Part G of transition to support modernize-use-default-member-init (#5860

)

* Part G of transition to support modernize-use-default-member-init

* Fixed clang-tidy, clang-format CI issues

* Made changes per inspection

* Made changes per review

* Made more changes per review

* Addressed review comments

* Addressed CI errors, omitted review comments

* Addressed more build errors

* Addressed additional review comment

registration/include/pcl/registration/impl/ndt.hpp

@@ -46,7 +46,7 @@ namespace pcl {
 template <typename PointSource, typename PointTarget, typename Scalar>
 NormalDistributionsTransform<PointSource, PointTarget, Scalar>::
     NormalDistributionsTransform()
-: target_cells_(), trans_likelihood_()
+: target_cells_()
 {
   reg_name_ = "NormalDistributionsTransform";
 

segmentation/include/pcl/segmentation/approximate_progressive_morphological_filter.h

@@ -144,28 +144,28 @@ namespace pcl
     protected:
 
       /** \brief Maximum window size to be used in filtering ground returns. */
-      int max_window_size_;
+      int max_window_size_{33};
 
       /** \brief Slope value to be used in computing the height threshold. */
-      float slope_;
+      float slope_{0.7f};
 
       /** \brief Maximum height above the parameterized ground surface to be considered a ground return. */
-      float max_distance_;
+      float max_distance_{10.0f};
 
       /** \brief Initial height above the parameterized ground surface to be considered a ground return. */
-      float initial_distance_;
+      float initial_distance_{0.15f};
 
       /** \brief Cell size. */
-      float cell_size_;
+      float cell_size_{1.0f};
 
       /** \brief Base to be used in computing progressive window sizes. */
-      float base_;
+      float base_{2.0f};
 
       /** \brief Exponentially grow window sizes? */
-      bool exponential_;
+      bool exponential_{true};
 
       /** \brief Number of threads to be used. */
-      unsigned int threads_;      
+      unsigned int threads_{0};      
   };
 }
 

segmentation/include/pcl/segmentation/conditional_euclidean_clustering.h

@@ -99,9 +99,6 @@ namespace pcl
       ConditionalEuclideanClustering (bool extract_removed_clusters = false) :
           searcher_ (),
           condition_function_ (),
-          cluster_tolerance_ (0.0f),
-          min_cluster_size_ (1),
-          max_cluster_size_ (std::numeric_limits<int>::max ()),
           extract_removed_clusters_ (extract_removed_clusters),
           small_clusters_ (new pcl::IndicesClusters),
           large_clusters_ (new pcl::IndicesClusters)
@@ -237,28 +234,28 @@ namespace pcl
 
     private:
       /** \brief A pointer to the spatial search object */
-      SearcherPtr searcher_;
+      SearcherPtr searcher_{nullptr};
 
       /** \brief The condition function that needs to hold for clustering */
       std::function<bool (const PointT&, const PointT&, float)> condition_function_;
 
       /** \brief The distance to scan for cluster candidates (default = 0.0) */
-      float cluster_tolerance_;
+      float cluster_tolerance_{0.0f};
 
       /** \brief The minimum cluster size (default = 1) */
-      int min_cluster_size_;
+      int min_cluster_size_{1};
 
       /** \brief The maximum cluster size (default = unlimited) */
-      int max_cluster_size_;
+      int max_cluster_size_{std::numeric_limits<int>::max ()};
 
       /** \brief Set to true if you want to be able to extract the clusters that are too large or too small (default = false) */
       bool extract_removed_clusters_;
 
       /** \brief The resultant clusters that contain less than min_cluster_size points */
-      pcl::IndicesClustersPtr small_clusters_;
+      pcl::IndicesClustersPtr small_clusters_{nullptr};
 
