scan-context/src2/cpp/module/Scancontext/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,
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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/** \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 <algorithm>
#include <array>
#include <cassert>
#include <cmath> // for abs()
#include <cstdio> // for fwrite()
#include <cstdlib> // for abs()
#include <functional>
#include <limits> // std::reference_wrapper
#include <stdexcept>
#include <vector>
/** Library version: 0xMmP (M=Major,m=minor,P=patch) */
#define NANOFLANN_VERSION 0x132
// 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
* @{ */
/** the PI constant (required to avoid MSVC missing symbols) */
template <typename T> T pi_const() {
return static_cast<T>(3.14159265358979323846);
}
/**
* Traits if object is resizable and assignable (typically has a resize | assign
* method)
*/
template <typename T, typename = int> struct has_resize : std::false_type {};
template <typename T>
struct has_resize<T, decltype((void)std::declval<T>().resize(1), 0)>
: std::true_type {};
template <typename T, typename = int> struct has_assign : std::false_type {};
template <typename T>
struct has_assign<T, decltype((void)std::declval<T>().assign(1, 0), 0)>
: std::true_type {};
/**
* Free function to resize a resizable object
*/
template <typename Container>
inline typename std::enable_if<has_resize<Container>::value, void>::type
resize(Container &c, const size_t nElements) {
c.resize(nElements);
}
/**
* Free function that has no effects on non resizable containers (e.g.
* std::array) It raises an exception if the expected size does not match
*/
template <typename Container>
inline typename std::enable_if<!has_resize<Container>::value, void>::type
resize(Container &c, const size_t nElements) {
if (nElements != c.size())
throw std::logic_error("Try to change the size of a std::array.");
}
/**
* Free function to assign to a container
*/
template <typename Container, typename T>
inline typename std::enable_if<has_assign<Container>::value, void>::type
assign(Container &c, const size_t nElements, const T &value) {
c.assign(nElements, value);
}
/**
* Free function to assign to a std::array
*/
template <typename Container, typename T>
inline typename std::enable_if<!has_assign<Container>::value, void>::type
assign(Container &c, const size_t nElements, const T &value) {
for (size_t i = 0; i < nElements; i++)
c[i] = value;
}
/** @addtogroup result_sets_grp Result set classes
* @{ */
template <typename _DistanceType, typename _IndexType = size_t,
typename _CountType = size_t>
class KNNResultSet {
public:
typedef _DistanceType DistanceType;
typedef _IndexType IndexType;
typedef _CountType CountType;
private:
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; }
/**
* Called during search to add an element matching the criteria.
* @return true if the search should be continued, false if the results are
* sufficient
*/
inline bool 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++;
// tell caller that the search shall continue
return true;
}
inline DistanceType worstDist() const { return dists[capacity - 1]; }
};
/** 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;
}
};
/**
* A result-set class used when performing a radius based search.
*/
template <typename _DistanceType, typename _IndexType = size_t>
class RadiusResultSet {
public:
typedef _DistanceType DistanceType;
typedef _IndexType IndexType;
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 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; }
/**
* Called during search to add an element matching the criteria.
* @return true if the search should be continued, false if the results are
* sufficient
*/
inline bool addPoint(DistanceType dist, IndexType index) {
if (dist < radius)
m_indices_dists.push_back(std::make_pair(index, dist));
return true;
}
inline DistanceType worstDist() const { return radius; }
/**
* 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(),
IndexDist_Sorter());
return *it;
}
};
/** @} */
/** @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
* @{ */
struct Metric {};
/** 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 evalMetric(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, const size_t) 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 evalMetric(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, const size_t) 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 evalMetric(const T *a, const size_t b_idx,
size_t size) const {
DistanceType result = DistanceType();
for (size_t i = 0; i < size; ++i) {
const DistanceType diff = a[i] - data_source.kdtree_get_pt(b_idx, i);
result += diff * diff;
}
return result;
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, const size_t) const {
return (a - b) * (a - b);
}
};
/** SO2 distance functor
* Corresponding distance traits: nanoflann::metric_SO2
* \tparam T Type of the elements (e.g. double, float)
* \tparam _DistanceType Type of distance variables (must be signed) (e.g.
