n2<0 || n2>3)
{
std::cerr << "Vocabulary loading failure: This is not a correct text file!" << endl;
return false;
}
m_scoring = (ScoringType)n1;
m_weighting = (WeightingType)n2;
createScoringObject();
// nodes
int expected_nodes =
(int)((pow((double)m_k, (double)m_L + 1) - 1)/(m_k - 1));
m_nodes.reserve(expected_nodes);
m_words.reserve(pow((double)m_k, (double)m_L + 1));
m_nodes.resize(1);
m_nodes[0].id = 0;
while(!f.eof())
{
string snode;
getline(f,snode);
stringstream ssnode;
ssnode << snode;
int nid = m_nodes.size();
m_nodes.resize(m_nodes.size()+1);
m_nodes[nid].id = nid;
int pid ;
ssnode >> pid;
m_nodes[nid].parent = pid;
m_nodes[pid].children.push_back(nid);
int nIsLeaf;
ssnode >> nIsLeaf;
stringstream ssd;
for(int iD=0;iD<F::L;iD++)
{
string sElement;
ssnode >> sElement;
ssd << sElement << " ";
}
F::fromString(m_nodes[nid].descriptor, ssd.str());
ssnode >> m_nodes[nid].weight;
if(nIsLeaf>0)
{
int wid = m_words.size();
m_words.resize(wid+1);
m_nodes[nid].word_id = wid;
m_words[wid] = &m_nodes[nid];
}
else
{
m_nodes[nid].children.reserve(m_k);
}
}
return true;
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::saveToTextFile(const std::string &filename) const
{
fstream f;
f.open(filename.c_str(),ios_base::out);
f << m_k << " " << m_L << " " << " " << m_scoring << " " << m_weighting << endl;
for(size_t i=1; i<m_nodes.size();i++)
{
const Node& node = m_nodes[i];
f << node.parent << " ";
if(node.isLeaf())
f << 1 << " ";
else
f << 0 << " ";
f << F::toString(node.descriptor) << " " << (double)node.weight << endl;
}
f.close();
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::save(const std::string &filename) const
{
cv::FileStorage fs(filename.c_str(), cv::FileStorage::WRITE);
if(!fs.isOpened()) throw string("Could not open file ") + filename;
save(fs);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::load(const std::string &filename)
{
cv::FileStorage fs(filename.c_str(), cv::FileStorage::READ);
if(!fs.isOpened()) throw string("Could not open file ") + filename;
this->load(fs);
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::save(cv::FileStorage &f,
const std::string &name) const
{
// Format YAML:
// vocabulary
// {
// k:
// L:
// scoringType:
// weightingType:
// nodes
// [
// {
// nodeId:
// parentId:
// weight:
// descriptor:
// }
// ]
// words
// [
// {
// wordId:
// nodeId:
// }
// ]
// }
//
// The root node (index 0) is not included in the node vector
//
f << name << "{";
f << "k" << m_k;
f << "L" << m_L;
f << "scoringType" << m_scoring;
f << "weightingType" << m_weighting;
// tree
f << "nodes" << "[";
vector<NodeId> parents, children;
vector<NodeId>::const_iterator pit;
parents.push_back(0); // root
while(!parents.empty())
{
NodeId pid = parents.back();
parents.pop_back();
const Node& parent = m_nodes[pid];
children = parent.children;
for(pit = children.begin(); pit != children.end(); pit++)
{
const Node& child = m_nodes[*pit];
// save node data
f << "{:";
f << "nodeId" << (int)child.id;
f << "parentId" << (int)pid;
f << "weight" << (double)child.weight;
f << "descriptor" << F::toString(child.descriptor);
f << "}";
// add to parent list
if(!child.isLeaf())
{
parents.push_back(*pit);
}
}
}
f << "]"; // nodes
// words
f << "words" << "[";
typename vector<Node*>::const_iterator wit;
for(wit = m_words.begin(); wit != m_words.end(); wit++)
{
WordId id = wit - m_words.begin();
f << "{:";
f << "wordId" << (int)id;
f << "nodeId" << (int)(*wit)->id;
f << "}";
}
f << "]"; // words
f << "}";
}
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::load(const cv::FileStorage &fs,
const std::string &name)
{
m_words.clear();
m_nodes.clear();
cv::FileNode fvoc = fs[name];
m_k = (int)fvoc["k"];
m_L = (int)fvoc["L"];
m_scoring = (ScoringType)((int)fvoc["scoringType"]);
m_weighting = (WeightingType)((int)fvoc["weightingType"]);
createScoringObject();
// nodes
cv::FileNode fn = fvoc["nodes"];
m_nodes.resize(fn.size() + 1); // +1 to include root
m_nodes[0].id = 0;
for(unsigned int i = 0; i < fn.size(); ++i)
{
NodeId nid = (int)fn[i]["nodeId"];
NodeId pid = (int)fn[i]["parentId"];
WordValue weight = (WordValue)fn[i]["weight"];
string d = (string)fn[i]["descriptor"];
m_nodes[nid].id = nid;
m_nodes[nid].parent = pid;
m_nodes[nid].weight = weight;
m_nodes[pid].children.push_back(nid);
F::fromString(m_nodes[nid].descriptor, d);
}
// words
fn = fvoc["words"];
m_words.resize(fn.size());
for(unsigned int i = 0; i < fn.size(); ++i)
{
NodeId wid = (int)fn[i]["wordId"];
NodeId nid = (int)fn[i]["nodeId"];
m_nodes[nid].word_id = wid;
m_words[wid] = &m_nodes[nid];
}
}
// --------------------------------------------------------------------------
/**
* Writes printable information of the vocabulary
* @param os stream to write to
* @param voc
*/
template<class TDescriptor, class F>
std::ostream& operator<<(std::ostream &os,
const TemplatedVocabulary<TDescriptor,F> &voc)
{
os << "Vocabulary: k = " << voc.getBranchingFactor()
<< ", L = " << voc.getDepthLevels()
<< ", Weighting = ";
switch(voc.getWeightingType())
{
case TF_IDF: os << "tf-idf"; break;
case TF: os << "tf"; break;
case IDF: os << "idf"; break;
case BINARY: os << "binary"; break;
}
os << ", Scoring = ";
switch(voc.getScoringType())
{
case L1_NORM: os << "L1-norm"; break;
case L2_NORM: os << "L2-norm"; break;
case CHI_SQUARE: os << "Chi square distance"; break;
case KL: os << "KL-divergence"; break;
case BHATTACHARYYA: os << "Bhattacharyya coefficient"; break;
case DOT_PRODUCT: os << "Dot product"; break;
}
os << ", Number of words = " << voc.size();
return os;
}
} // namespace DBoW2
#endif