├── plot2.png
├── plot3.png
├── README.md
├── dbscan.hpp
├── LICENSE
├── dbscan.cpp
├── example.cpp
├── sample2d.csv
├── sample3d.csv
└── vendor
    └── nanoflann
        └── nanoflann.hpp


/plot2.png:
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https://raw.githubusercontent.com/Eleobert/dbscan/HEAD/plot2.png


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/plot3.png:
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https://raw.githubusercontent.com/Eleobert/dbscan/HEAD/plot3.png


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/README.md:
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 1 | # Density-based spatial clustering of applications with noise (DBSCAN)
 2 | 
 3 | This is a fast C++ implementation of dbscan clustering algorithm.
 4 | 
 5 | Compiling and running the example:
 6 | 
 7 | ```bash
 8 | g++ example.cpp dbscan.cpp -I vendor/ -std=c++20 -o example
 9 | ./example sample2d.csv 0.2 10 > output.csv
10 | ```
11 | 
12 | <p align="center">
13 |   <img src="plot2.png" />
14 | </p>
15 | 
16 | Or run with three dimensional data:
17 | ```
18 | ./example sample3d.csv 1 10 > output.csv
19 | ```
20 | 
21 | <p align="center">
22 |   <img src="plot3.png" />
23 | </p>


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/dbscan.hpp:
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 1 | #pragma once
 2 | 
 3 | 
 4 | #include <cassert>
 5 | #include <cstddef>
 6 | #include <span>
 7 | #include <vector>
 8 | #include <cstdlib>
 9 | 
10 | struct point2
11 | {
12 |     float x, y;
13 | };
14 | 
15 | struct point3
16 | {
17 |     float x, y, z;
18 | };
19 | 
20 | auto dbscan(const std::span<const point2>& data, float eps, int min_pts) -> std::vector<std::vector<size_t>>;
21 | auto dbscan(const std::span<const point3>& data, float eps, int min_pts) -> std::vector<std::vector<size_t>>;
22 | 
23 | // template<size_t dim>
24 | // auto dbscan(const std::span<float>& data, float eps, int min_pts)
25 | // {
26 | //     static_assert(dim == 2 or dim == 3, "This only supports either 2D or 3D points");
27 | //     assert(data.size() % dim == 0);
28 |     
29 | //     if(dim == 2)
30 | //     {
31 | //         auto * const ptr = reinterpret_cast<float const*> (data.data());
32 | //         auto points = std::span<const point2> 
33 | //     }
34 | // }


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2021 Eleobert do Espírito Santo
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.


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/dbscan.cpp:
--------------------------------------------------------------------------------
  1 | #include "dbscan.hpp"
  2 | 
  3 | #include <cstddef>
  4 | #include <nanoflann/nanoflann.hpp>
  5 | 
  6 | #include <type_traits>
  7 | #include <vector>
  8 | 
  9 | // And this is the "dataset to kd-tree" adaptor class:
 10 | 
 11 | inline auto get_pt(const point2& p, std::size_t dim)
 12 | {
 13 |     if(dim == 0) return p.x;
 14 |     return p.y;
 15 | }
 16 | 
 17 | 
 18 | inline auto get_pt(const point3& p, std::size_t dim)
 19 | {
 20 |     if(dim == 0) return p.x;
 21 |     if(dim == 1) return p.y;
 22 |     return p.z;
 23 | }
 24 | 
 25 | 
 26 | template<typename Point>
 27 | struct adaptor
 28 | {
 29 |     const std::span<const Point>&  points;
 30 |     adaptor(const std::span<const Point>&  points) : points(points) { }
 31 | 
 32 |     /// CRTP helper method
 33 |     //inline const Derived& derived() const { return obj; }
 34 | 
 35 |     // Must return the number of data points
 36 |     inline std::size_t kdtree_get_point_count() const { return points.size(); }
 37 | 
 38 |     // Returns the dim'th component of the idx'th point in the class:
 39 |     // Since this is inlined and the "dim" argument is typically an immediate value, the
 40 |     //  "if/else's" are actually solved at compile time.
 41 |     inline float kdtree_get_pt(const std::size_t idx, const std::size_t dim) const
 42 |     {
 43 |         return get_pt(points[idx], dim);
 44 |     }
 45 | 
 46 |     // Optional bounding-box computation: return false to default to a standard bbox computation loop.
 47 |     //   Return true if the BBOX was already computed by the class and returned in "bb" so it can be avoided to redo it again.
 48 |     //   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3 for point clouds)
 49 |     template <class BBOX>
 50 |     bool kdtree_get_bbox(BBOX& /*bb*/) const { return false; }
 51 | 
 52 |     auto const * elem_ptr(const std::size_t idx) const
 53 |     {
 54 |         return &points[idx].x;
 55 |     }
 56 | };
 57 | 
 58 | 
 59 | 
 60 | auto sort_clusters(std::vector<std::vector<size_t>>& clusters)
 61 | {
 62 |     for(auto& cluster: clusters)
 63 |     {
 64 |         std::sort(cluster.begin(), cluster.end());
 65 |     }
 66 | }
 67 | 
 68 | 
 69 | template<int n_cols, typename Adaptor>
 70 | auto dbscan(const Adaptor& adapt, float eps, int min_pts)
 71 | {
 72 |     eps *= eps;
 73 |     using namespace nanoflann;
 74 |     using  my_kd_tree_t = KDTreeSingleIndexAdaptor<L2_Simple_Adaptor<float, decltype(adapt)>, decltype(adapt), n_cols>;
 75 | 
 76 |     auto index = my_kd_tree_t(n_cols, adapt, KDTreeSingleIndexAdaptorParams(10));
 77 |     index.buildIndex();
 78 | 
 79 |     const auto n_points = adapt.kdtree_get_point_count();
 80 |     auto visited  = std::vector<bool>(n_points);
 81 |     auto clusters = std::vector<std::vector<size_t>>();
 82 |     auto matches  = std::vector<std::pair<size_t, float>>();
 83 |     auto sub_matches = std::vector<std::pair<size_t, float>>();
 84 | 
 85 |     for(size_t i = 0; i < n_points; i++)
 86 |     {
 87 |         if (visited[i]) continue;
 88 | 
 89 |         index.radiusSearch(adapt.elem_ptr(i), eps, matches, SearchParams(32, 0.f, false));
 90 |         if (matches.size() < static_cast<size_t>(min_pts)) continue;
 91 |         visited[i] = true;
 92 | 
 93 |         auto cluster = std::vector({i});
 94 | 
 95 |         while (matches.empty() == false)
 96 |         {
 97 |             auto nb_idx = matches.back().first;
 98 |             matches.pop_back();
 99 |             if (visited[nb_idx]) continue;
100 |             visited[nb_idx] = true;
101 | 
102 |             index.radiusSearch(adapt.elem_ptr(nb_idx), eps, sub_matches, SearchParams(32, 0.f, false));
103 | 
104 |             if (sub_matches.size() >= static_cast<size_t>(min_pts))
105 |             {
106 |                 std::copy(sub_matches.begin(), sub_matches.end(), std::back_inserter(matches));
107 |             }
108 |             cluster.push_back(nb_idx);
109 |         }
110 |         clusters.emplace_back(std::move(cluster));
111 |     }
112 |     sort_clusters(clusters);
113 |     return clusters;
114 | }
115 | 
116 | 
117 | auto dbscan(const std::span<const point2>& data, float eps, int min_pts) -> std::vector<std::vector<size_t>>
118 | {
119 |     const auto adapt = adaptor<point2>(data);
120 | 
121 |     return dbscan<2>(adapt, eps, min_pts);
122 | }
123 | 
124 | 
125 | auto dbscan(const std::span<const point3>& data, float eps, int min_pts) -> std::vector<std::vector<size_t>>
126 | {
127 |     const auto adapt = adaptor<point3>(data);
128 | 
129 |     return dbscan<3>(adapt, eps, min_pts);
130 | }


--------------------------------------------------------------------------------
/example.cpp:
--------------------------------------------------------------------------------
  1 | #include "dbscan.hpp"
  2 | #include <iostream>
  3 | #include <string>
  4 | #include <system_error>
  5 | #include <vector>
  6 | #include <utility>
  7 | #include <fstream>
  8 | #include <charconv>
  9 | #include <cassert>
 10 | #include <tuple>
 11 | #include <cstring>
 12 | 
 13 | 
 14 | auto check_from_chars_error(std::errc err, const std::string_view& line, int line_counter)
 15 | {
 16 |     if(err == std::errc())
 17 |         return;
 18 |     
 19 |     if(err == std::errc::invalid_argument)
 20 |     {
 21 |         std::cerr << "Error: Invalid value \"" << line
 22 |             << "\" at line " << line_counter << "\n";
 23 |         std::exit(1);
 24 |     }
 25 | 
 26 |     if(err == std::errc::result_out_of_range)
 27 |     {
 28 |         std::cerr << "Error: Value \"" << line << "\"out of range at line " 
 29 |                   <<  line_counter << "\n";
 30 |         std::exit(1);
 31 |     }
 32 | 
 33 | }
 34 | 
 35 | 
 36 | auto push_values(std::vector<float>& store, const std::string_view& line, int line_counter)
 37 | {
 38 |     auto ptr = line.data();
 39 |     auto ec  = std::errc();
 40 |     auto n_pushed = 0;
 41 | 
 42 |     do
 43 |     {
 44 |         float value;
 45 |         auto [p, ec] =  std::from_chars(ptr, line.data() + line.size(), value);
 46 |         ptr = p + 1;
 47 |         check_from_chars_error(ec, line, line_counter);
 48 |         n_pushed++;
 49 |         store.push_back(value);
 50 | 
 51 |     }while(ptr < line.data() + line.size());
 52 | 
 53 |     return n_pushed;
 54 | }
 55 | 
 56 | 
 57 | auto read_values(const std::string& filename)
 58 | {
 59 |     std::ifstream file(filename);
 60 | 
 61 |     if(not file.good())
 62 |     {
 63 |         std::perror(filename.c_str());
 64 |         std::exit(2);
 65 |     }
 66 | 
 67 |     auto count = 0;
 68 | 
 69 |     auto points = std::vector<float>();
 70 |     auto dim    = 0;
 71 | 
 72 |     while(not file.eof())
 73 |     {
 74 |         count++;
 75 |         auto line = std::string();
 76 |         std::getline(file, line);
 77 | 
 78 |         if(not line.empty())
 79 |         {
 80 |             auto n_pushed = push_values(points, line, count);
 81 | 
 82 |             if(count != 1)
 83 |             {
 84 |                 if(n_pushed != dim)
 85 |                 {
 86 |                     std::cerr << "Inconsistent number of dimensions at line '" << count << "'\n";
 87 |                     std::exit(1);
 88 |                 }
 89 |             }
 90 |             dim = n_pushed;
 91 |         }
 92 |     }
 93 | 
 94 |     return std::tuple(points, dim);
 95 | }
 96 | 
 97 | 
 98 | template<typename T>
 99 | auto to_num(const std::string& str)
100 | {
101 |     T value = 0;
102 |     auto [ptr, ec] = std::from_chars(str.data(), str.data() + str.size(), value);
103 | 
104 |     if(ec != std::errc())
105 |     {
106 |         std::cerr << "Error converting value '" << str << "'\n";
107 |         std::exit(1);
108 |     }
109 |     return value;
110 | }
111 | 
112 | 
113 | // noise will be labelled as 0
114 | auto label(const std::vector<std::vector<size_t>>& clusters, size_t n)
115 | {
116 |     auto flat_clusters = std::vector<size_t>(n);
117 | 
118 |     for(size_t i = 0; i < clusters.size(); i++)
119 |     {
120 |         for(auto p: clusters[i])
121 |         {
122 |             flat_clusters[p] = i + 1;
123 |         }
124 |     }
125 | 
126 |     return flat_clusters;
127 | }
128 | 
129 | 
130 | auto dbscan2d(const std::span<const float>& data, float eps, int min_pts)
131 | {
132 |     auto points = std::vector<point2>(data.size() / 2);
133 | 
134 |     std::memcpy(points.data(), data.data(), sizeof(float) * data.size());
135 | 
136 |     auto clusters = dbscan(points, eps, min_pts);
137 |     auto flat     = label (clusters, points.size());
138 | 
139 |     for(size_t i = 0; i < points.size(); i++)
140 |     {
141 |         std::cout << points[i].x << ',' << points[i].y << ','  << flat[i] << '\n';
142 |     }
143 | }
144 | 
145 | 
146 | auto dbscan3d(const std::span<const float>& data, float eps, int min_pts)
147 | {
148 |     auto points = std::vector<point3>(data.size() / 3);
149 | 
150 |     std::memcpy(points.data(), data.data(), sizeof(float) * data.size());
151 | 
152 |     auto clusters = dbscan(points, eps, min_pts);
153 |     auto flat     = label (clusters, points.size());
154 | 
155 |     for(size_t i = 0; i < points.size(); i++)
156 |     {
157 |         std::cout << points[i].x << ',' << points[i].y << ',' << points[i].z << ',' << flat[i] << '\n';
158 |     }
159 | }
160 | 
161 | 
162 | 
163 | int main(int argc, char** argv)
164 | {
165 |     if(argc != 4)
166 |     {
167 |         std::cerr << "usage: example <tsv file> <epsilon> <min points>\n";
168 |         return 1;
169 |     }
170 | 
171 |     auto epsilon  = to_num<float>(argv[2]);
172 |     auto min_pts  = to_num<int>  (argv[3]);
173 |     auto [values, dim] = read_values(argv[1]);
174 | 
175 |     if(dim == 2)
176 |     {
177 |         dbscan2d(values, epsilon, min_pts);
178 |     }
179 |     else if (dim == 3)
180 |     {
181 |         dbscan3d(values, epsilon, min_pts);
182 |     }
183 | }


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/sample2d.csv:
--------------------------------------------------------------------------------
   1 | -1.3303058582391545,-0.434325580615089
   2 | -0.5924972261074593,-0.46453047635748296
   3 | -1.1514663829680862,0.41611224001806546
   4 | -1.5492485381753465,0.8027038296916662
   5 | -0.029999384901909565,0.16778807157434542
   6 | -2.550898666403688,0.8951192208886832
   7 | -1.204236560105029,0.5776550847730018
   8 | -2.0076783977997246,0.3298131705764211
   9 | -2.3929007725439684,1.0023550116130524
  10 | -0.43406313639471383,-0.37119599704158546
  11 | -1.0243161051435994,0.09476345784093444
