Point Cloud Library (PCL)  1.14.1-dev
gasd.hpp
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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/common.h> // for getMinMax3D
44 #include <pcl/common/transforms.h>
45 
46 #include <vector>
47 
48 //////////////////////////////////////////////////////////////////////////////////////////////
49 template <typename PointInT, typename PointOutT> void
51 {
53  {
54  output.width = output.height = 0;
55  output.clear ();
56  return;
57  }
58 
59  // Resize the output dataset
60  output.resize (1);
61 
62  // Copy header and is_dense flag from input
63  output.header = surface_->header;
64  output.is_dense = surface_->is_dense;
65 
66  // Perform the actual feature computation
67  computeFeature (output);
68 
70 }
71 
72 //////////////////////////////////////////////////////////////////////////////////////////////
73 template <typename PointInT, typename PointOutT> void
75 {
76  Eigen::Vector4f centroid;
77  Eigen::Matrix3f covariance_matrix;
78 
79  // compute centroid of the object's partial view, then compute covariance matrix from points and centroid of the object's partial view
80  if (pcl::compute3DCentroid (*surface_, *indices_, centroid) == 0 ||
81  pcl::computeCovarianceMatrix (*surface_, *indices_, centroid, covariance_matrix) == 0) {
82  PCL_ERROR("[pcl::GASDEstimation::computeAlignmentTransform] Surface cloud or indices are empty!\n");
83  return;
84  }
85 
86  Eigen::Matrix3f eigenvectors;
87  Eigen::Vector3f eigenvalues;
88 
89  // compute eigenvalues and eigenvectors of the covariance matrix
90  pcl::eigen33 (covariance_matrix, eigenvectors, eigenvalues);
91 
92  // z axis of the reference frame is the eigenvector associated with the minimal eigenvalue
93  Eigen::Vector3f z_axis = eigenvectors.col (0);
94 
95  // if angle between z axis and viewing direction is in the [-90 deg, 90 deg] range, then z axis is negated
96  if (z_axis.dot (view_direction_) > 0)
97  {
98  z_axis = -z_axis;
99  }
100 
101  // x axis of the reference frame is the eigenvector associated with the maximal eigenvalue
102  const Eigen::Vector3f x_axis = eigenvectors.col (2);
103 
104  // y axis is the cross product of z axis and x axis
105  const Eigen::Vector3f y_axis = z_axis.cross (x_axis);
106 
107  const Eigen::Vector3f centroid_xyz = centroid.head<3> ();
108 
109  // compute alignment transform from axes and centroid
110  transform_ << x_axis.transpose (), -x_axis.dot (centroid_xyz),
111  y_axis.transpose (), -y_axis.dot (centroid_xyz),
112  z_axis.transpose (), -z_axis.dot (centroid_xyz),
113  0.0f, 0.0f, 0.0f, 1.0f;
114 }
115 
116 //////////////////////////////////////////////////////////////////////////////////////////////
117 template <typename PointInT, typename PointOutT> void
119  const float max_coord,
120  const std::size_t half_grid_size,
121  const HistogramInterpolationMethod interp,
122  const float hbin,
123  const float hist_incr,
124  std::vector<Eigen::VectorXf> &hists)
125 {
126  const std::size_t grid_size = half_grid_size * 2;
127 
128  // compute normalized coordinates with respect to axis-aligned bounding cube centered on the origin
129  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);
130 
131  // compute histograms array coords
132  Eigen::Vector4f coords (scaled[0] + half_grid_size, scaled[1] + half_grid_size, scaled[2] + half_grid_size, hbin);
133 
134  // if using histogram interpolation, subtract 0.5 so samples with the central value of the bin have full weight in it
135  if (interp != INTERP_NONE)
136  {
137  coords -= Eigen::Vector4f (0.5f, 0.5f, 0.5f, 0.5f);
138  }
139 
140  // compute histograms bins indices
141  const Eigen::Vector4f bins (std::floor (coords[0]), std::floor (coords[1]), std::floor (coords[2]), std::floor (coords[3]));
142 
143  // compute indices of the bin where the sample falls into
144  const std::size_t grid_idx = ( (bins[0] + 1) * (grid_size + 2) + bins[1] + 1) * (grid_size + 2) + bins[2] + 1;
