GainCompensator.cpp
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2 Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
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27 
28 #include "rtabmap/core/GainCompensator.h"
29 
30 #include <rtabmap/utilite/ULogger.h>
31 #include <rtabmap/utilite/UStl.h>
32 #include <rtabmap/utilite/UConversion.h>
33 #include <rtabmap/core/util3d_transforms.h>
34 #include <pcl/search/kdtree.h>
35 #include <pcl/common/common.h>
36 #include <pcl/common/transforms.h>
37 #include <pcl/correspondence.h>
38 
39 namespace rtabmap {
40 
41 double sqr(uchar v)
42 {
43  return double(v)*double(v);
44 }
45 
46 GainCompensator::GainCompensator(double maxCorrespondenceDistance, double minOverlap, double alpha, double beta) :
47  maxCorrespondenceDistance_(maxCorrespondenceDistance),
48  minOverlap_(minOverlap),
49  alpha_(alpha),
50  beta_(beta)
51 {
52 }
53 
54 GainCompensator::~GainCompensator() {
55 }
56 
57 void GainCompensator::feed(
58  const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloudA,
59  const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloudB,
60  const Transform & transformB)
61 {
62  std::multimap<int, Link> links;
63  links.insert(std::make_pair(0, Link(0,1,Link::kUserClosure, transformB)));
64  std::map<int, pcl::PointCloud<pcl::PointXYZRGB>::Ptr> clouds;
65  clouds.insert(std::make_pair(0, cloudA));
66  clouds.insert(std::make_pair(1, cloudB));
67  feed(clouds, links);
68 }
69 
70 void GainCompensator::feed(
71  const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloudA,
72  const pcl::IndicesPtr & indicesA,
73  const pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloudB,
74  const pcl::IndicesPtr & indicesB,
75  const Transform & transformB)
76 {
77  std::multimap<int, Link> links;
78  links.insert(std::make_pair(0, Link(0,1,Link::kUserClosure, transformB)));
79  std::map<int, pcl::PointCloud<pcl::PointXYZRGB>::Ptr> clouds;
80  clouds.insert(std::make_pair(0, cloudA));
81  clouds.insert(std::make_pair(1, cloudB));
82  std::map<int, pcl::IndicesPtr> indices;
83  indices.insert(std::make_pair(0, indicesA));
84  indices.insert(std::make_pair(1, indicesB));
85  feed(clouds, indices, links);
86 }
87 
88 void GainCompensator::feed(
89  const std::map<int, pcl::PointCloud<pcl::PointXYZRGB>::Ptr> & clouds,
90  const std::multimap<int, Link> & links)
91 {
92  std::map<int, pcl::IndicesPtr> indices;
93  feed(clouds, indices, links);
94 }
95 
96 // @see https://studiofreya.com/3d-math-and-physics/simple-aabb-vs-aabb-collision-detection/
97 struct AABB
98 {
99  AABB() : c(), r() {}
100 
101  AABB(const Eigen::Vector3f & center, const Eigen::Vector3f & halfwidths)
102  : c(center)
103  , r(halfwidths)
104  {}
105 
106  Eigen::Vector3f c; // center point
107  Eigen::Vector3f r; // halfwidths
108 };
109 
110 bool testAABBAABB(const AABB &a, const AABB &b)
111 {
112  if ( fabs(a.c[0] - b.c[0]) > (a.r[0] + b.r[0]) ) return false;
113  if ( fabs(a.c[1] - b.c[1]) > (a.r[1] + b.r[1]) ) return false;