  12 | -1.7303188927974933,-0.04281514187958224
  13 | -2.835142083623058,0.7128372359333114
  14 | -2.6237108800255813,0.7176294993959645
  15 | -1.8602891561940256,0.9923774293633051
  16 | -1.1314973855494095,-0.5159850609542471
  17 | -0.5818103680025788,-0.4266784189158561
  18 | -3.0441854407425044,0.03707690902891613
  19 | -1.2125651724538922,0.7102035888785216
  20 | -0.9991949806876501,0.009184374561152328
  21 | -1.6080788691091472,0.8890972913187793
  22 | -0.9952721569244962,-0.59617144409241
  23 | -1.700705861626605,0.896771448707588
  24 | -2.780827136834604,0.64280336743606
  25 | -1.292603276247485,0.6652461841800765
  26 | -1.9778544183194762,0.21589716727848582
  27 | -2.819583871326087,0.678417100150437
  28 | -1.8993123929673383,-0.053849421809319176
  29 | -1.2189033205776099,-0.4286013002108381
  30 | -0.791744427377514,-0.4854203504851642
  31 | -0.31077175947557456,-0.07167431749980765
  32 | -1.6522549541506881,-0.335223381072491
  33 | -0.9334505907294359,0.07029505658828776
  34 | -0.6034725585008933,-0.43058689893087404
  35 | -1.6457197324606816,-0.11112708156685057
  36 | -0.36047101280325355,-0.18269465497515341
  37 | -1.4938865126045393,-0.4398130341163262
  38 | -3.034666824636589,0.20236356974545477
  39 | -1.3440397545722442,-0.4046411760875248
  40 | -1.4777054900584912,-0.3469778397153196
  41 | -1.9730268101343804,0.5019435746007844
  42 | -2.284838566790355,0.8864313268451619
  43 | -2.2635042328486987,0.906075562674943
  44 | -0.6207677698554193,-0.43493124092621654
  45 | -1.1206294210437668,0.3915235866807588
  46 | -2.869880629393536,0.5878038966453305
  47 | -0.9603324957002513,-0.4835181035237305
  48 | -2.6600544513649687,0.7523609815657362
  49 | -0.7082954505693906,-0.48389049147922564
  50 | -1.6587685318184242,-0.23097527506769672
  51 | -1.7664280399920893,0.9507262140096867
  52 | -1.382793764162855,-0.34735213125302
  53 | -1.4606504849560245,-0.34443633479666663
  54 | -1.360691046650401,-0.33879176678426665
  55 | -0.17343079564010422,0.013909120584047604
  56 | -1.005888834231635,0.3481366079223894
  57 | -1.5741493172377918,-0.30535184355842604
  58 | -3.083667027635564,0.07189553239242846
  59 | -1.8946527382845548,0.34084585183535554
  60 | -1.8038959736437525,1.0253440939282148
  61 | -0.5250567188318576,-0.39020059732201506
  62 | -2.9966427851291253,0.26876779562932385
  63 | -1.9242671872756456,0.06620605431200255
  64 | -1.5447276482796466,-0.2792445266194328
  65 | -0.8458140333713551,-0.5546887857745991
  66 | -1.4148076997192787,0.8998170624488967
  67 | -1.9623801598227377,1.011897967383911
  68 | -2.0433269197174453,0.41390496702516066
  69 | -2.8649384926100865,0.3987656310127547
  70 | -2.9243982483176434,0.2859219039943523
  71 | -1.1089383314541403,0.4943017968113612
  72 | -0.9787775377547561,-0.5039453939797348
  73 | -1.7249032773441928,0.9719415828148369
  74 | -0.7151734059746804,-0.45672915184754076
  75 | -0.05512974495953005,0.34836109749389754
  76 | -1.971727820572869,0.44497730614213055
  77 | -0.9664395204260849,0.23231255176290683
  78 | -1.1817996593933504,0.41723508842136503
  79 | -2.6598983141035637,0.800168503912098
  80 | -1.5790951472500931,1.0219877766834784
  81 | -2.639682248700582,0.7757867503297922
  82 | -0.4102140095240072,-0.28212055223875143
  83 | -1.0109050094585987,0.37152033696252557
  84 | -2.65010299586981,0.9107456739257127
  85 | -0.10016997429064389,0.3851235381662591
  86 | -0.9458097258682938,0.29010430624643
  87 | -1.3236673325509019,-0.5138027801494128
  88 | -0.03560587643006463,0.2565636413631235
  89 | -3.0151680401528385,0.021461296424950278
  90 | -1.275447100658896,-0.37951270796055864
  91 | -0.9275542859419348,0.18722089256514687
  92 | -2.859455970166303,0.5215984184159529
  93 | -0.3029046982396666,-0.24758601457005835
  94 | -2.0667133262101403,1.0281110882606044
  95 | -1.2077389663388478,0.6480408030712447
  96 | -2.765738935134479,0.7891044927150201
  97 | -2.4839390493774207,0.8568448838299919
  98 | -1.9940235702836244,0.9378924426902338
  99 | -2.7324673390909666,0.7662733937107853
 100 | -0.2197196637994383,-0.2667177160837247
 101 | -0.9752241934754586,0.35158102809799996
 102 | -0.03876059428566858,0.2591861540827761
 103 | -0.05032023376267447,0.18198546315953743
 104 | -1.845743365427094,1.0261961062551863
 105 | -1.373027775956463,-0.4315942011348258
 106 | -1.9072554662358465,0.2954772666223846
 107 | -1.884592724972176,1.015399644194677
 108 | -0.5370146104486488,-0.35075887853296966
 109 | -1.0858385889735804,-0.5279030212913971
 110 | -1.9502435864191046,0.39759090321350254
 111 | -1.212392490145117,0.6262918930812117
 112 | -1.2168164360026172,-0.5165586839812111
 113 | -0.9923093685212152,0.090116852417676
 114 | -2.965519831050109,0.272941198921126
 115 | -1.0182390343260224,0.28136510577915197
 116 | -2.683655147205113,0.6824550143155623
 117 | -0.17089899776848005,-0.02720349273039014
 118 | -2.419067684312586,0.9099576369701589
 119 | -0.009737035306594644,0.456223424336874
 120 | -1.0951523053165524,0.3022485966331948
 121 | -2.0248796472169937,0.961121522125862
 122 | -2.2215065722567626,1.0222314776631556
 123 | -1.0800915642045268,0.5545308333497937
 124 | -2.6612414385647214,0.703067228199621
 125 | -1.7568334996757489,0.9508451843917397
 126 | -0.25591685636279093,0.061472424964867683
 127 | -0.04674055545263922,0.1579668461498365
 128 | -1.0403703848002221,0.3153383340022235
 129 | -0.25838830899941456,-0.08506499583670192
 130 | -1.6207725768073418,-0.3191036206016011
 131 | -0.18037443163505396,0.05592226249813386
 132 | -2.8634038861919984,0.4500159893984662
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 984 | 0.46783738724730384,8.196975659932535,8.642789295993383
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 990 | -1.1598274434328888,7.331088312898528,8.523672506248023
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 995 | 0.9668854086764306,0.4465477629631287,4.154783303714957
 996 | 0.5967055690807888,4.160564093370887,0.8291267310621453
 997 | -1.403240267764577,7.440178148673143,8.472791217035006
 998 | 1.3384216382618366,3.8902912554592195,3.5956564334153245
 999 | -0.25432303813402624,2.345515717943158,1.53730483530035
1000 | 0.7288550305114597,5.895845668088949,2.6181875205838585
1001 | 


--------------------------------------------------------------------------------
/vendor/nanoflann/nanoflann.hpp:
--------------------------------------------------------------------------------
   1 | /***********************************************************************
   2 |  * Software License Agreement (BSD License)
   3 |  *
   4 |  * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
   5 |  * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
   6 |  * Copyright 2011-2016  Jose Luis Blanco (joseluisblancoc@gmail.com).
   7 |  *   All rights reserved.
   8 |  *
   9 |  * THE BSD LICENSE
  10 |  *
  11 |  * Redistribution and use in source and binary forms, with or without
  12 |  * modification, are permitted provided that the following conditions
  13 |  * are met:
  14 |  *
  15 |  * 1. Redistributions of source code must retain the above copyright
  16 |  *    notice, this list of conditions and the following disclaimer.
  17 |  * 2. Redistributions in binary form must reproduce the above copyright
  18 |  *    notice, this list of conditions and the following disclaimer in the
  19 |  *    documentation and/or other materials provided with the distribution.
  20 |  *
  21 |  * THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
  22 |  * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
  23 |  * OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
  24 |  * IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
  25 |  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
  26 |  * NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
  27 |  * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
  28 |  * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
  29 |  * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
  30 |  * THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
  31 |  *************************************************************************/
  32 | 
  33 | /** \mainpage nanoflann C++ API documentation
  34 |  *  nanoflann is a C++ header-only library for building KD-Trees, mostly
  35 |  *  optimized for 2D or 3D point clouds.
  36 |  *
  37 |  *  nanoflann does not require compiling or installing, just an
  38 |  *  #include <nanoflann.hpp> in your code.
  39 |  *
  40 |  *  See:
  41 |  *   - <a href="modules.html" >C++ API organized by modules</a>
  42 |  *   - <a href="https://github.com/jlblancoc/nanoflann" >Online README</a>
  43 |  *   - <a href="http://jlblancoc.github.io/nanoflann/" >Doxygen
  44 |  * documentation</a>
  45 |  */
  46 | 
  47 | #ifndef NANOFLANN_HPP_
  48 | #define NANOFLANN_HPP_
  49 | 
  50 | #include <algorithm>
  51 | #include <array>
  52 | #include <cassert>
  53 | #include <cmath>   // for abs()
  54 | #include <cstdio>  // for fwrite()
  55 | #include <cstdlib> // for abs()
  56 | #include <functional>
  57 | #include <limits> // std::reference_wrapper
  58 | #include <stdexcept>
  59 | #include <vector>
  60 | 
  61 | /** Library version: 0xMmP (M=Major,m=minor,P=patch) */
  62 | #define NANOFLANN_VERSION 0x132
  63 | 
  64 | // Avoid conflicting declaration of min/max macros in windows headers
  65 | #if !defined(NOMINMAX) &&                                                      \
  66 |     (defined(_WIN32) || defined(_WIN32_) || defined(WIN32) || defined(_WIN64))
  67 | #define NOMINMAX
  68 | #ifdef max
  69 | #undef max
  70 | #undef min
  71 | #endif
  72 | #endif
  73 | 
  74 | namespace nanoflann {
  75 | /** @addtogroup nanoflann_grp nanoflann C++ library for ANN
  76 |  *  @{ */
  77 | 
  78 | /** the PI constant (required to avoid MSVC missing symbols) */
  79 | template <typename T> T pi_const() {
  80 |   return static_cast<T>(3.14159265358979323846);
  81 | }
  82 | 
  83 | /**
  84 |  * Traits if object is resizable and assignable (typically has a resize | assign
  85 |  * method)
  86 |  */
  87 | template <typename T, typename = int> struct has_resize : std::false_type {};
  88 | 
  89 | template <typename T>
  90 | struct has_resize<T, decltype((void)std::declval<T>().resize(1), 0)>
  91 |     : std::true_type {};
  92 | 
  93 | template <typename T, typename = int> struct has_assign : std::false_type {};
  94 | 
  95 | template <typename T>
  96 | struct has_assign<T, decltype((void)std::declval<T>().assign(1, 0), 0)>
  97 |     : std::true_type {};
  98 | 
  99 | /**
 100 |  * Free function to resize a resizable object
 101 |  */
 102 | template <typename Container>
 103 | inline typename std::enable_if<has_resize<Container>::value, void>::type
 104 | resize(Container &c, const size_t nElements) {
 105 |   c.resize(nElements);
 106 | }
 107 | 
 108 | /**
 109 |  * Free function that has no effects on non resizable containers (e.g.
 110 |  * std::array) It raises an exception if the expected size does not match
 111 |  */
 112 | template <typename Container>
 113 | inline typename std::enable_if<!has_resize<Container>::value, void>::type
 114 | resize(Container &c, const size_t nElements) {
 115 |   if (nElements != c.size())
 116 |     throw std::logic_error("Try to change the size of a std::array.");
 117 | }
 118 | 
 119 | /**
 120 |  * Free function to assign to a container
 121 |  */
 122 | template <typename Container, typename T>
 123 | inline typename std::enable_if<has_assign<Container>::value, void>::type
 124 | assign(Container &c, const size_t nElements, const T &value) {
 125 |   c.assign(nElements, value);
 126 | }
 127 | 
 128 | /**
 129 |  * Free function to assign to a std::array
 130 |  */
 131 | template <typename Container, typename T>
 132 | inline typename std::enable_if<!has_assign<Container>::value, void>::type
 133 | assign(Container &c, const size_t nElements, const T &value) {
 134 |   for (size_t i = 0; i < nElements; i++)
 135 |     c[i] = value;
 136 | }
 137 | 
 138 | /** @addtogroup result_sets_grp Result set classes
 139 |  *  @{ */
 140 | template <typename _DistanceType, typename _IndexType = size_t,
 141 |           typename _CountType = size_t>
 142 | class KNNResultSet {
 143 | public:
 144 |   typedef _DistanceType DistanceType;
 145 |   typedef _IndexType IndexType;
 146 |   typedef _CountType CountType;
 147 | 
 148 | private:
 149 |   IndexType *indices;
 150 |   DistanceType *dists;
 151 |   CountType capacity;
 152 |   CountType count;
 153 | 
 154 | public:
 155 |   inline KNNResultSet(CountType capacity_)
 156 |       : indices(0), dists(0), capacity(capacity_), count(0) {}
 157 | 
 158 |   inline void init(IndexType *indices_, DistanceType *dists_) {
 159 |     indices = indices_;
 160 |     dists = dists_;
 161 |     count = 0;
 162 |     if (capacity)
 163 |       dists[capacity - 1] = (std::numeric_limits<DistanceType>::max)();
 164 |   }
 165 | 
 166 |   inline CountType size() const { return count; }
 167 | 
 168 |   inline bool full() const { return count == capacity; }
 169 | 
 170 |   /**
 171 |    * Called during search to add an element matching the criteria.
