544 lines
15 KiB
C++
544 lines
15 KiB
C++
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#ifndef OPENCV_FLANN_RESULTSET_H
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#define OPENCV_FLANN_RESULTSET_H
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#include <algorithm>
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#include <cstring>
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#include <iostream>
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#include <limits>
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#include <set>
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#include <vector>
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namespace cvflann
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{
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/* This record represents a branch point when finding neighbors in
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the tree. It contains a record of the minimum distance to the query
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point, as well as the node at which the search resumes.
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*/
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template <typename T, typename DistanceType>
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struct BranchStruct
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{
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T node; /* Tree node at which search resumes */
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DistanceType mindist; /* Minimum distance to query for all nodes below. */
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BranchStruct() {}
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BranchStruct(const T& aNode, DistanceType dist) : node(aNode), mindist(dist) {}
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bool operator<(const BranchStruct<T, DistanceType>& rhs) const
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{
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return mindist<rhs.mindist;
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}
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};
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template <typename DistanceType>
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class ResultSet
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{
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public:
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virtual ~ResultSet() {}
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virtual bool full() const = 0;
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virtual void addPoint(DistanceType dist, int index) = 0;
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virtual DistanceType worstDist() const = 0;
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};
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/**
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* KNNSimpleResultSet does not ensure that the element it holds are unique.
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* Is used in those cases where the nearest neighbour algorithm used does not
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* attempt to insert the same element multiple times.
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*/
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template <typename DistanceType>
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class KNNSimpleResultSet : public ResultSet<DistanceType>
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{
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int* indices;
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DistanceType* dists;
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int capacity;
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int count;
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DistanceType worst_distance_;
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public:
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KNNSimpleResultSet(int capacity_) : capacity(capacity_), count(0)
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{
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}
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void init(int* indices_, DistanceType* dists_)
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{
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indices = indices_;
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dists = dists_;
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count = 0;
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worst_distance_ = (std::numeric_limits<DistanceType>::max)();
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dists[capacity-1] = worst_distance_;
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}
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size_t size() const
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{
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return count;
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}
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bool full() const
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{
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return count == capacity;
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}
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void addPoint(DistanceType dist, int index)
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{
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if (dist >= worst_distance_) return;
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int i;
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for (i=count; i>0; --i) {
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#ifdef FLANN_FIRST_MATCH
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if ( (dists[i-1]>dist) || ((dist==dists[i-1])&&(indices[i-1]>index)) )
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#else
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if (dists[i-1]>dist)
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#endif
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{
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if (i<capacity) {
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dists[i] = dists[i-1];
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indices[i] = indices[i-1];
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}
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}
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else break;
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}
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if (count < capacity) ++count;
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dists[i] = dist;
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indices[i] = index;
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worst_distance_ = dists[capacity-1];
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}
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DistanceType worstDist() const
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{
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return worst_distance_;
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}
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};
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/**
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* K-Nearest neighbour result set. Ensures that the elements inserted are unique
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*/
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template <typename DistanceType>
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class KNNResultSet : public ResultSet<DistanceType>
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{
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int* indices;
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DistanceType* dists;
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int capacity;
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int count;
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DistanceType worst_distance_;
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public:
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KNNResultSet(int capacity_) : capacity(capacity_), count(0)
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{
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}
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void init(int* indices_, DistanceType* dists_)
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{
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indices = indices_;
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dists = dists_;
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count = 0;
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worst_distance_ = (std::numeric_limits<DistanceType>::max)();
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dists[capacity-1] = worst_distance_;
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}
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size_t size() const
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{
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return count;
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}
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bool full() const
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{
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return count == capacity;
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}
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void addPoint(DistanceType dist, int index)
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{
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if (dist >= worst_distance_) return;
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int i;
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for (i = count; i > 0; --i) {
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#ifdef FLANN_FIRST_MATCH
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if ( (dists[i-1]<=dist) && ((dist!=dists[i-1])||(indices[i-1]<=index)) )
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#else
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if (dists[i-1]<=dist)
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#endif
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{
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// Check for duplicate indices
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int j = i - 1;
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while ((j >= 0) && (dists[j] == dist)) {
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if (indices[j] == index) {
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return;
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}
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--j;
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}
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break;
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}
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}
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if (count < capacity) ++count;
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for (int j = count-1; j > i; --j) {
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dists[j] = dists[j-1];
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indices[j] = indices[j-1];
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}
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dists[i] = dist;
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indices[i] = index;
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worst_distance_ = dists[capacity-1];
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}
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DistanceType worstDist() const
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{
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return worst_distance_;
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}
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};
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/**
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* A result-set class used when performing a radius based search.
