Point Cloud Library (PCL)  1.11.1-dev
gfpfh.hpp
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40 
41 #ifndef PCL_FEATURES_IMPL_GFPFH_H_
42 #define PCL_FEATURES_IMPL_GFPFH_H_
43 
44 #include <pcl/features/gfpfh.h>
45 #include <pcl/octree/octree_search.h>
46 #include <Eigen/Core> // for Vector3f
47 
48 #include <algorithm>
49 #include <fstream>
50 
51 //////////////////////////////////////////////////////////////////////////////////////////////
52 template<typename PointInT, typename PointNT, typename PointOutT> void
54 {
56  {
57  output.width = output.height = 0;
58  output.clear ();
59  return;
60  }
61  // Copy the header
62  output.header = input_->header;
63 
64  // Resize the output dataset
65  // Important! We should only allocate precisely how many elements we will need, otherwise
66  // we risk at pre-allocating too much memory which could lead to bad_alloc
67  // (see http://dev.pointclouds.org/issues/657)
68  output.width = output.height = 1;
69  output.is_dense = input_->is_dense;
70  output.resize (1);
71 
72  // Perform the actual feature computation
73  computeFeature (output);
74 
76 }
77 
78 //////////////////////////////////////////////////////////////////////////////////////////////
79 template <typename PointInT, typename PointNT, typename PointOutT> void
81 {
82  pcl::octree::OctreePointCloudSearch<PointInT> octree (octree_leaf_size_);
83  octree.setInputCloud (input_);
84  octree.addPointsFromInputCloud ();
85 
86  typename pcl::PointCloud<PointInT>::VectorType occupied_cells;
87  octree.getOccupiedVoxelCenters (occupied_cells);
88 
89  // Determine the voxels crosses along the line segments
90  // formed by every pair of occupied cells.
91  std::vector< std::vector<int> > line_histograms;
92  for (std::size_t i = 0; i < occupied_cells.size (); ++i)
93  {
94  Eigen::Vector3f origin = occupied_cells[i].getVector3fMap ();
95 
96  for (std::size_t j = i+1; j < occupied_cells.size (); ++j)
97  {
98  typename pcl::PointCloud<PointInT>::VectorType intersected_cells;
99  Eigen::Vector3f end = occupied_cells[j].getVector3fMap ();
100  octree.getApproxIntersectedVoxelCentersBySegment (origin, end, intersected_cells, 0.5f);
101 
102  // Intersected cells are ordered from closest to furthest w.r.t. the origin.
103  std::vector<int> histogram;
104  for (std::size_t k = 0; k < intersected_cells.size (); ++k)
105  {
106  std::vector<int> indices;
107  octree.voxelSearch (intersected_cells[k], indices);
108  int label = emptyLabel ();
109  if (!indices.empty ())
110  {
111  label = getDominantLabel (indices);
112  }
113  histogram.push_back (label);
114  }
115 
116  line_histograms.push_back(histogram);
117  }
118  }
119 
120  std::vector< std::vector<int> > transition_histograms;
121  computeTransitionHistograms (line_histograms, transition_histograms);
122 
123  std::vector<float> distances;
124  computeDistancesToMean (transition_histograms, distances);
125 
126  std::vector<float> gfpfh_histogram;
127  computeDistanceHistogram (distances, gfpfh_histogram);
128 
129  output.clear ();
130  output.width = 1;
131  output.height = 1;
132  output.resize (1);
133  std::copy (gfpfh_histogram.cbegin (), gfpfh_histogram.cend (), output[0].histogram);
134 }
135 
136 //////////////////////////////////////////////////////////////////////////////////////////////
137 template <typename PointInT, typename PointNT, typename PointOutT> void
138 pcl::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeTransitionHistograms (const std::vector< std::vector<int> >& label_histograms,
139  std::vector< std::vector<int> >& transition_histograms)
140 {
141  transition_histograms.resize (label_histograms.size ());
142 
143  for (std::size_t i = 0; i < label_histograms.size (); ++i)
144  {
145  transition_histograms[i].resize ((getNumberOfClasses () + 2) * (getNumberOfClasses () + 1) / 2, 0);
146 
147  std::vector< std::vector <int> > transitions (getNumberOfClasses () + 1);
148  for (auto &transition : transitions)
149  {
150  transition.resize (getNumberOfClasses () + 1, 0);
151  }
152 
153  for (std::size_t k = 1; k < label_histograms[i].size (); ++k)
154  {
155  std::uint32_t first_class = label_histograms[i][k-1];
156  std::uint32_t second_class = label_histograms[i][k];
157  // Order has no influence.
