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nanoflann.hpp
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nanoflann.hpp
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/***********************************************************************
* 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.
* Copyright 2011-2016 Jose Luis Blanco (joseluisblancoc@gmail.com).
* 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.
*************************************************************************/
/** \mainpage nanoflann C++ API documentation
* nanoflann is a C++ header-only library for building KD-Trees, mostly
* optimized for 2D or 3D point clouds.
*
* nanoflann does not require compiling or installing, just an
* #include <nanoflann.hpp> in your code.
*
* See:
* - <a href="modules.html" >C++ API organized by modules</a>
* - <a href="https://github.com/jlblancoc/nanoflann" >Online README</a>
* - <a href="http://jlblancoc.github.io/nanoflann/" >Doxygen documentation</a>
*/
#ifndef NANOFLANN_HPP_
#define NANOFLANN_HPP_
#include <vector>
#include <cassert>
#include <algorithm>
#include <stdexcept>
#include <cstdio> // for fwrite()
#include <cmath> // for abs()
#include <cstdlib> // for abs()
#include <limits>
// Avoid conflicting declaration of min/max macros in windows headers
#if !defined(NOMINMAX) && (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
# define NOMINMAX
# ifdef max
# undef max
# undef min
# endif
#endif
namespace nanoflann
{
/** @addtogroup nanoflann_grp nanoflann C++ library for ANN
* @{ */
/** Library version: 0xMmP (M=Major,m=minor,P=patch) */
#define NANOFLANN_VERSION 0x123
/** @addtogroup result_sets_grp Result set classes
* @{ */
template <typename DistanceType, typename IndexType = size_t, typename CountType = size_t>
class KNNResultSet
{
IndexType * indices;
DistanceType* dists;
CountType capacity;
CountType count;
public:
inline KNNResultSet(CountType capacity_) : indices(0), dists(0), capacity(capacity_), count(0)
{
}
inline void init(IndexType* indices_, DistanceType* dists_)
{
indices = indices_;
dists = dists_;
count = 0;
if (capacity)
dists[capacity-1] = (std::numeric_limits<DistanceType>::max)();
}
inline CountType size() const
{
return count;
}
inline bool full() const
{
return count == capacity;
}
inline void addPoint(DistanceType dist, IndexType index)
{
CountType i;
for (i=count; i>0; --i) {
#ifdef NANOFLANN_FIRST_MATCH // If defined and two points have the same distance, the one with the lowest-index will be returned first.
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 (i<capacity) {
dists[i] = dist;
indices[i] = index;
}
if (count<capacity) count++;
}
inline DistanceType worstDist() const
{
return dists[capacity-1];
}
};
/**
* A result-set class used when performing a radius based search.
*/
template <typename DistanceType, typename IndexType = size_t>
class RadiusResultSet
{
public:
const DistanceType radius;
std::vector<std::pair<IndexType,DistanceType> >& m_indices_dists;
inline RadiusResultSet(DistanceType radius_, std::vector<std::pair<IndexType,DistanceType> >& indices_dists) : radius(radius_), m_indices_dists(indices_dists)
{
init();
}
inline ~RadiusResultSet() { }
inline void init() { clear(); }
inline void clear() { m_indices_dists.clear(); }
inline size_t size() const { return m_indices_dists.size(); }
inline bool full() const { return true; }
inline void addPoint(DistanceType dist, IndexType index)
{
if (dist<radius)
m_indices_dists.push_back(std::make_pair(index,dist));
}
inline DistanceType worstDist() const { return radius; }
/** Clears the result set and adjusts the search radius. */
inline void set_radius_and_clear( const DistanceType r )
{
radius = r;
clear();
}
/**
* Find the worst result (furtherest neighbor) without copying or sorting
* Pre-conditions: size() > 0
*/
std::pair<IndexType,DistanceType> worst_item() const
{
if (m_indices_dists.empty()) throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on an empty list of results.");
typedef typename std::vector<std::pair<IndexType,DistanceType> >::const_iterator DistIt;
DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end());
return *it;
}
};
/** operator "<" for std::sort() */
struct IndexDist_Sorter
{
/** PairType will be typically: std::pair<IndexType,DistanceType> */
template <typename PairType>
inline bool operator()(const PairType &p1, const PairType &p2) const {
return p1.second < p2.second;
