diff --git a/CHANGELOG b/CHANGELOG index 8c584d1e4..c850008cf 100644 --- a/CHANGELOG +++ b/CHANGELOG @@ -4,6 +4,20 @@ Ponca changelog -------------------------------------------------------------------------------- Current head (v.1.1 RC) +This release improves the Spatial Partitioning Package with new features and +improved doc. + +- API + - [spatialPartitioning] Add kd-tree traits type (#80) + - [fitting] Add Primitive::getNumNeighbors (#86) + +- Bug-fixes and code improvements + - [spatialPartitioning] Fix unwanted function hiding with DryFit::setWeightFunc (#86) + - [fitting] Remove deadcode in Basket (#86) + +-Docs + - [spatialPartitioning] Update module page with a minimal doc and examples (#86) + - [spatialPartitioning] Add NanoFlann example (#86) -------------------------------------------------------------------------------- v.1.0 @@ -28,7 +42,6 @@ The changes introduced in this version mostly are introduced by PR #56. - [fitting] Rename normal() as primitiveGradient in MlsSphereFit (#72) - [fitting] Add new weight kernels (#71,#74) - [fitting] Add mechanism to check weight derivatives validity (#74) - - [spatialPartitioning] Add kd-tree traits type (#80) - Bug-fixes and code improvements - [fitting] Reduce objets sizes by removing unnecessary attributes (#56) diff --git a/Ponca/src/Fitting/basket.h b/Ponca/src/Fitting/basket.h index 224b775d3..4e2fcaf4b 100644 --- a/Ponca/src/Fitting/basket.h +++ b/Ponca/src/Fitting/basket.h @@ -64,13 +64,13 @@ namespace internal PONCA_MULTIARCH inline \ FIT_RESULT computeWithIds(IndexRange ids, const PointContainer& points){ \ FIT_RESULT res = UNDEFINED; \ - do { \ - for (const auto& i : ids){ \ - this->addNeighbor(points[i]); \ - } \ - res = this->finalize(); \ - } while ( res == NEED_OTHER_PASS ); \ - return res;return Self::computeWithIds(ids, points); \ + do { \ + for (const auto& i : ids){ \ + this->addNeighbor(points[i]); \ + } \ + res = this->finalize(); \ + } while ( res == NEED_OTHER_PASS ); \ + return res; \ } \ WRITE_BASKET_SINGLE_HOST_FUNCTIONS diff --git a/Ponca/src/Fitting/dryFit.h b/Ponca/src/Fitting/dryFit.h index af56a6ab5..0fe885d77 100644 --- a/Ponca/src/Fitting/dryFit.h +++ b/Ponca/src/Fitting/dryFit.h @@ -37,9 +37,6 @@ namespace Ponca PONCA_FITTING_APIDOC_FINALIZE PONCA_MULTIARCH inline FIT_RESULT finalize() { return Base::finalize(); } - PONCA_FITTING_APIDOC_SETWFUNC - PONCA_MULTIARCH inline void setWeightFunc (const WFunctor& /*_w*/) { } - //! \brief Simulate Scalar field computation PONCA_MULTIARCH inline Scalar potential ( ) const { return Scalar(0); } diff --git a/Ponca/src/Fitting/primitive.h b/Ponca/src/Fitting/primitive.h index bbd4c6d8c..72c322eaa 100644 --- a/Ponca/src/Fitting/primitive.h +++ b/Ponca/src/Fitting/primitive.h @@ -83,6 +83,9 @@ class PrimitiveBase and the result is stable, eq. having more than 6 neighbors) */ PONCA_MULTIARCH inline bool isStable() const { return m_eCurrentState == STABLE; } + /*! \brief Get number of points added in the neighborhood (with non negative weight) */ + PONCA_MULTIARCH inline int getNumNeighbors() const { return m_nbNeighbors; } + /*! \return the current test of the fit */ PONCA_MULTIARCH inline FIT_RESULT getCurrentState() const { diff --git a/doc/src/example.mdoc b/doc/src/example.mdoc index dcb88e1c8..02b225bb9 100644 --- a/doc/src/example.mdoc +++ b/doc/src/example.mdoc @@ -17,5 +17,7 @@ - \subpage example_cxx_pcl_page : How to use Ponca with Point cloud library (PCL). A basic example to compute and visualize surface curvature. - \subpage example_cu_ssc_page : Calculate Screen Space Curvature using CUDA/C++. - \subpage example_python_ssc_page : Calculate Screen Space Curvature using CUDA/Python. + - @ref spatialpartitioning "Spatial Partitioning Module" + - \subpage example_cxx_nanoflann_page : Comparison between Nanoflann and Ponca KdTree APIs. */ diff --git a/doc/src/example_cxx_nanoflann.mdoc b/doc/src/example_cxx_nanoflann.mdoc new file mode 100644 index 000000000..242d9e86f --- /dev/null +++ b/doc/src/example_cxx_nanoflann.mdoc @@ -0,0 +1,53 @@ +/* + This Source Code Form is subject to the terms of the Mozilla Public + License, v. 2.0. If a copy of the MPL was not distributed with this + file, You can obtain one at http://mozilla.org/MPL/2.0/. +*/ + + +/*! + \page example_cxx_nanoflann_page Comparison between Nanoflann and Ponca KdTree APIs + + \section nanoflann_intro_sec Introduction + + This example demonstrates how to use Ponca and + Nanoflann (v1.5.0) KdTrees on the same data, and compare runtime performances. + + \subsection nanoflann_compilation_sec Compilation + + As for other examples, you need to enable the compilation of the examples with `cmake`, and build the examples: + + \code + cmake [..] -DPONCA_CONFIGURE_EXAMPLES=ON + make ponca-examples // or make ponca_nanoflann + \endcode + + \section nanoflann_comparison_sec API comparisons + + Task | Ponca | Nanoflann + ---------------- | ----------------------------------------------------- | ------------- + KdTree Creation | \snippet ponca_nanoflann.cpp Create Ponca KdTree | \snippet ponca_nanoflann.cpp Create NanoFlann KdTree + Range Query | \snippet ponca_nanoflann.cpp Use Ponca KdTree | \snippet ponca_nanoflann.cpp Use NanoFlann KdTree + + The fitting object is a Ponca::DryFit, an object that filter the neighbors according to their weight (out of scale points + are not counted), but do not perform any other computation: + \snippet ponca_nanoflann.cpp Define Fit Type + + \section nanoflann_timings_sec Timings + The example output the time required to collect the neighbors, and the number of neighbors collected: + \code +Timings: +Raw : 0.168698 +Ponca : 0.025475 +Nanoflann : 0.027746 +Number of neighbors: +Raw : 950618 +Ponca : 950618 +Nanoflann : 950618 + \endcode + + \section nanoflann_sourcecode_sec Example source code + Source file: `ponca_nanoflann.cpp` + \include ponca_nanoflann.cpp + + */ diff --git a/doc/src/ponca_module_spatialpartitioning.mdoc b/doc/src/ponca_module_spatialpartitioning.mdoc index a897d51ef..ea77c9cd1 100644 --- a/doc/src/ponca_module_spatialpartitioning.mdoc +++ b/doc/src/ponca_module_spatialpartitioning.mdoc @@ -42,7 +42,13 @@ namespace Ponca \note As queries are objets that are independent from the KdTree, they can be created and used in parallel from multiple threads. - + \subsection spatialpartitioning_kdtree_examples Examples + KdTree usage is demonstrated both in tests and examples: + - `tests/src/basket.cpp` + - `tests/src/kdtree_knearest.cpp` + - `tests/src/kdtree_nearest.cpp` + - `tests/src/kdtree_range.cpp` + - `examples/cpp/nanoflann/ponca_nanoflann.cpp` \subsection spatialpartitioning_kdtree_extending Extending KdTree Ponca::KdTreeBase is a customizable version of Ponca::KdTree, which can be controlled using `Traits`. diff --git a/examples/cpp/CMakeLists.txt b/examples/cpp/CMakeLists.txt index 51d34a14c..53070e5a2 100644 --- a/examples/cpp/CMakeLists.txt +++ b/examples/cpp/CMakeLists.txt @@ -35,3 +35,4 @@ add_dependencies(ponca-examples ponca_fit_line) ponca_handle_eigen_dependency(ponca_fit_line) add_subdirectory(pcl) +add_subdirectory(nanoflann) diff --git a/examples/cpp/nanoflann/CMakeLists.txt b/examples/cpp/nanoflann/CMakeLists.txt new file mode 100644 index 000000000..c2ee5d572 --- /dev/null +++ b/examples/cpp/nanoflann/CMakeLists.txt @@ -0,0 +1,8 @@ +project(Ponca_Example_Nanoflann LANGUAGES CXX) + +set(CMAKE_INCLUDE_CURRENT_DIR ON) + +add_executable(ponca_nanoflann ponca_nanoflann.cpp) +target_include_directories(ponca_nanoflann PRIVATE ${PONCA_src_ROOT}) +add_dependencies(ponca-examples ponca_nanoflann) +ponca_handle_eigen_dependency(ponca_nanoflann) diff --git a/examples/cpp/nanoflann/nanoflann.hpp b/examples/cpp/nanoflann/nanoflann.hpp new file mode 100644 index 000000000..305b25a4d --- /dev/null +++ b/examples/cpp/nanoflann/nanoflann.hpp @@ -0,0 +1,2585 @@ +/*********************************************************************** + * 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 2011-2022 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 in your code. + * + * See: + * - [Online README](https://github.com/jlblancoc/nanoflann) + * - [C++ API documentation](https://jlblancoc.github.io/nanoflann/) + */ + +#pragma once + +#include +#include +#include +#include +#include // for abs() +#include // for abs() +#include // std::reference_wrapper +#include +#include +#include // std::numeric_limits +#include +#include +#include +#include + +/** Library version: 0xMmP (M=Major,m=minor,P=patch) */ +#define NANOFLANN_VERSION 0x150 + +// 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 +// Avoid conflicts with X11 headers +#ifdef None +#undef None +#endif + +namespace nanoflann +{ +/** @addtogroup nanoflann_grp nanoflann C++ library for KD-trees + * @{ */ + +/** the PI constant (required to avoid MSVC missing symbols) */ +template +T pi_const() +{ + return static_cast(3.14159265358979323846); +} + +/** + * Traits if object is resizable and assignable (typically has a resize | assign + * method) + */ +template +struct has_resize : std::false_type +{ +}; + +template +struct has_resize().resize(1), 0)> + : std::true_type +{ +}; + +template +struct has_assign : std::false_type +{ +}; + +template +struct has_assign().assign(1, 0), 0)> + : std::true_type +{ +}; + +/** + * Free function to resize a resizable object + */ +template +inline typename std::enable_if::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 +inline typename std::enable_if::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 +inline typename std::enable_if::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 +inline typename std::enable_if::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: + using DistanceType = _DistanceType; + using IndexType = _IndexType; + using CountType = _CountType; + + private: + IndexType* indices; + DistanceType* dists; + CountType capacity; + CountType count; + + public: + explicit KNNResultSet(CountType capacity_) + : indices(nullptr), dists(nullptr), capacity(capacity_), count(0) + { + } + + void init(IndexType* indices_, DistanceType* dists_) + { + indices = indices_; + dists = dists_; + count = 0; + if (capacity) + dists[capacity - 1] = (std::numeric_limits::max)(); + } + + CountType size() const { return count; } + + 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 + */ + bool addPoint(DistanceType dist, IndexType index) + { + CountType i; + for (i = count; i > 0; --i) + { + /** If defined and two points have the same distance, the one with + * the lowest-index will be returned first. */ +#ifdef NANOFLANN_FIRST_MATCH + if ((dists[i - 1] > dist) || + ((dist == dists[i - 1]) && (indices[i - 1] > index))) + { +#else + if (dists[i - 1] > dist) + { +#endif + if (i < capacity) + { + dists[i] = dists[i - 1]; + indices[i] = indices[i - 1]; + } + } + else + break; + } + if (i < capacity) + { + dists[i] = dist; + indices[i] = index; + } + if (count < capacity) count++; + + // tell caller that the search shall continue + return true; + } + + DistanceType worstDist() const { return dists[capacity - 1]; } +}; + +/** operator "<" for std::sort() */ +struct IndexDist_Sorter +{ + /** PairType will be typically: ResultItem */ + template + bool operator()(const PairType& p1, const PairType& p2) const + { + return p1.second < p2.second; + } +}; + +/** + * Each result element in RadiusResultSet. Note that distances and indices + * are named `first` and `second` to keep backward-compatibility with the + * `std::pair<>` type used in the past. In contrast, this structure is ensured + * to be `std::is_standard_layout` so it can be used in wrappers to other + * languages. + * See: https://github.com/jlblancoc/nanoflann/issues/166 + */ +template +struct ResultItem +{ + ResultItem() = default; + ResultItem(const IndexType index, const DistanceType distance) + : first(index), second(distance) + { + } + + IndexType first; //!< Index of the sample in the dataset + DistanceType second; //!< Distance from sample to query point +}; + +/** + * A result-set class used when performing a radius based search. + */ +template +class RadiusResultSet +{ + public: + using DistanceType = _DistanceType; + using IndexType = _IndexType; + + public: + const DistanceType radius; + + std::vector>& m_indices_dists; + + explicit RadiusResultSet( + DistanceType radius_, + std::vector>& indices_dists) + : radius(radius_), m_indices_dists(indices_dists) + { + init(); + } + + void init() { clear(); } + void clear() { m_indices_dists.clear(); } + + size_t size() const { return m_indices_dists.size(); } + size_t empty() const { return m_indices_dists.empty(); } + + 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 + */ + bool addPoint(DistanceType dist, IndexType index) + { + if (dist < radius) m_indices_dists.emplace_back(index, dist); + return true; + } + + DistanceType worstDist() const { return radius; } + + /** + * Find the worst result (farthest neighbor) without copying or sorting + * Pre-conditions: size() > 0 + */ + ResultItem worst_item() const + { + if (m_indices_dists.empty()) + throw std::runtime_error( + "Cannot invoke RadiusResultSet::worst_item() on " + "an empty list of results."); + auto it = std::max_element( + m_indices_dists.begin(), m_indices_dists.end(), IndexDist_Sorter()); + return *it; + } +}; + +/** @} */ + +/** @addtogroup loadsave_grp Load/save auxiliary functions + * @{ */ +template +void save_value(std::ostream& stream, const T& value) +{ + stream.write(reinterpret_cast(&value), sizeof(T)); +} + +template +void save_value(std::ostream& stream, const std::vector& value) +{ + size_t size = value.size(); + stream.write(reinterpret_cast(&size), sizeof(size_t)); + stream.write(reinterpret_cast(value.data()), sizeof(T) * size); +} + +template +void load_value(std::istream& stream, T& value) +{ + stream.read(reinterpret_cast(&value), sizeof(T)); +} + +template +void load_value(std::istream& stream, std::vector& value) +{ + size_t size; + stream.read(reinterpret_cast(&size), sizeof(size_t)); + value.resize(size); + stream.read(reinterpret_cast(value.data()), sizeof(T) * size); +} +/** @} */ + +/** @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 DataSource Source of the data, i.e. where the vectors are stored + * \tparam _DistanceType Type of distance variables (must be signed) + * \tparam IndexType Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + class T, class DataSource, typename _DistanceType = T, + typename IndexType = uint32_t> +struct L1_Adaptor +{ + using ElementType = T; + using DistanceType = _DistanceType; + + const DataSource& data_source; + + L1_Adaptor(const DataSource& _data_source) : data_source(_data_source) {} + + DistanceType evalMetric( + const T* a, const IndexType 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 + 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 DataSource Source of the data, i.e. where the vectors are stored + * \tparam _DistanceType Type of distance variables (must be signed) + * \tparam IndexType Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + class T, class DataSource, typename _DistanceType = T, + typename IndexType = uint32_t> +struct L2_Adaptor +{ + using ElementType = T; + using DistanceType = _DistanceType; + + const DataSource& data_source; + + L2_Adaptor(const DataSource& _data_source) : data_source(_data_source) {} + + DistanceType evalMetric( + const T* a, const IndexType 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 + 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 DataSource Source of the data, i.e. where the vectors are stored + * \tparam _DistanceType Type of distance variables (must be signed) + * \tparam IndexType Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + class T, class DataSource, typename _DistanceType = T, + typename IndexType = uint32_t> +struct L2_Simple_Adaptor +{ + using ElementType = T; + using DistanceType = _DistanceType; + + const DataSource& data_source; + + L2_Simple_Adaptor(const DataSource& _data_source) + : data_source(_data_source) + { + } + + DistanceType evalMetric( + const T* a, const IndexType 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 + 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, uint8_t) + * \tparam DataSource Source of the data, i.e. where the vectors are stored + * \tparam _DistanceType Type of distance variables (must be signed) (e.g. + * float, double) orientation is constrained to be in [-pi, pi] + * \tparam IndexType Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + class T, class DataSource, typename _DistanceType = T, + typename IndexType = uint32_t> +struct SO2_Adaptor +{ + using ElementType = T; + using DistanceType = _DistanceType; + + const DataSource& data_source; + + SO2_Adaptor(const DataSource& _data_source) : data_source(_data_source) {} + + DistanceType evalMetric( + const T* a, const IndexType 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 + DistanceType accum_dist(const U a, const V b, const size_t) const + { + DistanceType result = DistanceType(); + DistanceType PI = pi_const(); + 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, uint8_t) + * \tparam DataSource Source of the data, i.e. where the vectors are stored + * \tparam _DistanceType Type of distance variables (must be signed) (e.g. + * float, double) + * \tparam IndexType Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + class T, class DataSource, typename _DistanceType = T, + typename IndexType = uint32_t> +struct SO3_Adaptor +{ + using ElementType = T; + using DistanceType = _DistanceType; + + L2_Simple_Adaptor + distance_L2_Simple; + + SO3_Adaptor(const DataSource& _data_source) + : distance_L2_Simple(_data_source) + { + } + + DistanceType evalMetric( + const T* a, const IndexType b_idx, size_t size) const + { + return distance_L2_Simple.evalMetric(a, b_idx, size); + } + + template + 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 + struct traits + { + using distance_t = L1_Adaptor; + }; +}; +/** Metaprogramming helper traits class for the L2 (Euclidean) **squared** + * distance metric */ +struct metric_L2 : public Metric +{ + template + struct traits + { + using distance_t = L2_Adaptor; + }; +}; +/** Metaprogramming helper traits class for the L2_simple (Euclidean) + * **squared** distance metric */ +struct metric_L2_Simple : public Metric +{ + template + struct traits + { + using distance_t = L2_Simple_Adaptor; + }; +}; +/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */ +struct metric_SO2 : public Metric +{ + template + struct traits + { + using distance_t = SO2_Adaptor; + }; +}; +/** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */ +struct metric_SO3 : public Metric +{ + template + struct traits + { + using distance_t = SO3_Adaptor; + }; +}; + +/** @} */ + +/** @addtogroup param_grp Parameter structs + * @{ */ + +enum class KDTreeSingleIndexAdaptorFlags +{ + None = 0, + SkipInitialBuildIndex = 1 +}; + +inline std::underlying_type::type operator&( + KDTreeSingleIndexAdaptorFlags lhs, KDTreeSingleIndexAdaptorFlags rhs) +{ + using underlying = + typename std::underlying_type::type; + return static_cast(lhs) & static_cast(rhs); +} + +/** Parameters (see README.md) */ +struct KDTreeSingleIndexAdaptorParams +{ + KDTreeSingleIndexAdaptorParams( + size_t _leaf_max_size = 10, + KDTreeSingleIndexAdaptorFlags _flags = + KDTreeSingleIndexAdaptorFlags::None, + unsigned int _n_thread_build = 1) + : leaf_max_size(_leaf_max_size), + flags(_flags), + n_thread_build(_n_thread_build) + { + } + + size_t leaf_max_size; + KDTreeSingleIndexAdaptorFlags flags; + unsigned int n_thread_build; +}; + +/** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */ +struct SearchParameters +{ + SearchParameters(float eps_ = 0, bool sorted_ = true) + : eps(eps_), sorted(sorted_) + { + } + + 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 + * @{ */ + +/** + * 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. + * + */ +class PooledAllocator +{ + static constexpr size_t WORDSIZE = 16; + static constexpr size_t BLOCKSIZE = 8192; + + /* 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. */ + + using Offset = uint32_t; + using Size = uint32_t; + using Dimension = int32_t; + + Size remaining_ = 0; //!< Number of bytes left in current block of storage + void* base_ = nullptr; //!< Pointer to base of current block of storage + void* loc_ = nullptr; //!< Current location in block to next allocate + + void internal_init() + { + remaining_ = 0; + base_ = nullptr; + usedMemory = 0; + wastedMemory = 0; + } + + public: + Size usedMemory = 0; + Size wastedMemory = 0; + + /** + 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_ != nullptr) + { + // Get pointer to prev block + void* prev = *(static_cast(base_)); + ::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 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 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"); + throw std::bad_alloc(); + } + + /* Fill first word of new block with pointer to previous block. */ + static_cast(m)[0] = base_; + base_ = m; + + Size shift = 0; + // int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) & + // (WORDSIZE-1))) & (WORDSIZE-1); + + remaining_ = blocksize - sizeof(void*) - shift; + loc_ = (static_cast(m) + sizeof(void*) + shift); + } + void* rloc = loc_; + loc_ = static_cast(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 + T* allocate(const size_t count = 1) + { + T* mem = static_cast(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 +struct array_or_vector +{ + using type = std::array; +}; +/** Dynamic size version */ +template +struct array_or_vector<-1, T> +{ + using type = std::vector; +}; + +/** @} */ + +/** 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, which must be ensured to + * have a lifetime equal or longer than the instance of this class. + * \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 Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + class Derived, typename Distance, class DatasetAdaptor, int32_t DIM = -1, + typename IndexType = uint32_t> +class KDTreeBaseClass +{ + public: + /** Frees the previously-built index. Automatically called within + * buildIndex(). */ + void freeIndex(Derived& obj) + { + obj.pool_.free_all(); + obj.root_node_ = nullptr; + obj.size_at_index_build_ = 0; + } + + using ElementType = typename Distance::ElementType; + using DistanceType = typename Distance::DistanceType; + + /** + * Array of indices to vectors in the dataset_. + */ + std::vector vAcc_; + + using Offset = typename decltype(vAcc_)::size_type; + using Size = typename decltype(vAcc_)::size_type; + using Dimension = int32_t; + + /*--------------------------- + * 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 + { + Offset left, right; //!< Indices of points in leaf node + } lr; + struct nonleaf + { + Dimension divfeat; //!< Dimension used for subdivision. + /// The values used for subdivision. + DistanceType divlow, divhigh; + } sub; + } node_type; + + /** Child nodes (both=nullptr mean its a leaf node) */ + Node *child1 = nullptr, *child2 = nullptr; + }; + + using NodePtr = Node*; + using NodeConstPtr = const Node*; + + struct Interval + { + ElementType low, high; + }; + + NodePtr root_node_ = nullptr; + + Size leaf_max_size_ = 0; + + /// Number of thread for concurrent tree build + Size n_thread_build_ = 1; + /// Number of current points in the dataset + Size size_ = 0; + /// Number of points in the dataset when the index was built + Size size_at_index_build_ = 0; + Dimension dim_ = 0; //!