libnabo
1.0.7
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KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation. More...
#include <nabo_private.h>
Classes | |
struct | BucketEntry |
entry in a bucket More... | |
struct | Node |
search node More... | |
Public Types | |
typedef NearestNeighbourSearch < T, CloudType >::Vector | Vector |
typedef NearestNeighbourSearch < T, CloudType >::Matrix | Matrix |
typedef NearestNeighbourSearch < T, CloudType >::Index | Index |
typedef NearestNeighbourSearch < T, CloudType >::IndexVector | IndexVector |
typedef NearestNeighbourSearch < T, CloudType >::IndexMatrix | IndexMatrix |
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enum | SearchType |
type of search | |
enum | CreationOptionFlags |
creation option | |
enum | SearchOptionFlags |
search option | |
typedef Eigen::Matrix< T, Eigen::Dynamic, 1 > | Vector |
an Eigen vector of type T, to hold the coordinates of a point | |
typedef 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 | |
typedef CloudType | CloudType |
a column-major Eigen matrix in which each column is a point; this matrix has dim rows | |
typedef int | Index |
an index to a Vector or a Matrix, for refering to data points | |
typedef Eigen::Matrix< Index, Eigen::Dynamic, 1 > | IndexVector |
a vector of indices to data points | |
typedef Eigen::Matrix< Index, Eigen::Dynamic, Eigen::Dynamic > | IndexMatrix |
a matrix of indices to data points | |
Protected Types | |
typedef std::vector< Index > | BuildPoints |
indices of points during kd-tree construction | |
typedef BuildPoints::iterator | BuildPointsIt |
iterator to indices of points during kd-tree construction | |
typedef BuildPoints::const_iterator | BuildPointsCstIt |
const-iterator to indices of points during kd-tree construction | |
typedef std::vector< Node > | Nodes |
dense vector of search nodes, provides better memory performances than many small objects | |
typedef std::vector< BucketEntry > | Buckets |
bucket data | |
Protected Member Functions | |
uint32_t | createDimChildBucketSize (const uint32_t dim, const uint32_t childIndex) const |
create the compound index containing the dimension and the index of the child or the bucket size | |
uint32_t | getDim (const uint32_t dimChildBucketSize) const |
get the dimension out of the compound index | |
uint32_t | getChildBucketSize (const uint32_t dimChildBucketSize) const |
get the child index or the bucket size out of the coumpount index | |
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NearestNeighbourSearch (const CloudType &cloud, const Index dim, const unsigned creationOptionFlags) | |
constructor | |
void | checkSizesKnn (const Matrix &query, const IndexMatrix &indices, const Matrix &dists2, const Index k, const unsigned optionFlags, const Vector *maxRadii=0) const |
Make sure that the output matrices have the right sizes. Throw an exception otherwise. More... | |
Protected Attributes | |
const unsigned | bucketSize |
size of bucket | |
const uint32_t | dimBitCount |
number of bits required to store dimension index + number of dimensions | |
const uint32_t | dimMask |
mask to access dim | |
Nodes | nodes |
search nodes | |
Buckets | buckets |
buckets | |
std::pair< T, T > | getBounds (const BuildPointsIt first, const BuildPointsIt last, const unsigned dim) |
return the bounds of points from [first..last[ on dimension dim | |
unsigned | buildNodes (const BuildPointsIt first, const BuildPointsIt last, const Vector minValues, const Vector maxValues) |
construct nodes for points [first..last[ inside the hyperrectangle [minValues..maxValues] | |
unsigned long | onePointKnn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, int i, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2, const bool allowSelfMatch, const bool collectStatistics, const bool sortResults) const |
search one point, call recurseKnn with the correct template parameters More... | |
template<bool allowSelfMatch, bool collectStatistics> | |
unsigned long | recurseKnn (const T *query, const unsigned n, T rd, Heap &heap, std::vector< T > &off, const T maxError, const T maxRadius2) const |
recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993] More... | |
KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt (const CloudType &cloud, const Index dim, const unsigned creationOptionFlags, const Parameters &additionalParameters) | |
constructor, calls NearestNeighbourSearch<T>(cloud) | |
virtual unsigned long | knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Index k, const T epsilon, const unsigned optionFlags, const T maxRadius) const |
virtual unsigned long | knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Vector &maxRadii, const Index k=1, const T epsilon=0, const unsigned optionFlags=0) const |
Additional Inherited Members | |
