Point Cloud Library (PCL)  1.11.1-dev
multiscale_feature_persistence.hpp
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2011, Alexandru-Eugen Ichim
6  * Copyright (c) 2012-, Open Perception, Inc.
7  *
8  * All rights reserved.
9  *
10  * Redistribution and use in source and binary forms, with or without
11  * modification, are permitted provided that the following conditions
12  * are met:
13  *
14  * * Redistributions of source code must retain the above copyright
15  * notice, this list of conditions and the following disclaimer.
16  * * Redistributions in binary form must reproduce the above
17  * copyright notice, this list of conditions and the following
18  * disclaimer in the documentation and/or other materials provided
19  * with the distribution.
20  * * Neither the name of the copyright holder(s) nor the names of its
21  * contributors may be used to endorse or promote products derived
22  * from this software without specific prior written permission.
23  *
24  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
25  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
26  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
27  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
28  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
29  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
30  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
31  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
32  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
33  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
34  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
35  * POSSIBILITY OF SUCH DAMAGE.
36  *
37  * $Id$
38  */
39 
40 #ifndef PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_
41 #define PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_
42 
43 #include <pcl/features/multiscale_feature_persistence.h>
44 
45 //////////////////////////////////////////////////////////////////////////////////////////////
46 template <typename PointSource, typename PointFeature>
48  alpha_ (0),
49  distance_metric_ (L1),
50  feature_estimator_ (),
51  features_at_scale_ (),
52  feature_representation_ ()
53 {
54  feature_representation_.reset (new DefaultPointRepresentation<PointFeature>);
55  // No input is needed, hack around the initCompute () check from PCLBase
56  input_.reset (new pcl::PointCloud<PointSource> ());
57 }
58 
59 
60 //////////////////////////////////////////////////////////////////////////////////////////////
61 template <typename PointSource, typename PointFeature> bool
63 {
65  {
66  PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] PCLBase::initCompute () failed - no input cloud was given.\n");
67  return false;
68  }
69  if (!feature_estimator_)
70  {
71  PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] No feature estimator was set\n");
72  return false;
73  }
74  if (scale_values_.empty ())
75  {
76  PCL_ERROR ("[pcl::MultiscaleFeaturePersistence::initCompute] No scale values were given\n");
77  return false;
78  }
79 
80  mean_feature_.resize (feature_representation_->getNumberOfDimensions ());
81 
82  return true;
83 }
84 
85 
86 //////////////////////////////////////////////////////////////////////////////////////////////
87 template <typename PointSource, typename PointFeature> void
89 {
90  features_at_scale_.clear ();
91  features_at_scale_.reserve (scale_values_.size ());
92  features_at_scale_vectorized_.clear ();
93  features_at_scale_vectorized_.reserve (scale_values_.size ());
94  for (std::size_t scale_i = 0; scale_i < scale_values_.size (); ++scale_i)
95  {
96  FeatureCloudPtr feature_cloud (new FeatureCloud ());
97  computeFeatureAtScale (scale_values_[scale_i], feature_cloud);
98  features_at_scale_[scale_i] = feature_cloud;
99 
100  // Vectorize each feature and insert it into the vectorized feature storage
101  std::vector<std::vector<float> > feature_cloud_vectorized;
102  feature_cloud_vectorized.reserve (feature_cloud->size ());
103 
104  for (const auto& feature: feature_cloud->points)
105  {
106  std::vector<float> feature_vectorized (feature_representation_->getNumberOfDimensions ());
107  feature_representation_->vectorize (feature, feature_vectorized);
108  feature_cloud_vectorized.emplace_back (std::move(feature_vectorized));
109  }
110  features_at_scale_vectorized_.emplace_back (std::move(feature_cloud_vectorized));
111  }
112 }
113 
114 
115 //////////////////////////////////////////////////////////////////////////////////////////////
116 template <typename PointSource, typename PointFeature> void
118  FeatureCloudPtr &features)
119 {
120  feature_estimator_->setRadiusSearch (scale);
121  feature_estimator_->compute (*features);
122 }
123 
124 
125 //////////////////////////////////////////////////////////////////////////////////////////////
126 template <typename PointSource, typename PointFeature> float
128  const std::vector<float> &b)
129 {
130  return (pcl::selectNorm<std::vector<float> > (a, b, a.size (), distance_metric_));
131 }
132 
133 
134 //////////////////////////////////////////////////////////////////////////////////////////////
135 template <typename PointSource, typename PointFeature> void
137 {
138  // Reset mean feature
139  std::fill_n(mean_feature_.begin (), mean_feature_.size (), 0.f);
140 
141  std::size_t normalization_factor = 0;
142  for (const auto& scale: features_at_scale_vectorized_)
143  {
144  normalization_factor += scale.size (); // not using accumulate for cache efficiency
145  for (const auto &feature : scale)
146  std::transform(mean_feature_.cbegin (), mean_feature_.cend (),
147  feature.cbegin (), mean_feature_.begin (), std::plus<>{});
148  }
149 
150  const float factor = std::min<float>(1, normalization_factor);
151  std::transform(mean_feature_.cbegin(),
152  mean_feature_.cend(),
153  mean_feature_.begin(),
154  [factor](const auto& mean) {
155  return mean / factor;
156  });
157 }
158 
159 
160 //////////////////////////////////////////////////////////////////////////////////////////////
161 template <typename PointSource, typename PointFeature> void
163 {
164  unique_features_indices_.clear ();
165  unique_features_table_.clear ();
