Point Cloud Library (PCL)  1.12.1-dev
iss_3d.hpp
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37 
38 #ifndef PCL_ISS_KEYPOINT3D_IMPL_H_
39 #define PCL_ISS_KEYPOINT3D_IMPL_H_
40 
41 #include <Eigen/Eigenvalues> // for SelfAdjointEigenSolver
42 #include <pcl/features/boundary.h>
43 #include <pcl/features/normal_3d.h>
44 #include <pcl/features/integral_image_normal.h>
45 
46 #include <pcl/keypoints/iss_3d.h>
47 
48 //////////////////////////////////////////////////////////////////////////////////////////////
49 template<typename PointInT, typename PointOutT, typename NormalT> void
51 {
52  salient_radius_ = salient_radius;
53 }
54 
55 //////////////////////////////////////////////////////////////////////////////////////////////
56 template<typename PointInT, typename PointOutT, typename NormalT> void
58 {
59  non_max_radius_ = non_max_radius;
60 }
61 
62 //////////////////////////////////////////////////////////////////////////////////////////////
63 template<typename PointInT, typename PointOutT, typename NormalT> void
65 {
66  normal_radius_ = normal_radius;
67 }
68 
69 //////////////////////////////////////////////////////////////////////////////////////////////
70 template<typename PointInT, typename PointOutT, typename NormalT> void
72 {
73  border_radius_ = border_radius;
74 }
75 
76 //////////////////////////////////////////////////////////////////////////////////////////////
77 template<typename PointInT, typename PointOutT, typename NormalT> void
79 {
80  gamma_21_ = gamma_21;
81 }
82 
83 //////////////////////////////////////////////////////////////////////////////////////////////
84 template<typename PointInT, typename PointOutT, typename NormalT> void
86 {
87  gamma_32_ = gamma_32;
88 }
89 
90 //////////////////////////////////////////////////////////////////////////////////////////////
91 template<typename PointInT, typename PointOutT, typename NormalT> void
93 {
94  min_neighbors_ = min_neighbors;
95 }
96 
97 //////////////////////////////////////////////////////////////////////////////////////////////
98 template<typename PointInT, typename PointOutT, typename NormalT> void
100 {
101  normals_ = normals;
102 }
103 
104 //////////////////////////////////////////////////////////////////////////////////////////////
105 template<typename PointInT, typename PointOutT, typename NormalT> void
107 {
108  if (nr_threads == 0)
109 #ifdef _OPENMP
110  threads_ = omp_get_num_procs();
111 #else
112  threads_ = 1;
113 #endif
114  else
115  threads_ = nr_threads;
116 }
117 
118 //////////////////////////////////////////////////////////////////////////////////////////////
119 template<typename PointInT, typename PointOutT, typename NormalT> bool*
120 pcl::ISSKeypoint3D<PointInT, PointOutT, NormalT>::getBoundaryPoints (PointCloudIn &input, double border_radius, float angle_threshold)
121 {
122  bool* edge_points = new bool [input.size ()];
123 
124  Eigen::Vector4f u = Eigen::Vector4f::Zero ();
125  Eigen::Vector4f v = Eigen::Vector4f::Zero ();
126 
128  boundary_estimator.setInputCloud (input_);
129 
130 #pragma omp parallel for \
131  default(none) \
132  shared(angle_threshold, boundary_estimator, border_radius, edge_points, input) \
133  firstprivate(u, v) \
134  num_threads(threads_)
135  for (int index = 0; index < int (input.size ()); index++)
136  {
137  edge_points[index] = false;
138  PointInT current_point = input[index];
139 
140  if (pcl::isFinite(current_point))
141  {
142  pcl::Indices nn_indices;
143  std::vector<float> nn_distances;
144  int n_neighbors;
145 
146  this->searchForNeighbors (index, border_radius, nn_indices, nn_distances);
147 
148  n_neighbors = static_cast<int> (nn_indices.size ());
149 
150  if (n_neighbors >= min_neighbors_)
151  {
152  boundary_estimator.getCoordinateSystemOnPlane ((*normals_)[index], u, v);
153 
154  if (boundary_estimator.isBoundaryPoint (input, static_cast<int> (index), nn_indices, u, v, angle_threshold))
155  edge_points[index] = true;
156  }
157  }
158  }
159 
160  return (edge_points);
161 }
162 
163 //////////////////////////////////////////////////////////////////////////////////////////////
164 template<typename PointInT, typename PointOutT, typename NormalT> void
165 pcl::ISSKeypoint3D<PointInT, PointOutT, NormalT>::getScatterMatrix (const int& current_index, Eigen::Matrix3d &cov_m)
166 {
