Point Cloud Library (PCL)  1.7.1
region_growing.hpp
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39 
40 #ifndef PCL_SEGMENTATION_REGION_GROWING_HPP_
41 #define PCL_SEGMENTATION_REGION_GROWING_HPP_
42 
43 #include <pcl/segmentation/region_growing.h>
44 
45 #include <pcl/search/search.h>
46 #include <pcl/search/kdtree.h>
47 #include <pcl/point_cloud.h>
48 #include <pcl/point_types.h>
49 
50 #include <queue>
51 #include <list>
52 #include <cmath>
53 #include <time.h>
54 
55 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
56 template <typename PointT, typename NormalT>
58  min_pts_per_cluster_ (1),
59  max_pts_per_cluster_ (std::numeric_limits<int>::max ()),
60  smooth_mode_flag_ (true),
61  curvature_flag_ (true),
62  residual_flag_ (false),
63  theta_threshold_ (30.0f / 180.0f * static_cast<float> (M_PI)),
64  residual_threshold_ (0.05f),
65  curvature_threshold_ (0.05f),
66  neighbour_number_ (30),
67  search_ (),
68  normals_ (),
69  point_neighbours_ (0),
70  point_labels_ (0),
71  normal_flag_ (true),
72  num_pts_in_segment_ (0),
73  clusters_ (0),
74  number_of_segments_ (0)
75 {
76 }
77 
78 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
79 template <typename PointT, typename NormalT>
81 {
82  if (search_ != 0)
83  search_.reset ();
84  if (normals_ != 0)
85  normals_.reset ();
86 
87  point_neighbours_.clear ();
88  point_labels_.clear ();
89  num_pts_in_segment_.clear ();
90  clusters_.clear ();
91 }
92 
93 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
94 template <typename PointT, typename NormalT> int
96 {
97  return (min_pts_per_cluster_);
98 }
99 
100 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
101 template <typename PointT, typename NormalT> void
103 {
104  min_pts_per_cluster_ = min_cluster_size;
105 }
106 
107 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
108 template <typename PointT, typename NormalT> int
110 {
111  return (max_pts_per_cluster_);
112 }
113 
114 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
115 template <typename PointT, typename NormalT> void
117 {
118  max_pts_per_cluster_ = max_cluster_size;
119 }
120 
121 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
122 template <typename PointT, typename NormalT> bool
124 {
125  return (smooth_mode_flag_);
126 }
127 
128 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
129 template <typename PointT, typename NormalT> void
131 {
132  smooth_mode_flag_ = value;
133 }
134 
135 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
136 template <typename PointT, typename NormalT> bool
138 {
139  return (curvature_flag_);
140 }
141 
142 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
143 template <typename PointT, typename NormalT> void
145 {
146  curvature_flag_ = value;
147 
148  if (curvature_flag_ == false && residual_flag_ == false)
149  residual_flag_ = true;
150 }
151 
152 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
153 template <typename PointT, typename NormalT> bool
155 {
156  return (residual_flag_);
157 }
158 
159 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
160 template <typename PointT, typename NormalT> void
162 {
163  residual_flag_ = value;
164 
165  if (curvature_flag_ == false && residual_flag_ == false)
166  curvature_flag_ = true;
167 }
168 
169 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
170 template <typename PointT, typename NormalT> float
172 {
173  return (theta_threshold_);
174 }
175 
176 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
177 template <typename PointT, typename NormalT> void
179 {
180  theta_threshold_ = theta;
181 }
182 
183 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
184 template <typename PointT, typename NormalT> float
186 {
187  return (residual_threshold_);
188 }
189 
190 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
191 template <typename PointT, typename NormalT> void
193 {
194  residual_threshold_ = residual;
195 }
196 
197 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
198 template <typename PointT, typename NormalT> float
200 {
201  return (curvature_threshold_);
202 }
203 