* float, double) orientation is constrained to be in [-pi, pi]
*/
template <class T, class DataSource, typename _DistanceType = T>
struct SO2_Adaptor {
typedef T ElementType;
typedef _DistanceType DistanceType;
const DataSource &data_source;
SO2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
inline DistanceType evalMetric(const T *a, const size_t b_idx,
size_t size) const {
return accum_dist(a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1),
size - 1);
}
/** Note: this assumes that input angles are already in the range [-pi,pi] */
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, const size_t) const {
DistanceType result = DistanceType();
DistanceType PI = pi_const<DistanceType>();
result = b - a;
if (result > PI)
result -= 2 * PI;
else if (result < -PI)
result += 2 * PI;
return result;
}
};
/** SO3 distance functor (Uses L2_Simple)
* Corresponding distance traits: nanoflann::metric_SO3
* \tparam T Type of the elements (e.g. double, float)
* \tparam _DistanceType Type of distance variables (must be signed) (e.g.
* float, double)
*/
template <class T, class DataSource, typename _DistanceType = T>
struct SO3_Adaptor {
typedef T ElementType;
typedef _DistanceType DistanceType;
L2_Simple_Adaptor<T, DataSource> distance_L2_Simple;
SO3_Adaptor(const DataSource &_data_source)
: distance_L2_Simple(_data_source) {}
inline DistanceType evalMetric(const T *a, const size_t b_idx,
size_t size) const {
return distance_L2_Simple.evalMetric(a, b_idx, size);
}
template <typename U, typename V>
inline DistanceType accum_dist(const U a, const V b, const size_t idx) const {
return distance_L2_Simple.accum_dist(a, b, idx);
}
};
/** Metaprogramming helper traits class for the L1 (Manhattan) metric */
struct metric_L1 : public Metric {
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 : public Metric {
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 : public Metric {
template <class T, class DataSource> struct traits {
typedef L2_Simple_Adaptor<T, DataSource> distance_t;
};
};
/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
struct metric_SO2 : public Metric {
template <class T, class DataSource> struct traits {
typedef SO2_Adaptor<T, DataSource> distance_t;
};
};
/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
struct metric_SO3 : public Metric {
template <class T, class DataSource> struct traits {
typedef SO3_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
* @{ */
/** 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 std::array<T, DIM> container_t;
};
/** Dynamic size version */
template <typename T> struct array_or_vector_selector<-1, T> {
typedef std::vector<T> container_t;
};
/** @} */
/** kd-tree base-class
*
* Contains the member functions common to the classes KDTreeSingleIndexAdaptor
* and KDTreeSingleIndexDynamicAdaptor_.
*
* \tparam Derived The name of the class which inherits this class.
* \tparam DatasetAdaptor The user-provided adaptor (see comments above).
* \tparam Distance The distance metric to use, these are all classes derived
* from nanoflann::Metric \tparam DIM Dimensionality of data points (e.g. 3 for
* 3D points) \tparam IndexType Will be typically size_t or int
*/
template <class Derived, typename Distance, class DatasetAdaptor, int DIM = -1,
typename IndexType = size_t>
class KDTreeBaseClass {
public:
/** Frees the previously-built index. Automatically called within
* buildIndex(). */
void freeIndex(Derived &obj) {
obj.pool.free_all();
obj.root_node = NULL;
obj.m_size_at_index_build = 0;
}
typedef typename Distance::ElementType ElementType;
typedef typename Distance::DistanceType DistanceType;
/*--------------------- 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;
};
/**
* Array of indices to vectors in the dataset.
*/
std::vector<IndexType> vind;
NodePtr root_node;
size_t m_leaf_max_size;
size_t m_size; //!< Number of current points 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
/** 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 */
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;
/** Returns number of points in dataset */
size_t size(const Derived &obj) const { return obj.m_size; }
/** Returns the length of each point in the dataset */
size_t veclen(const Derived &obj) {
return static_cast<size_t>(DIM > 0 ? DIM : obj.dim);
}
/// Helper accessor to the dataset points:
inline ElementType dataset_get(const Derived &obj, size_t idx,
int component) const {
return obj.dataset.kdtree_get_pt(idx, component);
}
/**
* Computes the inde memory usage
* Returns: memory used by the index
*/
size_t usedMemory(Derived &obj) {
return obj.pool.usedMemory + obj.pool.wastedMemory +
obj.dataset.kdtree_get_point_count() *
sizeof(IndexType); // pool memory and vind array memory
}
void computeMinMax(const Derived &obj, IndexType *ind, IndexType count,
int element, ElementType &min_elem,
ElementType &max_elem) {
min_elem = dataset_get(obj, ind[0], element);
max_elem = dataset_get(obj, ind[0], element);
for (IndexType i = 1; i < count; ++i) {
ElementType val = dataset_get(obj, ind[i], element);
if (val < min_elem)
min_elem = val;
if (val > max_elem)
max_elem = val;
}
}
/**
* Create a tree node that subdivides the list of vecs from vind[first]
* to vind[last]. The routine is called recursively on each sublist.