}
};
/** @} */
/** @addtogroup loadsave_grp Load/save auxiliary functions
* @{ */
template<typename T>
void save_value(FILE* stream, const T& value, size_t count = 1)
{
fwrite(&value, sizeof(value),count, stream);
}
template<typename T>
void save_value(FILE* stream, const std::vector<T>& value)
{
size_t size = value.size();
fwrite(&size, sizeof(size_t), 1, stream);
fwrite(&value[0], sizeof(T), size, stream);
}
template<typename T>
void load_value(FILE* stream, T& value, size_t count = 1)
{
size_t read_cnt = fread(&value, sizeof(value), count, stream);
if (read_cnt != count) {
throw std::runtime_error("Cannot read from file");
}
}
template<typename T>
void load_value(FILE* stream, std::vector<T>& value)
{
size_t size;
size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
if (read_cnt!=1) {
throw std::runtime_error("Cannot read from file");
}
value.resize(size);
read_cnt = fread(&value[0], sizeof(T), size, stream);
if (read_cnt!=size) {
throw std::runtime_error("Cannot read from file");
}
}
/** @} */
/** @addtogroup metric_grp Metric (distance) classes
* @{ */
/** Manhattan distance functor (generic version, optimized for high-dimensionality data sets).
* Corresponding distance traits: nanoflann::metric_L1
* \tparam T Type of the elements (e.g. double, float, uint8_t)
* \tparam _DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
*/
template<class T, class DataSource, typename _DistanceType = T>
struct L1_Adaptor
{
typedef T ElementType;
typedef _DistanceType DistanceType;
const DataSource &data_source;
L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
{
DistanceType result = DistanceType();
const T* last = a + size;
const T* lastgroup = last - 3;
size_t d = 0;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
const DistanceType diff0 = std::abs(a[0] - data_source.kdtree_get_pt(b_idx,d++));
const DistanceType diff1 = std::abs(a[1] - data_source.kdtree_get_pt(b_idx,d++));
const DistanceType diff2 = std::abs(a[2] - data_source.kdtree_get_pt(b_idx,d++));
const DistanceType diff3 = std::abs(a[3] - data_source.kdtree_get_pt(b_idx,d++));
result += diff0 + diff1 + diff2 + diff3;
a += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 components. Not needed for standard vector lengths. */
while (a < last) {
result += std::abs( *a++ - data_source.kdtree_get_pt(b_idx,d++) );
}
return result;
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, int ) const
{
return std::abs(a-b);
}
};
/** Squared Euclidean distance functor (generic version, optimized for high-dimensionality data sets).
* Corresponding distance traits: nanoflann::metric_L2
* \tparam T Type of the elements (e.g. double, float, uint8_t)
* \tparam _DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
*/
template<class T, class DataSource, typename _DistanceType = T>
struct L2_Adaptor
{
typedef T ElementType;
typedef _DistanceType DistanceType;
const DataSource &data_source;
L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
inline DistanceType operator()(const T* a, const size_t b_idx, size_t size, DistanceType worst_dist = -1) const
{
DistanceType result = DistanceType();
const T* last = a + size;
const T* lastgroup = last - 3;
size_t d = 0;
/* Process 4 items with each loop for efficiency. */
while (a < lastgroup) {
const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx,d++);
const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx,d++);
const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx,d++);
const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx,d++);
result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
a += 4;
if ((worst_dist>0)&&(result>worst_dist)) {
return result;
}
}
/* Process last 0-3 components. Not needed for standard vector lengths. */
while (a < last) {
const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx,d++);
result += diff0 * diff0;
}
return result;
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, int ) const
{
return (a-b)*(a-b);
}
};
/** Squared Euclidean (L2) distance functor (suitable for low-dimensionality datasets, like 2D or 3D point clouds)
* Corresponding distance traits: nanoflann::metric_L2_Simple
* \tparam T Type of the elements (e.g. double, float, uint8_t)
* \tparam _DistanceType Type of distance variables (must be signed) (e.g. float, double, int64_t)
*/
template<class T, class DataSource, typename _DistanceType = T>
struct L2_Simple_Adaptor
{
typedef T ElementType;
