Point Cloud Library (PCL)  1.11.1
gasd.hpp
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2016-, Open Perception, Inc.
6  * Copyright (c) 2016, Voxar Labs, CIn-UFPE / DEINFO-UFRPE
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  */
38 
39 #ifndef PCL_FEATURES_IMPL_GASD_H_
40 #define PCL_FEATURES_IMPL_GASD_H_
41 
42 #include <pcl/features/gasd.h>
43 #include <pcl/common/transforms.h>
44 
45 #include <vector>
46 
47 //////////////////////////////////////////////////////////////////////////////////////////////
48 template <typename PointInT, typename PointOutT> void
50 {
52  {
53  output.width = output.height = 0;
54  output.points.clear ();
55  return;
56  }
57 
58  // Resize the output dataset
59  output.resize (1);
60 
61  // Copy header and is_dense flag from input
62  output.header = surface_->header;
63  output.is_dense = surface_->is_dense;
64 
65  // Perform the actual feature computation
66  computeFeature (output);
67 
69 }
70 
71 //////////////////////////////////////////////////////////////////////////////////////////////
72 template <typename PointInT, typename PointOutT> void
74 {
75  Eigen::Vector4f centroid;
76  Eigen::Matrix3f covariance_matrix;
77 
78  // compute centroid of the object's partial view
79  pcl::compute3DCentroid (*surface_, *indices_, centroid);
80 
81  // compute covariance matrix from points and centroid of the object's partial view
82  pcl::computeCovarianceMatrix (*surface_, *indices_, centroid, covariance_matrix);
83 
84  Eigen::Matrix3f eigenvectors;
85  Eigen::Vector3f eigenvalues;
86 
87  // compute eigenvalues and eigenvectors of the covariance matrix
88  pcl::eigen33 (covariance_matrix, eigenvectors, eigenvalues);
89 
90  // z axis of the reference frame is the eigenvector associated with the minimal eigenvalue
91  Eigen::Vector3f z_axis = eigenvectors.col (0);
92 
93  // if angle between z axis and viewing direction is in the [-90 deg, 90 deg] range, then z axis is negated
94  if (z_axis.dot (view_direction_) > 0)
95  {
96  z_axis = -z_axis;
97  }
98 
99  // x axis of the reference frame is the eigenvector associated with the maximal eigenvalue
100  const Eigen::Vector3f x_axis = eigenvectors.col (2);
101 
102  // y axis is the cross product of z axis and x axis
103  const Eigen::Vector3f y_axis = z_axis.cross (x_axis);
104 
105  const Eigen::Vector3f centroid_xyz = centroid.head<3> ();
106 
107  // compute alignment transform from axes and centroid
108  transform_ << x_axis.transpose (), -x_axis.dot (centroid_xyz),
109  y_axis.transpose (), -y_axis.dot (centroid_xyz),
110  z_axis.transpose (), -z_axis.dot (centroid_xyz),
111  0.0f, 0.0f, 0.0f, 1.0f;
112 }
113 
114 //////////////////////////////////////////////////////////////////////////////////////////////
115 template <typename PointInT, typename PointOutT> void
117  const float max_coord,
118  const std::size_t half_grid_size,
119  const HistogramInterpolationMethod interp,
120  const float hbin,
121  const float hist_incr,
122  std::vector<Eigen::VectorXf> &hists)
123 {
124  const std::size_t grid_size = half_grid_size * 2;
125 
126  // compute normalized coordinates with respect to axis-aligned bounding cube centered on the origin
127  const Eigen::Vector3f scaled ( (p[0] / max_coord) * half_grid_size, (p[1] / max_coord) * half_grid_size, (p[2] / max_coord) * half_grid_size);
128 
129  // compute histograms array coords
130  Eigen::Vector4f coords (scaled[0] + half_grid_size, scaled[1] + half_grid_size, scaled[2] + half_grid_size, hbin);
131 
132  // if using histogram interpolation, subtract 0.5 so samples with the central value of the bin have full weight in it
133  if (interp != INTERP_NONE)
134  {
135  coords -= Eigen::Vector4f (0.5f, 0.5f, 0.5f, 0.5f);
136  }
137 
138  // compute histograms bins indices
139  const Eigen::Vector4f bins (std::floor (coords[0]), std::floor (coords[1]), std::floor (coords[2]), std::floor (coords[3]));
140 
141  // compute indices of the bin where the sample falls into
142  const std::size_t grid_idx = ( (bins[0] + 1) * (grid_size + 2) + bins[1] + 1) * (grid_size + 2) + bins[2] + 1;
143  const std::size_t h_idx = bins[3] + 1;
144 
145  if (interp == INTERP_NONE)
