[SpatialPartitioning] Change the API of kd-tree accessors

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);},

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);},

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->sample_indices();
 
         if (nodes.empty() || points.empty() || indices.empty())
             throw std::invalid_argument("Empty KdTree");

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->sample_indices();
         const auto& point   = QueryType::getInputPosition(points);
 
         auto descentDistanceThreshold = [this](){return QueryType::descentDistanceThreshold();};

Ponca/src/SpatialPartitioning/KdTree/kdTree.h

@@ -82,7 +82,7 @@ class KdTreeBase
 
     static_assert(std::is_same<typename PointContainer::value_type, DataPoint>::value,
         "PointContainer must contain DataPoints");
-    
+
     // Queries use a value of -1 for invalid indices
     static_assert(std::is_signed<IndexType>::value, "Index type must be signed");
 
@@ -195,52 +195,52 @@ class KdTreeBase
 
     // Accessors ---------------------------------------------------------------
 public:
+    /// \brief The number of nodes in the kd-tree (including leaves).
     inline NodeIndexType node_count() const
     {
         return m_nodes.size();
     }
 
-    inline IndexType index_count() const
+    /// \brief The number of points sampled by the kd-tree.
+    inline IndexType sample_count() const
     {
         return (IndexType)m_indices.size();
     }
 
+    /// \brief The number of points in the base point container.
     inline IndexType point_count() const
     {
         return (IndexType)m_points.size();
     }
 
+    /// \brief The number of leaf nodes in the kd-tree.
     inline NodeIndexType leaf_count() const
     {
         return m_leaf_count;
     }
 
-    inline PointContainer& point_data()
+    /// \brief The base points the kd-tree samples into.
+    /// \warning Changing the order of points will invalidate the kd-tree.
+    inline PointContainer& points()
     {
         return m_points;
     };
 
-    inline const PointContainer& point_data() const
+    /// \brief The base points the kd-tree samples into.
+    inline const PointContainer& points() const
     {
         return m_points;
     };
 
-    inline const NodeContainer& node_data() const
-    {
-        return m_nodes;
-    }
-
-    inline NodeContainer& node_data()
+    /// \brief The flat container of nodes used internally by the kd-tree.
+    inline const NodeContainer& nodes() const
     {
         return m_nodes;
     }
 
-    inline const IndexContainer& index_data() const
-    {
-        return m_indices;
-    }
-
-    inline IndexContainer& index_data()
+    /// \brief The indices of the points sampled by the kd-tree.
+    /// \note The order will vary depending on the geometry of the points.
+    inline const IndexContainer& sample_indices() const
     {
         return m_indices;
     }
@@ -296,7 +296,6 @@ public :
     {
         return KdTreeRangeIndexQuery<Traits>(this, r, index);
     }
-
 
     // Data --------------------------------------------------------------------
 protected:

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());
 }
@@ -58,7 +58,7 @@ inline void KdTreeBase<Traits>::rebuild(IndexUserContainer sampling)
 
     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());
 }
@@ -71,19 +71,19 @@ bool KdTreeBase<Traits>::valid() const
 
     if (m_points.empty())
         return m_nodes.empty() && m_indices.empty();
-        
+
     if(m_nodes.empty() || m_indices.empty())
     {
         PONCA_DEBUG_ERROR;
         return false;
     }
-        
-    if(point_count() < index_count())
+
+    if(point_count() < sample_count())
     {
         PONCA_DEBUG_ERROR;
         return false;
     }
-        
+
     std::vector<bool> b(point_count(), false);
     for(IndexType idx : m_indices)
     {
@@ -100,7 +100,7 @@ bool KdTreeBase<Traits>::valid() const
         const NodeType& node = m_nodes.operator[](n);
         if(node.is_leaf())
         {
-            if(index_count() <= node.leaf_start() || index_count() < node.leaf_start()+node.leaf_size())
+            if(sample_count() <= node.leaf_start() || sample_count() < node.leaf_start()+node.leaf_size())
             {
                 PONCA_DEBUG_ERROR;
                 return false;
@@ -128,10 +128,10 @@ template<typename Traits>
 std::string KdTreeBase<Traits>::to_string() const
 {
     if (m_indices.empty()) return "";
-    
+
     std::stringstream str;
-    str << "indices (" << index_count() << ") :\n";
-    for(IndexType i=0; i<index_count(); ++i)
+    str << "indices (" << sample_count() << ") :\n";
+    for(IndexType i=0; i<sample_count(); ++i)
     {
         str << "  " << i << ": " << m_indices.operator[](i) << "\n";
     }
@@ -192,13 +192,13 @@ auto KdTreeBase<Traits>::partition(IndexType start, IndexType end, int dim, Scal
 {
     const auto& points = m_points;
     auto& indices  = m_indices;
-    
+
     auto it = std::partition(indices.begin()+start, indices.begin()+end, [&](IndexType i)
     {
         return points[i].pos()[dim] < value;
     });
 
     auto distance = std::distance(m_indices.begin(), it);
-    
+
     return static_cast<IndexType>(distance);
 }

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);
         }
 

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

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