sac_model.h

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 * $Id: sac_model.h 3503 2011-12-12 06:07:28Z rusu $
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#ifndef PCL_SAMPLE_CONSENSUS_MODEL_H_
#define PCL_SAMPLE_CONSENSUS_MODEL_H_

#include <cfloat>
#include <ctime>
#include <limits.h>
#include <set>
#include <boost/random.hpp>

#include <pcl/console/print.h>
#include <pcl/point_cloud.h>
#include "pcl/sample_consensus/model_types.h"

namespace pcl
{
  template<class T> class ProgressiveSampleConsensus;

  template <typename PointT>
  class SampleConsensusModel
  {
    public:
      typedef typename pcl::PointCloud<PointT> PointCloud;
      typedef typename pcl::PointCloud<PointT>::ConstPtr PointCloudConstPtr;
      typedef typename pcl::PointCloud<PointT>::Ptr PointCloudPtr;

      typedef boost::shared_ptr<SampleConsensusModel> Ptr;
      typedef boost::shared_ptr<const SampleConsensusModel> ConstPtr;

    private:
      SampleConsensusModel (bool random = false) : radius_min_ (-DBL_MAX), radius_max_ (DBL_MAX) 
      {
        // Create a random number generator object
        if (random)
          rng_alg_.seed (static_cast<unsigned> (std::time(0)));
        else
          rng_alg_.seed (12345u);

        rng_dist_.reset (new boost::uniform_int<> (0, std::numeric_limits<int>::max ()));
        rng_gen_.reset (new boost::variate_generator<boost::mt19937&, boost::uniform_int<> > (rng_alg_, *rng_dist_)); 
       }

    public:
      SampleConsensusModel (const PointCloudConstPtr &cloud, bool random = false) : 
        radius_min_ (-DBL_MAX), radius_max_ (DBL_MAX)
      {
        if (random)
          rng_alg_.seed (static_cast<unsigned> (std::time(0)));
        else
          rng_alg_.seed (12345u);

        // Sets the input cloud and creates a vector of "fake" indices
        setInputCloud (cloud);

        // Create a random number generator object
        rng_dist_.reset (new boost::uniform_int<> (0, std::numeric_limits<int>::max ()));
        rng_gen_.reset (new boost::variate_generator<boost::mt19937&, boost::uniform_int<> > (rng_alg_, *rng_dist_)); 
       }

      SampleConsensusModel (const PointCloudConstPtr &cloud, const std::vector<int> &indices, bool random = false) :
                            input_ (cloud),
                            radius_min_ (-DBL_MAX), radius_max_ (DBL_MAX) 
    
      {
        if (random)
          rng_alg_.seed (static_cast<unsigned> (std::time(0)));
        else
          rng_alg_.seed (12345u);

        indices_.reset (new std::vector<int> (indices));
        if (indices_->size () > input_->points.size ())
        {
          PCL_ERROR ("[pcl::SampleConsensusModel] Invalid index vector given with size %lu while the input PointCloud has size %lu!\n", (unsigned long)indices_->size (), (unsigned long)input_->points.size ());
          indices_->clear ();
        }
        shuffled_indices_ = *indices_;

        // Create a random number generator object
        rng_dist_.reset (new boost::uniform_int<> (0, std::numeric_limits<int>::max ()));
        rng_gen_.reset (new boost::variate_generator<boost::mt19937&, boost::uniform_int<> > (rng_alg_, *rng_dist_)); 
       };

      virtual ~SampleConsensusModel () {};

      void 
      getSamples (int &iterations, std::vector<int> &samples)
      {
        // We're assuming that indices_ have already been set in the constructor
        if (indices_->size () < getSampleSize ())
        {
          PCL_ERROR ("[pcl::SampleConsensusModel::getSamples] Can not select %lu unique points out of %lu!\n",
                     (unsigned long)samples.size (), (unsigned long)indices_->size ());
          // one of these will make it stop :)
          samples.clear ();
          iterations = INT_MAX - 1;
          return;
        }

        // Get a second point which is different than the first
        samples.resize (getSampleSize());
        for (unsigned int iter = 0; iter < max_sample_checks_; ++iter)
        {
          // Choose the random indices
          SampleConsensusModel<PointT>::drawIndexSample (samples);

          // If it's a good sample, stop here
          if (isSampleGood (samples))
            return;
        }
        PCL_DEBUG ("[pcl::SampleConsensusModel::getSamples] WARNING: Could not select %d sample points in %d iterations!\n", getSampleSize (), max_sample_checks_);
        samples.clear ();
      }

      virtual bool 
      computeModelCoefficients (const std::vector<int> &samples, 
                                Eigen::VectorXf &model_coefficients) = 0;

      virtual void 
      optimizeModelCoefficients (const std::vector<int> &inliers, 
                                 const Eigen::VectorXf &model_coefficients,
                                 Eigen::VectorXf &optimized_coefficients) = 0;

      virtual void 
      getDistancesToModel (const Eigen::VectorXf &model_coefficients, 
                           std::vector<double> &distances) = 0;

      virtual void 
      selectWithinDistance (const Eigen::VectorXf &model_coefficients, 
                            const double threshold,
                            std::vector<int> &inliers) = 0;

      virtual int
      countWithinDistance (const Eigen::VectorXf &model_coefficients, 
                           const double threshold) = 0;

