Point Cloud Library (PCL)  1.11.1
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 <pcl/features/boundary.h>
42 #include <pcl/features/normal_3d.h>
43 #include <pcl/features/integral_image_normal.h>
44 
45 #include <pcl/keypoints/iss_3d.h>
46 
47 //////////////////////////////////////////////////////////////////////////////////////////////
48 template<typename PointInT, typename PointOutT, typename NormalT> void
50 {
51  salient_radius_ = salient_radius;
52 }
53 
54 //////////////////////////////////////////////////////////////////////////////////////////////
55 template<typename PointInT, typename PointOutT, typename NormalT> void
57 {
58  non_max_radius_ = non_max_radius;
59 }
60 
61 //////////////////////////////////////////////////////////////////////////////////////////////
62 template<typename PointInT, typename PointOutT, typename NormalT> void
64 {
65  normal_radius_ = normal_radius;
66 }
67 
68 //////////////////////////////////////////////////////////////////////////////////////////////
69 template<typename PointInT, typename PointOutT, typename NormalT> void
71 {
72  border_radius_ = border_radius;
73 }
74 
75 //////////////////////////////////////////////////////////////////////////////////////////////
76 template<typename PointInT, typename PointOutT, typename NormalT> void
78 {
79  gamma_21_ = gamma_21;
80 }
81 
82 //////////////////////////////////////////////////////////////////////////////////////////////
83 template<typename PointInT, typename PointOutT, typename NormalT> void
85 {
86  gamma_32_ = gamma_32;
87 }
88 
89 //////////////////////////////////////////////////////////////////////////////////////////////
90 template<typename PointInT, typename PointOutT, typename NormalT> void
92 {
93  min_neighbors_ = min_neighbors;
94 }
95 
96 //////////////////////////////////////////////////////////////////////////////////////////////
97 template<typename PointInT, typename PointOutT, typename NormalT> void
99 {
100  normals_ = normals;
101 }
102 
103 //////////////////////////////////////////////////////////////////////////////////////////////
104 template<typename PointInT, typename PointOutT, typename NormalT> bool*
105 pcl::ISSKeypoint3D<PointInT, PointOutT, NormalT>::getBoundaryPoints (PointCloudIn &input, double border_radius, float angle_threshold)
106 {
107  bool* edge_points = new bool [input.size ()];
108 
109  Eigen::Vector4f u = Eigen::Vector4f::Zero ();
110  Eigen::Vector4f v = Eigen::Vector4f::Zero ();
111 
113  boundary_estimator.setInputCloud (input_);
114 
115 #pragma omp parallel for \
116  default(none) \
117  shared(angle_threshold, boundary_estimator, border_radius, edge_points, input) \
118  firstprivate(u, v) \
119  num_threads(threads_)
120  for (int index = 0; index < int (input.size ()); index++)
121  {
122  edge_points[index] = false;
123  PointInT current_point = input[index];
124 
125  if (pcl::isFinite(current_point))
126  {
127  std::vector<int> nn_indices;
128  std::vector<float> nn_distances;
129  int n_neighbors;
130 
131  this->searchForNeighbors (static_cast<int> (index), border_radius, nn_indices, nn_distances);
132 
133  n_neighbors = static_cast<int> (nn_indices.size ());
134 
135  if (n_neighbors >= min_neighbors_)
136  {
137  boundary_estimator.getCoordinateSystemOnPlane ((*normals_)[index], u, v);
138 
139  if (boundary_estimator.isBoundaryPoint (input, static_cast<int> (index), nn_indices, u, v, angle_threshold))
