pca.h

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#pragma once
 
#include <pcl/pcl_base.h>
#include <pcl/pcl_macros.h>
 
namespace pcl
{
  /** Principal Component analysis (PCA) class.\n
    *  Principal components are extracted by singular values decomposition on the
    * covariance matrix of the centered input cloud. Available data after pca computation
    * are:\n
    * - The Mean of the input data\n
    * - The Eigenvectors: Ordered set of vectors representing the resultant principal components and the eigenspace cartesian basis (right-handed coordinate system).\n
    * - The Eigenvalues: Eigenvectors correspondent loadings ordered in descending order.\n\n
    * Other methods allow projection in the eigenspace, reconstruction from eigenspace and
    *  update of the eigenspace with a new datum (according Matej Artec, Matjaz Jogan and
    * Ales Leonardis: "Incremental PCA for On-line Visual Learning and Recognition").
    *
    * \author Nizar Sallem
    * \ingroup common
    */
  template <typename PointT>
  class PCA : public pcl::PCLBase <PointT>
  {
    public:
      using Base = pcl::PCLBase<PointT>;
      using PointCloud = typename Base::PointCloud;
      using PointCloudPtr = typename Base::PointCloudPtr;
      using PointCloudConstPtr = typename Base::PointCloudConstPtr;
      using PointIndicesPtr = typename Base::PointIndicesPtr;
      using PointIndicesConstPtr = typename Base::PointIndicesConstPtr;
 
      using Base::input_;
      using Base::indices_;
      using Base::initCompute;
      using Base::setInputCloud;
 
      /** Updating method flag */
      enum FLAG
      {
        /** keep the new basis vectors if possible */
        increase,
        /** preserve subspace dimension */
        preserve
      };
 
      /** \brief Default Constructor
        * \param basis_only flag to compute only the PCA basis
        */
      PCA (bool basis_only = false)
        : Base ()
        , basis_only_ (basis_only)
      {}
 
      /** Copy Constructor
        * \param[in] pca PCA object
        */
      PCA (PCA const & pca)
        : Base (pca)
        , compute_done_ (pca.compute_done_)
        , basis_only_ (pca.basis_only_)
        , eigenvectors_ (pca.eigenvectors_)
        , coefficients_ (pca.coefficients_)
        , mean_ (pca.mean_)
        , eigenvalues_  (pca.eigenvalues_)
      {}
 
      /** Assignment operator
        * \param[in] pca PCA object
        */
      inline PCA&
      operator= (PCA const & pca)
      {
        eigenvectors_ = pca.eigenvectors_;
        coefficients_ = pca.coefficients_;
        eigenvalues_  = pca.eigenvalues_;
        mean_         = pca.mean_;
        return (*this);
      }
 
      /** \brief Provide a pointer to the input dataset
        * \param cloud the const boost shared pointer to a PointCloud message
        */
      inline void
      setInputCloud (const PointCloudConstPtr &cloud) override
      {
        Base::setInputCloud (cloud);
        compute_done_ = false;
      }
 
      /** \brief Provide a pointer to the vector of indices that represents the input data.
        * \param[in] indices a pointer to the indices that represent the input data.
        */
      void
      setIndices (const IndicesPtr &indices) override
      {
        Base::setIndices (indices);
        compute_done_ = false;
      }
 
      /** \brief Provide a pointer to the vector of indices that represents the input data.
        * \param[in] indices a pointer to the indices that represent the input data.
        */
      void
      setIndices (const IndicesConstPtr &indices) override
      {
        Base::setIndices (indices);
        compute_done_ = false;
      }
 
      /** \brief Provide a pointer to the vector of indices that represents the input data.
        * \param[in] indices a pointer to the indices that represent the input data.
        */
      void
      setIndices (const PointIndicesConstPtr &indices) override
      {
        Base::setIndices (indices);
        compute_done_ = false;
      }
 
      /** \brief Set the indices for the points laying within an interest region of
        * the point cloud.
        * \note you shouldn't call this method on unorganized point clouds!
        * \param[in] row_start the offset on rows
        * \param[in] col_start the offset on columns
        * \param[in] nb_rows the number of rows to be considered row_start included
        * \param[in] nb_cols the number of columns to be considered col_start included
        */
      void
      setIndices (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols) override
      {
        Base::setIndices (row_start, col_start, nb_rows, nb_cols);
        compute_done_ = false;
      }
 
      /** \brief Mean accessor
        * \throw InitFailedException
        */
      inline Eigen::Vector4f&
      getMean ()
      {
        if (!compute_done_)
          initCompute ();
        if (!compute_done_)
          PCL_THROW_EXCEPTION (InitFailedException,
                               "[pcl::PCA::getMean] PCA initCompute failed");
        return (mean_);
      }
 
      /** Eigen Vectors accessor
        * \return Column ordered eigenvectors, representing the eigenspace cartesian basis (right-handed coordinate system).
        * \throw InitFailedException
        */
      inline Eigen::Matrix3f&
      getEigenVectors ()
      {
        if (!compute_done_)
          initCompute ();
        if (!compute_done_)
          PCL_THROW_EXCEPTION (InitFailedException,
                               "[pcl::PCA::getEigenVectors] PCA initCompute failed");
        return (eigenvectors_);
      }
 
      /** Eigen Values accessor
        * \throw InitFailedException
        */
      inline Eigen::Vector3f&
      getEigenValues ()
      {
        if (!compute_done_)
          initCompute ();
        if (!compute_done_)
          PCL_THROW_EXCEPTION (InitFailedException,
                               "[pcl::PCA::getEigenVectors] PCA getEigenValues failed");
        return (eigenvalues_);
      }
 
      /** Coefficients accessor
        * \throw InitFailedException
        */
      inline Eigen::MatrixXf&
      getCoefficients ()
      {
        if (!compute_done_)
          initCompute ();
        if (!compute_done_)
          PCL_THROW_EXCEPTION (InitFailedException,
                               "[pcl::PCA::getEigenVectors] PCA getCoefficients failed");
        return (coefficients_);
      }
 
      /** update PCA with a new point
        * \param[in] input input point
        * \param[in] flag update flag
        * \throw InitFailedException
        */
      inline void
      update (const PointT& input, FLAG flag = preserve);
 
      /** Project point on the eigenspace.
        * \param[in] input point from original dataset
        * \param[out] projection the point in eigen vectors space
        * \throw InitFailedException
        */
      inline void
      project (const PointT& input, PointT& projection);
 
      /** Project cloud on the eigenspace.
        * \param[in] input cloud from original dataset
        * \param[out] projection the cloud in eigen vectors space
        * \throw InitFailedException
        */
      inline void
      project (const PointCloud& input, PointCloud& projection);
 
      /** Reconstruct point from its projection
        * \param[in] projection point from eigenvector space
        * \param[out] input reconstructed point
        * \throw InitFailedException
        */
      inline void
      reconstruct (const PointT& projection, PointT& input);
 
      /** Reconstruct cloud from its projection
        * \param[in] projection cloud from eigenvector space
        * \param[out] input reconstructed cloud
        * \throw InitFailedException
        */
      inline void
      reconstruct (const PointCloud& projection, PointCloud& input);
    private:
      inline bool
      initCompute ();
 
      bool compute_done_{false};
      bool basis_only_;
      Eigen::Matrix3f eigenvectors_;
      Eigen::MatrixXf coefficients_;
      Eigen::Vector4f mean_;
      Eigen::Vector3f eigenvalues_;
  }; // class PCA
} // namespace pcl
 
#include <pcl/common/impl/pca.hpp>