114  if ( fabs(a.c[2] - b.c[2]) > (a.r[2] + b.r[2]) ) return false;
115 
116  // We have an overlap
117  return true;
118 };
119 
123 template<typename PointT>
124 void feedImpl(
125  const std::map<int, typename pcl::PointCloud<PointT>::Ptr> & clouds,
126  const std::map<int, pcl::IndicesPtr> & indices,
127  const std::multimap<int, Link> & links,
128  float maxCorrespondenceDistance,
129  double minOverlap,
130  double alpha,
131  double beta,
132  cv::Mat_<double> & gains,
133  std::map<int, int> & idToIndex)
134 {
135  UDEBUG("Exposure compensation...");
136 
137  UASSERT(maxCorrespondenceDistance > 0.0f);
138  UASSERT(indices.size() == 0 || clouds.size() == indices.size());
139 
140  const int num_images = static_cast<int>(clouds.size());
141  cv::Mat_<int> N(num_images, num_images); N.setTo(0);
142  cv::Mat_<double> I(num_images, num_images); I.setTo(0);
143 
144  cv::Mat_<double> IR(num_images, num_images); IR.setTo(0);
145  cv::Mat_<double> IG(num_images, num_images); IG.setTo(0);
146  cv::Mat_<double> IB(num_images, num_images); IB.setTo(0);
147 
148  // make id to index map
149  idToIndex.clear();
150  std::vector<int> indexToId(clouds.size());
151  int oi=0;
152  std::map<int, std::pair<pcl::PointXYZ, pcl::PointXYZ> > boundingBoxes;
153  for(typename std::map<int, typename pcl::PointCloud<PointT>::Ptr>::const_iterator iter=clouds.begin(); iter!=clouds.end(); ++iter)
154  {
155  idToIndex.insert(std::make_pair(iter->first, oi));
156  indexToId[oi] = iter->first;
157  UASSERT(indices.empty() || uContains(indices, iter->first));
158  Eigen::Vector4f minPt(0,0,0,0);
159  Eigen::Vector4f maxPt(0,0,0,0);
160  if(indices.empty() || indices.at(iter->first)->empty())
161  {
162  N(oi,oi) = iter->second->size();
163  pcl::getMinMax3D(*iter->second, minPt, maxPt);
164  }
165  else
166  {
167  N(oi,oi) = indices.at(iter->first)->size();
168  pcl::getMinMax3D(*iter->second, *indices.at(iter->first), minPt, maxPt);
169  }
170  minPt[0] -= maxCorrespondenceDistance;
171  minPt[1] -= maxCorrespondenceDistance;
172  minPt[2] -= maxCorrespondenceDistance;
173  maxPt[0] += maxCorrespondenceDistance;
174  maxPt[1] += maxCorrespondenceDistance;
175  maxPt[2] += maxCorrespondenceDistance;
176  boundingBoxes.insert(std::make_pair(iter->first, std::make_pair(pcl::PointXYZ(minPt[0], minPt[1], minPt[2]), pcl::PointXYZ(maxPt[0], maxPt[1], maxPt[2]))));
177  ++oi;
178  }
179 
180  typename pcl::search::KdTree<PointT> kdtree;
181  int lastKdTreeId = 0;
182  for(std::multimap<int, Link>::const_iterator iter=links.begin(); iter!=links.end(); ++iter)
183  {
184  if(uContains(idToIndex, iter->second.from()) && uContains(idToIndex, iter->second.to()))
185  {
186  const typename pcl::PointCloud<PointT>::Ptr & cloudFrom = clouds.at(iter->second.from());
187  const typename pcl::PointCloud<PointT>::Ptr & cloudTo = clouds.at(iter->second.to());
188  if(cloudFrom->size() && cloudTo->size())
189  {
190  //Are bounding boxes intersect?
