Point Cloud Library (PCL)  1.7.0
fast_bilateral_omp.hpp
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2012-, Open Perception, Inc.
6  * Copyright (c) 2004, Sylvain Paris and Francois Sillion
7 
8  * All rights reserved.
9 
10  * Redistribution and use in source and binary forms, with or without
11  * modification, are permitted provided that the following conditions
12  * are met:
13  *
14  * * Redistributions of source code must retain the above copyright
15  * notice, this list of conditions and the following disclaimer.
16  * * Redistributions in binary form must reproduce the above
17  * copyright notice, this list of conditions and the following
18  * disclaimer in the documentation and/or other materials provided
19  * with the distribution.
20  * * Neither the name of the copyright holder(s) nor the names of its
21  * contributors may be used to endorse or promote products derived
22  * from this software without specific prior written permission.
23  *
24  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
25  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
26  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
27  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
28  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
29  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
30  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
31  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
32  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
33  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
34  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
35  * POSSIBILITY OF SUCH DAMAGE.
36  *
37  * $Id: fast_bilateral_omp.hpp 8381 2013-01-02 23:12:44Z sdmiller $
38  *
39  */
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 <pcl/console/time.h>
46 #include <assert.h>
47 
48 //////////////////////////////////////////////////////////////////////////////////////////////
49 template <typename PointT> void
51 {
52  if (!input_->isOrganized ())
53  {
54  PCL_ERROR ("[pcl::FastBilateralFilterOMP] Input cloud needs to be organized.\n");
55  return;
56  }
57 
58  copyPointCloud (*input_, output);
59  float base_max = -std::numeric_limits<float>::max (),
60  base_min = std::numeric_limits<float>::max ();
61  bool found_finite = false;
62  for (size_t x = 0; x < output.width; ++x)
63  {
64  for (size_t y = 0; y < output.height; ++y)
65  {
66  if (pcl_isfinite (output (x, y).z))
67  {
68  if (base_max < output (x, y).z)
69  base_max = output (x, y).z;
70  if (base_min > output (x, y).z)
71  base_min = output (x, y).z;
72  found_finite = true;
73  }
74  }
75  }
76  if (!found_finite)
77  {
78  PCL_WARN ("[pcl::FastBilateralFilterOMP] Given an empty cloud. Doing nothing.\n");
79  return;
80  }
81 #ifdef _OPENMP
82 #pragma omp parallel for num_threads (threads_)
83 #endif
84  for (long int i = 0; i < static_cast<long int> (output.size ()); ++i)
85  if (!pcl_isfinite (output.at(i).z))
86  output.at(i).z = base_max;
87 
88  const float base_delta = base_max - base_min;
89 
90  const size_t padding_xy = 2;
91  const size_t padding_z = 2;
92 
93  const size_t small_width = static_cast<size_t> (static_cast<float> (input_->width - 1) / sigma_s_) + 1 + 2 * padding_xy;
94  const size_t small_height = static_cast<size_t> (static_cast<float> (input_->height - 1) / sigma_s_) + 1 + 2 * padding_xy;
95  const size_t small_depth = static_cast<size_t> (base_delta / sigma_r_) + 1 + 2 * padding_z;
96 
97  Array3D data (small_width, small_height, small_depth);
98 #ifdef _OPENMP
99 #pragma omp parallel for num_threads (threads_)
100 #endif
101  for (long int i = 0; i < static_cast<long int> (small_width * small_height); ++i)
102  {
103  size_t small_x = static_cast<size_t> (i % small_width);
104  size_t small_y = static_cast<size_t> (i / small_width);