146  {
147  // no interpolation
148  hists[grid_idx][h_idx] += hist_incr;
149  }
150  else
151  {
152  // if using histogram interpolation, compute trilinear interpolation
153  coords -= Eigen::Vector4f (bins[0], bins[1], bins[2], 0.0f);
154 
155  const float v_x1 = hist_incr * coords[0];
156  const float v_x0 = hist_incr - v_x1;
157 
158  const float v_xy11 = v_x1 * coords[1];
159  const float v_xy10 = v_x1 - v_xy11;
160  const float v_xy01 = v_x0 * coords[1];
161  const float v_xy00 = v_x0 - v_xy01;
162 
163  const float v_xyz111 = v_xy11 * coords[2];
164  const float v_xyz110 = v_xy11 - v_xyz111;
165  const float v_xyz101 = v_xy10 * coords[2];
166  const float v_xyz100 = v_xy10 - v_xyz101;
167  const float v_xyz011 = v_xy01 * coords[2];
168  const float v_xyz010 = v_xy01 - v_xyz011;
169  const float v_xyz001 = v_xy00 * coords[2];
170  const float v_xyz000 = v_xy00 - v_xyz001;
171 
172  if (interp == INTERP_TRILINEAR)
173  {
174  // trilinear interpolation
175  hists[grid_idx][h_idx] += v_xyz000;
176  hists[grid_idx + 1][h_idx] += v_xyz001;
177  hists[grid_idx + (grid_size + 2)][h_idx] += v_xyz010;
178  hists[grid_idx + (grid_size + 3)][h_idx] += v_xyz011;
179  hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx] += v_xyz100;
180  hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx] += v_xyz101;
181  hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx] += v_xyz110;
182  hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx] += v_xyz111;
183  }
184  else
185  {
186  // quadrilinear interpolation
187  coords[3] -= bins[3];
188 
189  const float v_xyzh1111 = v_xyz111 * coords[3];
190  const float v_xyzh1110 = v_xyz111 - v_xyzh1111;
191  const float v_xyzh1101 = v_xyz110 * coords[3];
192  const float v_xyzh1100 = v_xyz110 - v_xyzh1101;
193  const float v_xyzh1011 = v_xyz101 * coords[3];
194  const float v_xyzh1010 = v_xyz101 - v_xyzh1011;
195  const float v_xyzh1001 = v_xyz100 * coords[3];
196  const float v_xyzh1000 = v_xyz100 - v_xyzh1001;
197  const float v_xyzh0111 = v_xyz011 * coords[3];
198  const float v_xyzh0110 = v_xyz011 - v_xyzh0111;
199  const float v_xyzh0101 = v_xyz010 * coords[3];
200  const float v_xyzh0100 = v_xyz010 - v_xyzh0101;
201  const float v_xyzh0011 = v_xyz001 * coords[3];
202  const float v_xyzh0010 = v_xyz001 - v_xyzh0011;
203  const float v_xyzh0001 = v_xyz000 * coords[3];
204  const float v_xyzh0000 = v_xyz000 - v_xyzh0001;
205 
206  hists[grid_idx][h_idx] += v_xyzh0000;
207  hists[grid_idx][h_idx + 1] += v_xyzh0001;
208  hists[grid_idx + 1][h_idx] += v_xyzh0010;
209  hists[grid_idx + 1][h_idx + 1] += v_xyzh0011;
210  hists[grid_idx + (grid_size + 2)][h_idx] += v_xyzh0100;
211  hists[grid_idx + (grid_size + 2)][h_idx + 1] += v_xyzh0101;
212  hists[grid_idx + (grid_size + 3)][h_idx] += v_xyzh0110;
213  hists[grid_idx + (grid_size + 3)][h_idx + 1] += v_xyzh0111;
214  hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx] += v_xyzh1000;
215  hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx + 1] += v_xyzh1001;
216  hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx] += v_xyzh1010;
217  hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx + 1] += v_xyzh1011;
218  hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx] += v_xyzh1100;
219  hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx + 1] += v_xyzh1101;
220  hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx] += v_xyzh1110;
221  hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx + 1] += v_xyzh1111;
222  }
223  }
224 }
225 
226 //////////////////////////////////////////////////////////////////////////////////////////////
227 template <typename PointInT, typename PointOutT> void
229  const std::size_t hists_size,
230  const std::vector<Eigen::VectorXf> &hists,
231  PointCloudOut &output,
232  std::size_t &pos)
233 {
234  for (std::size_t i = 0; i < grid_size; ++i)
235  {
236  for (std::size_t j = 0; j < grid_size; ++j)
237  {
238  for (std::size_t k = 0; k < grid_size; ++k)
239  {
240  const std::size_t idx = ( (i + 1) * (grid_size + 2) + (j + 1)) * (grid_size + 2) + (k + 1);
241 
242  std::copy (hists[idx].data () + 1, hists[idx].data () + hists_size + 1, output[0].histogram + pos);