191  std::pair<pcl::PointXYZ, pcl::PointXYZ> bbMinMaxFrom = boundingBoxes.at(iter->second.from());
192  std::pair<pcl::PointXYZ, pcl::PointXYZ> bbMinMaxTo = boundingBoxes.at(iter->second.to());
193  Eigen::Affine3f t = Transform::getIdentity().toEigen3f();
194  if(!iter->second.transform().isIdentity() && !iter->second.transform().isNull())
195  {
196  t = iter->second.transform().toEigen3f();
197  bbMinMaxTo.first = pcl::transformPoint(bbMinMaxTo.first, t);
198  bbMinMaxTo.second = pcl::transformPoint(bbMinMaxTo.second, t);
199  }
200  AABB bbFrom(Eigen::Vector3f((bbMinMaxFrom.second.x + bbMinMaxFrom.first.x)/2.0f, (bbMinMaxFrom.second.y + bbMinMaxFrom.first.y)/2.0f, (bbMinMaxFrom.second.z + bbMinMaxFrom.first.z)/2.0f),
201  Eigen::Vector3f((bbMinMaxFrom.second.x - bbMinMaxFrom.first.x)/2.0f, (bbMinMaxFrom.second.y - bbMinMaxFrom.first.y)/2.0f, (bbMinMaxFrom.second.z - bbMinMaxFrom.first.z)/2.0f));
202  AABB bbTo(Eigen::Vector3f((bbMinMaxTo.second.x + bbMinMaxTo.first.x)/2.0f, (bbMinMaxTo.second.y + bbMinMaxTo.first.y)/2.0f, (bbMinMaxTo.second.z + bbMinMaxTo.first.z)/2.0f),
203  Eigen::Vector3f((bbMinMaxTo.second.x - bbMinMaxTo.first.x)/2.0f, (bbMinMaxTo.second.y - bbMinMaxTo.first.y)/2.0f, (bbMinMaxTo.second.z - bbMinMaxTo.first.z)/2.0f));
204  //UDEBUG("%d = %f,%f,%f %f,%f,%f", iter->second.from(), bbMinMaxFrom.first[0], bbMinMaxFrom.first[1], bbMinMaxFrom.first[2], bbMinMaxFrom.second[0], bbMinMaxFrom.second[1], bbMinMaxFrom.second[2]);
205  //UDEBUG("%d = %f,%f,%f %f,%f,%f", iter->second.to(), bbMinMaxTo.first[0], bbMinMaxTo.first[1], bbMinMaxTo.first[2], bbMinMaxTo.second[0], bbMinMaxTo.second[1], bbMinMaxTo.second[2]);
206  if(testAABBAABB(bbFrom, bbTo))
207  {
208  if(lastKdTreeId <= 0 || lastKdTreeId!=iter->second.from())
209  {
210  //reconstruct kdtree
211  if(indices.size() && indices.at(iter->second.from())->size())
212  {
213  kdtree.setInputCloud(cloudFrom, indices.at(iter->second.from()));
214  }
215  else
216  {
217  kdtree.setInputCloud(cloudFrom);
218  }
219  }
220 
221  pcl::Correspondences correspondences;
222  pcl::IndicesPtr indicesTo(new std::vector<int>);
223  std::set<int> addedFrom;
224  if(indices.size() && indices.at(iter->second.to())->size())
225  {
226  const pcl::IndicesPtr & indicesTo = indices.at(iter->second.to());
227  correspondences.resize(indicesTo->size());
228  int oi=0;
229  for(unsigned int i=0; i<indicesTo->size(); ++i)
230  {
231  std::vector<int> k_indices;
232  std::vector<float> k_sqr_distances;
233  if(kdtree.radiusSearch(pcl::transformPoint(cloudTo->at(indicesTo->at(i)), t), maxCorrespondenceDistance, k_indices, k_sqr_distances, 1))
234  {
235  if(addedFrom.find(k_indices[0]) == addedFrom.end())
236  {
237  correspondences[oi].index_match = k_indices[0];
238  correspondences[oi].index_query = indicesTo->at(i);
239  correspondences[oi].distance = k_sqr_distances[0];
240  addedFrom.insert(k_indices[0]);
241  ++oi;
242  }
243  }
244  }
245  correspondences.resize(oi);
246  }
247  else
248  {
249  correspondences.resize(cloudTo->size());
250  int oi=0;
251  for(unsigned int i=0; i<cloudTo->size(); ++i)
252  {
253  std::vector<int> k_indices;
