pca_norm.hpp

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#pragma once
 
#include <cpp-toolbox/cpp-toolbox_export.hpp>
#include <cpp-toolbox/pcl/knn/bfknn_parallel.hpp>
#include <cpp-toolbox/pcl/knn/kdtree.hpp>
#include <cpp-toolbox/pcl/norm/base_norm.hpp>
 
namespace toolbox::pcl
{
 
template<typename DataType, typename KNN = kdtree_generic_t<point_t<DataType>, toolbox::metrics::L2Metric<DataType>>>
class CPP_TOOLBOX_EXPORT pca_norm_extractor_t
    : public base_norm_extractor_t<pca_norm_extractor_t<DataType, KNN>,
                                   DataType,
                                   KNN>
{
public:
  using base_type =
      base_norm_extractor_t<pca_norm_extractor_t<DataType, KNN>, DataType, KNN>;
  using data_type = typename base_type::data_type;
  using knn_type = typename base_type::knn_type;
  using point_cloud = typename base_type::point_cloud;
  using point_cloud_ptr = typename base_type::point_cloud_ptr;
 
  std::size_t set_input_impl(const point_cloud& cloud);
 
  std::size_t set_input_impl(const point_cloud_ptr& cloud);
 
  std::size_t set_knn_impl(const knn_type& knn);
 
  std::size_t set_num_neighbors_impl(std::size_t num_neighbors);
 
  point_cloud extract_impl();
 
  void extract_impl(point_cloud_ptr output);
 
  void enable_parallel(bool enable) { m_enable_parallel = enable; }
 
private:
  void compute_normals_range(point_cloud_ptr output, std::size_t start_idx, std::size_t end_idx);
  
  point_t<data_type> compute_pca_normal(const std::vector<std::size_t>& indices);
 
  bool m_enable_parallel = false;  
  std::size_t m_num_neighbors = 0;  
  point_cloud_ptr m_cloud;  
  knn_type* m_knn = nullptr;  
};  // class pca_norm_extractor_t
 
}  // namespace toolbox::pcl
 
#include <cpp-toolbox/pcl/norm/impl/pca_norm_impl.hpp>