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40 
41 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
42 #define PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
43 
44 #include <pcl/sample_consensus/rmsac.h>
45 
46 //////////////////////////////////////////////////////////////////////////
47 template <typename PointT> bool
computeModel(int debug_verbosity_level)48 pcl::RandomizedMEstimatorSampleConsensus<PointT>::computeModel (int debug_verbosity_level)
49 {
50   // Warn and exit if no threshold was set
51   if (threshold_ == std::numeric_limits<double>::max())
52   {
53     PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] No threshold set!\n");
54     return (false);
55   }
56 
57   iterations_ = 0;
58   double d_best_penalty = std::numeric_limits<double>::max();
59   double k = 1.0;
60 
61   Indices selection;
62   Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
63   std::vector<double> distances;
64   std::set<index_t> indices_subset;
65 
66   int n_inliers_count = 0;
67   unsigned skipped_count = 0;
68   // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
69   const unsigned max_skip = max_iterations_ * 10;
70 
71   // Number of samples to try randomly
72   std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
73 
74   // Iterate
75   while (iterations_ < k && skipped_count < max_skip)
76   {
77     // Get X samples which satisfy the model criteria
78     sac_model_->getSamples (iterations_, selection);
79 
80     if (selection.empty ()) break;
81 
82     // Search for inliers in the point cloud for the current plane model M
83     if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
84     {
85       //iterations_++;
86       ++ skipped_count;
87       continue;
88     }
89 
90     // RMSAC addon: verify a random fraction of the data
91     // Get X random samples which satisfy the model criterion
92     this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
93 
94     if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
95     {
96       // Unfortunately we cannot "continue" after the first iteration, because k might not be set, while iterations gets incremented
97       if (k != 1.0)
98       {
99         ++iterations_;
100         continue;
101       }
102     }
103 
104     double d_cur_penalty = 0;
105     // Iterate through the 3d points and calculate the distances from them to the model
106     sac_model_->getDistancesToModel (model_coefficients, distances);
107 
108     if (distances.empty () && k > 1.0)
109       continue;
110 
111     for (const double &distance : distances)
112       d_cur_penalty += std::min (distance, threshold_);
113 
114     // Better match ?
115     if (d_cur_penalty < d_best_penalty)
116     {
117       d_best_penalty = d_cur_penalty;
118 
119       // Save the current model/coefficients selection as being the best so far
120       model_              = selection;
121       model_coefficients_ = model_coefficients;
122 
123       n_inliers_count = 0;
124       // Need to compute the number of inliers for this model to adapt k
125       for (const double &distance : distances)
126         if (distance <= threshold_)
127           n_inliers_count++;
128 
129       // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
130       double w = static_cast<double> (n_inliers_count) / static_cast<double>(sac_model_->getIndices ()->size ());
131       double p_no_outliers = 1 - std::pow (w, static_cast<double> (selection.size ()));
132       p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers);       // Avoid division by -Inf
133       p_no_outliers = (std::min) (1 - std::numeric_limits<double>::epsilon (), p_no_outliers);   // Avoid division by 0.
134       k = std::log (1 - probability_) / std::log (p_no_outliers);
135     }
136 
137     ++iterations_;
138     if (debug_verbosity_level > 1)
139       PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Trial %d out of %d. Best penalty is %f.\n", iterations_, static_cast<int> (std::ceil (k)), d_best_penalty);
140     if (iterations_ > max_iterations_)
141     {
142       if (debug_verbosity_level > 0)
143         PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] MSAC reached the maximum number of trials.\n");
144       break;
145     }
146   }
147 
148   if (model_.empty ())
149   {
150     if (debug_verbosity_level > 0)
151       PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Unable to find a solution!\n");
152     return (false);
153   }
154 
155   // Iterate through the 3d points and calculate the distances from them to the model again
156   sac_model_->getDistancesToModel (model_coefficients_, distances);
157   Indices &indices = *sac_model_->getIndices ();
158   if (distances.size () != indices.size ())
159   {
160     PCL_ERROR ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Estimated distances (%lu) differs than the normal of indices (%lu).\n", distances.size (), indices.size ());
161     return (false);
162   }
163 
164   inliers_.resize (distances.size ());
165   // Get the inliers for the best model found
166   n_inliers_count = 0;
167   for (std::size_t i = 0; i < distances.size (); ++i)
168     if (distances[i] <= threshold_)
169       inliers_[n_inliers_count++] = indices[i];
170 
171   // Resize the inliers vector
172   inliers_.resize (n_inliers_count);
173 
174   if (debug_verbosity_level > 0)
175     PCL_DEBUG ("[pcl::RandomizedMEstimatorSampleConsensus::computeModel] Model: %lu size, %d inliers.\n", model_.size (), n_inliers_count);
176 
177   return (true);
178 }
179 
180 #define PCL_INSTANTIATE_RandomizedMEstimatorSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedMEstimatorSampleConsensus<T>;
181 
182 #endif    // PCL_SAMPLE_CONSENSUS_IMPL_RMSAC_H_
183 
184