       /** \brief The resultant clusters that contain more than max_cluster_size points */
-      pcl::IndicesClustersPtr large_clusters_;
+      pcl::IndicesClustersPtr large_clusters_{nullptr};
 
     public:
       PCL_MAKE_ALIGNED_OPERATOR_NEW

segmentation/include/pcl/segmentation/cpc_segmentation.h

@@ -138,25 +138,25 @@ namespace pcl
       ///  *** Parameters *** ///
 
       /** \brief Maximum number of cuts */
-      std::uint32_t max_cuts_;
+      std::uint32_t max_cuts_{20};
 
       /** \brief Minimum segment size for cutting */
-      std::uint32_t min_segment_size_for_cutting_;
+      std::uint32_t min_segment_size_for_cutting_{400};
 
       /** \brief Cut_score threshold */
-      float min_cut_score_;
+      float min_cut_score_{0.16};
 
       /** \brief Use local constrains for cutting */
-      bool use_local_constrains_;
+      bool use_local_constrains_{true};
 
       /** \brief Use directed weights for the cutting */
-      bool use_directed_weights_;
+      bool use_directed_weights_{true};
 
       /** \brief Use clean cutting */
-      bool use_clean_cutting_;
+      bool use_clean_cutting_{false};
 
       /** \brief Iterations for RANSAC */
-      std::uint32_t ransac_itrs_;
+      std::uint32_t ransac_itrs_{10000};
 
 
 /******************************************* Directional weighted RANSAC declarations ******************************************************************/

segmentation/include/pcl/segmentation/extract_clusters.h

@@ -337,11 +337,7 @@ namespace pcl
 
       //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
       /** \brief Empty constructor. */
-      EuclideanClusterExtraction () : tree_ (), 
-                                      cluster_tolerance_ (0),
-                                      min_pts_per_cluster_ (1), 
-                                      max_pts_per_cluster_ (std::numeric_limits<pcl::uindex_t>::max ())
-      {};
+      EuclideanClusterExtraction () = default;
 
       /** \brief Provide a pointer to the search object.
         * \param[in] tree a pointer to the spatial search object.
@@ -423,16 +419,16 @@ namespace pcl
       using BasePCLBase::deinitCompute;
 
       /** \brief A pointer to the spatial search object. */
-      KdTreePtr tree_;
+      KdTreePtr tree_{nullptr};
 
       /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
-      double cluster_tolerance_;
+      double cluster_tolerance_{0.0};
 
       /** \brief The minimum number of points that a cluster needs to contain in order to be considered valid (default = 1). */
-      pcl::uindex_t min_pts_per_cluster_;
+      pcl::uindex_t min_pts_per_cluster_{1};
 
       /** \brief The maximum number of points that a cluster needs to contain in order to be considered valid (default = MAXINT). */
-      pcl::uindex_t max_pts_per_cluster_;
+      pcl::uindex_t max_pts_per_cluster_{std::numeric_limits<pcl::uindex_t>::max()};
 
       /** \brief Class getName method. */
       virtual std::string getClassName () const { return ("EuclideanClusterExtraction"); }

segmentation/include/pcl/segmentation/extract_labeled_clusters.h

@@ -88,12 +88,7 @@ class LabeledEuclideanClusterExtraction : public PCLBase<PointT> {
 
   //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
   /** \brief Empty constructor. */
-  LabeledEuclideanClusterExtraction()
-  : tree_()
-  , cluster_tolerance_(0)
-  , min_pts_per_cluster_(1)
-  , max_pts_per_cluster_(std::numeric_limits<int>::max())
-  , max_label_(std::numeric_limits<int>::max()){};
+  LabeledEuclideanClusterExtraction() = default;
 
   /** \brief Provide a pointer to the search object.
    * \param[in] tree a pointer to the spatial search object.
@@ -177,22 +172,22 @@ class LabeledEuclideanClusterExtraction : public PCLBase<PointT> {
   using BasePCLBase::input_;
 
   /** \brief A pointer to the spatial search object. */
-  KdTreePtr tree_;
+  KdTreePtr tree_{nullptr};
 
   /** \brief The spatial cluster tolerance as a measure in the L2 Euclidean space. */
-  double cluster_tolerance_;
+  double cluster_tolerance_{0};
 
