Features2d.cpp
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1 /*
2 Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
3 All rights reserved.
4 
5 Redistribution and use in source and binary forms, with or without
6 modification, are permitted provided that the following conditions are met:
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8  notice, this list of conditions and the following disclaimer.
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10  notice, this list of conditions and the following disclaimer in the
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14  derived from this software without specific prior written permission.
15 
16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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20 DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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25 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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27 
28 #include "rtabmap/core/Features2d.h"
29 #include "rtabmap/core/util3d.h"
30 #include "rtabmap/core/util3d_features.h"
31 #include "rtabmap/core/Stereo.h"
32 #include "rtabmap/core/util2d.h"
33 #include "rtabmap/utilite/UStl.h"
34 #include "rtabmap/utilite/UConversion.h"
35 #include "rtabmap/utilite/ULogger.h"
36 #include "rtabmap/utilite/UMath.h"
37 #include "rtabmap/utilite/ULogger.h"
38 #include "rtabmap/utilite/UTimer.h"
39 #include <opencv2/imgproc/imgproc_c.h>
40 #include <opencv2/core/version.hpp>
41 #include <opencv2/opencv_modules.hpp>
42 
43 #ifdef RTABMAP_ORB_OCTREE
44 #include "opencv/ORBextractor.h"
45 #endif
46 
47 #ifdef RTABMAP_TORCH
48 #include "superpoint_torch/SuperPoint.h"
49 #endif
50 
51 #ifdef RTABMAP_PYTHON
52 #include "python/PyDetector.h"
53 #endif
54 
55 #if CV_MAJOR_VERSION < 3
56 #include "opencv/Orb.h"
57 #ifdef HAVE_OPENCV_GPU
58 #include <opencv2/gpu/gpu.hpp>
59 #endif
60 #else
61 #include <opencv2/core/cuda.hpp>
62 #endif
63 
64 #ifdef HAVE_OPENCV_NONFREE
65  #if CV_MAJOR_VERSION == 2 && CV_MINOR_VERSION >=4
66  #include <opencv2/nonfree/gpu.hpp>
67  #include <opencv2/nonfree/features2d.hpp>
68  #endif
69 #endif
70 #ifdef HAVE_OPENCV_XFEATURES2D
71  #include <opencv2/xfeatures2d.hpp>
72  #include <opencv2/xfeatures2d/nonfree.hpp>
73  #include <opencv2/xfeatures2d/cuda.hpp>
74 #endif
75 #ifdef HAVE_OPENCV_CUDAFEATURES2D
76  #include <opencv2/cudafeatures2d.hpp>
77 #endif
78 
79 #ifdef RTABMAP_FASTCV
80 #include <fastcv.h>
81 #endif
82 
83 namespace rtabmap {
84 
85 void Feature2D::filterKeypointsByDepth(
86  std::vector<cv::KeyPoint> & keypoints,
87  const cv::Mat & depth,
88  float minDepth,
89  float maxDepth)
90 {
91  cv::Mat descriptors;
92  filterKeypointsByDepth(keypoints, descriptors, depth, minDepth, maxDepth);
93 }
94 
95 void Feature2D::filterKeypointsByDepth(
96  std::vector<cv::KeyPoint> & keypoints,
97  cv::Mat & descriptors,
98  const cv::Mat & depth,
99  float minDepth,
100  float maxDepth)
101 {
102  UASSERT(minDepth >= 0.0f);
103  UASSERT(maxDepth <= 0.0f || maxDepth > minDepth);
104  if(!depth.empty() && (descriptors.empty() || descriptors.rows == (int)keypoints.size()))
105  {
106  std::vector<cv::KeyPoint> output(keypoints.size());
107  std::vector<int> indexes(keypoints.size(), 0);
108  int oi=0;
109  bool isInMM = depth.type() == CV_16UC1;
