Point Cloud Library (PCL) 1.13.0
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feature.h
1/*
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40
41#pragma once
42
43#if defined __GNUC__
44# pragma GCC system_header
45#endif
46
47// PCL includes
48#include <pcl/memory.h>
49#include <pcl/pcl_base.h>
50#include <pcl/pcl_macros.h>
51#include <pcl/search/search.h>
52
53#include <functional>
54
55namespace pcl
56{
57 /** \brief Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares
58 * plane normal and surface curvature.
59 * \param covariance_matrix the 3x3 covariance matrix
60 * \param point a point lying on the least-squares plane (SSE aligned)
61 * \param plane_parameters the resultant plane parameters as: a, b, c, d (ax + by + cz + d = 0)
62 * \param curvature the estimated surface curvature as a measure of
63 * \f[
64 * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
65 * \f]
66 * \ingroup features
67 */
68 inline void
69 solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
70 const Eigen::Vector4f &point,
71 Eigen::Vector4f &plane_parameters, float &curvature);
72
73 /** \brief Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squares
74 * plane normal and surface curvature.
75 * \param covariance_matrix the 3x3 covariance matrix
76 * \param nx the resultant X component of the plane normal
77 * \param ny the resultant Y component of the plane normal
78 * \param nz the resultant Z component of the plane normal
79 * \param curvature the estimated surface curvature as a measure of
80 * \f[
81 * \lambda_0 / (\lambda_0 + \lambda_1 + \lambda_2)
82 * \f]
83 * \ingroup features
84 */
85 inline void
86 solvePlaneParameters (const Eigen::Matrix3f &covariance_matrix,
87 float &nx, float &ny, float &nz, float &curvature);
88
89 ////////////////////////////////////////////////////////////////////////////////////////////
90 ////////////////////////////////////////////////////////////////////////////////////////////
91 ////////////////////////////////////////////////////////////////////////////////////////////
92 /** \brief Feature represents the base feature class. Some generic 3D operations that
93 * are applicable to all features are defined here as static methods.
94 *
95 * \attention
96 * The convention for a feature descriptor is:
97 * - if the nearest neighbors for the query point at which the descriptor is to be computed cannot be
98 * determined, the descriptor values will be set to NaN (not a number)
99 * - it is impossible to estimate a feature descriptor for a point that doesn't have finite 3D coordinates.
100 * Therefore, any point that has NaN data on x, y, or z, will most likely have its descriptor set to NaN.
101 *
102 * \author Radu B. Rusu
103 * \ingroup features
104 */
105 template <typename PointInT, typename PointOutT>
106 class Feature : public PCLBase<PointInT>
107 {
108 public:
109 using PCLBase<PointInT>::indices_;
110 using PCLBase<PointInT>::input_;
111
113
114 using Ptr = shared_ptr< Feature<PointInT, PointOutT> >;
115 using ConstPtr = shared_ptr< const Feature<PointInT, PointOutT> >;
116
118 using KdTreePtr = typename KdTree::Ptr;
119
123
125
126 using SearchMethod = std::function<int (std::size_t, double, pcl::Indices &, std::vector<float> &)>;
127 using SearchMethodSurface = std::function<int (const PointCloudIn &cloud, std::size_t index, double, pcl::Indices &, std::vector<float> &)>;
128
129 public:
130 /** \brief Empty constructor. */
133 surface_(), tree_(),
135 fake_surface_(false)
136 {}
137
138 /** \brief Provide a pointer to a dataset to add additional information
139 * to estimate the features for every point in the input dataset. This
140 * is optional, if this is not set, it will only use the data in the
141 * input cloud to estimate the features. This is useful when you only
142 * need to compute the features for a downsampled cloud.
143 * \param[in] cloud a pointer to a PointCloud message
144 */
145 inline void
147 {
148 surface_ = cloud;
149 fake_surface_ = false;
150 //use_surface_ = true;
151 }
152
153 /** \brief Get a pointer to the surface point cloud dataset. */
156 {
157 return (surface_);
158 }
159
160 /** \brief Provide a pointer to the search object.
161 * \param[in] tree a pointer to the spatial search object.
162 */
163 inline void
164 setSearchMethod (const KdTreePtr &tree) { tree_ = tree; }
165
166 /** \brief Get a pointer to the search method used. */
167 inline KdTreePtr
169 {
170 return (tree_);
171 }
172
173 /** \brief Get the internal search parameter. */
174 inline double
176 {
177 return (search_parameter_);
178 }
179
180 /** \brief Set the number of k nearest neighbors to use for the feature estimation.
