MatchersImpl.h

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/*

Copyright (c) 2010--2012,
François Pomerleau and Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the authors at <f dot pomerleau at gmail dot com> and
<stephane at magnenat dot net>

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#ifndef __POINTMATCHER_MATCHERS_H
#define __POINTMATCHER_MATCHERS_H

#include "PointMatcher.h"

#include "nabo/nabo.h"
#if NABO_VERSION_INT < 10007
        #error "You need libnabo version 1.0.7 or greater"
#endif

template<typename T>
struct MatchersImpl
{
        typedef PointMatcherSupport::Parametrizable Parametrizable;
        typedef PointMatcherSupport::Parametrizable P;
        typedef Parametrizable::Parameters Parameters;
        typedef Parametrizable::ParameterDoc ParameterDoc;
        typedef Parametrizable::ParametersDoc ParametersDoc;
        
        typedef typename Nabo::NearestNeighbourSearch<T> NNS;
        typedef typename NNS::SearchType NNSearchType;
        
        typedef typename PointMatcher<T>::DataPoints DataPoints;
        typedef typename PointMatcher<T>::Matcher Matcher;
        typedef typename PointMatcher<T>::Matches Matches;
        
        struct NullMatcher: public Matcher
        {
                inline static const std::string description()
                {
                        return "Does nothing, returns no match.";
                }

                NullMatcher() : Matcher("NullMatcher",  ParametersDoc(), Parameters()) {}
                virtual void init(const DataPoints& filteredReference);
                virtual Matches findClosests(const DataPoints& filteredReading);
        };

        struct KDTreeMatcher: public Matcher
        {
                inline static const std::string description()
                {
                        return "This matcher matches a point from the reading to its closest neighbors in the reference.";
                }
                inline static const ParametersDoc availableParameters()
                {
                        return {
                                {"knn", "number of nearest neighbors to consider it the reference", "1", "1", "2147483647", &P::Comp<unsigned>},
                                {"epsilon", "approximation to use for the nearest-neighbor search", "0", "0", "inf", &P::Comp<T>},
                                {"searchType", "Nabo search type. 0: brute force, check distance to every point in the data (very slow), 1: kd-tree with linear heap, good for small knn (~up to 30) and 2: kd-tree with tree heap, good for large knn (~from 30)", "1", "0", "2", &P::Comp<unsigned>},
                                {"maxDist", "maximum distance to consider for neighbors", "inf", "0", "inf", &P::Comp<T>}
                        };
                }
                
                const int knn;
                const T epsilon;
                const NNSearchType searchType;
                const T maxDist;

        protected:
                std::shared_ptr<NNS> featureNNS;

        public:
                KDTreeMatcher(const Parameters& params = Parameters());
                virtual ~KDTreeMatcher();
                virtual void init(const DataPoints& filteredReference);
                virtual Matches findClosests(const DataPoints& filteredReading);
        };

        struct KDTreeVarDistMatcher: public Matcher
        {
                inline static const std::string description()
                {
                        return "This matcher matches a point from the reading to its closest neighbors in the reference. A maximum search radius per point can be defined.";
                }
                inline static const ParametersDoc availableParameters()
                {
                        return {
                                {"knn", "number of nearest neighbors to consider it the reference", "1", "1", "2147483647", &P::Comp<unsigned>},
                                {"epsilon", "approximation to use for the nearest-neighbor search", "0", "0", "inf", &P::Comp<T>},
                                {"searchType", "Nabo search type. 0: brute force, check distance to every point in the data (very slow), 1: kd-tree with linear heap, good for small knn (~up to 30) and 2: kd-tree with tree heap, good for large knn (~from 30)", "1", "0", "2", &P::Comp<unsigned>},
                                {"maxDistField", "descriptor field name used to set a maximum distance to consider for neighbors per point", "maxSearchDist"}
                        };
                }
                
                const int knn;
                const T epsilon;
                const NNSearchType searchType;
                const std::string maxDistField;

        protected:
                std::shared_ptr<NNS> featureNNS;

        public:
                KDTreeVarDistMatcher(const Parameters& params = Parameters());
                virtual ~KDTreeVarDistMatcher();
                virtual void init(const DataPoints& filteredReference);
                virtual Matches findClosests(const DataPoints& filteredReading);
        };

}; // MatchersImpl

#endif // __POINTMATCHER_MATCHERS_H