typedef _DistanceType DistanceType;
const DataSource &data_source;
L2_Simple_Adaptor(const DataSource &_data_source) : data_source(_data_source) { }
inline DistanceType operator()(const T* a, const size_t b_idx, size_t size) const {
return data_source.kdtree_distance(a,b_idx,size);
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, int ) const
{
return (a-b)*(a-b);
}
};
/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
struct metric_L1 {
template<class T, class DataSource>
struct traits {
typedef L1_Adaptor<T,DataSource> distance_t;
};
};
/** Metaprogramming helper traits class for the L2 (Euclidean) metric */
struct metric_L2 {
template<class T, class DataSource>
struct traits {
typedef L2_Adaptor<T,DataSource> distance_t;
};
};
/** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
struct metric_L2_Simple {
template<class T, class DataSource>
struct traits {
typedef L2_Simple_Adaptor<T,DataSource> distance_t;
};
};
/** @} */
/** @addtogroup param_grp Parameter structs
* @{ */
/** Parameters (see README.md) */
struct KDTreeSingleIndexAdaptorParams
{
KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10) :
leaf_max_size(_leaf_max_size)
{}
size_t leaf_max_size;
};
/** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */
struct SearchParams
{
/** Note: The first argument (checks_IGNORED_) is ignored, but kept for compatibility with the FLANN interface */
SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true ) :
checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
int checks; //!< Ignored parameter (Kept for compatibility with the FLANN interface).
float eps; //!< search for eps-approximate neighbours (default: 0)
bool sorted; //!< only for radius search, require neighbours sorted by distance (default: true)
};
/** @} */
/** @addtogroup memalloc_grp Memory allocation
* @{ */
/**
* Allocates (using C's malloc) a generic type T.
*
* Params:
* count = number of instances to allocate.
* Returns: pointer (of type T*) to memory buffer
*/
template <typename T>
inline T* allocate(size_t count = 1)
{
T* mem = static_cast<T*>( ::malloc(sizeof(T)*count));
return mem;
}
/**
* Pooled storage allocator
*
* The following routines allow for the efficient allocation of storage in
* small chunks from a specified pool. Rather than allowing each structure
* to be freed individually, an entire pool of storage is freed at once.
* This method has two advantages over just using malloc() and free(). First,
* it is far more efficient for allocating small objects, as there is
* no overhead for remembering all the information needed to free each
* object or consolidating fragmented memory. Second, the decision about
* how long to keep an object is made at the time of allocation, and there
* is no need to track down all the objects to free them.
*
*/
const size_t WORDSIZE=16;
const size_t BLOCKSIZE=8192;
class PooledAllocator
{
/* We maintain memory alignment to word boundaries by requiring that all
allocations be in multiples of the machine wordsize. */
/* Size of machine word in bytes. Must be power of 2. */
/* Minimum number of bytes requested at a time from the system. Must be multiple of WORDSIZE. */
size_t remaining; /* Number of bytes left in current block of storage. */
void* base; /* Pointer to base of current block of storage. */
void* loc; /* Current location in block to next allocate memory. */
void internal_init()
{
remaining = 0;
base = NULL;
usedMemory = 0;
wastedMemory = 0;
}
public:
size_t usedMemory;
size_t wastedMemory;
/**
Default constructor. Initializes a new pool.
*/
PooledAllocator() {
internal_init();
}
/**
* Destructor. Frees all the memory allocated in this pool.
*/
~PooledAllocator() {
free_all();
}
/** Frees all allocated memory chunks */
void free_all()
{
while (base != NULL) {
void *prev = *(static_cast<void**>( base)); /* Get pointer to prev block. */
::free(base);
base = prev;
}
internal_init();
}
/**
* Returns a pointer to a piece of new memory of the given size in bytes
* allocated from the pool.
*/
void* malloc(const size_t req_size)
{
/* Round size up to a multiple of wordsize. The following expression
only works for WORDSIZE that is a power of 2, by masking last bits of
incremented size to zero.
*/
const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
/* Check whether a new block must be allocated. Note that the first word
of a block is reserved for a pointer to the previous block.
