Point Cloud Library (PCL)  1.13.1-dev
cpc_segmentation.hpp
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37 
38 #ifndef PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_
39 #define PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_
40 
41 #include <pcl/sample_consensus/sac_model_plane.h> // for SampleConsensusModelPlane
42 #include <pcl/segmentation/cpc_segmentation.h>
43 
44 template <typename PointT>
46  max_cuts_ (20),
47  min_segment_size_for_cutting_ (400),
48  min_cut_score_ (0.16),
49  use_local_constrains_ (true),
50  use_directed_weights_ (true),
51  ransac_itrs_ (10000)
52 {
53 }
54 
55 template <typename PointT>
57 
58 template <typename PointT> void
60 {
61  if (supervoxels_set_)
62  {
63  // Calculate for every Edge if the connection is convex or invalid
64  // This effectively performs the segmentation.
65  calculateConvexConnections (sv_adjacency_list_);
66 
67  // Correct edge relations using extended convexity definition if k>0
68  applyKconvexity (k_factor_);
69 
70  // Determine whether to use cutting planes
71  doGrouping ();
72 
73  grouping_data_valid_ = true;
74 
75  applyCuttingPlane (max_cuts_);
76 
77  // merge small segments
78  mergeSmallSegments ();
79  }
80  else
81  PCL_WARN ("[pcl::CPCSegmentation::segment] WARNING: Call function setInputSupervoxels first. Nothing has been done. \n");
82 }
83 
84 template <typename PointT> void
85 pcl::CPCSegmentation<PointT>::applyCuttingPlane (std::uint32_t depth_levels_left)
86 {
87  using SegLabel2ClusterMap = std::map<std::uint32_t, pcl::PointCloud<WeightSACPointType>::Ptr>;
88 
89  pcl::console::print_info ("Cutting at level %d (maximum %d)\n", max_cuts_ - depth_levels_left + 1, max_cuts_);
90  // stop if we reached the 0 level
91  if (depth_levels_left <= 0)
92  return;
93 
94  pcl::IndicesPtr support_indices (new pcl::Indices);
95  SegLabel2ClusterMap seg_to_edge_points_map;
96  std::map<std::uint32_t, std::vector<EdgeID> > seg_to_edgeIDs_map;
97  EdgeIterator edge_itr, edge_itr_end, next_edge;
98  boost::tie (edge_itr, edge_itr_end) = boost::edges (sv_adjacency_list_);
99  for (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)
100  {
101  next_edge++; // next_edge iterator is necessary, because removing an edge invalidates the iterator to the current edge
102  std::uint32_t source_sv_label = sv_adjacency_list_[boost::source (*edge_itr, sv_adjacency_list_)];
103  std::uint32_t target_sv_label = sv_adjacency_list_[boost::target (*edge_itr, sv_adjacency_list_)];
104 
105  std::uint32_t source_segment_label = sv_label_to_seg_label_map_[source_sv_label];
106  std::uint32_t target_segment_label = sv_label_to_seg_label_map_[target_sv_label];
107 
108  // do not process edges which already split two segments
109  if (source_segment_label != target_segment_label)
110  continue;
111 
112  // if edge has been used for cutting already do not use it again
113  if (sv_adjacency_list_[*edge_itr].used_for_cutting)
114  continue;
115  // get centroids of vertices
116  const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
117  const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
118 
119  // stores the information about the edge cloud (used for the weighted ransac)
120  // we use the normal to express the direction of the connection
121  // we use the intensity to express the normal differences between supervoxel patches. <=0: Convex, >0: Concave
122  WeightSACPointType edge_centroid;
123  edge_centroid.getVector3fMap () = (source_centroid.getVector3fMap () + target_centroid.getVector3fMap ()) / 2;
124 
125  // we use the normal to express the direction of the connection!