243  pos += hists_size;
244  }
245  }
246  }
247 }
248 
249 //////////////////////////////////////////////////////////////////////////////////////////////
250 template <typename PointInT, typename PointOutT> void
252 {
253  // compute alignment transform using reference frame
254  computeAlignmentTransform ();
255 
256  // align point cloud
257  pcl::transformPointCloud (*surface_, *indices_, shape_samples_, transform_);
258 
259  const std::size_t shape_grid_size = shape_half_grid_size_ * 2;
260 
261  // each histogram dimension has 2 additional bins, 1 in each boundary, for performing interpolation
262  std::vector<Eigen::VectorXf> shape_hists ((shape_grid_size + 2) * (shape_grid_size + 2) * (shape_grid_size + 2),
263  Eigen::VectorXf::Zero (shape_hists_size_ + 2));
264 
265  Eigen::Vector4f centroid_p = Eigen::Vector4f::Zero ();
266 
267  // compute normalization factor for distances between samples and centroid
268  Eigen::Vector4f far_pt;
269  pcl::getMaxDistance (shape_samples_, centroid_p, far_pt);
270  far_pt[3] = 0;
271  const float distance_normalization_factor = (centroid_p - far_pt).norm ();
272 
273  // compute normalization factor with respect to axis-aligned bounding cube centered on the origin
274  Eigen::Vector4f min_pt, max_pt;
275  pcl::getMinMax3D (shape_samples_, min_pt, max_pt);
276 
277  max_coord_ = std::max (min_pt.head<3> ().cwiseAbs ().maxCoeff (), max_pt.head<3> ().cwiseAbs ().maxCoeff ());
278 
279  // normalize sample contribution with respect to the total number of points in the cloud
280  hist_incr_ = 100.0f / static_cast<float> (shape_samples_.size () - 1);
281 
282  // for each sample
283  for (const auto& sample: shape_samples_)
284  {
285  // compute shape histogram array coord based on distance between sample and centroid
286  const Eigen::Vector4f p (sample.x, sample.y, sample.z, 0.0f);
287  const float d = p.norm ();
288 
289  const float shape_grid_step = distance_normalization_factor / shape_half_grid_size_;
290 
291  float integral;
292  const float dist_hist_val = std::modf(d / shape_grid_step, &integral);
293 
294  const float dbin = dist_hist_val * shape_hists_size_;
295 
296  // add sample to shape histograms, optionally performing interpolation
297  addSampleToHistograms (p, max_coord_, shape_half_grid_size_, shape_interp_, dbin, hist_incr_, shape_hists);
298  }
299 
300  pos_ = 0;
301 
302  // copy shape histograms to output
303  copyShapeHistogramsToOutput (shape_grid_size, shape_hists_size_, shape_hists, output, pos_);
304 
305  // set remaining values of the descriptor to zero (if any)
306  std::fill (output[0].histogram + pos_, output[0].histogram + output[0].descriptorSize (), 0.0f);
307 }
308 
309 //////////////////////////////////////////////////////////////////////////////////////////////
310 template <typename PointInT, typename PointOutT> void
312  const std::size_t hists_size,
313  std::vector<Eigen::VectorXf> &hists,
314  PointCloudOut &output,
315  std::size_t &pos)
316 {
317  for (std::size_t i = 0; i < grid_size; ++i)
318  {
319  for (std::size_t j = 0; j < grid_size; ++j)
320  {
321  for (std::size_t k = 0; k < grid_size; ++k)
322  {
323  const std::size_t idx = ( (i + 1) * (grid_size + 2) + (j + 1)) * (grid_size + 2) + (k + 1);
324 
325  hists[idx][1] += hists[idx][hists_size + 1];
326  hists[idx][hists_size] += hists[idx][0];
327 
328  std::copy (hists[idx].data () + 1, hists[idx].data () + hists_size + 1, output[0].histogram + pos);
329  pos += hists_size;
330  }
331  }
332  }
333 }
334 
335 //////////////////////////////////////////////////////////////////////////////////////////////
336 template <typename PointInT, typename PointOutT> void
338 {
339  // call shape feature computation
340  GASDEstimation<PointInT, PointOutT>::computeFeature (output);
341 
342  const std::size_t color_grid_size = color_half_grid_size_ * 2;
343 
344  // each histogram dimension has 2 additional bins, 1 in each boundary, for performing interpolation
345  std::vector<Eigen::VectorXf> color_hists ((color_grid_size + 2) * (color_grid_size + 2) * (color_grid_size + 2),
346  Eigen::VectorXf::Zero (color_hists_size_ + 2));
347 
