Point Cloud Library (PCL)  1.12.1-dev
min_cut_segmentation.h
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38 
39 #pragma once
40 
41 #include <pcl/memory.h>
42 #include <pcl/pcl_base.h>
43 #include <pcl/pcl_macros.h>
44 #include <pcl/point_cloud.h>
45 #include <pcl/point_types.h>
46 #include <pcl/search/search.h>
47 #include <string>
48 #include <set>
49 #include <boost/graph/adjacency_list.hpp> // for adjacency_list
50 
51 namespace pcl
52 {
53  /** \brief This class implements the segmentation algorithm based on minimal cut of the graph.
54  * The description can be found in the article:
55  * "Min-Cut Based Segmentation of Point Clouds"
56  * \author: Aleksey Golovinskiy and Thomas Funkhouser.
57  */
58  template <typename PointT>
60  {
61  public:
62 
64  using KdTreePtr = typename KdTree::Ptr;
67 
72 
73  public:
74 
75  using Traits = boost::adjacency_list_traits< boost::vecS, boost::vecS, boost::directedS >;
76 
77  using mGraph = boost::adjacency_list< boost::vecS, boost::vecS, boost::directedS,
78  boost::property< boost::vertex_name_t, std::string,
79  boost::property< boost::vertex_index_t, long,
80  boost::property< boost::vertex_color_t, boost::default_color_type,
81  boost::property< boost::vertex_distance_t, long,
82  boost::property< boost::vertex_predecessor_t, Traits::edge_descriptor > > > > >,
83  boost::property< boost::edge_capacity_t, double,
84  boost::property< boost::edge_residual_capacity_t, double,
85  boost::property< boost::edge_reverse_t, Traits::edge_descriptor > > > >;
86 
87  using CapacityMap = boost::property_map< mGraph, boost::edge_capacity_t >::type;
88 
89  using ReverseEdgeMap = boost::property_map< mGraph, boost::edge_reverse_t>::type;
90 
91  using VertexDescriptor = Traits::vertex_descriptor;
92 
93  using EdgeDescriptor = boost::graph_traits<mGraph>::edge_descriptor;
94 
95  using OutEdgeIterator = boost::graph_traits<mGraph>::out_edge_iterator;
96 
97  using VertexIterator = boost::graph_traits<mGraph>::vertex_iterator;
98 
99  using ResidualCapacityMap = boost::property_map< mGraph, boost::edge_residual_capacity_t >::type;
100 
101  using IndexMap = boost::property_map< mGraph, boost::vertex_index_t >::type;
102 
103  using InEdgeIterator = boost::graph_traits<mGraph>::in_edge_iterator;
104 
105  using mGraphPtr = shared_ptr<mGraph>;
106 
107  public:
108 
109  /** \brief Constructor that sets default values for member variables. */
111 
112  /** \brief Destructor that frees memory. */
113 
114  ~MinCutSegmentation () override;
115 
116  /** \brief This method simply sets the input point cloud.
117  * \param[in] cloud the const boost shared pointer to a PointCloud
118  */
119  void
120  setInputCloud (const PointCloudConstPtr &cloud) override;
121 
122  /** \brief Returns normalization value for binary potentials. For more information see the article. */
123  double
124  getSigma () const;
125 
126  /** \brief Allows to set the normalization value for the binary potentials as described in the article.
127  * \param[in] sigma new normalization value
128  */
129  void
130  setSigma (double sigma);
131 
132  /** \brief Returns radius to the background. */
133  double
134  getRadius () const;
135 
136  /** \brief Allows to set the radius to the background.
137  * \param[in] radius new radius to the background
138  */
139  void
140  setRadius (double radius);
141 
142  /** \brief Returns weight that every edge from the source point has. */
143  double
144  getSourceWeight () const;
145 
146  /** \brief Allows to set weight for source edges. Every edge that comes from the source point will have that weight.
147  * \param[in] weight new weight
148  */
149  void
150  setSourceWeight (double weight);
151 
152  /** \brief Returns search method that is used for finding KNN.
153  * The graph is build such way that it contains the edges that connect point and its KNN.
154  */
155  KdTreePtr
156  getSearchMethod () const;
157 
158  /** \brief Allows to set search method for finding KNN.
159  * The graph is build such way that it contains the edges that connect point and its KNN.
160  * \param[in] tree search method that will be used for finding KNN.
161  */
162  void
163  setSearchMethod (const KdTreePtr& tree);
164 
165  /** \brief Returns the number of neighbours to find. */
166  unsigned int
167  getNumberOfNeighbours () const;
168 
169  /** \brief Allows to set the number of neighbours to find.
