Point Cloud Library (PCL)  1.7.1
sift_keypoint.hpp
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2010-2011, Willow Garage, Inc.
6  *
7  * All rights reserved.
8  *
9  * Redistribution and use in source and binary forms, with or without
10  * modification, are permitted provided that the following conditions
11  * are met:
12  *
13  * * Redistributions of source code must retain the above copyright
14  * notice, this list of conditions and the following disclaimer.
15  * * Redistributions in binary form must reproduce the above
16  * copyright notice, this list of conditions and the following
17  * disclaimer in the documentation and/or other materials provided
18  * with the distribution.
19  * * Neither the name of Willow Garage, Inc. nor the names of its
20  * contributors may be used to endorse or promote products derived
21  * from this software without specific prior written permission.
22  *
23  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
24  * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
25  * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
26  * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
27  * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
28  * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
29  * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
30  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
31  * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
32  * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
33  * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
34  * POSSIBILITY OF SUCH DAMAGE.
35  *
36  */
37 
38 #ifndef PCL_SIFT_KEYPOINT_IMPL_H_
39 #define PCL_SIFT_KEYPOINT_IMPL_H_
40 
41 #include <pcl/keypoints/sift_keypoint.h>
42 #include <pcl/common/io.h>
43 #include <pcl/filters/voxel_grid.h>
44 
45 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
46 template <typename PointInT, typename PointOutT> void
47 pcl::SIFTKeypoint<PointInT, PointOutT>::setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
48 {
49  min_scale_ = min_scale;
50  nr_octaves_ = nr_octaves;
51  nr_scales_per_octave_ = nr_scales_per_octave;
52 }
53 
54 
55 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
56 template <typename PointInT, typename PointOutT> void
58 {
59  min_contrast_ = min_contrast;
60 }
61 
62 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
63 template <typename PointInT, typename PointOutT> bool
65 {
66  if (min_scale_ <= 0)
67  {
68  PCL_ERROR ("[pcl::%s::initCompute] : Minimum scale (%f) must be strict positive!\n",
69  name_.c_str (), min_scale_);
70  return (false);
71  }
72  if (nr_octaves_ < 1)
73  {
74  PCL_ERROR ("[pcl::%s::initCompute] : Number of octaves (%d) must be at least 1!\n",
75  name_.c_str (), nr_octaves_);
76  return (false);
77  }
78  if (nr_scales_per_octave_ < 1)
79  {
80  PCL_ERROR ("[pcl::%s::initCompute] : Number of scales per octave (%d) must be at least 1!\n",
81  name_.c_str (), nr_scales_per_octave_);
82  return (false);
83  }
84  if (min_contrast_ < 0)
85  {
86  PCL_ERROR ("[pcl::%s::initCompute] : Minimum contrast (%f) must be non-negative!\n",
87  name_.c_str (), min_contrast_);
88  return (false);
89  }
90 
91  this->setKSearch (1);
92  tree_.reset (new pcl::search::KdTree<PointInT> (true));
93  return (true);
94 }
95 
96 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
97 template <typename PointInT, typename PointOutT> void
99 {
100  if (surface_ && surface_ != input_)
101  {
102  PCL_WARN ("[pcl::%s::detectKeypoints] : ", name_.c_str ());
103  PCL_WARN ("A search surface has been set by setSearchSurface, but this SIFT keypoint detection algorithm does ");
104  PCL_WARN ("not support search surfaces other than the input cloud. ");
105  PCL_WARN ("The cloud provided in setInputCloud is being used instead.\n");
106  }
107 
108  // Check if the output has a "scale" field
109  scale_idx_ = pcl::getFieldIndex<PointOutT> (output, "scale", out_fields_);
110 
111  // Make sure the output cloud is empty
112  output.points.clear ();
113 
114  // Create a local copy of the input cloud that will be resized for each octave