*/
if (size > remaining) {
wastedMemory += remaining;
/* Allocate new storage. */
const size_t blocksize = (size + sizeof(void*) + (WORDSIZE-1) > BLOCKSIZE) ?
size + sizeof(void*) + (WORDSIZE-1) : BLOCKSIZE;
// use the standard C malloc to allocate memory
void* m = ::malloc(blocksize);
if (!m) {
fprintf(stderr,"Failed to allocate memory.\n");
return NULL;
}
/* Fill first word of new block with pointer to previous block. */
static_cast<void**>(m)[0] = base;
base = m;
size_t shift = 0;
//int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & (WORDSIZE-1))) & (WORDSIZE-1);
remaining = blocksize - sizeof(void*) - shift;
loc = (static_cast<char*>(m) + sizeof(void*) + shift);
}
void* rloc = loc;
loc = static_cast<char*>(loc) + size;
remaining -= size;
usedMemory += size;
return rloc;
}
/**
* Allocates (using this pool) a generic type T.
*
* Params:
* count = number of instances to allocate.
* Returns: pointer (of type T*) to memory buffer
*/
template <typename T>
T* allocate(const size_t count = 1)
{
T* mem = static_cast<T*>(this->malloc(sizeof(T)*count));
return mem;
}
};
/** @} */
/** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
* @{ */
// ---------------- CArray -------------------------
/** A STL container (as wrapper) for arrays of constant size defined at compile time (class imported from the MRPT project)
* This code is an adapted version from Boost, modifed for its integration
* within MRPT (JLBC, Dec/2009) (Renamed array -> CArray to avoid possible potential conflicts).
* See
* http://www.josuttis.com/cppcode
* for details and the latest version.
* See
* http://www.boost.org/libs/array for Documentation.
* for documentation.
*
* (C) Copyright Nicolai M. Josuttis 2001.
* Permission to copy, use, modify, sell and distribute this software
* is granted provided this copyright notice appears in all copies.
* This software is provided "as is" without express or implied
* warranty, and with no claim as to its suitability for any purpose.
*
* 29 Jan 2004 - minor fixes (Nico Josuttis)
* 04 Dec 2003 - update to synch with library TR1 (Alisdair Meredith)
* 23 Aug 2002 - fix for Non-MSVC compilers combined with MSVC libraries.
* 05 Aug 2001 - minor update (Nico Josuttis)
* 20 Jan 2001 - STLport fix (Beman Dawes)
* 29 Sep 2000 - Initial Revision (Nico Josuttis)
*
* Jan 30, 2004
*/
template <typename T, std::size_t N>
class CArray {
public:
T elems[N]; // fixed-size array of elements of type T
public:
// type definitions
typedef T value_type;
typedef T* iterator;
typedef const T* const_iterator;
typedef T& reference;
typedef const T& const_reference;
typedef std::size_t size_type;
typedef std::ptrdiff_t difference_type;
// iterator support
inline iterator begin() { return elems; }
inline const_iterator begin() const { return elems; }
inline iterator end() { return elems+N; }
inline const_iterator end() const { return elems+N; }
// reverse iterator support
#if !defined(BOOST_NO_TEMPLATE_PARTIAL_SPECIALIZATION) && !defined(BOOST_MSVC_STD_ITERATOR) && !defined(BOOST_NO_STD_ITERATOR_TRAITS)
typedef std::reverse_iterator<iterator> reverse_iterator;
typedef std::reverse_iterator<const_iterator> const_reverse_iterator;
#elif defined(_MSC_VER) && (_MSC_VER == 1300) && defined(BOOST_DINKUMWARE_STDLIB) && (BOOST_DINKUMWARE_STDLIB == 310)
// workaround for broken reverse_iterator in VC7
typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, iterator,
reference, iterator, reference> > reverse_iterator;
typedef std::reverse_iterator<std::_Ptrit<value_type, difference_type, const_iterator,
const_reference, iterator, reference> > const_reverse_iterator;
#else
// workaround for broken reverse_iterator implementations
typedef std::reverse_iterator<iterator,T> reverse_iterator;
typedef std::reverse_iterator<const_iterator,T> const_reverse_iterator;
#endif
reverse_iterator rbegin() { return reverse_iterator(end()); }
const_reverse_iterator rbegin() const { return const_reverse_iterator(end()); }
reverse_iterator rend() { return reverse_iterator(begin()); }
const_reverse_iterator rend() const { return const_reverse_iterator(begin()); }
// operator[]
inline reference operator[](size_type i) { return elems[i]; }