   /** \brief The minimum number of points that a cluster needs to contain in order to be
    * considered valid (default = 1). */
-  int min_pts_per_cluster_;
+  int min_pts_per_cluster_{1};
 
   /** \brief The maximum number of points that a cluster needs to contain in order to be
    * considered valid (default = MAXINT). */
-  int max_pts_per_cluster_;
+  int max_pts_per_cluster_{std::numeric_limits<int>::max()};
 
   /** \brief The maximum number of labels we can find in this pointcloud (default =
    * MAXINT)*/
-  unsigned int max_label_;
+  unsigned int max_label_{std::numeric_limits<int>::max()};
 
   /** \brief Class getName method. */
   virtual std::string

segmentation/include/pcl/segmentation/extract_polygonal_prism_data.h

@@ -115,11 +115,7 @@ namespace pcl
       using PointIndicesConstPtr = PointIndices::ConstPtr;
 
       /** \brief Empty constructor. */
-      ExtractPolygonalPrismData () : planar_hull_ (), min_pts_hull_ (3), 
-                                     height_limit_min_ (0),
-                                     height_limit_max_(std::numeric_limits<float>::max()),
-                                     vpx_ (0), vpy_ (0), vpz_ (0)
-      {};
+      ExtractPolygonalPrismData () = default;
 
       /** \brief Provide a pointer to the input planar hull dataset.
         * \note Please see the example in the class description for how to obtain this.
@@ -187,23 +183,23 @@ namespace pcl
 
     protected:
       /** \brief A pointer to the input planar hull dataset. */
-      PointCloudConstPtr planar_hull_;
+      PointCloudConstPtr planar_hull_{nullptr};
 
       /** \brief The minimum number of points needed on the convex hull. */
-      int min_pts_hull_;
+      int min_pts_hull_{3};
 
       /** \brief The minimum allowed height (distance to the model) a point
         * will be considered from. 
         */
-      double height_limit_min_;
+      double height_limit_min_{0.0};
 
       /** \brief The maximum allowed height (distance to the model) a point
         * will be considered from. 
         */
-      double height_limit_max_;
+      double height_limit_max_{std::numeric_limits<float>::max()};
 
       /** \brief Values describing the data acquisition viewpoint. Default: 0,0,0. */
-      float vpx_, vpy_, vpz_;
+      float vpx_{0}, vpy_{0}, vpz_{0};
 
       /** \brief Class getName method. */
       virtual std::string 

segmentation/include/pcl/segmentation/grabcut_segmentation.h

@@ -157,7 +157,7 @@ namespace pcl
           /// nodes and their outgoing internal edges
           std::vector<capacitated_edge> nodes_;
           /// current flow value (includes constant)
-          double flow_value_;
+          double flow_value_{0.0};
           /// identifies which side of the cut a node falls
           std::vector<unsigned char> cut_;
 
@@ -256,12 +256,9 @@ namespace pcl
       class GaussianFitter
       {
         public:
-        GaussianFitter (float epsilon = 0.0001)
-          : sum_ (Eigen::Vector3f::Zero ())
-          , accumulator_ (Eigen::Matrix3f::Zero ())
-          , count_ (0)
-          , epsilon_ (epsilon)
-        { }
+        GaussianFitter (float epsilon = 0.0001f)
+          : epsilon_ (epsilon)
+        {}
 
         /// Add a color sample
         void
@@ -281,11 +278,11 @@ namespace pcl
 
         private:
         /// sum of r,g, and b
-        Eigen::Vector3f sum_;
+        Eigen::Vector3f sum_{Eigen::Vector3f::Zero ()};
         /// matrix of products (i.e. r*r, r*g, r*b), some values are duplicated.
-        Eigen::Matrix3f accumulator_;
+        Eigen::Matrix3f accumulator_{Eigen::Matrix3f::Zero ()};
         /// count of color samples added to the gaussian
-        std::uint32_t count_;
+        std::uint32_t count_{0};
         /// small value to add to covariance matrix diagonal to avoid singular values
         float epsilon_;
         PCL_MAKE_ALIGNED_OPERATOR_NEW
@@ -329,12 +326,8 @@ namespace pcl
       using PCLBase<PointT>::fake_indices_;
 
