sac_segmentation.h

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#ifndef PCL_SEGMENTATION_SAC_SEGMENTATION_H_
#define PCL_SEGMENTATION_SAC_SEGMENTATION_H_

#include <pcl/pcl_base.h>
#include <pcl/PointIndices.h>
#include <pcl/ModelCoefficients.h>

// Sample Consensus methods
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/sac.h>
// Sample Consensus models
#include <pcl/sample_consensus/model_types.h>
#include <pcl/sample_consensus/sac_model.h>

#include <pcl/search/search.h>

namespace pcl
{
  template <typename PointT>
  class SACSegmentation : public PCLBase<PointT>
  {
    using PCLBase<PointT>::initCompute;
    using PCLBase<PointT>::deinitCompute;

     public:
      using PCLBase<PointT>::input_;
      using PCLBase<PointT>::indices_;

      typedef pcl::PointCloud<PointT> PointCloud;
      typedef typename PointCloud::Ptr PointCloudPtr;
      typedef typename PointCloud::ConstPtr PointCloudConstPtr;
      typedef typename pcl::search::Search<PointT>::Ptr SearchPtr;

      typedef typename SampleConsensus<PointT>::Ptr SampleConsensusPtr;
      typedef typename SampleConsensusModel<PointT>::Ptr SampleConsensusModelPtr;

      SACSegmentation (bool random = false) 
        : model_ ()
        , sac_ ()
        , model_type_ (-1)
        , method_type_ (0)
        , threshold_ (0)
        , optimize_coefficients_ (true)
        , radius_min_ (-std::numeric_limits<double>::max ())
        , radius_max_ (std::numeric_limits<double>::max ())
        , samples_radius_ (0.0)
        , samples_radius_search_ ()
        , eps_angle_ (0.0)
        , axis_ (Eigen::Vector3f::Zero ())
        , max_iterations_ (50)
        , probability_ (0.99)
        , random_ (random)
      {
      }

      virtual ~SACSegmentation () { /*srv_.reset ();*/ };

      inline void 
      setModelType (int model) { model_type_ = model; }

      inline int 
      getModelType () const { return (model_type_); }

      inline SampleConsensusPtr 
      getMethod () const { return (sac_); }

      inline SampleConsensusModelPtr 
      getModel () const { return (model_); }

      inline void 
      setMethodType (int method) { method_type_ = method; }

      inline int 
      getMethodType () const { return (method_type_); }

      inline void 
      setDistanceThreshold (double threshold) { threshold_ = threshold; }

      inline double 
      getDistanceThreshold () const { return (threshold_); }

      inline void 
      setMaxIterations (int max_iterations) { max_iterations_ = max_iterations; }

      inline int 
      getMaxIterations () const { return (max_iterations_); }

      inline void 
      setProbability (double probability) { probability_ = probability; }

      inline double 
      getProbability () const { return (probability_); }

      inline void 
      setOptimizeCoefficients (bool optimize) { optimize_coefficients_ = optimize; }

      inline bool 
      getOptimizeCoefficients () const { return (optimize_coefficients_); }

      inline void
      setRadiusLimits (const double &min_radius, const double &max_radius)
      {
        radius_min_ = min_radius;
        radius_max_ = max_radius;
      }

      inline void
      getRadiusLimits (double &min_radius, double &max_radius)
      {
        min_radius = radius_min_;
        max_radius = radius_max_;
      }

      inline void
      setSamplesMaxDist (const double &radius, SearchPtr search)
      {
        samples_radius_ = radius;
        samples_radius_search_ = search;
      }

      inline void
      getSamplesMaxDist (double &radius)
      {
        radius = samples_radius_;
      }

      inline void 
      setAxis (const Eigen::Vector3f &ax) { axis_ = ax; }

      inline Eigen::Vector3f 
      getAxis () const { return (axis_); }

      inline void 
      setEpsAngle (double ea) { eps_angle_ = ea; }

      inline double 
      getEpsAngle () const { return (eps_angle_); }

      virtual void 
      segment (PointIndices &inliers, ModelCoefficients &model_coefficients);

    protected:
      virtual bool 
      initSACModel (const int model_type);

      virtual void 
      initSAC (const int method_type);