254  std::vector<float> k_sqr_distances;
255  if(kdtree.radiusSearch(pcl::transformPoint(cloudTo->at(i), t), maxCorrespondenceDistance, k_indices, k_sqr_distances, 1))
256  {
257  if(addedFrom.find(k_indices[0]) == addedFrom.end())
258  {
259  correspondences[oi].index_match = k_indices[0];
260  correspondences[oi].index_query = i;
261  correspondences[oi].distance = k_sqr_distances[0];
262  addedFrom.insert(k_indices[0]);
263  ++oi;
264  }
265  }
266  }
267  correspondences.resize(oi);
268  }
269 
270  UDEBUG("%d->%d: correspondences = %d", iter->second.from(), iter->second.to(), (int)correspondences.size());
271  if(correspondences.size() && (minOverlap <= 0.0 ||
272  (double(correspondences.size()) / double(clouds.at(iter->second.from())->size()) >= minOverlap &&
273  double(correspondences.size()) / double(clouds.at(iter->second.to())->size()) >= minOverlap)))
274  {
275  int i = idToIndex.at(iter->second.from());
276  int j = idToIndex.at(iter->second.to());
277 
278  double Isum1 = 0, Isum2 = 0;
279  double IRsum1 = 0, IRsum2 = 0;
280  double IGsum1 = 0, IGsum2 = 0;
281  double IBsum1 = 0, IBsum2 = 0;
282  for (unsigned int c = 0; c < correspondences.size(); ++c)
283  {
284  const PointT & pt1 = cloudFrom->at(correspondences.at(c).index_match);
285  const PointT & pt2 = cloudTo->at(correspondences.at(c).index_query);
286 
287  Isum1 += std::sqrt(static_cast<double>(sqr(pt1.r) + sqr(pt1.g) + sqr(pt1.b)));
288  Isum2 += std::sqrt(static_cast<double>(sqr(pt2.r) + sqr(pt2.g) + sqr(pt2.b)));
289 
290  IRsum1 += static_cast<double>(pt1.r);
291  IRsum2 += static_cast<double>(pt2.r);
292  IGsum1 += static_cast<double>(pt1.g);
293  IGsum2 += static_cast<double>(pt2.g);
294  IBsum1 += static_cast<double>(pt1.b);
295  IBsum2 += static_cast<double>(pt2.b);
296  }
297  N(i, j) = N(j, i) = correspondences.size();
298  I(i, j) = Isum1 / N(i, j);
299  I(j, i) = Isum2 / N(i, j);
300 
301  IR(i, j) = IRsum1 / N(i, j);
302  IR(j, i) = IRsum2 / N(i, j);
303  IG(i, j) = IGsum1 / N(i, j);
304  IG(j, i) = IGsum2 / N(i, j);
305  IB(i, j) = IBsum1 / N(i, j);
306  IB(j, i) = IBsum2 / N(i, j);
307  }
308  }
309  }
310  }
311  }
312 
313  cv::Mat_<double> A(num_images, num_images); A.setTo(0);
314  cv::Mat_<double> b(num_images, 1); b.setTo(0);
315  cv::Mat_<double> AR(num_images, num_images); AR.setTo(0);
316  cv::Mat_<double> AG(num_images, num_images); AG.setTo(0);
317  cv::Mat_<double> AB(num_images, num_images); AB.setTo(0);
318  for (int i = 0; i < num_images; ++i)
319  {
320  for (int j = 0; j < num_images; ++j)
321  {
322  b(i, 0) += beta * N(i, j);
323  A(i, i) += beta * N(i, j);
324  AR(i, i) += beta * N(i, j);
325  AG(i, i) += beta * N(i, j);
326  AB(i, i) += beta * N(i, j);
327  if (j == i) continue;
328  A(i, i) += 2 * alpha * I(i, j) * I(i, j) * N(i, j);
329  A(i, j) -= 2 * alpha * I(i, j) * I(j, i) * N(i, j);
330 
331  AR(i, i) += 2 * alpha * IR(i, j) * IR(i, j) * N(i, j);
332  AR(i, j) -= 2 * alpha * IR(i, j) * IR(j, i) * N(i, j);
333 
334  AG(i, i) += 2 * alpha * IG(i, j) * IG(i, j) * N(i, j);
335  AG(i, j) -= 2 * alpha * IG(i, j) * IG(j, i) * N(i, j);
336 
337  AB(i, i) += 2 * alpha * IB(i, j) * IB(i, j) * N(i, j);