204 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
205 template <typename PointT, typename NormalT> void
207 {
208  curvature_threshold_ = curvature;
209 }
210 
211 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
212 template <typename PointT, typename NormalT> unsigned int
214 {
215  return (neighbour_number_);
216 }
217 
218 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
219 template <typename PointT, typename NormalT> void
221 {
222  neighbour_number_ = neighbour_number;
223 }
224 
225 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
226 template <typename PointT, typename NormalT> typename pcl::RegionGrowing<PointT, NormalT>::KdTreePtr
228 {
229  return (search_);
230 }
231 
232 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
233 template <typename PointT, typename NormalT> void
235 {
236  if (search_ != 0)
237  search_.reset ();
238 
239  search_ = tree;
240 }
241 
242 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
243 template <typename PointT, typename NormalT> typename pcl::RegionGrowing<PointT, NormalT>::NormalPtr
245 {
246  return (normals_);
247 }
248 
249 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
250 template <typename PointT, typename NormalT> void
252 {
253  if (normals_ != 0)
254  normals_.reset ();
255 
256  normals_ = norm;
257 }
258 
259 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
260 template <typename PointT, typename NormalT> void
261 pcl::RegionGrowing<PointT, NormalT>::extract (std::vector <pcl::PointIndices>& clusters)
262 {
263  clusters_.clear ();
264  clusters.clear ();
265  point_neighbours_.clear ();
266  point_labels_.clear ();
267  num_pts_in_segment_.clear ();
268  number_of_segments_ = 0;
269 
270  bool segmentation_is_possible = initCompute ();
271  if ( !segmentation_is_possible )
272  {
273  deinitCompute ();
274  return;
275  }
276 
277  segmentation_is_possible = prepareForSegmentation ();
278  if ( !segmentation_is_possible )
279  {
280  deinitCompute ();
281  return;
282  }
283 
284  findPointNeighbours ();
285  applySmoothRegionGrowingAlgorithm ();
286  assembleRegions ();
287 
288  clusters.resize (clusters_.size ());
289  std::vector<pcl::PointIndices>::iterator cluster_iter_input = clusters.begin ();
290  for (std::vector<pcl::PointIndices>::const_iterator cluster_iter = clusters_.begin (); cluster_iter != clusters_.end (); cluster_iter++)
291  {
292  if ((cluster_iter->indices.size () >= min_pts_per_cluster_) &&
293  (cluster_iter->indices.size () <= max_pts_per_cluster_))
294  {
295  *cluster_iter_input = *cluster_iter;
296  cluster_iter_input++;
297  }
298  }
299 
300  clusters_ = std::vector<pcl::PointIndices> (clusters.begin (), cluster_iter_input);
301  clusters.resize(clusters_.size());
302 
303  deinitCompute ();
304 }
305 
306 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
307 template <typename PointT, typename NormalT> bool
309 {
310  // if user forgot to pass point cloud or if it is empty
311  if ( input_->points.size () == 0 )
312  return (false);
313 
314  // if user forgot to pass normals or the sizes of point and normal cloud are different
315  if ( normals_ == 0 || input_->points.size () != normals_->points.size () )
316  return (false);
317 
318  // if residual test is on then we need to check if all needed parameters were correctly initialized
319  if (residual_flag_)
320  {
321  if (residual_threshold_ <= 0.0f)
322  return (false);
323  }
324 
325  // if curvature test is on ...
326  // if (curvature_flag_)
327  // {
328  // in this case we do not need to check anything that related to it
329  // so we simply commented it
330  // }
331 
332  // from here we check those parameters that are always valuable
333  if (neighbour_number_ == 0)
334  return (false);
335 
336  // if user didn't set search method
337  if (!search_)
338  search_.reset (new pcl::search::KdTree<PointT>);
339 
340  if (indices_)
341  {
342  if (indices_->empty ())
343  PCL_ERROR ("[pcl::RegionGrowing::prepareForSegmentation] Empty given indices!\n");
344  search_->setInputCloud (input_, indices_);
345  }
346  else
347  search_->setInputCloud (input_);
348 
349  return (true);