*
* @param left index of the first vector
* @param right index of the last vector
*/
NodePtr divideTree(Derived &obj, const IndexType left, const IndexType right,
BoundingBox &bbox) {
NodePtr node = obj.pool.template allocate<Node>(); // allocate memory
/* If too few exemplars remain, then make this a leaf node. */
if ((right - left) <= static_cast<IndexType>(obj.m_leaf_max_size)) {
node->child1 = node->child2 = NULL; /* Mark as leaf node. */
node->node_type.lr.left = left;
node->node_type.lr.right = right;
// compute bounding-box of leaf points
for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
bbox[i].low = dataset_get(obj, obj.vind[left], i);
bbox[i].high = dataset_get(obj, obj.vind[left], i);
}
for (IndexType k = left + 1; k < right; ++k) {
for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
if (bbox[i].low > dataset_get(obj, obj.vind[k], i))
bbox[i].low = dataset_get(obj, obj.vind[k], i);
if (bbox[i].high < dataset_get(obj, obj.vind[k], i))
bbox[i].high = dataset_get(obj, obj.vind[k], i);
}
}
} else {
IndexType idx;
int cutfeat;
DistanceType cutval;
middleSplit_(obj, &obj.vind[0] + left, right - left, idx, cutfeat, cutval,
bbox);
node->node_type.sub.divfeat = cutfeat;
BoundingBox left_bbox(bbox);
left_bbox[cutfeat].high = cutval;
node->child1 = divideTree(obj, left, left + idx, left_bbox);
BoundingBox right_bbox(bbox);
right_bbox[cutfeat].low = cutval;
node->child2 = divideTree(obj, left + idx, right, right_bbox);
node->node_type.sub.divlow = left_bbox[cutfeat].high;
node->node_type.sub.divhigh = right_bbox[cutfeat].low;
for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
}
}
return node;
}
void middleSplit_(Derived &obj, IndexType *ind, IndexType count,
IndexType &index, int &cutfeat, DistanceType &cutval,
const BoundingBox &bbox) {
const DistanceType EPS = static_cast<DistanceType>(0.00001);
ElementType max_span = bbox[0].high - bbox[0].low;
for (int i = 1; i < (DIM > 0 ? DIM : obj.dim); ++i) {
ElementType span = bbox[i].high - bbox[i].low;
if (span > max_span) {
max_span = span;
}
}
ElementType max_spread = -1;
cutfeat = 0;
for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
ElementType span = bbox[i].high - bbox[i].low;
if (span > (1 - EPS) * max_span) {
ElementType min_elem, max_elem;
computeMinMax(obj, ind, count, i, min_elem, max_elem);
ElementType spread = max_elem - min_elem;
;
if (spread > max_spread) {
cutfeat = i;
max_spread = spread;
}
}
}
// split in the middle
DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
ElementType min_elem, max_elem;
computeMinMax(obj, ind, count, cutfeat, min_elem, max_elem);
if (split_val < min_elem)
cutval = min_elem;
else if (split_val > max_elem)
cutval = max_elem;
else
cutval = split_val;
IndexType lim1, lim2;
planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
if (lim1 > count / 2)
index = lim1;
else if (lim2 < count / 2)
index = lim2;
else
index = count / 2;
}
/**
* Subdivide the list of points by a plane perpendicular on axe corresponding
* to the 'cutfeat' dimension at 'cutval' position.
*
* On return:
* dataset[ind[0..lim1-1]][cutfeat]<cutval
* dataset[ind[lim1..lim2-1]][cutfeat]==cutval
* dataset[ind[lim2..count]][cutfeat]>cutval
*/
void planeSplit(Derived &obj, IndexType *ind, const IndexType count,
int cutfeat, DistanceType &cutval, IndexType &lim1,
IndexType &lim2) {
/* Move vector indices for left subtree to front of list. */
IndexType left = 0;
IndexType right = count - 1;
for (;;) {
while (left <= right && dataset_get(obj, ind[left], cutfeat) < cutval)
++left;
while (right && left <= right &&
dataset_get(obj, ind[right], cutfeat) >= cutval)
--right;
if (left > right || !right)
break; // "!right" was added to support unsigned Index types
std::swap(ind[left], ind[right]);
++left;
--right;
}
/* If either list is empty, it means that all remaining features
* are identical. Split in the middle to maintain a balanced tree.
