Point Cloud Library (PCL)  1.15.1-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 
47 template <typename PointT>
49 
50 template <typename PointT> void
52 {
53  if (supervoxels_set_)
54  {
55  // Calculate for every Edge if the connection is convex or invalid
56  // This effectively performs the segmentation.
57  calculateConvexConnections (sv_adjacency_list_);
58 
59  // Correct edge relations using extended convexity definition if k>0
60  applyKconvexity (k_factor_);
61 
62  // Determine whether to use cutting planes
63  doGrouping ();
64 
65  grouping_data_valid_ = true;
66 
67  applyCuttingPlane (max_cuts_);
68 
69  // merge small segments
70  mergeSmallSegments ();
71  }
72  else
73  PCL_WARN ("[pcl::CPCSegmentation::segment] WARNING: Call function setInputSupervoxels first. Nothing has been done. \n");
74 }
75 
76 template <typename PointT> void
77 pcl::CPCSegmentation<PointT>::applyCuttingPlane (std::uint32_t depth_levels_left)
78 {
79  using SegLabel2ClusterMap = std::map<std::uint32_t, pcl::PointCloud<WeightSACPointType>::Ptr>;
80 
81  pcl::console::print_info ("Cutting at level %d (maximum %d)\n", max_cuts_ - depth_levels_left + 1, max_cuts_);
82  // stop if we reached the 0 level
83  if (depth_levels_left <= 0)
84  return;
85 
86  pcl::IndicesPtr support_indices (new pcl::Indices);
87  SegLabel2ClusterMap seg_to_edge_points_map;
88  std::map<std::uint32_t, std::vector<EdgeID> > seg_to_edgeIDs_map;
89  EdgeIterator edge_itr, edge_itr_end, next_edge;
90  boost::tie (edge_itr, edge_itr_end) = boost::edges (sv_adjacency_list_);
91  for (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)
92  {
93  next_edge++; // next_edge iterator is necessary, because removing an edge invalidates the iterator to the current edge
94  std::uint32_t source_sv_label = sv_adjacency_list_[boost::source (*edge_itr, sv_adjacency_list_)];
95  std::uint32_t target_sv_label = sv_adjacency_list_[boost::target (*edge_itr, sv_adjacency_list_)];
96 
97  std::uint32_t source_segment_label = sv_label_to_seg_label_map_[source_sv_label];
98  std::uint32_t target_segment_label = sv_label_to_seg_label_map_[target_sv_label];
99 
100  // do not process edges which already split two segments
101  if (source_segment_label != target_segment_label)
102  continue;
103 
104  // if edge has been used for cutting already do not use it again
105  if (sv_adjacency_list_[*edge_itr].used_for_cutting)
106  continue;
107  // get centroids of vertices
108  const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
109  const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
110 
111  // stores the information about the edge cloud (used for the weighted ransac)
112  // we use the normal to express the direction of the connection
113  // we use the intensity to express the normal differences between supervoxel patches. <=0: Convex, >0: Concave
114  WeightSACPointType edge_centroid;
115  edge_centroid.getVector3fMap () = (source_centroid.getVector3fMap () + target_centroid.getVector3fMap ()) / 2;
116 
117  // we use the normal to express the direction of the connection!