350 }
351 
352 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
353 template <typename PointT, typename NormalT> void
355 {
356  int point_number = static_cast<int> (indices_->size ());
357  std::vector<int> neighbours;
358  std::vector<float> distances;
359 
360  point_neighbours_.resize (input_->points.size (), neighbours);
361 
362  for (int i_point = 0; i_point < point_number; i_point++)
363  {
364  int point_index = (*indices_)[i_point];
365  neighbours.clear ();
366  search_->nearestKSearch (i_point, neighbour_number_, neighbours, distances);
367  point_neighbours_[point_index].swap (neighbours);
368  }
369 }
370 
371 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
372 template <typename PointT, typename NormalT> void
374 {
375  int num_of_pts = static_cast<int> (indices_->size ());
376  point_labels_.resize (input_->points.size (), -1);
377 
378  std::vector< std::pair<float, int> > point_residual;
379  std::pair<float, int> pair;
380  point_residual.resize (num_of_pts, pair);
381 
382  if (normal_flag_ == true)
383  {
384  for (int i_point = 0; i_point < num_of_pts; i_point++)
385  {
386  int point_index = (*indices_)[i_point];
387  point_residual[i_point].first = normals_->points[point_index].curvature;
388  point_residual[i_point].second = point_index;
389  }
390  std::sort (point_residual.begin (), point_residual.end (), comparePair);
391  }
392  else
393  {
394  for (int i_point = 0; i_point < num_of_pts; i_point++)
395  {
396  int point_index = (*indices_)[i_point];
397  point_residual[i_point].first = 0;
398  point_residual[i_point].second = point_index;
399  }
400  }
401  int seed_counter = 0;
402  int seed = point_residual[seed_counter].second;
403 
404  int segmented_pts_num = 0;
405  int number_of_segments = 0;
406  while (segmented_pts_num < num_of_pts)
407  {
408  int pts_in_segment;
409  pts_in_segment = growRegion (seed, number_of_segments);
410  segmented_pts_num += pts_in_segment;
411  num_pts_in_segment_.push_back (pts_in_segment);
412  number_of_segments++;
413 
414  //find next point that is not segmented yet
415  for (int i_seed = seed_counter + 1; i_seed < num_of_pts; i_seed++)
416  {
417  int index = point_residual[i_seed].second;
418  if (point_labels_[index] == -1)
419  {
420  seed = index;
421  break;
422  }
423  }
424  }
425 }
426 
427 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
428 template <typename PointT, typename NormalT> int
429 pcl::RegionGrowing<PointT, NormalT>::growRegion (int initial_seed, int segment_number)
430 {
431  std::queue<int> seeds;
432  seeds.push (initial_seed);
433  point_labels_[initial_seed] = segment_number;
434 
435  int num_pts_in_segment = 1;
436 
437  while (!seeds.empty ())
438  {
439  int curr_seed;
440  curr_seed = seeds.front ();
441  seeds.pop ();
442 
443  size_t i_nghbr = 0;
444  while ( i_nghbr < neighbour_number_ && i_nghbr < point_neighbours_[curr_seed].size () )
445  {
446  int index = point_neighbours_[curr_seed][i_nghbr];
447  if (point_labels_[index] != -1)
448  {
449  i_nghbr++;
450  continue;
451  }
452 
453  bool is_a_seed = false;
454  bool belongs_to_segment = validatePoint (initial_seed, curr_seed, index, is_a_seed);
455 
456  if (belongs_to_segment == false)
457  {
458  i_nghbr++;
459  continue;
460  }
461 
462  point_labels_[index] = segment_number;
463  num_pts_in_segment++;
464 
465  if (is_a_seed)
466  {
467  seeds.push (index);
468  }
469 
470  i_nghbr++;
471  }// next neighbour
472  }// next seed
473 
474  return (num_pts_in_segment);
475 }
476 
477 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
478 template <typename PointT, typename NormalT> bool
479 pcl::RegionGrowing<PointT, NormalT>::validatePoint (int initial_seed, int point, int nghbr, bool& is_a_seed) const
480 {
481  is_a_seed = true;
482 
483  float cosine_threshold = cosf (theta_threshold_);
484  float data[4];
485 
486  data[0] = input_->points[point].data[0];
487  data[1] = input_->points[point].data[1];
488  data[2] = input_->points[point].data[2];
489  data[3] = input_->points[point].data[3];
490  Eigen::Map<Eigen::Vector3f> initial_point (static_cast<float*> (data));
