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-/*M///////////////////////////////////////////////////////////////////////////////////////
-//
-//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
-//
-//  By downloading, copying, installing or using the software you agree to this license.
-//  If you do not agree to this license, do not download, install,
-//  copy or use the software.
-//
-// This file originates from the openFABMAP project:
-// [http://code.google.com/p/openfabmap/]
-//
-// For published work which uses all or part of OpenFABMAP, please cite:
-// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
-//
-// Original Algorithm by Mark Cummins and Paul Newman:
-// [http://ijr.sagepub.com/content/27/6/647.short]
-// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
-// [http://ijr.sagepub.com/content/30/9/1100.abstract]
-//
-//                           License Agreement
-//
-// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
-//                    Will Maddern [w.maddern@qut.edu.au], all rights reserved.
-//
-//
-// Redistribution and use in source and binary forms, with or without modification,
-// are permitted provided that the following conditions are met:
-//
-//   * Redistribution's of source code must retain the above copyright notice,
-//     this list of conditions and the following disclaimer.
-//
-//   * Redistribution's in binary form must reproduce the above copyright notice,
-//     this list of conditions and the following disclaimer in the documentation
-//     and/or other materials provided with the distribution.
-//
-//   * The name of the copyright holders may not be used to endorse or promote products
-//     derived from this software without specific prior written permission.
-//
-// This software is provided by the copyright holders and contributors "as is" and
-// any express or implied warranties, including, but not limited to, the implied
-// warranties of merchantability and fitness for a particular purpose are disclaimed.
-// In no event shall the Intel Corporation or contributors be liable for any direct,
-// indirect, incidental, special, exemplary, or consequential damages
-// (including, but not limited to, procurement of substitute goods or services;
-// loss of use, data, or profits; or business interruption) however caused
-// and on any theory of liability, whether in contract, strict liability,
-// or tort (including negligence or otherwise) arising in any way out of
-// the use of this software, even if advised of the possibility of such damage.
-//
-//M*/
-
-#ifndef __OPENCV_OPENFABMAP_H_
-#define __OPENCV_OPENFABMAP_H_
-
-#include "opencv2/core/core.hpp"
-#include "opencv2/features2d/features2d.hpp"
-
-#include <vector>
-#include <list>
-#include <map>
-#include <set>
-#include <valarray>
-
-namespace cv {
-
-namespace of2 {
-
-using std::list;
-using std::map;
-using std::multiset;
-
-/*
-    Return data format of a FABMAP compare call
-*/
-struct CV_EXPORTS IMatch {
-
-    IMatch() :
-        queryIdx(-1), imgIdx(-1), likelihood(-DBL_MAX), match(-DBL_MAX) {
-    }
-    IMatch(int _queryIdx, int _imgIdx, double _likelihood, double _match) :
-        queryIdx(_queryIdx), imgIdx(_imgIdx), likelihood(_likelihood), match(
-                _match) {
-    }
-
-    int queryIdx;    //query index
-    int imgIdx;      //test index
-
-    double likelihood;  //raw loglikelihood
-    double match;      //normalised probability
-
-    bool operator<(const IMatch& m) const {
-        return match < m.match;
-    }
-
-};
-
-/*
-    Base FabMap class. Each FabMap method inherits from this class.
-*/
-class CV_EXPORTS FabMap {
-public:
-
-    //FabMap options
-    enum {
-        MEAN_FIELD = 1,
-        SAMPLED = 2,
-        NAIVE_BAYES = 4,
-        CHOW_LIU = 8,
-        MOTION_MODEL = 16
-    };
-
-    FabMap(const Mat& clTree, double PzGe, double PzGNe, int flags,
-            int numSamples = 0);
-    virtual ~FabMap();
-
-    //methods to add training data for sampling method
-    virtual void addTraining(const Mat& queryImgDescriptor);
-    virtual void addTraining(const vector<Mat>& queryImgDescriptors);
-
-    //methods to add to the test data
-    virtual void add(const Mat& queryImgDescriptor);
-    virtual void add(const vector<Mat>& queryImgDescriptors);
-
-    //accessors
-    const vector<Mat>& getTrainingImgDescriptors() const;
-    const vector<Mat>& getTestImgDescriptors() const;
-
-    //Main FabMap image comparison
-    void compare(const Mat& queryImgDescriptor,
-            vector<IMatch>& matches, bool addQuery = false,
-            const Mat& mask = Mat());
-    void compare(const Mat& queryImgDescriptor,
-            const Mat& testImgDescriptors, vector<IMatch>& matches,
-            const Mat& mask = Mat());
-    void compare(const Mat& queryImgDescriptor,
-            const vector<Mat>& testImgDescriptors,
-            vector<IMatch>& matches, const Mat& mask = Mat());
-    void compare(const vector<Mat>& queryImgDescriptors, vector<
-            IMatch>& matches, bool addQuery = false, const Mat& mask =
-            Mat());
-    void compare(const vector<Mat>& queryImgDescriptors,
-            const vector<Mat>& testImgDescriptors,
-            vector<IMatch>& matches, const Mat& mask = Mat());
-
-protected:
-
-    void compareImgDescriptor(const Mat& queryImgDescriptor,
-            int queryIndex, const vector<Mat>& testImgDescriptors,
-            vector<IMatch>& matches);
-
-    void addImgDescriptor(const Mat& queryImgDescriptor);
-
-    //the getLikelihoods method is overwritten for each different FabMap
-    //method.
