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Merge pull request #128 from poncateam/easeKdTreeNodesChange
Simplify KdTree Node type specialization
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| /* | ||
| This Source Code Form is subject to the terms of the Mozilla Public | ||
| License, v. 2.0. If a copy of the MPL was not distributed with this | ||
| file, You can obtain one at http://mozilla.org/MPL/2.0/. | ||
| */ | ||
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||
| #include <iostream> | ||
| #include <optional> | ||
| #include <Ponca/SpatialPartitioning> | ||
| #include <Eigen/Core> | ||
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| struct DataPoint | ||
| { | ||
| enum {Dim = 3}; | ||
| using Scalar = float; | ||
| using VectorType = Eigen::Vector<Scalar,Dim>; | ||
| inline const auto& pos() const {return m_pos;} | ||
| VectorType m_pos; | ||
| }; | ||
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| //! [CustomInnerNodeDefinition] | ||
| template <typename NodeIndex, typename Scalar, int DIM, typename _AabbType = Eigen::AlignedBox<Scalar, DIM>> | ||
| struct MyKdTreeInnerNode : public Ponca::KdTreeDefaultInnerNode<NodeIndex, Scalar, DIM> { | ||
| using AabbType = _AabbType; | ||
| AabbType m_aabb{}; | ||
| }; | ||
| //! [CustomInnerNodeDefinition] | ||
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| //! [CustomNodeDefinition] | ||
| template <typename Index, typename NodeIndex, typename DataPoint, typename LeafSize = Index> | ||
| struct MyKdTreeNode : Ponca::KdTreeCustomizableNode<Index, NodeIndex, DataPoint, LeafSize, | ||
| MyKdTreeInnerNode<NodeIndex, typename DataPoint::Scalar, DataPoint::Dim>> { | ||
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| using Base = Ponca::KdTreeCustomizableNode<Index, NodeIndex, DataPoint, LeafSize, | ||
| MyKdTreeInnerNode<NodeIndex, typename DataPoint::Scalar, DataPoint::Dim>>; | ||
| using AabbType = typename Base::AabbType; | ||
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| void configure_range(Index start, Index size, const AabbType &aabb) | ||
| { | ||
| Base::configure_range(start, size, aabb); | ||
| if (! Base::is_leaf() ) | ||
| { | ||
| Base::getAsInner().m_aabb = aabb; | ||
| } | ||
| } | ||
| [[nodiscard]] inline std::optional<AabbType> getAabb() const { | ||
| if (! Base::is_leaf()) | ||
| return Base::getAsInner().m_aabb; | ||
| else | ||
| return std::optional<AabbType>(); | ||
| } | ||
| }; | ||
| //! [CustomNodeDefinition] | ||
|
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| int main() | ||
| { | ||
| // generate N random points | ||
| constexpr int N = 1e5; | ||
| std::vector<DataPoint> points(N); | ||
| std::generate(points.begin(), points.end(), [](){ | ||
| return DataPoint{100 * DataPoint::VectorType::Random()};}); | ||
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| //! [KdTreeTypeWithCustomNode] | ||
| using CustomKdTree = Ponca::KdTreeBase<Ponca::KdTreeDefaultTraits<DataPoint,MyKdTreeNode>>; | ||
| //! [KdTreeTypeWithCustomNode] | ||
|
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| // build the k-d tree | ||
| const CustomKdTree kdtree(points); | ||
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| // neighbor searches are done below from these arbitrary index and point | ||
| const int query_idx = 10; | ||
| const DataPoint::VectorType query_pt{-10.0, 0.5, 75.0}; | ||
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| // | ||
| // nearest neighbor search | ||
| // | ||
| std::cout << "the nearest neighbor of the point at index " << query_idx << " is at index " | ||
| << *kdtree.nearest_neighbor(query_idx).begin() << std::endl; | ||
| std::cout << "the nearest neighbor of the point (" << query_pt.transpose() << ") is at index " | ||
| << *kdtree.nearest_neighbor(query_pt).begin() << std::endl; | ||
|
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| //! [ReadCustomProperties] | ||
| auto bbox = kdtree.node_data()[0].getAabb(); | ||
| if (bbox) { | ||
| std::cout << "Root bounding box is as follows: \n" | ||
| << " Center: " << bbox->center() | ||
| << " Diagonal: " << bbox->diagonal() | ||
| << std::endl; | ||
| } | ||
| //! [ReadCustomProperties] | ||
|
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||
| return 1; | ||
| } |