Point Cloud Library (PCL)  1.11.1-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 }
59 
60 template <typename PointT> void
62 {
63  if (supervoxels_set_)
64  {
65  // Calculate for every Edge if the connection is convex or invalid
66  // This effectively performs the segmentation.
67  calculateConvexConnections (sv_adjacency_list_);
68 
69  // Correct edge relations using extended convexity definition if k>0
70  applyKconvexity (k_factor_);
71 
72  // Determine whether to use cutting planes
73  doGrouping ();
74 
75  grouping_data_valid_ = true;
76 
77  applyCuttingPlane (max_cuts_);
78 
79  // merge small segments
80  mergeSmallSegments ();
81  }
82  else
83  PCL_WARN ("[pcl::CPCSegmentation::segment] WARNING: Call function setInputSupervoxels first. Nothing has been done. \n");
84 }
85 
86 template <typename PointT> void
87 pcl::CPCSegmentation<PointT>::applyCuttingPlane (std::uint32_t depth_levels_left)
88 {
89  using SegLabel2ClusterMap = std::map<std::uint32_t, pcl::PointCloud<WeightSACPointType>::Ptr>;
90 
91  pcl::console::print_info ("Cutting at level %d (maximum %d)\n", max_cuts_ - depth_levels_left + 1, max_cuts_);
92  // stop if we reached the 0 level
93  if (depth_levels_left <= 0)
94  return;
95 
96  pcl::IndicesPtr support_indices (new pcl::Indices);
97  SegLabel2ClusterMap seg_to_edge_points_map;
98  std::map<std::uint32_t, std::vector<EdgeID> > seg_to_edgeIDs_map;
99  EdgeIterator edge_itr, edge_itr_end, next_edge;
100  boost::tie (edge_itr, edge_itr_end) = boost::edges (sv_adjacency_list_);
101  for (next_edge = edge_itr; edge_itr != edge_itr_end; edge_itr = next_edge)
102  {
103  next_edge++; // next_edge iterator is necessary, because removing an edge invalidates the iterator to the current edge
104  std::uint32_t source_sv_label = sv_adjacency_list_[boost::source (*edge_itr, sv_adjacency_list_)];
105  std::uint32_t target_sv_label = sv_adjacency_list_[boost::target (*edge_itr, sv_adjacency_list_)];
106 
107  std::uint32_t source_segment_label = sv_label_to_seg_label_map_[source_sv_label];
108  std::uint32_t target_segment_label = sv_label_to_seg_label_map_[target_sv_label];
109 
110  // do not process edges which already split two segments
111  if (source_segment_label != target_segment_label)
112  continue;
113 
114  // if edge has been used for cutting already do not use it again
115  if (sv_adjacency_list_[*edge_itr].used_for_cutting)
116  continue;
117  // get centroids of vertices
118  const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
119  const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
120 
121  // stores the information about the edge cloud (used for the weighted ransac)
122  // we use the normal to express the direction of the connection
123  // we use the intensity to express the normal differences between supervoxel patches. <=0: Convex, >0: Concave
124  WeightSACPointType edge_centroid;
125  edge_centroid.getVector3fMap () = (source_centroid.getVector3fMap () + target_centroid.getVector3fMap ()) / 2;
126 
127  // we use the normal to express the direction of the connection!
