Point Cloud Library (PCL)  1.11.1-dev
brisk_2d.hpp
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39 
40 
41 #ifndef PCL_FEATURES_IMPL_BRISK_2D_HPP_
42 #define PCL_FEATURES_IMPL_BRISK_2D_HPP_
43 
44 
45 namespace pcl
46 {
47 
48 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT>
50  : rotation_invariance_enabled_ (true)
51  , scale_invariance_enabled_ (true)
52  , pattern_scale_ (1.0f)
53  , input_cloud_ (), keypoints_ (), scale_range_ (), pattern_points_ (), points_ ()
54  , n_rot_ (1024), scale_list_ (nullptr), size_list_ (nullptr)
55  , scales_ (64)
56  , scalerange_ (30)
57  , basic_size_ (12.0)
58  , strings_ (0), d_max_ (0.0f), d_min_ (0.0f), short_pairs_ (), long_pairs_ ()
59  , no_short_pairs_ (0), no_long_pairs_ (0)
60  , intensity_ ()
61  , name_ ("BRISK2Destimation")
62 {
63  // Since we do not assume pattern_scale_ should be changed by the user, we
64  // can initialize the kernel in the constructor
65  std::vector<float> r_list;
66  std::vector<int> n_list;
67 
68  // this is the standard pattern found to be suitable also
69  r_list.resize (5);
70  n_list.resize (5);
71  const float f = 0.85f * pattern_scale_;
72 
73  r_list[0] = f * 0.0f;
74  r_list[1] = f * 2.9f;
75  r_list[2] = f * 4.9f;
76  r_list[3] = f * 7.4f;
77  r_list[4] = f * 10.8f;
78 
79  n_list[0] = 1;
80  n_list[1] = 10;
81  n_list[2] = 14;
82  n_list[3] = 15;
83  n_list[4] = 20;
84 
85  generateKernel (r_list, n_list, 5.85f * pattern_scale_, 8.2f * pattern_scale_);
86 }
87 
88 
89 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT>
91 {
92  delete [] pattern_points_;
93  delete [] short_pairs_;
94  delete [] long_pairs_;
95  delete [] scale_list_;
96  delete [] size_list_;
97 }
98 
99 
100 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> void
102  std::vector<float> &radius_list,
103  std::vector<int> &number_list, float d_max, float d_min,
104  std::vector<int> index_change)
105 {
106  d_max_ = d_max;
107  d_min_ = d_min;
108 
109  // get the total number of points
110  const auto rings = radius_list.size ();
111  assert (radius_list.size () != 0 && radius_list.size () == number_list.size ());
112  points_ = 0; // remember the total number of points
113  for (const auto number: number_list)
114  points_ += number;
115 
116  // set up the patterns
117  pattern_points_ = new BriskPatternPoint[points_*scales_*n_rot_];
118  BriskPatternPoint* pattern_iterator = pattern_points_;
119 
120  // define the scale discretization:
121  static const float lb_scale = std::log (scalerange_) / std::log (2.0);
122  static const float lb_scale_step = lb_scale / (float (scales_));
123 
124  scale_list_ = new float[scales_];
125  size_list_ = new unsigned int[scales_];
126 
127  const float sigma_scale = 1.3f;
128 
129  for (unsigned int scale = 0; scale < scales_; ++scale)
130  {
131  scale_list_[scale] = static_cast<float> (pow (double (2.0), static_cast<double> (float (scale) * lb_scale_step)));
132  size_list_[scale] = 0;
133 
134  // generate the pattern points look-up
135  for (std::size_t rot = 0; rot < n_rot_; ++rot)
136  {
137  // this is the rotation of the feature
138  double theta = double (rot) * 2 * M_PI / double (n_rot_);
139  for (int ring = 0; ring < static_cast<int>(rings); ++ring)
140  {
141  for (int num = 0; num < number_list[ring]; ++num)
142  {
143  // the actual coordinates on the circle
144  double alpha = double (num) * 2 * M_PI / double (number_list[ring]);
145 
146  // feature rotation plus angle of the point
147  pattern_iterator->x = scale_list_[scale] * radius_list[ring] * static_cast<float> (std::cos (alpha + theta));
148  pattern_iterator->y = scale_list_[scale] * radius_list[ring] * static_cast<float> (sin (alpha + theta));
149  // and the gaussian kernel sigma
150  if (ring == 0)
151  pattern_iterator->sigma = sigma_scale * scale_list_[scale] * 0.5f;