338  AB(i, j) -= 2 * alpha * IB(i, j) * IB(j, i) * N(i, j);
339  }
340  }
341 
342  cv::Mat_<double> gainsGray, gainsR, gainsG, gainsB;
343  cv::solve(A, b, gainsGray);
344 
345  cv::solve(AR, b, gainsR);
346  cv::solve(AG, b, gainsG);
347  cv::solve(AB, b, gainsB);
348 
349  gains = cv::Mat_<double>(gainsGray.rows, 4);
350  gainsGray.copyTo(gains.col(0));
351  gainsR.copyTo(gains.col(1));
352  gainsG.copyTo(gains.col(2));
353  gainsB.copyTo(gains.col(3));
354 
355  if(ULogger::kInfo)
356  {
357  for(int i=0; i<gains.rows; ++i)
358  {
359  UINFO("Gain index=%d (id=%d) = %f (%f,%f,%f)", i, indexToId[i], gains(i, 0), gains(i, 1), gains(i, 2), gains(i, 3));
360  }
361  }
362 }
363 
364 void GainCompensator::feed(
365  const std::map<int, pcl::PointCloud<pcl::PointXYZRGB>::Ptr> & clouds,
366  const std::map<int, pcl::IndicesPtr> & indices,
367  const std::multimap<int, Link> & links)
368 {
369  feedImpl<pcl::PointXYZRGB>(clouds, indices, links, maxCorrespondenceDistance_, minOverlap_, alpha_, beta_, gains_, idToIndex_);
370 }
371 void GainCompensator::feed(
372  const std::map<int, pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr> & clouds,
373  const std::map<int, pcl::IndicesPtr> & indices,
374  const std::multimap<int, Link> & links)
375 {
376  feedImpl<pcl::PointXYZRGBNormal>(clouds, indices, links, maxCorrespondenceDistance_, minOverlap_, alpha_, beta_, gains_, idToIndex_);
377 }
378 void GainCompensator::feed(
379  const std::map<int, std::pair<pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr, pcl::IndicesPtr> > & cloudsIndices,
380  const std::multimap<int, Link> & links)
381 {
382  std::map<int, pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr> clouds;
383  std::map<int, pcl::IndicesPtr> indices;
384  for(std::map<int, std::pair<pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr, pcl::IndicesPtr> >::const_iterator iter=cloudsIndices.begin(); iter!=cloudsIndices.end(); ++iter)
385  {
386  clouds.insert(std::make_pair(iter->first, iter->second.first));
387  indices.insert(std::make_pair(iter->first, iter->second.second));
388  }
389  feedImpl<pcl::PointXYZRGBNormal>(clouds, indices, links, maxCorrespondenceDistance_, minOverlap_, alpha_, beta_, gains_, idToIndex_);
390 }
391 
392 template<typename PointT>
393 void applyImpl(
394  int index,
395  typename pcl::PointCloud<PointT>::Ptr & cloud,
396  const pcl::IndicesPtr & indices,
397  const cv::Mat_<double> & gains,
398  bool rgb)
399 {
400  double gainR = gains(index, rgb?1:0);
401  double gainG = gains(index, rgb?2:0);
402  double gainB = gains(index, rgb?3:0);
403  UDEBUG("index=%d gain=%f (%f,%f,%f)", index, gains(index, 0), gainR, gainG, gainB);
404  if(indices->size())
405  {
406  for(unsigned int i=0; i<indices->size(); ++i)
407  {
408  PointT & pt = cloud->at(indices->at(i));
409  pt.r = uchar(std::max(0.0, std::min(255.0, double(pt.r) * gainR)));
410  pt.g = uchar(std::max(0.0, std::min(255.0, double(pt.g) * gainG)));
411  pt.b = uchar(std::max(0.0, std::min(255.0, double(pt.b) * gainB)));
412  }
413  }
414  else
415  {
416  for(unsigned int i=0; i<cloud->size(); ++i)
417  {
418  PointT & pt = cloud->at(i);