167  const PointInT& current_point = (*input_)[current_index];
168 
169  double central_point[3]{};
170 
171  central_point[0] = current_point.x;
172  central_point[1] = current_point.y;
173  central_point[2] = current_point.z;
174 
175  cov_m = Eigen::Matrix3d::Zero ();
176 
177  pcl::Indices nn_indices;
178  std::vector<float> nn_distances;
179  int n_neighbors;
180 
181  this->searchForNeighbors (current_index, salient_radius_, nn_indices, nn_distances);
182 
183  n_neighbors = static_cast<int> (nn_indices.size ());
184 
185  if (n_neighbors < min_neighbors_)
186  return;
187 
188  double cov[9]{};
189 
190  for (const auto& n_idx : nn_indices)
191  {
192  const PointInT& n_point = (*input_)[n_idx];
193 
194  double neigh_point[3]{};
195 
196  neigh_point[0] = n_point.x;
197  neigh_point[1] = n_point.y;
198  neigh_point[2] = n_point.z;
199 
200  for (int i = 0; i < 3; i++)
201  for (int j = 0; j < 3; j++)
202  cov[i * 3 + j] += (neigh_point[i] - central_point[i]) * (neigh_point[j] - central_point[j]);
203  }
204 
205  cov_m << cov[0], cov[1], cov[2],
206  cov[3], cov[4], cov[5],
207  cov[6], cov[7], cov[8];
208 }
209 
210 //////////////////////////////////////////////////////////////////////////////////////////////
211 template<typename PointInT, typename PointOutT, typename NormalT> bool
213 {
215  {
216  PCL_ERROR ("[pcl::%s::initCompute] init failed!\n", name_.c_str ());
217  return (false);
218  }
219  if (salient_radius_ <= 0)
220  {
221  PCL_ERROR ("[pcl::%s::initCompute] : the salient radius (%f) must be strict positive!\n",
222  name_.c_str (), salient_radius_);
223  return (false);
224  }
225  if (non_max_radius_ <= 0)
226  {
227  PCL_ERROR ("[pcl::%s::initCompute] : the non maxima radius (%f) must be strict positive!\n",
228  name_.c_str (), non_max_radius_);
229  return (false);
230  }
231  if (gamma_21_ <= 0)
232  {
233  PCL_ERROR ("[pcl::%s::initCompute] : the threshold on the ratio between the 2nd and the 1rst eigenvalue (%f) must be strict positive!\n",
234  name_.c_str (), gamma_21_);
235  return (false);
236  }
237  if (gamma_32_ <= 0)
238  {
239  PCL_ERROR ("[pcl::%s::initCompute] : the threshold on the ratio between the 3rd and the 2nd eigenvalue (%f) must be strict positive!\n",
240  name_.c_str (), gamma_32_);
241  return (false);
242  }
243  if (min_neighbors_ <= 0)
244  {
245  PCL_ERROR ("[pcl::%s::initCompute] : the minimum number of neighbors (%f) must be strict positive!\n",
246  name_.c_str (), min_neighbors_);
247  return (false);
248  }
249 
250  delete[] third_eigen_value_;
251 
252  third_eigen_value_ = new double[input_->size ()]{};
253 
254  delete[] edge_points_;
255 
256  if (border_radius_ > 0.0)
257  {
258  if (normals_->empty ())
259  {
260  if (normal_radius_ <= 0.)
261  {
262  PCL_ERROR ("[pcl::%s::initCompute] : the radius used to estimate surface normals (%f) must be positive!\n",
263  name_.c_str (), normal_radius_);
264  return (false);
265  }
266 
267  PointCloudNPtr normal_ptr (new PointCloudN ());
268  if (input_->height == 1 )
269  {
271  normal_estimation.setInputCloud (surface_);
272  normal_estimation.setRadiusSearch (normal_radius_);
273  normal_estimation.compute (*normal_ptr);
274  }
275  else
276  {
279  normal_estimation.setInputCloud (surface_);
280  normal_estimation.setNormalSmoothingSize (5.0);
281  normal_estimation.compute (*normal_ptr);
282  }
283  normals_ = normal_ptr;
284  }
285  if (normals_->size () != surface_->size ())
286  {
287  PCL_ERROR ("[pcl::%s::initCompute] normals given, but the number of normals does not match the number of input points!\n", name_.c_str ());
288  return (false);
289  }
290  }
291  else if (border_radius_ < 0.0)
292  {
293  PCL_ERROR ("[pcl::%s::initCompute] : the border radius used to estimate boundary points (%f) must be positive!\n",
294  name_.c_str (), border_radius_);
295  return (false);
296  }
297 
298  return (true);
299 }
300 
301 //////////////////////////////////////////////////////////////////////////////////////////////
302 template<typename PointInT, typename PointOutT, typename NormalT> void
304 {
305  // Make sure the output cloud is empty
306  output.clear ();
307 
308  if (border_radius_ > 0.0)
309  edge_points_ = getBoundaryPoints (*(input_->makeShared ()), border_radius_, angle_threshold_);
310 
311  bool* borders = new bool [input_->size()];
312 