152  else
153  pattern_iterator->sigma = static_cast<float> (sigma_scale * scale_list_[scale] * (double (radius_list[ring])) * sin (M_PI / double (number_list[ring])));
154 
155  // adapt the sizeList if necessary
156  const unsigned int size = static_cast<unsigned int> (std::ceil (((scale_list_[scale] * radius_list[ring]) + pattern_iterator->sigma)) + 1);
157 
158  if (size_list_[scale] < size)
159  size_list_[scale] = size;
160 
161  // increment the iterator
162  ++pattern_iterator;
163  }
164  }
165  }
166  }
167 
168  // now also generate pairings
169  short_pairs_ = new BriskShortPair[points_ * (points_ - 1) / 2];
170  long_pairs_ = new BriskLongPair[points_ * (points_ - 1) / 2];
171  no_short_pairs_ = 0;
172  no_long_pairs_ = 0;
173 
174  // fill index_change with 0..n if empty
175  if (index_change.empty ())
176  {
177  index_change.resize (points_ * (points_ - 1) / 2);
178  }
179  std::iota(index_change.begin (), index_change.end (), 0);
180 
181  const float d_min_sq = d_min_ * d_min_;
182  const float d_max_sq = d_max_ * d_max_;
183  for (unsigned int i = 1; i < points_; i++)
184  {
185  for (unsigned int j = 0; j < i; j++)
186  { //(find all the pairs)
187  // point pair distance:
188  const float dx = pattern_points_[j].x - pattern_points_[i].x;
189  const float dy = pattern_points_[j].y - pattern_points_[i].y;
190  const float norm_sq = (dx*dx+dy*dy);
191  if (norm_sq > d_min_sq)
192  {
193  // save to long pairs
194  BriskLongPair& longPair = long_pairs_[no_long_pairs_];
195  longPair.weighted_dx = int ((dx / (norm_sq)) * 2048.0 + 0.5);
196  longPair.weighted_dy = int ((dy / (norm_sq)) * 2048.0 + 0.5);
197  longPair.i = i;
198  longPair.j = j;
199  ++no_long_pairs_;
200  }
201  else if (norm_sq < d_max_sq)
202  {
203  // save to short pairs
204  assert (no_short_pairs_ < index_change.size ()); // make sure the user passes something sensible
205  BriskShortPair& shortPair = short_pairs_[index_change[no_short_pairs_]];
206  shortPair.j = j;
207  shortPair.i = i;
208  ++no_short_pairs_;
209  }
210  }
211  }
212 
213  // no bits:
214  strings_ = int (std::ceil ((float (no_short_pairs_)) / 128.0)) * 4 * 4;
215 }
216 
217 
218 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> inline int
220  const std::vector<unsigned char> &image,
221  int image_width, int,
222  //const Stefan& integral,
223  const std::vector<int> &integral_image,
224  const float key_x, const float key_y, const unsigned int scale,
225  const unsigned int rot, const unsigned int point) const
226 {
227  // get the float position
228  const BriskPatternPoint& brisk_point = pattern_points_[scale * n_rot_*points_ + rot * points_ + point];
229  const float xf = brisk_point.x + key_x;
230  const float yf = brisk_point.y + key_y;
231  const int x = int (xf);
232  const int y = int (yf);
233  const int& imagecols = image_width;
234 
235  // get the sigma:
236  const float sigma_half = brisk_point.sigma;
237  const float area = 4.0f * sigma_half * sigma_half;
238 
239  // Get the point step
240 
241  // calculate output:
242  int ret_val;
243  if (sigma_half < 0.5)
244  {
245  // interpolation multipliers:
246  const int r_x = static_cast<int> ((xf - float (x)) * 1024);
247  const int r_y = static_cast<int> ((yf - float (y)) * 1024);
248  const int r_x_1 = (1024 - r_x);
249  const int r_y_1 = (1024 - r_y);
250 
251  //+const unsigned char* ptr = static_cast<const unsigned char*> (&image[0].r) + x + y * imagecols;
252  const unsigned char* ptr = static_cast<const unsigned char*>(&image[0]) + x + y * imagecols;
253 
254  // just interpolate:
255  ret_val = (r_x_1 * r_y_1 * int (*ptr));
256 
257  //+ptr += sizeof (PointInT);
258  ptr++;
259 
260  ret_val += (r_x * r_y_1 * int (*ptr));
261 
262  //+ptr += (imagecols * sizeof (PointInT));
263  ptr += imagecols;
264 