< Dimensionality of each data point + + /** Define "BoundingBox" as a fixed-size or variable-size container + * depending on "DIM" */ + using BoundingBox = typename array_or_vector::type; + + /** Define "distance_vector_t" as a fixed-size or variable-size container + * depending on "DIM" */ + using distance_vector_t = typename array_or_vector::type; + + /** 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 size(const Derived& obj) const { return obj.size_; } + + /** Returns the length of each point in the dataset */ + Size veclen(const Derived& obj) { return DIM > 0 ? DIM : obj.dim; } + + /// Helper accessor to the dataset points: + ElementType dataset_get( + const Derived& obj, IndexType element, Dimension component) const + { + return obj.dataset_.kdtree_get_pt(element, component); + } + + /** + * Computes the inde memory usage + * Returns: memory used by the index + */ + Size 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, Offset ind, Size count, Dimension element, + ElementType& min_elem, ElementType& max_elem) + { + min_elem = dataset_get(obj, vAcc_[ind], element); + max_elem = min_elem; + for (Offset i = 1; i < count; ++i) + { + ElementType val = dataset_get(obj, vAcc_[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 Offset left, const Offset right, BoundingBox& bbox) + { + NodePtr node = obj.pool_.template allocate(); // allocate memory + const auto dims = (DIM > 0 ? DIM : obj.dim_); + + /* If too few exemplars remain, then make this a leaf node. */ + if ((right - left) <= static_cast(obj.leaf_max_size_)) + { + node->child1 = node->child2 = nullptr; /* Mark as leaf node. */ + node->node_type.lr.left = left; + node->node_type.lr.right = right; + + // compute bounding-box of leaf points + for (Dimension i = 0; i < dims; ++i) + { + bbox[i].low = dataset_get(obj, obj.vAcc_[left], i); + bbox[i].high = dataset_get(obj, obj.vAcc_[left], i); + } + for (Offset k = left + 1; k < right; ++k) + { + for (Dimension i = 0; i < dims; ++i) + { + const auto val = dataset_get(obj, obj.vAcc_[k], i); + if (bbox[i].low > val) bbox[i].low = val; + if (bbox[i].high < val) bbox[i].high = val; + } + } + } + else + { + Offset idx; + Dimension cutfeat; + DistanceType cutval; + middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox); + + node->node_type.sub.divfeat = cutfeat; + + BoundingBox left_bbox(bbox); + left_bbox[cutfeat].high = cutval; + node->child1 = this->divideTree(obj, left, left + idx, left_bbox); + + BoundingBox right_bbox(bbox); + right_bbox[cutfeat].low = cutval; + node->child2 = this->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 (Dimension i = 0; i < dims; ++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; + } + + /** + * Create a tree node that subdivides the list of vecs from vind[first] to + * vind[last] concurrently. The routine is called recursively on each + * sublist. + * + * @param left index of the first vector + * @param right index of the last vector + * @param thread_count count of std::async threads + * @param mutex mutex for mempool allocation + */ + NodePtr divideTreeConcurrent( + Derived& obj, const Offset left, const Offset right, BoundingBox& bbox, + std::atomic& thread_count, std::mutex& mutex) + { + std::unique_lock lock(mutex); + NodePtr node = obj.pool_.template allocate(); // allocate memory + lock.unlock(); + + const auto dims = (DIM > 0 ? DIM : obj.dim_); + + /* If too few exemplars remain, then make this a leaf node. */ + if ((right - left) <= static_cast(obj.leaf_max_size_)) + { + node->child1 = node->child2 = nullptr; /* Mark as leaf node. */ + node->node_type.lr.left = left; + node->node_type.lr.right = right; + + // compute bounding-box of leaf points + for (Dimension i = 0; i < dims; ++i) + { + bbox[i].low = dataset_get(obj, obj.vAcc_[left], i); + bbox[i].high = dataset_get(obj, obj.vAcc_[left], i); + } + for (Offset k = left + 1; k < right; ++k) + { + for (Dimension i = 0; i < dims; ++i) + { + const auto val = dataset_get(obj, obj.vAcc_[k], i); + if (bbox[i].low > val) bbox[i].low = val; + if (bbox[i].high < val) bbox[i].high = val; + } + } + } + else + { + Offset idx; + Dimension cutfeat; + DistanceType cutval; + middleSplit_(obj, left, right - left, idx, cutfeat, cutval, bbox); + + node->node_type.sub.divfeat = cutfeat; + + std::future left_future, right_future; + + BoundingBox left_bbox(bbox); + left_bbox[cutfeat].high = cutval; + if (++thread_count < n_thread_build_) + { + left_future = std::async( + std::launch::async, &KDTreeBaseClass::divideTreeConcurrent, + this, std::ref(obj), left, left + idx, std::ref(left_bbox), + std::ref(thread_count), std::ref(mutex)); + } + else + { + --thread_count; + node->child1 = this->divideTreeConcurrent( + obj, left, left + idx, left_bbox, thread_count, mutex); + } + + BoundingBox right_bbox(bbox); + right_bbox[cutfeat].low = cutval; + if (++thread_count < n_thread_build_) + { + right_future = std::async( + std::launch::async, &KDTreeBaseClass::divideTreeConcurrent, + this, std::ref(obj), left + idx, right, + std::ref(right_bbox), std::ref(thread_count), + std::ref(mutex)); + } + else + { + --thread_count; + node->child2 = this->divideTreeConcurrent( + obj, left + idx, right, right_bbox, thread_count, mutex); + } + + if (left_future.valid()) + { + node->child1 = left_future.get(); + --thread_count; + } + if (right_future.valid()) + { + node->child2 = right_future.get(); + --thread_count; + } + + node->node_type.sub.divlow = left_bbox[cutfeat].high; + node->node_type.sub.divhigh = right_bbox[cutfeat].low; + + for (Dimension i = 0; i < dims; ++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_( + const Derived& obj, const Offset ind, const Size count, Offset& index, + Dimension& cutfeat, DistanceType& cutval, const BoundingBox& bbox) + { + const auto dims = (DIM > 0 ? DIM : obj.dim_); + const auto EPS = static_cast(0.00001); + ElementType max_span = bbox[0].high - bbox[0].low; + for (Dimension i = 1; i < dims; ++i) + { + ElementType span = bbox[i].high - bbox[i].low; + if (span > max_span) { max_span = span; } + } + ElementType max_spread = -1; + cutfeat = 0; + for (Dimension i = 0; i < dims; ++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; + + Offset 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 the axis + * corresponding to the 'cutfeat' dimension at 'cutval' position. + * + * On return: + * dataset[ind[0..lim1-1]][cutfeat]cutval + */ + void planeSplit( + const Derived& obj, const Offset ind, const Size count, + const Dimension cutfeat, const DistanceType& cutval, Offset& lim1, + Offset& lim2) + { + /* Move vector indices for left subtree to front of list. */ + Offset left = 0; + Offset right = count - 1; + for (;;) + { + while (left <= right && + dataset_get(obj, vAcc_[ind + left], cutfeat) < cutval) + ++left; + while (right && left <= right && + dataset_get(obj, vAcc_[ind + right], cutfeat) >= cutval) + --right; + if (left > right || !right) + break; // "!right" was added to support unsigned Index types + std::swap(vAcc_[ind + left], vAcc_[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, vAcc_[ind + left], cutfeat) <= cutval) + ++left; + while (right && left <= right && + dataset_get(obj, vAcc_[ind + right], cutfeat) > cutval) + --right; + if (left > right || !right) + break; // "!right" was added to support unsigned Index types + std::swap(vAcc_[ind + left], vAcc_[ind + right]); + ++left; + --right; + } + lim2 = left; + } + + DistanceType computeInitialDistances( + const Derived& obj, const ElementType* vec, + distance_vector_t& dists) const + { + assert(vec); + DistanceType dist = DistanceType(); + + for (Dimension 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); + dist += dists[i]; + } + if (vec[i] > obj.root_bbox_[i].high) + { + dists[i] = + obj.distance_.accum_dist(vec[i], obj.root_bbox_[i].high, i); + dist += dists[i]; + } + } + return dist; + } + + static void save_tree( + const Derived& obj, std::ostream& stream, const NodeConstPtr tree) + { + save_value(stream, *tree); + if (tree->child1 != nullptr) { save_tree(obj, stream, tree->child1); } + if (tree->child2 != nullptr) { save_tree(obj, stream, tree->child2); } + } + + static void load_tree(Derived& obj, std::istream& stream, NodePtr& tree) + { + tree = obj.pool_.template allocate(); + load_value(stream, *tree); + if (tree->child1 != nullptr) { load_tree(obj, stream, tree->child1); } + if (tree->child2 != nullptr) { 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(const Derived& obj, std::ostream& stream) const + { + save_value(stream, obj.size_); + save_value(stream, obj.dim_); + save_value(stream, obj.root_bbox_); + save_value(stream, obj.leaf_max_size_); + save_value(stream, obj.vAcc_); + 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, std::istream& stream) + { + load_value(stream, obj.size_); + load_value(stream, obj.dim_); + load_value(stream, obj.root_bbox_); + load_value(stream, obj.leaf_max_size_); + load_value(stream, obj.vAcc_); + 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 + * size_t kdtree_get_point_count() const { ... } + * + * + * // Must return the dim'th component of the idx'th point in the class: + * 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 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, which must be ensured to + * have a lifetime equal or longer than the instance of this class. + * \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, int32_t DIM = -1, + typename IndexType = uint32_t> +class KDTreeSingleIndexAdaptor + : public KDTreeBaseClass< + KDTreeSingleIndexAdaptor, + Distance, DatasetAdaptor, DIM, IndexType> +{ + public: + /** Deleted copy constructor*/ + explicit KDTreeSingleIndexAdaptor( + const KDTreeSingleIndexAdaptor< + Distance, DatasetAdaptor, DIM, IndexType>&) = delete; + + /** The data source used by this index */ + const DatasetAdaptor& dataset_; + + const KDTreeSingleIndexAdaptorParams indexParams; + + Distance distance_; + + using Base = typename nanoflann::KDTreeBaseClass< + nanoflann::KDTreeSingleIndexAdaptor< + Distance, DatasetAdaptor, DIM, IndexType>, + Distance, DatasetAdaptor, DIM, IndexType>; + + using Offset = typename Base::Offset; + using Size = typename Base::Size; + using Dimension = typename Base::Dimension; + + using ElementType = typename Base::ElementType; + using DistanceType = typename Base::DistanceType; + + using Node = typename Base::Node; + using NodePtr = Node*; + + using Interval = typename Base::Interval; + + /** Define "BoundingBox" as a fixed-size or variable-size container + * depending on "DIM" */ + using BoundingBox = typename Base::BoundingBox; + + /** Define "distance_vector_t" as a fixed-size or variable-size container + * depending on "DIM" */ + using distance_vector_t = typename Base::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. Its lifetime must be + * equal or longer than that of the instance of this class. + * @param params Basically, the maximum leaf node size + * + * Note that there is a variable number of optional additional parameters + * which will be forwarded to the metric class constructor. Refer to example + * `examples/pointcloud_custom_metric.cpp` for a use case. + * + */ + template + explicit KDTreeSingleIndexAdaptor( + const Dimension dimensionality, const DatasetAdaptor& inputData, + const KDTreeSingleIndexAdaptorParams& params, Args&&... args) + : dataset_(inputData), + indexParams(params), + distance_(inputData, std::forward(args)...) + { + init(dimensionality, params); + } + + explicit KDTreeSingleIndexAdaptor( + const Dimension dimensionality, const DatasetAdaptor& inputData, + const KDTreeSingleIndexAdaptorParams& params = {}) + : dataset_(inputData), indexParams(params), distance_(inputData) + { + init(dimensionality, params); + } + + private: + void init( + const Dimension dimensionality, + const KDTreeSingleIndexAdaptorParams& params) + { + Base::size_ = dataset_.kdtree_get_point_count(); + Base::size_at_index_build_ = Base::size_; + Base::dim_ = dimensionality; + if (DIM > 0) Base::dim_ = DIM; + Base::leaf_max_size_ = params.leaf_max_size; + if (params.n_thread_build > 0) + { + Base::n_thread_build_ = params.n_thread_build; + } + else + { + Base::n_thread_build_ = + std::max(std::thread::hardware_concurrency(), 1u); + } + + if (!(params.flags & + KDTreeSingleIndexAdaptorFlags::SkipInitialBuildIndex)) + { + // Build KD-tree: + buildIndex(); + } + } + + public: + /** + * Builds the index + */ + void buildIndex() + { + Base::size_ = dataset_.kdtree_get_point_count(); + Base::size_at_index_build_ = Base::size_; + init_vind(); + this->freeIndex(*this); + Base::size_at_index_build_ = Base::size_; + if (Base::size_ == 0) return; + computeBoundingBox(Base::root_bbox_); + // construct the tree + if (Base::n_thread_build_ == 1) + { + Base::root_node_ = + this->divideTree(*this, 0, Base::size_, Base::root_bbox_); + } + else + { + std::atomic thread_count(0u); + std::mutex mutex; + Base::root_node_ = this->divideTreeConcurrent( + *this, 0, Base::size_, Base::root_bbox_, thread_count, mutex); + } + } + + /** \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 + * \return True if the requested neighbors could be found. + * \sa knnSearch, radiusSearch + * + * \note If L2 norms are used, all returned distances are actually squared + * distances. + */ + template + bool findNeighbors( + RESULTSET& result, const ElementType* vec, + const SearchParameters& searchParams = {}) const + { + assert(vec); + if (this->size(*this) == 0) return false; + if (!Base::root_node_) + throw std::runtime_error( + "[nanoflann] findNeighbors() called before building the " + "index."); + float epsError = 1 + searchParams.eps; + + // fixed or variable-sized container (depending on DIM) + distance_vector_t dists; + // Fill it with zeros. + auto zero = static_cast(0); + assign(dists, (DIM > 0 ? DIM : Base::dim_), zero); + DistanceType dist = this->computeInitialDistances(*this, vec, dists); + searchLevel(result, vec, Base::root_node_, dist, dists, epsError); + return result.full(); + } + + /** + * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1]. + * Their indices and distances are stored in the provided pointers to + * array/vector. + * + * \sa radiusSearch, findNeighbors + * \return Number `N` of valid points in the result set. + * + * \note If L2 norms are used, all returned distances are actually squared + * distances. + * + * \note Only the first `N` entries in `out_indices` and `out_distances` + * will be valid. Return is less than `num_closest` only if the + * number of elements in the tree is less than `num_closest`. + */ + Size knnSearch( + const ElementType* query_point, const Size num_closest, + IndexType* out_indices, DistanceType* out_distances) const + { + nanoflann::KNNResultSet resultSet(num_closest); + resultSet.init(out_indices, out_distances); + findNeighbors(resultSet, query_point); + 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() ) + * + * \note If L2 norms are used, search radius and all returned distances + * are actually squared distances. + */ + Size radiusSearch( + const ElementType* query_point, const DistanceType& radius, + std::vector>& IndicesDists, + const SearchParameters& searchParams = {}) const + { + RadiusResultSet resultSet( + radius, IndicesDists); + const Size 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 + Size radiusSearchCustomCallback( + const ElementType* query_point, SEARCH_CALLBACK& resultSet, + const SearchParameters& searchParams = {}) const + { + 