313 #pragma omp parallel for \
314  default(none) \
315  shared(borders) \
316  num_threads(threads_)
317  for (int index = 0; index < int (input_->size ()); index++)
318  {
319  borders[index] = false;
320  PointInT current_point = (*input_)[index];
321 
322  if ((border_radius_ > 0.0) && (pcl::isFinite(current_point)))
323  {
324  pcl::Indices nn_indices;
325  std::vector<float> nn_distances;
326 
327  this->searchForNeighbors (index, border_radius_, nn_indices, nn_distances);
328 
329  for (const auto &nn_index : nn_indices)
330  {
331  if (edge_points_[nn_index])
332  {
333  borders[index] = true;
334  break;
335  }
336  }
337  }
338  }
339 
340 #ifdef _OPENMP
341  Eigen::Vector3d *omp_mem = new Eigen::Vector3d[threads_];
342 
343  for (std::size_t i = 0; i < threads_; i++)
344  omp_mem[i].setZero (3);
345 #else
346  auto *omp_mem = new Eigen::Vector3d[1];
347 
348  omp_mem[0].setZero (3);
349 #endif
350 
351  double *prg_local_mem = new double[input_->size () * 3];
352  double **prg_mem = new double * [input_->size ()];
353 
354  for (std::size_t i = 0; i < input_->size (); i++)
355  prg_mem[i] = prg_local_mem + 3 * i;
356 
357 #pragma omp parallel for \
358  default(none) \
359  shared(borders, omp_mem, prg_mem) \
360  num_threads(threads_)
361  for (int index = 0; index < static_cast<int> (input_->size ()); index++)
362  {
363 #ifdef _OPENMP
364  int tid = omp_get_thread_num ();
365 #else
366  int tid = 0;
367 #endif
368  PointInT current_point = (*input_)[index];
369 
370  if ((!borders[index]) && pcl::isFinite(current_point))
371  {
372  //if the considered point is not a border point and the point is "finite", then compute the scatter matrix
373  Eigen::Matrix3d cov_m = Eigen::Matrix3d::Zero ();
374  getScatterMatrix (static_cast<int> (index), cov_m);
375 
376  Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> solver (cov_m);
377 
378  const double& e1c = solver.eigenvalues ()[2];
379  const double& e2c = solver.eigenvalues ()[1];
380  const double& e3c = solver.eigenvalues ()[0];
381 
382  if (!std::isfinite (e1c) || !std::isfinite (e2c) || !std::isfinite (e3c))
383  continue;
384 
385  if (e3c < 0)
386  {
387  PCL_WARN ("[pcl::%s::detectKeypoints] : The third eigenvalue is negative! Skipping the point with index %i.\n",
388  name_.c_str (), index);
389  continue;
390  }
391 
392  omp_mem[tid][0] = e2c / e1c;
393  omp_mem[tid][1] = e3c / e2c;
394  omp_mem[tid][2] = e3c;
395  }
396 
397  for (Eigen::Index d = 0; d < omp_mem[tid].size (); d++)
398  prg_mem[index][d] = omp_mem[tid][d];
399  }
400 
401  for (int index = 0; index < int (input_->size ()); index++)
402  {
403  if (!borders[index])
404  {
405  if ((prg_mem[index][0] < gamma_21_) && (prg_mem[index][1] < gamma_32_))
406  third_eigen_value_[index] = prg_mem[index][2];
407  }
408  }
409 
410  bool* feat_max = new bool [input_->size()];
411 
412 #pragma omp parallel for \
413  default(none) \
414  shared(feat_max) \
415  num_threads(threads_)
416  for (int index = 0; index < int (input_->size ()); index++)
417  {
418  feat_max [index] = false;
419  PointInT current_point = (*input_)[index];
420 
421  if ((third_eigen_value_[index] > 0.0) && (pcl::isFinite(current_point)))
422  {
423  pcl::Indices nn_indices;
424  std::vector<float> nn_distances;
425  int n_neighbors;
426 
427  this->searchForNeighbors (index, non_max_radius_, nn_indices, nn_distances);
428 
429  n_neighbors = static_cast<int> (nn_indices.size ());
430 
431  if (n_neighbors >= min_neighbors_)
432  {
433  bool is_max = true;
434 
435  for (const auto& j : nn_indices)
436  if (third_eigen_value_[index] < third_eigen_value_[j])
437  is_max = false;
438  if (is_max)
439  feat_max[index] = true;
440  }
441  }
442  }
443 
444 #pragma omp parallel for \
445  default(none) \
446  shared(feat_max, output) \
447  num_threads(threads_)
448  for (int index = 0; index < int (input_->size ()); index++)
449  {
450  if (feat_max[index])
451 #pragma omp critical
452  {
453  PointOutT p;
454  p.getVector3fMap () = (*input_)[index].getVector3fMap ();
455  output.push_back(p);
456  keypoints_indices_->indices.push_back (index);
457  }
458  }
459 
460  output.header = input_->header;
461  output.width = output.size ();
462  output.height = 1;
463 
464  // Clear the contents of variables and arrays before the beginning of the next computation.