265  ret_val += (r_x * r_y * int (*ptr));
266 
267  //+ptr -= sizeof (PointInT);
268  ptr--;
269 
270  ret_val += (r_x_1 * r_y * int (*ptr));
271  return (ret_val + 512) / 1024;
272  }
273 
274  // this is the standard case (simple, not speed optimized yet):
275 
276  // scaling:
277  const int scaling = static_cast<int> (4194304.0f / area);
278  const int scaling2 = static_cast<int> (float (scaling) * area / 1024.0f);
279 
280  // the integral image is larger:
281  const int integralcols = imagecols + 1;
282 
283  // calculate borders
284  const float x_1 = xf - sigma_half;
285  const float x1 = xf + sigma_half;
286  const float y_1 = yf - sigma_half;
287  const float y1 = yf + sigma_half;
288 
289  const int x_left = int (x_1 + 0.5);
290  const int y_top = int (y_1 + 0.5);
291  const int x_right = int (x1 + 0.5);
292  const int y_bottom = int (y1 + 0.5);
293 
294  // overlap area - multiplication factors:
295  const float r_x_1 = float (x_left) - x_1 + 0.5f;
296  const float r_y_1 = float (y_top) - y_1 + 0.5f;
297  const float r_x1 = x1 - float (x_right) + 0.5f;
298  const float r_y1 = y1 - float (y_bottom) + 0.5f;
299  const int dx = x_right - x_left - 1;
300  const int dy = y_bottom - y_top - 1;
301  const int A = static_cast<int> ((r_x_1 * r_y_1) * float (scaling));
302  const int B = static_cast<int> ((r_x1 * r_y_1) * float (scaling));
303  const int C = static_cast<int> ((r_x1 * r_y1) * float (scaling));
304  const int D = static_cast<int> ((r_x_1 * r_y1) * float (scaling));
305  const int r_x_1_i = static_cast<int> (r_x_1 * float (scaling));
306  const int r_y_1_i = static_cast<int> (r_y_1 * float (scaling));
307  const int r_x1_i = static_cast<int> (r_x1 * float (scaling));
308  const int r_y1_i = static_cast<int> (r_y1 * float (scaling));
309 
310  if (dx + dy > 2)
311  {
312  // now the calculation:
313 
314  //+const unsigned char* ptr = static_cast<const unsigned char*> (&image[0].r) + x_left + imagecols * y_top;
315  const unsigned char* ptr = static_cast<const unsigned char*>(&image[0]) + x_left + imagecols * y_top;
316 
317  // first the corners:
318  ret_val = A * int (*ptr);
319 
320  //+ptr += (dx + 1) * sizeof (PointInT);
321  ptr += dx + 1;
322 
323  ret_val += B * int (*ptr);
324 
325  //+ptr += (dy * imagecols + 1) * sizeof (PointInT);
326  ptr += dy * imagecols + 1;
327 
328  ret_val += C * int (*ptr);
329 
330  //+ptr -= (dx + 1) * sizeof (PointInT);
331  ptr -= dx + 1;
332 
333  ret_val += D * int (*ptr);
334 
335  // next the edges:
336  //+int* ptr_integral;// = static_cast<int*> (integral.data) + x_left + integralcols * y_top + 1;
337  const int* ptr_integral = static_cast<const int*> (&integral_image[0]) + x_left + integralcols * y_top + 1;
338 
339  // find a simple path through the different surface corners
340  const int tmp1 = (*ptr_integral);
341  ptr_integral += dx;
342  const int tmp2 = (*ptr_integral);
343  ptr_integral += integralcols;
344  const int tmp3 = (*ptr_integral);
345  ptr_integral++;
346  const int tmp4 = (*ptr_integral);
347  ptr_integral += dy * integralcols;
348  const int tmp5 = (*ptr_integral);
349  ptr_integral--;
350  const int tmp6 = (*ptr_integral);
351  ptr_integral += integralcols;
352  const int tmp7 = (*ptr_integral);
353  ptr_integral -= dx;
354  const int tmp8 = (*ptr_integral);
355  ptr_integral -= integralcols;
356  const int tmp9 = (*ptr_integral);
357  ptr_integral--;
358  const int tmp10 = (*ptr_integral);
359  ptr_integral -= dy * integralcols;
360  const int tmp11 = (*ptr_integral);
361  ptr_integral++;
362  const int tmp12 = (*ptr_integral);
363 
364  // assign the weighted surface integrals:
365  const int upper = (tmp3 -tmp2 +tmp1 -tmp12) * r_y_1_i;
366  const int middle = (tmp6 -tmp3 +tmp12 -tmp9) * scaling;
367  const int left = (tmp9 -tmp12 +tmp11 -tmp10) * r_x_1_i;
