nabo/nabo.cpp
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1 /*
2 
3 Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland
4 You can contact the author at <stephane at magnenat dot net>
5 
6 All rights reserved.
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8 Redistribution and use in source and binary forms, with or without
9 modification, are permitted provided that the following conditions are met:
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19 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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28 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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30 */
31 
32 #include "nabo.h"
33 #include "nabo_private.h"
34 #include "index_heap.h"
35 #include <limits>
36 #include <algorithm>
37 #include <stdexcept>
38 #include <sstream>
39 
45 namespace Nabo
46 {
47  using namespace std;
48 
49  // Custom exception class supporting stream-style message constructions.
50  struct runtime_error : std::runtime_error {
51  stringstream ss;
52 
53  template<typename T> runtime_error& operator<<(const T& t) {
54  ss << t;
55 
56  // Linux executor would not print correctly by overiding "virtual const char* what()".
57  // One solution is refreshing underlying std::runtime_error every time message is changed.
58  static_cast<std::runtime_error&>(*this) = std::runtime_error(ss.str());
59 
60  return *this;
61  }
62 
64  runtime_error(const runtime_error& re): std::runtime_error(re.ss.str()), ss(re.ss.str()) {}
65  virtual ~runtime_error() throw () {}
66  };
67 
68  template<typename T, typename CloudType>
69  NearestNeighbourSearch<T, CloudType>::NearestNeighbourSearch(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags):
70  cloud(cloud),
71  dim(min(dim, int(cloud.rows()))),
72  creationOptionFlags(creationOptionFlags),
73  minBound(Vector::Constant(this->dim, numeric_limits<T>::max())),
74  maxBound(Vector::Constant(this->dim, numeric_limits<T>::min()))
75  {
76  if (cloud.cols() == 0)
77  throw runtime_error() << "Cloud has no points";
78  if (cloud.rows() == 0)
79  throw runtime_error() << "Cloud has 0 dimensions";
80  }
81 
82  template<typename T, typename CloudType>
83  unsigned long NearestNeighbourSearch<T, CloudType>::knn(const Vector& query, IndexVector& indices, Vector& dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const
84  {
85 #ifdef EIGEN3_API
86  const Eigen::Map<const Matrix> queryMatrix(&query.coeff(0,0), dim, 1);
87 #else // EIGEN3_API
88  const Eigen::Map<Matrix> queryMatrix(&query.coeff(0,0), dim, 1);
89 #endif // EIGEN3_API
90  // note: this is inefficient, because we copy memory, due to the template-
91  // based abstraction of Eigen. High-performance implementation should
92  // take care of knnM and then implement knn on top of it.
93  // C++0x should solve this with rvalue
94  IndexMatrix indexMatrix(k, 1);
95  Matrix dists2Matrix(k, 1);
96  const unsigned long stats = knn(queryMatrix, indexMatrix, dists2Matrix, k, epsilon, optionFlags, maxRadius);
97  indices = indexMatrix.col(0);
98  dists2 = dists2Matrix.col(0);
99  return stats;
100  }
101 
102  template<typename T, typename CloudType>
103  void NearestNeighbourSearch<T, CloudType>::checkSizesKnn(const Matrix& query, const IndexMatrix& indices, const Matrix& dists2, const Index k, const unsigned optionFlags, const Vector* maxRadii) const
104  {
105  const bool allowSelfMatch(optionFlags & NearestNeighbourSearch<T, CloudType>::ALLOW_SELF_MATCH);
106  if (allowSelfMatch)
107  {
108  if (k > cloud.cols())
109  throw runtime_error() << "Requesting more points (" << k << ") than available in cloud (" << cloud.cols() << ")";
110  }
111  else
112  {
113  if (k > cloud.cols()-1)
114  throw runtime_error() << "Requesting more points (" << k << ") than available in cloud minus 1 (" << cloud.cols()-1 << ") (as self match is forbidden)";
115  }
116  if (query.rows() < dim)
117  throw runtime_error() << "Query has less dimensions (" << query.rows() << ") than requested for cloud (" << dim << ")";
118  if (indices.rows() != k)
119  throw runtime_error() << "Index matrix has a different number of rows (" << indices.rows() << ") than k (" << k << ")";
120  if (indices.cols() != query.cols())
121  throw runtime_error() << "Index matrix has a different number of columns (" << indices.rows() << ") than query (" << query.cols() << ")";
122  if (dists2.rows() != k)
123  throw runtime_error() << "Distance matrix has a different number of rows (" << dists2.rows() << ") than k (" << k << ")";
124  if (dists2.cols() != query.cols())
125  throw runtime_error() << "Distance matrix has a different number of columns (" << dists2.rows() << ") than query (" << query.cols() << ")";
126  if (maxRadii && (maxRadii->size() != query.cols()))
127  throw runtime_error() << "Maximum radii vector has not the same length (" << maxRadii->size() << ") than query has columns (" << k << ")";
128  const unsigned maxOptionFlagsValue(ALLOW_SELF_MATCH|SORT_RESULTS);
129  if (optionFlags > maxOptionFlagsValue)
130  throw runtime_error() << "OR-ed value of option flags (" << optionFlags << ") is larger than maximal valid value (" << maxOptionFlagsValue << ")";
131  }
132 
133 
134  template<typename T, typename CloudType>
135  NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::create(const CloudType& cloud, const Index dim, const SearchType preferedType, const unsigned creationOptionFlags, const Parameters& additionalParameters)
136  {
137  if (dim <= 0)