 172 |    * @return true if the search should be continued, false if the results are
 173 |    * sufficient
 174 |    */
 175 |   inline bool addPoint(DistanceType dist, IndexType index) {
 176 |     CountType i;
 177 |     for (i = count; i > 0; --i) {
 178 | #ifdef NANOFLANN_FIRST_MATCH // If defined and two points have the same
 179 |                              // distance, the one with the lowest-index will be
 180 |                              // returned first.
 181 |       if ((dists[i - 1] > dist) ||
 182 |           ((dist == dists[i - 1]) && (indices[i - 1] > index))) {
 183 | #else
 184 |       if (dists[i - 1] > dist) {
 185 | #endif
 186 |         if (i < capacity) {
 187 |           dists[i] = dists[i - 1];
 188 |           indices[i] = indices[i - 1];
 189 |         }
 190 |       } else
 191 |         break;
 192 |     }
 193 |     if (i < capacity) {
 194 |       dists[i] = dist;
 195 |       indices[i] = index;
 196 |     }
 197 |     if (count < capacity)
 198 |       count++;
 199 | 
 200 |     // tell caller that the search shall continue
 201 |     return true;
 202 |   }
 203 | 
 204 |   inline DistanceType worstDist() const { return dists[capacity - 1]; }
 205 | };
 206 | 
 207 | /** operator "<" for std::sort() */
 208 | struct IndexDist_Sorter {
 209 |   /** PairType will be typically: std::pair<IndexType,DistanceType> */
 210 |   template <typename PairType>
 211 |   inline bool operator()(const PairType &p1, const PairType &p2) const {
 212 |     return p1.second < p2.second;
 213 |   }
 214 | };
 215 | 
 216 | /**
 217 |  * A result-set class used when performing a radius based search.
 218 |  */
 219 | template <typename _DistanceType, typename _IndexType = size_t>
 220 | class RadiusResultSet {
 221 | public:
 222 |   typedef _DistanceType DistanceType;
 223 |   typedef _IndexType IndexType;
 224 | 
 225 | public:
 226 |   const DistanceType radius;
 227 | 
 228 |   std::vector<std::pair<IndexType, DistanceType>> &m_indices_dists;
 229 | 
 230 |   inline RadiusResultSet(
 231 |       DistanceType radius_,
 232 |       std::vector<std::pair<IndexType, DistanceType>> &indices_dists)
 233 |       : radius(radius_), m_indices_dists(indices_dists) {
 234 |     init();
 235 |   }
 236 | 
 237 |   inline void init() { clear(); }
 238 |   inline void clear() { m_indices_dists.clear(); }
 239 | 
 240 |   inline size_t size() const { return m_indices_dists.size(); }
 241 | 
 242 |   inline bool full() const { return true; }
 243 | 
 244 |   /**
 245 |    * Called during search to add an element matching the criteria.
 246 |    * @return true if the search should be continued, false if the results are
 247 |    * sufficient
 248 |    */
 249 |   inline bool addPoint(DistanceType dist, IndexType index) {
 250 |     if (dist < radius)
 251 |       m_indices_dists.push_back(std::make_pair(index, dist));
 252 |     return true;
 253 |   }
 254 | 
 255 |   inline DistanceType worstDist() const { return radius; }
 256 | 
 257 |   /**
 258 |    * Find the worst result (furtherest neighbor) without copying or sorting
 259 |    * Pre-conditions: size() > 0
 260 |    */
 261 |   std::pair<IndexType, DistanceType> worst_item() const {
 262 |     if (m_indices_dists.empty())
 263 |       throw std::runtime_error("Cannot invoke RadiusResultSet::worst_item() on "
 264 |                                "an empty list of results.");
 265 |     typedef
 266 |         typename std::vector<std::pair<IndexType, DistanceType>>::const_iterator
 267 |             DistIt;
 268 |     DistIt it = std::max_element(m_indices_dists.begin(), m_indices_dists.end(),
 269 |                                  IndexDist_Sorter());
 270 |     return *it;
 271 |   }
 272 | };
 273 | 
 274 | /** @} */
 275 | 
 276 | /** @addtogroup loadsave_grp Load/save auxiliary functions
 277 |  * @{ */
 278 | template <typename T>
 279 | void save_value(FILE *stream, const T &value, size_t count = 1) {
 280 |   fwrite(&value, sizeof(value), count, stream);
 281 | }
 282 | 
 283 | template <typename T>
 284 | void save_value(FILE *stream, const std::vector<T> &value) {
 285 |   size_t size = value.size();
 286 |   fwrite(&size, sizeof(size_t), 1, stream);
 287 |   fwrite(&value[0], sizeof(T), size, stream);
 288 | }
 289 | 
 290 | template <typename T>
 291 | void load_value(FILE *stream, T &value, size_t count = 1) {
 292 |   size_t read_cnt = fread(&value, sizeof(value), count, stream);
 293 |   if (read_cnt != count) {
 294 |     throw std::runtime_error("Cannot read from file");
 295 |   }
 296 | }
 297 | 
 298 | template <typename T> void load_value(FILE *stream, std::vector<T> &value) {
 299 |   size_t size;
 300 |   size_t read_cnt = fread(&size, sizeof(size_t), 1, stream);
 301 |   if (read_cnt != 1) {
 302 |     throw std::runtime_error("Cannot read from file");
 303 |   }
 304 |   value.resize(size);
 305 |   read_cnt = fread(&value[0], sizeof(T), size, stream);
 306 |   if (read_cnt != size) {
 307 |     throw std::runtime_error("Cannot read from file");
 308 |   }
 309 | }
 310 | /** @} */
 311 | 
 312 | /** @addtogroup metric_grp Metric (distance) classes
 313 |  * @{ */
 314 | 
 315 | struct Metric {};
 316 | 
 317 | /** Manhattan distance functor (generic version, optimized for
 318 |  * high-dimensionality data sets). Corresponding distance traits:
 319 |  * nanoflann::metric_L1 \tparam T Type of the elements (e.g. double, float,
 320 |  * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
 321 |  * (e.g. float, double, int64_t)
 322 |  */
 323 | template <class T, class DataSource, typename _DistanceType = T>
 324 | struct L1_Adaptor {
 325 |   typedef T ElementType;
 326 |   typedef _DistanceType DistanceType;
 327 | 
 328 |   const DataSource &data_source;
 329 | 
 330 |   L1_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
 331 | 
 332 |   inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
 333 |                                  DistanceType worst_dist = -1) const {
 334 |     DistanceType result = DistanceType();
 335 |     const T *last = a + size;
 336 |     const T *lastgroup = last - 3;
 337 |     size_t d = 0;
 338 | 
 339 |     /* Process 4 items with each loop for efficiency. */
 340 |     while (a < lastgroup) {
 341 |       const DistanceType diff0 =
 342 |           std::abs(a[0] - data_source.kdtree_get_pt(b_idx, d++));
 343 |       const DistanceType diff1 =
 344 |           std::abs(a[1] - data_source.kdtree_get_pt(b_idx, d++));
 345 |       const DistanceType diff2 =
 346 |           std::abs(a[2] - data_source.kdtree_get_pt(b_idx, d++));
 347 |       const DistanceType diff3 =
 348 |           std::abs(a[3] - data_source.kdtree_get_pt(b_idx, d++));
 349 |       result += diff0 + diff1 + diff2 + diff3;
 350 |       a += 4;
 351 |       if ((worst_dist > 0) && (result > worst_dist)) {
 352 |         return result;
 353 |       }
 354 |     }
 355 |     /* Process last 0-3 components.  Not needed for standard vector lengths. */
 356 |     while (a < last) {
 357 |       result += std::abs(*a++ - data_source.kdtree_get_pt(b_idx, d++));
 358 |     }
 359 |     return result;
 360 |   }
 361 | 
 362 |   template <typename U, typename V>
 363 |   inline DistanceType accum_dist(const U a, const V b, const size_t) const {
 364 |     return std::abs(a - b);
 365 |   }
 366 | };
 367 | 
 368 | /** Squared Euclidean distance functor (generic version, optimized for
 369 |  * high-dimensionality data sets). Corresponding distance traits:
 370 |  * nanoflann::metric_L2 \tparam T Type of the elements (e.g. double, float,
 371 |  * uint8_t) \tparam _DistanceType Type of distance variables (must be signed)
 372 |  * (e.g. float, double, int64_t)
 373 |  */
 374 | template <class T, class DataSource, typename _DistanceType = T>
 375 | struct L2_Adaptor {
 376 |   typedef T ElementType;
 377 |   typedef _DistanceType DistanceType;
 378 | 
 379 |   const DataSource &data_source;
 380 | 
 381 |   L2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
 382 | 
 383 |   inline DistanceType evalMetric(const T *a, const size_t b_idx, size_t size,
 384 |                                  DistanceType worst_dist = -1) const {
 385 |     DistanceType result = DistanceType();
 386 |     const T *last = a + size;
 387 |     const T *lastgroup = last - 3;
 388 |     size_t d = 0;
 389 | 
 390 |     /* Process 4 items with each loop for efficiency. */
 391 |     while (a < lastgroup) {
 392 |       const DistanceType diff0 = a[0] - data_source.kdtree_get_pt(b_idx, d++);
 393 |       const DistanceType diff1 = a[1] - data_source.kdtree_get_pt(b_idx, d++);
 394 |       const DistanceType diff2 = a[2] - data_source.kdtree_get_pt(b_idx, d++);
 395 |       const DistanceType diff3 = a[3] - data_source.kdtree_get_pt(b_idx, d++);
 396 |       result += diff0 * diff0 + diff1 * diff1 + diff2 * diff2 + diff3 * diff3;
 397 |       a += 4;
 398 |       if ((worst_dist > 0) && (result > worst_dist)) {
 399 |         return result;
 400 |       }
 401 |     }
 402 |     /* Process last 0-3 components.  Not needed for standard vector lengths. */
 403 |     while (a < last) {
 404 |       const DistanceType diff0 = *a++ - data_source.kdtree_get_pt(b_idx, d++);
 405 |       result += diff0 * diff0;
 406 |     }
 407 |     return result;
 408 |   }
 409 | 
 410 |   template <typename U, typename V>
 411 |   inline DistanceType accum_dist(const U a, const V b, const size_t) const {
 412 |     return (a - b) * (a - b);
 413 |   }
 414 | };
 415 | 
 416 | /** Squared Euclidean (L2) distance functor (suitable for low-dimensionality
 417 |  * datasets, like 2D or 3D point clouds) Corresponding distance traits:
 418 |  * nanoflann::metric_L2_Simple \tparam T Type of the elements (e.g. double,
 419 |  * float, uint8_t) \tparam _DistanceType Type of distance variables (must be
 420 |  * signed) (e.g. float, double, int64_t)
 421 |  */
 422 | template <class T, class DataSource, typename _DistanceType = T>
 423 | struct L2_Simple_Adaptor {
 424 |   typedef T ElementType;
 425 |   typedef _DistanceType DistanceType;
 426 | 
 427 |   const DataSource &data_source;
 428 | 
 429 |   L2_Simple_Adaptor(const DataSource &_data_source)
 430 |       : data_source(_data_source) {}
 431 | 
 432 |   inline DistanceType evalMetric(const T *a, const size_t b_idx,
 433 |                                  size_t size) const {
 434 |     DistanceType result = DistanceType();
 435 |     for (size_t i = 0; i < size; ++i) {
 436 |       const DistanceType diff = a[i] - data_source.kdtree_get_pt(b_idx, i);
 437 |       result += diff * diff;
 438 |     }
 439 |     return result;
 440 |   }
 441 | 
 442 |   template <typename U, typename V>
 443 |   inline DistanceType accum_dist(const U a, const V b, const size_t) const {
 444 |     return (a - b) * (a - b);
 445 |   }
 446 | };
 447 | 
 448 | /** SO2 distance functor
 449 |  *  Corresponding distance traits: nanoflann::metric_SO2
 450 |  * \tparam T Type of the elements (e.g. double, float)
 451 |  * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
 452 |  * float, double) orientation is constrained to be in [-pi, pi]
 453 |  */
 454 | template <class T, class DataSource, typename _DistanceType = T>
 455 | struct SO2_Adaptor {
 456 |   typedef T ElementType;
 457 |   typedef _DistanceType DistanceType;
 458 | 
 459 |   const DataSource &data_source;
 460 | 
 461 |   SO2_Adaptor(const DataSource &_data_source) : data_source(_data_source) {}
 462 | 
 463 |   inline DistanceType evalMetric(const T *a, const size_t b_idx,
 464 |                                  size_t size) const {
 465 |     return accum_dist(a[size - 1], data_source.kdtree_get_pt(b_idx, size - 1),
 466 |                       size - 1);
 467 |   }
 468 | 
 469 |   /** Note: this assumes that input angles are already in the range [-pi,pi] */
 470 |   template <typename U, typename V>
 471 |   inline DistanceType accum_dist(const U a, const V b, const size_t) const {
 472 |     DistanceType result = DistanceType();
 473 |     DistanceType PI = pi_const<DistanceType>();
 474 |     result = b - a;
 475 |     if (result > PI)
 476 |       result -= 2 * PI;
 477 |     else if (result < -PI)
 478 |       result += 2 * PI;
 479 |     return result;
 480 |   }
 481 | };
 482 | 
 483 | /** SO3 distance functor (Uses L2_Simple)
 484 |  *  Corresponding distance traits: nanoflann::metric_SO3
 485 |  * \tparam T Type of the elements (e.g. double, float)
 486 |  * \tparam _DistanceType Type of distance variables (must be signed) (e.g.
