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OpenCV
4.8.0
Open Source Computer Vision
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The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built. More...
#include <opencv2/flann.hpp>
Public Types | |
typedef Distance::ResultType | DistanceType |
typedef Distance::ElementType | ElementType |
Public Member Functions | |
GenericIndex (const Mat &features, const ::cvflann::IndexParams ¶ms, Distance distance=Distance()) | |
Constructs a nearest neighbor search index for a given dataset. More... | |
~GenericIndex () | |
const ::cvflann::IndexParams * | getIndexParameters () |
::cvflann::IndexParams | getParameters () |
void | knnSearch (const std::vector< ElementType > &query, std::vector< int > &indices, std::vector< DistanceType > &dists, int knn, const ::cvflann::SearchParams ¶ms) |
Performs a K-nearest neighbor search for a given query point using the index. More... | |
void | knnSearch (const Mat &queries, Mat &indices, Mat &dists, int knn, const ::cvflann::SearchParams ¶ms) |
int | radiusSearch (const std::vector< ElementType > &query, std::vector< int > &indices, std::vector< DistanceType > &dists, DistanceType radius, const ::cvflann::SearchParams ¶ms) |
Performs a radius nearest neighbor search for a given query point using the index. More... | |
int | radiusSearch (const Mat &query, Mat &indices, Mat &dists, DistanceType radius, const ::cvflann::SearchParams ¶ms) |
void | save (String filename) |
int | size () const |
int | veclen () const |
The FLANN nearest neighbor index class. This class is templated with the type of elements for which the index is built.
Distance
functor specifies the metric to be used to calculate the distance between two points. There are several Distance
functors that are readily available:
cv::cvflann::L2_Simple - Squared Euclidean distance functor. This is the simpler, unrolled version. This is preferable for very low dimensionality data (eg 3D points)
cv::flann::L2 - Squared Euclidean distance functor, optimized version.
cv::flann::L1 - Manhattan distance functor, optimized version.
cv::flann::MinkowskiDistance - The Minkowski distance functor. This is highly optimised with loop unrolling. The computation of squared root at the end is omitted for efficiency.
cv::flann::MaxDistance - The max distance functor. It computes the maximum distance between two vectors. This distance is not a valid kdtree distance, it's not dimensionwise additive.
cv::flann::HammingLUT - Hamming distance functor. It counts the bit differences between two strings using a lookup table implementation.
cv::flann::Hamming - Hamming distance functor. Population count is performed using library calls, if available. Lookup table implementation is used as a fallback.
cv::flann::Hamming2 - Hamming distance functor. Population count is implemented in 12 arithmetic operations (one of which is multiplication).
cv::flann::DNAmmingLUT - Adaptation of the Hamming distance functor to DNA comparison. As the four bases A, C, G, T of the DNA (or A, G, C, U for RNA) can be coded on 2 bits, it counts the bits pairs differences between two sequences using a lookup table implementation.
cv::flann::DNAmming2 - Adaptation of the Hamming distance functor to DNA comparison. Bases differences count are vectorised thanks to arithmetic operations using standard registers (AVX2 and AVX-512 should come in a near future).
cv::flann::HistIntersectionDistance - The histogram intersection distance functor.
cv::flann::HellingerDistance - The Hellinger distance functor.
cv::flann::ChiSquareDistance - The chi-square distance functor.
cv::flann::KL_Divergence - The Kullback-Leibler divergence functor.
Although the provided implementations cover a vast range of cases, it is also possible to use a custom implementation. The distance functor is a class whose operator()
computes the distance between two features. If the distance is also a kd-tree compatible distance, it should also provide an accum_dist()
method that computes the distance between individual feature dimensions.
In addition to operator()
and accum_dist()
, a distance functor should also define the ElementType
and the ResultType
as the types of the elements it operates on and the type of the result it computes. If a distance functor can be used as a kd-tree distance (meaning that the full distance between a pair of features can be accumulated from the partial distances between the individual dimensions) a typedef is_kdtree_distance
should be present inside the distance functor. If the distance is not a kd-tree distance, but it's a distance in a vector space (the individual dimensions of the elements it operates on can be accessed independently) a typedef is_vector_space_distance
should be defined inside the functor. If neither typedef is defined, the distance is assumed to be a metric distance and will only be used with indexes operating on generic metric distances.
typedef Distance::ResultType cv::flann::GenericIndex< Distance >::DistanceType |
typedef Distance::ElementType cv::flann::GenericIndex< Distance >::ElementType |
cv::flann::GenericIndex< Distance >::GenericIndex | ( | const Mat & | features, |
const ::cvflann::IndexParams & | params, | ||
Distance | distance = Distance() |
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Constructs a nearest neighbor search index for a given dataset.
features | Matrix of containing the features(points) to index. The size of the matrix is num_features x feature_dimensionality and the data type of the elements in the matrix must coincide with the type of the index. |
params | Structure containing the index parameters. The type of index that will be constructed depends on the type of this parameter. See the description. |
distance | The method constructs a fast search structure from a set of features using the specified algorithm with specified parameters, as defined by params. params is a reference to one of the following class IndexParams descendants: |
cv::flann::GenericIndex< Distance >::~GenericIndex | ( | ) |
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void cv::flann::GenericIndex< Distance >::knnSearch | ( | const std::vector< ElementType > & | query, |
std::vector< int > & | indices, | ||
std::vector< DistanceType > & | dists, | ||
int | knn, | ||
const ::cvflann::SearchParams & | params | ||
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Performs a K-nearest neighbor search for a given query point using the index.
query | The query point |
indices | Vector that will contain the indices of the K-nearest neighbors found. It must have at least knn size. |
dists | Vector that will contain the distances to the K-nearest neighbors found. It must have at least knn size. |
knn | Number of nearest neighbors to search for. |
params | SearchParams |
void cv::flann::GenericIndex< Distance >::knnSearch | ( | const Mat & | queries, |
Mat & | indices, | ||
Mat & | dists, | ||
int | knn, | ||
const ::cvflann::SearchParams & | params | ||
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int cv::flann::GenericIndex< Distance >::radiusSearch | ( | const std::vector< ElementType > & | query, |
std::vector< int > & | indices, | ||
std::vector< DistanceType > & | dists, | ||
DistanceType | radius, | ||
const ::cvflann::SearchParams & | params | ||
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Performs a radius nearest neighbor search for a given query point using the index.
query | The query point. |
indices | Vector that will contain the indices of the nearest neighbors found. |
dists | Vector that will contain the distances to the nearest neighbors found. It has the same number of elements as indices. |
radius | The search radius. |
params | SearchParams |
This function returns the number of nearest neighbors found.
int cv::flann::GenericIndex< Distance >::radiusSearch | ( | const Mat & | query, |
Mat & | indices, | ||
Mat & | dists, | ||
DistanceType | radius, | ||
const ::cvflann::SearchParams & | params | ||
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