166  unique_features_indices_.reserve (scale_values_.size ());
167  unique_features_table_.reserve (scale_values_.size ());
168 
169  for (std::size_t scale_i = 0; scale_i < features_at_scale_vectorized_.size (); ++scale_i)
170  {
171  // Calculate standard deviation within the scale
172  float standard_dev = 0.0;
173  std::vector<float> diff_vector (features_at_scale_vectorized_[scale_i].size ());
174  diff_vector.clear();
175 
176  for (const auto& feature: features_at_scale_vectorized_[scale_i])
177  {
178  float diff = distanceBetweenFeatures (feature, mean_feature_);
179  standard_dev += diff * diff;
180  diff_vector.emplace_back (diff);
181  }
182  standard_dev = std::sqrt (standard_dev / static_cast<float> (features_at_scale_vectorized_[scale_i].size ()));
183  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::extractUniqueFeatures] Standard deviation for scale %f is %f\n", scale_values_[scale_i], standard_dev);
184 
185  // Select only points outside (mean +/- alpha * standard_dev)
186  std::list<std::size_t> indices_per_scale;
187  std::vector<bool> indices_table_per_scale (features_at_scale_[scale_i]->size (), false);
188  for (std::size_t point_i = 0; point_i < features_at_scale_[scale_i]->size (); ++point_i)
189  {
190  if (diff_vector[point_i] > alpha_ * standard_dev)
191  {
192  indices_per_scale.emplace_back (point_i);
193  indices_table_per_scale[point_i] = true;
194  }
195  }
196  unique_features_indices_.emplace_back (std::move(indices_per_scale));
197  unique_features_table_.emplace_back (std::move(indices_table_per_scale));
198  }
199 }
200 
201 
202 //////////////////////////////////////////////////////////////////////////////////////////////
203 template <typename PointSource, typename PointFeature> void
205  shared_ptr<std::vector<int> > &output_indices)
206 {
207  if (!initCompute ())
208  return;
209 
210  // Compute the features for all scales with the given feature estimator
211  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Computing features ...\n");
212  computeFeaturesAtAllScales ();
213 
214  // Compute mean feature
215  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Calculating mean feature ...\n");
216  calculateMeanFeature ();
217 
218  // Get the 'unique' features at each scale
219  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Extracting unique features ...\n");
220  extractUniqueFeatures ();
221 
222  PCL_DEBUG ("[pcl::MultiscaleFeaturePersistence::determinePersistentFeatures] Determining persistent features between scales ...\n");
223  // Determine persistent features between scales
224 
225 /*
226  // Method 1: a feature is considered persistent if it is 'unique' in at least 2 different scales
227  for (std::size_t scale_i = 0; scale_i < features_at_scale_vectorized_.size () - 1; ++scale_i)
228  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)
229  {
230  if (unique_features_table_[scale_i][*feature_it] == true)
231  {
232  output_features.push_back ((*features_at_scale_[scale_i])[*feature_it]);
233  output_indices->push_back (feature_estimator_->getIndices ()->at (*feature_it));
234  }
235  }
236 */
237  // Method 2: a feature is considered persistent if it is 'unique' in all the scales
238  for (const auto& feature: unique_features_indices_.front ())
239  {
240  bool present_in_all = true;
241  for (std::size_t scale_i = 0; scale_i < features_at_scale_.size (); ++scale_i)
242  present_in_all = present_in_all && unique_features_table_[scale_i][feature];
243 
244  if (present_in_all)
245  {
246  output_features.emplace_back ((*features_at_scale_.front ())[feature]);
247  output_indices->emplace_back (feature_estimator_->getIndices ()->at (feature));
248  }
249  }
250 
251  // Consider that output cloud is unorganized
252  output_features.header = feature_estimator_->getInputCloud ()->header;
253  output_features.is_dense = feature_estimator_->getInputCloud ()->is_dense;
254  output_features.width = output_features.size ();
255  output_features.height = 1;
256 }
257 
258 
259 #define PCL_INSTANTIATE_MultiscaleFeaturePersistence(InT, Feature) template class PCL_EXPORTS pcl::MultiscaleFeaturePersistence<InT, Feature>;
260 
261 #endif /* PCL_FEATURES_IMPL_MULTISCALE_FEATURE_PERSISTENCE_H_ */
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:394
pcl::PCLBase< PointSource >::input_
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
pcl::MultiscaleFeaturePersistence::computeFeaturesAtAllScales
void computeFeaturesAtAllScales()
Method that calls computeFeatureAtScale () for each scale parameter.
Definition: multiscale_feature_persistence.hpp:88
pcl::DefaultPointRepresentation
DefaultPointRepresentation extends PointRepresentation to define default behavior for common point ty...
Definition: point_representation.h:179
pcl::L1
@ L1
Definition: norms.h:54
pcl::PCLBase
PCL base class.
Definition: pcl_base.h:69
pcl::PointCloud< PointSource >
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:392
pcl::selectNorm
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
pcl::PointCloud::is_dense
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:397
pcl::MultiscaleFeaturePersistence::FeatureCloudPtr
typename pcl::PointCloud< PointFeature >::Ptr FeatureCloudPtr
Definition: multiscale_feature_persistence.h:69
pcl::MultiscaleFeaturePersistence::determinePersistentFeatures
void determinePersistentFeatures(FeatureCloud &output_features, shared_ptr< std::vector< int > > &output_indices)
Central function that computes the persistent features.
Definition: multiscale_feature_persistence.hpp:204
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:386
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:437
pcl::MultiscaleFeaturePersistence
Generic class for extracting the persistent features from an input point cloud It can be given any Fe...
Definition: multiscale_feature_persistence.h:63
pcl::PointCloud::emplace_back
reference emplace_back(Args &&...args)
Emplace a new point in the cloud, at the end of the container.
Definition: point_cloud.h:665
pcl::MultiscaleFeaturePersistence::MultiscaleFeaturePersistence
MultiscaleFeaturePersistence()
Empty constructor.
Definition: multiscale_feature_persistence.hpp:47