Point Cloud Library (PCL)  1.9.1-dev
fast_bilateral_omp.hpp
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40 #ifndef PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_
41 #define PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_
42 
43 #include <pcl/filters/fast_bilateral_omp.h>
44 #include <pcl/common/io.h>
45 #include <cassert>
46 
47 //////////////////////////////////////////////////////////////////////////////////////////////
48 template <typename PointT> void
50 {
51  if (nr_threads == 0)
52 #ifdef _OPENMP
53  threads_ = omp_get_num_procs();
54 #else
55  threads_ = 1;
56 #endif
57  else
58  threads_ = nr_threads;
59 }
60 
61 //////////////////////////////////////////////////////////////////////////////////////////////
62 template <typename PointT> void
64 {
65  if (!input_->isOrganized ())
66  {
67  PCL_ERROR ("[pcl::FastBilateralFilterOMP] Input cloud needs to be organized.\n");
68  return;
69  }
70 
71  copyPointCloud (*input_, output);
72  float base_max = -std::numeric_limits<float>::max (),
73  base_min = std::numeric_limits<float>::max ();
74  bool found_finite = false;
75  for (size_t x = 0; x < output.width; ++x)
76  {
77  for (size_t y = 0; y < output.height; ++y)
78  {
79  if (std::isfinite (output (x, y).z))
80  {
81  if (base_max < output (x, y).z)
82  base_max = output (x, y).z;
83  if (base_min > output (x, y).z)
84  base_min = output (x, y).z;
85  found_finite = true;
86  }
87  }
88  }
89  if (!found_finite)
90  {
91  PCL_WARN ("[pcl::FastBilateralFilterOMP] Given an empty cloud. Doing nothing.\n");
92  return;
93  }
94 #ifdef _OPENMP
95 #pragma omp parallel for num_threads (threads_)
96 #endif
97  for (long int i = 0; i < static_cast<long int> (output.size ()); ++i)
98  if (!std::isfinite (output.at(i).z))
99  output.at(i).z = base_max;
100 
101  const float base_delta = base_max - base_min;
102 
103  const size_t padding_xy = 2;
104  const size_t padding_z = 2;
105 
106  const size_t small_width = static_cast<size_t> (static_cast<float> (input_->width - 1) / sigma_s_) + 1 + 2 * padding_xy;
107  const size_t small_height = static_cast<size_t> (static_cast<float> (input_->height - 1) / sigma_s_) + 1 + 2 * padding_xy;
108  const size_t small_depth = static_cast<size_t> (base_delta / sigma_r_) + 1 + 2 * padding_z;
109 
110  Array3D data (small_width, small_height, small_depth);
111 #ifdef _OPENMP
112 #pragma omp parallel for num_threads (threads_)
113 #endif
114  for (long int i = 0; i < static_cast<long int> (small_width * small_height); ++i)
115  {
116  size_t small_x = static_cast<size_t> (i % small_width);
117  size_t small_y = static_cast<size_t> (i / small_width);
118  size_t start_x = static_cast<size_t>(
119  std::max ((static_cast<float> (small_x) - static_cast<float> (padding_xy) - 0.5f) * sigma_s_ + 1, 0.f));
120  size_t end_x = static_cast<size_t>(
121  std::max ((static_cast<float> (small_x) - static_cast<float> (padding_xy) + 0.5f) * sigma_s_ + 1, 0.f));
122  size_t start_y = static_cast<size_t>(
123  std::max ((static_cast<float> (small_y) - static_cast<float> (padding_xy) - 0.5f) * sigma_s_ + 1, 0.f));
124  size_t end_y = static_cast<size_t>(
125  std::max ((static_cast<float> (small_y) - static_cast<float> (padding_xy) + 0.5f) * sigma_s_ + 1, 0.f));
126  for (size_t x = start_x; x < end_x && x < input_->width; ++x)
127  {
128  for (size_t y = start_y; y < end_y && y < input_->height; ++y)
129  {
130  const float z = output (x,y).z - base_min;
131  const size_t small_z = static_cast<size_t> (static_cast<float> (z) / sigma_r_ + 0.5f) + padding_z;
132  Eigen::Vector2f& d = data (small_x, small_y, small_z);
133  d[0] += output (x,y).z;
134  d[1] += 1.0f;
135  }
136  }
137  }
138 
139  std::vector<long int> offset (3);
140  offset[0] = &(data (1,0,0)) - &(data (0,0,0));