110  for(unsigned int i=0; i<keypoints.size(); ++i)
111  {
112  int u = int(keypoints[i].pt.x+0.5f);
113  int v = int(keypoints[i].pt.y+0.5f);
114  if(u >=0 && u<depth.cols && v >=0 && v<depth.rows)
115  {
116  float d = isInMM?(float)depth.at<uint16_t>(v,u)*0.001f:depth.at<float>(v,u);
117  if(uIsFinite(d) && d>minDepth && (maxDepth <= 0.0f || d < maxDepth))
118  {
119  output[oi++] = keypoints[i];
120  indexes[i] = 1;
121  }
122  }
123  }
124  output.resize(oi);
125  keypoints = output;
126 
127  if(!descriptors.empty() && (int)keypoints.size() != descriptors.rows)
128  {
129  if(keypoints.size() == 0)
130  {
131  descriptors = cv::Mat();
132  }
133  else
134  {
135  cv::Mat newDescriptors((int)keypoints.size(), descriptors.cols, descriptors.type());
136  int di = 0;
137  for(unsigned int i=0; i<indexes.size(); ++i)
138  {
139  if(indexes[i] == 1)
140  {
141  if(descriptors.type() == CV_32FC1)
142  {
143  memcpy(newDescriptors.ptr<float>(di++), descriptors.ptr<float>(i), descriptors.cols*sizeof(float));
144  }
145  else // CV_8UC1
146  {
147  memcpy(newDescriptors.ptr<char>(di++), descriptors.ptr<char>(i), descriptors.cols*sizeof(char));
148  }
149  }
150  }
151  descriptors = newDescriptors;
152  }
153  }
154  }
155 }
156 
157 void Feature2D::filterKeypointsByDepth(
158  std::vector<cv::KeyPoint> & keypoints,
159  cv::Mat & descriptors,
160  std::vector<cv::Point3f> & keypoints3D,
161  float minDepth,
162  float maxDepth)
163 {
164  UDEBUG("");
165  //remove all keypoints/descriptors with no valid 3D points
166  UASSERT(((int)keypoints.size() == descriptors.rows || descriptors.empty()) &&
167  keypoints3D.size() == keypoints.size());
168  std::vector<cv::KeyPoint> validKeypoints(keypoints.size());
169  std::vector<cv::Point3f> validKeypoints3D(keypoints.size());
170  cv::Mat validDescriptors(descriptors.size(), descriptors.type());
171 
172  int oi=0;
173  float minDepthSqr = minDepth * minDepth;
174  float maxDepthSqr = maxDepth * maxDepth;
175  for(unsigned int i=0; i<keypoints3D.size(); ++i)
176  {
177  cv::Point3f & pt = keypoints3D[i];
178  if(util3d::isFinite(pt))
179  {
180  float distSqr = pt.x*pt.x+pt.y*pt.y+pt.z*pt.z;
181  if(distSqr >= minDepthSqr && (maxDepthSqr==0.0f || distSqr <= maxDepthSqr))
182  {
183  validKeypoints[oi] = keypoints[i];
184  validKeypoints3D[oi] = pt;
185  if(!descriptors.empty())
186  {
187  descriptors.row(i).copyTo(validDescriptors.row(oi));
188  }
189  ++oi;
190  }
191  }
192  }
193  UDEBUG("Removed %d invalid 3D points", (int)keypoints3D.size()-oi);
194  validKeypoints.resize(oi);
195  validKeypoints3D.resize(oi);
196  keypoints = validKeypoints;
197  keypoints3D = validKeypoints3D;
198  if(!descriptors.empty())
199  {
200  descriptors = validDescriptors.rowRange(0, oi).clone();
201  }
202 }
203 
204 void Feature2D::filterKeypointsByDisparity(
205  std::vector<cv::KeyPoint> & keypoints,
206  const cv::Mat & disparity,
207  float minDisparity)
208 {
209  cv::Mat descriptors;
210  filterKeypointsByDisparity(keypoints, descriptors, disparity, minDisparity);
211 }
212 
213 void Feature2D::filterKeypointsByDisparity(
214  std::vector<cv::KeyPoint> & keypoints,
215  cv::Mat & descriptors,
216  const cv::Mat & disparity,
217  float minDisparity)
218 {
219  if(!disparity.empty() && minDisparity > 0.0f && (descriptors.empty() || descriptors.rows == (int)keypoints.size()))
220  {