419  pt.r = uchar(std::max(0.0, std::min(255.0, double(pt.r) * gainR)));
420  pt.g = uchar(std::max(0.0, std::min(255.0, double(pt.g) * gainG)));
421  pt.b = uchar(std::max(0.0, std::min(255.0, double(pt.b) * gainB)));
422  }
423  }
424 }
425 
426 void GainCompensator::apply(
427  int id,
428  pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
429  bool rgb) const
430 {
431  pcl::IndicesPtr indices(new std::vector<int>);
432  apply(id, cloud, indices, rgb);
433 }
434 void GainCompensator::apply(
435  int id,
436  pcl::PointCloud<pcl::PointXYZRGB>::Ptr & cloud,
437  const pcl::IndicesPtr & indices,
438  bool rgb) const
439 {
440  UASSERT_MSG(uContains(idToIndex_, id), uFormat("id=%d idToIndex_.size()=%d", id, (int)idToIndex_.size()).c_str());
441  applyImpl<pcl::PointXYZRGB>(idToIndex_.at(id), cloud, indices, gains_, rgb);
442 }
443 void GainCompensator::apply(
444  int id,
445  pcl::PointCloud<pcl::PointXYZRGBNormal>::Ptr & cloud,
446  const pcl::IndicesPtr & indices,
447  bool rgb) const
448 {
449  UASSERT_MSG(uContains(idToIndex_, id), uFormat("id=%d idToIndex_.size()=%d", id, (int)idToIndex_.size()).c_str());
450  applyImpl<pcl::PointXYZRGBNormal>(idToIndex_.at(id), cloud, indices, gains_, rgb);
451 }
452 
453 void GainCompensator::apply(
454  int id,
455  cv::Mat & image,
456  bool rgb) const
457 {
458  UASSERT_MSG(uContains(idToIndex_, id), uFormat("id=%d idToIndex_.size()=%d", id, (int)idToIndex_.size()).c_str());
459  if(image.channels() == 1 || !rgb)
460  {
461  cv::multiply(image, gains_(idToIndex_.at(id), 0), image);
462  }
463  else if(image.channels()>=3)
464  {
465  std::vector<cv::Mat> channels;
466  cv::split(image, channels);
467  // assuming BGR
468  cv::multiply(channels[0], gains_(idToIndex_.at(id), 3), channels[0]);
469  cv::multiply(channels[1], gains_(idToIndex_.at(id), 2), channels[1]);
470  cv::multiply(channels[2], gains_(idToIndex_.at(id), 1), channels[2]);
471  cv::merge(channels, image);
472  }
473 }
474 
475 double GainCompensator::getGain(int id, double * r, double * g, double * b) const
476 {
477  UASSERT_MSG(uContains(idToIndex_, id), uFormat("id=%d idToIndex_.size()=%d", id, (int)idToIndex_.size()).c_str());
478 
479  if(r)
480  {
481  *r = gains_(idToIndex_.at(id), 1);
482  }
483  if(g)
484  {
485  *g = gains_(idToIndex_.at(id), 2);
486  }
487  if(b)
488  {
489  *b = gains_(idToIndex_.at(id), 3);
490  }
491 
492  return gains_(idToIndex_.at(id), 0);
493 }
494 
495 int GainCompensator::getIndex(int id) const
496 {
497  if(uContains(idToIndex_, id))
498  {
499  return idToIndex_.at(id);
500  }
501  return -1;
502 }
503 
504 } /* namespace rtabmap */
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Definition: GainCompensator.h:97
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GLM_FUNC_DECL genType min(genType const &x, genType const &y)
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f
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Definition: Transform.cpp:344
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Definition: GainCompensator.cpp:110
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rtabmap
Author(s): Mathieu Labbe
autogenerated on Wed Jun 5 2019 22:41:31