*/
lim1 = left;
right = count - 1;
for (;;) {
while (left <= right && dataset_get(obj, ind[left], cutfeat) <= cutval)
++left;
while (right && left <= right &&
dataset_get(obj, ind[right], cutfeat) > cutval)
--right;
if (left > right || !right)
break; // "!right" was added to support unsigned Index types
std::swap(ind[left], ind[right]);
++left;
--right;
}
lim2 = left;
}
DistanceType computeInitialDistances(const Derived &obj,
const ElementType *vec,
distance_vector_t &dists) const {
assert(vec);
DistanceType distsq = DistanceType();
for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
if (vec[i] < obj.root_bbox[i].low) {
dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].low, i);
distsq += dists[i];
}
if (vec[i] > obj.root_bbox[i].high) {
dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].high, i);
distsq += dists[i];
}
}
return distsq;
}
void save_tree(Derived &obj, FILE *stream, NodePtr tree) {
save_value(stream, *tree);
if (tree->child1 != NULL) {
save_tree(obj, stream, tree->child1);
}
if (tree->child2 != NULL) {
save_tree(obj, stream, tree->child2);
}
}
void load_tree(Derived &obj, FILE *stream, NodePtr &tree) {
tree = obj.pool.template allocate<Node>();
load_value(stream, *tree);
if (tree->child1 != NULL) {
load_tree(obj, stream, tree->child1);
}
if (tree->child2 != NULL) {
load_tree(obj, stream, tree->child2);
}
}
/** Stores the index in a binary file.
* IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
* loading the index object it must be constructed associated to the same
* source of data points used while building it. See the example:
* examples/saveload_example.cpp \sa loadIndex */
void saveIndex_(Derived &obj, FILE *stream) {
save_value(stream, obj.m_size);
save_value(stream, obj.dim);
save_value(stream, obj.root_bbox);
save_value(stream, obj.m_leaf_max_size);
save_value(stream, obj.vind);
save_tree(obj, stream, obj.root_node);
}
/** Loads a previous index from a binary file.
* IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
* index object must be constructed associated to the same source of data
* points used while building the index. See the example:
* examples/saveload_example.cpp \sa loadIndex */
void loadIndex_(Derived &obj, FILE *stream) {
load_value(stream, obj.m_size);
load_value(stream, obj.dim);
load_value(stream, obj.root_bbox);
load_value(stream, obj.m_leaf_max_size);
load_value(stream, obj.vind);
load_tree(obj, stream, obj.root_node);
}
};
/** @addtogroup kdtrees_grp KD-tree classes and adaptors
* @{ */
/** kd-tree static 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 { ... }
*
*
* // Must return the dim'th component of the idx'th point in the class:
* inline T kdtree_get_pt(const size_t idx, const size_t 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
: public KDTreeBaseClass<
KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>,
Distance, DatasetAdaptor, DIM, IndexType> {
public:
/** Deleted copy constructor*/
KDTreeSingleIndexAdaptor(
const KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>
&) = delete;
/**
* The dataset used by this index
*/
const DatasetAdaptor &dataset; //!< The source of our data
const KDTreeSingleIndexAdaptorParams index_params;
Distance distance;
typedef typename nanoflann::KDTreeBaseClass<
nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM,
IndexType>,
Distance, DatasetAdaptor, DIM, IndexType>
BaseClassRef;
typedef typename BaseClassRef::ElementType ElementType;
typedef typename BaseClassRef::DistanceType DistanceType;
typedef typename BaseClassRef::Node Node;
typedef Node *NodePtr;
typedef typename BaseClassRef::Interval Interval;
/** Define "BoundingBox" as a fixed-size or variable-size container depending
* on "DIM" */
typedef typename BaseClassRef::BoundingBox BoundingBox;
/** Define "distance_vector_t" as a fixed-size or variable-size container
* depending on "DIM" */
typedef typename BaseClassRef::distance_vector_t distance_vector_t;
/**
* 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), distance(inputData) {
BaseClassRef::root_node = NULL;
BaseClassRef::m_size = dataset.kdtree_get_point_count();
BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
BaseClassRef::dim = dimensionality;
if (DIM > 0)
BaseClassRef::dim = DIM;
BaseClassRef::m_leaf_max_size = params.leaf_max_size;
// Create a permutable array of indices to the input vectors.
init_vind();
}
/**
* Builds the index
*/
void buildIndex() {
BaseClassRef::m_size = dataset.kdtree_get_point_count();
BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
init_vind();
this->freeIndex(*this);
BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
if (BaseClassRef::m_size == 0)
return;
computeBoundingBox(BaseClassRef::root_bbox);
BaseClassRef::root_node =
this->divideTree(*this, 0, BaseClassRef::m_size,
BaseClassRef::root_bbox); // construct the tree
}
/** \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 (this->size(*this) == 0)
return false;
if (!BaseClassRef::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)
auto zero = static_cast<decltype(result.worstDist())>(0);
assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
zero); // Fill it with zeros.
DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
searchLevel(result, vec, BaseClassRef::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();
}
/** @} */
public:
/** 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.
BaseClassRef::m_size = dataset.kdtree_get_point_count();
if (BaseClassRef::vind.size() != BaseClassRef::m_size)
BaseClassRef::vind.resize(BaseClassRef::m_size);
for (size_t i = 0; i < BaseClassRef::m_size; i++)
BaseClassRef::vind[i] = i;
}
void computeBoundingBox(BoundingBox &bbox) {
resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
if (dataset.kdtree_get_bbox(bbox)) {
// Done! It was implemented in derived class
} else {
const size_t N = dataset.kdtree_get_point_count();
if (!N)
throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
"no data points found.");
for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
bbox[i].low = bbox[i].high = this->dataset_get(*this, 0, i);
}
for (size_t k = 1; k < N; ++k) {
for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
if (this->dataset_get(*this, k, i) < bbox[i].low)
bbox[i].low = this->dataset_get(*this, k, i);
if (this->dataset_get(*this, k, i) > bbox[i].high)
bbox[i].high = this->dataset_get(*this, k, i);
}
}
}
}
/**
* Performs an exact search in the tree starting from a node.
* \tparam RESULTSET Should be any ResultSet<DistanceType>
* \return true if the search should be continued, false if the results are
* sufficient
*/
template <class RESULTSET>
bool searchLevel(RESULTSET &result_set, const ElementType *vec,
const NodePtr node, DistanceType mindistsq,
distance_vector_t &dists, const float epsError) const {
/* If this is a leaf node, then do check and return. */
if ((node->child1 == NULL) && (node->child2 == NULL)) {
// count_leaf += (node->lr.right-node->lr.left); // Removed since was
// neither used nor returned to the user.
DistanceType worst_dist = result_set.worstDist();
for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
++i) {
const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
DistanceType dist = distance.evalMetric(
vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
if (dist < worst_dist) {
if (!result_set.addPoint(dist, BaseClassRef::vind[i])) {
// the resultset doesn't want to receive any more points, we're done
// searching!
return false;
}
}
}
return true;
}
/* Which child branch should be taken first? */
int idx = node->node_type.sub.divfeat;
ElementType val = vec[idx];
DistanceType diff1 = val - node->node_type.sub.divlow;
DistanceType diff2 = val - node->node_type.sub.divhigh;
NodePtr bestChild;
NodePtr otherChild;
DistanceType cut_dist;
if ((diff1 + diff2) < 0) {
bestChild = node->child1;
otherChild = node->child2;
cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
} else {
bestChild = node->child2;
otherChild = node->child1;
cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
}
/* Call recursively to search next level down. */
if (!searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError)) {
// the resultset doesn't want to receive any more points, we're done
// searching!
return false;
}
DistanceType dst = dists[idx];
mindistsq = mindistsq + cut_dist - dst;
dists[idx] = cut_dist;
if (mindistsq * epsError <= result_set.worstDist()) {
if (!searchLevel(result_set, vec, otherChild, mindistsq, dists,
epsError)) {
// the resultset doesn't want to receive any more points, we're done
// searching!
return false;
}
}
dists[idx] = dst;
return true;
}
public:
/** Stores the index in a binary file.
* IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
* loading the index object it must be constructed associated to the same
* source of data points used while building it. See the example:
* examples/saveload_example.cpp \sa loadIndex */
void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
/** Loads a previous index from a binary file.
* IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
* index object must be constructed associated to the same source of data
* points used while building the index. See the example:
* examples/saveload_example.cpp \sa loadIndex */
void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
}; // class KDTree
/** kd-tree dynamic 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 { ... }
*
* // Must return the dim'th component of the idx'th point in the class:
* inline T kdtree_get_pt(const size_t idx, const size_t 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 KDTreeSingleIndexDynamicAdaptor_
: public KDTreeBaseClass<KDTreeSingleIndexDynamicAdaptor_<
Distance, DatasetAdaptor, DIM, IndexType>,
Distance, DatasetAdaptor, DIM, IndexType> {
public:
/**
* The dataset used by this index
*/
const DatasetAdaptor &dataset; //!< The source of our data
KDTreeSingleIndexAdaptorParams index_params;
std::vector<int> &treeIndex;
Distance distance;
typedef typename nanoflann::KDTreeBaseClass<
nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM,
IndexType>,
Distance, DatasetAdaptor, DIM, IndexType>
BaseClassRef;
typedef typename BaseClassRef::ElementType ElementType;
typedef typename BaseClassRef::DistanceType DistanceType;
typedef typename BaseClassRef::Node Node;
typedef Node *NodePtr;
typedef typename BaseClassRef::Interval Interval;
/** Define "BoundingBox" as a fixed-size or variable-size container depending
* on "DIM" */
typedef typename BaseClassRef::BoundingBox BoundingBox;
/** Define "distance_vector_t" as a fixed-size or variable-size container
* depending on "DIM" */
typedef typename BaseClassRef::distance_vector_t distance_vector_t;
/**
* 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
*/
KDTreeSingleIndexDynamicAdaptor_(
const int dimensionality, const DatasetAdaptor &inputData,
std::vector<int> &treeIndex_,
const KDTreeSingleIndexAdaptorParams &params =
KDTreeSingleIndexAdaptorParams())
: dataset(inputData), index_params(params), treeIndex(treeIndex_),
distance(inputData) {
BaseClassRef::root_node = NULL;
BaseClassRef::m_size = 0;
BaseClassRef::m_size_at_index_build = 0;
BaseClassRef::dim = dimensionality;
if (DIM > 0)
BaseClassRef::dim = DIM;
BaseClassRef::m_leaf_max_size = params.leaf_max_size;
}
/** Assignment operator definiton */
KDTreeSingleIndexDynamicAdaptor_
operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs) {
KDTreeSingleIndexDynamicAdaptor_ tmp(rhs);
std::swap(BaseClassRef::vind, tmp.BaseClassRef::vind);
std::swap(BaseClassRef::m_leaf_max_size, tmp.BaseClassRef::m_leaf_max_size);
std::swap(index_params, tmp.index_params);
std::swap(treeIndex, tmp.treeIndex);
std::swap(BaseClassRef::m_size, tmp.BaseClassRef::m_size);
std::swap(BaseClassRef::m_size_at_index_build,
tmp.BaseClassRef::m_size_at_index_build);
std::swap(BaseClassRef::root_node, tmp.BaseClassRef::root_node);
std::swap(BaseClassRef::root_bbox, tmp.BaseClassRef::root_bbox);
std::swap(BaseClassRef::pool, tmp.BaseClassRef::pool);
return *this;
}
/**
* Builds the index
*/
void buildIndex() {
BaseClassRef::m_size = BaseClassRef::vind.size();
this->freeIndex(*this);
BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
if (BaseClassRef::m_size == 0)
return;
computeBoundingBox(BaseClassRef::root_bbox);
BaseClassRef::root_node =
this->divideTree(*this, 0, BaseClassRef::m_size,
BaseClassRef::root_bbox); // construct the tree
}
/** \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 (this->size(*this) == 0)
return false;
if (!BaseClassRef::root_node)
return false;
float epsError = 1 + searchParams.eps;
// fixed or variable-sized container (depending on DIM)
distance_vector_t dists;
// Fill it with zeros.
assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
static_cast<typename distance_vector_t::value_type>(0));
DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
searchLevel(result, vec, BaseClassRef::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();
}
/** @} */
public:
void computeBoundingBox(BoundingBox &bbox) {
resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
if (dataset.kdtree_get_bbox(bbox)) {
// Done! It was implemented in derived class
} else {
const size_t N = BaseClassRef::m_size;
if (!N)
throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
"no data points found.");
for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
bbox[i].low = bbox[i].high =
this->dataset_get(*this, BaseClassRef::vind[0], i);
}
for (size_t k = 1; k < N; ++k) {
for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
if (this->dataset_get(*this, BaseClassRef::vind[k], i) < bbox[i].low)
bbox[i].low = this->dataset_get(*this, BaseClassRef::vind[k], i);
if (this->dataset_get(*this, BaseClassRef::vind[k], i) > bbox[i].high)
bbox[i].high = this->dataset_get(*this, BaseClassRef::vind[k], i);
}
}
}
}
/**
* Performs an exact search in the tree starting from a node.
* \tparam RESULTSET Should be any ResultSet<DistanceType>
*/
template <class RESULTSET>
void searchLevel(RESULTSET &result_set, const ElementType *vec,
const NodePtr node, DistanceType mindistsq,
distance_vector_t &dists, const float epsError) const {
/* If this is a leaf node, then do check and return. */
if ((node->child1 == NULL) && (node->child2 == NULL)) {
// count_leaf += (node->lr.right-node->lr.left); // Removed since was
// neither used nor returned to the user.