491  Eigen::Map<Eigen::Vector3f> initial_normal (static_cast<float*> (normals_->points[point].normal));
492 
493  //check the angle between normals
494  if (smooth_mode_flag_ == true)
495  {
496  Eigen::Map<Eigen::Vector3f> nghbr_normal (static_cast<float*> (normals_->points[nghbr].normal));
497  float dot_product = fabsf (nghbr_normal.dot (initial_normal));
498  if (dot_product < cosine_threshold)
499  {
500  return (false);
501  }
502  }
503  else
504  {
505  Eigen::Map<Eigen::Vector3f> nghbr_normal (static_cast<float*> (normals_->points[nghbr].normal));
506  Eigen::Map<Eigen::Vector3f> initial_seed_normal (static_cast<float*> (normals_->points[initial_seed].normal));
507  float dot_product = fabsf (nghbr_normal.dot (initial_seed_normal));
508  if (dot_product < cosine_threshold)
509  return (false);
510  }
511 
512  // check the curvature if needed
513  if (curvature_flag_ && normals_->points[nghbr].curvature > curvature_threshold_)
514  {
515  is_a_seed = false;
516  }
517 
518  // check the residual if needed
519  data[0] = input_->points[nghbr].data[0];
520  data[1] = input_->points[nghbr].data[1];
521  data[2] = input_->points[nghbr].data[2];
522  data[3] = input_->points[nghbr].data[3];
523  Eigen::Map<Eigen::Vector3f> nghbr_point (static_cast<float*> (data));
524  float residual = fabsf (initial_normal.dot (initial_point - nghbr_point));
525  if (residual_flag_ && residual > residual_threshold_)
526  is_a_seed = false;
527 
528  return (true);
529 }
530 
531 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
532 template <typename PointT, typename NormalT> void
534 {
535  int number_of_segments = static_cast<int> (num_pts_in_segment_.size ());
536  int number_of_points = static_cast<int> (input_->points.size ());
537 
538  pcl::PointIndices segment;
539  clusters_.resize (number_of_segments, segment);
540 
541  for (int i_seg = 0; i_seg < number_of_segments; i_seg++)
542  {
543  clusters_[i_seg].indices.resize ( num_pts_in_segment_[i_seg], 0);
544  }
545 
546  std::vector<int> counter;
547  counter.resize (number_of_segments, 0);
548 
549  for (int i_point = 0; i_point < number_of_points; i_point++)
550  {
551  int segment_index = point_labels_[i_point];
552  if (segment_index != -1)
553  {
554  int point_index = counter[segment_index];
555  clusters_[segment_index].indices[point_index] = i_point;
556  counter[segment_index] = point_index + 1;
557  }
558  }
559 
560  number_of_segments_ = number_of_segments;
561 }
562 
563 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
564 template <typename PointT, typename NormalT> void
566 {
567  cluster.indices.clear ();
568 
569  bool segmentation_is_possible = initCompute ();
570  if ( !segmentation_is_possible )
571  {
572  deinitCompute ();
573  return;
574  }
575 
576  // first of all we need to find out if this point belongs to cloud
577  bool point_was_found = false;
578  int number_of_points = static_cast <int> (indices_->size ());
579  for (size_t point = 0; point < number_of_points; point++)
580  if ( (*indices_)[point] == index)
581  {
582  point_was_found = true;
583  break;
584  }
585 
586  if (point_was_found)
587  {
588  if (clusters_.empty ())
589  {
590  point_neighbours_.clear ();
591  point_labels_.clear ();
592  num_pts_in_segment_.clear ();
593  number_of_segments_ = 0;
594 
595  segmentation_is_possible = prepareForSegmentation ();
596  if ( !segmentation_is_possible )
597  {
598  deinitCompute ();
599  return;
600  }
601 
602  findPointNeighbours ();
603  applySmoothRegionGrowingAlgorithm ();
604  assembleRegions ();
605  }
606  // if we have already made the segmentation, then find the segment
607  // to which this point belongs
608  std::vector <pcl::PointIndices>::iterator i_segment;
609  for (i_segment = clusters_.begin (); i_segment != clusters_.end (); i_segment++)
610  {
611  bool segment_was_found = false;
612  for (size_t i_point = 0; i_point < i_segment->indices.size (); i_point++)
613  {
614  if (i_segment->indices[i_point] == index)
615  {
616  segment_was_found = true;
617  cluster.indices.clear ();
618  cluster.indices.reserve (i_segment->indices.size ());
