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49 lines
1.3 KiB
49 lines
1.3 KiB
#pragma once
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#include <PointCloud.hpp>
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#include <nanoflann.hpp>
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#include <Eigen/Core>
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namespace meshless {
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class KDTree {
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private:
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typedef nanoflann::KDTreeSingleIndexAdaptor<nanoflann::L2_Simple_Adaptor<double, PointCloud>, PointCloud, 3, int> kd_tree_t;
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PointCloud points_;
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kd_tree_t tree;
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public:
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explicit KDTree(const std::vector<Eigen::Vector3d>& points) :points_(points), tree(3, points_, nanoflann::KDTreeSingleIndexAdaptorParams(20)) {
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tree.buildIndex();
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}
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KDTree() :points_(), 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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tree.buildIndex();
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}
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std::pair<std::vector<int>, std::vector<double>> query(const Eigen::Vector3d& point, int k = 1)const;
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std::pair<std::vector<int>, std::vector<double>> query(const Eigen::Vector3d& point, const double& radius_squared) const;
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Eigen::Vector3d get(int index) const {
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return points_.get(index);
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}
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std::vector<Eigen::Vector3d> get(const std::vector<int>& indexes) const {
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const int n = indexes.size();
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std::vector<Eigen::Vector3d> result(n);
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for(int i = 0; i < n; i++) {
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result[i] = points_.get(indexes[i]);
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}
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return result;
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}
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int size() const {
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return points_.kdtree_get_point_count();
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}
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};
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};
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