368  const int right = (tmp5 -tmp4 +tmp3 -tmp6) * r_x1_i;
369  const int bottom = (tmp7 -tmp6 +tmp9 -tmp8) * r_y1_i;
370 
371  return (ret_val + upper + middle + left + right + bottom + scaling2 / 2) / scaling2;
372  }
373 
374  // now the calculation:
375 
376  //const unsigned char* ptr = static_cast<const unsigned char*> (&image[0].r) + x_left + imagecols * y_top;
377  const unsigned char* ptr = static_cast<const unsigned char*>(&image[0]) + x_left + imagecols * y_top;
378 
379  // first row:
380  ret_val = A * int (*ptr);
381 
382  //+ptr += sizeof (PointInT);
383  ptr++;
384 
385  //+const unsigned char* end1 = ptr + (dx * sizeof (PointInT));
386  const unsigned char* end1 = ptr + dx;
387 
388  //+for (; ptr < end1; ptr += sizeof (PointInT))
389  for (; ptr < end1; ptr++)
390  ret_val += r_y_1_i * int (*ptr);
391  ret_val += B * int (*ptr);
392 
393  // middle ones:
394  //+ptr += (imagecols - dx - 1) * sizeof (PointInT);
395  ptr += imagecols - dx - 1;
396 
397  //+const unsigned char* end_j = ptr + (dy * imagecols) * sizeof (PointInT);
398  const unsigned char* end_j = ptr + dy * imagecols;
399 
400  //+for (; ptr < end_j; ptr += (imagecols - dx - 1) * sizeof (PointInT))
401  for (; ptr < end_j; ptr += imagecols - dx - 1)
402  {
403  ret_val += r_x_1_i * int (*ptr);
404 
405  //+ptr += sizeof (PointInT);
406  ptr++;
407 
408  //+const unsigned char* end2 = ptr + (dx * sizeof (PointInT));
409  const unsigned char* end2 = ptr + dx;
410 
411  //+for (; ptr < end2; ptr += sizeof (PointInT))
412  for (; ptr < end2; ptr++)
413  ret_val += int (*ptr) * scaling;
414 
415  ret_val += r_x1_i * int (*ptr);
416  }
417  // last row:
418  ret_val += D * int (*ptr);
419 
420  //+ptr += sizeof (PointInT);
421  ptr++;
422 
423  //+const unsigned char* end3 = ptr + (dx * sizeof (PointInT));
424  const unsigned char* end3 = ptr + dx;
425 
426  //+for (; ptr<end3; ptr += sizeof (PointInT))
427  for (; ptr<end3; ptr++)
428  ret_val += r_y1_i * int (*ptr);
429 
430  ret_val += C * int (*ptr);
431 
432  return (ret_val + scaling2 / 2) / scaling2;
433 }
434 
435 
436 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> bool
438  const float min_x, const float min_y,
439  const float max_x, const float max_y, const KeypointT& pt)
440 {
441  return ((pt.x < min_x) || (pt.x >= max_x) || (pt.y < min_y) || (pt.y >= max_y));
442 }
443 
444 
445 template <typename PointInT, typename PointOutT, typename KeypointT, typename IntensityT> void
447  PointCloudOutT &output)
448 {
449  if (!input_cloud_->isOrganized ())
450  {
451  PCL_ERROR ("[pcl::%s::initCompute] %s doesn't support non organized clouds!\n", name_.c_str ());
452  return;
453  }
454 
455  // image size
456  const index_t width = static_cast<index_t>(input_cloud_->width);
457  const index_t height = static_cast<index_t>(input_cloud_->height);
458 
459  // destination for intensity data; will be forwarded to BRISK
460  std::vector<unsigned char> image_data (width*height);
461 
462  for (std::size_t i = 0; i < image_data.size (); ++i)
463  image_data[i] = static_cast<unsigned char> (intensity_ ((*input_cloud_)[i]));
464 
465  // Remove keypoints very close to the border
466  auto ksize = keypoints_->size ();
467  std::vector<int> kscales; // remember the scale per keypoint
468  kscales.resize (ksize);
469 
470  // initialize constants
471  static const float lb_scalerange = std::log2 (scalerange_);
472 
473  typename std::vector<KeypointT, Eigen::aligned_allocator<KeypointT> >::iterator beginning = keypoints_->points.begin ();
474  std::vector<int>::iterator beginningkscales = kscales.begin ();
475 
476  static const float basic_size_06 = basic_size_ * 0.6f;
477  unsigned int basicscale = 0;
478 
479  if (!scale_invariance_enabled_)
480  basicscale = std::max (static_cast<int> (float (scales_) / lb_scalerange * (std::log2 (1.45f * basic_size_ / (basic_size_06))) + 0.5f), 0);
