Point Cloud Library (PCL)  1.7.2
esf.hpp
1 /*
2  * Software License Agreement (BSD License)
3  *
4  * Point Cloud Library (PCL) - www.pointclouds.org
5  * Copyright (c) 2010-2012, Willow Garage, Inc.
6  * Copyright (c) 2012-, Open Perception, Inc.
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: pfh.hpp 5027 2012-03-12 03:10:45Z rusu $
38  *
39  */
40 
41 #ifndef PCL_FEATURES_IMPL_ESF_H_
42 #define PCL_FEATURES_IMPL_ESF_H_
43 
44 #include <pcl/features/esf.h>
45 #include <pcl/common/common.h>
46 #include <pcl/common/distances.h>
47 #include <pcl/common/transforms.h>
48 #include <vector>
49 
50 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
51 template <typename PointInT, typename PointOutT> void
53  PointCloudIn &pc, std::vector<float> &hist)
54 {
55  const int binsize = 64;
56  unsigned int sample_size = 20000;
57  srand (static_cast<unsigned int> (time (0)));
58  int maxindex = static_cast<int> (pc.points.size ());
59 
60  int index1, index2, index3;
61  std::vector<float> d2v, d1v, d3v, wt_d3;
62  std::vector<int> wt_d2;
63  d1v.reserve (sample_size);
64  d2v.reserve (sample_size * 3);
65  d3v.reserve (sample_size);
66  wt_d2.reserve (sample_size * 3);
67  wt_d3.reserve (sample_size);
68 
69  float h_in[binsize] = {0};
70  float h_out[binsize] = {0};
71  float h_mix[binsize] = {0};
72  float h_mix_ratio[binsize] = {0};
73 
74  float h_a3_in[binsize] = {0};
75  float h_a3_out[binsize] = {0};
76  float h_a3_mix[binsize] = {0};
77 
78  float h_d3_in[binsize] = {0};
79  float h_d3_out[binsize] = {0};
80  float h_d3_mix[binsize] = {0};
81 
82  float ratio=0.0;
83  float pih = static_cast<float>(M_PI) / 2.0f;
84  float a,b,c,s;
85  int th1,th2,th3;
86  int vxlcnt = 0;
87  int pcnt1,pcnt2,pcnt3;
88  for (size_t nn_idx = 0; nn_idx < sample_size; ++nn_idx)
89  {
90  // get a new random point
91  index1 = rand()%maxindex;
92  index2 = rand()%maxindex;
93  index3 = rand()%maxindex;
94 
95  if (index1==index2 || index1 == index3 || index2 == index3)
96  {
97  nn_idx--;
98  continue;
99  }
100 
101  Eigen::Vector4f p1 = pc.points[index1].getVector4fMap ();
102  Eigen::Vector4f p2 = pc.points[index2].getVector4fMap ();
103  Eigen::Vector4f p3 = pc.points[index3].getVector4fMap ();
104 
105  // A3
106  Eigen::Vector4f v21 (p2 - p1);
107  Eigen::Vector4f v31 (p3 - p1);
108  Eigen::Vector4f v23 (p2 - p3);
109  a = v21.norm (); b = v31.norm (); c = v23.norm (); s = (a+b+c) * 0.5f;
110  if (s * (s-a) * (s-b) * (s-c) <= 0.001f)
111  continue;
112 
113  v21.normalize ();
114  v31.normalize ();
115  v23.normalize ();
116 
117  //TODO: .dot gives nan's
118  th1 = static_cast<int> (pcl_round (acos (fabs (v21.dot (v31))) / pih * (binsize-1)));
119  th2 = static_cast<int> (pcl_round (acos (fabs (v23.dot (v31))) / pih * (binsize-1)));
120  th3 = static_cast<int> (pcl_round (acos (fabs (v23.dot (v21))) / pih * (binsize-1)));
121  if (th1 < 0 || th1 >= binsize)
122  {
123  nn_idx--;
124  continue;
125  }
126  if (th2 < 0 || th2 >= binsize)
127  {
128  nn_idx--;
129  continue;
130  }
131  if (th3 < 0 || th3 >= binsize)
132  {
133  nn_idx--;
134  continue;
135  }
136 
137  // D2
138  d2v.push_back (pcl::euclideanDistance (pc.points[index1], pc.points[index2]));
