sac.h

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#ifndef PCL_SAMPLE_CONSENSUS_H_
#define PCL_SAMPLE_CONSENSUS_H_

#include <pcl/sample_consensus/boost.h>
#include <pcl/sample_consensus/sac_model.h>
#include <ctime>
#include <set>

namespace pcl
{
  template <typename T>
  class SampleConsensus
  {
    typedef typename SampleConsensusModel<T>::Ptr SampleConsensusModelPtr;

    private:
      SampleConsensus () {};

    public:
      typedef boost::shared_ptr<SampleConsensus> Ptr;
      typedef boost::shared_ptr<const SampleConsensus> ConstPtr;

      SampleConsensus (const SampleConsensusModelPtr &model, bool random = false) 
        : sac_model_ (model)
        , model_ ()
        , inliers_ ()
        , model_coefficients_ ()
        , probability_ (0.99)
        , iterations_ (0)
        , threshold_ (std::numeric_limits<double>::max ())
        , max_iterations_ (1000)
        , rng_alg_ ()
        , rng_ (new boost::uniform_01<boost::mt19937> (rng_alg_))
      {
         // Create a random number generator object
         if (random)
           rng_->base ().seed (static_cast<unsigned> (std::time (0)));
         else
           rng_->base ().seed (12345u);
      };

      SampleConsensus (const SampleConsensusModelPtr &model, 
                       double threshold, 
                       bool random = false)
        : sac_model_ (model)
        , model_ ()
        , inliers_ ()
        , model_coefficients_ ()
        , probability_ (0.99)
        , iterations_ (0)
        , threshold_ (threshold)
        , max_iterations_ (1000)
        , rng_alg_ ()
        , rng_ (new boost::uniform_01<boost::mt19937> (rng_alg_))
      {
         // Create a random number generator object
         if (random)
           rng_->base ().seed (static_cast<unsigned> (std::time (0)));
         else
           rng_->base ().seed (12345u);
      };

      void
      setSampleConsensusModel (const SampleConsensusModelPtr &model)
      {
        sac_model_ = model;
      }

      SampleConsensusModelPtr
      getSampleConsensusModel () const
      {
        return (sac_model_);
      }

      virtual ~SampleConsensus () {};

      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_); }

      virtual bool 
      computeModel (int debug_verbosity_level = 0) = 0;

      virtual bool 
      refineModel (const double sigma = 3.0, const unsigned int max_iterations = 1000)
      {
        if (!sac_model_)
        {
          PCL_ERROR ("[pcl::SampleConsensus::refineModel] Critical error: NULL model!\n");
          return (false);
        }

        double inlier_distance_threshold_sqr = threshold_ * threshold_, 
               error_threshold = threshold_;
        double sigma_sqr = sigma * sigma;
        unsigned int refine_iterations = 0;
        bool inlier_changed = false, oscillating = false;
        std::vector<int> new_inliers, prev_inliers = inliers_;
        std::vector<size_t> inliers_sizes;
        Eigen::VectorXf new_model_coefficients = model_coefficients_;
        do
        {
          // Optimize the model coefficients
          sac_model_->optimizeModelCoefficients (prev_inliers, new_model_coefficients, new_model_coefficients);
          inliers_sizes.push_back (prev_inliers.size ());

          // Select the new inliers based on the optimized coefficients and new threshold
          sac_model_->selectWithinDistance (new_model_coefficients, error_threshold, new_inliers);
          PCL_DEBUG ("[pcl::SampleConsensus::refineModel] Number of inliers found (before/after): %zu/%zu, with an error threshold of %g.\n", prev_inliers.size (), new_inliers.size (), error_threshold);
        
          if (new_inliers.empty ())
          {
            refine_iterations++;
            if (refine_iterations >= max_iterations)
              break;
            continue;
            //return (false);
          }

          // Estimate the variance and the new threshold
          double variance = sac_model_->computeVariance ();
          error_threshold = sqrt (std::min (inlier_distance_threshold_sqr, sigma_sqr * variance));

          PCL_DEBUG ("[pcl::SampleConsensus::refineModel] New estimated error threshold: %g on iteration %d out of %d.\n", error_threshold, refine_iterations, max_iterations);
          inlier_changed = false;
          std::swap (prev_inliers, new_inliers);
          // If the number of inliers changed, then we are still optimizing
          if (new_inliers.size () != prev_inliers.size ())
          {
            // Check if the number of inliers is oscillating in between two values
            if (inliers_sizes.size () >= 4)
            {
              if (inliers_sizes[inliers_sizes.size () - 1] == inliers_sizes[inliers_sizes.size () - 3] &&
                  inliers_sizes[inliers_sizes.size () - 2] == inliers_sizes[inliers_sizes.size () - 4])
              {
                oscillating = true;
                break;
              }
            }
            inlier_changed = true;
            continue;
          }

          // Check the values of the inlier set
          for (size_t i = 0; i < prev_inliers.size (); ++i)
          {
            // If the value of the inliers changed, then we are still optimizing
            if (prev_inliers[i] != new_inliers[i])
            {
              inlier_changed = true;
              break;
            }
          }
        }
        while (inlier_changed && ++refine_iterations < max_iterations);
      
        // If the new set of inliers is empty, we didn't do a good job refining
        if (new_inliers.empty ())
        {
          PCL_ERROR ("[pcl::SampleConsensus::refineModel] Refinement failed: got an empty set of inliers!\n");
          return (false);
        }

        if (oscillating)
        {
          PCL_DEBUG ("[pcl::SampleConsensus::refineModel] Detected oscillations in the model refinement.\n");
          return (true);
        }

        // If no inliers have been changed anymore, then the refinement was successful
        if (!inlier_changed)
        {
          std::swap (inliers_, new_inliers);
          model_coefficients_ = new_model_coefficients;
          return (true);
        }
        return (false);
      }

      inline void
      getRandomSamples (const boost::shared_ptr <std::vector<int> > &indices, 
                        size_t nr_samples, 
                        std::set<int> &indices_subset)
      {
        indices_subset.clear ();
        while (indices_subset.size () < nr_samples)
          //indices_subset.insert ((*indices)[(int) (indices->size () * (rand () / (RAND_MAX + 1.0)))]);
          indices_subset.insert ((*indices)[static_cast<int> (static_cast<double>(indices->size ()) * rnd ())]);
      }

      inline void 
      getModel (std::vector<int> &model) { model = model_; }

      inline void 
      getInliers (std::vector<int> &inliers) { inliers = inliers_; }

      inline void 
      getModelCoefficients (Eigen::VectorXf &model_coefficients) { model_coefficients = model_coefficients_; }

    protected:
      SampleConsensusModelPtr sac_model_;

      std::vector<int> model_;

      std::vector<int> inliers_;

      Eigen::VectorXf model_coefficients_;

      double probability_;

      int iterations_;
      
      double threshold_;
      
      int max_iterations_;

      boost::mt19937 rng_alg_;

      boost::shared_ptr<boost::uniform_01<boost::mt19937> > rng_;

      inline double
      rnd ()
      {
        return ((*rng_) ());
      }
   };
}

#endif  //#ifndef PCL_SAMPLE_CONSENSUS_H_