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[SpatialPartitioning] Add test for KdTree datastructure
Aim at detecting problems with - the structure KdTreeDefaultTraits (copies, references, ...) - duplicated samples
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| /* | ||
| 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/. | ||
| \file Test general properties of the KdTree | ||
| */ | ||
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| #include "../common/testing.h" | ||
| #include "../common/testUtils.h" | ||
| #include "../common/has_duplicate.h" | ||
| #include "../common/kdtree_utils.h" | ||
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| #include <Ponca/src/SpatialPartitioning/KdTree/kdTree.h> | ||
| #include <Ponca/src/SpatialPartitioning/KdTree/kdTreeTraits.h> | ||
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| using namespace Ponca; | ||
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| template<typename DataPoint> | ||
| void testKdtreeWithDuplicate() | ||
| { | ||
| using Scalar = typename DataPoint::Scalar; | ||
| using VectorContainer = typename KdTreeSparse<DataPoint>::PointContainer; | ||
| using VectorType = typename DataPoint::VectorType; | ||
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| // Number of point samples in each KdTree leaf | ||
| #ifdef PONCA_DEBUG | ||
| const int cellSize = 6; | ||
| const int nbCells = 2; | ||
| const int N = nbCells*cellSize; | ||
| #else | ||
| const int cellSize = 64; | ||
| const int nbCells = 100; | ||
| const int N = nbCells*cellSize; | ||
| #endif | ||
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| const Scalar r = 0.001; | ||
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| auto test_tree = [r] (const auto& points, const auto&indices, const int cellSize) -> void | ||
| { | ||
| KdTreeSparse<DataPoint> tree(points, indices, cellSize); | ||
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| #ifndef PONCA_DEBUG | ||
| #pragma omp parallel for default(none) shared(tree, points, indices, g_test_stack, r) | ||
| #endif | ||
| for (int i = 0; i < points.size(); ++i) | ||
| { | ||
| VectorType point = points[i].pos();//VectorType::Random(); // values between [-1:1] | ||
| std::vector<int> results; | ||
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| for (int j : tree.range_neighbors(point, r)) { | ||
| results.push_back(j); | ||
| } | ||
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| bool res = check_range_neighbors<Scalar, VectorType, VectorContainer>(points, indices, point, r, results, true); | ||
| VERIFY(res); | ||
| } | ||
| }; | ||
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| // Generate N random points | ||
| typename KdTreeDense<DataPoint>::IndexContainer ids(N); | ||
| std::iota(ids.begin(), ids.end(), 0); | ||
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| auto points = VectorContainer(N); | ||
| std::generate(points.begin(), points.end(), []() {return DataPoint(VectorType::Random()); }); | ||
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| // Test on 100% random points | ||
| { | ||
| test_tree(points, ids, cellSize); | ||
| } | ||
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| // Generate a small part of duplicates by extending the index container | ||
| { | ||
| const int nbDuplicates = N; | ||
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| ids.resize(nbDuplicates+N); | ||
| std::generate(ids.begin()+N, ids.end(), [N]() {return Eigen::internal::random<int>(0,N-1); }); | ||
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| test_tree(points, ids, cellSize); | ||
| } | ||
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| // Generate duplicated coordinates samples TODO | ||
| // { | ||
| // const int nbDuplicates = N/10; | ||
| // const int nbUniques = N; | ||
| // | ||
| // auto points = VectorContainer(nbUniques); | ||
| // std::generate(points.begin(), points.end(), []() {return DataPoint(VectorType::Random()); }); | ||
| // | ||
| // typename KdTreeDense<DataPoint>::IndexContainer ids(nbUniques); | ||
| // std::iota(ids.begin(), ids.end(), 0); | ||
| // ids.resize(nbDuplicates*nbUniques); | ||
| // | ||
| // for (int i = 1; i < nbDuplicates; ++i) | ||
| // { | ||
| // std::copy(ids.begin(), ids.begin() + nbUniques, ids.begin()+(nbUniques*i)); | ||
| // } | ||
| // test_tree(points, ids, cellSize); | ||
| // } | ||
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| } | ||
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| template<typename NodeType> | ||
| void testKdTreeNode() { | ||
| std::vector<NodeType> buffer; | ||
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| buffer.resize(10); | ||
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| // simple predicate that only check if a node is a leaf or not | ||
| auto nodePredicate = [](const NodeType& n1, const NodeType& n2) -> bool { | ||
| return n1.is_leaf() == n2.is_leaf(); | ||
| }; | ||
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| auto checkProperties = [nodePredicate](std::vector<NodeType>& buf, bool targetLeafState) -> void{ | ||
| // Check that references works well: | ||
| for (auto& b : buf ) b.set_is_leaf(targetLeafState); | ||
| for (const auto& b : buf ) VERIFY( b.is_leaf() == targetLeafState ); | ||
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| // Check that copies are working well | ||
| std::vector<NodeType> other; | ||
| other.reserve(buf.size()); | ||
| other = buf; | ||
| VERIFY(std::equal(buf.cbegin(), buf.cend(), other.cbegin(), other.cend(), nodePredicate)); | ||
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| // Check that reallocation works well | ||
| other.resize(buf.size()*2); | ||
| VERIFY(std::equal(buf.cbegin(), buf.cend(), other.cbegin(), other.cbegin()+buf.size(), nodePredicate)); | ||
| }; | ||
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| checkProperties(buffer, true); | ||
| checkProperties(buffer, false); | ||
| } | ||
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| int main(int argc, char** argv) | ||
| { | ||
| if (!init_testing(argc, argv)) | ||
| { | ||
| return EXIT_FAILURE; | ||
| } | ||
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| using PointType = TestPoint<float, 3>; | ||
| using KdTreeTraits = KdTreeDefaultTraits<PointType>; | ||
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| cout << "Test KdTreeDefaultNode" << endl; | ||
| testKdTreeNode<typename KdTreeTraits::NodeType>(); | ||
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| cout << "Test KdTreeRange with large number of duplicated points" << endl; | ||
| testKdtreeWithDuplicate<PointType>(); | ||
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| } |