140  edge_points[index] = true;
141  }
142  }
143  }
144 
145  return (edge_points);
146 }
147 
148 //////////////////////////////////////////////////////////////////////////////////////////////
149 template<typename PointInT, typename PointOutT, typename NormalT> void
150 pcl::ISSKeypoint3D<PointInT, PointOutT, NormalT>::getScatterMatrix (const int& current_index, Eigen::Matrix3d &cov_m)
151 {
152  const PointInT& current_point = (*input_)[current_index];
153 
154  double central_point[3];
155  memset(central_point, 0, sizeof(double) * 3);
156 
157  central_point[0] = current_point.x;
158  central_point[1] = current_point.y;
159  central_point[2] = current_point.z;
160 
161  cov_m = Eigen::Matrix3d::Zero ();
162 
163  std::vector<int> nn_indices;
164  std::vector<float> nn_distances;
165  int n_neighbors;
166 
167  this->searchForNeighbors (current_index, salient_radius_, nn_indices, nn_distances);
168 
169  n_neighbors = static_cast<int> (nn_indices.size ());
170 
171  if (n_neighbors < min_neighbors_)
172  return;
173 
174  double cov[9];
175  memset(cov, 0, sizeof(double) * 9);
176 
177  for (int n_idx = 0; n_idx < n_neighbors; n_idx++)
178  {
179  const PointInT& n_point = (*input_)[nn_indices[n_idx]];
180 
181  double neigh_point[3];
182  memset(neigh_point, 0, sizeof(double) * 3);
183 
184  neigh_point[0] = n_point.x;
185  neigh_point[1] = n_point.y;
186  neigh_point[2] = n_point.z;
187 
188  for (int i = 0; i < 3; i++)
189  for (int j = 0; j < 3; j++)
190  cov[i * 3 + j] += (neigh_point[i] - central_point[i]) * (neigh_point[j] - central_point[j]);
191  }
192 
193  cov_m << cov[0], cov[1], cov[2],
194  cov[3], cov[4], cov[5],
195  cov[6], cov[7], cov[8];
196 }
197 
198 //////////////////////////////////////////////////////////////////////////////////////////////
199 template<typename PointInT, typename PointOutT, typename NormalT> bool
201 {
203  {
204  PCL_ERROR ("[pcl::%s::initCompute] init failed!\n", name_.c_str ());
205  return (false);
206  }
207  if (salient_radius_ <= 0)
208  {
209  PCL_ERROR ("[pcl::%s::initCompute] : the salient radius (%f) must be strict positive!\n",
210  name_.c_str (), salient_radius_);
211  return (false);
212  }
213  if (non_max_radius_ <= 0)
214  {
215  PCL_ERROR ("[pcl::%s::initCompute] : the non maxima radius (%f) must be strict positive!\n",
216  name_.c_str (), non_max_radius_);
217  return (false);
218  }
219  if (gamma_21_ <= 0)
220  {
221  PCL_ERROR ("[pcl::%s::initCompute] : the threshold on the ratio between the 2nd and the 1rst eigenvalue (%f) must be strict positive!\n",
222  name_.c_str (), gamma_21_);
223  return (false);
224  }
225  if (gamma_32_ <= 0)
226  {
227  PCL_ERROR ("[pcl::%s::initCompute] : the threshold on the ratio between the 3rd and the 2nd eigenvalue (%f) must be strict positive!\n",
228  name_.c_str (), gamma_32_);
229  return (false);
230  }
231  if (min_neighbors_ <= 0)
232  {
233  PCL_ERROR ("[pcl::%s::initCompute] : the minimum number of neighbors (%f) must be strict positive!\n",
234  name_.c_str (), min_neighbors_);
235  return (false);
236  }
237 
238  delete[] third_eigen_value_;
239 
240  third_eigen_value_ = new double[input_->size ()];
241  memset(third_eigen_value_, 0, sizeof(double) * input_->size ());
242 
243  delete[] edge_points_;
244 
245  if (border_radius_ > 0.0)
246  {
247  if (normals_->empty ())
248  {
249  if (normal_radius_ <= 0.)