      SampleConsensusModelPtr model_;

      SampleConsensusPtr sac_;

      int model_type_;

      int method_type_;

      double threshold_;

      bool optimize_coefficients_;

      double radius_min_, radius_max_;

      double samples_radius_;

      SearchPtr samples_radius_search_;

      double eps_angle_;

      Eigen::Vector3f axis_;

      int max_iterations_;

      double probability_;

      bool random_;

      virtual std::string 
      getClassName () const { return ("SACSegmentation"); }
  };

  template <typename PointT, typename PointNT>
  class SACSegmentationFromNormals: public SACSegmentation<PointT>
  {
    using SACSegmentation<PointT>::model_;
    using SACSegmentation<PointT>::model_type_;
    using SACSegmentation<PointT>::radius_min_;
    using SACSegmentation<PointT>::radius_max_;
    using SACSegmentation<PointT>::eps_angle_;
    using SACSegmentation<PointT>::axis_;
    using SACSegmentation<PointT>::random_;

    public:
      using PCLBase<PointT>::input_;
      using PCLBase<PointT>::indices_;

      typedef typename SACSegmentation<PointT>::PointCloud PointCloud;
      typedef typename PointCloud::Ptr PointCloudPtr;
      typedef typename PointCloud::ConstPtr PointCloudConstPtr;

      typedef typename pcl::PointCloud<PointNT> PointCloudN;
      typedef typename PointCloudN::Ptr PointCloudNPtr;
      typedef typename PointCloudN::ConstPtr PointCloudNConstPtr;

      typedef typename SampleConsensus<PointT>::Ptr SampleConsensusPtr;
      typedef typename SampleConsensusModel<PointT>::Ptr SampleConsensusModelPtr;
      typedef typename SampleConsensusModelFromNormals<PointT, PointNT>::Ptr SampleConsensusModelFromNormalsPtr;

      SACSegmentationFromNormals (bool random = false) 
        : SACSegmentation<PointT> (random)
        , normals_ ()
        , distance_weight_ (0.1)
        , distance_from_origin_ (0)
        , min_angle_ ()
        , max_angle_ ()
      {};

      inline void 
      setInputNormals (const PointCloudNConstPtr &normals) { normals_ = normals; }

      inline PointCloudNConstPtr 
      getInputNormals () const { return (normals_); }

      inline void 
      setNormalDistanceWeight (double distance_weight) { distance_weight_ = distance_weight; }

      inline double 
      getNormalDistanceWeight () const { return (distance_weight_); }

      inline void
      setMinMaxOpeningAngle (const double &min_angle, const double &max_angle)
      {
        min_angle_ = min_angle;
        max_angle_ = max_angle;
      }
 
      inline void
      getMinMaxOpeningAngle (double &min_angle, double &max_angle)
      {
        min_angle = min_angle_;
        max_angle = max_angle_;
      }

      inline void
      setDistanceFromOrigin (const double d) { distance_from_origin_ = d; }

      inline double
      getDistanceFromOrigin () const { return (distance_from_origin_); }

    protected:
      PointCloudNConstPtr normals_;

      double distance_weight_;

      double distance_from_origin_;

      double min_angle_;
      double max_angle_;

      virtual bool 
      initSACModel (const int model_type);

      virtual std::string 
      getClassName () const { return ("SACSegmentationFromNormals"); }
  };
}

#ifdef PCL_NO_PRECOMPILE
#include <pcl/segmentation/impl/sac_segmentation.hpp>
#endif

#endif  //#ifndef PCL_SEGMENTATION_SAC_SEGMENTATION_H_