126  edge_centroid.getNormalVector3fMap () = (target_centroid.getVector3fMap () - source_centroid.getVector3fMap ()).normalized ();
127 
128  // we use the intensity to express the normal differences between supervoxel patches. <=0: Convex, >0: Concave
129  edge_centroid.intensity = sv_adjacency_list_[*edge_itr].is_convex ? -sv_adjacency_list_[*edge_itr].normal_difference : sv_adjacency_list_[*edge_itr].normal_difference;
130  if (seg_to_edge_points_map.find (source_segment_label) == seg_to_edge_points_map.end ())
131  {
132  seg_to_edge_points_map[source_segment_label] = pcl::PointCloud<WeightSACPointType>::Ptr (new pcl::PointCloud<WeightSACPointType> ());
133  }
134  seg_to_edge_points_map[source_segment_label]->push_back (edge_centroid);
135  seg_to_edgeIDs_map[source_segment_label].push_back (*edge_itr);
136  }
137  bool cut_found = false;
138  // do the following processing for each segment separately
139  for (const auto &seg_to_edge_points : seg_to_edge_points_map)
140  {
141  // if too small do not process
142  if (seg_to_edge_points.second->size () < min_segment_size_for_cutting_)
143  {
144  continue;
145  }
146 
147  std::vector<double> weights;
148  weights.resize (seg_to_edge_points.second->size ());
149  for (std::size_t cp = 0; cp < seg_to_edge_points.second->size (); ++cp)
150  {
151  float& cur_weight = (*seg_to_edge_points.second)[cp].intensity;
152  cur_weight = cur_weight < concavity_tolerance_threshold_ ? 0 : 1;
153  weights[cp] = cur_weight;
154  }
155 
156  pcl::PointCloud<WeightSACPointType>::Ptr edge_cloud_cluster = seg_to_edge_points.second;
158 
159  WeightedRandomSampleConsensus weight_sac (model_p, seed_resolution_, true);
160 
161  weight_sac.setWeights (weights, use_directed_weights_);
162  weight_sac.setMaxIterations (ransac_itrs_);
163 
164  // if not enough inliers are found
165  if (!weight_sac.computeModel ())
166  {
167  continue;
168  }
169 
170  Eigen::VectorXf model_coefficients;
171  weight_sac.getModelCoefficients (model_coefficients);
172 
173  model_coefficients[3] += std::numeric_limits<float>::epsilon ();
174 
175  weight_sac.getInliers (*support_indices);
176 
177  // the support_indices which are actually cut (if not locally constrain: cut_support_indices = support_indices
178  pcl::Indices cut_support_indices;
179 
180  if (use_local_constrains_)
181  {
182  Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
183  // Cut the connections.
184  // We only iterate through the points which are within the support (when we are local, otherwise all points in the segment).
185  // We also just actually cut when the edge goes through the plane. This is why we check the planedistance
186  std::vector<pcl::PointIndices> cluster_indices;
189  tree->setInputCloud (edge_cloud_cluster);
190  euclidean_clusterer.setClusterTolerance (seed_resolution_);
191  euclidean_clusterer.setMinClusterSize (1);
192  euclidean_clusterer.setMaxClusterSize (25000);
193  euclidean_clusterer.setSearchMethod (tree);
194  euclidean_clusterer.setInputCloud (edge_cloud_cluster);
195  euclidean_clusterer.setIndices (support_indices);
196  euclidean_clusterer.extract (cluster_indices);
197 // sv_adjacency_list_[seg_to_edgeID_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
198 
199  for (const auto &cluster_index : cluster_indices)
200  {
201  // get centroids of vertices
202  float cluster_score = 0;
203 // std::cout << "Cluster has " << cluster_indices[cc].indices.size () << " points" << std::endl;
204  for (const auto &current_index : cluster_index.indices)
205  {
206  double index_score = weights[current_index];
207  if (use_directed_weights_)
208  index_score *= 1.414 * (std::abs (plane_normal.dot (edge_cloud_cluster->at (current_index).getNormalVector3fMap ())));
209  cluster_score += index_score;
210  }
211  // check if the score is below the threshold. If that is the case this segment should not be split
212  cluster_score /= cluster_index.indices.size ();
213 // std::cout << "Cluster score: " << cluster_score << std::endl;
214  if (cluster_score >= min_cut_score_)
215  {
216  cut_support_indices.insert (cut_support_indices.end (), cluster_index.indices.begin (), cluster_index.indices.end ());
217  }
218  }
219  if (cut_support_indices.empty ())
220  {
221 // std::cout << "Could not find planes which exceed required minimum score (threshold " << min_cut_score_ << "), not cutting" << std::endl;
222  continue;
223  }
224  }
225  else
226  {
227  double current_score = weight_sac.getBestScore ();
228  cut_support_indices = *support_indices;
229  // check if the score is below the threshold. If that is the case this segment should not be split
230  if (current_score < min_cut_score_)
231  {
232 // std::cout << "Score too low, no cutting" << std::endl;
233  continue;
234  }
235  }
236 
237  int number_connections_cut = 0;
238  for (const auto &point_index : cut_support_indices)
239  {
240  if (use_clean_cutting_)
241  {
242  // skip edges where both centroids are on one side of the cutting plane
243  std::uint32_t source_sv_label = sv_adjacency_list_[boost::source (seg_to_edgeIDs_map[seg_to_edge_points.first][point_index], sv_adjacency_list_)];
244  std::uint32_t target_sv_label = sv_adjacency_list_[boost::target (seg_to_edgeIDs_map[seg_to_edge_points.first][point_index], sv_adjacency_list_)];
245  // get centroids of vertices
246  const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
247  const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
248  // this makes a clean cut
249  if (pcl::pointToPlaneDistanceSigned (source_centroid, model_coefficients) * pcl::pointToPlaneDistanceSigned (target_centroid, model_coefficients) > 0)
250  {
251  continue;
252  }
253  }
254  sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
255  if (sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid)
256  {
257  ++number_connections_cut;
258  sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid = false;
259  }
260  }