115  boost::shared_ptr<pcl::PointCloud<PointInT> > cloud (new pcl::PointCloud<PointInT> (*input_));
116 
117  VoxelGrid<PointInT> voxel_grid;
118  // Search for keypoints at each octave
119  float scale = min_scale_;
120  for (int i_octave = 0; i_octave < nr_octaves_; ++i_octave)
121  {
122  // Downsample the point cloud
123  const float s = 1.0f * scale; // note: this can be adjusted
124  voxel_grid.setLeafSize (s, s, s);
125  voxel_grid.setInputCloud (cloud);
126  boost::shared_ptr<pcl::PointCloud<PointInT> > temp (new pcl::PointCloud<PointInT>);
127  voxel_grid.filter (*temp);
128  cloud = temp;
129 
130  // Make sure the downsampled cloud still has enough points
131  const size_t min_nr_points = 25;
132  if (cloud->points.size () < min_nr_points)
133  break;
134 
135  // Update the KdTree with the downsampled points
136  tree_->setInputCloud (cloud);
137 
138  // Detect keypoints for the current scale
139  detectKeypointsForOctave (*cloud, *tree_, scale, nr_scales_per_octave_, output);
140 
141  // Increase the scale by another octave
142  scale *= 2;
143  }
144 
145  output.height = 1;
146  output.width = static_cast<uint32_t> (output.points.size ());
147 }
148 
149 
150 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
151 template <typename PointInT, typename PointOutT> void
153  const PointCloudIn &input, KdTree &tree, float base_scale, int nr_scales_per_octave,
154  PointCloudOut &output)
155 {
156  // Compute the difference of Gaussians (DoG) scale space
157  std::vector<float> scales (nr_scales_per_octave + 3);
158  for (int i_scale = 0; i_scale <= nr_scales_per_octave + 2; ++i_scale)
159  {
160  scales[i_scale] = base_scale * powf (2.0f, (1.0f * static_cast<float> (i_scale) - 1.0f) / static_cast<float> (nr_scales_per_octave));
161  }
162  Eigen::MatrixXf diff_of_gauss;
163  computeScaleSpace (input, tree, scales, diff_of_gauss);
164 
165  // Find extrema in the DoG scale space
166  std::vector<int> extrema_indices, extrema_scales;
167  findScaleSpaceExtrema (input, tree, diff_of_gauss, extrema_indices, extrema_scales);
168 
169  output.points.reserve (output.points.size () + extrema_indices.size ());
170  // Save scale?
171  if (scale_idx_ != -1)
172  {
173  // Add keypoints to output
174  for (size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
175  {
176  PointOutT keypoint;
177  const int &keypoint_index = extrema_indices[i_keypoint];
178 
179  keypoint.x = input.points[keypoint_index].x;
180  keypoint.y = input.points[keypoint_index].y;
181  keypoint.z = input.points[keypoint_index].z;
182  memcpy (reinterpret_cast<char*> (&keypoint) + out_fields_[scale_idx_].offset,
183  &scales[extrema_scales[i_keypoint]], sizeof (float));
184  output.points.push_back (keypoint);
185  }
186  }
187  else
188  {
189  // Add keypoints to output
190  for (size_t i_keypoint = 0; i_keypoint < extrema_indices.size (); ++i_keypoint)
191  {
192  PointOutT keypoint;
193  const int &keypoint_index = extrema_indices[i_keypoint];
194 
195  keypoint.x = input.points[keypoint_index].x;
196  keypoint.y = input.points[keypoint_index].y;
197  keypoint.z = input.points[keypoint_index].z;
198 
199  output.points.push_back (keypoint);
200  }
201  }
202 }
203 
204 
205 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
206 template <typename PointInT, typename PointOutT>
208  const PointCloudIn &input, KdTree &tree, const std::vector<float> &scales,
209  Eigen::MatrixXf &diff_of_gauss)
210 {
211  diff_of_gauss.resize (input.size (), scales.size () - 1);
212 
213  // For efficiency, we will only filter over points within 3 standard deviations
214  const float max_radius = 3.0f * scales.back ();
215 
216  for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
217  {
218  std::vector<int> nn_indices;
219  std::vector<float> nn_dist;
220  tree.radiusSearch (i_point, max_radius, nn_indices, nn_dist); // *
221  // * note: at this stage of the algorithm, we must find all points within a radius defined by the maximum scale,
222  // regardless of the configurable search method specified by the user, so we directly employ tree.radiusSearch
223  // here instead of using searchForNeighbors.