181 * \param[in] k the number of k-nearest neighbors
182 */
183 inline void
184 setKSearch (int k) { k_ = k; }
185
186 /** \brief get the number of k nearest neighbors used for the feature estimation. */
187 inline int
188 getKSearch () const
189 {
190 return (k_);
191 }
192
193 /** \brief Set the sphere radius that is to be used for determining the nearest neighbors used for the feature
194 * estimation.
195 * \param[in] radius the sphere radius used as the maximum distance to consider a point a neighbor
196 */
197 inline void
198 setRadiusSearch (double radius)
199 {
200 search_radius_ = radius;
201 }
202
203 /** \brief Get the sphere radius used for determining the neighbors. */
204 inline double
206 {
207 return (search_radius_);
208 }
209
210 /** \brief Base method for feature estimation for all points given in
211 * <setInputCloud (), setIndices ()> using the surface in setSearchSurface ()
212 * and the spatial locator in setSearchMethod ()
213 * \param[out] output the resultant point cloud model dataset containing the estimated features
214 */
215 void
216 compute (PointCloudOut &output);
217
218 protected:
219 /** \brief The feature name. */
220 std::string feature_name_;
221
222 /** \brief The search method template for points. */
224
225 /** \brief An input point cloud describing the surface that is to be used
226 * for nearest neighbors estimation.
227 */
229
230 /** \brief A pointer to the spatial search object. */
232
233 /** \brief The actual search parameter (from either \a search_radius_ or \a k_). */
235
236 /** \brief The nearest neighbors search radius for each point. */
238
239 /** \brief The number of K nearest neighbors to use for each point. */
240 int k_;
241
242 /** \brief Get a string representation of the name of this class. */
243 inline const std::string&
244 getClassName () const { return (feature_name_); }
245
246 /** \brief This method should get called before starting the actual computation. */
247 virtual bool
248 initCompute ();
249
250 /** \brief This method should get called after ending the actual computation. */
251 virtual bool
252 deinitCompute ();
253
254 /** \brief If no surface is given, we use the input PointCloud as the surface. */
256
257 /** \brief Search for k-nearest neighbors using the spatial locator from
258 * \a setSearchmethod, and the given surface from \a setSearchSurface.
259 * \param[in] index the index of the query point
260 * \param[in] parameter the search parameter (either k or radius)
261 * \param[out] indices the resultant vector of indices representing the k-nearest neighbors
262 * \param[out] distances the resultant vector of distances representing the distances from the query point to the
263 * k-nearest neighbors
264 *
265 * \return the number of neighbors found. If no neighbors are found or an error occurred, return 0.
266 */
267 inline int
268 searchForNeighbors (std::size_t index, double parameter,
269 pcl::Indices &indices, std::vector<float> &distances) const
270 {
271 return (search_method_surface_ (*input_, index, parameter, indices, distances));
272 }
273
274 /** \brief Search for k-nearest neighbors using the spatial locator from
275 * \a setSearchmethod, and the given surface from \a setSearchSurface.
276 * \param[in] cloud the query point cloud
277 * \param[in] index the index of the query point in \a cloud
278 * \param[in] parameter the search parameter (either k or radius)
279 * \param[out] indices the resultant vector of indices representing the k-nearest neighbors
280 * \param[out] distances the resultant vector of distances representing the distances from the query point to the
281 * k-nearest neighbors
282 *
283 * \return the number of neighbors found. If no neighbors are found or an error occurred, return 0.
284 */
285 inline int
286 searchForNeighbors (const PointCloudIn &cloud, std::size_t index, double parameter,
287 pcl::Indices &indices, std::vector<float> &distances) const
288 {
289 return (search_method_surface_ (cloud, index, parameter, indices, distances));
290 }
291
292 private:
293 /** \brief Abstract feature estimation method.