139  d2v.push_back (pcl::euclideanDistance (pc.points[index1], pc.points[index3]));
140  d2v.push_back (pcl::euclideanDistance (pc.points[index2], pc.points[index3]));
141 
142  int vxlcnt_sum = 0;
143  int p_cnt = 0;
144  // IN, OUT, MIXED, Ratio line tracing, index1->index2
145  {
146  const int xs = p1[0] < 0.0? static_cast<int>(floor(p1[0])+GRIDSIZE_H): static_cast<int>(ceil(p1[0])+GRIDSIZE_H-1);
147  const int ys = p1[1] < 0.0? static_cast<int>(floor(p1[1])+GRIDSIZE_H): static_cast<int>(ceil(p1[1])+GRIDSIZE_H-1);
148  const int zs = p1[2] < 0.0? static_cast<int>(floor(p1[2])+GRIDSIZE_H): static_cast<int>(ceil(p1[2])+GRIDSIZE_H-1);
149  const int xt = p2[0] < 0.0? static_cast<int>(floor(p2[0])+GRIDSIZE_H): static_cast<int>(ceil(p2[0])+GRIDSIZE_H-1);
150  const int yt = p2[1] < 0.0? static_cast<int>(floor(p2[1])+GRIDSIZE_H): static_cast<int>(ceil(p2[1])+GRIDSIZE_H-1);
151  const int zt = p2[2] < 0.0? static_cast<int>(floor(p2[2])+GRIDSIZE_H): static_cast<int>(ceil(p2[2])+GRIDSIZE_H-1);
152  wt_d2.push_back (this->lci (xs, ys, zs, xt, yt, zt, ratio, vxlcnt, pcnt1));
153  if (wt_d2.back () == 2)
154  h_mix_ratio[static_cast<int> (pcl_round (ratio * (binsize-1)))]++;
155  vxlcnt_sum += vxlcnt;
156  p_cnt += pcnt1;
157  }
158  // IN, OUT, MIXED, Ratio line tracing, index1->index3
159  {
160  const int xs = p1[0] < 0.0? static_cast<int>(floor(p1[0])+GRIDSIZE_H): static_cast<int>(ceil(p1[0])+GRIDSIZE_H-1);
161  const int ys = p1[1] < 0.0? static_cast<int>(floor(p1[1])+GRIDSIZE_H): static_cast<int>(ceil(p1[1])+GRIDSIZE_H-1);
162  const int zs = p1[2] < 0.0? static_cast<int>(floor(p1[2])+GRIDSIZE_H): static_cast<int>(ceil(p1[2])+GRIDSIZE_H-1);
163  const int xt = p3[0] < 0.0? static_cast<int>(floor(p3[0])+GRIDSIZE_H): static_cast<int>(ceil(p3[0])+GRIDSIZE_H-1);
164  const int yt = p3[1] < 0.0? static_cast<int>(floor(p3[1])+GRIDSIZE_H): static_cast<int>(ceil(p3[1])+GRIDSIZE_H-1);
165  const int zt = p3[2] < 0.0? static_cast<int>(floor(p3[2])+GRIDSIZE_H): static_cast<int>(ceil(p3[2])+GRIDSIZE_H-1);
166  wt_d2.push_back (this->lci (xs, ys, zs, xt, yt, zt, ratio, vxlcnt, pcnt2));
167  if (wt_d2.back () == 2)
168  h_mix_ratio[static_cast<int>(pcl_round (ratio * (binsize-1)))]++;
169  vxlcnt_sum += vxlcnt;
170  p_cnt += pcnt2;
171  }
172  // IN, OUT, MIXED, Ratio line tracing, index2->index3
173  {
174  const int xs = p2[0] < 0.0? static_cast<int>(floor(p2[0])+GRIDSIZE_H): static_cast<int>(ceil(p2[0])+GRIDSIZE_H-1);
175  const int ys = p2[1] < 0.0? static_cast<int>(floor(p2[1])+GRIDSIZE_H): static_cast<int>(ceil(p2[1])+GRIDSIZE_H-1);
176  const int zs = p2[2] < 0.0? static_cast<int>(floor(p2[2])+GRIDSIZE_H): static_cast<int>(ceil(p2[2])+GRIDSIZE_H-1);
177  const int xt = p3[0] < 0.0? static_cast<int>(floor(p3[0])+GRIDSIZE_H): static_cast<int>(ceil(p3[0])+GRIDSIZE_H-1);
178  const int yt = p3[1] < 0.0? static_cast<int>(floor(p3[1])+GRIDSIZE_H): static_cast<int>(ceil(p3[1])+GRIDSIZE_H-1);
179  const int zt = p3[2] < 0.0? static_cast<int>(floor(p3[2])+GRIDSIZE_H): static_cast<int>(ceil(p3[2])+GRIDSIZE_H-1);
180  wt_d2.push_back (this->lci (xs,ys,zs,xt,yt,zt,ratio,vxlcnt,pcnt3));
181  if (wt_d2.back () == 2)