 487 |  * float, double)
 488 |  */
 489 | template <class T, class DataSource, typename _DistanceType = T>
 490 | struct SO3_Adaptor {
 491 |   typedef T ElementType;
 492 |   typedef _DistanceType DistanceType;
 493 | 
 494 |   L2_Simple_Adaptor<T, DataSource> distance_L2_Simple;
 495 | 
 496 |   SO3_Adaptor(const DataSource &_data_source)
 497 |       : distance_L2_Simple(_data_source) {}
 498 | 
 499 |   inline DistanceType evalMetric(const T *a, const size_t b_idx,
 500 |                                  size_t size) const {
 501 |     return distance_L2_Simple.evalMetric(a, b_idx, size);
 502 |   }
 503 | 
 504 |   template <typename U, typename V>
 505 |   inline DistanceType accum_dist(const U a, const V b, const size_t idx) const {
 506 |     return distance_L2_Simple.accum_dist(a, b, idx);
 507 |   }
 508 | };
 509 | 
 510 | /** Metaprogramming helper traits class for the L1 (Manhattan) metric */
 511 | struct metric_L1 : public Metric {
 512 |   template <class T, class DataSource> struct traits {
 513 |     typedef L1_Adaptor<T, DataSource> distance_t;
 514 |   };
 515 | };
 516 | /** Metaprogramming helper traits class for the L2 (Euclidean) metric */
 517 | struct metric_L2 : public Metric {
 518 |   template <class T, class DataSource> struct traits {
 519 |     typedef L2_Adaptor<T, DataSource> distance_t;
 520 |   };
 521 | };
 522 | /** Metaprogramming helper traits class for the L2_simple (Euclidean) metric */
 523 | struct metric_L2_Simple : public Metric {
 524 |   template <class T, class DataSource> struct traits {
 525 |     typedef L2_Simple_Adaptor<T, DataSource> distance_t;
 526 |   };
 527 | };
 528 | /** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
 529 | struct metric_SO2 : public Metric {
 530 |   template <class T, class DataSource> struct traits {
 531 |     typedef SO2_Adaptor<T, DataSource> distance_t;
 532 |   };
 533 | };
 534 | /** Metaprogramming helper traits class for the SO3_InnerProdQuat metric */
 535 | struct metric_SO3 : public Metric {
 536 |   template <class T, class DataSource> struct traits {
 537 |     typedef SO3_Adaptor<T, DataSource> distance_t;
 538 |   };
 539 | };
 540 | 
 541 | /** @} */
 542 | 
 543 | /** @addtogroup param_grp Parameter structs
 544 |  * @{ */
 545 | 
 546 | /**  Parameters (see README.md) */
 547 | struct KDTreeSingleIndexAdaptorParams {
 548 |   KDTreeSingleIndexAdaptorParams(size_t _leaf_max_size = 10)
 549 |       : leaf_max_size(_leaf_max_size) {}
 550 | 
 551 |   size_t leaf_max_size;
 552 | };
 553 | 
 554 | /** Search options for KDTreeSingleIndexAdaptor::findNeighbors() */
 555 | struct SearchParams {
 556 |   /** Note: The first argument (checks_IGNORED_) is ignored, but kept for
 557 |    * compatibility with the FLANN interface */
 558 |   SearchParams(int checks_IGNORED_ = 32, float eps_ = 0, bool sorted_ = true)
 559 |       : checks(checks_IGNORED_), eps(eps_), sorted(sorted_) {}
 560 | 
 561 |   int checks;  //!< Ignored parameter (Kept for compatibility with the FLANN
 562 |                //!< interface).
 563 |   float eps;   //!< search for eps-approximate neighbours (default: 0)
 564 |   bool sorted; //!< only for radius search, require neighbours sorted by
 565 |                //!< distance (default: true)
 566 | };
 567 | /** @} */
 568 | 
 569 | /** @addtogroup memalloc_grp Memory allocation
 570 |  * @{ */
 571 | 
 572 | /**
 573 |  * Allocates (using C's malloc) a generic type T.
 574 |  *
 575 |  * Params:
 576 |  *     count = number of instances to allocate.
 577 |  * Returns: pointer (of type T*) to memory buffer
 578 |  */
 579 | template <typename T> inline T *allocate(size_t count = 1) {
 580 |   T *mem = static_cast<T *>(::malloc(sizeof(T) * count));
 581 |   return mem;
 582 | }
 583 | 
 584 | /**
 585 |  * Pooled storage allocator
 586 |  *
 587 |  * The following routines allow for the efficient allocation of storage in
 588 |  * small chunks from a specified pool.  Rather than allowing each structure
 589 |  * to be freed individually, an entire pool of storage is freed at once.
 590 |  * This method has two advantages over just using malloc() and free().  First,
 591 |  * it is far more efficient for allocating small objects, as there is
 592 |  * no overhead for remembering all the information needed to free each
 593 |  * object or consolidating fragmented memory.  Second, the decision about
 594 |  * how long to keep an object is made at the time of allocation, and there
 595 |  * is no need to track down all the objects to free them.
 596 |  *
 597 |  */
 598 | 
 599 | const size_t WORDSIZE = 16;
 600 | const size_t BLOCKSIZE = 8192;
 601 | 
 602 | class PooledAllocator {
 603 |   /* We maintain memory alignment to word boundaries by requiring that all
 604 |       allocations be in multiples of the machine wordsize.  */
 605 |   /* Size of machine word in bytes.  Must be power of 2. */
 606 |   /* Minimum number of bytes requested at a time from	the system.  Must be
 607 |    * multiple of WORDSIZE. */
 608 | 
 609 |   size_t remaining; /* Number of bytes left in current block of storage. */
 610 |   void *base;       /* Pointer to base of current block of storage. */
 611 |   void *loc;        /* Current location in block to next allocate memory. */
 612 | 
 613 |   void internal_init() {
 614 |     remaining = 0;
 615 |     base = NULL;
 616 |     usedMemory = 0;
 617 |     wastedMemory = 0;
 618 |   }
 619 | 
 620 | public:
 621 |   size_t usedMemory;
 622 |   size_t wastedMemory;
 623 | 
 624 |   /**
 625 |       Default constructor. Initializes a new pool.
 626 |    */
 627 |   PooledAllocator() { internal_init(); }
 628 | 
 629 |   /**
 630 |    * Destructor. Frees all the memory allocated in this pool.
 631 |    */
 632 |   ~PooledAllocator() { free_all(); }
 633 | 
 634 |   /** Frees all allocated memory chunks */
 635 |   void free_all() {
 636 |     while (base != NULL) {
 637 |       void *prev =
 638 |           *(static_cast<void **>(base)); /* Get pointer to prev block. */
 639 |       ::free(base);
 640 |       base = prev;
 641 |     }
 642 |     internal_init();
 643 |   }
 644 | 
 645 |   /**
 646 |    * Returns a pointer to a piece of new memory of the given size in bytes
 647 |    * allocated from the pool.
 648 |    */
 649 |   void *malloc(const size_t req_size) {
 650 |     /* Round size up to a multiple of wordsize.  The following expression
 651 |         only works for WORDSIZE that is a power of 2, by masking last bits of
 652 |         incremented size to zero.
 653 |      */
 654 |     const size_t size = (req_size + (WORDSIZE - 1)) & ~(WORDSIZE - 1);
 655 | 
 656 |     /* Check whether a new block must be allocated.  Note that the first word
 657 |         of a block is reserved for a pointer to the previous block.
 658 |      */
 659 |     if (size > remaining) {
 660 | 
 661 |       wastedMemory += remaining;
 662 | 
 663 |       /* Allocate new storage. */
 664 |       const size_t blocksize =
 665 |           (size + sizeof(void *) + (WORDSIZE - 1) > BLOCKSIZE)
 666 |               ? size + sizeof(void *) + (WORDSIZE - 1)
 667 |               : BLOCKSIZE;
 668 | 
 669 |       // use the standard C malloc to allocate memory
 670 |       void *m = ::malloc(blocksize);
 671 |       if (!m) {
 672 |         fprintf(stderr, "Failed to allocate memory.\n");
 673 |         throw std::bad_alloc();
 674 |       }
 675 | 
 676 |       /* Fill first word of new block with pointer to previous block. */
 677 |       static_cast<void **>(m)[0] = base;
 678 |       base = m;
 679 | 
 680 |       size_t shift = 0;
 681 |       // int size_t = (WORDSIZE - ( (((size_t)m) + sizeof(void*)) &
 682 |       // (WORDSIZE-1))) & (WORDSIZE-1);
 683 | 
 684 |       remaining = blocksize - sizeof(void *) - shift;
 685 |       loc = (static_cast<char *>(m) + sizeof(void *) + shift);
 686 |     }
 687 |     void *rloc = loc;
 688 |     loc = static_cast<char *>(loc) + size;
 689 |     remaining -= size;
 690 | 
 691 |     usedMemory += size;
 692 | 
 693 |     return rloc;
 694 |   }
 695 | 
 696 |   /**
 697 |    * Allocates (using this pool) a generic type T.
 698 |    *
 699 |    * Params:
 700 |    *     count = number of instances to allocate.
 701 |    * Returns: pointer (of type T*) to memory buffer
 702 |    */
 703 |   template <typename T> T *allocate(const size_t count = 1) {
 704 |     T *mem = static_cast<T *>(this->malloc(sizeof(T) * count));
 705 |     return mem;
 706 |   }
 707 | };
 708 | /** @} */
 709 | 
 710 | /** @addtogroup nanoflann_metaprog_grp Auxiliary metaprogramming stuff
 711 |  * @{ */
 712 | 
 713 | /** Used to declare fixed-size arrays when DIM>0, dynamically-allocated vectors
 714 |  * when DIM=-1. Fixed size version for a generic DIM:
 715 |  */
 716 | template <int DIM, typename T> struct array_or_vector_selector {
 717 |   typedef std::array<T, DIM> container_t;
 718 | };
 719 | /** Dynamic size version */
 720 | template <typename T> struct array_or_vector_selector<-1, T> {
 721 |   typedef std::vector<T> container_t;
 722 | };
 723 | 
 724 | /** @} */
 725 | 
 726 | /** kd-tree base-class
 727 |  *
 728 |  * Contains the member functions common to the classes KDTreeSingleIndexAdaptor
 729 |  * and KDTreeSingleIndexDynamicAdaptor_.
 730 |  *
 731 |  * \tparam Derived The name of the class which inherits this class.
 732 |  * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
 733 |  * \tparam Distance The distance metric to use, these are all classes derived
 734 |  * from nanoflann::Metric \tparam DIM Dimensionality of data points (e.g. 3 for
 735 |  * 3D points) \tparam IndexType Will be typically size_t or int
 736 |  */
 737 | 
 738 | template <class Derived, typename Distance, class DatasetAdaptor, int DIM = -1,
 739 |           typename IndexType = size_t>
 740 | class KDTreeBaseClass {
 741 | 
 742 | public:
 743 |   /** Frees the previously-built index. Automatically called within
 744 |    * buildIndex(). */
 745 |   void freeIndex(Derived &obj) {
 746 |     obj.pool.free_all();
 747 |     obj.root_node = NULL;
 748 |     obj.m_size_at_index_build = 0;
 749 |   }
 750 | 
 751 |   typedef typename Distance::ElementType ElementType;
 752 |   typedef typename Distance::DistanceType DistanceType;
 753 | 
 754 |   /*--------------------- Internal Data Structures --------------------------*/
 755 |   struct Node {
 756 |     /** Union used because a node can be either a LEAF node or a non-leaf node,
 757 |      * so both data fields are never used simultaneously */
 758 |     union {
 759 |       struct leaf {
 760 |         IndexType left, right; //!< Indices of points in leaf node
 761 |       } lr;
 762 |       struct nonleaf {
 763 |         int divfeat;                  //!< Dimension used for subdivision.
 764 |         DistanceType divlow, divhigh; //!< The values used for subdivision.
 765 |       } sub;
 766 |     } node_type;
 767 |     Node *child1, *child2; //!< Child nodes (both=NULL mean its a leaf node)
 768 |   };
 769 | 
 770 |   typedef Node *NodePtr;
 771 | 
 772 |   struct Interval {
 773 |     ElementType low, high;
 774 |   };
 775 | 
 776 |   /**
 777 |    *  Array of indices to vectors in the dataset.
 778 |    */
 779 |   std::vector<IndexType> vind;
 780 | 
 781 |   NodePtr root_node;
 782 | 
 783 |   size_t m_leaf_max_size;
 784 | 
 785 |   size_t m_size;                //!< Number of current points in the dataset
 786 |   size_t m_size_at_index_build; //!< Number of points in the dataset when the
 787 |                                 //!< index was built
 788 |   int dim;                      //!< Dimensionality of each data point
 789 | 
 790 |   /** Define "BoundingBox" as a fixed-size or variable-size container depending
 791 |    * on "DIM" */
 792 |   typedef
 793 |       typename array_or_vector_selector<DIM, Interval>::container_t BoundingBox;
 794 | 
 795 |   /** Define "distance_vector_t" as a fixed-size or variable-size container
 796 |    * depending on "DIM" */
 797 |   typedef typename array_or_vector_selector<DIM, DistanceType>::container_t
 798 |       distance_vector_t;
 799 | 
 800 |   /** The KD-tree used to find neighbours */
 801 | 
 802 |   BoundingBox root_bbox;
 803 | 
 804 |   /**
 805 |    * Pooled memory allocator.
 806 |    *
 807 |    * Using a pooled memory allocator is more efficient
 808 |    * than allocating memory directly when there is a large
 809 |    * number small of memory allocations.
