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40 
41 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
42 #define PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
43 
44 #include <pcl/sample_consensus/rransac.h>
45 
46 //////////////////////////////////////////////////////////////////////////
47 template <typename PointT> bool
computeModel(int debug_verbosity_level)48 pcl::RandomizedRandomSampleConsensus<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::RandomizedRandomSampleConsensus::computeModel] No threshold set!\n");
54     return (false);
55   }
56 
57   iterations_ = 0;
58   std::size_t n_best_inliers_count = 0;
59   double k = std::numeric_limits<double>::max();
60 
61   Indices selection;
62   Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
63   std::set<index_t> indices_subset;
64 
65   const double log_probability  = std::log (1.0 - probability_);
66   const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
67 
68   std::size_t n_inliers_count;
69   unsigned skipped_count = 0;
70   // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
71   const unsigned max_skip = max_iterations_ * 10;
72 
73   // Number of samples to try randomly
74   const std::size_t fraction_nr_points = pcl_lrint (static_cast<double>(sac_model_->getIndices ()->size ()) * fraction_nr_pretest_ / 100.0);
75 
76   // Iterate
77   while (iterations_ < k)
78   {
79     // Get X samples which satisfy the model criteria
80     sac_model_->getSamples (iterations_, selection);
81 
82     if (selection.empty ())
83     {
84       PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] No samples could be selected!\n");
85       break;
86     }
87 
88     // Search for inliers in the point cloud for the current plane model M
89     if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
90     {
91       //iterations_++;
92       ++skipped_count;
93       if (skipped_count < max_skip)
94       {
95         PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, so continue with next iteration.\n");
96         continue;
97       }
98       else
99       {
100         PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function computeModelCoefficients failed, and RRANSAC reached the maximum number of trials.\n");
101         break;
102       }
103     }
104 
105     // RRANSAC addon: verify a random fraction of the data
106     // Get X random samples which satisfy the model criterion
107     this->getRandomSamples (sac_model_->getIndices (), fraction_nr_points, indices_subset);
108     if (!sac_model_->doSamplesVerifyModel (indices_subset, model_coefficients, threshold_))
109     {
110       ++iterations_;
111       PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] The function doSamplesVerifyModel failed, so continue with next iteration.\n");
112       continue;
113     }
114 
115     // Select the inliers that are within threshold_ from the model
116     n_inliers_count = sac_model_->countWithinDistance (model_coefficients, threshold_);
117 
118     // Better match ?
119     if (n_inliers_count > n_best_inliers_count)
120     {
121       n_best_inliers_count = n_inliers_count;
122 
123       // Save the current model/inlier/coefficients selection as being the best so far
124       model_              = selection;
125       model_coefficients_ = model_coefficients;
126 
127       // Compute the k parameter (k=std::log(z)/std::log(1-w^n))
128       const double w = static_cast<double> (n_inliers_count) * one_over_indices;
129       double p_no_outliers = 1.0 - std::pow (w, static_cast<double> (selection.size ()));
130       p_no_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_no_outliers);       // Avoid division by -Inf
131       p_no_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_no_outliers);   // Avoid division by 0.
132       k = log_probability / std::log (p_no_outliers);
133     }
134 
135     ++iterations_;
136 
137     if (debug_verbosity_level > 1)
138       PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Trial %d out of %d: %u inliers (best is: %u so far).\n", iterations_, static_cast<int> (std::ceil (k)), n_inliers_count, n_best_inliers_count);
139     if (iterations_ > max_iterations_)
140     {
141       if (debug_verbosity_level > 0)
142         PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC reached the maximum number of trials.\n");
143       break;
144     }
145   }
146 
147   if (debug_verbosity_level > 0)
148     PCL_DEBUG ("[pcl::RandomizedRandomSampleConsensus::computeModel] Model: %lu size, %u inliers.\n", model_.size (), n_best_inliers_count);
149 
150   if (model_.empty ())
151   {
152     PCL_ERROR ("[pcl::RandomizedRandomSampleConsensus::computeModel] RRANSAC found no model.\n");
153     inliers_.clear ();
154     return (false);
155   }
156 
157   // Get the set of inliers that correspond to the best model found so far
158   sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
159   return (true);
160 }
161 
162 #define PCL_INSTANTIATE_RandomizedRandomSampleConsensus(T) template class PCL_EXPORTS pcl::RandomizedRandomSampleConsensus<T>;
163 
164 #endif    // PCL_SAMPLE_CONSENSUS_IMPL_RRANSAC_H_
165 
166