Point Cloud Library (PCL) 1.14.0
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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
51namespace 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 * \ingroup segmentation
58 */
59 template <typename PointT>
60 class PCL_EXPORTS MinCutSegmentation : public pcl::PCLBase<PointT>
61 {
62 public:
63
65 using KdTreePtr = typename KdTree::Ptr;
68
69 using PCLBase <PointT>::input_;
70 using PCLBase <PointT>::indices_;
71 using PCLBase <PointT>::initCompute;
72 using PCLBase <PointT>::deinitCompute;
73
74 public:
75
76 using Traits = boost::adjacency_list_traits< boost::vecS, boost::vecS, boost::directedS >;
77
78 using mGraph = boost::adjacency_list< boost::vecS, boost::vecS, boost::directedS,
79 boost::property< boost::vertex_name_t, std::string,
80 boost::property< boost::vertex_index_t, long,
81 boost::property< boost::vertex_color_t, boost::default_color_type,
82 boost::property< boost::vertex_distance_t, long,
83 boost::property< boost::vertex_predecessor_t, Traits::edge_descriptor > > > > >,
84 boost::property< boost::edge_capacity_t, double,
85 boost::property< boost::edge_residual_capacity_t, double,
86 boost::property< boost::edge_reverse_t, Traits::edge_descriptor > > > >;
87
88 using CapacityMap = boost::property_map< mGraph, boost::edge_capacity_t >::type;
89
90 using ReverseEdgeMap = boost::property_map< mGraph, boost::edge_reverse_t>::type;
91
92 using VertexDescriptor = Traits::vertex_descriptor;
93
94 using EdgeDescriptor = boost::graph_traits<mGraph>::edge_descriptor;
95
96 using OutEdgeIterator = boost::graph_traits<mGraph>::out_edge_iterator;
97
98 using VertexIterator = boost::graph_traits<mGraph>::vertex_iterator;
99
100 using ResidualCapacityMap = boost::property_map< mGraph, boost::edge_residual_capacity_t >::type;
101
102 using IndexMap = boost::property_map< mGraph, boost::vertex_index_t >::type;
103
104 using InEdgeIterator = boost::graph_traits<mGraph>::in_edge_iterator;
105
106 using mGraphPtr = shared_ptr<mGraph>;
107
108 public:
109
110 /** \brief Constructor that sets default values for member variables. */
112
113 /** \brief Destructor that frees memory. */
114
115 ~MinCutSegmentation () override;
116
117 /** \brief This method simply sets the input point cloud.
118 * \param[in] cloud the const boost shared pointer to a PointCloud
119 */
120 void
121 setInputCloud (const PointCloudConstPtr &cloud) override;
122
123 /** \brief Returns normalization value for binary potentials. For more information see the article. */
124 double
125 getSigma () const;
126
127 /** \brief Allows to set the normalization value for the binary potentials as described in the article.
128 * \param[in] sigma new normalization value
129 */
130 void
131 setSigma (double sigma);
132
133 /** \brief Returns radius to the background. */
134 double
135 getRadius () const;
136
137 /** \brief Allows to set the radius to the background.
138 * \param[in] radius new radius to the background
139 */
140 void
141 setRadius (double radius);
142
143 /** \brief Returns weight that every edge from the source point has. */
144 double
145 getSourceWeight () const;
146
147 /** \brief Allows to set weight for source edges. Every edge that comes from the source point will have that weight.
148 * \param[in] weight new weight
149 */
150 void
151 setSourceWeight (double weight);
152
153 /** \brief Returns search method that is used for finding KNN.
154 * The graph is build such way that it contains the edges that connect point and its KNN.
155 */
157 getSearchMethod () const;
158
159 /** \brief Allows to set search method for finding KNN.
160 * The graph is build such way that it contains the edges that connect point and its KNN.
161 * \param[in] tree search method that will be used for finding KNN.
162 */
163 void
164 setSearchMethod (const KdTreePtr& tree);
165
166 /** \brief Returns the number of neighbours to find. */
167 unsigned int
168 getNumberOfNeighbours () const;
169
170 /** \brief Allows to set the number of neighbours to find.
171 * \param[in] neighbour_number new number of neighbours
172 */
173 void
174 setNumberOfNeighbours (unsigned int neighbour_number);
175
176 /** \brief Returns the points that must belong to foreground. */
177 std::vector<PointT, Eigen::aligned_allocator<PointT> >
178 getForegroundPoints () const;
179
180 /** \brief Allows to specify points which are known to be the points of the object.
181 * \param[in] foreground_points point cloud that contains foreground points. At least one point must be specified.
182 */
183 void
184 setForegroundPoints (typename pcl::PointCloud<PointT>::Ptr foreground_points);
185
186 /** \brief Returns the points that must belong to background. */
187 std::vector<PointT, Eigen::aligned_allocator<PointT> >
188 getBackgroundPoints () const;
189
190 /** \brief Allows to specify points which are known to be the points of the background.
191 * \param[in] background_points point cloud that contains background points.
192 */
193 void
194 setBackgroundPoints (typename pcl::PointCloud<PointT>::Ptr background_points);
195
196 /** \brief This method launches the segmentation algorithm and returns the clusters that were
197 * obtained during the segmentation. The indices of points that belong to the object will be stored
198 * in the cluster with index 1, other indices will be stored in the cluster with index 0.
199 * \param[out] clusters clusters that were obtained. Each cluster is an array of point indices.