261 // std::cout << "We cut " << number_connections_cut << " connections" << std::endl;
262  if (number_connections_cut > 0)
263  cut_found = true;
264  }
265 
266  // if not cut has been performed we can stop the recursion
267  if (cut_found)
268  {
269  doGrouping ();
270  --depth_levels_left;
271  applyCuttingPlane (depth_levels_left);
272  }
273  else
274  pcl::console::print_info ("Could not find any more cuts, stopping recursion\n");
275 }
276 
277 /******************************************* Directional weighted RANSAC definitions ******************************************************************/
278 
279 
280 template <typename PointT> bool
282 {
283  // Warn and exit if no threshold was set
284  if (threshold_ == std::numeric_limits<double>::max ())
285  {
286  PCL_ERROR ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] No threshold set!\n");
287  return (false);
288  }
289 
290  iterations_ = 0;
291  best_score_ = -std::numeric_limits<double>::max ();
292 
293  pcl::Indices selection;
294  Eigen::VectorXf model_coefficients;
295 
296  unsigned skipped_count = 0;
297  // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
298  const unsigned max_skip = max_iterations_ * 10;
299 
300  // Iterate
301  while (iterations_ < max_iterations_ && skipped_count < max_skip)
302  {
303  // Get X samples which satisfy the model criteria and which have a weight > 0
304  sac_model_->setIndices (model_pt_indices_);
305  sac_model_->getSamples (iterations_, selection);
306 
307  if (selection.empty ())
308  {
309  PCL_ERROR ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] No samples could be selected!\n");
310  break;
311  }
312 
313  // Search for inliers in the point cloud for the current plane model M
314  if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
315  {
316  //++iterations_;
317  ++skipped_count;
318  continue;
319  }
320  // weight distances to get the score (only using connected inliers)
321  sac_model_->setIndices (full_cloud_pt_indices_);
322 
323  pcl::IndicesPtr current_inliers (new pcl::Indices);
324  sac_model_->selectWithinDistance (model_coefficients, threshold_, *current_inliers);
325  double current_score = 0;
326  Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
327  for (const auto &current_index : *current_inliers)
328  {
329  double index_score = weights_[current_index];
330  if (use_directed_weights_)
331  // the sqrt(2) factor was used in the paper and was meant for making the scores better comparable between directed and undirected weights
332  index_score *= 1.414 * (std::abs (plane_normal.dot (point_cloud_ptr_->at (current_index).getNormalVector3fMap ())));
333 
334  current_score += index_score;
335  }
336  // normalize by the total number of inliers
337  current_score /= current_inliers->size ();
338 
339  // Better match ?
340  if (current_score > best_score_)
341  {
342  best_score_ = current_score;
343  // Save the current model/inlier/coefficients selection as being the best so far
344  model_ = selection;
345  model_coefficients_ = model_coefficients;
346  }
347 
348  ++iterations_;
349  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] Trial %d (max %d): score is %f (best is: %f so far).\n", iterations_, max_iterations_, current_score, best_score_);
350  if (iterations_ > max_iterations_)
351  {
352  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n");
353  break;
354  }
355  }
356 // std::cout << "Took us " << iterations_ - 1 << " iterations" << std::endl;
357  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] Model: %lu size, %f score.\n", model_.size (), best_score_);
358 
359  if (model_.empty ())
360  {
361  inliers_.clear ();
362  return (false);
363  }
364 
365  // Get the set of inliers that correspond to the best model found so far
366  sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
367  return (true);
368 }
369 
370 #endif // PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_
A segmentation algorithm partitioning a supervoxel graph.
void segment()
Merge supervoxels using cuts through local convexities.
~CPCSegmentation() override
EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sen...
void extract(std::vector< PointIndices > &clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
void setClusterTolerance(double tolerance)
Set the spatial cluster tolerance as a measure in the L2 Euclidean space.
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
void setMaxClusterSize(pcl::uindex_t max_cluster_size)
Set the maximum number of points that a cluster needs to contain in order to be considered valid.
void setMinClusterSize(pcl::uindex_t min_cluster_size)
Set the minimum number of points that a cluster needs to contain in order to be considered valid.
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:65
virtual void setIndices(const IndicesPtr &indices)
Provide a pointer to the vector of indices that represents the input data.
Definition: pcl_base.hpp:72
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
const PointT & at(int column, int row) const
Obtain the point given by the (column, row) coordinates.
Definition: point_cloud.h:262
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
SampleConsensusModelPlane defines a model for 3D plane segmentation.
shared_ptr< SampleConsensusModelPlane< PointT > > Ptr
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:62
shared_ptr< KdTree< PointT, Tree > > Ptr
Definition: kdtree.h:75
double pointToPlaneDistanceSigned(const Point &p, double a, double b, double c, double d)
Get the distance from a point to a plane (signed) defined by ax+by+cz+d=0.
PCL_EXPORTS void print_info(const char *format,...)
Print an info message on stream with colors.
int cp(int from, int to)
Returns field copy operation code.
Definition: repacks.hpp:56
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
shared_ptr< Indices > IndicesPtr
Definition: pcl_base.h:58
A point structure representing Euclidean xyz coordinates, and the RGBA color.