Point Cloud Library (PCL)  1.11.0
multiscale_feature_persistence.hpp
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39 
40 #ifndef PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_
41 #define PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_
42 
43 #include <numeric>
44 #include <pcl/features/multiscale_feature_persistence.h>
45 
46 //////////////////////////////////////////////////////////////////////////////////////////////
47 template <typename PointSource, typename PointFeature>
49  alpha_ (0),
50  distance_metric_ (L1),
51  feature_estimator_ (),
52  features_at_scale_ (),
53  feature_representation_ ()
54 {
55  feature_representation_.reset (new DefaultPointRepresentation<PointFeature>);
56  // No input is needed, hack around the initCompute () check from PCLBase
57  input_.reset (new pcl::PointCloud<PointSource> ());
58 }
59 
60 
61 //////////////////////////////////////////////////////////////////////////////////////////////
62 template <typename PointSource, typename PointFeature> bool
64 {
66  {
67  PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] PCLBase::initCompute () failed - no input cloud was given.\n");
68  return false;
69  }
70  if (!feature_estimator_)
71  {
72  PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] No feature estimator was set\n");
73  return false;
74  }
75  if (scale_values_.empty ())
76  {
77  PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] No scale values were given\n");
78  return false;
79  }
80 
81  mean_feature_.resize (feature_representation_->getNumberOfDimensions ());
82 
83  return true;
84 }
85 
86 
87 //////////////////////////////////////////////////////////////////////////////////////////////
88 template <typename PointSource, typename PointFeature> void
90 {
91  features_at_scale_.clear ();
92  features_at_scale_.reserve (scale_values_.size ());
93  features_at_scale_vectorized_.clear ();
94  features_at_scale_vectorized_.reserve (scale_values_.size ());
95  for (std::size_t scale_i = 0; scale_i < scale_values_.size (); ++scale_i)
96  {
97  FeatureCloudPtr feature_cloud (new FeatureCloud ());
98  computeFeatureAtScale (scale_values_[scale_i], feature_cloud);
99  features_at_scale_[scale_i] = feature_cloud;
100 
101  // Vectorize each feature and insert it into the vectorized feature storage
102  std::vector<std::vector<float> > feature_cloud_vectorized;
103  feature_cloud_vectorized.reserve (feature_cloud->points.size ());
104 
105  for (const auto& feature: feature_cloud->points)
106  {
107  std::vector<float> feature_vectorized (feature_representation_->getNumberOfDimensions ());
108  feature_representation_->vectorize (feature, feature_vectorized);
109  feature_cloud_vectorized.emplace_back (std::move(feature_vectorized));
110  }
111  features_at_scale_vectorized_.emplace_back (std::move(feature_cloud_vectorized));
112  }
113 }
114 
115 
116 //////////////////////////////////////////////////////////////////////////////////////////////
117 template <typename PointSource, typename PointFeature> void
119  FeatureCloudPtr &features)
120 {
121  feature_estimator_->setRadiusSearch (scale);
122  feature_estimator_->compute (*features);
123 }
124 
125 
126 //////////////////////////////////////////////////////////////////////////////////////////////
127 template <typename PointSource, typename PointFeature> float
129  const std::vector<float> &b)
130 {
131  return (pcl::selectNorm<std::vector<float> > (a, b, a.size (), distance_metric_));
132 }
133 
134 
135 //////////////////////////////////////////////////////////////////////////////////////////////
136 template <typename PointSource, typename PointFeature> void
138 {
139  // Reset mean feature
140  std::fill_n(mean_feature_.begin (), mean_feature_.size (), 0.f);
141 
142  std::size_t normalization_factor = 0;
143  for (const auto& scale: features_at_scale_vectorized_)
144  {
145  normalization_factor += scale.size (); // not using accumulate for cache efficiency
146  for (const auto &feature : scale)
147  std::transform(mean_feature_.cbegin (), mean_feature_.cend (),
148  feature.cbegin (), mean_feature_.begin (), std::plus<>{});
149  }
150 
151  const float factor = std::min<float>(1, normalization_factor);
152  std::transform(mean_feature_.cbegin(),
153  mean_feature_.cend(),
154  mean_feature_.begin(),
155  [factor](const auto& mean) {
156  return mean / factor;
157  });
158 }
159 
160 
161 //////////////////////////////////////////////////////////////////////////////////////////////
162 template <typename PointSource, typename PointFeature> void
164 {
165  unique_features_indices_.clear ();
166  unique_features_table_.clear ();
167  unique_features_indices_.reserve (scale_values_.size ());
168  unique_features_table_.reserve (scale_values_.size ());
169 
170  for (std::size_t scale_i = 0; scale_i < features_at_scale_vectorized_.size (); ++scale_i)
171  {
172  // Calculate standard deviation within the scale
173  float standard_dev = 0.0;
174  std::vector<float> diff_vector (features_at_scale_vectorized_[scale_i].size ());
175  diff_vector.clear();
176 
