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10 #ifndef mrpt_vision_descriptor_kdtrees_H
11 #define mrpt_vision_descriptor_kdtrees_H
58 ASSERT_(!feats.
empty() && feats[0]->descriptors.hasDescriptorSIFT())
115 ASSERT_(!feats.
empty() && feats[0]->descriptors.hasDescriptorSIFT())
150 template <
typename distance_t,
typename element_t>
151 struct TSIFTDesc2KDTree_Adaptor
160 const size_t dim=
m_feats[idx_p2]->descriptors.SIFT.
size();
161 const element_t *p2 = &
m_feats[idx_p2]->descriptors.SIFT[0];
163 for (
size_t i=0;i<dim;i++)
165 d+=(*p1-*p2)*(*p1-*p2);
172 inline element_t
kdtree_get_pt(
const size_t idx,
int dim)
const {
return m_feats[idx]->descriptors.SIFT[dim]; }
176 template <
typename distance_t,
typename element_t>
177 struct TSURFDesc2KDTree_Adaptor
186 const size_t dim=
m_feats[idx_p2]->descriptors.SURF.
size();
187 const element_t *p2 = &
m_feats[idx_p2]->descriptors.SURF[0];
189 for (
size_t i=0;i<dim;i++)
191 d+=(*p1-*p2)*(*p1-*p2);
198 inline element_t
kdtree_get_pt(
const size_t idx,
int dim)
const {
return m_feats[idx]->descriptors.SURF[dim]; }
distance_t kdtree_distance(const element_t *p1, const size_t idx_p2, size_t size) const
bool kdtree_get_bbox(BBOX &bb) const
kdtree_t & get_kdtree()
Access to the kd-tree object.
detail::TSIFTDesc2KDTree_Adaptor< distance_t > m_adaptor
void regenerate_kdtreee()
Re-creates the kd-tree, which must be done whenever the data source (the CFeatureList) changes.
kdtree_t & get_kdtree()
Access to the kd-tree object.
TSIFTDescriptorsKDTreeIndex(const CFeatureList &feats)
Constructor from a list of SIFT features.
~TSURFDescriptorsKDTreeIndex()
size_t size(const MATRIXLIKE &m, int dim)
const kdtree_t & get_kdtree() const
This is the global namespace for all Mobile Robot Programming Toolkit (MRPT) libraries.
~TSIFTDescriptorsKDTreeIndex()
nanoflann::KDTreeSingleIndexAdaptor< metric_t, detail::TSURFDesc2KDTree_Adaptor< distance_t > > kdtree_t
nanoflann::KDTreeSingleIndexAdaptor< metric_t, detail::TSIFTDesc2KDTree_Adaptor< distance_t > > kdtree_t
void buildIndex()
Builds the index.
Squared Euclidean (L2) distance functor (suitable for low-dimensionality datasets,...
A list of visual features, to be used as output by detectors, as input/output by trackers,...
void regenerate_kdtreee()
Re-creates the kd-tree, which must be done whenever the data source (the CFeatureList) changes.
element_t kdtree_get_pt(const size_t idx, int dim) const
A kd-tree builder for sets of features with SURF descriptors.
detail::TSURFDesc2KDTree_Adaptor< distance_t > m_adaptor
const CFeatureList & m_feats
element_t kdtree_get_pt(const size_t idx, int dim) const
TSURFDesc2KDTree_Adaptor(const CFeatureList &feats)
const CFeatureList & m_feats
const CFeatureList & m_feats
Parameters (see http://code.google.com/p/nanoflann/ for help choosing the parameters)
bool kdtree_get_bbox(BBOX &bb) const
size_t kdtree_get_point_count() const
distance_t kdtree_distance(const element_t *p1, const size_t idx_p2, size_t size) const
TSURFDescriptorsKDTreeIndex(const CFeatureList &feats)
Constructor from a list of SIFT features.
const kdtree_t & get_kdtree() const
const CFeatureList & m_feats
A kd-tree builder for sets of features with SIFT descriptors.
TSIFTDesc2KDTree_Adaptor(const CFeatureList &feats)
size_t kdtree_get_point_count() const
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