182  h_mix_ratio[static_cast<int>(pcl_round(ratio * (binsize-1)))]++;
183  vxlcnt_sum += vxlcnt;
184  p_cnt += pcnt3;
185  }
186 
187  // D3 ( herons formula )
188  d3v.push_back (sqrtf (sqrtf (s * (s-a) * (s-b) * (s-c))));
189  if (vxlcnt_sum <= 21)
190  {
191  wt_d3.push_back (0);
192  h_a3_out[th1] += static_cast<float> (pcnt3) / 32.0f;
193  h_a3_out[th2] += static_cast<float> (pcnt1) / 32.0f;
194  h_a3_out[th3] += static_cast<float> (pcnt2) / 32.0f;
195  }
196  else
197  if (p_cnt - vxlcnt_sum < 4)
198  {
199  h_a3_in[th1] += static_cast<float> (pcnt3) / 32.0f;
200  h_a3_in[th2] += static_cast<float> (pcnt1) / 32.0f;
201  h_a3_in[th3] += static_cast<float> (pcnt2) / 32.0f;
202  wt_d3.push_back (1);
203  }
204  else
205  {
206  h_a3_mix[th1] += static_cast<float> (pcnt3) / 32.0f;
207  h_a3_mix[th2] += static_cast<float> (pcnt1) / 32.0f;
208  h_a3_mix[th3] += static_cast<float> (pcnt2) / 32.0f;
209  wt_d3.push_back (static_cast<float> (vxlcnt_sum) / static_cast<float> (p_cnt));
210  }
211  }
212  // Normalizing, get max
213  float maxd2 = 0;
214  float maxd3 = 0;
215 
216  for (size_t nn_idx = 0; nn_idx < sample_size; ++nn_idx)
217  {
218  // get max of Dx
219  if (d2v[nn_idx] > maxd2)
220  maxd2 = d2v[nn_idx];
221  if (d2v[sample_size + nn_idx] > maxd2)
222  maxd2 = d2v[sample_size + nn_idx];
223  if (d2v[sample_size*2 +nn_idx] > maxd2)
224  maxd2 = d2v[sample_size*2 +nn_idx];
225  if (d3v[nn_idx] > maxd3)
226  maxd3 = d3v[nn_idx];
227  }
228 
229  // Normalize and create histogram
230  int index;
231  for (size_t nn_idx = 0; nn_idx < sample_size; ++nn_idx)
232  {
233  if (wt_d3[nn_idx] >= 0.999) // IN
234  {
235  index = static_cast<int>(pcl_round (d3v[nn_idx] / maxd3 * (binsize-1)));
236  if (index >= 0 && index < binsize)
237  h_d3_in[index]++;
238  }
239  else
240  {
241  if (wt_d3[nn_idx] <= 0.001) // OUT
242  {
243  index = static_cast<int>(pcl_round (d3v[nn_idx] / maxd3 * (binsize-1)));
244  if (index >= 0 && index < binsize)
245  h_d3_out[index]++ ;
246  }
247  else
248  {
249  index = static_cast<int>(pcl_round (d3v[nn_idx] / maxd3 * (binsize-1)));
250  if (index >= 0 && index < binsize)
251  h_d3_mix[index]++;
252  }
253  }
254  }
255  //normalize and create histogram
256  for (size_t nn_idx = 0; nn_idx < d2v.size(); ++nn_idx )
257  {
258  if (wt_d2[nn_idx] == 0)
259  h_in[static_cast<int>(pcl_round (d2v[nn_idx] / maxd2 * (binsize-1)))]++ ;
260  if (wt_d2[nn_idx] == 1)
261  h_out[static_cast<int>(pcl_round (d2v[nn_idx] / maxd2 * (binsize-1)))]++;
262  if (wt_d2[nn_idx] == 2)
263  h_mix[static_cast<int>(pcl_round (d2v[nn_idx] / maxd2 * (binsize-1)))]++ ;
264  }
265 
266  //float weights[10] = {1, 1, 1, 1, 1, 1, 1, 1 , 1 , 1};
267  float weights[10] = {0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f};
268 
269  hist.reserve (binsize * 10);
270  for (int i = 0; i < binsize; i++)
271  hist.push_back (h_a3_in[i] * weights[0]);
272  for (int i = 0; i < binsize; i++)
273  hist.push_back (h_a3_out[i] * weights[1]);
274  for (int i = 0; i < binsize; i++)
275  hist.push_back (h_a3_mix[i] * weights[2]);
276 
277  for (int i = 0; i < binsize; i++)
278  hist.push_back (h_d3_in[i] * weights[3]);
279  for (int i = 0; i < binsize; i++)
280  hist.push_back (h_d3_out[i] * weights[4]);