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. + Base::size_ = dataset_.kdtree_get_point_count(); + if (Base::vAcc_.size() != Base::size_) Base::vAcc_.resize(Base::size_); + for (Size i = 0; i < Base::size_; i++) Base::vAcc_[i] = i; + } + + void computeBoundingBox(BoundingBox& bbox) + { + const auto dims = (DIM > 0 ? DIM : Base::dim_); + resize(bbox, dims); + if (dataset_.kdtree_get_bbox(bbox)) + { + // Done! It was implemented in derived class + } + else + { + const Size N = dataset_.kdtree_get_point_count(); + if (!N) + throw std::runtime_error( + "[nanoflann] computeBoundingBox() called but " + "no data points found."); + for (Dimension i = 0; i < dims; ++i) + { + bbox[i].low = bbox[i].high = + this->dataset_get(*this, Base::vAcc_[0], i); + } + for (Offset k = 1; k < N; ++k) + { + for (Dimension i = 0; i < dims; ++i) + { + const auto val = + this->dataset_get(*this, Base::vAcc_[k], i); + if (val < bbox[i].low) bbox[i].low = val; + if (val > bbox[i].high) bbox[i].high = val; + } + } + } + } + + /** + * Performs an exact search in the tree starting from a node. + * \tparam RESULTSET Should be any ResultSet + * \return true if the search should be continued, false if the results are + * sufficient + */ + template + bool searchLevel( + RESULTSET& result_set, const ElementType* vec, const NodePtr node, + DistanceType mindist, distance_vector_t& dists, + const float epsError) const + { + /* If this is a leaf node, then do check and return. */ + if ((node->child1 == nullptr) && (node->child2 == nullptr)) + { + DistanceType worst_dist = result_set.worstDist(); + for (Offset i = node->node_type.lr.left; + i < node->node_type.lr.right; ++i) + { + const IndexType accessor = Base::vAcc_[i]; // reorder... : i; + DistanceType dist = distance_.evalMetric( + vec, accessor, (DIM > 0 ? DIM : Base::dim_)); + if (dist < worst_dist) + { + if (!result_set.addPoint(dist, Base::vAcc_[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? */ + Dimension 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, mindist, dists, epsError)) + { + // the resultset doesn't want to receive any more points, we're done + // searching! + return false; + } + + DistanceType dst = dists[idx]; + mindist = mindist + cut_dist - dst; + dists[idx] = cut_dist; + if (mindist * epsError <= result_set.worstDist()) + { + if (!searchLevel( + result_set, vec, otherChild, mindist, 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(std::ostream& stream) const + { + Base::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(std::istream& stream) { Base::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 + * size_t kdtree_get_point_count() const { ... } + * + * // Must return the dim'th component of the idx'th point in the class: + * 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 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 Type of the arguments with which the data can be + * accessed (e.g. float, double, int64_t, T*) + */ +template < + typename Distance, class DatasetAdaptor, int32_t DIM = -1, + typename IndexType = uint32_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& treeIndex_; + + Distance distance_; + + using Base = typename nanoflann::KDTreeBaseClass< + nanoflann::KDTreeSingleIndexDynamicAdaptor_< + Distance, DatasetAdaptor, DIM, IndexType>, + Distance, DatasetAdaptor, DIM, IndexType>; + + using ElementType = typename Base::ElementType; + using DistanceType = typename Base::DistanceType; + + using Offset = typename Base::Offset; + using Size = typename Base::Size; + using Dimension = typename Base::Dimension; + + using Node = typename Base::Node; + using NodePtr = Node*; + + using Interval = typename Base::Interval; + /** Define "BoundingBox" as a fixed-size or variable-size container + * depending on "DIM" */ + using BoundingBox = typename Base::BoundingBox; + + /** Define "distance_vector_t" as a fixed-size or variable-size container + * depending on "DIM" */ + using distance_vector_t = typename Base::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. Its lifetime must be + * equal or longer than that of the instance of this class. + * @param params Basically, the maximum leaf node size + */ + KDTreeSingleIndexDynamicAdaptor_( + const Dimension dimensionality, const DatasetAdaptor& inputData, + std::vector& treeIndex, + const KDTreeSingleIndexAdaptorParams& params = + KDTreeSingleIndexAdaptorParams()) + : dataset_(inputData), + index_params_(params), + treeIndex_(treeIndex), + distance_(inputData) + { + Base::size_ = 0; + Base::size_at_index_build_ = 0; + for (auto& v : Base::root_bbox_) v = {}; + Base::dim_ = dimensionality; + if (DIM > 0) Base::dim_ = DIM; + Base::leaf_max_size_ = params.leaf_max_size; + if (params.n_thread_build > 0) + { + Base::n_thread_build_ = params.n_thread_build; + } + else + { + Base::n_thread_build_ = + std::max(std::thread::hardware_concurrency(), 1u); + } + } + + /** Explicitly default the copy constructor */ + KDTreeSingleIndexDynamicAdaptor_( + const KDTreeSingleIndexDynamicAdaptor_& rhs) = default; + + /** Assignment operator definiton */ + KDTreeSingleIndexDynamicAdaptor_ operator=( + const KDTreeSingleIndexDynamicAdaptor_& rhs) + { + KDTreeSingleIndexDynamicAdaptor_ tmp(rhs); + std::swap(Base::vAcc_, tmp.Base::vAcc_); + std::swap(Base::leaf_max_size_, tmp.Base::leaf_max_size_); + std::swap(index_params_, tmp.index_params_); + std::swap(treeIndex_, tmp.treeIndex_); + std::swap(Base::size_, tmp.Base::size_); + std::swap(Base::size_at_index_build_, tmp.Base::size_at_index_build_); + std::swap(Base::root_node_, tmp.Base::root_node_); + std::swap(Base::root_bbox_, tmp.Base::root_bbox_); + std::swap(Base::pool_, tmp.Base::pool_); + return *this; + } + + /** + * Builds the index + */ + void buildIndex() + { + Base::size_ = Base::vAcc_.size(); + this->freeIndex(*this); + Base::size_at_index_build_ = Base::size_; + if (Base::size_ == 0) return; + computeBoundingBox(Base::root_bbox_); + // construct the tree + if (Base::n_thread_build_ == 1) + { + Base::root_node_ = + this->divideTree(*this, 0, Base::size_, Base::root_bbox_); + } + else + { + std::atomic thread_count(0u); + std::mutex mutex; + Base::root_node_ = this->divideTreeConcurrent( + *this, 0, Base::size_, Base::root_bbox_, thread_count, mutex); + } + } + + /** \name Query methods + * @{ */ + + /** + * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored + * inside the result object. + * This is the core search function, all others are wrappers around this + * one. + * + * \param result The result object in which the indices of the + * nearest-neighbors are stored. + * \param vec The vector of the query point for which to search the + * nearest neighbors. + * \param searchParams Optional parameters for the search. + * + * \tparam RESULTSET Should be any ResultSet + * \return True if the requested neighbors could be found. + * + * \sa knnSearch(), radiusSearch(), radiusSearchCustomCallback() + * + * \note If L2 norms are used, all returned distances are actually squared + * distances. + */ + template + bool findNeighbors( + RESULTSET& result, const ElementType* vec, + const SearchParameters& searchParams = {}) const + { + assert(vec); + if (this->size(*this) == 0) return false; + if (!Base::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 : Base::dim_), + static_cast(0)); + DistanceType dist = this->computeInitialDistances(*this, vec, dists); + searchLevel(result, vec, Base::root_node_, dist, dists, epsError); + 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 + * \return Number `N` of valid points in + * the result set. + * + * \note If L2 norms are used, all returned distances are actually squared + * distances. + * + * \note Only the first `N` entries in `out_indices` and `out_distances` + * 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 