250  {
251  PCL_ERROR ("[pcl::%s::initCompute] : the radius used to estimate surface normals (%f) must be positive!\n",
252  name_.c_str (), normal_radius_);
253  return (false);
254  }
255 
256  PointCloudNPtr normal_ptr (new PointCloudN ());
257  if (input_->height == 1 )
258  {
260  normal_estimation.setInputCloud (surface_);
261  normal_estimation.setRadiusSearch (normal_radius_);
262  normal_estimation.compute (*normal_ptr);
263  }
264  else
265  {
268  normal_estimation.setInputCloud (surface_);
269  normal_estimation.setNormalSmoothingSize (5.0);
270  normal_estimation.compute (*normal_ptr);
271  }
272  normals_ = normal_ptr;
273  }
274  if (normals_->size () != surface_->size ())
275  {
276  PCL_ERROR ("[pcl::%s::initCompute] normals given, but the number of normals does not match the number of input points!\n", name_.c_str ());
277  return (false);
278  }
279  }
280  else if (border_radius_ < 0.0)
281  {
282  PCL_ERROR ("[pcl::%s::initCompute] : the border radius used to estimate boundary points (%f) must be positive!\n",
283  name_.c_str (), border_radius_);
284  return (false);
285  }
286 
287  return (true);
288 }
289 
290 //////////////////////////////////////////////////////////////////////////////////////////////
291 template<typename PointInT, typename PointOutT, typename NormalT> void
293 {
294  // Make sure the output cloud is empty
295  output.points.clear ();
296 
297  if (border_radius_ > 0.0)
298  edge_points_ = getBoundaryPoints (*(input_->makeShared ()), border_radius_, angle_threshold_);
299 
300  bool* borders = new bool [input_->size()];
301 
302 #pragma omp parallel for \
303  default(none) \
304  shared(borders) \
305  num_threads(threads_)
306  for (int index = 0; index < int (input_->size ()); index++)
307  {
308  borders[index] = false;
309  PointInT current_point = (*input_)[index];
310 
311  if ((border_radius_ > 0.0) && (pcl::isFinite(current_point)))
312  {
313  std::vector<int> nn_indices;
314  std::vector<float> nn_distances;
315 
316  this->searchForNeighbors (static_cast<int> (index), border_radius_, nn_indices, nn_distances);
317 
318  for (const int &nn_index : nn_indices)
319  {
320  if (edge_points_[nn_index])
321  {
322  borders[index] = true;
323  break;
324  }
325  }
326  }
327  }
328 
329 #ifdef _OPENMP
330  Eigen::Vector3d *omp_mem = new Eigen::Vector3d[threads_];
331 
332  for (std::size_t i = 0; i < threads_; i++)
333  omp_mem[i].setZero (3);
334 #else
335  Eigen::Vector3d *omp_mem = new Eigen::Vector3d[1];
336 
337  omp_mem[0].setZero (3);
338 #endif
339 
340  double *prg_local_mem = new double[input_->size () * 3];
341  double **prg_mem = new double * [input_->size ()];
342 
343  for (std::size_t i = 0; i < input_->size (); i++)
344  prg_mem[i] = prg_local_mem + 3 * i;
345 
346 #pragma omp parallel for \
347  default(none) \
348  shared(borders, omp_mem, prg_mem) \
349  num_threads(threads_)
350  for (int index = 0; index < static_cast<int> (input_->size ()); index++)
351  {
352 #ifdef _OPENMP
353  int tid = omp_get_thread_num ();
354 #else
355  int tid = 0;
356 #endif
357  PointInT current_point = (*input_)[index];
358 
359  if ((!borders[index]) && pcl::isFinite(current_point))
360  {
361  //if the considered point is not a border point and the point is "finite", then compute the scatter matrix
362  Eigen::Matrix3d cov_m = Eigen::Matrix3d::Zero ();
363  getScatterMatrix (static_cast<int> (index), cov_m);
364 
365  Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> solver (cov_m);
366 
367  const double& e1c = solver.eigenvalues ()[2];
368  const double& e2c = solver.eigenvalues ()[1];
369  const double& e3c = solver.eigenvalues ()[0];
370 
371  if (!std::isfinite (e1c) || !std::isfinite (e2c) || !std::isfinite (e3c))
372  continue;
373 
374  if (e3c < 0)
375  {
376  PCL_WARN ("[pcl::%s::detectKeypoints] : The third eigenvalue is negative! Skipping the point with index %i.\n",
377  name_.c_str (), index);
378  continue;
379  }
380 
381  omp_mem[tid][0] = e2c / e1c;
382  omp_mem[tid][1] = e3c / e2c;;
383  omp_mem[tid][2] = e3c;
384  }
385 
386  for (Eigen::Index d = 0; d < omp_mem[tid].size (); d++)
387  prg_mem[index][d] = omp_mem[tid][d];
388  }
389 
390  for (int index = 0; index < int (input_->size ()); index++)
391  {
392  if (!borders[index])
393  {
394  if ((prg_mem[index][0] < gamma_21_) && (prg_mem[index][1] < gamma_32_))