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*/
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template <typename DistanceType>
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class RadiusResultSet : public ResultSet<DistanceType>
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{
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DistanceType radius;
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int* indices;
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DistanceType* dists;
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size_t capacity;
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size_t count;
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public:
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RadiusResultSet(DistanceType radius_, int* indices_, DistanceType* dists_, int capacity_) :
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radius(radius_), indices(indices_), dists(dists_), capacity(capacity_)
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{
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init();
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}
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~RadiusResultSet()
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{
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}
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void init()
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{
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count = 0;
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}
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size_t size() const
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{
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return count;
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}
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bool full() const
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{
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return true;
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}
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void addPoint(DistanceType dist, int index)
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{
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if (dist<radius) {
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if ((capacity>0)&&(count < capacity)) {
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dists[count] = dist;
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indices[count] = index;
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}
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count++;
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}
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}
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DistanceType worstDist() const
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{
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return radius;
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}
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** Class that holds the k NN neighbors
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* Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
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*/
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template<typename DistanceType>
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class UniqueResultSet : public ResultSet<DistanceType>
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{
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public:
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struct DistIndex
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{
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DistIndex(DistanceType dist, unsigned int index) :
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dist_(dist), index_(index)
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{
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}
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bool operator<(const DistIndex dist_index) const
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{
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return (dist_ < dist_index.dist_) || ((dist_ == dist_index.dist_) && index_ < dist_index.index_);
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}
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DistanceType dist_;
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unsigned int index_;
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};
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/** Default cosntructor */
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UniqueResultSet() :
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worst_distance_(std::numeric_limits<DistanceType>::max())
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{
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}
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/** Check the status of the set
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* @return true if we have k NN
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*/
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inline bool full() const
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{
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return is_full_;
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}
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/** Remove all elements in the set
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*/
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virtual void clear() = 0;
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/** Copy the set to two C arrays
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* @param indices pointer to a C array of indices
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* @param dist pointer to a C array of distances
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* @param n_neighbors the number of neighbors to copy
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*/
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virtual void copy(int* indices, DistanceType* dist, int n_neighbors = -1) const
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{
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if (n_neighbors < 0) {
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for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
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dist_indices_.end(); dist_index != dist_index_end; ++dist_index, ++indices, ++dist) {
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*indices = dist_index->index_;
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*dist = dist_index->dist_;
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}
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}
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else {
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int i = 0;
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for (typename std::set<DistIndex>::const_iterator dist_index = dist_indices_.begin(), dist_index_end =
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dist_indices_.end(); (dist_index != dist_index_end) && (i < n_neighbors); ++dist_index, ++indices, ++dist, ++i) {
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*indices = dist_index->index_;
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*dist = dist_index->dist_;
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}
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}
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}
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/** Copy the set to two C arrays but sort it according to the distance first
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* @param indices pointer to a C array of indices
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* @param dist pointer to a C array of distances
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* @param n_neighbors the number of neighbors to copy
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*/
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virtual void sortAndCopy(int* indices, DistanceType* dist, int n_neighbors = -1) const
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{
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copy(indices, dist, n_neighbors);
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}
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/** The number of neighbors in the set
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* @return
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*/
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size_t size() const
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{
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return dist_indices_.size();
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}
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/** The distance of the furthest neighbor
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* If we don't have enough neighbors, it returns the max possible value
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* @return
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*/
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inline DistanceType worstDist() const
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{
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return worst_distance_;
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}
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protected:
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/** Flag to say if the set is full */
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bool is_full_;
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/** The worst distance found so far */
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DistanceType worst_distance_;
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/** The best candidates so far */
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std::set<DistIndex> dist_indices_;
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** Class that holds the k NN neighbors
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* Faster than KNNResultSet as it uses a binary heap and does not maintain two arrays
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*/
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template<typename DistanceType>