118  edge_centroid.getNormalVector3fMap () = (target_centroid.getVector3fMap () - source_centroid.getVector3fMap ()).normalized ();
119 
120  // we use the intensity to express the normal differences between supervoxel patches. <=0: Convex, >0: Concave
121  edge_centroid.intensity = sv_adjacency_list_[*edge_itr].is_convex ? -sv_adjacency_list_[*edge_itr].normal_difference : sv_adjacency_list_[*edge_itr].normal_difference;
122  if (seg_to_edge_points_map.find (source_segment_label) == seg_to_edge_points_map.end ())
123  {
124  seg_to_edge_points_map[source_segment_label] = pcl::PointCloud<WeightSACPointType>::Ptr (new pcl::PointCloud<WeightSACPointType> ());
125  }
126  seg_to_edge_points_map[source_segment_label]->push_back (edge_centroid);
127  seg_to_edgeIDs_map[source_segment_label].push_back (*edge_itr);
128  }
129  bool cut_found = false;
130  // do the following processing for each segment separately
131  for (const auto &seg_to_edge_points : seg_to_edge_points_map)
132  {
133  // if too small do not process
134  if (seg_to_edge_points.second->size () < min_segment_size_for_cutting_)
135  {
136  continue;
137  }
138 
139  std::vector<double> weights;
140  weights.resize (seg_to_edge_points.second->size ());
141  for (std::size_t cp = 0; cp < seg_to_edge_points.second->size (); ++cp)
142  {
143  float& cur_weight = (*seg_to_edge_points.second)[cp].intensity;
144  cur_weight = cur_weight < concavity_tolerance_threshold_ ? 0 : 1;
145  weights[cp] = cur_weight;
146  }
147 
148  pcl::PointCloud<WeightSACPointType>::Ptr edge_cloud_cluster = seg_to_edge_points.second;
150 
151  WeightedRandomSampleConsensus weight_sac (model_p, seed_resolution_, true);
152 
153  weight_sac.setWeights (weights, use_directed_weights_);
154  weight_sac.setMaxIterations (ransac_itrs_);
155 
156  // if not enough inliers are found
157  if (!weight_sac.computeModel ())
158  {
159  continue;
160  }
161 
162  Eigen::VectorXf model_coefficients = weight_sac.getModelCoefficients ();
163 
164  model_coefficients[3] += std::numeric_limits<float>::epsilon ();
165 
166  *support_indices = weight_sac.getInliers ();
167 
168  // the support_indices which are actually cut (if not locally constrain: cut_support_indices = support_indices
169  pcl::Indices cut_support_indices;
170 
171  if (use_local_constrains_)
172  {
173  Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
174  // Cut the connections.
175  // We only iterate through the points which are within the support (when we are local, otherwise all points in the segment).
176  // We also just actually cut when the edge goes through the plane. This is why we check the planedistance
177  std::vector<pcl::PointIndices> cluster_indices;
180  tree->setInputCloud (edge_cloud_cluster);
181  euclidean_clusterer.setClusterTolerance (seed_resolution_);
182  euclidean_clusterer.setMinClusterSize (1);
183  euclidean_clusterer.setMaxClusterSize (25000);
184  euclidean_clusterer.setSearchMethod (tree);
185  euclidean_clusterer.setInputCloud (edge_cloud_cluster);
186  euclidean_clusterer.setIndices (support_indices);
187  euclidean_clusterer.extract (cluster_indices);
188 // sv_adjacency_list_[seg_to_edgeID_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
189 
190  for (const auto &cluster_index : cluster_indices)
191  {
192  // get centroids of vertices
193  float cluster_score = 0;
194 // std::cout << "Cluster has " << cluster_indices[cc].indices.size () << " points" << std::endl;
195  for (const auto &current_index : cluster_index.indices)
196  {
197  double index_score = weights[current_index];
198  if (use_directed_weights_)
199  index_score *= 1.414 * (std::abs (plane_normal.dot (edge_cloud_cluster->at (current_index).getNormalVector3fMap ())));
200  cluster_score += index_score;
201  }
202  // check if the score is below the threshold. If that is the case this segment should not be split
203  cluster_score /= cluster_index.indices.size ();
204 // std::cout << "Cluster score: " << cluster_score << std::endl;
205  if (cluster_score >= min_cut_score_)
206  {
207  cut_support_indices.insert (cut_support_indices.end (), cluster_index.indices.begin (), cluster_index.indices.end ());
208  }
209  }
210  if (cut_support_indices.empty ())
211  {
212 // std::cout << "Could not find planes which exceed required minimum score (threshold " << min_cut_score_ << "), not cutting" << std::endl;
213  continue;
214  }
215  }
216  else
217  {
218  double current_score = weight_sac.getBestScore ();
219  cut_support_indices = *support_indices;
220  // check if the score is below the threshold. If that is the case this segment should not be split
221  if (current_score < min_cut_score_)
222  {
223 // std::cout << "Score too low, no cutting" << std::endl;
224  continue;
225  }
226  }
227 
228  int number_connections_cut = 0;
229  for (const auto &point_index : cut_support_indices)
230  {
231  if (use_clean_cutting_)
232  {