481 
482  for (std::size_t k = 0; k < ksize; k++)
483  {
484  unsigned int scale;
485  if (scale_invariance_enabled_)
486  {
487  scale = std::max (static_cast<int> (float (scales_) / lb_scalerange * (std::log2 ((*keypoints_)[k].size / (basic_size_06))) + 0.5f), 0);
488  // saturate
489  if (scale >= scales_) scale = scales_ - 1;
490  kscales[k] = scale;
491  }
492  else
493  {
494  scale = basicscale;
495  kscales[k] = scale;
496  }
497 
498  const int border = size_list_[scale];
499  const int border_x = width - border;
500  const int border_y = height - border;
501 
502  if (RoiPredicate (float (border), float (border), float (border_x), float (border_y), (*keypoints_)[k]))
503  {
504  //std::cerr << "remove keypoint" << std::endl;
505  keypoints_->points.erase (beginning + k);
506  kscales.erase (beginningkscales + k);
507  if (k == 0)
508  {
509  beginning = keypoints_->points.begin ();
510  beginningkscales = kscales.begin ();
511  }
512  ksize--;
513  k--;
514  }
515  }
516 
517  keypoints_->width = keypoints_->size ();
518  keypoints_->height = 1;
519 
520  // first, calculate the integral image over the whole image:
521  // current integral image
522  std::vector<int> integral ((width+1)*(height+1), 0); // the integral image
523 
524  for (index_t row_index = 1; row_index < height; ++row_index)
525  {
526  for (index_t col_index = 1; col_index < width; ++col_index)
527  {
528  const std::size_t index = row_index*width+col_index;
529  const std::size_t index2 = (row_index)*(width+1)+(col_index);
530 
531  integral[index2] = static_cast<int> (image_data[index])
532  - integral[index2-1-(width+1)]
533  + integral[index2-(width+1)]
534  + integral[index2-1];
535  }
536  }
537 
538  int* values = new int[points_]; // for temporary use
539 
540  // resize the descriptors:
541  //output = zeros (ksize, strings_);
542 
543  // now do the extraction for all keypoints:
544 
545  // temporary variables containing gray values at sample points:
546  int t1;
547  int t2;
548 
549  // the feature orientation
550  int direction0;
551  int direction1;
552 
553  output.resize (ksize);
554  //output.width = ksize;
555  //output.height = 1;
556  for (std::size_t k = 0; k < ksize; k++)
557  {
558  unsigned char* ptr = &output[k].descriptor[0];
559 
560  int theta;
561  KeypointT &kp = (*keypoints_)[k];
562  const int& scale = kscales[k];
563  int shifter = 0;
564  int* pvalues = values;
565  const float& x = float (kp.x);
566  const float& y = float (kp.y);
567  if (true) // kp.angle==-1
568  {
569  if (!rotation_invariance_enabled_)
570  // don't compute the gradient direction, just assign a rotation of 0 degree
571  theta = 0;
572  else
573  {
574  // get the gray values in the unrotated pattern
575  for (unsigned int i = 0; i < points_; i++)
576  *(pvalues++) = smoothedIntensity (image_data, width, height, integral, x, y, scale, 0, i);
577 
578  direction0 = 0;
579  direction1 = 0;
580  // now iterate through the long pairings
581  const BriskLongPair* max = long_pairs_ + no_long_pairs_;
582 
583  for (BriskLongPair* iter = long_pairs_; iter < max; ++iter)
584  {
585  t1 = *(values + iter->i);
586  t2 = *(values + iter->j);
587  const int delta_t = (t1 - t2);
588 
589  // update the direction:
590  const int tmp0 = delta_t * (iter->weighted_dx) / 1024;
591  const int tmp1 = delta_t * (iter->weighted_dy) / 1024;
592  direction0 += tmp0;
593  direction1 += tmp1;
594  }
595  kp.angle = std::atan2 (float (direction1), float (direction0)) / float (M_PI) * 180.0f;
596  theta = static_cast<int> ((float (n_rot_) * kp.angle) / (360.0f) + 0.5f);
597  if (theta < 0)
598  theta += n_rot_;
599  if (theta >= int (n_rot_))
600  theta -= n_rot_;
601  }
602  }
603  else
604  {
605  // figure out the direction:
