Point Cloud Library (PCL)  1.11.1-dev
normal_space.hpp
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37 
38 #ifndef PCL_FILTERS_IMPL_NORMAL_SPACE_SAMPLE_H_
39 #define PCL_FILTERS_IMPL_NORMAL_SPACE_SAMPLE_H_
40 
41 #include <pcl/filters/normal_space.h>
42 
43 #include <vector>
44 #include <list>
45 
46 ///////////////////////////////////////////////////////////////////////////////
47 template<typename PointT, typename NormalT> bool
49 {
51  return false;
52 
53  // If sample size is 0 or if the sample size is greater then input cloud size then return entire copy of cloud
54  if (sample_ >= input_->size ())
55  {
56  PCL_ERROR ("[NormalSpaceSampling::initCompute] Requested more samples than the input cloud size: %d vs %lu\n",
57  sample_, input_->size ());
58  return false;
59  }
60 
61  rng_.seed (seed_);
62  return (true);
63 }
64 
65 ///////////////////////////////////////////////////////////////////////////////
66 template<typename PointT, typename NormalT> bool
68  unsigned int start_index,
69  unsigned int length)
70 {
71  bool status = true;
72  for (unsigned int i = start_index; i < start_index + length; i++)
73  {
74  status &= array.test (i);
75  }
76  return status;
77 }
78 
79 ///////////////////////////////////////////////////////////////////////////////
80 template<typename PointT, typename NormalT> unsigned int
82 {
83  const unsigned ix = static_cast<unsigned> (std::round (0.5f * (binsx_ - 1.f) * (normal[0] + 1.f)));
84  const unsigned iy = static_cast<unsigned> (std::round (0.5f * (binsy_ - 1.f) * (normal[1] + 1.f)));
85  const unsigned iz = static_cast<unsigned> (std::round (0.5f * (binsz_ - 1.f) * (normal[2] + 1.f)));
86  return ix * (binsy_*binsz_) + iy * binsz_ + iz;
87 }
88 
89 ///////////////////////////////////////////////////////////////////////////////
90 template<typename PointT, typename NormalT> void
92 {
93  if (!initCompute ())
94  {
95  indices = *indices_;
96  return;
97  }
98 
99  unsigned int max_values = (std::min) (sample_, static_cast<unsigned int> (input_normals_->size ()));
100  // Resize output indices to sample size
101  indices.resize (max_values);
102  removed_indices_->resize (max_values);
103 
104  // Allocate memory for the histogram of normals. Normals will then be sampled from each bin.
105  unsigned int n_bins = binsx_ * binsy_ * binsz_;
106  // list<int> holds the indices of points in that bin. Using list to avoid repeated resizing of vectors.
107  // Helps when the point cloud is large.
108  std::vector<std::list <int> > normals_hg;
109  normals_hg.reserve (n_bins);
110  for (unsigned int i = 0; i < n_bins; i++)
111  normals_hg.emplace_back();
112 
113  for (const auto index : *indices_)
114  {
115  unsigned int bin_number = findBin ((*input_normals_)[index].normal);
116  normals_hg[bin_number].push_back (index);
117  }
118 
119 
120  // Setting up random access for the list created above. Maintaining the iterators to individual elements of the list
121  // in a vector. Using vector now as the size of the list is known.
122  std::vector<std::vector<std::list<int>::iterator> > random_access (normals_hg.size ());
123  for (std::size_t i = 0; i < normals_hg.size (); i++)
124  {
125  random_access.emplace_back();
126  random_access[i].resize (normals_hg[i].size ());
127 
128  std::size_t j = 0;
129  for (std::list<int>::iterator itr = normals_hg[i].begin (); itr != normals_hg[i].end (); ++itr, ++j)
130  random_access[i][j] = itr;
131  }
132  std::vector<std::size_t> start_index (normals_hg.size ());
133  start_index[0] = 0;
134  std::size_t prev_index = 0;
135  for (std::size_t i = 1; i < normals_hg.size (); i++)
136  {
137  start_index[i] = prev_index + normals_hg[i-1].size ();
138  prev_index = start_index[i];
139  }
140 
141  // Maintaining flags to check if a point is sampled
142  boost::dynamic_bitset<> is_sampled_flag (input_normals_->size ());
143  // Maintaining flags to check if all points in the bin are sampled
144  boost::dynamic_bitset<> bin_empty_flag (normals_hg.size ());
145  unsigned int i = 0;
146  while (i < sample_)
147  {
148  // Iterating through every bin and picking one point at random, until the required number of points are sampled.
149  for (std::size_t j = 0; j < normals_hg.size (); j++)
150  {
151  unsigned int M = static_cast<unsigned int> (normals_hg[j].size ());
152  if (M == 0 || bin_empty_flag.test (j)) // bin_empty_flag(i) is set if all points in that bin are sampled..
153  continue;
154 
155  unsigned int pos = 0;
156  unsigned int random_index = 0;
157  std::uniform_int_distribution<unsigned> rng_uniform_distribution (0u, M - 1u);
158 
159  // Picking up a sample at random from jth bin
160  do
161  {
162  random_index = rng_uniform_distribution (rng_);
163  pos = start_index[j] + random_index;
164  } while (is_sampled_flag.test (pos));
165 
166  is_sampled_flag.flip (start_index[j] + random_index);
167 
168  // Checking if all points in bin j are sampled.
169  if (isEntireBinSampled (is_sampled_flag, start_index[j], static_cast<unsigned int> (normals_hg[j].size ())))
170  bin_empty_flag.flip (j);
171 
172  unsigned int index = *(random_access[j][random_index]);
173  indices[i] = index;
174  i++;
175  if (i == sample_)
176  break;
177  }
178  }
179 
180  // If we need to return the indices that we haven't sampled
181  if (extract_removed_indices_)
182  {
183  std::vector<int> indices_temp = indices;
184  std::sort (indices_temp.begin (), indices_temp.end ());
185 
186  std::vector<int> all_indices_temp = *indices_;
187  std::sort (all_indices_temp.begin (), all_indices_temp.end ());
188  set_difference (all_indices_temp.begin (), all_indices_temp.end (),
189  indices_temp.begin (), indices_temp.end (),
190  inserter (*removed_indices_, removed_indices_->begin ()));
191  }
192 }
193 
194 #define PCL_INSTANTIATE_NormalSpaceSampling(T,NT) template class PCL_EXPORTS pcl::NormalSpaceSampling<T,NT>;
195 
196 #endif // PCL_FILTERS_IMPL_NORMAL_SPACE_SAMPLE_H_
pcl::NormalSpaceSampling::initCompute
bool initCompute()
Definition: normal_space.hpp:48
pcl::NormalSpaceSampling
NormalSpaceSampling samples the input point cloud in the space of normal directions computed at every...
Definition: normal_space.h:51
pcl::NormalSpaceSampling::applyFilter
void applyFilter(std::vector< int > &indices) override
Sample of point indices.
Definition: normal_space.hpp:91
pcl::FilterIndices
FilterIndices represents the base class for filters that are about binary point removal.
Definition: filter_indices.h:74