395  third_eigen_value_[index] = prg_mem[index][2];
396  }
397  }
398 
399  bool* feat_max = new bool [input_->size()];
400 
401 #pragma omp parallel for \
402  default(none) \
403  shared(feat_max) \
404  num_threads(threads_)
405  for (int index = 0; index < int (input_->size ()); index++)
406  {
407  feat_max [index] = false;
408  PointInT current_point = (*input_)[index];
409 
410  if ((third_eigen_value_[index] > 0.0) && (pcl::isFinite(current_point)))
411  {
412  std::vector<int> nn_indices;
413  std::vector<float> nn_distances;
414  int n_neighbors;
415 
416  this->searchForNeighbors (static_cast<int> (index), non_max_radius_, nn_indices, nn_distances);
417 
418  n_neighbors = static_cast<int> (nn_indices.size ());
419 
420  if (n_neighbors >= min_neighbors_)
421  {
422  bool is_max = true;
423 
424  for (int j = 0 ; j < n_neighbors; j++)
425  if (third_eigen_value_[index] < third_eigen_value_[nn_indices[j]])
426  is_max = false;
427  if (is_max)
428  feat_max[index] = true;
429  }
430  }
431  }
432 
433 #pragma omp parallel for \
434  default(none) \
435  shared(feat_max, output) \
436  num_threads(threads_)
437  for (int index = 0; index < int (input_->size ()); index++)
438  {
439  if (feat_max[index])
440 #pragma omp critical
441  {
442  PointOutT p;
443  p.getVector3fMap () = (*input_)[index].getVector3fMap ();
444  output.points.push_back(p);
445  keypoints_indices_->indices.push_back (index);
446  }
447  }
448 
449  output.header = input_->header;
450  output.width = output.size ();
451  output.height = 1;
452 
453  // Clear the contents of variables and arrays before the beginning of the next computation.
454  if (border_radius_ > 0.0)
455  normals_.reset (new pcl::PointCloud<NormalT>);
456 
457  delete[] borders;
458  delete[] prg_mem;
459  delete[] prg_local_mem;
460  delete[] feat_max;
461  delete[] omp_mem;
462 }
463 
464 #define PCL_INSTANTIATE_ISSKeypoint3D(T,U,N) template class PCL_EXPORTS pcl::ISSKeypoint3D<T,U,N>;
465 
466 #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:81
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:160
bool isBoundaryPoint(const pcl::PointCloud< PointInT > &cloud, int q_idx, const std::vector< int > &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:201
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition: feature.hpp:193
typename PointCloudN::ConstPtr PointCloudNConstPtr
Definition: iss_3d.h:95
typename Keypoint< PointInT, PointOutT >::PointCloudIn PointCloudIn
Definition: iss_3d.h:90
void setNormals(const PointCloudNConstPtr &normals)
Set the normals if pre-calculated normals are available.
Definition: iss_3d.hpp:98
void setBorderRadius(double border_radius)
Set the radius used for the estimation of the boundary points.
Definition: iss_3d.hpp:70
void setSalientRadius(double salient_radius)
Set the radius of the spherical neighborhood used to compute the scatter matrix.
Definition: iss_3d.hpp:49
void getScatterMatrix(const int &current_index, Eigen::Matrix3d &cov_m)
Compute the scatter matrix for a point index.
Definition: iss_3d.hpp:150
void setThreshold21(double gamma_21)
Set the upper bound on the ratio between the second and the first eigenvalue.
Definition: iss_3d.hpp:77
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:91
void setNormalRadius(double normal_radius)
Set the radius used for the estimation of the surface normals of the input cloud.
Definition: iss_3d.hpp:63
void setThreshold32(double gamma_32)
Set the upper bound on the ratio between the third and the second eigenvalue.
Definition: iss_3d.hpp:84
bool initCompute() override
Perform the initial checks before computing the keypoints.
Definition: iss_3d.hpp:200
typename PointCloudN::Ptr PointCloudNPtr
Definition: iss_3d.h:94
void setNonMaxRadius(double non_max_radius)
Set the radius for the application of the non maxima supression algorithm.
Definition: iss_3d.hpp:56
bool * getBoundaryPoints(PointCloudIn &input, double border_radius, float angle_threshold)
Compute the boundary points for the given input cloud.
Definition: iss_3d.hpp:105
void detectKeypoints(PointCloudOut &output) override
Detect the keypoints by performing the EVD of the scatter matrix.
Definition: iss_3d.hpp:292
typename Keypoint< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition: iss_3d.h:91
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