DistanceType worst_dist = result_set.worstDist();
for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
++i) {
const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
if (treeIndex[index] == -1)
continue;
DistanceType dist = distance.evalMetric(
vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
if (dist < worst_dist) {
if (!result_set.addPoint(
static_cast<typename RESULTSET::DistanceType>(dist),
static_cast<typename RESULTSET::IndexType>(
BaseClassRef::vind[i]))) {
// the resultset doesn't want to receive any more points, we're done
// searching!
return; // false;
}
}
}
return;
}
/* Which child branch should be taken first? */
int idx = node->node_type.sub.divfeat;
ElementType val = vec[idx];
DistanceType diff1 = val - node->node_type.sub.divlow;
DistanceType diff2 = val - node->node_type.sub.divhigh;
NodePtr bestChild;
NodePtr otherChild;
DistanceType cut_dist;
if ((diff1 + diff2) < 0) {
bestChild = node->child1;
otherChild = node->child2;
cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
} else {
bestChild = node->child2;
otherChild = node->child1;
cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
}
/* Call recursively to search next level down. */
searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
DistanceType dst = dists[idx];
mindistsq = mindistsq + cut_dist - dst;
dists[idx] = cut_dist;
if (mindistsq * epsError <= result_set.worstDist()) {
searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
}
dists[idx] = dst;
}
public:
/** Stores the index in a binary file.
* IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
* loading the index object it must be constructed associated to the same
* source of data points used while building it. See the example:
* examples/saveload_example.cpp \sa loadIndex */
void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
/** Loads a previous index from a binary file.
* IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
* index object must be constructed associated to the same source of data
* points used while building the index. See the example:
* examples/saveload_example.cpp \sa loadIndex */
void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
};
/** kd-tree dynaimic index
*
* class to create multiple static index and merge their results to behave as
* single dynamic index as proposed in Logarithmic Approach.
*
* Example of usage:
* examples/dynamic_pointcloud_example.cpp
*
* \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 KDTreeSingleIndexDynamicAdaptor {
public:
typedef typename Distance::ElementType ElementType;
typedef typename Distance::DistanceType DistanceType;
protected:
size_t m_leaf_max_size;
size_t treeCount;
size_t pointCount;
/**
* The dataset used by this index
*/
const DatasetAdaptor &dataset; //!< The source of our data
std::vector<int> treeIndex; //!< treeIndex[idx] is the index of tree in which
//!< point at idx is stored. treeIndex[idx]=-1
//!< means that point has been removed.
KDTreeSingleIndexAdaptorParams index_params;
int dim; //!< Dimensionality of each data point
typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
index_container_t;
std::vector<index_container_t> index;
public:
/** Get a const ref to the internal list of indices; the number of indices is
* adapted dynamically as the dataset grows in size. */
const std::vector<index_container_t> &getAllIndices() const { return index; }
private:
/** finds position of least significant unset bit */
int First0Bit(IndexType num) {
int pos = 0;
while (num & 1) {
num = num >> 1;
pos++;
}
return pos;
}
/** Creates multiple empty trees to handle dynamic support */
void init() {
typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
my_kd_tree_t;
std::vector<my_kd_tree_t> index_(
treeCount, my_kd_tree_t(dim /*dim*/, dataset, treeIndex, index_params));
index = index_;
}
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
*/
KDTreeSingleIndexDynamicAdaptor(const int dimensionality,
const DatasetAdaptor &inputData,
const KDTreeSingleIndexAdaptorParams &params =
KDTreeSingleIndexAdaptorParams(),
const size_t maximumPointCount = 1000000000U)
: dataset(inputData), index_params(params), distance(inputData) {
treeCount = static_cast<size_t>(std::log2(maximumPointCount));
pointCount = 0U;
dim = dimensionality;
treeIndex.clear();
if (DIM > 0)
dim = DIM;
m_leaf_max_size = params.leaf_max_size;
init();
const size_t num_initial_points = dataset.kdtree_get_point_count();
if (num_initial_points > 0) {
addPoints(0, num_initial_points - 1);
}
}
/** Deleted copy constructor*/
KDTreeSingleIndexDynamicAdaptor(
const KDTreeSingleIndexDynamicAdaptor<Distance, DatasetAdaptor, DIM,
IndexType> &) = delete;
/** Add points to the set, Inserts all points from [start, end] */
void addPoints(IndexType start, IndexType end) {
size_t count = end - start + 1;
treeIndex.resize(treeIndex.size() + count);
for (IndexType idx = start; idx <= end; idx++) {
int pos = First0Bit(pointCount);
index[pos].vind.clear();
treeIndex[pointCount] = pos;
for (int i = 0; i < pos; i++) {
for (int j = 0; j < static_cast<int>(index[i].vind.size()); j++) {
index[pos].vind.push_back(index[i].vind[j]);
if (treeIndex[index[i].vind[j]] != -1)
treeIndex[index[i].vind[j]] = pos;
}
index[i].vind.clear();
index[i].freeIndex(index[i]);
}
index[pos].vind.push_back(idx);
index[pos].buildIndex();
pointCount++;
}
}
/** Remove a point from the set (Lazy Deletion) */
void removePoint(size_t idx) {
if (idx >= pointCount)
return;
treeIndex[idx] = -1;
}
/**
* 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 {
for (size_t i = 0; i < treeCount; i++) {
index[i].findNeighbors(result, &vec[0], searchParams);
}
return result.full();
}
};
/** An L2-metric KD-tree adaptor for working with data directly stored in an
* Eigen Matrix, without duplicating the data storage. Each row in the matrix
* represents a point in the state space.