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class KNNUniqueResultSet : public UniqueResultSet<DistanceType>
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{
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public:
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/** Constructor
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* @param capacity the number of neighbors to store at max
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*/
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KNNUniqueResultSet(unsigned int capacity) : capacity_(capacity)
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{
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this->is_full_ = false;
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this->clear();
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}
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/** Add a possible candidate to the best neighbors
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* @param dist distance for that neighbor
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* @param index index of that neighbor
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*/
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inline void addPoint(DistanceType dist, int index)
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{
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// Don't do anything if we are worse than the worst
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if (dist >= worst_distance_) return;
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dist_indices_.insert(DistIndex(dist, index));
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if (is_full_) {
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if (dist_indices_.size() > capacity_) {
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dist_indices_.erase(*dist_indices_.rbegin());
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worst_distance_ = dist_indices_.rbegin()->dist_;
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}
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}
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else if (dist_indices_.size() == capacity_) {
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is_full_ = true;
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worst_distance_ = dist_indices_.rbegin()->dist_;
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}
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}
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/** Remove all elements in the set
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*/
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void clear()
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{
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dist_indices_.clear();
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worst_distance_ = std::numeric_limits<DistanceType>::max();
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is_full_ = false;
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}
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protected:
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typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
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using UniqueResultSet<DistanceType>::is_full_;
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using UniqueResultSet<DistanceType>::worst_distance_;
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using UniqueResultSet<DistanceType>::dist_indices_;
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/** The number of neighbors to keep */
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unsigned int capacity_;
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** Class that holds the radius nearest neighbors
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* It is more accurate than RadiusResult as it is not limited in the number of neighbors
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*/
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template<typename DistanceType>
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class RadiusUniqueResultSet : public UniqueResultSet<DistanceType>
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{
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public:
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/** Constructor
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* @param radius the maximum distance of a neighbor
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*/
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RadiusUniqueResultSet(DistanceType radius) :
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radius_(radius)
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{
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is_full_ = true;
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}
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/** Add a possible candidate to the best neighbors
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* @param dist distance for that neighbor
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* @param index index of that neighbor
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*/
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void addPoint(DistanceType dist, int index)
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{
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if (dist <= radius_) dist_indices_.insert(DistIndex(dist, index));
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}
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/** Remove all elements in the set
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*/
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inline void clear()
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{
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dist_indices_.clear();
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}
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/** Check the status of the set
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* @return alwys false
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*/
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inline bool full() const
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{
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return true;
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}
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/** The distance of the furthest neighbor
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* If we don't have enough neighbors, it returns the max possible value
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* @return
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*/
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inline DistanceType worstDist() const
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{
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return radius_;
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}
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private:
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typedef typename UniqueResultSet<DistanceType>::DistIndex DistIndex;
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using UniqueResultSet<DistanceType>::dist_indices_;
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using UniqueResultSet<DistanceType>::is_full_;
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/** The furthest distance a neighbor can be */
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DistanceType radius_;
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/** Class that holds the k NN neighbors within a radius distance
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*/
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template<typename DistanceType>
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class KNNRadiusUniqueResultSet : public KNNUniqueResultSet<DistanceType>
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{
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public:
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/** Constructor
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* @param capacity the number of neighbors to store at max
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* @param radius the maximum distance of a neighbor
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*/
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KNNRadiusUniqueResultSet(unsigned int capacity, DistanceType radius)
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{
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this->capacity_ = capacity;
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this->radius_ = radius;
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this->dist_indices_.reserve(capacity_);
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this->clear();
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}
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/** Remove all elements in the set
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*/
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void clear()
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{
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dist_indices_.clear();
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worst_distance_ = radius_;
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is_full_ = false;
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}
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private:
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using KNNUniqueResultSet<DistanceType>::dist_indices_;
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using KNNUniqueResultSet<DistanceType>::is_full_;
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using KNNUniqueResultSet<DistanceType>::worst_distance_;
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/** The maximum number of neighbors to consider */
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unsigned int capacity_;
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/** The maximum distance of a neighbor */
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DistanceType radius_;
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};
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}
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#endif //OPENCV_FLANN_RESULTSET_H
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