[SpatialPartitioning] Change part of the kdtree API

poncateam · Dec 11, 2023 · 7ef62b9 · 7ef62b9
1 parent 9b42731
commit 7ef62b9
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Showing 9 changed files with 37 additions and 31 deletions.
diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeKNearestQueries.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeKNearestQueries.h
@@ -41,7 +41,7 @@ class KdTreeKNearestQueryBase : public KdTreeQuery<Traits>, public QueryType
 
 protected:
     inline void search(){
-        KdTreeQuery<Traits>::search_internal(QueryType::getInputPosition(QueryAccelType::m_kdtree->point_data()),
+        KdTreeQuery<Traits>::search_internal(QueryType::getInputPosition(QueryAccelType::m_kdtree->points()),
                                              [](IndexType, IndexType){},
                                              [this](){return QueryType::descentDistanceThreshold();},
                                              [this](IndexType idx){return QueryType::skipIndexFunctor(idx);},

diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeNearestQueries.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeNearestQueries.h
@@ -41,7 +41,7 @@ class KdTreeNearestQueryBase : public KdTreeQuery<Traits>, public QueryType
 
 protected:
     inline void search(){
-        KdTreeQuery<Traits>::search_internal(QueryType::getInputPosition(QueryAccelType::m_kdtree->point_data()),
+        KdTreeQuery<Traits>::search_internal(QueryType::getInputPosition(QueryAccelType::m_kdtree->points()),
                                              [](IndexType, IndexType){},
                                              [this](){return QueryType::descentDistanceThreshold();},
                                              [this](IndexType idx){return QueryType::skipIndexFunctor(idx);},

diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeQuery.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeQuery.h
@@ -44,9 +44,9 @@ class KdTreeQuery
                          ProcessNeighborFunctor processNeighborFunctor
                          )
     {
-        const auto& nodes   = m_kdtree->node_data();
-        const auto& points  = m_kdtree->point_data();
-        const auto& indices = m_kdtree->index_data();
+        const auto& nodes   = m_kdtree->nodes();
+        const auto& points  = m_kdtree->points();
+        const auto& indices = m_kdtree->samples();
 
         if (nodes.empty() || points.empty() || indices.empty())
             throw std::invalid_argument("Empty KdTree");

diff --git a/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeRangeQueries.h b/Ponca/src/SpatialPartitioning/KdTree/Query/kdTreeRangeQueries.h
@@ -47,8 +47,8 @@ class KdTreeRangeQueryBase : public KdTreeQuery<Traits>, public QueryType
 
 protected:
     inline void advance(Iterator& it){
-        const auto& points  = QueryAccelType::m_kdtree->point_data();
-        const auto& indices = QueryAccelType::m_kdtree->index_data();
+        const auto& points  = QueryAccelType::m_kdtree->points();
+        const auto& indices = QueryAccelType::m_kdtree->samples();
         const auto& point   = QueryType::getInputPosition(points);
 
         auto descentDistanceThreshold = [this](){return QueryType::descentDistanceThreshold();};

diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTree.h b/Ponca/src/SpatialPartitioning/KdTree/kdTree.h
@@ -8,6 +8,7 @@
 
 #include "./kdTreeTraits.h"
 
+#include <iostream>
 #include <memory>
 #include <numeric>
 #include <type_traits>
@@ -186,7 +187,7 @@ class KdTreeBase
                                   Converter c);
 
     inline bool valid() const;
-    inline std::string to_string() const;
+    inline void print(std::ostream& os) const;
 
     // Accessors ---------------------------------------------------------------
 public:
@@ -195,7 +196,7 @@ class KdTreeBase
         return m_nodes.size();
     }
 
-    inline IndexType index_count() const
+    inline IndexType sample_count() const
     {
         return (IndexType)m_indices.size();
     }
@@ -210,22 +211,22 @@ class KdTreeBase
         return m_leaf_count;
     }
 
-    inline PointContainer& point_data()
+    inline PointContainer& points()
     {
         return m_points;
     };
 
-    inline const PointContainer& point_data() const
+    inline const PointContainer& points() const
     {
         return m_points;
     };
 
-    inline const NodeContainer& node_data() const
+    inline const NodeContainer& nodes() const
     {
         return m_nodes;
     }
 
-    inline const IndexContainer& index_data() const
+    inline const IndexContainer& samples() const
     {
         return m_indices;
     }
@@ -295,3 +296,10 @@ public :
 
 #include "./kdTree.hpp"
 } // namespace Ponca
+
+template <typename Traits>
+std::ostream& operator<<(std::ostream& os, Ponca::KdTreeBase<Traits>& kdtree)
+{
+    kdtree.print(os);
+    return os;
+}
diff --git a/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp b/Ponca/src/SpatialPartitioning/KdTree/kdTree.hpp
@@ -42,7 +42,7 @@ inline void KdTreeBase<Traits>::buildWithSampling(PointUserContainer&& points,
 
     m_indices = std::move(sampling);
 
-    this->build_rec(0, 0, index_count(), 1);
+    this->build_rec(0, 0, sample_count(), 1);
 