145  const std::size_t h_idx = bins[3] + 1;
146 
147  if (interp == INTERP_NONE)
148  {
149  // no interpolation
150  hists[grid_idx][h_idx] += hist_incr;
151  }
152  else
153  {
154  // if using histogram interpolation, compute trilinear interpolation
155  coords -= Eigen::Vector4f (bins[0], bins[1], bins[2], 0.0f);
156 
157  const float v_x1 = hist_incr * coords[0];
158  const float v_x0 = hist_incr - v_x1;
159 
160  const float v_xy11 = v_x1 * coords[1];
161  const float v_xy10 = v_x1 - v_xy11;
162  const float v_xy01 = v_x0 * coords[1];
163  const float v_xy00 = v_x0 - v_xy01;
164 
165  const float v_xyz111 = v_xy11 * coords[2];
166  const float v_xyz110 = v_xy11 - v_xyz111;
167  const float v_xyz101 = v_xy10 * coords[2];
168  const float v_xyz100 = v_xy10 - v_xyz101;
169  const float v_xyz011 = v_xy01 * coords[2];
170  const float v_xyz010 = v_xy01 - v_xyz011;
171  const float v_xyz001 = v_xy00 * coords[2];
172  const float v_xyz000 = v_xy00 - v_xyz001;
173 
174  if (interp == INTERP_TRILINEAR)
175  {
176  // trilinear interpolation
177  hists[grid_idx][h_idx] += v_xyz000;
178  hists[grid_idx + 1][h_idx] += v_xyz001;
179  hists[grid_idx + (grid_size + 2)][h_idx] += v_xyz010;
180  hists[grid_idx + (grid_size + 3)][h_idx] += v_xyz011;
181  hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx] += v_xyz100;
182  hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx] += v_xyz101;
183  hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx] += v_xyz110;
184  hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx] += v_xyz111;
185  }
186  else
187  {
188  // quadrilinear interpolation
189  coords[3] -= bins[3];
190 
191  const float v_xyzh1111 = v_xyz111 * coords[3];
192  const float v_xyzh1110 = v_xyz111 - v_xyzh1111;
193  const float v_xyzh1101 = v_xyz110 * coords[3];
194  const float v_xyzh1100 = v_xyz110 - v_xyzh1101;
195  const float v_xyzh1011 = v_xyz101 * coords[3];
196  const float v_xyzh1010 = v_xyz101 - v_xyzh1011;
197  const float v_xyzh1001 = v_xyz100 * coords[3];
198  const float v_xyzh1000 = v_xyz100 - v_xyzh1001;
199  const float v_xyzh0111 = v_xyz011 * coords[3];
200  const float v_xyzh0110 = v_xyz011 - v_xyzh0111;
201  const float v_xyzh0101 = v_xyz010 * coords[3];
202  const float v_xyzh0100 = v_xyz010 - v_xyzh0101;
203  const float v_xyzh0011 = v_xyz001 * coords[3];
204  const float v_xyzh0010 = v_xyz001 - v_xyzh0011;
205  const float v_xyzh0001 = v_xyz000 * coords[3];
206  const float v_xyzh0000 = v_xyz000 - v_xyzh0001;
207 
208  hists[grid_idx][h_idx] += v_xyzh0000;
209  hists[grid_idx][h_idx + 1] += v_xyzh0001;
210  hists[grid_idx + 1][h_idx] += v_xyzh0010;
211  hists[grid_idx + 1][h_idx + 1] += v_xyzh0011;
212  hists[grid_idx + (grid_size + 2)][h_idx] += v_xyzh0100;
213  hists[grid_idx + (grid_size + 2)][h_idx + 1] += v_xyzh0101;
214  hists[grid_idx + (grid_size + 3)][h_idx] += v_xyzh0110;
215  hists[grid_idx + (grid_size + 3)][h_idx + 1] += v_xyzh0111;
216  hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx] += v_xyzh1000;
217  hists[grid_idx + (grid_size + 2) * (grid_size + 2)][h_idx + 1] += v_xyzh1001;
218  hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx] += v_xyzh1010;
219  hists[grid_idx + (grid_size + 2) * (grid_size + 2) + 1][h_idx + 1] += v_xyzh1011;
220  hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx] += v_xyzh1100;
221  hists[grid_idx + (grid_size + 3) * (grid_size + 2)][h_idx + 1] += v_xyzh1101;
222  hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx] += v_xyzh1110;
223  hists[grid_idx + (grid_size + 3) * (grid_size + 2) + 1][h_idx + 1] += v_xyzh1111;
224  }
225  }
226 }
227 
228 //////////////////////////////////////////////////////////////////////////////////////////////
229 template <typename PointInT, typename PointOutT> void
231  const std::size_t hists_size,
232  const std::vector<Eigen::VectorXf> &hists,