knnSearch( + const ElementType* query_point, const Size num_closest, + IndexType* out_indices, DistanceType* out_distances) const + { + nanoflann::KNNResultSet resultSet(num_closest); + resultSet.init(out_indices, out_distances); + findNeighbors(resultSet, query_point); + 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() ) + * + * \note If L2 norms are used, search radius and all returned distances + * are actually squared distances. + */ + Size radiusSearch( + const ElementType* query_point, const DistanceType& radius, + std::vector>& IndicesDists, + const SearchParameters& searchParams = {}) const + { + RadiusResultSet 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 + Size radiusSearchCustomCallback( + const ElementType* query_point, SEARCH_CALLBACK& resultSet, + const SearchParameters& searchParams = {}) const + { + findNeighbors(resultSet, query_point, searchParams); + return resultSet.size(); + } + + /** @} */ + + public: + void computeBoundingBox(BoundingBox& bbox) + { + const auto dims = (DIM > 0 ? DIM : Base::dim_); + resize(bbox, dims); + + if (dataset_.kdtree_get_bbox(bbox)) + { + // Done! It was implemented in derived class + } + else + { + const Size N = Base::size_; + if (!N) + throw std::runtime_error( + "[nanoflann] computeBoundingBox() called but " + "no data points found."); + for (Dimension i = 0; i < dims; ++i) + { + bbox[i].low = bbox[i].high = + this->dataset_get(*this, Base::vAcc_[0], i); + } + for (Offset k = 1; k < N; ++k) + { + for (Dimension i = 0; i < dims; ++i) + { + const auto val = + this->dataset_get(*this, Base::vAcc_[k], i); + if (val < bbox[i].low) bbox[i].low = val; + if (val > bbox[i].high) bbox[i].high = val; + } + } + } + } + + /** + * Performs an exact search in the tree starting from a node. + * \tparam RESULTSET Should be any ResultSet + */ + template + void searchLevel( + RESULTSET& result_set, const ElementType* vec, const NodePtr node, + DistanceType mindist, distance_vector_t& dists, + const float epsError) const + { + /* If this is a leaf node, then do check and return. */ + if ((node->child1 == nullptr) && (node->child2 == nullptr)) + { + DistanceType worst_dist = result_set.worstDist(); + for (Offset i = node->node_type.lr.left; + i < node->node_type.lr.right; ++i) + { + const IndexType index = Base::vAcc_[i]; // reorder... : i; + if (treeIndex_[index] == -1) continue; + DistanceType dist = distance_.evalMetric( + vec, index, (DIM > 0 ? DIM : Base::dim_)); + if (dist < worst_dist) + { + if (!result_set.addPoint( + static_cast(dist), + static_cast( + Base::vAcc_[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? */ + Dimension 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, mindist, dists, epsError); + + DistanceType dst = dists[idx]; + mindist = mindist + cut_dist - dst; + dists[idx] = cut_dist; + if (mindist * epsError <= result_set.worstDist()) + { + searchLevel( + result_set, vec, otherChild, mindist, 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(std::ostream& stream) { 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(std::istream& stream) { 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, int32_t DIM = -1, + typename IndexType = uint32_t> +class KDTreeSingleIndexDynamicAdaptor +{ + public: + using ElementType = typename Distance::ElementType; + using DistanceType = typename Distance::DistanceType; + + using Offset = typename KDTreeSingleIndexDynamicAdaptor_< + Distance, DatasetAdaptor, DIM>::Offset; + using Size = typename KDTreeSingleIndexDynamicAdaptor_< + Distance, DatasetAdaptor, DIM>::Size; + using Dimension = typename KDTreeSingleIndexDynamicAdaptor_< + Distance, DatasetAdaptor, DIM>::Dimension; + + protected: + Size leaf_max_size_; + Size treeCount_; + Size pointCount_; + + /** + * The dataset used by this index + */ + const DatasetAdaptor& dataset_; //!< The source of our data + + /** treeIndex[idx] is the index of tree in which point at idx is stored. + * treeIndex[idx]=-1 means that point has been removed. */ + std::vector treeIndex_; + std::unordered_set removedPoints_; + + KDTreeSingleIndexAdaptorParams index_params_; + + Dimension dim_; //!< Dimensionality of each data point + + using index_container_t = KDTreeSingleIndexDynamicAdaptor_< + Distance, DatasetAdaptor, DIM, IndexType>; + std::vector 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& 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() + { + using my_kd_tree_t = KDTreeSingleIndexDynamicAdaptor_< + Distance, DatasetAdaptor, DIM, IndexType>; + std::vector 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. Its lifetime must be + * equal or longer than that of the instance of this class. + * @param params Basically, the maximum leaf node size + */ + explicit 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(std::log2(maximumPointCount)) + 1; + pointCount_ = 0U; + dim_ = dimensionality; + treeIndex_.clear(); + if (DIM > 0) dim_ = DIM; + 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*/ + explicit 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) + { + const Size count = end - start + 1; + int maxIndex = 0; + treeIndex_.resize(treeIndex_.size() + count); + for (IndexType idx = start; idx <= end; idx++) + { + const int pos = First0Bit(pointCount_); + maxIndex = std::max(pos, maxIndex); + treeIndex_[pointCount_] = pos; + + const auto it = removedPoints_.find(idx); + if (it != removedPoints_.end()) + { + removedPoints_.erase(it); + treeIndex_[idx] = pos; + } + + for (int i = 0; i < pos; i++) + { + for (int j = 0; j < static_cast(index_[i].vAcc_.size()); + j++) + { + index_[pos].vAcc_.push_back(index_[i].vAcc_[j]); + if (treeIndex_[index_[i].vAcc_[j]] != -1) + treeIndex_[index_[i].vAcc_[j]] = pos; + } + index_[i].vAcc_.clear(); + } + index_[pos].vAcc_.push_back(idx); + pointCount_++; + } + + for (int i = 0; i <= maxIndex; ++i) + { + index_[i].freeIndex(index_[i]); + if (!index_[i].vAcc_.empty()) index_[i].buildIndex(); + } + } + + /** Remove a point from the set (Lazy Deletion) */ + void removePoint(size_t idx) + { + if (idx >= pointCount_) return; + removedPoints_.insert(idx); + 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 + * \return True if the requested neighbors could be found. + * \sa knnSearch, radiusSearch + * + * \note If L2 norms are used, all returned distances are actually squared + * distances. + */ + template + bool findNeighbors( + RESULTSET& result, const ElementType* vec, + const SearchParameters& 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. You can select whether a + * row or column in the matrix represents a point in the state space. + * + * Example of usage: + * \code + * Eigen::Matrix mat; + * + * // Fill out "mat"... + * using my_kd_tree_t = nanoflann::KDTreeEigenMatrixAdaptor< + * Eigen::Matrix>; + * + * const int max_leaf = 10; + * my_kd_tree_t mat_index(mat, max_leaf); + * 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. + * \tparam row_major If set to true the rows of the matrix are used as the + * points, if set to false the columns of the matrix are used as the + * points. + */ +template < + class MatrixType, int32_t DIM = -1, class Distance = nanoflann::metric_L2, + bool row_major = true> +struct KDTreeEigenMatrixAdaptor +{ + using self_t = + KDTreeEigenMatrixAdaptor; + using num_t = typename MatrixType::Scalar; + using IndexType = typename MatrixType::Index; + using metric_t = typename Distance::template traits< + num_t, self_t, IndexType>::distance_t; + + using index_t = KDTreeSingleIndexAdaptor< + metric_t, self_t, + row_major ? MatrixType::ColsAtCompileTime + : MatrixType::RowsAtCompileTime, + IndexType>; + + index_t* index_; //! The kd-tree index for the user to call its methods as + //! usual with any other FLANN index. + + using Offset = typename index_t::Offset; + using Size = typename