619  std::copy (i_segment->indices.begin (), i_segment->indices.end (), std::back_inserter (cluster.indices));
620  break;
621  }
622  }
623  if (segment_was_found)
624  {
625  break;
626  }
627  }// next segment
628  }// end if point was found
629 
630  deinitCompute ();
631 }
632 
633 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
634 template <typename PointT, typename NormalT> pcl::PointCloud<pcl::PointXYZRGB>::Ptr
636 {
638 
639  if (!clusters_.empty ())
640  {
641  colored_cloud = (new pcl::PointCloud<pcl::PointXYZRGB>)->makeShared ();
642 
643  srand (static_cast<unsigned int> (time (0)));
644  std::vector<unsigned char> colors;
645  for (size_t i_segment = 0; i_segment < clusters_.size (); i_segment++)
646  {
647  colors.push_back (static_cast<unsigned char> (rand () % 256));
648  colors.push_back (static_cast<unsigned char> (rand () % 256));
649  colors.push_back (static_cast<unsigned char> (rand () % 256));
650  }
651 
652  colored_cloud->width = input_->width;
653  colored_cloud->height = input_->height;
654  colored_cloud->is_dense = input_->is_dense;
655  for (size_t i_point = 0; i_point < input_->points.size (); i_point++)
656  {
657  pcl::PointXYZRGB point;
658  point.x = *(input_->points[i_point].data);
659  point.y = *(input_->points[i_point].data + 1);
660  point.z = *(input_->points[i_point].data + 2);
661  point.r = 255;
662  point.g = 0;
663  point.b = 0;
664  colored_cloud->points.push_back (point);
665  }
666 
667  std::vector< pcl::PointIndices >::iterator i_segment;
668  int next_color = 0;
669  for (i_segment = clusters_.begin (); i_segment != clusters_.end (); i_segment++)
670  {
671  std::vector<int>::iterator i_point;
672  for (i_point = i_segment->indices.begin (); i_point != i_segment->indices.end (); i_point++)
673  {
674  int index;
675  index = *i_point;
676  colored_cloud->points[index].r = colors[3 * next_color];
677  colored_cloud->points[index].g = colors[3 * next_color + 1];
678  colored_cloud->points[index].b = colors[3 * next_color + 2];
679  }
680  next_color++;
681  }
682  }
683 
684  return (colored_cloud);
685 }
686 
687 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
688 template <typename PointT, typename NormalT> pcl::PointCloud<pcl::PointXYZRGBA>::Ptr
690 {
692 
693  if (!clusters_.empty ())
694  {
695  colored_cloud = (new pcl::PointCloud<pcl::PointXYZRGBA>)->makeShared ();
696 
697  srand (static_cast<unsigned int> (time (0)));
698  std::vector<unsigned char> colors;
699  for (size_t i_segment = 0; i_segment < clusters_.size (); i_segment++)
700  {
701  colors.push_back (static_cast<unsigned char> (rand () % 256));
702  colors.push_back (static_cast<unsigned char> (rand () % 256));
703  colors.push_back (static_cast<unsigned char> (rand () % 256));
704  }
705 
706  colored_cloud->width = input_->width;
707  colored_cloud->height = input_->height;
708  colored_cloud->is_dense = input_->is_dense;
709  for (size_t i_point = 0; i_point < input_->points.size (); i_point++)
710  {
711  pcl::PointXYZRGBA point;
712  point.x = *(input_->points[i_point].data);
713  point.y = *(input_->points[i_point].data + 1);
714  point.z = *(input_->points[i_point].data + 2);
715  point.r = 255;
716  point.g = 0;
717  point.b = 0;
718  point.a = 0;
719  colored_cloud->points.push_back (point);
720  }
721 
722  std::vector< pcl::PointIndices >::iterator i_segment;
723  int next_color = 0;
724  for (i_segment = clusters_.begin (); i_segment != clusters_.end (); i_segment++)
725  {
726  std::vector<int>::iterator i_point;
727  for (i_point = i_segment->indices.begin (); i_point != i_segment->indices.end (); i_point++)
728  {
729  int index;
730  index = *i_point;
731  colored_cloud->points[index].r = colors[3 * next_color];
732  colored_cloud->points[index].g = colors[3 * next_color + 1];
733  colored_cloud->points[index].b = colors[3 * next_color + 2];
734  }
735  next_color++;
736  }
737  }
738 
739  return (colored_cloud);
740 }
741 
742 #define PCL_INSTANTIATE_RegionGrowing(T) template class pcl::RegionGrowing<T, pcl::Normal>;
743 
744 #endif // PCL_SEGMENTATION_REGION_GROWING_HPP_