inline const_reference operator[](size_type i) const { return elems[i]; }
// at() with range check
reference at(size_type i) { rangecheck(i); return elems[i]; }
const_reference at(size_type i) const { rangecheck(i); return elems[i]; }
// front() and back()
reference front() { return elems[0]; }
const_reference front() const { return elems[0]; }
reference back() { return elems[N-1]; }
const_reference back() const { return elems[N-1]; }
// size is constant
static inline size_type size() { return N; }
static bool empty() { return false; }
static size_type max_size() { return N; }
enum { static_size = N };
/** This method has no effects in this class, but raises an exception if the expected size does not match */
inline void resize(const size_t nElements) { if (nElements!=N) throw std::logic_error("Try to change the size of a CArray."); }
// swap (note: linear complexity in N, constant for given instantiation)
void swap (CArray<T,N>& y) { std::swap_ranges(begin(),end(),y.begin()); }
// direct access to data (read-only)
const T* data() const { return elems; }
// use array as C array (direct read/write access to data)
T* data() { return elems; }
// assignment with type conversion
template <typename T2> CArray<T,N>& operator= (const CArray<T2,N>& rhs) {
std::copy(rhs.begin(),rhs.end(), begin());
return *this;
}
// assign one value to all elements
inline void assign (const T& value) { for (size_t i=0;i<N;i++) elems[i]=value; }
// assign (compatible with std::vector's one) (by JLBC for MRPT)
void assign (const size_t n, const T& value) { assert(N==n); for (size_t i=0;i<N;i++) elems[i]=value; }
private:
// check range (may be private because it is static)
static void rangecheck (size_type i) { if (i >= size()) { throw std::out_of_range("CArray<>: index out of range"); } }
}; // end of CArray
/** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors when DIM=-1.
* Fixed size version for a generic DIM:
*/
template <int DIM, typename T>
struct array_or_vector_selector
{
typedef CArray<T,DIM> container_t;
};
/** Dynamic size version */
template <typename T>
struct array_or_vector_selector<-1,T> {
typedef std::vector<T> container_t;
};
/** @} */
/** @addtogroup kdtrees_grp KD-tree classes and adaptors
* @{ */
/** kd-tree index
*
* Contains the k-d trees and other information for indexing a set of points
* for nearest-neighbor matching.
*
* The class "DatasetAdaptor" must provide the following interface (can be non-virtual, inlined methods):
*
* \code
* // Must return the number of data poins
* inline size_t kdtree_get_point_count() const { ... }
*
* // [Only if using the metric_L2_Simple type] Must return the Euclidean (L2) distance between the vector "p1[0:size-1]" and the data point with index "idx_p2" stored in the class:
* inline DistanceType kdtree_distance(const T *p1, const size_t idx_p2,size_t size) const { ... }
*
* // Must return the dim'th component of the idx'th point in the class:
* inline T kdtree_get_pt(const size_t idx, int dim) const { ... }
*
* // Optional bounding-box computation: return false to default to a standard bbox computation loop.
* // Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
* // Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
* template <class BBOX>
* bool kdtree_get_bbox(BBOX &bb) const
* {
* bb[0].low = ...; bb[0].high = ...; // 0th dimension limits
* bb[1].low = ...; bb[1].high = ...; // 1st dimension limits
* ...
* return true;
* }
*
* \endcode
*
* \tparam DatasetAdaptor The user-provided adaptor (see comments above).
* \tparam Distance The distance metric to use: nanoflann::metric_L1, nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
* \tparam DIM Dimensionality of data points (e.g. 3 for 3D points)
* \tparam IndexType Will be typically size_t or int
*/
template <typename Distance, class DatasetAdaptor,int DIM = -1, typename IndexType = size_t>
class KDTreeSingleIndexAdaptor
{
private:
/** Hidden copy constructor, to disallow copying indices (Not implemented) */
KDTreeSingleIndexAdaptor(const KDTreeSingleIndexAdaptor<Distance,DatasetAdaptor,DIM,IndexType>&);
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::DistanceType DistanceType;
protected:
/**
* Array of indices to vectors in the dataset.