128  edge_centroid.getNormalVector3fMap () = (target_centroid.getVector3fMap () - source_centroid.getVector3fMap ()).normalized ();
129 
130  // we use the intensity to express the normal differences between supervoxel patches. <=0: Convex, >0: Concave
131  edge_centroid.intensity = sv_adjacency_list_[*edge_itr].is_convex ? -sv_adjacency_list_[*edge_itr].normal_difference : sv_adjacency_list_[*edge_itr].normal_difference;
132  if (seg_to_edge_points_map.find (source_segment_label) == seg_to_edge_points_map.end ())
133  {
134  seg_to_edge_points_map[source_segment_label] = pcl::PointCloud<WeightSACPointType>::Ptr (new pcl::PointCloud<WeightSACPointType> ());
135  }
136  seg_to_edge_points_map[source_segment_label]->push_back (edge_centroid);
137  seg_to_edgeIDs_map[source_segment_label].push_back (*edge_itr);
138  }
139  bool cut_found = false;
140  // do the following processing for each segment separately
141  for (const auto &seg_to_edge_points : seg_to_edge_points_map)
142  {
143  // if too small do not process
144  if (seg_to_edge_points.second->size () < min_segment_size_for_cutting_)
145  {
146  continue;
147  }
148 
149  std::vector<double> weights;
150  weights.resize (seg_to_edge_points.second->size ());
151  for (std::size_t cp = 0; cp < seg_to_edge_points.second->size (); ++cp)
152  {
153  float& cur_weight = (*seg_to_edge_points.second)[cp].intensity;
154  cur_weight = cur_weight < concavity_tolerance_threshold_ ? 0 : 1;
155  weights[cp] = cur_weight;
156  }
157 
158  pcl::PointCloud<WeightSACPointType>::Ptr edge_cloud_cluster = seg_to_edge_points.second;
160 
161  WeightedRandomSampleConsensus weight_sac (model_p, seed_resolution_, true);
162 
163  weight_sac.setWeights (weights, use_directed_weights_);
164  weight_sac.setMaxIterations (ransac_itrs_);
165 
166  // if not enough inliers are found
167  if (!weight_sac.computeModel ())
168  {
169  continue;
170  }
171 
172  Eigen::VectorXf model_coefficients;
173  weight_sac.getModelCoefficients (model_coefficients);
174 
175  model_coefficients[3] += std::numeric_limits<float>::epsilon ();
176 
177  weight_sac.getInliers (*support_indices);
178 
179  // the support_indices which are actually cut (if not locally constrain: cut_support_indices = support_indices
180  pcl::Indices cut_support_indices;
181 
182  if (use_local_constrains_)
183  {
184  Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
185  // Cut the connections.
186  // We only iterate through the points which are within the support (when we are local, otherwise all points in the segment).
187  // We also just actually cut when the edge goes through the plane. This is why we check the planedistance
188  std::vector<pcl::PointIndices> cluster_indices;
191  tree->setInputCloud (edge_cloud_cluster);
192  euclidean_clusterer.setClusterTolerance (seed_resolution_);
193  euclidean_clusterer.setMinClusterSize (1);
194  euclidean_clusterer.setMaxClusterSize (25000);
195  euclidean_clusterer.setSearchMethod (tree);
196  euclidean_clusterer.setInputCloud (edge_cloud_cluster);
197  euclidean_clusterer.setIndices (support_indices);
198  euclidean_clusterer.extract (cluster_indices);
199 // sv_adjacency_list_[seg_to_edgeID_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
200 
201  for (const auto &cluster_index : cluster_indices)
202  {
203  // get centroids of vertices
204  int cluster_concave_pts = 0;
205  float cluster_score = 0;
206 // std::cout << "Cluster has " << cluster_indices[cc].indices.size () << " points" << std::endl;
207  for (const auto &current_index : cluster_index.indices)
208  {
209  double index_score = weights[current_index];
210  if (use_directed_weights_)
211  index_score *= 1.414 * (std::abs (plane_normal.dot (edge_cloud_cluster->at (current_index).getNormalVector3fMap ())));
212  cluster_score += index_score;
213  if (weights[current_index] > 0)
214  ++cluster_concave_pts;
215  }
216  // check if the score is below the threshold. If that is the case this segment should not be split
217  cluster_score /= cluster_index.indices.size ();
218 // std::cout << "Cluster score: " << cluster_score << std::endl;
219  if (cluster_score >= min_cut_score_)
220  {
221  cut_support_indices.insert (cut_support_indices.end (), cluster_index.indices.begin (), cluster_index.indices.end ());
222  }
223  }
224  if (cut_support_indices.empty ())
225  {
226 // std::cout << "Could not find planes which exceed required minimum score (threshold " << min_cut_score_ << "), not cutting" << std::endl;