281  for (int i = 0; i < binsize; i++)
282  hist.push_back (h_d3_mix[i] * weights[5]);
283 
284  for (int i = 0; i < binsize; i++)
285  hist.push_back (h_in[i]*0.5f * weights[6]);
286  for (int i = 0; i < binsize; i++)
287  hist.push_back (h_out[i] * weights[7]);
288  for (int i = 0; i < binsize; i++)
289  hist.push_back (h_mix[i] * weights[8]);
290  for (int i = 0; i < binsize; i++)
291  hist.push_back (h_mix_ratio[i]*0.5f * weights[9]);
292 
293  float sm = 0;
294  for (size_t i = 0; i < hist.size (); i++)
295  sm += hist[i];
296 
297  for (size_t i = 0; i < hist.size (); i++)
298  hist[i] /= sm;
299 }
300 
301 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
302 template <typename PointInT, typename PointOutT> int
304  const int x1, const int y1, const int z1,
305  const int x2, const int y2, const int z2,
306  float &ratio, int &incnt, int &pointcount)
307 {
308  int voxelcount = 0;
309  int voxel_in = 0;
310  int act_voxel[3];
311  act_voxel[0] = x1;
312  act_voxel[1] = y1;
313  act_voxel[2] = z1;
314  int x_inc, y_inc, z_inc;
315  int dx = x2 - x1;
316  int dy = y2 - y1;
317  int dz = z2 - z1;
318  if (dx < 0)
319  x_inc = -1;
320  else
321  x_inc = 1;
322  int l = abs (dx);
323  if (dy < 0)
324  y_inc = -1 ;
325  else
326  y_inc = 1;
327  int m = abs (dy);
328  if (dz < 0)
329  z_inc = -1 ;
330  else
331  z_inc = 1;
332  int n = abs (dz);
333  int dx2 = 2 * l;
334  int dy2 = 2 * m;
335  int dz2 = 2 * n;
336  if ((l >= m) & (l >= n))
337  {
338  int err_1 = dy2 - l;
339  int err_2 = dz2 - l;
340  for (int i = 1; i<l; i++)
341  {
342  voxelcount++;;
343  voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
344  if (err_1 > 0)
345  {
346  act_voxel[1] += y_inc;
347  err_1 -= dx2;
348  }
349  if (err_2 > 0)
350  {
351  act_voxel[2] += z_inc;
352  err_2 -= dx2;
353  }
354  err_1 += dy2;
355  err_2 += dz2;
356  act_voxel[0] += x_inc;
357  }
358  }
359  else if ((m >= l) & (m >= n))
360  {
361  int err_1 = dx2 - m;
362  int err_2 = dz2 - m;
363  for (int i=1; i<m; i++)
364  {
365  voxelcount++;
366  voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
367  if (err_1 > 0)
368  {
369  act_voxel[0] += x_inc;
370  err_1 -= dy2;
371  }
372  if (err_2 > 0)
373  {
374  act_voxel[2] += z_inc;
375  err_2 -= dy2;
376  }
377  err_1 += dx2;
378  err_2 += dz2;
379  act_voxel[1] += y_inc;
380  }
381  }
382  else
383  {
384  int err_1 = dy2 - n;
385  int err_2 = dx2 - n;
386  for (int i=1; i<n; i++)
387  {
388  voxelcount++;
389  voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
390  if (err_1 > 0)
391  {
392  act_voxel[1] += y_inc;
393  err_1 -= dz2;
394  }
395  if (err_2 > 0)
396  {
397  act_voxel[0] += x_inc;
398  err_2 -= dz2;
399  }
400  err_1 += dy2;
401  err_2 += dx2;
402  act_voxel[2] += z_inc;
403  }
404  }
405  voxelcount++;
406  voxel_in += static_cast<int>(lut_[act_voxel[0]][act_voxel[1]][act_voxel[2]] == 1);
407  incnt = voxel_in;
408  pointcount = voxelcount;
409 
410  if (voxel_in >= voxelcount-1)
411  return (0);
412 
413  if (voxel_in <= 7)
414  return (1);
415 
416  ratio = static_cast<float>(voxel_in) / static_cast<float>(voxelcount);
417  return (2);
418 }
419 
420 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