170  * \param[in] neighbour_number new number of neighbours
171  */
172  void
173  setNumberOfNeighbours (unsigned int neighbour_number);
174 
175  /** \brief Returns the points that must belong to foreground. */
176  std::vector<PointT, Eigen::aligned_allocator<PointT> >
177  getForegroundPoints () const;
178 
179  /** \brief Allows to specify points which are known to be the points of the object.
180  * \param[in] foreground_points point cloud that contains foreground points. At least one point must be specified.
181  */
182  void
183  setForegroundPoints (typename pcl::PointCloud<PointT>::Ptr foreground_points);
184 
185  /** \brief Returns the points that must belong to background. */
186  std::vector<PointT, Eigen::aligned_allocator<PointT> >
187  getBackgroundPoints () const;
188 
189  /** \brief Allows to specify points which are known to be the points of the background.
190  * \param[in] background_points point cloud that contains background points.
191  */
192  void
193  setBackgroundPoints (typename pcl::PointCloud<PointT>::Ptr background_points);
194 
195  /** \brief This method launches the segmentation algorithm and returns the clusters that were
196  * obtained during the segmentation. The indices of points that belong to the object will be stored
197  * in the cluster with index 1, other indices will be stored in the cluster with index 0.
198  * \param[out] clusters clusters that were obtained. Each cluster is an array of point indices.
199  */
200  void
201  extract (std::vector <pcl::PointIndices>& clusters);
202 
203  /** \brief Returns that flow value that was calculated during the segmentation. */
204  double
205  getMaxFlow () const;
206 
207  /** \brief Returns the graph that was build for finding the minimum cut. */
208  mGraphPtr
209  getGraph () const;
210 
211  /** \brief Returns the colored cloud. Points that belong to the object have the same color. */
213  getColoredCloud ();
214 
215  protected:
216 
217  /** \brief This method simply builds the graph that will be used during the segmentation. */
218  bool
219  buildGraph ();
220 
221  /** \brief Returns unary potential(data cost) for the given point index.
222  * In other words it calculates weights for (source, point) and (point, sink) edges.
223  * \param[in] point index of the point for which weights will be calculated
224  * \param[out] source_weight calculated weight for the (source, point) edge
225  * \param[out] sink_weight calculated weight for the (point, sink) edge
226  */
227  void
228  calculateUnaryPotential (int point, double& source_weight, double& sink_weight) const;
229 
230  /** \brief This method simply adds the edge from the source point to the target point with a given weight.
231  * \param[in] source index of the source point of the edge
232  * \param[in] target index of the target point of the edge
233  * \param[in] weight weight that will be assigned to the (source, target) edge
234  */
235  bool
236  addEdge (int source, int target, double weight);
237 
238  /** \brief Returns the binary potential(smooth cost) for the given indices of points.
239  * In other words it returns weight that must be assigned to the edge from source to target point.
240  * \param[in] source index of the source point of the edge
241  * \param[in] target index of the target point of the edge
242  */
243  double
244  calculateBinaryPotential (int source, int target) const;
245 
246  /** \brief This method recalculates unary potentials(data cost) if some changes were made, instead of creating new graph. */
247  bool
248  recalculateUnaryPotentials ();
249 
250  /** \brief This method recalculates binary potentials(smooth cost) if some changes were made, instead of creating new graph. */
251  bool
252  recalculateBinaryPotentials ();
253 
254  /** \brief This method analyzes the residual network and assigns a label to every point in the cloud.
255  * \param[in] residual_capacity residual network that was obtained during the segmentation
256  */
257  void
258  assembleLabels (ResidualCapacityMap& residual_capacity);
259 
260  protected:
261 
262  /** \brief Stores the sigma coefficient. It is used for finding smooth costs. More information can be found in the article. */
264 
265  /** \brief Signalizes if the binary potentials are valid. */
267 
268  /** \brief Used for comparison of the floating point numbers. */
269  double epsilon_;
270 
271  /** \brief Stores the distance to the background. */
272  double radius_;
273 
274  /** \brief Signalizes if the unary potentials are valid. */
276 
277  /** \brief Stores the weight for every edge that comes from source point. */
279 
280  /** \brief Stores the search method that will be used for finding K nearest neighbors. Neighbours are used for building the graph. */
282 
283  /** \brief Stores the number of neighbors to find. */
284  unsigned int number_of_neighbours_;
285 
286  /** \brief Signalizes if the graph is valid. */
288 
289  /** \brief Stores the points that are known to be in the foreground. */
290  std::vector<PointT, Eigen::aligned_allocator<PointT> > foreground_points_;
291 
292  /** \brief Stores the points that are known to be in the background. */
293  std::vector<PointT, Eigen::aligned_allocator<PointT> > background_points_;
294 
295  /** \brief After the segmentation it will contain the segments. */
296  std::vector <pcl::PointIndices> clusters_;
297 
298  /** \brief Stores the graph for finding the maximum flow. */
300 
301  /** \brief Stores the capacity of every edge in the graph. */
302  std::shared_ptr<CapacityMap> capacity_;
303 
304  /** \brief Stores reverse edges for every edge in the graph. */
305  std::shared_ptr<ReverseEdgeMap> reverse_edges_;
306 
307  /** \brief Stores the vertices of the graph. */
308  std::vector< VertexDescriptor > vertices_;
309 
310  /** \brief Stores the information about the edges that were added to the graph. It is used to avoid the duplicate edges. */
311  std::vector< std::set<int> > edge_marker_;
312 
313  /** \brief Stores the vertex that serves as source. */
315 
316  /** \brief Stores the vertex that serves as sink. */
318 
319  /** \brief Stores the maximum flow value that was calculated during the segmentation. */
320  double max_flow_;
321 
322  public:
324  };
325 }
326 
327 #ifdef PCL_NO_PRECOMPILE
328 #include <pcl/segmentation/impl/min_cut_segmentation.hpp>
329 #endif
shared_ptr< KdTree< PointT > > Ptr
Definition: kdtree.h:68
This class implements the segmentation algorithm based on minimal cut of the graph.