224 
225  // For each scale, compute the Gaussian "filter response" at the current point
226  float filter_response = 0.0f;
227  float previous_filter_response;
228  for (size_t i_scale = 0; i_scale < scales.size (); ++i_scale)
229  {
230  float sigma_sqr = powf (scales[i_scale], 2.0f);
231 
232  float numerator = 0.0f;
233  float denominator = 0.0f;
234  for (size_t i_neighbor = 0; i_neighbor < nn_indices.size (); ++i_neighbor)
235  {
236  const float &value = getFieldValue_ (input.points[nn_indices[i_neighbor]]);
237  const float &dist_sqr = nn_dist[i_neighbor];
238  if (dist_sqr <= 9*sigma_sqr)
239  {
240  float w = expf (-0.5f * dist_sqr / sigma_sqr);
241  numerator += value * w;
242  denominator += w;
243  }
244  else break; // i.e. if dist > 3 standard deviations, then terminate early
245  }
246  previous_filter_response = filter_response;
247  filter_response = numerator / denominator;
248 
249  // Compute the difference between adjacent scales
250  if (i_scale > 0)
251  diff_of_gauss (i_point, i_scale - 1) = filter_response - previous_filter_response;
252  }
253  }
254 }
255 
256 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
257 template <typename PointInT, typename PointOutT> void
259  const PointCloudIn &input, KdTree &tree, const Eigen::MatrixXf &diff_of_gauss,
260  std::vector<int> &extrema_indices, std::vector<int> &extrema_scales)
261 {
262  const int k = 25;
263  std::vector<int> nn_indices (k);
264  std::vector<float> nn_dist (k);
265 
266  const int nr_scales = static_cast<int> (diff_of_gauss.cols ());
267  std::vector<float> min_val (nr_scales), max_val (nr_scales);
268 
269  for (int i_point = 0; i_point < static_cast<int> (input.size ()); ++i_point)
270  {
271  // Define the local neighborhood around the current point
272  const size_t nr_nn = tree.nearestKSearch (i_point, k, nn_indices, nn_dist); //*
273  // * note: the neighborhood for finding local extrema is best defined as a small fixed-k neighborhood, regardless of
274  // the configurable search method specified by the user, so we directly employ tree.nearestKSearch here instead
275  // of using searchForNeighbors
276 
277  // At each scale, find the extreme values of the DoG within the current neighborhood
278  for (int i_scale = 0; i_scale < nr_scales; ++i_scale)
279  {
280  min_val[i_scale] = std::numeric_limits<float>::max ();
281  max_val[i_scale] = -std::numeric_limits<float>::max ();
282 
283  for (size_t i_neighbor = 0; i_neighbor < nr_nn; ++i_neighbor)
284  {
285  const float &d = diff_of_gauss (nn_indices[i_neighbor], i_scale);
286 
287  min_val[i_scale] = (std::min) (min_val[i_scale], d);
288  max_val[i_scale] = (std::max) (max_val[i_scale], d);
289  }
290  }
291 
292  // If the current point is an extreme value with high enough contrast, add it as a keypoint
293  for (int i_scale = 1; i_scale < nr_scales - 1; ++i_scale)
294  {
295  const float &val = diff_of_gauss (i_point, i_scale);
296 
297  // Does the point have sufficient contrast?
298  if (fabs (val) >= min_contrast_)
299  {
300  // Is it a local minimum?
301  if ((val == min_val[i_scale]) &&
302  (val < min_val[i_scale - 1]) &&
303  (val < min_val[i_scale + 1]))
304  {
305  extrema_indices.push_back (i_point);
306  extrema_scales.push_back (i_scale);
307  }
308  // Is it a local maximum?
309  else if ((val == max_val[i_scale]) &&
310  (val > max_val[i_scale - 1]) &&
311  (val > max_val[i_scale + 1]))
312  {
313  extrema_indices.push_back (i_point);
314  extrema_scales.push_back (i_scale);
315  }
316  }
317  }
318  }
319 }
320 
321 #define PCL_INSTANTIATE_SIFTKeypoint(T,U) template class PCL_EXPORTS pcl::SIFTKeypoint<T,U>;
322 
323 #endif // #ifndef PCL_SIFT_KEYPOINT_IMPL_H_
324