233  PointCloudOut &output,
234  std::size_t &pos)
235 {
236  for (std::size_t i = 0; i < grid_size; ++i)
237  {
238  for (std::size_t j = 0; j < grid_size; ++j)
239  {
240  for (std::size_t k = 0; k < grid_size; ++k)
241  {
242  const std::size_t idx = ( (i + 1) * (grid_size + 2) + (j + 1)) * (grid_size + 2) + (k + 1);
243 
244  std::copy (hists[idx].data () + 1, hists[idx].data () + 1 + hists_size, output[0].histogram + pos);
245  pos += hists_size;
246  }
247  }
248  }
249 }
250 
251 //////////////////////////////////////////////////////////////////////////////////////////////
252 template <typename PointInT, typename PointOutT> void
254 {
255  // compute alignment transform using reference frame
256  computeAlignmentTransform ();
257 
258  // align point cloud
259  pcl::transformPointCloud (*surface_, *indices_, shape_samples_, transform_);
260 
261  const std::size_t shape_grid_size = shape_half_grid_size_ * 2;
262 
263  // each histogram dimension has 2 additional bins, 1 in each boundary, for performing interpolation
264  std::vector<Eigen::VectorXf> shape_hists ((shape_grid_size + 2) * (shape_grid_size + 2) * (shape_grid_size + 2),
265  Eigen::VectorXf::Zero (shape_hists_size_ + 2));
266 
267  Eigen::Vector4f centroid_p = Eigen::Vector4f::Zero ();
268 
269  // compute normalization factor for distances between samples and centroid
270  Eigen::Vector4f far_pt;
271  pcl::getMaxDistance (shape_samples_, centroid_p, far_pt);
272  far_pt[3] = 0;
273  const float distance_normalization_factor = (centroid_p - far_pt).norm ();
274 
275  // compute normalization factor with respect to axis-aligned bounding cube centered on the origin
276  Eigen::Vector4f min_pt, max_pt;
277  pcl::getMinMax3D (shape_samples_, min_pt, max_pt);
278 
279  max_coord_ = std::max (min_pt.head<3> ().cwiseAbs ().maxCoeff (), max_pt.head<3> ().cwiseAbs ().maxCoeff ());
280 
281  // normalize sample contribution with respect to the total number of points in the cloud
282  hist_incr_ = 100.0f / static_cast<float> (shape_samples_.size () - 1);
283 
284  // for each sample
285  for (const auto& sample: shape_samples_)
286  {
287  // compute shape histogram array coord based on distance between sample and centroid
288  const Eigen::Vector4f p (sample.x, sample.y, sample.z, 0.0f);
289  const float d = p.norm ();
290 
291  const float shape_grid_step = distance_normalization_factor / shape_half_grid_size_;
292 
293  float integral;
294  const float dist_hist_val = std::modf(d / shape_grid_step, &integral);
295 
296  const float dbin = dist_hist_val * shape_hists_size_;
297 
298  // add sample to shape histograms, optionally performing interpolation
299  addSampleToHistograms (p, max_coord_, shape_half_grid_size_, shape_interp_, dbin, hist_incr_, shape_hists);
300  }
301 
302  pos_ = 0;
303 
304  // copy shape histograms to output
305  copyShapeHistogramsToOutput (shape_grid_size, shape_hists_size_, shape_hists, output, pos_);
306 
307  // set remaining values of the descriptor to zero (if any)
308  std::fill (output[0].histogram + pos_, output[0].histogram + output[0].descriptorSize (), 0.0f);
309 }
310 
311 //////////////////////////////////////////////////////////////////////////////////////////////
312 template <typename PointInT, typename PointOutT> void
314  const std::size_t hists_size,
315  std::vector<Eigen::VectorXf> &hists,
316  PointCloudOut &output,
317  std::size_t &pos)
318 {
319  for (std::size_t i = 0; i < grid_size; ++i)
320  {
321  for (std::size_t j = 0; j < grid_size; ++j)
322  {
323  for (std::size_t k = 0; k < grid_size; ++k)
324  {
325  const std::size_t idx = ( (i + 1) * (grid_size + 2) + (j + 1)) * (grid_size + 2) + (k + 1);
326 
327  hists[idx][1] += hists[idx][hists_size + 1];
328  hists[idx][hists_size] += hists[idx][0];
329 
330  std::copy (hists[idx].data () + 1, hists[idx].data () + 1 + hists_size, output[0].histogram + pos);
331  pos += hists_size;