*/
std::vector<IndexType> vind;
size_t m_leaf_max_size;
/**
* The dataset used by this index
*/
const DatasetAdaptor &dataset; //!< The source of our data
const KDTreeSingleIndexAdaptorParams index_params;
size_t m_size; //!< Number of current poins in the dataset
size_t m_size_at_index_build; //!< Number of points in the dataset when the index was built
int dim; //!< Dimensionality of each data point
/*--------------------- Internal Data Structures --------------------------*/
struct Node
{
/** Union used because a node can be either a LEAF node or a non-leaf node, so both data fields are never used simultaneously */
union {
struct leaf
{
IndexType left, right; //!< Indices of points in leaf node
} lr;
struct nonleaf
{
int divfeat; //!< Dimension used for subdivision.
DistanceType divlow, divhigh; //!< The values used for subdivision.
} sub;
} node_type;
Node* child1, * child2; //!< Child nodes (both=NULL mean its a leaf node)
};
typedef Node* NodePtr;
struct Interval
{
ElementType low, high;
};
/** Define "BoundingBox" as a fixed-size or variable-size container depending on "DIM" */
typedef typename array_or_vector_selector<DIM,Interval>::container_t BoundingBox;
/** Define "distance_vector_t" as a fixed-size or variable-size container depending on "DIM" */
typedef typename array_or_vector_selector<DIM,DistanceType>::container_t distance_vector_t;
/** The KD-tree used to find neighbours */
NodePtr root_node;
BoundingBox root_bbox;
/**
* Pooled memory allocator.
*
* Using a pooled memory allocator is more efficient
* than allocating memory directly when there is a large
* number small of memory allocations.
*/
PooledAllocator pool;
public:
Distance distance;
/**
* KDTree constructor
*
* Refer to docs in README.md or online in https://github.com/jlblancoc/nanoflann
*
* The KD-Tree point dimension (the length of each point in the datase, e.g. 3 for 3D points)
* is determined by means of:
* - The \a DIM template parameter if >0 (highest priority)
* - Otherwise, the \a dimensionality parameter of this constructor.
*
* @param inputData Dataset with the input features
* @param params Basically, the maximum leaf node size
*/
KDTreeSingleIndexAdaptor(const int dimensionality, const DatasetAdaptor& inputData, const KDTreeSingleIndexAdaptorParams& params = KDTreeSingleIndexAdaptorParams() ) :
dataset(inputData), index_params(params), root_node(NULL), distance(inputData)
{
m_size = dataset.kdtree_get_point_count();
m_size_at_index_build = m_size;
dim = dimensionality;
if (DIM>0) dim=DIM;
m_leaf_max_size = params.leaf_max_size;
// Create a permutable array of indices to the input vectors.
init_vind();
}
/** Standard destructor */
~KDTreeSingleIndexAdaptor() { }
/** Frees the previously-built index. Automatically called within buildIndex(). */
void freeIndex()
{
pool.free_all();
root_node=NULL;
m_size_at_index_build = 0;
}
/**
* Builds the index
*/
void buildIndex()
{
init_vind();
freeIndex();
m_size_at_index_build = m_size;
if(m_size == 0) return;
computeBoundingBox(root_bbox);
root_node = divideTree(0, m_size, root_bbox ); // construct the tree
}
/** Returns number of points in dataset */
size_t size() const { return m_size; }
/** Returns the length of each point in the dataset */
size_t veclen() const {
return static_cast<size_t>(DIM>0 ? DIM : dim);
}
/**
* Computes the inde memory usage
* Returns: memory used by the index
*/
size_t usedMemory() const
{
return pool.usedMemory+pool.wastedMemory+dataset.kdtree_get_point_count()*sizeof(IndexType); // pool memory and vind array memory
}
/** \name Query methods
* @{ */
/**
* Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored inside
* the result object.