138  throw runtime_error() << "Your space must have at least one dimension";
139  switch (preferedType)
140  {
141  case BRUTE_FORCE: return new BruteForceSearch<T, CloudType>(cloud, dim, creationOptionFlags);
142  case KDTREE_LINEAR_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapBruteForceVector<Index,T>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
143  case KDTREE_TREE_HEAP: return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapSTL<Index,T>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
144  #ifdef HAVE_OPENCL
145  case KDTREE_CL_PT_IN_NODES: return new KDTreeBalancedPtInNodesStackOpenCL<T, CloudType>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
146  case KDTREE_CL_PT_IN_LEAVES: return new KDTreeBalancedPtInLeavesStackOpenCL<T, CloudType>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
147  case BRUTE_FORCE_CL: return new BruteForceSearchOpenCL<T, CloudType>(cloud, dim, creationOptionFlags, CL_DEVICE_TYPE_GPU);
148  #else // HAVE_OPENCL
149  case KDTREE_CL_PT_IN_NODES: throw runtime_error() << "OpenCL not found during compilation";
150  case KDTREE_CL_PT_IN_LEAVES: throw runtime_error() << "OpenCL not found during compilation";
151  case BRUTE_FORCE_CL: throw runtime_error() << "OpenCL not found during compilation";
152  #endif // HAVE_OPENCL
153  default: throw runtime_error() << "Unknown search type";
154  }
155  }
156 
157  template<typename T, typename CloudType>
159  {
160  if (dim <= 0)
161  throw runtime_error() << "Your space must have at least one dimension";
162  return new BruteForceSearch<T, CloudType>(cloud, dim, creationOptionFlags);
163  }
164 
165  template<typename T, typename CloudType>
166  NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::createKDTreeLinearHeap(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters)
167  {
168  if (dim <= 0)
169  throw runtime_error() << "Your space must have at least one dimension";
170  return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapBruteForceVector<Index,T>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
171  }
172 
173  template<typename T, typename CloudType>
174  NearestNeighbourSearch<T, CloudType>* NearestNeighbourSearch<T, CloudType>::createKDTreeTreeHeap(const CloudType& cloud, const Index dim, const unsigned creationOptionFlags, const Parameters& additionalParameters)
175  {
176  if (dim <= 0)
177  throw runtime_error() << "Your space must have at least one dimension";
178  return new KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<T, IndexHeapSTL<Index,T>, CloudType>(cloud, dim, creationOptionFlags, additionalParameters);
179  }
180 
181  template struct NearestNeighbourSearch<float>;
182  template struct NearestNeighbourSearch<double>;
187 }
Nabo
Namespace for Nabo.
Definition: experimental/kdtree_cpu.cpp:40
index_heap.h
implementation of index heaps
Nabo::Parameters
Parameter vector.
Definition: nabo.h:231
Nabo::NearestNeighbourSearch::NearestNeighbourSearch
NearestNeighbourSearch(const CloudType &cloud, const Index dim, const unsigned creationOptionFlags)
constructor
Definition: nabo/nabo.cpp:69
nabo_private.h
header for implementation
nabo.h
public interface
Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt
KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementati...
Definition: nabo_private.h:94
Nabo::runtime_error
Definition: nabo/nabo.cpp:50
Nabo::NearestNeighbourSearch< T, CloudType >::IndexVector
Eigen::Matrix< Index, Eigen::Dynamic, 1 > IndexVector
a vector of indices to data points
Definition: nabo.h:269
Nabo::NearestNeighbourSearch< T, CloudType >::SearchType
SearchType
type of search
Definition: nabo.h:290
Nabo::NearestNeighbourSearch< T, CloudType >::CloudType
CloudType CloudType
a column-major Eigen matrix in which each column is a point; this matrix has dim rows
Definition: nabo.h:265
Nabo::NearestNeighbourSearch< T, CloudType >::IndexMatrix
Eigen::Matrix< Index, Eigen::Dynamic, Eigen::Dynamic > IndexMatrix
a matrix of indices to data points
Definition: nabo.h:271
Nabo::runtime_error::runtime_error
runtime_error(const runtime_error &re)
Definition: nabo/nabo.cpp:64
Nabo::NearestNeighbourSearch< T, CloudType >::Matrix
Eigen::Matrix< T, Eigen::Dynamic, Eigen::Dynamic > Matrix
a column-major Eigen matrix in which each column is a point; this matrix has dim rows
Definition: nabo.h:263
Nabo::runtime_error::operator<<
runtime_error & operator<<(const T &t)
Definition: nabo/nabo.cpp:53
Nabo::NearestNeighbourSearch
Nearest neighbour search interface, templatized on scalar type.
Definition: nabo.h:258
Nabo::runtime_error::ss
stringstream ss
Definition: nabo/nabo.cpp:51
Nabo::runtime_error::runtime_error
runtime_error()
Definition: nabo/nabo.cpp:63
Nabo::runtime_error::~runtime_error
virtual ~runtime_error()
Definition: nabo/nabo.cpp:65
NearestNeighbourSearch::knn
tuple knn(const object query, const Index k=1, const double epsilon=0, const unsigned optionFlags=0, const double maxRadius=infD)
Definition: python/nabo.cpp:137
NearestNeighbourSearch::cloud
NNSNabo::Matrix cloud
Definition: python/nabo.cpp:162
Nabo::NearestNeighbourSearch< T, CloudType >::Vector
Eigen::Matrix< T, Eigen::Dynamic, 1 > Vector
an Eigen vector of type T, to hold the coordinates of a point
Definition: nabo.h:261
std
Definition: any.hpp:450
Nabo::NearestNeighbourSearch< T, CloudType >::Index
int Index
an index to a Vector or a Matrix, for refering to data points
Definition: nabo.h:267
Nabo::BruteForceSearch
Brute-force nearest neighbour.
Definition: nabo_private.h:72


libnabo
Author(s): St├ęphane Magnenat
autogenerated on Sat Mar 23 2024 02:52:25