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69 lines
1.9 KiB
69 lines
1.9 KiB
#pragma once
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#include "nanoflann.hpp"
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#include <array>
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#include <iosfwd>
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#include "PointCloud.hpp"
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namespace meshless {
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class KDTreeMutable {
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private:
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typedef nanoflann::KDTreeSingleIndexDynamicAdaptor<nanoflann::L2_Simple_Adaptor<double, PointCloud>, PointCloud, 3, int> kd_tree_t;
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int size_;
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PointCloud points_;
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kd_tree_t tree;
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public:
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explicit KDTreeMutable(const std::vector<Eigen::Vector3d>& points) :points_(points), size_(points.size()), tree(3, points_, nanoflann::KDTreeSingleIndexAdaptorParams(20)) {}
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KDTreeMutable() :points_(), size_(0), tree(3, points_, nanoflann::KDTreeSingleIndexAdaptorParams(20)) {}
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void reset(const std::vector<Eigen::Vector3d>& points) {
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points_.setPts(points);
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size_ = points.size();
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tree.reset();
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}
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void insert(const Eigen::Vector3d& point);
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void insert(const std::vector<Eigen::Vector3d>& points);
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/// Check if any point exists in sphere centered at `p` with radius `r`.
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bool existsPointInSphere(const Eigen::Vector3d& p, double r) {
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if(size_ == 0)
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return false;
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return query(p).second[0] <= r * r;
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}
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/**
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* Removes a point with given index from the tree. The indexes of the points are given
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* sequentially at insertion and do not change. The removal is lazy and point still takes
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* up memory.
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*/
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void remove(int index) {
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size_ -= tree.removePoint(index);
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}
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/// Returns number of points in the tree.
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int size() const {
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return size_;
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}
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/**
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* Find `k` nearest neighbors to given point.
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* @param point Find closest points to this point.
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* @param k How many nearest points to find.
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*
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* @return A pair of two vectors of size `k` containing
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* indices of nearest neighbors and squared distances to said neighbors.
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* @throw Assertion fails if there are not enough points in the tree.
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*/
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std::pair<std::vector<int>, std::vector<double>> query(const Eigen::Vector3d& point, int k = 1);
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};
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};
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