-    virtual void getLikelihoods(const Mat& queryImgDescriptor,
-            const vector<Mat>& testImgDescriptors,
-            vector<IMatch>& matches);
-    virtual double getNewPlaceLikelihood(const Mat& queryImgDescriptor);
-
-    //turn likelihoods into probabilities (also add in motion model if used)
-    void normaliseDistribution(vector<IMatch>& matches);
-
-    //Chow-Liu Tree
-    int pq(int q);
-    double Pzq(int q, bool zq);
-    double PzqGzpq(int q, bool zq, bool zpq);
-
-    //FAB-MAP Core
-    double PzqGeq(bool zq, bool eq);
-    double PeqGL(int q, bool Lzq, bool eq);
-    double PzqGL(int q, bool zq, bool zpq, bool Lzq);
-    double PzqGzpqL(int q, bool zq, bool zpq, bool Lzq);
-    double (FabMap::*PzGL)(int q, bool zq, bool zpq, bool Lzq);
-
-    //data
-    Mat clTree;
-    vector<Mat> trainingImgDescriptors;
-    vector<Mat> testImgDescriptors;
-    vector<IMatch> priorMatches;
-
-    //parameters
-    double PzGe;
-    double PzGNe;
-    double Pnew;
-
-    double mBias;
-    double sFactor;
-
-    int flags;
-    int numSamples;
-
-};
-
-/*
-    The original FAB-MAP algorithm, developed based on:
-    http://ijr.sagepub.com/content/27/6/647.short
-*/
-class CV_EXPORTS FabMap1: public FabMap {
-public:
-    FabMap1(const Mat& clTree, double PzGe, double PzGNe, int flags,
-            int numSamples = 0);
-    virtual ~FabMap1();
-protected:
-
-    //FabMap1 implementation of likelihood comparison
-    void getLikelihoods(const Mat& queryImgDescriptor, const vector<
-            Mat>& testImgDescriptors, vector<IMatch>& matches);
-};
-
-/*
-    A computationally faster version of the original FAB-MAP algorithm. A look-
-    up-table is used to precompute many of the reoccuring calculations
-*/
-class CV_EXPORTS FabMapLUT: public FabMap {
-public:
-    FabMapLUT(const Mat& clTree, double PzGe, double PzGNe,
-            int flags, int numSamples = 0, int precision = 6);
-    virtual ~FabMapLUT();
-protected:
-
-    //FabMap look-up-table implementation of the likelihood comparison
-    void getLikelihoods(const Mat& queryImgDescriptor, const vector<
-            Mat>& testImgDescriptors, vector<IMatch>& matches);
-
-    //precomputed data
-    int (*table)[8];
-
-    //data precision
-    int precision;
-};
-
-/*
-    The Accelerated FAB-MAP algorithm, developed based on:
-    http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942
-*/
-class CV_EXPORTS FabMapFBO: public FabMap {
-public:
-    FabMapFBO(const Mat& clTree, double PzGe, double PzGNe, int flags,
-            int numSamples = 0, double rejectionThreshold = 1e-8, double PsGd =
-                    1e-8, int bisectionStart = 512, int bisectionIts = 9);
-    virtual ~FabMapFBO();
-
-protected:
-
-    //FabMap Fast Bail-out implementation of the likelihood comparison
-    void getLikelihoods(const Mat& queryImgDescriptor, const vector<
-            Mat>& testImgDescriptors, vector<IMatch>& matches);
-
-    //stucture used to determine word comparison order
-    struct WordStats {
-        WordStats() :
-            q(0), info(0), V(0), M(0) {
-        }
-
-        WordStats(int _q, double _info) :
-            q(_q), info(_info), V(0), M(0) {
-        }
-
-        int q;
-        double info;
-        mutable double V;
-        mutable double M;
-
-        bool operator<(const WordStats& w) const {
-            return info < w.info;
-        }
-
-    };
-
-    //private fast bail-out necessary functions
-    void setWordStatistics(const Mat& queryImgDescriptor, multiset<WordStats>& wordData);
-    double limitbisection(double v, double m);
-    double bennettInequality(double v, double m, double delta);
-    static bool compInfo(const WordStats& first, const WordStats& second);
-
-    //parameters
-    double PsGd;
-    double rejectionThreshold;