233  // skip edges where both centroids are on one side of the cutting plane
234  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_)];
235  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_)];
236  // get centroids of vertices
237  const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
238  const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
239  // this makes a clean cut
240  if (pcl::pointToPlaneDistanceSigned (source_centroid, model_coefficients) * pcl::pointToPlaneDistanceSigned (target_centroid, model_coefficients) > 0)
241  {
242  continue;
243  }
244  }
245  sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
246  if (sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid)
247  {
248  ++number_connections_cut;
249  sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid = false;
250  }
251  }
252 // std::cout << "We cut " << number_connections_cut << " connections" << std::endl;
253  if (number_connections_cut > 0)
254  cut_found = true;
255  }
256 
257  // if not cut has been performed we can stop the recursion
258  if (cut_found)
259  {
260  doGrouping ();
261  --depth_levels_left;
262  applyCuttingPlane (depth_levels_left);
263  }
264  else
265  pcl::console::print_info ("Could not find any more cuts, stopping recursion\n");
266 }
267 
268 /******************************************* Directional weighted RANSAC definitions ******************************************************************/
269 
270 
271 template <typename PointT> bool
273 {
274  // Warn and exit if no threshold was set
275  if (threshold_ == std::numeric_limits<double>::max ())
276  {
277  PCL_ERROR ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] No threshold set!\n");
278  return (false);
279  }
280 
281  iterations_ = 0;
282  best_score_ = -std::numeric_limits<double>::max ();
283 
284  pcl::Indices selection;
285  Eigen::VectorXf model_coefficients;
286 
287  unsigned skipped_count = 0;
288  // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
289  const unsigned max_skip = max_iterations_ * 10;
290 
291  // Iterate
292  while (iterations_ < max_iterations_ && skipped_count < max_skip)
293  {
294  // Get X samples which satisfy the model criteria and which have a weight > 0
295  sac_model_->setIndices (model_pt_indices_);
296  sac_model_->getSamples (iterations_, selection);
297 
298  if (selection.empty ())
299  {
300  PCL_ERROR ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] No samples could be selected!\n");
301  break;
302  }
303 
304  // Search for inliers in the point cloud for the current plane model M
305  if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
306  {
307  //++iterations_;
308  ++skipped_count;
309  continue;
310  }
311  // weight distances to get the score (only using connected inliers)
312  sac_model_->setIndices (full_cloud_pt_indices_);
313 
314  pcl::IndicesPtr current_inliers (new pcl::Indices);
315  sac_model_->selectWithinDistance (model_coefficients, threshold_, *current_inliers);
316  double current_score = 0;
317  Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
318  for (const auto &current_index : *current_inliers)
319  {
320  double index_score = weights_[current_index];
321  if (use_directed_weights_)
322  // the sqrt(2) factor was used in the paper and was meant for making the scores better comparable between directed and undirected weights
323  index_score *= 1.414 * (std::abs (plane_normal.dot (point_cloud_ptr_->at (current_index).getNormalVector3fMap ())));
324 
325  current_score += index_score;
326  }
327  // normalize by the total number of inliers
328  current_score /= current_inliers->size ();
329 
330  // Better match ?
331  if (current_score > best_score_)
332  {
333  best_score_ = current_score;
334  // Save the current model/inlier/coefficients selection as being the best so far
335  model_ = selection;
336  model_coefficients_ = model_coefficients;
337  }
338 
339  ++iterations_;
340  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_);
341  if (iterations_ > max_iterations_)
342  {
343  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n");
344  break;
345  }
346  }
347 // std::cout << "Took us " << iterations_ - 1 << " iterations" << std::endl;
348  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] Model: %lu size, %f score.\n", model_.size (), best_score_);
349 
350  if (model_.empty ())
351  {
352  inliers_.clear ();
353  return (false);
354  }
355 
356  // Get the set of inliers that correspond to the best model found so far
357  sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
358  return (true);
359 }
360 
361 #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:174
const PointT & at(int column, int row) const
Obtain the point given by the (column, row) coordinates.
Definition: point_cloud.h:263
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:414
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:54
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.