       /// Constructor
-      GrabCut (std::uint32_t K = 5, float lambda = 50.f)
-        : K_ (K)
-        , lambda_ (lambda)
-        , nb_neighbours_ (9)
-        , initialized_ (false)
-      {}
+      GrabCut(std::uint32_t K = 5, float lambda = 50.f) : K_(K), lambda_(lambda) {}
+
       /// Destructor
       ~GrabCut () override = default;
       // /// Set input cloud
@@ -399,12 +392,12 @@ namespace pcl
       // Storage for N-link weights, each pixel stores links to nb_neighbours
       struct NLinks
       {
-        NLinks () : nb_links (0), indices (0), dists (0), weights (0) {}
+        NLinks () = default;
 
-        int nb_links;
-        Indices indices;
-        std::vector<float> dists;
-        std::vector<float> weights;
+        int nb_links{0};
+        Indices indices{};
+        std::vector<float> dists{};
+        std::vector<float> weights{};
       };
       bool
       initCompute ();
@@ -460,9 +453,9 @@ namespace pcl
       /// Pointer to the spatial search object.
       KdTreePtr tree_;
       /// Number of neighbours
-      int nb_neighbours_;
+      int nb_neighbours_{9};
       /// is segmentation initialized
-      bool initialized_;
+      bool initialized_{false};
       /// Precomputed N-link weights
       std::vector<NLinks> n_links_;
       /// Converted input

segmentation/include/pcl/segmentation/impl/approximate_progressive_morphological_filter.hpp

@@ -49,17 +49,7 @@
 
 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 template <typename PointT>
-pcl::ApproximateProgressiveMorphologicalFilter<PointT>::ApproximateProgressiveMorphologicalFilter () :
-  max_window_size_ (33),
-  slope_ (0.7f),
-  max_distance_ (10.0f),
-  initial_distance_ (0.15f),
-  cell_size_ (1.0f),
-  base_ (2.0f),
-  exponential_ (true),
-  threads_ (0)
-{
-}
+pcl::ApproximateProgressiveMorphologicalFilter<PointT>::ApproximateProgressiveMorphologicalFilter () = default;
 
 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 template <typename PointT>

segmentation/include/pcl/segmentation/impl/cpc_segmentation.hpp

@@ -42,15 +42,7 @@
 #include <pcl/segmentation/cpc_segmentation.h>
 
 template <typename PointT>
-pcl::CPCSegmentation<PointT>::CPCSegmentation () :
-    max_cuts_ (20),
-    min_segment_size_for_cutting_ (400),
-    min_cut_score_ (0.16),
-    use_local_constrains_ (true),
-    use_directed_weights_ (true),
-    ransac_itrs_ (10000)
-{
-}
+pcl::CPCSegmentation<PointT>::CPCSegmentation () = default;
 
 template <typename PointT>
 pcl::CPCSegmentation<PointT>::~CPCSegmentation () = default;

segmentation/include/pcl/segmentation/impl/lccp_segmentation.hpp

@@ -51,19 +51,7 @@
 
 
 template <typename PointT>
-pcl::LCCPSegmentation<PointT>::LCCPSegmentation () :
-  concavity_tolerance_threshold_ (10),
-  grouping_data_valid_ (false),
-  supervoxels_set_ (false),
-  use_smoothness_check_ (false),
-  smoothness_threshold_ (0.1),
-  use_sanity_check_ (false),  
-  seed_resolution_ (0),
-  voxel_resolution_ (0),
-  k_factor_ (0),
-  min_segment_size_ (0)
-{
-}
+pcl::LCCPSegmentation<PointT>::LCCPSegmentation () = default;
 
 template <typename PointT>
 pcl::LCCPSegmentation<PointT>::~LCCPSegmentation () = default;