105  size_t start_x = static_cast<size_t>(
106  std::max ((static_cast<float> (small_x) - static_cast<float> (padding_xy) - 0.5f) * sigma_s_ + 1, 0.f));
107  size_t end_x = static_cast<size_t>(
108  std::max ((static_cast<float> (small_x) - static_cast<float> (padding_xy) + 0.5f) * sigma_s_ + 1, 0.f));
109  size_t start_y = static_cast<size_t>(
110  std::max ((static_cast<float> (small_y) - static_cast<float> (padding_xy) - 0.5f) * sigma_s_ + 1, 0.f));
111  size_t end_y = static_cast<size_t>(
112  std::max ((static_cast<float> (small_y) - static_cast<float> (padding_xy) + 0.5f) * sigma_s_ + 1, 0.f));
113  for (size_t x = start_x; x < end_x && x < input_->width; ++x)
114  {
115  for (size_t y = start_y; y < end_y && y < input_->height; ++y)
116  {
117  const float z = output (x,y).z - base_min;
118  const size_t small_z = static_cast<size_t> (static_cast<float> (z) / sigma_r_ + 0.5f) + padding_z;
119  Eigen::Vector2f& d = data (small_x, small_y, small_z);
120  d[0] += output (x,y).z;
121  d[1] += 1.0f;
122  }
123  }
124  }
125 
126  std::vector<long int> offset (3);
127  offset[0] = &(data (1,0,0)) - &(data (0,0,0));
128  offset[1] = &(data (0,1,0)) - &(data (0,0,0));
129  offset[2] = &(data (0,0,1)) - &(data (0,0,0));
130 
131  Array3D buffer (small_width, small_height, small_depth);
132 
133  for (size_t dim = 0; dim < 3; ++dim)
134  {
135  for (size_t n_iter = 0; n_iter < 2; ++n_iter)
136  {
137  Array3D* current_buffer = (n_iter % 2 == 1 ? &buffer : &data);
138  Array3D* current_data =(n_iter % 2 == 1 ? &data : &buffer);
139 #ifdef _OPENMP
140 #pragma omp parallel for num_threads (threads_)
141 #endif
142  for(long int i = 0; i < static_cast<long int> ((small_width - 2)*(small_height - 2)); ++i)
143  {
144  size_t x = static_cast<size_t> (i % (small_width - 2) + 1);
145  size_t y = static_cast<size_t> (i / (small_width - 2) + 1);
146  const long int off = offset[dim];
147  Eigen::Vector2f* d_ptr = &(current_data->operator() (x,y,1));
148  Eigen::Vector2f* b_ptr = &(current_buffer->operator() (x,y,1));
149 
150  for(size_t z = 1; z < small_depth - 1; ++z, ++d_ptr, ++b_ptr)
151  *d_ptr = (*(b_ptr - off) + *(b_ptr + off) + 2.0 * (*b_ptr)) / 4.0;
152  }
153  }
154  }
155  // Note: this works because there are an even number of iterations.
156  // If there were an odd number, we would need to end with a:
157  // std::swap (data, buffer);
158 
159  if (early_division_)
160  {
161  for (std::vector<Eigen::Vector2f >::iterator d = data.begin (); d != data.end (); ++d)
162  *d /= ((*d)[0] != 0) ? (*d)[1] : 1;
163 
164 #ifdef _OPENMP
165 #pragma omp parallel for num_threads (threads_)
166 #endif
167  for (long int i = 0; i < static_cast<long int> (input_->size ()); ++i)
168  {
169  size_t x = static_cast<size_t> (i % input_->width);
170  size_t y = static_cast<size_t> (i / input_->width);
171  const float z = output (x,y).z - base_min;
172  const Eigen::Vector2f D = data.trilinear_interpolation (static_cast<float> (x) / sigma_s_ + padding_xy,
173  static_cast<float> (y) / sigma_s_ + padding_xy,
174  z / sigma_r_ + padding_z);
175  output(x,y).z = D[0];
176  }
177  }
178  else
179  {
180 #ifdef _OPENMP
181 #pragma omp parallel for num_threads (threads_)
182 #endif
183  for (long i = 0; i < static_cast<long int> (input_->size ()); ++i)
184  {
185  size_t x = static_cast<size_t> (i % input_->width);
186  size_t y = static_cast<size_t> (i / input_->width);
187  const float z = output (x,y).z - base_min;
188  const Eigen::Vector2f D = data.trilinear_interpolation (static_cast<float> (x) / sigma_s_ + padding_xy,
189  static_cast<float> (y) / sigma_s_ + padding_xy,
190  z / sigma_r_ + padding_z);
191  output (x,y).z = D[0] / D[1];
192  }
193  }
194 }
195 
196 
197 
198 #endif /* PCL_FILTERS_IMPL_FAST_BILATERAL_OMP_HPP_ */
199