294 * \param[out] output the resultant features
295 */
296 virtual void
297 computeFeature (PointCloudOut &output) = 0;
298
299 public:
301 };
302
303
304 ////////////////////////////////////////////////////////////////////////////////////////////
305 ////////////////////////////////////////////////////////////////////////////////////////////
306 ////////////////////////////////////////////////////////////////////////////////////////////
307 template <typename PointInT, typename PointNT, typename PointOutT>
308 class FeatureFromNormals : public Feature<PointInT, PointOutT>
309 {
310 using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
311 using PointCloudInPtr = typename PointCloudIn::Ptr;
312 using PointCloudInConstPtr = typename PointCloudIn::ConstPtr;
313 using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
314
315 public:
319
320 using Ptr = shared_ptr< FeatureFromNormals<PointInT, PointNT, PointOutT> >;
321 using ConstPtr = shared_ptr< const FeatureFromNormals<PointInT, PointNT, PointOutT> >;
322
323 // Members derived from the base class
324 using Feature<PointInT, PointOutT>::input_;
325 using Feature<PointInT, PointOutT>::surface_;
326 using Feature<PointInT, PointOutT>::getClassName;
327
328 /** \brief Empty constructor. */
330
331 /** \brief Provide a pointer to the input dataset that contains the point normals of
332 * the XYZ dataset.
333 * In case of search surface is set to be different from the input cloud,
334 * normals should correspond to the search surface, not the input cloud!
335 * \param[in] normals the const boost shared pointer to a PointCloud of normals.
336 * By convention, L2 norm of each normal should be 1.
337 */
338 inline void
339 setInputNormals (const PointCloudNConstPtr &normals) { normals_ = normals; }
340
341 /** \brief Get a pointer to the normals of the input XYZ point cloud dataset. */
343 getInputNormals () const { return (normals_); }
344
345 protected:
346 /** \brief A pointer to the input dataset that contains the point normals of the XYZ
347 * dataset.
348 */
350
351 /** \brief This method should get called before starting the actual computation. */
352 virtual bool
353 initCompute ();
354
355 public:
357 };
358
359 ////////////////////////////////////////////////////////////////////////////////////////////
360 ////////////////////////////////////////////////////////////////////////////////////////////
361 ////////////////////////////////////////////////////////////////////////////////////////////
362 template <typename PointInT, typename PointLT, typename PointOutT>
363 class FeatureFromLabels : public Feature<PointInT, PointOutT>
364 {
365 using PointCloudIn = typename Feature<PointInT, PointOutT>::PointCloudIn;
366 using PointCloudInPtr = typename PointCloudIn::Ptr;
367 using PointCloudInConstPtr = typename PointCloudIn::ConstPtr;
368
369 using PointCloudL = pcl::PointCloud<PointLT>;
370 using PointCloudNPtr = typename PointCloudL::Ptr;
371 using PointCloudLConstPtr = typename PointCloudL::ConstPtr;
372
373 using PointCloudOut = typename Feature<PointInT, PointOutT>::PointCloudOut;
374
375 public:
376 using Ptr = shared_ptr< FeatureFromLabels<PointInT, PointLT, PointOutT> >;
377 using ConstPtr = shared_ptr< const FeatureFromLabels<PointInT, PointLT, PointOutT> >;
378
379 // Members derived from the base class
380 using Feature<PointInT, PointOutT>::input_;
381 using Feature<PointInT, PointOutT>::surface_;
382 using Feature<PointInT, PointOutT>::getClassName;
383 using Feature<PointInT, PointOutT>::k_;
384
385 /** \brief Empty constructor. */
387 {
388 k_ = 1; // Search tree is not always used here.
389 }
390
391 /** \brief Provide a pointer to the input dataset that contains the point labels of
392 * the XYZ dataset.
393 * In case of search surface is set to be different from the input cloud,
394 * labels should correspond to the search surface, not the input cloud!
395 * \param[in] labels the const boost shared pointer to a PointCloud of labels.
396 */
397 inline void
398 setInputLabels (const PointCloudLConstPtr &labels)
399 {
400 labels_ = labels;
401 }
402
403 /** \brief Get a pointer to the labels of the input XYZ point cloud dataset. */
404 inline PointCloudLConstPtr
406 {
407 return (labels_);
408 }
409
410 protected:
411 /** \brief A pointer to the input dataset that contains the point labels of the XYZ
412 * dataset.
413 */
414 PointCloudLConstPtr labels_;
415
416 /** \brief This method should get called before starting the actual computation. */
417 virtual bool
418 initCompute ();
419
420 public:
422 };
423
424 ////////////////////////////////////////////////////////////////////////////////////////////
425 ////////////////////////////////////////////////////////////////////////////////////////////
426 ////////////////////////////////////////////////////////////////////////////////////////////
427 /** \brief FeatureWithLocalReferenceFrames provides a public interface for descriptor
428 * extractor classes which need a local reference frame at each input keypoint.
429 *
430 * \attention
431 * This interface is for backward compatibility with existing code and in the future it could be
432 * merged with pcl::Feature. Subclasses should call the protected method initLocalReferenceFrames ()
433 * to correctly initialize the frames_ member.