*
* Example of usage:
* \code
* Eigen::Matrix<num_t,Dynamic,Dynamic> mat;
* // Fill out "mat"...
*
* typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> >
* my_kd_tree_t; const int max_leaf = 10; my_kd_tree_t mat_index(mat, max_leaf
* ); mat_index.index->buildIndex(); mat_index.index->... \endcode
*
* \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality
* for the points in the data set, allowing more compiler optimizations. \tparam
* Distance The distance metric to use: nanoflann::metric_L1,
* nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
*/
template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2>
struct KDTreeEigenMatrixAdaptor {
typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance> self_t;
typedef typename MatrixType::Scalar num_t;
typedef typename MatrixType::Index IndexType;
typedef
typename Distance::template traits<num_t, self_t>::distance_t metric_t;
typedef KDTreeSingleIndexAdaptor<metric_t, self_t,
MatrixType::ColsAtCompileTime, IndexType>
index_t;
index_t *index; //! The kd-tree index for the user to call its methods as
//! usual with any other FLANN index.
/// Constructor: takes a const ref to the matrix object with the data points
KDTreeEigenMatrixAdaptor(const size_t dimensionality,
const std::reference_wrapper<const MatrixType> &mat,
const int leaf_max_size = 10)
: m_data_matrix(mat) {
const auto dims = mat.get().cols();
if (size_t(dims) != dimensionality)
throw std::runtime_error(
"Error: 'dimensionality' must match column count in data matrix");
if (DIM > 0 && int(dims) != DIM)
throw std::runtime_error(
"Data set dimensionality does not match the 'DIM' template argument");
index =
new index_t(static_cast<int>(dims), *this /* adaptor */,
nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
index->buildIndex();
}
public:
/** Deleted copy constructor */
KDTreeEigenMatrixAdaptor(const self_t &) = delete;
~KDTreeEigenMatrixAdaptor() { delete index; }
const std::reference_wrapper<const MatrixType> m_data_matrix;
/** Query for the \a num_closest closest points to a given point (entered as
* query_point[0:dim-1]). Note that this is a short-cut method for
* index->findNeighbors(). The user can also call index->... methods as
* desired. \note nChecks_IGNORED is ignored but kept for compatibility with
* the original FLANN interface.
*/
inline void query(const num_t *query_point, const size_t num_closest,
IndexType *out_indices, num_t *out_distances_sq,
const int /* nChecks_IGNORED */ = 10) const {
nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
resultSet.init(out_indices, out_distances_sq);
index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
}
/** @name Interface expected by KDTreeSingleIndexAdaptor
* @{ */
const self_t &derived() const { return *this; }
self_t &derived() { return *this; }
// Must return the number of data points
inline size_t kdtree_get_point_count() const {
return m_data_matrix.get().rows();
}
// Returns the dim'th component of the idx'th point in the class:
inline num_t kdtree_get_pt(const IndexType idx, size_t dim) const {
return m_data_matrix.get().coeff(idx, IndexType(dim));
}
// 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 {
return false;
}
/** @} */
}; // end of KDTreeEigenMatrixAdaptor
/** @} */
/** @} */ // end of grouping
} // namespace nanoflann
#endif /* NANOFLANN_HPP_ */
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