421 template <typename PointInT, typename PointOutT> void
423 {
424  int xi,yi,zi,xx,yy,zz;
425  for (size_t i = 0; i < cluster.points.size (); ++i)
426  {
427  xx = cluster.points[i].x<0.0? static_cast<int>(floor(cluster.points[i].x)+GRIDSIZE_H) : static_cast<int>(ceil(cluster.points[i].x)+GRIDSIZE_H-1);
428  yy = cluster.points[i].y<0.0? static_cast<int>(floor(cluster.points[i].y)+GRIDSIZE_H) : static_cast<int>(ceil(cluster.points[i].y)+GRIDSIZE_H-1);
429  zz = cluster.points[i].z<0.0? static_cast<int>(floor(cluster.points[i].z)+GRIDSIZE_H) : static_cast<int>(ceil(cluster.points[i].z)+GRIDSIZE_H-1);
430 
431  for (int x = -1; x < 2; x++)
432  for (int y = -1; y < 2; y++)
433  for (int z = -1; z < 2; z++)
434  {
435  xi = xx + x;
436  yi = yy + y;
437  zi = zz + z;
438 
439  if (yi >= GRIDSIZE || xi >= GRIDSIZE || zi>=GRIDSIZE || yi < 0 || xi < 0 || zi < 0)
440  {
441  ;
442  }
443  else
444  this->lut_[xi][yi][zi] = 1;
445  }
446  }
447 }
448 
449 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
450 template <typename PointInT, typename PointOutT> void
452 {
453  int xi,yi,zi,xx,yy,zz;
454  for (size_t i = 0; i < cluster.points.size (); ++i)
455  {
456  xx = cluster.points[i].x<0.0? static_cast<int>(floor(cluster.points[i].x)+GRIDSIZE_H) : static_cast<int>(ceil(cluster.points[i].x)+GRIDSIZE_H-1);
457  yy = cluster.points[i].y<0.0? static_cast<int>(floor(cluster.points[i].y)+GRIDSIZE_H) : static_cast<int>(ceil(cluster.points[i].y)+GRIDSIZE_H-1);
458  zz = cluster.points[i].z<0.0? static_cast<int>(floor(cluster.points[i].z)+GRIDSIZE_H) : static_cast<int>(ceil(cluster.points[i].z)+GRIDSIZE_H-1);
459 
460  for (int x = -1; x < 2; x++)
461  for (int y = -1; y < 2; y++)
462  for (int z = -1; z < 2; z++)
463  {
464  xi = xx + x;
465  yi = yy + y;
466  zi = zz + z;
467 
468  if (yi >= GRIDSIZE || xi >= GRIDSIZE || zi>=GRIDSIZE || yi < 0 || xi < 0 || zi < 0)
469  {
470  ;
471  }
472  else
473  this->lut_[xi][yi][zi] = 0;
474  }
475  }
476 }
477 
478 //////////////////////////////////////////////////////////////////////////////////////////////////////////////////
479 template <typename PointInT, typename PointOutT> void
481  const pcl::PointCloud<PointInT> &pc, float scalefactor, Eigen::Vector4f& centroid)
482 {
483  pcl::compute3DCentroid (pc, centroid);
484  pcl::demeanPointCloud (pc, centroid, local_cloud_);
485 
486  float max_distance = 0, d;
487  pcl::PointXYZ cog (0, 0, 0);
488 
489  for (size_t i = 0; i < local_cloud_.points.size (); ++i)
490  {
491  d = pcl::euclideanDistance(cog,local_cloud_.points[i]);
492  if (d > max_distance)
493  max_distance = d;
494  }
495 
496  float scale_factor = 1.0f / max_distance * scalefactor;
497 
498  Eigen::Affine3f matrix = Eigen::Affine3f::Identity();
499  matrix.scale (scale_factor);
500  pcl::transformPointCloud (local_cloud_, local_cloud_, matrix);
501 }
502 
503 //////////////////////////////////////////////////////////////////////////////////////////////
504 template<typename PointInT, typename PointOutT> void
506 {
508  {
509  output.width = output.height = 0;
510  output.points.clear ();
511  return;
512  }
513  // Copy the header
514  output.header = input_->header;