348  // for each sample
349  for (const auto& sample: shape_samples_)
350  {
351  // compute shape histogram array coord based on distance between sample and centroid
352  const Eigen::Vector4f p (sample.x, sample.y, sample.z, 0.0f);
353 
354  // compute hue value
355  float hue = 0.f;
356 
357  const unsigned char max = std::max (sample.r, std::max (sample.g, sample.b));
358  const unsigned char min = std::min (sample.r, std::min (sample.g, sample.b));
359 
360  const float diff_inv = 1.f / static_cast <float> (max - min);
361 
362  if (std::isfinite (diff_inv))
363  {
364  if (max == sample.r)
365  {
366  hue = 60.f * (static_cast <float> (sample.g - sample.b) * diff_inv);
367  }
368  else if (max == sample.g)
369  {
370  hue = 60.f * (2.f + static_cast <float> (sample.b - sample.r) * diff_inv);
371  }
372  else
373  {
374  hue = 60.f * (4.f + static_cast <float> (sample.r - sample.g) * diff_inv); // max == b
375  }
376 
377  if (hue < 0.f)
378  {
379  hue += 360.f;
380  }
381  }
382 
383  // compute color histogram array coord based on hue value
384  const float hbin = (hue / 360) * color_hists_size_;
385 
386  // add sample to color histograms, optionally performing interpolation
387  GASDEstimation<PointInT, PointOutT>::addSampleToHistograms (p, max_coord_, color_half_grid_size_, color_interp_, hbin, hist_incr_, color_hists);
388  }
389 
390  // copy color histograms to output
391  copyColorHistogramsToOutput (color_grid_size, color_hists_size_, color_hists, output, pos_);
392 
393  // set remaining values of the descriptor to zero (if any)
394  std::fill (output[0].histogram + pos_, output[0].histogram + output[0].descriptorSize (), 0.0f);
395 }
396 
397 #define PCL_INSTANTIATE_GASDEstimation(InT, OutT) template class PCL_EXPORTS pcl::GASDEstimation<InT, OutT>;
398 #define PCL_INSTANTIATE_GASDColorEstimation(InT, OutT) template class PCL_EXPORTS pcl::GASDColorEstimation<InT, OutT>;
399 
400 #endif // PCL_FEATURES_IMPL_GASD_H_
void computeFeature(PointCloudOut &output) override
Estimate GASD descriptor.
Definition: gasd.hpp:251
trilinear interpolation
Definition: gasd.h:51
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:411
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition: gasd.hpp:49
GASDColorEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given...
Definition: gasd.h:258
void getMaxDistance(const pcl::PointCloud< PointT > &cloud, const Eigen::Vector4f &pivot_pt, Eigen::Vector4f &max_pt)
Get the point at maximum distance from a given point and a given pointcloud.
Definition: common.hpp:145
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:414
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform, bool copy_all_fields)
Apply an affine transform defined by an Eigen Transform.
Definition: transforms.hpp:221
unsigned int computeCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix)
Compute the 3x3 covariance matrix of a given set of points.
Definition: centroid.hpp:180
void getMinMax3D(const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt)
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud...
Definition: common.hpp:243
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:478
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:416
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:408
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition: eigen.hpp:296
HistogramInterpolationMethod
Different histogram interpolation methods.
Definition: gasd.h:48
no interpolation
Definition: gasd.h:50
void addSampleToHistograms(const Eigen::Vector4f &p, const float max_coord, const std::size_t half_grid_size, const HistogramInterpolationMethod interp, const float hbin, const float hist_incr, std::vector< Eigen::VectorXf > &hists)
add a sample to its respective histogram, optionally performing interpolation.
Definition: gasd.hpp:116
GASDEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given poin...
Definition: gasd.h:76
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields)...
Definition: point_cloud.h:419
Feature represents the base feature class.
Definition: feature.h:106
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition: centroid.hpp:56