227  continue;
228  }
229  }
230  else
231  {
232  double current_score = weight_sac.getBestScore ();
233  cut_support_indices = *support_indices;
234  // check if the score is below the threshold. If that is the case this segment should not be split
235  if (current_score < min_cut_score_)
236  {
237 // std::cout << "Score too low, no cutting" << std::endl;
238  continue;
239  }
240  }
241 
242  int number_connections_cut = 0;
243  for (const auto &point_index : cut_support_indices)
244  {
245  if (use_clean_cutting_)
246  {
247  // skip edges where both centroids are on one side of the cutting plane
248  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_)];
249  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_)];
250  // get centroids of vertices
251  const pcl::PointXYZRGBA source_centroid = sv_label_to_supervoxel_map_[source_sv_label]->centroid_;
252  const pcl::PointXYZRGBA target_centroid = sv_label_to_supervoxel_map_[target_sv_label]->centroid_;
253  // this makes a clean cut
254  if (pcl::pointToPlaneDistanceSigned (source_centroid, model_coefficients) * pcl::pointToPlaneDistanceSigned (target_centroid, model_coefficients) > 0)
255  {
256  continue;
257  }
258  }
259  sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].used_for_cutting = true;
260  if (sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid)
261  {
262  ++number_connections_cut;
263  sv_adjacency_list_[seg_to_edgeIDs_map[seg_to_edge_points.first][point_index]].is_valid = false;
264  }
265  }
266 // std::cout << "We cut " << number_connections_cut << " connections" << std::endl;
267  if (number_connections_cut > 0)
268  cut_found = true;
269  }
270 
271  // if not cut has been performed we can stop the recursion
272  if (cut_found)
273  {
274  doGrouping ();
275  --depth_levels_left;
276  applyCuttingPlane (depth_levels_left);
277  }
278  else
279  pcl::console::print_info ("Could not find any more cuts, stopping recursion\n");
280 }
281 
282 /******************************************* Directional weighted RANSAC definitions ******************************************************************/
283 
284 
285 template <typename PointT> bool
287 {
288  // Warn and exit if no threshold was set
289  if (threshold_ == std::numeric_limits<double>::max ())
290  {
291  PCL_ERROR ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] No threshold set!\n");
292  return (false);
293  }
294 
295  iterations_ = 0;
296  best_score_ = -std::numeric_limits<double>::max ();
297 
298  pcl::Indices selection;
299  Eigen::VectorXf model_coefficients;
300 
301  unsigned skipped_count = 0;
302  // suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
303  const unsigned max_skip = max_iterations_ * 10;
304 
305  // Iterate
306  while (iterations_ < max_iterations_ && skipped_count < max_skip)
307  {
308  // Get X samples which satisfy the model criteria and which have a weight > 0
309  sac_model_->setIndices (model_pt_indices_);
310  sac_model_->getSamples (iterations_, selection);
311 
312  if (selection.empty ())
313  {
314  PCL_ERROR ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] No samples could be selected!\n");
315  break;
316  }
317 
318  // Search for inliers in the point cloud for the current plane model M
319  if (!sac_model_->computeModelCoefficients (selection, model_coefficients))
320  {
321  //++iterations_;
322  ++skipped_count;
323  continue;
324  }
325  // weight distances to get the score (only using connected inliers)
326  sac_model_->setIndices (full_cloud_pt_indices_);
327 
328  pcl::IndicesPtr current_inliers (new pcl::Indices);
329  sac_model_->selectWithinDistance (model_coefficients, threshold_, *current_inliers);
330  double current_score = 0;
331  Eigen::Vector3f plane_normal (model_coefficients[0], model_coefficients[1], model_coefficients[2]);
332  for (const auto &current_index : *current_inliers)
333  {
334  double index_score = weights_[current_index];
335  if (use_directed_weights_)
336  // the sqrt(2) factor was used in the paper and was meant for making the scores better comparable between directed and undirected weights
337  index_score *= 1.414 * (std::abs (plane_normal.dot (point_cloud_ptr_->at (current_index).getNormalVector3fMap ())));
338 
339  current_score += index_score;
340  }
341  // normalize by the total number of inliers
342  current_score /= current_inliers->size ();
343 
344  // Better match ?