200 */
201 void
202 extract (std::vector <pcl::PointIndices>& clusters);
203
204 /** \brief Returns that flow value that was calculated during the segmentation. */
205 double
206 getMaxFlow () const;
207
208 /** \brief Returns the graph that was build for finding the minimum cut. */
210 getGraph () const;
211
212 /** \brief Returns the colored cloud. Points that belong to the object have the same color. */
214 getColoredCloud ();
215
216 protected:
217
218 /** \brief This method simply builds the graph that will be used during the segmentation. */
219 bool
220 buildGraph ();
221
222 /** \brief Returns unary potential(data cost) for the given point index.
223 * In other words it calculates weights for (source, point) and (point, sink) edges.
224 * \param[in] point index of the point for which weights will be calculated
225 * \param[out] source_weight calculated weight for the (source, point) edge
226 * \param[out] sink_weight calculated weight for the (point, sink) edge
227 */
228 void
229 calculateUnaryPotential (int point, double& source_weight, double& sink_weight) const;
230
231 /** \brief This method simply adds the edge from the source point to the target point with a given weight.
232 * \param[in] source index of the source point of the edge
233 * \param[in] target index of the target point of the edge
234 * \param[in] weight weight that will be assigned to the (source, target) edge
235 */
236 bool
237 addEdge (int source, int target, double weight);
238
239 /** \brief Returns the binary potential(smooth cost) for the given indices of points.
240 * In other words it returns weight that must be assigned to the edge from source to target point.
241 * \param[in] source index of the source point of the edge
242 * \param[in] target index of the target point of the edge
243 */
244 double
245 calculateBinaryPotential (int source, int target) const;
246
247 /** \brief This method recalculates unary potentials(data cost) if some changes were made, instead of creating new graph. */
248 bool
249 recalculateUnaryPotentials ();
250
251 /** \brief This method recalculates binary potentials(smooth cost) if some changes were made, instead of creating new graph. */
252 bool
253 recalculateBinaryPotentials ();
254
255 /** \brief This method analyzes the residual network and assigns a label to every point in the cloud.
256 * \param[in] residual_capacity residual network that was obtained during the segmentation
257 */
258 void
259 assembleLabels (ResidualCapacityMap& residual_capacity);
260
261 protected:
262
263 /** \brief Stores the sigma coefficient. It is used for finding smooth costs. More information can be found in the article. */
264 double inverse_sigma_{16.0};
265
266 /** \brief Signalizes if the binary potentials are valid. */
267 bool binary_potentials_are_valid_{false};
268
269 /** \brief Used for comparison of the floating point numbers. */
270 double epsilon_{0.0001};
271
272 /** \brief Stores the distance to the background. */
273 double radius_{16.0};
274
275 /** \brief Signalizes if the unary potentials are valid. */
276 bool unary_potentials_are_valid_{false};
277
278 /** \brief Stores the weight for every edge that comes from source point. */
279 double source_weight_{0.8};
280
281 /** \brief Stores the search method that will be used for finding K nearest neighbors. Neighbours are used for building the graph. */
282 KdTreePtr search_{nullptr};
283
284 /** \brief Stores the number of neighbors to find. */
285 unsigned int number_of_neighbours_{14};
286
287 /** \brief Signalizes if the graph is valid. */
288 bool graph_is_valid_{false};
289
290 /** \brief Stores the points that are known to be in the foreground. */
291 std::vector<PointT, Eigen::aligned_allocator<PointT> > foreground_points_{};
292
293 /** \brief Stores the points that are known to be in the background. */
294 std::vector<PointT, Eigen::aligned_allocator<PointT> > background_points_{};
295
296 /** \brief After the segmentation it will contain the segments. */
297 std::vector <pcl::PointIndices> clusters_{};
298
299 /** \brief Stores the graph for finding the maximum flow. */
300 mGraphPtr graph_{nullptr};
301
302 /** \brief Stores the capacity of every edge in the graph. */
303 std::shared_ptr<CapacityMap> capacity_{nullptr};
304
305 /** \brief Stores reverse edges for every edge in the graph. */
306 std::shared_ptr<ReverseEdgeMap> reverse_edges_{nullptr};
307
308 /** \brief Stores the vertices of the graph. */
309 std::vector< VertexDescriptor > vertices_{};
310
311 /** \brief Stores the information about the edges that were added to the graph. It is used to avoid the duplicate edges. */
312 std::vector< std::set<int> > edge_marker_{};
313
314 /** \brief Stores the vertex that serves as source. */
316
317 /** \brief Stores the vertex that serves as sink. */
319
320 /** \brief Stores the maximum flow value that was calculated during the segmentation. */
321 double max_flow_{0.0};
322
323 public:
325 };
326}
327
328#ifdef PCL_NO_PRECOMPILE
329#include <pcl/segmentation/impl/min_cut_segmentation.hpp>
330#endif
This class implements the segmentation algorithm based on minimal cut of the graph.
boost::property_map< mGraph, boost::vertex_index_t >::type IndexMap
MinCutSegmentation()
Constructor that sets default values for member variables.
boost::graph_traits< mGraph >::out_edge_iterator OutEdgeIterator
boost::property_map< mGraph, boost::edge_capacity_t >::type CapacityMap
boost::property_map< mGraph, boost::edge_reverse_t >::type ReverseEdgeMap
shared_ptr< mGraph > mGraphPtr
Traits::vertex_descriptor VertexDescriptor
typename PointCloud::ConstPtr PointCloudConstPtr
boost::graph_traits< mGraph >::vertex_iterator VertexIterator
typename KdTree::Ptr KdTreePtr
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
PCL base class.
Definition pcl_base.h:70
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
Generic search class.
Definition search.h:75
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
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.
A point structure representing Euclidean xyz coordinates, and the RGB color.