332  }
333  }
334  }
335 }
336 
337 //////////////////////////////////////////////////////////////////////////////////////////////
338 template <typename PointInT, typename PointOutT> void
340 {
341  // call shape feature computation
342  GASDEstimation<PointInT, PointOutT>::computeFeature (output);
343 
344  const std::size_t color_grid_size = color_half_grid_size_ * 2;
345 
346  // each histogram dimension has 2 additional bins, 1 in each boundary, for performing interpolation
347  std::vector<Eigen::VectorXf> color_hists ((color_grid_size + 2) * (color_grid_size + 2) * (color_grid_size + 2),
348  Eigen::VectorXf::Zero (color_hists_size_ + 2));
349 
350  // for each sample
351  for (const auto& sample: shape_samples_)
352  {
353  // compute shape histogram array coord based on distance between sample and centroid
354  const Eigen::Vector4f p (sample.x, sample.y, sample.z, 0.0f);
355 
356  // compute hue value
357  float hue = 0.f;
358 
359  const unsigned char max = std::max (sample.r, std::max (sample.g, sample.b));
360  const unsigned char min = std::min (sample.r, std::min (sample.g, sample.b));
361 
362  const float diff_inv = 1.f / static_cast <float> (max - min);
363 
364  if (std::isfinite (diff_inv))
365  {
366  if (max == sample.r)
367  {
368  hue = 60.f * (static_cast <float> (sample.g - sample.b) * diff_inv);
369  }
370  else if (max == sample.g)
371  {
372  hue = 60.f * (2.f + static_cast <float> (sample.b - sample.r) * diff_inv);
373  }
374  else
375  {
376  hue = 60.f * (4.f + static_cast <float> (sample.r - sample.g) * diff_inv); // max == b
377  }
378 
379  if (hue < 0.f)
380  {
381  hue += 360.f;
382  }
383  }
384 
385  // compute color histogram array coord based on hue value
386  const float hbin = (hue / 360) * color_hists_size_;
387 
388  // add sample to color histograms, optionally performing interpolation
389  GASDEstimation<PointInT, PointOutT>::addSampleToHistograms (p, max_coord_, color_half_grid_size_, color_interp_, hbin, hist_incr_, color_hists);
390  }
391 
392  // copy color histograms to output
393  copyColorHistogramsToOutput (color_grid_size, color_hists_size_, color_hists, output, pos_);
394 
395  // set remaining values of the descriptor to zero (if any)
396  std::fill (output[0].histogram + pos_, output[0].histogram + output[0].descriptorSize (), 0.0f);
397 }
398 
399 #define PCL_INSTANTIATE_GASDEstimation(InT, OutT) template class PCL_EXPORTS pcl::GASDEstimation<InT, OutT>;
400 #define PCL_INSTANTIATE_GASDColorEstimation(InT, OutT) template class PCL_EXPORTS pcl::GASDColorEstimation<InT, OutT>;
401 
402 #endif // PCL_FEATURES_IMPL_GASD_H_
Feature represents the base feature class.
Definition: feature.h:107
GASDColorEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given...
Definition: gasd.h:257
GASDEstimation estimates the Globally Aligned Spatial Distribution (GASD) descriptor for a given poin...
Definition: gasd.h:75
void computeFeature(PointCloudOut &output) override
Estimate GASD descriptor.
Definition: gasd.hpp:253
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition: gasd.hpp: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:118
Define standard C methods and C++ classes that are common to all methods.
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:197
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:295
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:192
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Matrix< Scalar, 4, 4 > &transform, bool copy_all_fields)
Apply a rigid transform defined by a 4x4 matrix.
Definition: transforms.hpp:221
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:295
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:57
HistogramInterpolationMethod
Different histogram interpolation methods.
Definition: gasd.h:47
@ INTERP_NONE
no interpolation
Definition: gasd.h:48
@ INTERP_TRILINEAR
trilinear interpolation
Definition: gasd.h:49