*
* Params:
* result = the result object in which the indices of the nearest-neighbors are stored
* vec = the vector for which to search the nearest neighbors
*
* \tparam RESULTSET Should be any ResultSet<DistanceType>
* \return True if the requested neighbors could be found.
* \sa knnSearch, radiusSearch
*/
template <typename RESULTSET>
bool findNeighbors(RESULTSET& result, const ElementType* vec, const SearchParams& searchParams) const
{
assert(vec);
if (size() == 0)
return false;
if (!root_node)
throw std::runtime_error("[nanoflann] findNeighbors() called before building the index.");
float epsError = 1+searchParams.eps;
distance_vector_t dists; // fixed or variable-sized container (depending on DIM)
dists.assign((DIM>0 ? DIM : dim) ,0); // Fill it with zeros.
DistanceType distsq = computeInitialDistances(vec, dists);
searchLevel(result, vec, root_node, distsq, dists, epsError); // "count_leaf" parameter removed since was neither used nor returned to the user.
return result.full();
}
/**
* Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1]. Their indices are stored inside
* the result object.
* \sa radiusSearch, findNeighbors
* \note nChecks_IGNORED is ignored but kept for compatibility with the original FLANN interface.
* \return Number `N` of valid points in the result set. Only the first `N` entries in `out_indices` and `out_distances_sq` will be valid.
* Return may be less than `num_closest` only if the number of elements in the tree is less than `num_closest`.
*/
size_t knnSearch(const ElementType *query_point, const size_t num_closest, IndexType *out_indices, DistanceType *out_distances_sq, const int /* nChecks_IGNORED */ = 10) const
{
nanoflann::KNNResultSet<DistanceType,IndexType> resultSet(num_closest);
resultSet.init(out_indices, out_distances_sq);
this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
return resultSet.size();
}
/**
* Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
* The output is given as a vector of pairs, of which the first element is a point index and the second the corresponding distance.
* Previous contents of \a IndicesDists are cleared.
*
* If searchParams.sorted==true, the output list is sorted by ascending distances.
*
* For a better performance, it is advisable to do a .reserve() on the vector if you have any wild guess about the number of expected matches.
*
* \sa knnSearch, findNeighbors, radiusSearchCustomCallback
* \return The number of points within the given radius (i.e. indices.size() or dists.size() )
*/
size_t radiusSearch(const ElementType *query_point,const DistanceType &radius, std::vector<std::pair<IndexType,DistanceType> >& IndicesDists, const SearchParams& searchParams) const
{
RadiusResultSet<DistanceType,IndexType> resultSet(radius,IndicesDists);
const size_t nFound = radiusSearchCustomCallback(query_point,resultSet,searchParams);
if (searchParams.sorted)
std::sort(IndicesDists.begin(),IndicesDists.end(), IndexDist_Sorter() );
return nFound;
}
/**
* Just like radiusSearch() but with a custom callback class for each point found in the radius of the query.
* See the source of RadiusResultSet<> as a start point for your own classes.
* \sa radiusSearch
*/
template <class SEARCH_CALLBACK>
size_t radiusSearchCustomCallback(const ElementType *query_point,SEARCH_CALLBACK &resultSet, const SearchParams& searchParams = SearchParams() ) const
{
this->findNeighbors(resultSet, query_point, searchParams);
return resultSet.size();
}
/** @} */
private:
/** Make sure the auxiliary list \a vind has the same size than the current dataset, and re-generate if size has changed. */
void init_vind()
{
// Create a permutable array of indices to the input vectors.
m_size = dataset.kdtree_get_point_count();
if (vind.size()!=m_size) vind.resize(m_size);
for (size_t i = 0; i < m_size; i++) vind[i] = i;
}
/// Helper accessor to the dataset points:
inline ElementType dataset_get(size_t idx, int component) const {
return dataset.kdtree_get_pt(idx,component);
}
void save_tree(FILE* stream, NodePtr tree)
{
save_value(stream, *tree);
if (tree->child1!=NULL) {
save_tree(stream, tree->child1);
}
if (tree->child2!=NULL) {
save_tree(stream, tree->child2);
}
}
void load_tree(FILE* stream, NodePtr& tree)
{
tree = pool.allocate<Node>();
load_value(stream, *tree);
if (tree->child1!=NULL) {