 810 |    */
 811 |   PooledAllocator pool;
 812 | 
 813 |   /** Returns number of points in dataset  */
 814 |   size_t size(const Derived &obj) const { return obj.m_size; }
 815 | 
 816 |   /** Returns the length of each point in the dataset */
 817 |   size_t veclen(const Derived &obj) {
 818 |     return static_cast<size_t>(DIM > 0 ? DIM : obj.dim);
 819 |   }
 820 | 
 821 |   /// Helper accessor to the dataset points:
 822 |   inline ElementType dataset_get(const Derived &obj, size_t idx,
 823 |                                  int component) const {
 824 |     return obj.dataset.kdtree_get_pt(idx, component);
 825 |   }
 826 | 
 827 |   /**
 828 |    * Computes the inde memory usage
 829 |    * Returns: memory used by the index
 830 |    */
 831 |   size_t usedMemory(Derived &obj) {
 832 |     return obj.pool.usedMemory + obj.pool.wastedMemory +
 833 |            obj.dataset.kdtree_get_point_count() *
 834 |                sizeof(IndexType); // pool memory and vind array memory
 835 |   }
 836 | 
 837 |   void computeMinMax(const Derived &obj, IndexType *ind, IndexType count,
 838 |                      int element, ElementType &min_elem,
 839 |                      ElementType &max_elem) {
 840 |     min_elem = dataset_get(obj, ind[0], element);
 841 |     max_elem = dataset_get(obj, ind[0], element);
 842 |     for (IndexType i = 1; i < count; ++i) {
 843 |       ElementType val = dataset_get(obj, ind[i], element);
 844 |       if (val < min_elem)
 845 |         min_elem = val;
 846 |       if (val > max_elem)
 847 |         max_elem = val;
 848 |     }
 849 |   }
 850 | 
 851 |   /**
 852 |    * Create a tree node that subdivides the list of vecs from vind[first]
 853 |    * to vind[last].  The routine is called recursively on each sublist.
 854 |    *
 855 |    * @param left index of the first vector
 856 |    * @param right index of the last vector
 857 |    */
 858 |   NodePtr divideTree(Derived &obj, const IndexType left, const IndexType right,
 859 |                      BoundingBox &bbox) {
 860 |     NodePtr node = obj.pool.template allocate<Node>(); // allocate memory
 861 | 
 862 |     /* If too few exemplars remain, then make this a leaf node. */
 863 |     if ((right - left) <= static_cast<IndexType>(obj.m_leaf_max_size)) {
 864 |       node->child1 = node->child2 = NULL; /* Mark as leaf node. */
 865 |       node->node_type.lr.left = left;
 866 |       node->node_type.lr.right = right;
 867 | 
 868 |       // compute bounding-box of leaf points
 869 |       for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
 870 |         bbox[i].low = dataset_get(obj, obj.vind[left], i);
 871 |         bbox[i].high = dataset_get(obj, obj.vind[left], i);
 872 |       }
 873 |       for (IndexType k = left + 1; k < right; ++k) {
 874 |         for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
 875 |           if (bbox[i].low > dataset_get(obj, obj.vind[k], i))
 876 |             bbox[i].low = dataset_get(obj, obj.vind[k], i);
 877 |           if (bbox[i].high < dataset_get(obj, obj.vind[k], i))
 878 |             bbox[i].high = dataset_get(obj, obj.vind[k], i);
 879 |         }
 880 |       }
 881 |     } else {
 882 |       IndexType idx;
 883 |       int cutfeat;
 884 |       DistanceType cutval;
 885 |       middleSplit_(obj, &obj.vind[0] + left, right - left, idx, cutfeat, cutval,
 886 |                    bbox);
 887 | 
 888 |       node->node_type.sub.divfeat = cutfeat;
 889 | 
 890 |       BoundingBox left_bbox(bbox);
 891 |       left_bbox[cutfeat].high = cutval;
 892 |       node->child1 = divideTree(obj, left, left + idx, left_bbox);
 893 | 
 894 |       BoundingBox right_bbox(bbox);
 895 |       right_bbox[cutfeat].low = cutval;
 896 |       node->child2 = divideTree(obj, left + idx, right, right_bbox);
 897 | 
 898 |       node->node_type.sub.divlow = left_bbox[cutfeat].high;
 899 |       node->node_type.sub.divhigh = right_bbox[cutfeat].low;
 900 | 
 901 |       for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
 902 |         bbox[i].low = std::min(left_bbox[i].low, right_bbox[i].low);
 903 |         bbox[i].high = std::max(left_bbox[i].high, right_bbox[i].high);
 904 |       }
 905 |     }
 906 | 
 907 |     return node;
 908 |   }
 909 | 
 910 |   void middleSplit_(Derived &obj, IndexType *ind, IndexType count,
 911 |                     IndexType &index, int &cutfeat, DistanceType &cutval,
 912 |                     const BoundingBox &bbox) {
 913 |     const DistanceType EPS = static_cast<DistanceType>(0.00001);
 914 |     ElementType max_span = bbox[0].high - bbox[0].low;
 915 |     for (int i = 1; i < (DIM > 0 ? DIM : obj.dim); ++i) {
 916 |       ElementType span = bbox[i].high - bbox[i].low;
 917 |       if (span > max_span) {
 918 |         max_span = span;
 919 |       }
 920 |     }
 921 |     ElementType max_spread = -1;
 922 |     cutfeat = 0;
 923 |     for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
 924 |       ElementType span = bbox[i].high - bbox[i].low;
 925 |       if (span > (1 - EPS) * max_span) {
 926 |         ElementType min_elem, max_elem;
 927 |         computeMinMax(obj, ind, count, i, min_elem, max_elem);
 928 |         ElementType spread = max_elem - min_elem;
 929 |         ;
 930 |         if (spread > max_spread) {
 931 |           cutfeat = i;
 932 |           max_spread = spread;
 933 |         }
 934 |       }
 935 |     }
 936 |     // split in the middle
 937 |     DistanceType split_val = (bbox[cutfeat].low + bbox[cutfeat].high) / 2;
 938 |     ElementType min_elem, max_elem;
 939 |     computeMinMax(obj, ind, count, cutfeat, min_elem, max_elem);
 940 | 
 941 |     if (split_val < min_elem)
 942 |       cutval = min_elem;
 943 |     else if (split_val > max_elem)
 944 |       cutval = max_elem;
 945 |     else
 946 |       cutval = split_val;
 947 | 
 948 |     IndexType lim1, lim2;
 949 |     planeSplit(obj, ind, count, cutfeat, cutval, lim1, lim2);
 950 | 
 951 |     if (lim1 > count / 2)
 952 |       index = lim1;
 953 |     else if (lim2 < count / 2)
 954 |       index = lim2;
 955 |     else
 956 |       index = count / 2;
 957 |   }
 958 | 
 959 |   /**
 960 |    *  Subdivide the list of points by a plane perpendicular on axe corresponding
 961 |    *  to the 'cutfeat' dimension at 'cutval' position.
 962 |    *
 963 |    *  On return:
 964 |    *  dataset[ind[0..lim1-1]][cutfeat]<cutval
 965 |    *  dataset[ind[lim1..lim2-1]][cutfeat]==cutval
 966 |    *  dataset[ind[lim2..count]][cutfeat]>cutval
 967 |    */
 968 |   void planeSplit(Derived &obj, IndexType *ind, const IndexType count,
 969 |                   int cutfeat, DistanceType &cutval, IndexType &lim1,
 970 |                   IndexType &lim2) {
 971 |     /* Move vector indices for left subtree to front of list. */
 972 |     IndexType left = 0;
 973 |     IndexType right = count - 1;
 974 |     for (;;) {
 975 |       while (left <= right && dataset_get(obj, ind[left], cutfeat) < cutval)
 976 |         ++left;
 977 |       while (right && left <= right &&
 978 |              dataset_get(obj, ind[right], cutfeat) >= cutval)
 979 |         --right;
 980 |       if (left > right || !right)
 981 |         break; // "!right" was added to support unsigned Index types
 982 |       std::swap(ind[left], ind[right]);
 983 |       ++left;
 984 |       --right;
 985 |     }
 986 |     /* If either list is empty, it means that all remaining features
 987 |      * are identical. Split in the middle to maintain a balanced tree.
 988 |      */
 989 |     lim1 = left;
 990 |     right = count - 1;
 991 |     for (;;) {
 992 |       while (left <= right && dataset_get(obj, ind[left], cutfeat) <= cutval)
 993 |         ++left;
 994 |       while (right && left <= right &&
 995 |              dataset_get(obj, ind[right], cutfeat) > cutval)
 996 |         --right;
 997 |       if (left > right || !right)
 998 |         break; // "!right" was added to support unsigned Index types
 999 |       std::swap(ind[left], ind[right]);
1000 |       ++left;
1001 |       --right;
1002 |     }
1003 |     lim2 = left;
1004 |   }
1005 | 
1006 |   DistanceType computeInitialDistances(const Derived &obj,
1007 |                                        const ElementType *vec,
1008 |                                        distance_vector_t &dists) const {
1009 |     assert(vec);
1010 |     DistanceType distsq = DistanceType();
1011 | 
1012 |     for (int i = 0; i < (DIM > 0 ? DIM : obj.dim); ++i) {
1013 |       if (vec[i] < obj.root_bbox[i].low) {
1014 |         dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].low, i);
1015 |         distsq += dists[i];
1016 |       }
1017 |       if (vec[i] > obj.root_bbox[i].high) {
1018 |         dists[i] = obj.distance.accum_dist(vec[i], obj.root_bbox[i].high, i);
1019 |         distsq += dists[i];
1020 |       }
1021 |     }
1022 |     return distsq;
1023 |   }
1024 | 
1025 |   void save_tree(Derived &obj, FILE *stream, NodePtr tree) {
1026 |     save_value(stream, *tree);
1027 |     if (tree->child1 != NULL) {
1028 |       save_tree(obj, stream, tree->child1);
1029 |     }
1030 |     if (tree->child2 != NULL) {
1031 |       save_tree(obj, stream, tree->child2);
1032 |     }
1033 |   }
1034 | 
1035 |   void load_tree(Derived &obj, FILE *stream, NodePtr &tree) {
1036 |     tree = obj.pool.template allocate<Node>();
1037 |     load_value(stream, *tree);
1038 |     if (tree->child1 != NULL) {
1039 |       load_tree(obj, stream, tree->child1);
1040 |     }
1041 |     if (tree->child2 != NULL) {
1042 |       load_tree(obj, stream, tree->child2);
1043 |     }
1044 |   }
1045 | 
1046 |   /**  Stores the index in a binary file.
1047 |    *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
1048 |    * loading the index object it must be constructed associated to the same
1049 |    * source of data points used while building it. See the example:
1050 |    * examples/saveload_example.cpp \sa loadIndex  */
1051 |   void saveIndex_(Derived &obj, FILE *stream) {
1052 |     save_value(stream, obj.m_size);
1053 |     save_value(stream, obj.dim);
1054 |     save_value(stream, obj.root_bbox);
1055 |     save_value(stream, obj.m_leaf_max_size);
1056 |     save_value(stream, obj.vind);
1057 |     save_tree(obj, stream, obj.root_node);
1058 |   }
1059 | 
1060 |   /**  Loads a previous index from a binary file.
1061 |    *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
1062 |    * index object must be constructed associated to the same source of data
1063 |    * points used while building the index. See the example:
1064 |    * examples/saveload_example.cpp \sa loadIndex  */
1065 |   void loadIndex_(Derived &obj, FILE *stream) {
1066 |     load_value(stream, obj.m_size);
1067 |     load_value(stream, obj.dim);
1068 |     load_value(stream, obj.root_bbox);
1069 |     load_value(stream, obj.m_leaf_max_size);
1070 |     load_value(stream, obj.vind);
1071 |     load_tree(obj, stream, obj.root_node);
1072 |   }
1073 | };
1074 | 
1075 | /** @addtogroup kdtrees_grp KD-tree classes and adaptors
1076 |  * @{ */
1077 | 
1078 | /** kd-tree static index
1079 |  *
1080 |  * Contains the k-d trees and other information for indexing a set of points
1081 |  * for nearest-neighbor matching.
1082 |  *
1083 |  *  The class "DatasetAdaptor" must provide the following interface (can be
1084 |  * non-virtual, inlined methods):
1085 |  *
1086 |  *  \code
1087 |  *   // Must return the number of data poins
1088 |  *   inline size_t kdtree_get_point_count() const { ... }
1089 |  *
1090 |  *
1091 |  *   // Must return the dim'th component of the idx'th point in the class:
1092 |  *   inline T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
1093 |  *
1094 |  *   // Optional bounding-box computation: return false to default to a standard
1095 |  * bbox computation loop.
1096 |  *   //   Return true if the BBOX was already computed by the class and returned
1097 |  * in "bb" so it can be avoided to redo it again.
1098 |  *   //   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3
1099 |  * for point clouds) template <class BBOX> bool kdtree_get_bbox(BBOX &bb) const
1100 |  *   {
1101 |  *      bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
1102 |  *      bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
1103 |  *      ...
1104 |  *      return true;
1105 |  *   }
1106 |  *
1107 |  *  \endcode
1108 |  *
1109 |  * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
1110 |  * \tparam Distance The distance metric to use: nanoflann::metric_L1,
1111 |  * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
1112 |  * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
1113 |  * be typically size_t or int
1114 |  */
1115 | template <typename Distance, class DatasetAdaptor, int DIM = -1,
1116 |           typename IndexType = size_t>
1117 | class KDTreeSingleIndexAdaptor
1118 |     : public KDTreeBaseClass<
1119 |           KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>,
1120 |           Distance, DatasetAdaptor, DIM, IndexType> {
1121 | public:
1122 |   /** Deleted copy constructor*/
1123 |   KDTreeSingleIndexAdaptor(
1124 |       const KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM, IndexType>
1125 |           &) = delete;
1126 | 
1127 |   /**
1128 |    * The dataset used by this index
1129 |    */
1130 |   const DatasetAdaptor &dataset; //!< The source of our data
1131 | 
1132 |   const KDTreeSingleIndexAdaptorParams index_params;
1133 | 
1134 |   Distance distance;
1135 | 
1136 |   typedef typename nanoflann::KDTreeBaseClass<
1137 |       nanoflann::KDTreeSingleIndexAdaptor<Distance, DatasetAdaptor, DIM,
1138 |                                           IndexType>,
1139 |       Distance, DatasetAdaptor, DIM, IndexType>
1140 |       BaseClassRef;
1141 | 
1142 |   typedef typename BaseClassRef::ElementType ElementType;
1143 |   typedef typename BaseClassRef::DistanceType DistanceType;
1144 | 
1145 |   typedef typename BaseClassRef::Node Node;
1146 |   typedef Node *NodePtr;
1147 | 
1148 |   typedef typename BaseClassRef::Interval Interval;
1149 |   /** Define "BoundingBox" as a fixed-size or variable-size container depending
1150 |    * on "DIM" */
1151 |   typedef typename BaseClassRef::BoundingBox BoundingBox;
1152 | 
1153 |   /** Define "distance_vector_t" as a fixed-size or variable-size container
1154 |    * depending on "DIM" */
1155 |   typedef typename BaseClassRef::distance_vector_t distance_vector_t;
1156 | 
1157 |   /**
1158 |    * KDTree constructor
1159 |    *
1160 |    * Refer to docs in README.md or online in
1161 |    * https://github.com/jlblancoc/nanoflann
1162 |    *
1163 |    * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
1164 |    * for 3D points) is determined by means of:
1165 |    *  - The \a DIM template parameter if >0 (highest priority)
1166 |    *  - Otherwise, the \a dimensionality parameter of this constructor.