141  offset[1] = &(data (0,1,0)) - &(data (0,0,0));
142  offset[2] = &(data (0,0,1)) - &(data (0,0,0));
143 
144  Array3D buffer (small_width, small_height, small_depth);
145 
146  for (size_t dim = 0; dim < 3; ++dim)
147  {
148  for (size_t n_iter = 0; n_iter < 2; ++n_iter)
149  {
150  Array3D* current_buffer = (n_iter % 2 == 1 ? &buffer : &data);
151  Array3D* current_data =(n_iter % 2 == 1 ? &data : &buffer);
152 #ifdef _OPENMP
153 #pragma omp parallel for num_threads (threads_)
154 #endif
155  for(long int i = 0; i < static_cast<long int> ((small_width - 2)*(small_height - 2)); ++i)
156  {
157  size_t x = static_cast<size_t> (i % (small_width - 2) + 1);
158  size_t y = static_cast<size_t> (i / (small_width - 2) + 1);
159  const long int off = offset[dim];
160  Eigen::Vector2f* d_ptr = &(current_data->operator() (x,y,1));
161  Eigen::Vector2f* b_ptr = &(current_buffer->operator() (x,y,1));
162 
163  for(size_t z = 1; z < small_depth - 1; ++z, ++d_ptr, ++b_ptr)
164  *d_ptr = (*(b_ptr - off) + *(b_ptr + off) + 2.0 * (*b_ptr)) / 4.0;
165  }
166  }
167  }
168  // Note: this works because there are an even number of iterations.
169  // If there were an odd number, we would need to end with a:
170  // std::swap (data, buffer);
171 
172  if (early_division_)
173  {
174  for (std::vector<Eigen::Vector2f, Eigen::aligned_allocator<Eigen::Vector2f> >::iterator d = data.begin (); d != data.end (); ++d)
175  *d /= ((*d)[0] != 0) ? (*d)[1] : 1;
176 
177 #ifdef _OPENMP
178 #pragma omp parallel for num_threads (threads_)
179 #endif
180  for (long int i = 0; i < static_cast<long int> (input_->size ()); ++i)
181  {
182  size_t x = static_cast<size_t> (i % input_->width);
183  size_t y = static_cast<size_t> (i / input_->width);
184  const float z = output (x,y).z - base_min;
185  const Eigen::Vector2f D = data.trilinear_interpolation (static_cast<float> (x) / sigma_s_ + padding_xy,
186  static_cast<float> (y) / sigma_s_ + padding_xy,
187  z / sigma_r_ + padding_z);
188  output(x,y).z = D[0];
189  }
190  }
191  else
192  {
193 #ifdef _OPENMP
194 #pragma omp parallel for num_threads (threads_)
195 #endif
196  for (long i = 0; i < static_cast<long int> (input_->size ()); ++i)
197  {
198  size_t x = static_cast<size_t> (i % input_->width);
199  size_t y = static_cast<size_t> (i / input_->width);
200  const float z = output (x,y).z - base_min;
201  const Eigen::Vector2f D = data.trilinear_interpolation (static_cast<float> (x) / sigma_s_ + padding_xy,
202  static_cast<float> (y) / sigma_s_ + padding_xy,
203  z / sigma_r_ + padding_z);
204  output (x,y).z = D[0] / D[1];
205  }
206  }
207 }
208 
209 
210 
211 #endif /* PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_ */
212 
Eigen::Vector2f trilinear_interpolation(const float x, const float y, const float z)
size_t size() const
Definition: point_cloud.h:447
std::vector< Eigen::Vector2f, Eigen::aligned_allocator< Eigen::Vector2f > >::iterator end()
void setNumberOfThreads(unsigned int nr_threads=0)
Initialize the scheduler and set the number of threads to use.
PCL_EXPORTS void copyPointCloud(const pcl::PCLPointCloud2 &cloud_in, const std::vector< int > &indices, pcl::PCLPointCloud2 &cloud_out)
Extract the indices of a given point cloud as a new point cloud.
uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:414
uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:412
void applyFilter(PointCloud &output) override
Filter the input data and store the results into output.
PointCloud represents the base class in PCL for storing collections of 3D points. ...
std::vector< Eigen::Vector2f, Eigen::aligned_allocator< Eigen::Vector2f > >::iterator begin()
const PointT & at(int column, int row) const
Obtain the point given by the (column, row) coordinates.
Definition: point_cloud.h:282