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