1167 |    *
1168 |    * @param inputData Dataset with the input features
1169 |    * @param params Basically, the maximum leaf node size
1170 |    */
1171 |   KDTreeSingleIndexAdaptor(const int dimensionality,
1172 |                            const DatasetAdaptor &inputData,
1173 |                            const KDTreeSingleIndexAdaptorParams &params =
1174 |                                KDTreeSingleIndexAdaptorParams())
1175 |       : dataset(inputData), index_params(params), distance(inputData) {
1176 |     BaseClassRef::root_node = NULL;
1177 |     BaseClassRef::m_size = dataset.kdtree_get_point_count();
1178 |     BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
1179 |     BaseClassRef::dim = dimensionality;
1180 |     if (DIM > 0)
1181 |       BaseClassRef::dim = DIM;
1182 |     BaseClassRef::m_leaf_max_size = params.leaf_max_size;
1183 | 
1184 |     // Create a permutable array of indices to the input vectors.
1185 |     init_vind();
1186 |   }
1187 | 
1188 |   /**
1189 |    * Builds the index
1190 |    */
1191 |   void buildIndex() {
1192 |     BaseClassRef::m_size = dataset.kdtree_get_point_count();
1193 |     BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
1194 |     init_vind();
1195 |     this->freeIndex(*this);
1196 |     BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
1197 |     if (BaseClassRef::m_size == 0)
1198 |       return;
1199 |     computeBoundingBox(BaseClassRef::root_bbox);
1200 |     BaseClassRef::root_node =
1201 |         this->divideTree(*this, 0, BaseClassRef::m_size,
1202 |                          BaseClassRef::root_bbox); // construct the tree
1203 |   }
1204 | 
1205 |   /** \name Query methods
1206 |    * @{ */
1207 | 
1208 |   /**
1209 |    * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
1210 |    * inside the result object.
1211 |    *
1212 |    * Params:
1213 |    *     result = the result object in which the indices of the
1214 |    * nearest-neighbors are stored vec = the vector for which to search the
1215 |    * nearest neighbors
1216 |    *
1217 |    * \tparam RESULTSET Should be any ResultSet<DistanceType>
1218 |    * \return  True if the requested neighbors could be found.
1219 |    * \sa knnSearch, radiusSearch
1220 |    */
1221 |   template <typename RESULTSET>
1222 |   bool findNeighbors(RESULTSET &result, const ElementType *vec,
1223 |                      const SearchParams &searchParams) const {
1224 |     assert(vec);
1225 |     if (this->size(*this) == 0)
1226 |       return false;
1227 |     if (!BaseClassRef::root_node)
1228 |       throw std::runtime_error(
1229 |           "[nanoflann] findNeighbors() called before building the index.");
1230 |     float epsError = 1 + searchParams.eps;
1231 | 
1232 |     distance_vector_t
1233 |         dists; // fixed or variable-sized container (depending on DIM)
1234 |     auto zero = static_cast<decltype(result.worstDist())>(0);
1235 |     assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
1236 |            zero); // Fill it with zeros.
1237 |     DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
1238 |     searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
1239 |                 epsError); // "count_leaf" parameter removed since was neither
1240 |                            // used nor returned to the user.
1241 |     return result.full();
1242 |   }
1243 | 
1244 |   /**
1245 |    * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1].
1246 |    * Their indices are stored inside the result object. \sa radiusSearch,
1247 |    * findNeighbors \note nChecks_IGNORED is ignored but kept for compatibility
1248 |    * with the original FLANN interface. \return Number `N` of valid points in
1249 |    * the result set. Only the first `N` entries in `out_indices` and
1250 |    * `out_distances_sq` will be valid. Return may be less than `num_closest`
1251 |    * only if the number of elements in the tree is less than `num_closest`.
1252 |    */
1253 |   size_t knnSearch(const ElementType *query_point, const size_t num_closest,
1254 |                    IndexType *out_indices, DistanceType *out_distances_sq,
1255 |                    const int /* nChecks_IGNORED */ = 10) const {
1256 |     nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
1257 |     resultSet.init(out_indices, out_distances_sq);
1258 |     this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
1259 |     return resultSet.size();
1260 |   }
1261 | 
1262 |   /**
1263 |    * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
1264 |    *  The output is given as a vector of pairs, of which the first element is a
1265 |    * point index and the second the corresponding distance. Previous contents of
1266 |    * \a IndicesDists are cleared.
1267 |    *
1268 |    *  If searchParams.sorted==true, the output list is sorted by ascending
1269 |    * distances.
1270 |    *
1271 |    *  For a better performance, it is advisable to do a .reserve() on the vector
1272 |    * if you have any wild guess about the number of expected matches.
1273 |    *
1274 |    *  \sa knnSearch, findNeighbors, radiusSearchCustomCallback
1275 |    * \return The number of points within the given radius (i.e. indices.size()
1276 |    * or dists.size() )
1277 |    */
1278 |   size_t
1279 |   radiusSearch(const ElementType *query_point, const DistanceType &radius,
1280 |                std::vector<std::pair<IndexType, DistanceType>> &IndicesDists,
1281 |                const SearchParams &searchParams) const {
1282 |     RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
1283 |     const size_t nFound =
1284 |         radiusSearchCustomCallback(query_point, resultSet, searchParams);
1285 |     if (searchParams.sorted)
1286 |       std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
1287 |     return nFound;
1288 |   }
1289 | 
1290 |   /**
1291 |    * Just like radiusSearch() but with a custom callback class for each point
1292 |    * found in the radius of the query. See the source of RadiusResultSet<> as a
1293 |    * start point for your own classes. \sa radiusSearch
1294 |    */
1295 |   template <class SEARCH_CALLBACK>
1296 |   size_t radiusSearchCustomCallback(
1297 |       const ElementType *query_point, SEARCH_CALLBACK &resultSet,
1298 |       const SearchParams &searchParams = SearchParams()) const {
1299 |     this->findNeighbors(resultSet, query_point, searchParams);
1300 |     return resultSet.size();
1301 |   }
1302 | 
1303 |   /** @} */
1304 | 
1305 | public:
1306 |   /** Make sure the auxiliary list \a vind has the same size than the current
1307 |    * dataset, and re-generate if size has changed. */
1308 |   void init_vind() {
1309 |     // Create a permutable array of indices to the input vectors.
1310 |     BaseClassRef::m_size = dataset.kdtree_get_point_count();
1311 |     if (BaseClassRef::vind.size() != BaseClassRef::m_size)
1312 |       BaseClassRef::vind.resize(BaseClassRef::m_size);
1313 |     for (size_t i = 0; i < BaseClassRef::m_size; i++)
1314 |       BaseClassRef::vind[i] = i;
1315 |   }
1316 | 
1317 |   void computeBoundingBox(BoundingBox &bbox) {
1318 |     resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
1319 |     if (dataset.kdtree_get_bbox(bbox)) {
1320 |       // Done! It was implemented in derived class
1321 |     } else {
1322 |       const size_t N = dataset.kdtree_get_point_count();
1323 |       if (!N)
1324 |         throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
1325 |                                  "no data points found.");
1326 |       for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
1327 |         bbox[i].low = bbox[i].high = this->dataset_get(*this, 0, i);
1328 |       }
1329 |       for (size_t k = 1; k < N; ++k) {
1330 |         for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
1331 |           if (this->dataset_get(*this, k, i) < bbox[i].low)
1332 |             bbox[i].low = this->dataset_get(*this, k, i);
1333 |           if (this->dataset_get(*this, k, i) > bbox[i].high)
1334 |             bbox[i].high = this->dataset_get(*this, k, i);
1335 |         }
1336 |       }
1337 |     }
1338 |   }
1339 | 
1340 |   /**
1341 |    * Performs an exact search in the tree starting from a node.
1342 |    * \tparam RESULTSET Should be any ResultSet<DistanceType>
1343 |    * \return true if the search should be continued, false if the results are
1344 |    * sufficient
1345 |    */
1346 |   template <class RESULTSET>
1347 |   bool searchLevel(RESULTSET &result_set, const ElementType *vec,
1348 |                    const NodePtr node, DistanceType mindistsq,
1349 |                    distance_vector_t &dists, const float epsError) const {
1350 |     /* If this is a leaf node, then do check and return. */
1351 |     if ((node->child1 == NULL) && (node->child2 == NULL)) {
1352 |       // count_leaf += (node->lr.right-node->lr.left);  // Removed since was
1353 |       // neither used nor returned to the user.
1354 |       DistanceType worst_dist = result_set.worstDist();
1355 |       for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
1356 |            ++i) {
1357 |         const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
1358 |         DistanceType dist = distance.evalMetric(
1359 |             vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
1360 |         if (dist < worst_dist) {
1361 |           if (!result_set.addPoint(dist, BaseClassRef::vind[i])) {
1362 |             // the resultset doesn't want to receive any more points, we're done
1363 |             // searching!
1364 |             return false;
1365 |           }
1366 |         }
1367 |       }
1368 |       return true;
1369 |     }
1370 | 
1371 |     /* Which child branch should be taken first? */
1372 |     int idx = node->node_type.sub.divfeat;
1373 |     ElementType val = vec[idx];
1374 |     DistanceType diff1 = val - node->node_type.sub.divlow;
1375 |     DistanceType diff2 = val - node->node_type.sub.divhigh;
1376 | 
1377 |     NodePtr bestChild;
1378 |     NodePtr otherChild;
1379 |     DistanceType cut_dist;
1380 |     if ((diff1 + diff2) < 0) {
1381 |       bestChild = node->child1;
1382 |       otherChild = node->child2;
1383 |       cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
1384 |     } else {
1385 |       bestChild = node->child2;
1386 |       otherChild = node->child1;
1387 |       cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
1388 |     }
1389 | 
1390 |     /* Call recursively to search next level down. */
1391 |     if (!searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError)) {
1392 |       // the resultset doesn't want to receive any more points, we're done
1393 |       // searching!
1394 |       return false;
1395 |     }
1396 | 
1397 |     DistanceType dst = dists[idx];
1398 |     mindistsq = mindistsq + cut_dist - dst;
1399 |     dists[idx] = cut_dist;
1400 |     if (mindistsq * epsError <= result_set.worstDist()) {
1401 |       if (!searchLevel(result_set, vec, otherChild, mindistsq, dists,
1402 |                        epsError)) {
1403 |         // the resultset doesn't want to receive any more points, we're done
1404 |         // searching!
1405 |         return false;
1406 |       }
1407 |     }
1408 |     dists[idx] = dst;
1409 |     return true;
1410 |   }
1411 | 
1412 | public:
1413 |   /**  Stores the index in a binary file.
1414 |    *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
1415 |    * loading the index object it must be constructed associated to the same
1416 |    * source of data points used while building it. See the example:
1417 |    * examples/saveload_example.cpp \sa loadIndex  */
1418 |   void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
1419 | 
1420 |   /**  Loads a previous index from a binary file.
1421 |    *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
1422 |    * index object must be constructed associated to the same source of data
1423 |    * points used while building the index. See the example:
1424 |    * examples/saveload_example.cpp \sa loadIndex  */
1425 |   void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
1426 | 
1427 | }; // class KDTree
1428 | 
1429 | /** kd-tree dynamic index
1430 |  *
1431 |  * Contains the k-d trees and other information for indexing a set of points
1432 |  * for nearest-neighbor matching.
1433 |  *
1434 |  *  The class "DatasetAdaptor" must provide the following interface (can be
1435 |  * non-virtual, inlined methods):
1436 |  *
1437 |  *  \code
1438 |  *   // Must return the number of data poins
1439 |  *   inline size_t kdtree_get_point_count() const { ... }
1440 |  *
1441 |  *   // Must return the dim'th component of the idx'th point in the class:
1442 |  *   inline T kdtree_get_pt(const size_t idx, const size_t dim) const { ... }
1443 |  *
1444 |  *   // Optional bounding-box computation: return false to default to a standard
1445 |  * bbox computation loop.
1446 |  *   //   Return true if the BBOX was already computed by the class and returned
1447 |  * in "bb" so it can be avoided to redo it again.
1448 |  *   //   Look at bb.size() to find out the expected dimensionality (e.g. 2 or 3
1449 |  * for point clouds) template <class BBOX> bool kdtree_get_bbox(BBOX &bb) const
1450 |  *   {
1451 |  *      bb[0].low = ...; bb[0].high = ...;  // 0th dimension limits
1452 |  *      bb[1].low = ...; bb[1].high = ...;  // 1st dimension limits
1453 |  *      ...