     PONCA_DEBUG_ASSERT(this->valid());
 }
@@ -73,7 +73,7 @@ bool KdTreeBase<Traits>::valid() const
         const NodeType& node = m_nodes[n];
         if(node.is_leaf())
         {
-            if(index_count() <= node.leaf_start() || node.leaf_start()+node.leaf_size() > index_count())
+            if(sample_count() <= node.leaf_start() || node.leaf_start()+node.leaf_size() > sample_count())
             {
                 return false;
             }
@@ -95,31 +95,29 @@ bool KdTreeBase<Traits>::valid() const
 }
 
 template<typename Traits>
-std::string KdTreeBase<Traits>::to_string() const
+void KdTreeBase<Traits>::print(std::ostream& os) const
 {
-    if (m_indices.empty()) return "";
+    if (m_indices.empty()) return;
 
-    std::stringstream str;
-    str << "indices (" << index_count() << ") :\n";
-    for(IndexType i=0; i<index_count(); ++i)
+    os << "indices (" << sample_count() << ") :\n";
+    for(IndexType i=0; i<sample_count(); ++i)
     {
-        str << "  " << i << ": " << m_indices.operator[](i) << "\n";
+        os << "  " << i << ": " << m_indices.operator[](i) << "\n";
     }
-    str << "nodes (" << node_count() << ") :\n";
+    os << "nodes (" << node_count() << ") :\n";
     for(NodeIndexType n=0; n< node_count(); ++n)
     {
         const NodeType& node = m_nodes.operator[](n);
         if(node.is_leaf())
         {
             auto end = node.leaf_start() + node.leaf_size();
-            str << "  leaf: start=" << node.leaf_start() << " end=" << end << " (size=" << node.leaf_size() << ")\n";
+            os << "  leaf: start=" << node.leaf_start() << " end=" << end << " (size=" << node.leaf_size() << ")\n";
         }
         else
         {
-            str << "  node: dim=" << node.inner_dim() << " split=" << node.inner_split_value() << " child=" << node.inner_first_child_id() << "\n";
+            os << "  node: dim=" << node.inner_dim() << " split=" << node.inner_split_value() << " child=" << node.inner_first_child_id() << "\n";
         }
     }
-    return str.str();
 }
 
 template<typename Traits>

diff --git a/Ponca/src/SpatialPartitioning/KnnGraph/knnGraph.h b/Ponca/src/SpatialPartitioning/KnnGraph/knnGraph.h
@@ -70,8 +70,8 @@ template <typename Traits> class KnnGraphBase
     /// \warning KdTreeTraits compatibility is checked with static assertion
     template<typename KdTreeTraits>
     inline KnnGraphBase(const KdTreeBase<KdTreeTraits>& kdtree, int k = 6)
-            : m_k(std::min(k,kdtree.index_count()-1)),
-              m_kdTreePoints(kdtree.point_data())
+            : m_k(std::min(k,kdtree.sample_count()-1)),
+              m_kdTreePoints(kdtree.points())
     {
         static_assert( std::is_same<typename Traits::DataPoint, typename KdTreeTraits::DataPoint>::value,
                        "KdTreeTraits::DataPoint is not equal to Traits::DataPoint" );
@@ -85,7 +85,7 @@ template <typename Traits> class KnnGraphBase
         // \fixme Update API to properly handle kdtree subsampling
         const int cloudSize   = kdtree.point_count();
         {
-            const int samplesSize = kdtree.index_count();
+            const int samplesSize = kdtree.sample_count();
             eigen_assert(cloudSize == samplesSize);
         }
 

diff --git a/examples/cpp/ponca_basic_cpu.cpp b/examples/cpp/ponca_basic_cpu.cpp
@@ -89,7 +89,7 @@ void test_fit(Fit& _fit, const KdTree<MyPoint>& tree, const VectorType& _p)
   // Iterate over samples and _fit the primitive
   for(int i : tree.range_neighbors(_p, tmax) )
   {
-      _fit.addNeighbor( tree.point_data()[i] );
+      _fit.addNeighbor( tree.points()[i] );
   }
 
   //finalize fitting

diff --git a/tests/src/basket.cpp b/tests/src/basket.cpp
@@ -70,7 +70,7 @@ void testBasicFunctionalities(const KdTree<typename Fit::DataPoint>& tree, typen
     typedef typename DataPoint::VectorType VectorType;
     typedef typename Fit::WFunctor WeightFunc;
 
-    const auto& vectorPoints = tree.point_data();
+    const auto& vectorPoints = tree.points();
 
     // Test for each point if the fitted sphere correspond to the theoretical sphere
 #ifdef NDEBUG
@@ -135,7 +135,7 @@ void testIsSame(const KdTree<typename Fit1::DataPoint>& tree,
     typedef typename Fit1::Scalar     Scalar;
     typedef typename Fit1::VectorType VectorType;
     typedef typename Fit1::WFunctor   WeightFunc;
-    const auto& vectorPoints = tree.point_data();
+    const auto& vectorPoints = tree.points();
 
     // Test for each point if the fitted sphere correspond to the theoretical sphere
 #ifdef NDEBUG