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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