515 
516  // Resize the output dataset
517  // Important! We should only allocate precisely how many elements we will need, otherwise
518  // we risk at pre-allocating too much memory which could lead to bad_alloc
519  // (see http://dev.pointclouds.org/issues/657)
520  output.width = output.height = 1;
521  output.is_dense = input_->is_dense;
522  output.points.resize (1);
523 
524  // Perform the actual feature computation
525  computeFeature (output);
526 
528 }
529 
530 
531 //////////////////////////////////////////////////////////////////////////////////////////////
532 template <typename PointInT, typename PointOutT> void
534 {
535  Eigen::Vector4f xyz_centroid;
536  std::vector<float> hist;
537  scale_points_unit_sphere (*surface_, static_cast<float>(GRIDSIZE_H), xyz_centroid);
538  this->voxelize9 (local_cloud_);
539  this->computeESF (local_cloud_, hist);
540  this->cleanup9 (local_cloud_);
541 
542  // We only output _1_ signature
543  output.points.resize (1);
544  output.width = 1;
545  output.height = 1;
546 
547  for (size_t d = 0; d < hist.size (); ++d)
548  output.points[0].histogram[d] = hist[d];
549 }
550 
551 #define PCL_INSTANTIATE_ESFEstimation(T,OutT) template class PCL_EXPORTS pcl::ESFEstimation<T,OutT>;
552 
553 #endif // PCL_FEATURES_IMPL_ESF_H_
554 
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
void voxelize9(PointCloudIn &cluster)
...
Definition: esf.hpp:422
void computeFeature(PointCloudOut &output)
Estimate the Ensebmel of Shape Function (ESF) descriptors at a set of points given by
Definition: esf.hpp:533
void transformPointCloud(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform)
Apply an affine transform defined by an Eigen Transform.
Definition: transforms.hpp:42
uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:415
float euclideanDistance(const PointType1 &p1, const PointType2 &p2)
Calculate the euclidean distance between the two given points.
Definition: distances.h:196
void cleanup9(PointCloudIn &cluster)
...
Definition: esf.hpp:451
void scale_points_unit_sphere(const pcl::PointCloud< PointInT > &pc, float scalefactor, Eigen::Vector4f &centroid)
...
Definition: esf.hpp:480
uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:413
void demeanPointCloud(ConstCloudIterator< PointT > &cloud_iterator, const Eigen::Matrix< Scalar, 4, 1 > &centroid, pcl::PointCloud< PointT > &cloud_out, int npts=0)
Subtract a centroid from a point cloud and return the de-meaned representation.
Definition: centroid.hpp:632
A point structure representing Euclidean xyz coordinates.
int lci(const int x1, const int y1, const int z1, const int x2, const int y2, const int z2, float &ratio, int &incnt, int &pointcount)
...
Definition: esf.hpp:303
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:407
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values).
Definition: point_cloud.h:418
void computeESF(PointCloudIn &pc, std::vector< float > &hist)
...
Definition: esf.hpp:52
Feature represents the base feature class.
Definition: feature.h:105
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:50
void compute(PointCloudOut &output)
Overloaded computed method from pcl::Feature.
Definition: esf.hpp:505