index_t::Size; + using Dimension = typename index_t::Dimension; + + /// Constructor: takes a const ref to the matrix object with the data points + explicit KDTreeEigenMatrixAdaptor( + const Dimension dimensionality, + const std::reference_wrapper& mat, + const int leaf_max_size = 10) + : m_data_matrix(mat) + { + const auto dims = row_major ? mat.get().cols() : mat.get().rows(); + if (static_cast(dims) != dimensionality) + throw std::runtime_error( + "Error: 'dimensionality' must match column count in data " + "matrix"); + if (DIM > 0 && static_cast(dims) != DIM) + throw std::runtime_error( + "Data set dimensionality does not match the 'DIM' template " + "argument"); + index_ = new index_t( + dims, *this /* adaptor */, + nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size)); + } + + public: + /** Deleted copy constructor */ + KDTreeEigenMatrixAdaptor(const self_t&) = delete; + + ~KDTreeEigenMatrixAdaptor() { delete index_; } + + const std::reference_wrapper 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 If L2 norms are used, all returned distances are actually squared + * distances. + */ + void query( + const num_t* query_point, const Size num_closest, + IndexType* out_indices, num_t* out_distances) const + { + nanoflann::KNNResultSet resultSet(num_closest); + resultSet.init(out_indices, out_distances); + index_->findNeighbors(resultSet, query_point); + } + + /** @name Interface expected by KDTreeSingleIndexAdaptor + * @{ */ + + const self_t& derived() const { return *this; } + self_t& derived() { return *this; } + + // Must return the number of data points + Size kdtree_get_point_count() const + { + if (row_major) + return m_data_matrix.get().rows(); + else + return m_data_matrix.get().cols(); + } + + // Returns the dim'th component of the idx'th point in the class: + num_t kdtree_get_pt(const IndexType idx, size_t dim) const + { + if (row_major) + return m_data_matrix.get().coeff(idx, IndexType(dim)); + else + return m_data_matrix.get().coeff(IndexType(dim), idx); + } + + // 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 + bool kdtree_get_bbox(BBOX& /*bb*/) const + { + return false; + } + + /** @} */ + +}; // end of KDTreeEigenMatrixAdaptor +/** @} */ + +/** @} */ // end of grouping +} // namespace nanoflann diff --git a/examples/cpp/nanoflann/ponca_nanoflann.cpp b/examples/cpp/nanoflann/ponca_nanoflann.cpp new file mode 100644 index 000000000..4e14df1d5 --- /dev/null +++ b/examples/cpp/nanoflann/ponca_nanoflann.cpp @@ -0,0 +1,193 @@ + + +/*! +\file examples/cpp/nanoflann/ponca_nanoflann.cpp +\brief Demonstrate how to use Ponca with Nanoflann, and compare performances with built-in kdtree + + +\author: Nicolas Mellado +*/ + +#include +#include +#include "./nanoflann.hpp" + +#include + +using namespace Ponca; + + +// This class defines the input data format +class MyPoint +{ +public: + enum {Dim = 3}; + typedef double Scalar; + typedef Eigen::Matrix VectorType; + typedef Eigen::Matrix MatrixType; + + PONCA_MULTIARCH inline MyPoint(const VectorType& _pos = VectorType::Zero()) : m_pos(_pos) {} + PONCA_MULTIARCH inline const VectorType& pos() const { return m_pos; } + PONCA_MULTIARCH inline VectorType& pos() { return m_pos; } + + // Generate points on a sphere of radius 1 + static inline MyPoint Random() + { + return { VectorType::Random().normalized() * Eigen::internal::random(0.9,1.1) }; + } + +private: + VectorType m_pos; +}; + +typedef MyPoint::Scalar Scalar; +typedef MyPoint::VectorType VectorType; + +//! [Define Fit Type] +using WeightFunc = DistWeightFunc > ; +using FitType = Basket; // build a fitting object that does nothing +//! [Define Fit Type] + + +///// Nanoflann stuff +struct NFPointCloud +{ + using coord_t = typename MyPoint::Scalar; //!< The type of each coordinate + + const std::vector& pts; + + inline NFPointCloud(const std::vector& _pts) : pts(_pts) {} + + // Must return the number of data points + inline size_t kdtree_get_point_count() const { return pts.size(); } + + // Returns the dim'th component of the idx'th point in the class: + // Since this is inlined and the "dim" argument is typically an immediate + // value, the + // "if/else's" are actually solved at compile time. + inline coord_t kdtree_get_pt(const size_t idx, const size_t dim) const + { + if (dim == 0) + return pts[idx].pos()[0]; + else if (dim == 1) + return pts[idx].pos()[1]; + else + return pts[idx].pos()[2]; + } + + // 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 + bool kdtree_get_bbox(BBOX& /* bb */) const + { + return false; + } +}; +using my_kd_tree_t = nanoflann::KDTreeSingleIndexAdaptor< + nanoflann::L2_Simple_Adaptor< Scalar, NFPointCloud>, NFPointCloud, 3>; +///// Enf of nanoflann stuff + +int test_raw(FitType& f, const std::vector& _vecs, VectorType _p) +{ + f.init(_p); + if(! (f.compute( _vecs ) == STABLE) ) + std::cerr << "[raw] Something weird happened" << std::endl; + return f.getNumNeighbors(); +} + +int test_ponca_kdtree(FitType& f, const std::vector& _vecs, VectorType _p, const KdTree& tree, Scalar tmax){ + f.init(_p); + if(! ( + //! [Use Ponca KdTree] +f.computeWithIds( tree.range_neighbors(_p, tmax), _vecs ) + //! [Use Ponca KdTree] + == STABLE) ) + std::cerr << "[ponca_kdtree] Something weird happened" << std::endl; + return f.getNumNeighbors(); +} + +int test_nanflann_kdtree(FitType& f, const std::vector& _vecs, VectorType _p, const my_kd_tree_t& tree, Scalar tmax) +{ + // radius search: + const Scalar squaredRadius = 1; + std::vector> indices_dists; + nanoflann::RadiusResultSet resultSet( + tmax*tmax, indices_dists); + + tree.findNeighbors(resultSet, _p.data()); + + //! [Use NanoFlann KdTree] +f.init(_p); +// Compute +auto res = Ponca::UNDEFINED; +do { + for (const auto& r : resultSet.m_indices_dists){ + f.addNeighbor(_vecs[r.first]); + } + res = f.finalize(); +} while ( res == NEED_OTHER_PASS ); + //! [Use NanoFlann KdTree] + + if(res != STABLE) + std::cerr << "[nanoflann_kdtree] Something weird happened" << std::endl; + return f.getNumNeighbors(); +} + + +int main() +{ + using Scalar = typename MyPoint::Scalar; + // init random point cloud + int n = 100000; + std::vector vecs (n); + std::generate(vecs.begin(), vecs.end(), []() {return MyPoint::Random(); }); + + //! [Create Ponca KdTree] +KdTree ponca_tree(vecs); + //! [Create Ponca KdTree] + + //! [Create NanoFlann KdTree] +NFPointCloud nfcloud(vecs); +my_kd_tree_t mat_index(3, nfcloud); + //! [Create NanoFlann KdTree] + + Scalar tmax = 0.2; + + FitType fit; + fit.setWeightFunc(WeightFunc(tmax)); + + int nbrun = 1000; + std::vector queries (nbrun); + std::generate(queries.begin(), queries.end(), []() {return MyPoint::Random().pos(); }); + + int neiRaw {0}, neiPonca {0}, neiFlann {0}; + auto start = std::chrono::system_clock::now(); + for(int i = 0; i != nbrun; ++i) + neiRaw += test_raw(fit, vecs, queries[i]); + auto end = std::chrono::system_clock::now(); + std::chrono::duration rawDiff = (end-start); + + start = std::chrono::system_clock::now(); + for(int i = 0; i != nbrun; ++i) + neiPonca += test_ponca_kdtree(fit, vecs, queries[i], ponca_tree, tmax); + end = std::chrono::system_clock::now(); + std::chrono::duration poncaDiff = (end-start); + + start = std::chrono::system_clock::now(); + for(int i = 0; i != nbrun; ++i) + neiFlann += test_nanflann_kdtree(fit, vecs, queries[i], mat_index, tmax); + end = std::chrono::system_clock::now(); + std::chrono::duration nanoflannDiff = (end-start); + + std::cout << "Timings: " << "\n" + << "Raw : " << rawDiff.count() << "\n" + << "Ponca : " << poncaDiff.count() << "\n" + << "Nanoflann : " << nanoflannDiff.count() << "\n"; + std::cout << "Number of neighbors: " << "\n" + << "Raw : " << neiRaw << "\n" + << "Ponca : " << neiPonca << "\n" + << "Nanoflann : " << neiFlann << "\n"; +} \ No newline at end of file