std::shared_ptr< ReverseEdgeMap > reverse_edges_
Stores reverse edges for every edge in the graph.
double max_flow_
Stores the maximum flow value that was calculated during the segmentation.
double inverse_sigma_
Stores the sigma coefficient.
unsigned int number_of_neighbours_
Stores the number of neighbors to find.
double source_weight_
Stores the weight for every edge that comes from source point.
boost::property_map< mGraph, boost::vertex_index_t >::type IndexMap
std::vector< pcl::PointIndices > clusters_
After the segmentation it will contain the segments.
mGraphPtr graph_
Stores the graph for finding the maximum flow.
double epsilon_
Used for comparison of the floating point numbers.
std::vector< PointT, Eigen::aligned_allocator< PointT > > foreground_points_
Stores the points that are known to be in the foreground.
boost::graph_traits< mGraph >::out_edge_iterator OutEdgeIterator
VertexDescriptor sink_
Stores the vertex that serves as sink.
bool unary_potentials_are_valid_
Signalizes if the unary potentials are valid.
KdTreePtr search_
Stores the search method that will be used for finding K nearest neighbors.
std::shared_ptr< CapacityMap > capacity_
Stores the capacity of every edge in the graph.
boost::property_map< mGraph, boost::edge_capacity_t >::type CapacityMap
VertexDescriptor source_
Stores the vertex that serves as source.
bool graph_is_valid_
Signalizes if the graph is valid.
boost::property_map< mGraph, boost::edge_reverse_t >::type ReverseEdgeMap
shared_ptr< mGraph > mGraphPtr
Traits::vertex_descriptor VertexDescriptor
std::vector< VertexDescriptor > vertices_
Stores the vertices of the graph.
boost::graph_traits< mGraph >::vertex_iterator VertexIterator
std::vector< PointT, Eigen::aligned_allocator< PointT > > background_points_
Stores the points that are known to be in the background.
std::vector< std::set< int > > edge_marker_
Stores the information about the edges that were added to the graph.
typename KdTree::Ptr KdTreePtr
bool binary_potentials_are_valid_
Signalizes if the binary potentials are valid.
boost::adjacency_list< boost::vecS, boost::vecS, boost::directedS, boost::property< boost::vertex_name_t, std::string, boost::property< boost::vertex_index_t, long, boost::property< boost::vertex_color_t, boost::default_color_type, boost::property< boost::vertex_distance_t, long, boost::property< boost::vertex_predecessor_t, Traits::edge_descriptor > > > > >, boost::property< boost::edge_capacity_t, double, boost::property< boost::edge_residual_capacity_t, double, boost::property< boost::edge_reverse_t, Traits::edge_descriptor > > > > mGraph
boost::graph_traits< mGraph >::edge_descriptor EdgeDescriptor
boost::graph_traits< mGraph >::in_edge_iterator InEdgeIterator
boost::property_map< mGraph, boost::edge_residual_capacity_t >::type ResidualCapacityMap
boost::adjacency_list_traits< boost::vecS, boost::vecS, boost::directedS > Traits
double radius_
Stores the distance to the background.
PCL base class.
Definition: pcl_base.h:70
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: pcl_base.h:74
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
Generic search class.
Definition: search.h:75
Defines all the PCL implemented PointT point type structures.
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
Defines functions, macros and traits for allocating and using memory.
Defines all the PCL and non-PCL macros used.
#define PCL_EXPORTS
Definition: pcl_macros.h:323