345  if (current_score > best_score_)
346  {
347  best_score_ = current_score;
348  // Save the current model/inlier/coefficients selection as being the best so far
349  model_ = selection;
350  model_coefficients_ = model_coefficients;
351  }
352 
353  ++iterations_;
354  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_);
355  if (iterations_ > max_iterations_)
356  {
357  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n");
358  break;
359  }
360  }
361 // std::cout << "Took us " << iterations_ - 1 << " iterations" << std::endl;
362  PCL_DEBUG ("[pcl::CPCSegmentation<PointT>::WeightedRandomSampleConsensus::computeModel] Model: %lu size, %f score.\n", model_.size (), best_score_);
363 
364  if (model_.empty ())
365  {
366  inliers_.clear ();
367  return (false);
368  }
369 
370  // Get the set of inliers that correspond to the best model found so far
371  sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
372  return (true);
373 }
374 
375 #endif // PCL_SEGMENTATION_IMPL_CPC_SEGMENTATION_HPP_
pcl::CPCSegmentation
A segmentation algorithm partitioning a supervoxel graph.
Definition: cpc_segmentation.h:66
pcl::EuclideanClusterExtraction::setClusterTolerance
void setClusterTolerance(double tolerance)
Set the spatial cluster tolerance as a measure in the L2 Euclidean space.
Definition: extract_clusters.h:370
pcl::IndicesPtr
shared_ptr< Indices > IndicesPtr
Definition: pcl_base.h:58
pcl::EuclideanClusterExtraction::extract
void extract(std::vector< PointIndices > &clusters)
Cluster extraction in a PointCloud given by <setInputCloud (), setIndices ()>
Definition: extract_clusters.hpp:228
pcl::EuclideanClusterExtraction::setMinClusterSize
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.
Definition: extract_clusters.h:386
pcl::SampleConsensusModelPlane
SampleConsensusModelPlane defines a model for 3D plane segmentation.
Definition: sac_model_plane.h:142
pcl::CPCSegmentation::~CPCSegmentation
~CPCSegmentation()
Definition: cpc_segmentation.hpp:56
pcl::EuclideanClusterExtraction::setMaxClusterSize
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.
Definition: extract_clusters.h:402
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: distances.h:55
pcl::SampleConsensusModelPlane::Ptr
shared_ptr< SampleConsensusModelPlane< PointT > > Ptr
Definition: sac_model_plane.h:155
pcl::PCLBase::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:65
pcl::PointCloud::at
const PointT & at(int column, int row) const
Obtain the point given by the (column, row) coordinates.
Definition: point_cloud.h:262
pcl::EuclideanClusterExtraction::setSearchMethod
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
Definition: extract_clusters.h:352
pcl::search::KdTree::Ptr
shared_ptr< KdTree< PointT, Tree > > Ptr
Definition: kdtree.h:75
pcl::EuclideanClusterExtraction
EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sen...
Definition: extract_clusters.h:325
pcl::PointXYZRGBA
A point structure representing Euclidean xyz coordinates, and the RGBA color.
Definition: point_types.hpp:555
pcl::console::print_info
PCL_EXPORTS void print_info(const char *format,...)
Print an info message on stream with colors.
pcl::gpu::cp
int cp(int from, int to)
Returns field copy operation code.
Definition: repacks.hpp:56
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:61
pcl::PCLBase::setIndices
virtual void setIndices(const IndicesPtr &indices)
Provide a pointer to the vector of indices that represents the input data.
Definition: pcl_base.hpp:72
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::PointCloud::Ptr
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:407
pcl::CPCSegmentation::CPCSegmentation
CPCSegmentation()
Definition: cpc_segmentation.hpp:45
pcl::CPCSegmentation::segment
void segment()
Merge supervoxels using cuts through local convexities.
Definition: cpc_segmentation.hpp:61
pcl::pointToPlaneDistanceSigned
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.
Definition: sac_model_plane.h:89