606  //int theta=rotationInvariance*round((_n_rot*std::atan2(direction.at<int>(0,0),direction.at<int>(1,0)))/(2*M_PI));
607  if (!rotation_invariance_enabled_)
608  theta = 0;
609  else
610  {
611  theta = static_cast<int> (n_rot_ * (kp.angle / (360.0)) + 0.5);
612  if (theta < 0)
613  theta += n_rot_;
614  if (theta >= int (n_rot_))
615  theta -= n_rot_;
616  }
617  }
618 
619  // now also extract the stuff for the actual direction:
620  // let us compute the smoothed values
621  shifter = 0;
622 
623  //unsigned int mean=0;
624  pvalues = values;
625  // get the gray values in the rotated pattern
626  for (unsigned int i = 0; i < points_; i++)
627  *(pvalues++) = smoothedIntensity (image_data, width, height, integral, x, y, scale, theta, i);
628 
629 #ifdef __GNUC__
630  using UINT32_ALIAS = std::uint32_t;
631 #endif
632 #ifdef _MSC_VER
633  // Todo: find the equivalent to may_alias
634  #define UCHAR_ALIAS std::uint32_t //__declspec(noalias)
635  #define UINT32_ALIAS std::uint32_t //__declspec(noalias)
636 #endif
637 
638  // now iterate through all the pairings
639  UINT32_ALIAS* ptr2 = reinterpret_cast<UINT32_ALIAS*> (ptr);
640  const BriskShortPair* max = short_pairs_ + no_short_pairs_;
641 
642  for (BriskShortPair* iter = short_pairs_; iter < max; ++iter)
643  {
644  t1 = *(values + iter->i);
645  t2 = *(values + iter->j);
646 
647  if (t1 > t2)
648  *ptr2 |= ((1) << shifter);
649 
650  // else already initialized with zero
651  // take care of the iterators:
652  ++shifter;
653 
654  if (shifter == 32)
655  {
656  shifter = 0;
657  ++ptr2;
658  }
659  }
660 
661  //ptr += strings_;
662 
663  //// Account for the scale + orientation;
664  //ptr += sizeof (output[0].scale);
665  //ptr += sizeof (output[0].orientation);
666  }
667 
668  // we do not change the denseness
669  output.width = output.size ();
670  output.height = 1;
671  output.is_dense = true;
672 
673  // clean-up
674  delete [] values;
675 }
676 
677 } // namespace pcl
678 
679 #endif //#ifndef PCL_FEATURES_IMPL_BRISK_2D_HPP_
680 
pcl
Definition: convolution.h:46
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:394
pcl::BRISK2DEstimation::BRISK2DEstimation
BRISK2DEstimation()
Constructor.
Definition: brisk_2d.hpp:49
pcl::PointCloud< PointOutT >
pcl::index_t
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition: types.h:110
pcl::BRISK2DEstimation::compute
void compute(PointCloudOutT &output)
Computes the descriptors for the previously specified points and input data.
Definition: brisk_2d.hpp:446
pcl::BRISK2DEstimation::~BRISK2DEstimation
virtual ~BRISK2DEstimation()
Destructor.
Definition: brisk_2d.hpp:90
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:392
M_PI
#define M_PI
Definition: pcl_macros.h:201
pcl::PointCloud::is_dense
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:397
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:456
pcl::BRISK2DEstimation::smoothedIntensity
int smoothedIntensity(const std::vector< unsigned char > &image, int image_width, int image_height, const std::vector< int > &integral_image, const float key_x, const float key_y, const unsigned int scale, const unsigned int rot, const unsigned int point) const
Compute the smoothed intensity for a given x/y position in the image.
Definition: brisk_2d.hpp:219
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:437
pcl::BRISK2DEstimation::generateKernel
void generateKernel(std::vector< float > &radius_list, std::vector< int > &number_list, float d_max=5.85f, float d_min=8.2f, std::vector< int > index_change=std::vector< int >())
Call this to generate the kernel: circle of radius r (pixels), with n points; short pairings with dMa...
Definition: brisk_2d.hpp:101
pcl::B
@ B
Definition: norms.h:54
pcl::BRISK2DEstimation
Implementation of the BRISK-descriptor, based on the original code and paper reference by.
Definition: brisk_2d.h:66