158  if (second_class < first_class)
159  std::swap (first_class, second_class);
160 
161  transitions[first_class][second_class] += 1;
162  }
163 
164  // Build a one-dimension histogram out of it.
165  std::size_t flat_index = 0;
166  for (std::size_t m = 0; m < transitions.size (); ++m)
167  for (std::size_t n = m; n < transitions[m].size (); ++n)
168  {
169  transition_histograms[i][flat_index] = transitions[m][n];
170  ++flat_index;
171  }
172 
173  assert (flat_index == transition_histograms[i].size ());
174  }
175 }
176 
177 //////////////////////////////////////////////////////////////////////////////////////////////
178 template <typename PointInT, typename PointNT, typename PointOutT> void
179 pcl::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeDistancesToMean (const std::vector< std::vector<int> >& transition_histograms,
180  std::vector<float>& distances)
181 {
182  distances.resize (transition_histograms.size ());
183 
184  std::vector<float> mean_histogram;
185  computeMeanHistogram (transition_histograms, mean_histogram);
186 
187  for (std::size_t i = 0; i < transition_histograms.size (); ++i)
188  {
189  float d = computeHIKDistance (transition_histograms[i], mean_histogram);
190  distances[i] = d;
191  }
192 }
193 
194 //////////////////////////////////////////////////////////////////////////////////////////////
195 template <typename PointInT, typename PointNT, typename PointOutT> void
197  std::vector<float>& histogram)
198 {
199  std::vector<float>::const_iterator min_it, max_it;
200  std::tie (min_it, max_it) = std::minmax_element (distances.cbegin (), distances.cend ());
201  assert (min_it != distances.cend ());
202  assert (max_it != distances.cend ());
203 
204  const float min_value = *min_it;
205  const float max_value = *max_it;
206 
207  histogram.resize (descriptorSize (), 0);
208 
209  const float range = max_value - min_value;
210 
211  using binSizeT = decltype(descriptorSize());
212  const binSizeT max_bin = descriptorSize () - 1;
213  for (const float &distance : distances)
214  {
215  const auto raw_bin = descriptorSize () * (distance - min_value) / range;
216  const auto bin = std::min<binSizeT> (max_bin, static_cast<binSizeT> (std::floor (raw_bin)));
217  histogram[bin] += 1;
218  }
219 }
220 
221 //////////////////////////////////////////////////////////////////////////////////////////////
222 template <typename PointInT, typename PointNT, typename PointOutT> void
223 pcl::GFPFHEstimation<PointInT, PointNT, PointOutT>::computeMeanHistogram (const std::vector< std::vector<int> >& histograms,
224  std::vector<float>& mean_histogram)
225 {
226  assert (histograms.size () > 0);
227 
228  mean_histogram.resize (histograms[0].size (), 0);
229  for (const auto &histogram : histograms)
230  for (std::size_t j = 0; j < histogram.size (); ++j)
231  mean_histogram[j] += static_cast<float> (histogram[j]);
232 
233  for (float &i : mean_histogram)
234  i /= static_cast<float> (histograms.size ());
235 }
236 
237 //////////////////////////////////////////////////////////////////////////////////////////////
238 template <typename PointInT, typename PointNT, typename PointOutT> float
240  const std::vector<float>& mean_histogram)
241 {
242  assert (histogram.size () == mean_histogram.size ());
243 
244  float norm = 0.f;
245  for (std::size_t i = 0; i < histogram.size (); ++i)
246  norm += std::min (static_cast<float> (histogram[i]), mean_histogram[i]);
247 
248  norm /= static_cast<float> (histogram.size ());
249  return (norm);
250 }
251 
252 //////////////////////////////////////////////////////////////////////////////////////////////
253 template <typename PointInT, typename PointNT, typename PointOutT> std::uint32_t
255 {
256  std::vector<std::uint32_t> counts (getNumberOfClasses () + 1, 0);
257  for (const auto &nn_index : indices)
258  {
259  std::uint32_t label = (*labels_)[nn_index].label;
260  counts[label] += 1;
261  }
262 
263  const auto max_it = std::max_element (counts.cbegin (), counts.cend ());
264  if (max_it == counts.end ())
265  return (emptyLabel ());
266 
267  return std::distance(counts.cbegin (), max_it);
268 }
269 
270 #define PCL_INSTANTIATE_GFPFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::GFPFHEstimation<T,NT,OutT>;
271 
272 #endif // PCL_FEATURES_IMPL_GFPFH_H_
pcl::GFPFHEstimation::computeHIKDistance
float computeHIKDistance(const std::vector< int > &histogram, const std::vector< float > &mean_histogram)
Return the Intersection Kernel distance between two histograms.