1454 |  *      return true;
1455 |  *   }
1456 |  *
1457 |  *  \endcode
1458 |  *
1459 |  * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
1460 |  * \tparam Distance The distance metric to use: nanoflann::metric_L1,
1461 |  * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
1462 |  * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
1463 |  * be typically size_t or int
1464 |  */
1465 | template <typename Distance, class DatasetAdaptor, int DIM = -1,
1466 |           typename IndexType = size_t>
1467 | class KDTreeSingleIndexDynamicAdaptor_
1468 |     : public KDTreeBaseClass<KDTreeSingleIndexDynamicAdaptor_<
1469 |                                  Distance, DatasetAdaptor, DIM, IndexType>,
1470 |                              Distance, DatasetAdaptor, DIM, IndexType> {
1471 | public:
1472 |   /**
1473 |    * The dataset used by this index
1474 |    */
1475 |   const DatasetAdaptor &dataset; //!< The source of our data
1476 | 
1477 |   KDTreeSingleIndexAdaptorParams index_params;
1478 | 
1479 |   std::vector<int> &treeIndex;
1480 | 
1481 |   Distance distance;
1482 | 
1483 |   typedef typename nanoflann::KDTreeBaseClass<
1484 |       nanoflann::KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM,
1485 |                                                   IndexType>,
1486 |       Distance, DatasetAdaptor, DIM, IndexType>
1487 |       BaseClassRef;
1488 | 
1489 |   typedef typename BaseClassRef::ElementType ElementType;
1490 |   typedef typename BaseClassRef::DistanceType DistanceType;
1491 | 
1492 |   typedef typename BaseClassRef::Node Node;
1493 |   typedef Node *NodePtr;
1494 | 
1495 |   typedef typename BaseClassRef::Interval Interval;
1496 |   /** Define "BoundingBox" as a fixed-size or variable-size container depending
1497 |    * on "DIM" */
1498 |   typedef typename BaseClassRef::BoundingBox BoundingBox;
1499 | 
1500 |   /** Define "distance_vector_t" as a fixed-size or variable-size container
1501 |    * depending on "DIM" */
1502 |   typedef typename BaseClassRef::distance_vector_t distance_vector_t;
1503 | 
1504 |   /**
1505 |    * KDTree constructor
1506 |    *
1507 |    * Refer to docs in README.md or online in
1508 |    * https://github.com/jlblancoc/nanoflann
1509 |    *
1510 |    * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
1511 |    * for 3D points) is determined by means of:
1512 |    *  - The \a DIM template parameter if >0 (highest priority)
1513 |    *  - Otherwise, the \a dimensionality parameter of this constructor.
1514 |    *
1515 |    * @param inputData Dataset with the input features
1516 |    * @param params Basically, the maximum leaf node size
1517 |    */
1518 |   KDTreeSingleIndexDynamicAdaptor_(
1519 |       const int dimensionality, const DatasetAdaptor &inputData,
1520 |       std::vector<int> &treeIndex_,
1521 |       const KDTreeSingleIndexAdaptorParams &params =
1522 |           KDTreeSingleIndexAdaptorParams())
1523 |       : dataset(inputData), index_params(params), treeIndex(treeIndex_),
1524 |         distance(inputData) {
1525 |     BaseClassRef::root_node = NULL;
1526 |     BaseClassRef::m_size = 0;
1527 |     BaseClassRef::m_size_at_index_build = 0;
1528 |     BaseClassRef::dim = dimensionality;
1529 |     if (DIM > 0)
1530 |       BaseClassRef::dim = DIM;
1531 |     BaseClassRef::m_leaf_max_size = params.leaf_max_size;
1532 |   }
1533 | 
1534 |   /** Assignment operator definiton */
1535 |   KDTreeSingleIndexDynamicAdaptor_
1536 |   operator=(const KDTreeSingleIndexDynamicAdaptor_ &rhs) {
1537 |     KDTreeSingleIndexDynamicAdaptor_ tmp(rhs);
1538 |     std::swap(BaseClassRef::vind, tmp.BaseClassRef::vind);
1539 |     std::swap(BaseClassRef::m_leaf_max_size, tmp.BaseClassRef::m_leaf_max_size);
1540 |     std::swap(index_params, tmp.index_params);
1541 |     std::swap(treeIndex, tmp.treeIndex);
1542 |     std::swap(BaseClassRef::m_size, tmp.BaseClassRef::m_size);
1543 |     std::swap(BaseClassRef::m_size_at_index_build,
1544 |               tmp.BaseClassRef::m_size_at_index_build);
1545 |     std::swap(BaseClassRef::root_node, tmp.BaseClassRef::root_node);
1546 |     std::swap(BaseClassRef::root_bbox, tmp.BaseClassRef::root_bbox);
1547 |     std::swap(BaseClassRef::pool, tmp.BaseClassRef::pool);
1548 |     return *this;
1549 |   }
1550 | 
1551 |   /**
1552 |    * Builds the index
1553 |    */
1554 |   void buildIndex() {
1555 |     BaseClassRef::m_size = BaseClassRef::vind.size();
1556 |     this->freeIndex(*this);
1557 |     BaseClassRef::m_size_at_index_build = BaseClassRef::m_size;
1558 |     if (BaseClassRef::m_size == 0)
1559 |       return;
1560 |     computeBoundingBox(BaseClassRef::root_bbox);
1561 |     BaseClassRef::root_node =
1562 |         this->divideTree(*this, 0, BaseClassRef::m_size,
1563 |                          BaseClassRef::root_bbox); // construct the tree
1564 |   }
1565 | 
1566 |   /** \name Query methods
1567 |    * @{ */
1568 | 
1569 |   /**
1570 |    * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
1571 |    * inside the result object.
1572 |    *
1573 |    * Params:
1574 |    *     result = the result object in which the indices of the
1575 |    * nearest-neighbors are stored vec = the vector for which to search the
1576 |    * nearest neighbors
1577 |    *
1578 |    * \tparam RESULTSET Should be any ResultSet<DistanceType>
1579 |    * \return  True if the requested neighbors could be found.
1580 |    * \sa knnSearch, radiusSearch
1581 |    */
1582 |   template <typename RESULTSET>
1583 |   bool findNeighbors(RESULTSET &result, const ElementType *vec,
1584 |                      const SearchParams &searchParams) const {
1585 |     assert(vec);
1586 |     if (this->size(*this) == 0)
1587 |       return false;
1588 |     if (!BaseClassRef::root_node)
1589 |       return false;
1590 |     float epsError = 1 + searchParams.eps;
1591 | 
1592 |     // fixed or variable-sized container (depending on DIM)
1593 |     distance_vector_t dists;
1594 |     // Fill it with zeros.
1595 |     assign(dists, (DIM > 0 ? DIM : BaseClassRef::dim),
1596 |            static_cast<typename distance_vector_t::value_type>(0));
1597 |     DistanceType distsq = this->computeInitialDistances(*this, vec, dists);
1598 |     searchLevel(result, vec, BaseClassRef::root_node, distsq, dists,
1599 |                 epsError); // "count_leaf" parameter removed since was neither
1600 |                            // used nor returned to the user.
1601 |     return result.full();
1602 |   }
1603 | 
1604 |   /**
1605 |    * Find the "num_closest" nearest neighbors to the \a query_point[0:dim-1].
1606 |    * Their indices are stored inside the result object. \sa radiusSearch,
1607 |    * findNeighbors \note nChecks_IGNORED is ignored but kept for compatibility
1608 |    * with the original FLANN interface. \return Number `N` of valid points in
1609 |    * the result set. Only the first `N` entries in `out_indices` and
1610 |    * `out_distances_sq` will be valid. Return may be less than `num_closest`
1611 |    * only if the number of elements in the tree is less than `num_closest`.
1612 |    */
1613 |   size_t knnSearch(const ElementType *query_point, const size_t num_closest,
1614 |                    IndexType *out_indices, DistanceType *out_distances_sq,
1615 |                    const int /* nChecks_IGNORED */ = 10) const {
1616 |     nanoflann::KNNResultSet<DistanceType, IndexType> resultSet(num_closest);
1617 |     resultSet.init(out_indices, out_distances_sq);
1618 |     this->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
1619 |     return resultSet.size();
1620 |   }
1621 | 
1622 |   /**
1623 |    * Find all the neighbors to \a query_point[0:dim-1] within a maximum radius.
1624 |    *  The output is given as a vector of pairs, of which the first element is a
1625 |    * point index and the second the corresponding distance. Previous contents of
1626 |    * \a IndicesDists are cleared.
1627 |    *
1628 |    *  If searchParams.sorted==true, the output list is sorted by ascending
1629 |    * distances.
1630 |    *
1631 |    *  For a better performance, it is advisable to do a .reserve() on the vector
1632 |    * if you have any wild guess about the number of expected matches.
1633 |    *
1634 |    *  \sa knnSearch, findNeighbors, radiusSearchCustomCallback
1635 |    * \return The number of points within the given radius (i.e. indices.size()
1636 |    * or dists.size() )
1637 |    */
1638 |   size_t
1639 |   radiusSearch(const ElementType *query_point, const DistanceType &radius,
1640 |                std::vector<std::pair<IndexType, DistanceType>> &IndicesDists,
1641 |                const SearchParams &searchParams) const {
1642 |     RadiusResultSet<DistanceType, IndexType> resultSet(radius, IndicesDists);
1643 |     const size_t nFound =
1644 |         radiusSearchCustomCallback(query_point, resultSet, searchParams);
1645 |     if (searchParams.sorted)
1646 |       std::sort(IndicesDists.begin(), IndicesDists.end(), IndexDist_Sorter());
1647 |     return nFound;
1648 |   }
1649 | 
1650 |   /**
1651 |    * Just like radiusSearch() but with a custom callback class for each point
1652 |    * found in the radius of the query. See the source of RadiusResultSet<> as a
1653 |    * start point for your own classes. \sa radiusSearch
1654 |    */
1655 |   template <class SEARCH_CALLBACK>
1656 |   size_t radiusSearchCustomCallback(
1657 |       const ElementType *query_point, SEARCH_CALLBACK &resultSet,
1658 |       const SearchParams &searchParams = SearchParams()) const {
1659 |     this->findNeighbors(resultSet, query_point, searchParams);
1660 |     return resultSet.size();
1661 |   }
1662 | 
1663 |   /** @} */
1664 | 
1665 | public:
1666 |   void computeBoundingBox(BoundingBox &bbox) {
1667 |     resize(bbox, (DIM > 0 ? DIM : BaseClassRef::dim));
1668 | 
1669 |     if (dataset.kdtree_get_bbox(bbox)) {
1670 |       // Done! It was implemented in derived class
1671 |     } else {
1672 |       const size_t N = BaseClassRef::m_size;
1673 |       if (!N)
1674 |         throw std::runtime_error("[nanoflann] computeBoundingBox() called but "
1675 |                                  "no data points found.");
1676 |       for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
1677 |         bbox[i].low = bbox[i].high =
1678 |             this->dataset_get(*this, BaseClassRef::vind[0], i);
1679 |       }
1680 |       for (size_t k = 1; k < N; ++k) {
1681 |         for (int i = 0; i < (DIM > 0 ? DIM : BaseClassRef::dim); ++i) {
1682 |           if (this->dataset_get(*this, BaseClassRef::vind[k], i) < bbox[i].low)
1683 |             bbox[i].low = this->dataset_get(*this, BaseClassRef::vind[k], i);
1684 |           if (this->dataset_get(*this, BaseClassRef::vind[k], i) > bbox[i].high)
1685 |             bbox[i].high = this->dataset_get(*this, BaseClassRef::vind[k], i);
1686 |         }
1687 |       }
1688 |     }
1689 |   }
1690 | 
1691 |   /**
1692 |    * Performs an exact search in the tree starting from a node.
1693 |    * \tparam RESULTSET Should be any ResultSet<DistanceType>
1694 |    */
1695 |   template <class RESULTSET>
1696 |   void searchLevel(RESULTSET &result_set, const ElementType *vec,
1697 |                    const NodePtr node, DistanceType mindistsq,
1698 |                    distance_vector_t &dists, const float epsError) const {
1699 |     /* If this is a leaf node, then do check and return. */
1700 |     if ((node->child1 == NULL) && (node->child2 == NULL)) {
1701 |       // count_leaf += (node->lr.right-node->lr.left);  // Removed since was
1702 |       // neither used nor returned to the user.
1703 |       DistanceType worst_dist = result_set.worstDist();
1704 |       for (IndexType i = node->node_type.lr.left; i < node->node_type.lr.right;
1705 |            ++i) {
1706 |         const IndexType index = BaseClassRef::vind[i]; // reorder... : i;
1707 |         if (treeIndex[index] == -1)
1708 |           continue;
1709 |         DistanceType dist = distance.evalMetric(
1710 |             vec, index, (DIM > 0 ? DIM : BaseClassRef::dim));
1711 |         if (dist < worst_dist) {
1712 |           if (!result_set.addPoint(
1713 |                   static_cast<typename RESULTSET::DistanceType>(dist),
1714 |                   static_cast<typename RESULTSET::IndexType>(
1715 |                       BaseClassRef::vind[i]))) {
1716 |             // the resultset doesn't want to receive any more points, we're done
1717 |             // searching!
1718 |             return; // false;
1719 |           }
1720 |         }
1721 |       }
1722 |       return;
1723 |     }
1724 | 
1725 |     /* Which child branch should be taken first? */
1726 |     int idx = node->node_type.sub.divfeat;
1727 |     ElementType val = vec[idx];
1728 |     DistanceType diff1 = val - node->node_type.sub.divlow;
1729 |     DistanceType diff2 = val - node->node_type.sub.divhigh;
1730 | 
1731 |     NodePtr bestChild;
1732 |     NodePtr otherChild;
1733 |     DistanceType cut_dist;
1734 |     if ((diff1 + diff2) < 0) {
1735 |       bestChild = node->child1;
1736 |       otherChild = node->child2;
1737 |       cut_dist = distance.accum_dist(val, node->node_type.sub.divhigh, idx);
1738 |     } else {
1739 |       bestChild = node->child2;
1740 |       otherChild = node->child1;
1741 |       cut_dist = distance.accum_dist(val, node->node_type.sub.divlow, idx);
1742 |     }
1743 | 
1744 |     /* Call recursively to search next level down. */
1745 |     searchLevel(result_set, vec, bestChild, mindistsq, dists, epsError);
1746 | 
1747 |     DistanceType dst = dists[idx];
1748 |     mindistsq = mindistsq + cut_dist - dst;
1749 |     dists[idx] = cut_dist;
1750 |     if (mindistsq * epsError <= result_set.worstDist()) {
1751 |       searchLevel(result_set, vec, otherChild, mindistsq, dists, epsError);
1752 |     }
1753 |     dists[idx] = dst;
1754 |   }
1755 | 
1756 | public:
1757 |   /**  Stores the index in a binary file.