177  for (const auto& feature: features_at_scale_vectorized_[scale_i])
178  {
179  float diff = distanceBetweenFeatures (feature, mean_feature_);
180  standard_dev += diff * diff;
181  diff_vector.emplace_back (diff);
182  }
183  standard_dev = std::sqrt (standard_dev / static_cast<float> (features_at_scale_vectorized_[scale_i].size ()));
184  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::extractUniqueFeatures] Standard deviation for scale %f is %f\n", scale_values_[scale_i], standard_dev);
185 
186  // Select only points outside (mean +/- alpha * standard_dev)
187  std::list<std::size_t> indices_per_scale;
188  std::vector<bool> indices_table_per_scale (features_at_scale_[scale_i]->points.size (), false);
189  for (std::size_t point_i = 0; point_i < features_at_scale_[scale_i]->points.size (); ++point_i)
190  {
191  if (diff_vector[point_i] > alpha_ * standard_dev)
192  {
193  indices_per_scale.emplace_back (point_i);
194  indices_table_per_scale[point_i] = true;
195  }
196  }
197  unique_features_indices_.emplace_back (std::move(indices_per_scale));
198  unique_features_table_.emplace_back (std::move(indices_table_per_scale));
199  }
200 }
201 
202 
203 //////////////////////////////////////////////////////////////////////////////////////////////
204 template <typename PointSource, typename PointFeature> void
206  shared_ptr<std::vector<int> > &output_indices)
207 {
208  if (!initCompute ())
209  return;
210 
211  // Compute the features for all scales with the given feature estimator
212  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Computing features ...\n");
213  computeFeaturesAtAllScales ();
214 
215  // Compute mean feature
216  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Calculating mean feature ...\n");
217  calculateMeanFeature ();
218 
219  // Get the 'unique' features at each scale
220  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Extracting unique features ...\n");
221  extractUniqueFeatures ();
222 
223  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Determining persistent features between scales ...\n");
224  // Determine persistent features between scales
225 
226 /*
227  // Method 1: a feature is considered persistent if it is 'unique' in at least 2 different scales
228  for (std::size_t scale_i = 0; scale_i < features_at_scale_vectorized_.size () - 1; ++scale_i)
229  for (std::list<std::size_t>::iterator feature_it = unique_features_indices_[scale_i].begin (); feature_it != unique_features_indices_[scale_i].end (); ++feature_it)
230  {
231  if (unique_features_table_[scale_i][*feature_it] == true)
232  {
233  output_features.points.push_back (features_at_scale[scale_i]->points[*feature_it]);
234  output_indices->push_back (feature_estimator_->getIndices ()->at (*feature_it));
235  }
236  }
237 */
238  // Method 2: a feature is considered persistent if it is 'unique' in all the scales
239  for (const auto& feature: unique_features_indices_.front ())
240  {
241  bool present_in_all = true;
242  for (std::size_t scale_i = 0; scale_i < features_at_scale_.size (); ++scale_i)
243  present_in_all = present_in_all && unique_features_table_[scale_i][feature];
244 
245  if (present_in_all)
246  {
247  output_features.points.emplace_back (features_at_scale_.front ()->points[feature]);
248  output_indices->emplace_back (feature_estimator_->getIndices ()->at (feature));
249  }
250  }
251 
252  // Consider that output cloud is unorganized
253  output_features.header = feature_estimator_->getInputCloud ()->header;
254  output_features.is_dense = feature_estimator_->getInputCloud ()->is_dense;
255  output_features.width = static_cast<std::uint32_t> (output_features.points.size ());
256  output_features.height = 1;
257 }
258 
259 
260 #define PCL_INSTANTIATE_MultiscaleFeaturePersistence(InT, Feature) template class PCL_EXPORTS pcl::MultiscaleFeaturePersistence<InT, Feature>;
261 
262 #endif /* PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_ */
DefaultPointRepresentation extends PointRepresentation to define default behavior for common point ty...
Generic class for extracting the persistent features from an input point cloud It can be given any Fe...
void determinePersistentFeatures(FeatureCloud &output_features, shared_ptr< std::vector< int > > &output_indices)
Central function that computes the persistent features.
void computeFeaturesAtAllScales()
Method that calls computeFeatureAtScale () for each scale parameter.
typename pcl::PointCloud< PointFeature >::Ptr FeatureCloudPtr
PCL base class.
Definition: pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:418
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:413
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:407
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:415
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
float selectNorm(FloatVectorT a, FloatVectorT b, int dim, NormType norm_type)
Method that calculates any norm type available, based on the norm_type variable.
Definition: norms.hpp:50
@ L1
Definition: norms.h:54
std::uint32_t uint32_t
Definition: types.h:58