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unsigned long | knn (const Vector &query, IndexVector &indices, Vector &dists2, const Index k=1, const T epsilon=0, const unsigned optionFlags=0, const T maxRadius=std::numeric_limits< T >::infinity()) const |
Find the k nearest neighbours of query. More... | |
virtual unsigned long | knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Index k=1, const T epsilon=0, const unsigned optionFlags=0, const T maxRadius=std::numeric_limits< T >::infinity()) const =0 |
Find the k nearest neighbours for each point of query. More... | |
virtual unsigned long | knn (const Matrix &query, IndexMatrix &indices, Matrix &dists2, const Vector &maxRadii, const Index k=1, const T epsilon=0, const unsigned optionFlags=0) const =0 |
Find the k nearest neighbours for each point of query. More... | |
virtual | ~NearestNeighbourSearch () |
virtual destructor | |
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static NearestNeighbourSearch * | create (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const SearchType preferedType=KDTREE_LINEAR_HEAP, const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters()) |
Create a nearest-neighbour search. More... | |
static NearestNeighbourSearch * | create (const WrongMatrixType &cloud, const Index dim=std::numeric_limits< Index >::max(), const SearchType preferedType=KDTREE_LINEAR_HEAP, const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters()) |
Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported. | |
static NearestNeighbourSearch * | createBruteForce (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0) |
Create a nearest-neighbour search, using brute-force search, useful for comparison only. More... | |
static NearestNeighbourSearch * | createBruteForce (const WrongMatrixType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0) |
Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported. | |
static NearestNeighbourSearch * | createKDTreeLinearHeap (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters()) |
Create a nearest-neighbour search, using a kd-tree with linear heap, good for small k (~up to 30) More... | |
static NearestNeighbourSearch * | createKDTreeLinearHeap (const WrongMatrixType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters()) |
Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported. | |
static NearestNeighbourSearch * | createKDTreeTreeHeap (const CloudType &cloud, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters()) |
Create a nearest-neighbour search, using a kd-tree with tree heap, good for large k (~from 30) More... | |
static NearestNeighbourSearch * | createKDTreeTreeHeap (const WrongMatrixType &, const Index dim=std::numeric_limits< Index >::max(), const unsigned creationOptionFlags=0, const Parameters &additionalParameters=Parameters()) |
Prevent creation of trees with the wrong matrix type. Currently only dynamic size matrices are supported. | |
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const CloudType & | cloud |
the reference to the data-point cloud, which must remain valid during the lifetime of the NearestNeighbourSearch object | |
const Index | dim |
the dimensionality of the data-point cloud | |
const unsigned | creationOptionFlags |
creation options | |
const Vector | minBound |
the low bound of the search space (axis-aligned bounding box) | |
const Vector | maxBound |
the high bound of the search space (axis-aligned bounding box) | |
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static constexpr Index | InvalidIndex |
the invalid index | |
static constexpr T | InvalidValue |
the invalid value | |
KDTree, unbalanced, points in leaves, stack, implicit bounds, ANN_KD_SL_MIDPT, optimised implementation.
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protected |
search one point, call recurseKnn with the correct template parameters
query | pointer to query coordinates |
indices | indices of nearest neighbours, must be of size k x query.cols() |
dists2 | squared distances to nearest neighbours, must be of size k x query.cols() |
i | index of point to search |
heap | reference to heap |
off | reference to array of offsets |
maxError | error factor (1 + epsilon) |
maxRadius2 | square of maximum radius |
allowSelfMatch | whether to allow self match |
collectStatistics | whether to collect statistics |
sortResults | wether to sort results |
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protected |
recursive search, strongly inspired by ANN and [Arya & Mount, Algorithms for fast vector quantization, 1993]
query | pointer to query coordinates |
n | index of node to visit |
rd | squared dist to this rect |
heap | reference to heap |
off | reference to array of offsets |
maxError | error factor (1 + epsilon) |
maxRadius2 | square of maximum radius |