221  std::vector<cv::KeyPoint> output(keypoints.size());
222  std::vector<int> indexes(keypoints.size(), 0);
223  int oi=0;
224  for(unsigned int i=0; i<keypoints.size(); ++i)
225  {
226  int u = int(keypoints[i].pt.x+0.5f);
227  int v = int(keypoints[i].pt.y+0.5f);
228  if(u >=0 && u<disparity.cols && v >=0 && v<disparity.rows)
229  {
230  float d = disparity.type() == CV_16SC1?float(disparity.at<short>(v,u))/16.0f:disparity.at<float>(v,u);
231  if(d!=0.0f && uIsFinite(d) && d >= minDisparity)
232  {
233  output[oi++] = keypoints[i];
234  indexes[i] = 1;
235  }
236  }
237  }
238  output.resize(oi);
239  keypoints = output;
240 
241  if(!descriptors.empty() && (int)keypoints.size() != descriptors.rows)
242  {
243  if(keypoints.size() == 0)
244  {
245  descriptors = cv::Mat();
246  }
247  else
248  {
249  cv::Mat newDescriptors((int)keypoints.size(), descriptors.cols, descriptors.type());
250  int di = 0;
251  for(unsigned int i=0; i<indexes.size(); ++i)
252  {
253  if(indexes[i] == 1)
254  {
255  if(descriptors.type() == CV_32FC1)
256  {
257  memcpy(newDescriptors.ptr<float>(di++), descriptors.ptr<float>(i), descriptors.cols*sizeof(float));
258  }
259  else // CV_8UC1
260  {
261  memcpy(newDescriptors.ptr<char>(di++), descriptors.ptr<char>(i), descriptors.cols*sizeof(char));
262  }
263  }
264  }
265  descriptors = newDescriptors;
266  }
267  }
268  }
269 }
270 
271 void Feature2D::limitKeypoints(std::vector<cv::KeyPoint> & keypoints, int maxKeypoints, const cv::Size & imageSize, bool ssc)
272 {
273  cv::Mat descriptors;
274  limitKeypoints(keypoints, descriptors, maxKeypoints, imageSize, ssc);
275 }
276 
277 void Feature2D::limitKeypoints(std::vector<cv::KeyPoint> & keypoints, cv::Mat & descriptors, int maxKeypoints, const cv::Size & imageSize, bool ssc)
278 {
279  std::vector<cv::Point3f> keypoints3D;
280  limitKeypoints(keypoints, keypoints3D, descriptors, maxKeypoints, imageSize, ssc);
281 }
282 
283 void Feature2D::limitKeypoints(std::vector<cv::KeyPoint> & keypoints, std::vector<cv::Point3f> & keypoints3D, cv::Mat & descriptors, int maxKeypoints, const cv::Size & imageSize, bool ssc)
284 {
285  UASSERT_MSG((int)keypoints.size() == descriptors.rows || descriptors.rows == 0, uFormat("keypoints=%d descriptors=%d", (int)keypoints.size(), descriptors.rows).c_str());
286  UASSERT_MSG(keypoints.size() == keypoints3D.size() || keypoints3D.size() == 0, uFormat("keypoints=%d keypoints3D=%d", (int)keypoints.size(), (int)keypoints3D.size()).c_str());
287  if(maxKeypoints > 0 && (int)keypoints.size() > maxKeypoints)
288  {
289  UTimer timer;
290  int removed;
291  std::vector<cv::KeyPoint> kptsTmp;
292  std::vector<cv::Point3f> kpts3DTmp;
293  cv::Mat descriptorsTmp;
294  if(ssc)
295  {
296  ULOGGER_DEBUG("too much words (%d), removing words with SSC", keypoints.size());
297  static constexpr float tolerance = 0.1;
298  auto ResultVec = util2d::SSC(keypoints, maxKeypoints, tolerance, imageSize.width, imageSize.height);
299  removed = keypoints.size()-ResultVec.size();
300  // retrieve final keypoints
301  kptsTmp.resize(ResultVec.size());
302  if(!keypoints3D.empty())
303  {
304  kpts3DTmp.resize(ResultVec.size());
305  }
306  if(descriptors.rows)
307  {
308  descriptorsTmp = cv::Mat(ResultVec.size(), descriptors.cols, descriptors.type());
309  }
310  for(unsigned int k=0; k<ResultVec.size(); ++k)
311  {