      virtual void 
      projectPoints (const std::vector<int> &inliers, 
                     const Eigen::VectorXf &model_coefficients,
                     PointCloud &projected_points, 
                     bool copy_data_fields = true) = 0;

      virtual bool 
      doSamplesVerifyModel (const std::set<int> &indices, 
                            const Eigen::VectorXf &model_coefficients, 
                            const double threshold) = 0;

      inline virtual void
      setInputCloud (const PointCloudConstPtr &cloud)
      {
        input_ = cloud;
        if (!indices_)
          indices_.reset (new std::vector<int> ());
        if (indices_->empty ())
        {
          // Prepare a set of indices to be used (entire cloud)
          indices_->resize (cloud->points.size ());
          for (size_t i = 0; i < cloud->points.size (); ++i) 
            (*indices_)[i] = (int) i;
        }
        shuffled_indices_ = *indices_;
       }

      inline PointCloudConstPtr 
      getInputCloud () const { return (input_); }

      inline void 
      setIndices (const boost::shared_ptr <std::vector<int> > &indices) 
      { 
        indices_ = indices; 
        shuffled_indices_ = *indices_;
       }

      inline void 
      setIndices (const std::vector<int> &indices) 
      { 
        indices_.reset (new std::vector<int> (indices));
        shuffled_indices_ = indices;
       }

      inline boost::shared_ptr <std::vector<int> > 
      getIndices () const { return (indices_); }

      virtual SacModel 
      getModelType () const = 0;

      inline unsigned int 
      getSampleSize () const 
      { 
        std::map<pcl::SacModel, unsigned int>::const_iterator it = SAC_SAMPLE_SIZE.find (getModelType ());
        if (it == SAC_SAMPLE_SIZE.end ())
          throw InvalidSACModelTypeException ("No sample size defined for given model type!\n");
        return (it->second);
      }

      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_;
      }

    friend class ProgressiveSampleConsensus<PointT>;

    protected:
      inline void
      drawIndexSample (std::vector<int> &sample)
      {
        size_t sample_size = sample.size ();
        size_t index_size = shuffled_indices_.size ();
        for (unsigned int i = 0; i < sample_size; ++i)
          // The 1/(RAND_MAX+1.0) trick is when the random numbers are not uniformly distributed and for small modulo
          // elements, that does not matter (and nowadays, random number generators are good)
          //std::swap (shuffled_indices_[i], shuffled_indices_[i + (rand () % (index_size - i))]);
          std::swap (shuffled_indices_[i], shuffled_indices_[i + (rnd () % (index_size - i))]);
        std::copy (shuffled_indices_.begin (), shuffled_indices_.begin () + sample_size, sample.begin ());
      }

      virtual inline bool
      isModelValid (const Eigen::VectorXf &model_coefficients) = 0;

      virtual bool
      isSampleGood (const std::vector<int> &samples) const = 0;

      PointCloudConstPtr input_;

      boost::shared_ptr <std::vector<int> > indices_;

      static const unsigned int max_sample_checks_ = 1000;

      double radius_min_, radius_max_;

      std::vector<int> shuffled_indices_;

      boost::mt19937 rng_alg_;

      boost::shared_ptr<boost::uniform_int<> > rng_dist_;

      boost::shared_ptr<boost::variate_generator< boost::mt19937&, boost::uniform_int<> > > rng_gen_;

      inline int
      rnd ()
      {
        return ((*rng_gen_) ());
      }
  };

  template <typename PointT, typename PointNT>
  class SampleConsensusModelFromNormals
  {
    public:
      typedef typename pcl::PointCloud<PointNT>::ConstPtr PointCloudNConstPtr;
      typedef typename pcl::PointCloud<PointNT>::Ptr PointCloudNPtr;

      typedef boost::shared_ptr<SampleConsensusModelFromNormals> Ptr;
      typedef boost::shared_ptr<const SampleConsensusModelFromNormals> ConstPtr;

      SampleConsensusModelFromNormals () : normal_distance_weight_ (0.0) {};

      inline void 
      setNormalDistanceWeight (const double w) 
      { 
        normal_distance_weight_ = w; 
      }

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

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

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

    protected:
      double normal_distance_weight_;

      PointCloudNConstPtr normals_;
  };

  template<typename _Scalar, int NX=Eigen::Dynamic, int NY=Eigen::Dynamic>
  struct Functor
  {
    typedef _Scalar Scalar;
    enum {
      InputsAtCompileTime = NX,
      ValuesAtCompileTime = NY
    };
    typedef Eigen::Matrix<Scalar,InputsAtCompileTime,1> InputType;
    typedef Eigen::Matrix<Scalar,ValuesAtCompileTime,1> ValueType;
    typedef Eigen::Matrix<Scalar,ValuesAtCompileTime,InputsAtCompileTime> JacobianType;
    
    const int m_inputs, m_values;
    
    Functor() : m_inputs(InputsAtCompileTime), m_values(ValuesAtCompileTime) {}
    Functor(int inputs, int values) : m_inputs(inputs), m_values(values) {}
  
    int inputs() const { return m_inputs; }
    int values() const { return m_values; }
  };
}

#endif  //#ifndef PCL_SAMPLE_CONSENSUS_MODEL_H_