Definition: gfpfh.hpp:239
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:394
pcl::geometry::distance
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
pcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty >::getOccupiedVoxelCenters
int getOccupiedVoxelCenters(AlignedPointTVector &voxel_center_list_arg) const
Get a PointT vector of centers of all occupied voxels.
Definition: octree_pointcloud.hpp:252
pcl::GFPFHEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Estimate the Point Feature Histograms (PFH) descriptors at a set of points given by <setInputCloud ()...
Definition: gfpfh.hpp:80
pcl::octree::OctreePointCloudSearch::voxelSearch
bool voxelSearch(const PointT &point, std::vector< int > &point_idx_data)
Search for neighbors within a voxel at given point.
Definition: octree_search.hpp:50
pcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty >::addPointsFromInputCloud
void addPointsFromInputCloud()
Add points from input point cloud to octree.
Definition: octree_pointcloud.hpp:78
pcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty >::getApproxIntersectedVoxelCentersBySegment
int getApproxIntersectedVoxelCentersBySegment(const Eigen::Vector3f &origin, const Eigen::Vector3f &end, AlignedPointTVector &voxel_center_list, float precision=0.2)
Get a PointT vector of centers of voxels intersected by a line segment.
Definition: octree_pointcloud.hpp:269
pcl::PointCloud< PointOutT >
pcl::PointCloud::VectorType
std::vector< PointT, Eigen::aligned_allocator< PointT > > VectorType
Definition: point_cloud.h:405
pcl::GFPFHEstimation::getDominantLabel
std::uint32_t getDominantLabel(const std::vector< int > &indices)
Return the dominant label of a set of points.
Definition: gfpfh.hpp:254
pcl::octree::OctreePointCloud< PointT, OctreeContainerPointIndices, OctreeContainerEmpty >::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud_arg, const IndicesConstPtr &indices_arg=IndicesConstPtr())
Provide a pointer to the input data set.
Definition: octree_pointcloud.h:117
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:392
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::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:456
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::GFPFHEstimation::computeDistancesToMean
void computeDistancesToMean(const std::vector< std::vector< int > > &transition_histograms, std::vector< float > &distances)
Compute the distance of each transition histogram to the mean.
Definition: gfpfh.hpp:179
pcl::GFPFHEstimation::compute
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition: gfpfh.hpp:53
pcl::GFPFHEstimation::computeTransitionHistograms
void computeTransitionHistograms(const std::vector< std::vector< int > > &label_histograms, std::vector< std::vector< int > > &transition_histograms)
Compute the fixed-length histograms of transitions.
Definition: gfpfh.hpp:138
pcl::PointCloud::clear
void clear()
Removes all points in a cloud and sets the width and height to 0.
Definition: point_cloud.h:864
pcl::GFPFHEstimation::computeMeanHistogram
void computeMeanHistogram(const std::vector< std::vector< int > > &histograms, std::vector< float > &mean_histogram)
Compute the mean histogram of the given set of histograms.
Definition: gfpfh.hpp:223
pcl::octree::OctreePointCloudSearch
Octree pointcloud search class
Definition: octree_search.h:57
pcl::GFPFHEstimation::computeDistanceHistogram
void computeDistanceHistogram(const std::vector< float > &distances, std::vector< float > &histogram)
Compute the binned histogram of distance values.
Definition: gfpfh.hpp:196
pcl::Feature
Feature represents the base feature class.
Definition: feature.h:106