465  if (border_radius_ > 0.0)
466  normals_.reset (new pcl::PointCloud<NormalT>);
467 
468  delete[] borders;
469  delete[] prg_mem;
470  delete[] prg_local_mem;
471  delete[] feat_max;
472  delete[] omp_mem;
473 }
474 
475 #define PCL_INSTANTIATE_ISSKeypoint3D(T,U,N) template class PCL_EXPORTS pcl::ISSKeypoint3D<T,U,N>;
476 
477 #endif /* PCL_ISS_3D_IMPL_H_ */
BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle cr...
Definition: boundary.h:80
void getCoordinateSystemOnPlane(const PointNT &p_coeff, Eigen::Vector4f &u, Eigen::Vector4f &v)
Get a u-v-n coordinate system that lies on a plane defined by its normal.
Definition: boundary.h:159
bool isBoundaryPoint(const pcl::PointCloud< PointInT > &cloud, int q_idx, const pcl::Indices &indices, const Eigen::Vector4f &u, const Eigen::Vector4f &v, const float angle_threshold)
Check whether a point is a boundary point in a planar patch of projected points given by indices.
Definition: boundary.hpp:51
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
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition: feature.hpp:194
typename PointCloudN::ConstPtr PointCloudNConstPtr
Definition: iss_3d.h:96
void setNumberOfThreads(unsigned int nr_threads=0)
Initialize the scheduler and set the number of threads to use.
Definition: iss_3d.hpp:106
typename Keypoint< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition: iss_3d.h:91
void setNormals(const PointCloudNConstPtr &normals)
Set the normals if pre-calculated normals are available.
Definition: iss_3d.hpp:99
void setBorderRadius(double border_radius)
Set the radius used for the estimation of the boundary points.
Definition: iss_3d.hpp:71
void setSalientRadius(double salient_radius)
Set the radius of the spherical neighborhood used to compute the scatter matrix.
Definition: iss_3d.hpp:50
void getScatterMatrix(const int &current_index, Eigen::Matrix3d &cov_m)
Compute the scatter matrix for a point index.
Definition: iss_3d.hpp:165
void setThreshold21(double gamma_21)
Set the upper bound on the ratio between the second and the first eigenvalue.
Definition: iss_3d.hpp:78
void setMinNeighbors(int min_neighbors)
Set the minimum number of neighbors that has to be found while applying the non maxima suppression al...
Definition: iss_3d.hpp:92
void setNormalRadius(double normal_radius)
Set the radius used for the estimation of the surface normals of the input cloud.
Definition: iss_3d.hpp:64
void setThreshold32(double gamma_32)
Set the upper bound on the ratio between the third and the second eigenvalue.
Definition: iss_3d.hpp:85
bool initCompute() override
Perform the initial checks before computing the keypoints.
Definition: iss_3d.hpp:212
typename PointCloudN::Ptr PointCloudNPtr
Definition: iss_3d.h:95
void setNonMaxRadius(double non_max_radius)
Set the radius for the application of the non maxima supression algorithm.
Definition: iss_3d.hpp:57
bool * getBoundaryPoints(PointCloudIn &input, double border_radius, float angle_threshold)
Compute the boundary points for the given input cloud.
Definition: iss_3d.hpp:120
void detectKeypoints(PointCloudOut &output) override
Detect the keypoints by performing the EVD of the scatter matrix.
Definition: iss_3d.hpp:303
typename Keypoint< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: iss_3d.h:92
Surface normal estimation on organized data using integral images.
void setNormalEstimationMethod(NormalEstimationMethod normal_estimation_method)
Set the normal estimation method.
void setInputCloud(const typename PointCloudIn::ConstPtr &cloud) override
Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
void setNormalSmoothingSize(float normal_smoothing_size)
Set the normal smoothing size.
Keypoint represents the base class for key points.
Definition: keypoint.h:49
NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point.
Definition: normal_3d.h:244
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition: normal_3d.h:332
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: pcl_base.hpp:65
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition: point_tests.h:55
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133