1758 |    *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so when
1759 |    * loading the index object it must be constructed associated to the same
1760 |    * source of data points used while building it. See the example:
1761 |    * examples/saveload_example.cpp \sa loadIndex  */
1762 |   void saveIndex(FILE *stream) { this->saveIndex_(*this, stream); }
1763 | 
1764 |   /**  Loads a previous index from a binary file.
1765 |    *   IMPORTANT NOTE: The set of data points is NOT stored in the file, so the
1766 |    * index object must be constructed associated to the same source of data
1767 |    * points used while building the index. See the example:
1768 |    * examples/saveload_example.cpp \sa loadIndex  */
1769 |   void loadIndex(FILE *stream) { this->loadIndex_(*this, stream); }
1770 | };
1771 | 
1772 | /** kd-tree dynaimic index
1773 |  *
1774 |  * class to create multiple static index and merge their results to behave as
1775 |  * single dynamic index as proposed in Logarithmic Approach.
1776 |  *
1777 |  *  Example of usage:
1778 |  *  examples/dynamic_pointcloud_example.cpp
1779 |  *
1780 |  * \tparam DatasetAdaptor The user-provided adaptor (see comments above).
1781 |  * \tparam Distance The distance metric to use: nanoflann::metric_L1,
1782 |  * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam DIM
1783 |  * Dimensionality of data points (e.g. 3 for 3D points) \tparam IndexType Will
1784 |  * be typically size_t or int
1785 |  */
1786 | template <typename Distance, class DatasetAdaptor, int DIM = -1,
1787 |           typename IndexType = size_t>
1788 | class KDTreeSingleIndexDynamicAdaptor {
1789 | public:
1790 |   typedef typename Distance::ElementType ElementType;
1791 |   typedef typename Distance::DistanceType DistanceType;
1792 | 
1793 | protected:
1794 |   size_t m_leaf_max_size;
1795 |   size_t treeCount;
1796 |   size_t pointCount;
1797 | 
1798 |   /**
1799 |    * The dataset used by this index
1800 |    */
1801 |   const DatasetAdaptor &dataset; //!< The source of our data
1802 | 
1803 |   std::vector<int> treeIndex; //!< treeIndex[idx] is the index of tree in which
1804 |                               //!< point at idx is stored. treeIndex[idx]=-1
1805 |                               //!< means that point has been removed.
1806 | 
1807 |   KDTreeSingleIndexAdaptorParams index_params;
1808 | 
1809 |   int dim; //!< Dimensionality of each data point
1810 | 
1811 |   typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
1812 |       index_container_t;
1813 |   std::vector<index_container_t> index;
1814 | 
1815 | public:
1816 |   /** Get a const ref to the internal list of indices; the number of indices is
1817 |    * adapted dynamically as the dataset grows in size. */
1818 |   const std::vector<index_container_t> &getAllIndices() const { return index; }
1819 | 
1820 | private:
1821 |   /** finds position of least significant unset bit */
1822 |   int First0Bit(IndexType num) {
1823 |     int pos = 0;
1824 |     while (num & 1) {
1825 |       num = num >> 1;
1826 |       pos++;
1827 |     }
1828 |     return pos;
1829 |   }
1830 | 
1831 |   /** Creates multiple empty trees to handle dynamic support */
1832 |   void init() {
1833 |     typedef KDTreeSingleIndexDynamicAdaptor_<Distance, DatasetAdaptor, DIM>
1834 |         my_kd_tree_t;
1835 |     std::vector<my_kd_tree_t> index_(
1836 |         treeCount, my_kd_tree_t(dim /*dim*/, dataset, treeIndex, index_params));
1837 |     index = index_;
1838 |   }
1839 | 
1840 | public:
1841 |   Distance distance;
1842 | 
1843 |   /**
1844 |    * KDTree constructor
1845 |    *
1846 |    * Refer to docs in README.md or online in
1847 |    * https://github.com/jlblancoc/nanoflann
1848 |    *
1849 |    * The KD-Tree point dimension (the length of each point in the datase, e.g. 3
1850 |    * for 3D points) is determined by means of:
1851 |    *  - The \a DIM template parameter if >0 (highest priority)
1852 |    *  - Otherwise, the \a dimensionality parameter of this constructor.
1853 |    *
1854 |    * @param inputData Dataset with the input features
1855 |    * @param params Basically, the maximum leaf node size
1856 |    */
1857 |   KDTreeSingleIndexDynamicAdaptor(const int dimensionality,
1858 |                                   const DatasetAdaptor &inputData,
1859 |                                   const KDTreeSingleIndexAdaptorParams &params =
1860 |                                       KDTreeSingleIndexAdaptorParams(),
1861 |                                   const size_t maximumPointCount = 1000000000U)
1862 |       : dataset(inputData), index_params(params), distance(inputData) {
1863 |     treeCount = static_cast<size_t>(std::log2(maximumPointCount));
1864 |     pointCount = 0U;
1865 |     dim = dimensionality;
1866 |     treeIndex.clear();
1867 |     if (DIM > 0)
1868 |       dim = DIM;
1869 |     m_leaf_max_size = params.leaf_max_size;
1870 |     init();
1871 |     const size_t num_initial_points = dataset.kdtree_get_point_count();
1872 |     if (num_initial_points > 0) {
1873 |       addPoints(0, num_initial_points - 1);
1874 |     }
1875 |   }
1876 | 
1877 |   /** Deleted copy constructor*/
1878 |   KDTreeSingleIndexDynamicAdaptor(
1879 |       const KDTreeSingleIndexDynamicAdaptor<Distance, DatasetAdaptor, DIM,
1880 |                                             IndexType> &) = delete;
1881 | 
1882 |   /** Add points to the set, Inserts all points from [start, end] */
1883 |   void addPoints(IndexType start, IndexType end) {
1884 |     size_t count = end - start + 1;
1885 |     treeIndex.resize(treeIndex.size() + count);
1886 |     for (IndexType idx = start; idx <= end; idx++) {
1887 |       int pos = First0Bit(pointCount);
1888 |       index[pos].vind.clear();
1889 |       treeIndex[pointCount] = pos;
1890 |       for (int i = 0; i < pos; i++) {
1891 |         for (int j = 0; j < static_cast<int>(index[i].vind.size()); j++) {
1892 |           index[pos].vind.push_back(index[i].vind[j]);
1893 |           if (treeIndex[index[i].vind[j]] != -1)
1894 |             treeIndex[index[i].vind[j]] = pos;
1895 |         }
1896 |         index[i].vind.clear();
1897 |         index[i].freeIndex(index[i]);
1898 |       }
1899 |       index[pos].vind.push_back(idx);
1900 |       index[pos].buildIndex();
1901 |       pointCount++;
1902 |     }
1903 |   }
1904 | 
1905 |   /** Remove a point from the set (Lazy Deletion) */
1906 |   void removePoint(size_t idx) {
1907 |     if (idx >= pointCount)
1908 |       return;
1909 |     treeIndex[idx] = -1;
1910 |   }
1911 | 
1912 |   /**
1913 |    * Find set of nearest neighbors to vec[0:dim-1]. Their indices are stored
1914 |    * inside the result object.
1915 |    *
1916 |    * Params:
1917 |    *     result = the result object in which the indices of the
1918 |    * nearest-neighbors are stored vec = the vector for which to search the
1919 |    * nearest neighbors
1920 |    *
1921 |    * \tparam RESULTSET Should be any ResultSet<DistanceType>
1922 |    * \return  True if the requested neighbors could be found.
1923 |    * \sa knnSearch, radiusSearch
1924 |    */
1925 |   template <typename RESULTSET>
1926 |   bool findNeighbors(RESULTSET &result, const ElementType *vec,
1927 |                      const SearchParams &searchParams) const {
1928 |     for (size_t i = 0; i < treeCount; i++) {
1929 |       index[i].findNeighbors(result, &vec[0], searchParams);
1930 |     }
1931 |     return result.full();
1932 |   }
1933 | };
1934 | 
1935 | /** An L2-metric KD-tree adaptor for working with data directly stored in an
1936 |  * Eigen Matrix, without duplicating the data storage. You can select whether a 
1937 |  * row or column in the matrix represents a point in the state space.
1938 |  *
1939 |  *  Example of usage:
1940 |  * \code
1941 |  * 	Eigen::Matrix<num_t,Dynamic,Dynamic>  mat;
1942 |  * 	// Fill out "mat"...
1943 |  *
1944 |  * 	typedef KDTreeEigenMatrixAdaptor< Eigen::Matrix<num_t,Dynamic,Dynamic> >
1945 |  * my_kd_tree_t; const int max_leaf = 10; my_kd_tree_t   mat_index(mat, max_leaf
1946 |  * ); mat_index.index->buildIndex(); mat_index.index->... \endcode
1947 |  *
1948 |  *  \tparam DIM If set to >0, it specifies a compile-time fixed dimensionality
1949 |  * for the points in the data set, allowing more compiler optimizations. \tparam
1950 |  * Distance The distance metric to use: nanoflann::metric_L1,
1951 |  * nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc. \tparam row_major 
1952 |  * If set to true the rows of the matrix are used as the points, if set to false
1953 |  * the columns of the matrix are used as the points.
1954 |  */
1955 | template <class MatrixType, int DIM = -1, class Distance = nanoflann::metric_L2,
1956 | 	  bool row_major = true>
1957 | struct KDTreeEigenMatrixAdaptor {
1958 |   typedef KDTreeEigenMatrixAdaptor<MatrixType, DIM, Distance, row_major> self_t;
1959 |   typedef typename MatrixType::Scalar num_t;
1960 |   typedef typename MatrixType::Index IndexType;
1961 |   typedef
1962 |       typename Distance::template traits<num_t, self_t>::distance_t metric_t;
1963 |   typedef KDTreeSingleIndexAdaptor<metric_t, self_t,
1964 |                                    MatrixType::ColsAtCompileTime, IndexType>
1965 |       index_t;
1966 | 
1967 |   index_t *index; //! The kd-tree index for the user to call its methods as
1968 |                   //! usual with any other FLANN index.
1969 | 
1970 |   /// Constructor: takes a const ref to the matrix object with the data points
1971 |   KDTreeEigenMatrixAdaptor(const size_t dimensionality,
1972 |                            const std::reference_wrapper<const MatrixType> &mat,
1973 |                            const int leaf_max_size = 10)
1974 |       : m_data_matrix(mat) {
1975 |     const auto dims = row_major ? mat.get().cols() : mat.get().rows();
1976 |     if (size_t(dims) != dimensionality)
1977 |       throw std::runtime_error(
1978 |           "Error: 'dimensionality' must match column count in data matrix");
1979 |     if (DIM > 0 && int(dims) != DIM)
1980 |       throw std::runtime_error(
1981 |           "Data set dimensionality does not match the 'DIM' template argument");
1982 |     index =
1983 |         new index_t(static_cast<int>(dims), *this /* adaptor */,
1984 |                     nanoflann::KDTreeSingleIndexAdaptorParams(leaf_max_size));
1985 |     index->buildIndex();
1986 |   }
1987 | 
1988 | public:
1989 |   /** Deleted copy constructor */
1990 |   KDTreeEigenMatrixAdaptor(const self_t &) = delete;
1991 | 
1992 |   ~KDTreeEigenMatrixAdaptor() { delete index; }
1993 | 
1994 |   const std::reference_wrapper<const MatrixType> m_data_matrix;
1995 | 
1996 |   /** Query for the \a num_closest closest points to a given point (entered as
1997 |    * query_point[0:dim-1]). Note that this is a short-cut method for
1998 |    * index->findNeighbors(). The user can also call index->... methods as
1999 |    * desired. \note nChecks_IGNORED is ignored but kept for compatibility with
2000 |    * the original FLANN interface.
2001 |    */
2002 |   inline void query(const num_t *query_point, const size_t num_closest,
2003 |                     IndexType *out_indices, num_t *out_distances_sq,
2004 |                     const int /* nChecks_IGNORED */ = 10) const {
2005 |     nanoflann::KNNResultSet<num_t, IndexType> resultSet(num_closest);
2006 |     resultSet.init(out_indices, out_distances_sq);
2007 |     index->findNeighbors(resultSet, query_point, nanoflann::SearchParams());
2008 |   }
2009 | 
2010 |   /** @name Interface expected by KDTreeSingleIndexAdaptor
2011 |    * @{ */
2012 | 
2013 |   const self_t &derived() const { return *this; }
2014 |   self_t &derived() { return *this; }
2015 | 
2016 |   // Must return the number of data points
2017 |   inline size_t kdtree_get_point_count() const {
2018 |     if(row_major)
2019 |       return m_data_matrix.get().rows();
2020 |     else
2021 |       return m_data_matrix.get().cols();
2022 |   }
2023 | 
2024 |   // Returns the dim'th component of the idx'th point in the class:
2025 |   inline num_t kdtree_get_pt(const IndexType idx, size_t dim) const {
2026 |     if(row_major)
2027 |       return m_data_matrix.get().coeff(idx, IndexType(dim));
2028 |     else
2029 |       return m_data_matrix.get().coeff(IndexType(dim), idx);
2030 |   }
2031 | 
2032 |   // Optional bounding-box computation: return false to default to a standard
2033 |   // bbox computation loop.
2034 |   //   Return true if the BBOX was already computed by the class and returned in
2035 |   //   "bb" so it can be avoided to redo it again. Look at bb.size() to find out
2036 |   //   the expected dimensionality (e.g. 2 or 3 for point clouds)
2037 |   template <class BBOX> bool kdtree_get_bbox(BBOX & /*bb*/) const {
2038 |     return false;
2039 |   }
2040 | 
2041 |   /** @} */
2042 | 
2043 | }; // end of KDTreeEigenMatrixAdaptor
2044 |    /** @} */
2045 | 
2046 | /** @} */ // end of grouping
2047 | } // namespace nanoflann
2048 | 
2049 | #endif /* NANOFLANN_HPP_ */
2050 | 


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