sac_segmentation.h

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 * $Id: sac_segmentation.h 35810 2011-02-08 00:03:46Z rusu $
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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/sac.h"
// Sample Consensus models
#include "pcl/sample_consensus/sac_model.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 SampleConsensus<PointT>::Ptr SampleConsensusPtr;
      typedef typename SampleConsensusModel<PointT>::Ptr SampleConsensusModelPtr;

      SACSegmentation () :  model_type_ (-1), method_type_ (0), optimize_coefficients_ (true), 
                            radius_min_ (-DBL_MAX), radius_max_ (DBL_MAX), eps_angle_ (0.0),
                            max_iterations_ (50), probability_ (0.99)
      {
        axis_.setZero ();
        //srand ((unsigned)time (0)); // set a random seed
      }

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

      inline double 
      getEpsAngle () { 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 eps_angle_;

      Eigen::Vector3f axis_;

      int max_iterations_;

      double probability_;

      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_;

    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 () : distance_weight_ (0.1) {};

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

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

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

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

    protected:
      PointCloudNConstPtr normals_;

      double distance_weight_;

      virtual bool 
      initSACModel (const int model_type);

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

#endif  //#ifndef PCL_SEGMENTATION_SAC_SEGMENTATION_H_

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pcl
Author(s): See http://pcl.ros.org/authors for the complete list of authors.
autogenerated on Fri Jan 11 09:55:23 2013