434 *
435 * \author Nicola Fioraio
436 * \ingroup features
437 */
438 template <typename PointInT, typename PointRFT>
440 {
441 public:
443 using PointCloudLRFPtr = typename PointCloudLRF::Ptr;
444 using PointCloudLRFConstPtr = typename PointCloudLRF::ConstPtr;
445
446 /** \brief Empty constructor. */
448
449 /** \brief Default virtual destructor. */
450 virtual
452
453 /** \brief Provide a pointer to the input dataset that contains the local
454 * reference frames of the XYZ dataset.
455 * In case of search surface is set to be different from the input cloud,
456 * local reference frames should correspond to the input cloud, not the search surface!
457 * \param[in] frames the const boost shared pointer to a PointCloud of reference frames.
458 */
459 inline void
461 {
462 frames_ = frames;
463 frames_never_defined_ = false;
464 }
465
466 /** \brief Get a pointer to the local reference frames. */
469 {
470 return (frames_);
471 }
472
473 protected:
474 /** \brief A boost shared pointer to the local reference frames. */
476 /** \brief The user has never set the frames. */
478
479 /** \brief Check if frames_ has been correctly initialized and compute it if needed.
480 * \param input the subclass' input cloud dataset.
481 * \param lrf_estimation a pointer to a local reference frame estimation class to be used as default.
482 * \return true if frames_ has been correctly initialized.
483 */
485 virtual bool
486 initLocalReferenceFrames (const std::size_t& indices_size,
487 const LRFEstimationPtr& lrf_estimation = LRFEstimationPtr());
488 };
489}
490
491#include <pcl/features/impl/feature.hpp>
void setInputLabels(const PointCloudLConstPtr &labels)
Provide a pointer to the input dataset that contains the point labels of the XYZ dataset.
Definition feature.h:398
shared_ptr< const FeatureFromLabels< PointInT, PointLT, PointOutT > > ConstPtr
Definition feature.h:377
FeatureFromLabels()
Empty constructor.
Definition feature.h:386
PointCloudLConstPtr labels_
A pointer to the input dataset that contains the point labels of the XYZ dataset.
Definition feature.h:414
virtual bool initCompute()
This method should get called before starting the actual computation.
Definition feature.hpp:265
shared_ptr< FeatureFromLabels< PointInT, PointLT, PointOutT > > Ptr
Definition feature.h:376
PointCloudLConstPtr getInputLabels() const
Get a pointer to the labels of the input XYZ point cloud dataset.
Definition feature.h:405
FeatureFromNormals()
Empty constructor.
Definition feature.h:329
virtual bool initCompute()
This method should get called before starting the actual computation.
Definition feature.hpp:232
PointCloudNConstPtr getInputNormals() const
Get a pointer to the normals of the input XYZ point cloud dataset.
Definition feature.h:343
void setInputNormals(const PointCloudNConstPtr &normals)
Provide a pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition feature.h:339
shared_ptr< FeatureFromNormals< PointInT, PointNT, PointOutT > > Ptr
Definition feature.h:320
typename PointCloudN::Ptr PointCloudNPtr
Definition feature.h:317
typename PointCloudN::ConstPtr PointCloudNConstPtr
Definition feature.h:318
PointCloudNConstPtr normals_
A pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition feature.h:349
shared_ptr< const FeatureFromNormals< PointInT, PointNT, PointOutT > > ConstPtr
Definition feature.h:321
Feature represents the base feature class.
Definition feature.h:107
typename PointCloudIn::Ptr PointCloudInPtr
Definition feature.h:121
PointCloudInConstPtr getSearchSurface() const
Get a pointer to the surface point cloud dataset.
Definition feature.h:155
double search_parameter_
The actual search parameter (from either search_radius_ or k_).
Definition feature.h:234
const std::string & getClassName() const
Get a string representation of the name of this class.
Definition feature.h:244
int searchForNeighbors(std::size_t index, double parameter, pcl::Indices &indices, std::vector< float > &distances) const
Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface ...
Definition feature.h:268
void setSearchSurface(const PointCloudInConstPtr &cloud)
Provide a pointer to a dataset to add additional information to estimate the features for every point...
Definition feature.h:146
pcl::PointCloud< PointOutT > PointCloudOut
Definition feature.h:124
double search_radius_
The nearest neighbors search radius for each point.
Definition feature.h:237
virtual bool initCompute()
This method should get called before starting the actual computation.