-    int bisectionStart;
-    int bisectionIts;
-};
-
-/*
-    The FAB-MAP2.0 algorithm, developed based on:
-    http://ijr.sagepub.com/content/30/9/1100.abstract
-*/
-class CV_EXPORTS FabMap2: public FabMap {
-public:
-
-    FabMap2(const Mat& clTree, double PzGe, double PzGNe, int flags);
-    virtual ~FabMap2();
-
-    //FabMap2 builds the inverted index and requires an additional training/test
-    //add function
-    void addTraining(const Mat& queryImgDescriptors) {
-        FabMap::addTraining(queryImgDescriptors);
-    }
-    void addTraining(const vector<Mat>& queryImgDescriptors);
-
-    void add(const Mat& queryImgDescriptors) {
-        FabMap::add(queryImgDescriptors);
-    }
-    void add(const vector<Mat>& queryImgDescriptors);
-
-protected:
-
-    //FabMap2 implementation of the likelihood comparison
-    void getLikelihoods(const Mat& queryImgDescriptor, const vector<
-            Mat>& testImgDescriptors, vector<IMatch>& matches);
-    double getNewPlaceLikelihood(const Mat& queryImgDescriptor);
-
-    //the likelihood function using the inverted index
-    void getIndexLikelihoods(const Mat& queryImgDescriptor, vector<
-                             double>& defaults, map<int, vector<int> >& invertedMap,
-            vector<IMatch>& matches);
-    void addToIndex(const Mat& queryImgDescriptor,
-            vector<double>& defaults,
-            map<int, vector<int> >& invertedMap);
-
-    //data
-    vector<double> d1, d2, d3, d4;
-    vector<vector<int> > children;
-
-    // TODO: inverted map a vector?
-
-    vector<double> trainingDefaults;
-    map<int, vector<int> > trainingInvertedMap;
-
-    vector<double> testDefaults;
-    map<int, vector<int> > testInvertedMap;
-
-};
-/*
-    A Chow-Liu tree is required by FAB-MAP. The Chow-Liu tree provides an
-    estimate of the full distribution of visual words using a minimum spanning
-    tree. The tree is generated through training data.
-*/
-class CV_EXPORTS ChowLiuTree {
-public:
-    ChowLiuTree();
-    virtual ~ChowLiuTree();
-
-    //add data to the chow-liu tree before calling make
-    void add(const Mat& imgDescriptor);
-    void add(const vector<Mat>& imgDescriptors);
-
-    const vector<Mat>& getImgDescriptors() const;
-
-    Mat make(double infoThreshold = 0.0);
-
-private:
-    vector<Mat> imgDescriptors;
-    Mat mergedImgDescriptors;
-
-    typedef struct info {
-        float score;
-        short word1;
-        short word2;
-    } info;
-
-    //probabilities extracted from mergedImgDescriptors
-    double P(int a, bool za);
-    double JP(int a, bool za, int b, bool zb); //a & b
-    double CP(int a, bool za, int b, bool zb); // a | b
-
-    //calculating mutual information of all edges
-    void createBaseEdges(list<info>& edges, double infoThreshold);
-    double calcMutInfo(int word1, int word2);
-    static bool sortInfoScores(const info& first, const info& second);
-
-    //selecting minimum spanning egdges with maximum information
-    bool reduceEdgesToMinSpan(list<info>& edges);
-
-    //building the tree sctructure
-    Mat buildTree(int root_word, list<info> &edges);
-    void recAddToTree(Mat &cltree, int q, int pq,
-        list<info> &remaining_edges);
-    vector<int> extractChildren(list<info> &remaining_edges, int q);
-
-};
-
-/*
-    A custom vocabulary training method based on:
-    http://www.springerlink.com/content/d1h6j8x552532003/
-*/
-class CV_EXPORTS BOWMSCTrainer: public BOWTrainer {
-public:
-    BOWMSCTrainer(double clusterSize = 0.4);
-    virtual ~BOWMSCTrainer();
-
-    // Returns trained vocabulary (i.e. cluster centers).
-    virtual Mat cluster() const;
-    virtual Mat cluster(const Mat& descriptors) const;
-
-protected:
-
-    double clusterSize;
-
-};
-
-}
-
-}
-
-#endif /* OPENFABMAP_H_ */