Definition feature.hpp:95
std::function< int(std::size_t, double, pcl::Indices &, std::vector< float > &)> SearchMethod
Definition feature.h:126
int k_
The number of K nearest neighbors to use for each point.
Definition feature.h:240
void setRadiusSearch(double radius)
Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...
Definition feature.h:198
shared_ptr< Feature< PointInT, PointOutT > > Ptr
Definition feature.h:114
void setKSearch(int k)
Set the number of k nearest neighbors to use for the feature estimation.
Definition feature.h:184
std::string feature_name_
The feature name.
Definition feature.h:220
shared_ptr< const Feature< PointInT, PointOutT > > ConstPtr
Definition feature.h:115
KdTreePtr tree_
A pointer to the spatial search object.
Definition feature.h:231
typename KdTree::Ptr KdTreePtr
Definition feature.h:118
double getSearchParameter() const
Get the internal search parameter.
Definition feature.h:175
int getKSearch() const
get the number of k nearest neighbors used for the feature estimation.
Definition feature.h:188
std::function< int(const PointCloudIn &cloud, std::size_t index, double, pcl::Indices &, std::vector< float > &)> SearchMethodSurface
Definition feature.h:127
Feature()
Empty constructor.
Definition feature.h:131
PointCloudInConstPtr surface_
An input point cloud describing the surface that is to be used for nearest neighbors estimation.
Definition feature.h:228
typename PointCloudIn::ConstPtr PointCloudInConstPtr
Definition feature.h:122
bool fake_surface_
If no surface is given, we use the input PointCloud as the surface.
Definition feature.h:255
int searchForNeighbors(const PointCloudIn &cloud, std::size_t index, double parameter, pcl::Indices &indices, std::vector< float > &distances) const
Search for k-nearest neighbors using the spatial locator from setSearchmethod, and the given surface ...
Definition feature.h:286
virtual bool deinitCompute()
This method should get called after ending the actual computation.
Definition feature.hpp:181
double getRadiusSearch() const
Get the sphere radius used for determining the neighbors.
Definition feature.h:205
KdTreePtr getSearchMethod() const
Get a pointer to the search method used.
Definition feature.h:168
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
Definition feature.h:164
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition feature.hpp:194
SearchMethodSurface search_method_surface_
The search method template for points.
Definition feature.h:223
FeatureWithLocalReferenceFrames provides a public interface for descriptor extractor classes which ne...
Definition feature.h:440
PointCloudLRFConstPtr frames_
A boost shared pointer to the local reference frames.
Definition feature.h:475
typename PointCloudLRF::Ptr PointCloudLRFPtr
Definition feature.h:443
PointCloudLRFConstPtr getInputReferenceFrames() const
Get a pointer to the local reference frames.
Definition feature.h:468
typename Feature< PointInT, PointRFT >::Ptr LRFEstimationPtr
Check if frames_ has been correctly initialized and compute it if needed.
Definition feature.h:484
virtual bool initLocalReferenceFrames(const std::size_t &indices_size, const LRFEstimationPtr &lrf_estimation=LRFEstimationPtr())
Definition feature.hpp:294
pcl::PointCloud< PointRFT > PointCloudLRF
Definition feature.h:442
virtual ~FeatureWithLocalReferenceFrames()=default
Default virtual destructor.
void setInputReferenceFrames(const PointCloudLRFConstPtr &frames)
Provide a pointer to the input dataset that contains the local reference frames of the XYZ dataset.
Definition feature.h:460
typename PointCloudLRF::ConstPtr PointCloudLRFConstPtr
Definition feature.h:444
FeatureWithLocalReferenceFrames()
Empty constructor.
Definition feature.h:447
bool frames_never_defined_
The user has never set the frames.
Definition feature.h:477
PCL base class.
Definition pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
shared_ptr< PointCloud< PointInT > > Ptr
shared_ptr< const PointCloud< PointInT > > ConstPtr
shared_ptr< pcl::search::Search< PointInT > > Ptr
Definition search.h:81
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition memory.h:63
void solvePlaneParameters(const Eigen::Matrix3f &covariance_matrix, const Eigen::Vector4f &point, Eigen::Vector4f &plane_parameters, float &curvature)
Solve the eigenvalues and eigenvectors of a given 3x3 covariance matrix, and estimate the least-squar...
Definition feature.hpp:52
Defines functions, macros and traits for allocating and using memory.
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
Defines all the PCL and non-PCL macros used.