312  kptsTmp[k] = keypoints[ResultVec[k]];
313  if(keypoints3D.size())
314  {
315  kpts3DTmp[k] = keypoints3D[ResultVec[k]];
316  }
317  if(descriptors.rows)
318  {
319  if(descriptors.type() == CV_32FC1)
320  {
321  memcpy(descriptorsTmp.ptr<float>(k), descriptors.ptr<float>(ResultVec[k]), descriptors.cols*sizeof(float));
322  }
323  else
324  {
325  memcpy(descriptorsTmp.ptr<char>(k), descriptors.ptr<char>(ResultVec[k]), descriptors.cols*sizeof(char));
326  }
327  }
328  }
329  }
330  else
331  {
332  ULOGGER_DEBUG("too much words (%d), removing words with the hessian threshold", keypoints.size());
333  // Remove words under the new hessian threshold
334 
335  // Sort words by hessian
336  std::multimap<float, int> hessianMap; // <hessian,id>
337  for(unsigned int i = 0; i <keypoints.size(); ++i)
338  {
339  //Keep track of the data, to be easier to manage the data in the next step
340  hessianMap.insert(std::pair<float, int>(fabs(keypoints[i].response), i));
341  }
342 
343  // Remove them from the signature
344  removed = (int)hessianMap.size()-maxKeypoints;
345  std::multimap<float, int>::reverse_iterator iter = hessianMap.rbegin();
346  kptsTmp.resize(maxKeypoints);
347  if(!keypoints3D.empty())
348  {
349  kpts3DTmp.resize(maxKeypoints);
350  }
351  if(descriptors.rows)
352  {
353  descriptorsTmp = cv::Mat(maxKeypoints, descriptors.cols, descriptors.type());
354  }
355  for(unsigned int k=0; k<kptsTmp.size() && iter!=hessianMap.rend(); ++k, ++iter)
356  {
357  kptsTmp[k] = keypoints[iter->second];
358  if(keypoints3D.size())
359  {
360  kpts3DTmp[k] = keypoints3D[iter->second];
361  }
362  if(descriptors.rows)
363  {
364  if(descriptors.type() == CV_32FC1)
365  {
366  memcpy(descriptorsTmp.ptr<float>(k), descriptors.ptr<float>(iter->second), descriptors.cols*sizeof(float));
367  }
368  else
369  {
370  memcpy(descriptorsTmp.ptr<char>(k), descriptors.ptr<char>(iter->second), descriptors.cols*sizeof(char));
371  }
372  }
373  }
374  }
375  ULOGGER_DEBUG("%d keypoints removed, (kept %d), minimum response=%f", removed, (int)kptsTmp.size(), !ssc&&kptsTmp.size()?kptsTmp.back().response:0.0f);
376  ULOGGER_DEBUG("removing words time = %f s", timer.ticks());
377  keypoints = kptsTmp;
378  keypoints3D = kpts3DTmp;
379  if(descriptors.rows)
380  {
381  descriptors = descriptorsTmp;
382  }
383  }
384 }
385 
386 void Feature2D::limitKeypoints(const std::vector<cv::KeyPoint> & keypoints, std::vector<bool> & inliers, int maxKeypoints, const cv::Size & imageSize, bool ssc)
387 {
388  if(maxKeypoints > 0 && (int)keypoints.size() > maxKeypoints)
389  {
390  UTimer timer;
391  float minimumHessian = 0.0f;
392  int removed;
393  inliers.resize(keypoints.size(), false);
394  if(ssc)
395  {
396  ULOGGER_DEBUG("too much words (%d), removing words with SSC", keypoints.size());
397  static constexpr float tolerance = 0.1;
398  auto ResultVec = util2d::SSC(keypoints, maxKeypoints, tolerance, imageSize.width, imageSize.height);
399  removed = keypoints.size()-ResultVec.size();
400  for(unsigned int k=0; k<ResultVec.size(); ++k)
401  {
402  inliers[ResultVec[k]] = true;
403  }
404  }
405  else
406  {
407  ULOGGER_DEBUG("too much words (%d), removing words with the hessian threshold", keypoints.size());
408  // Remove words under the new hessian threshold
409 
410  // Sort words by hessian
411  std::multimap<float, int> hessianMap; // <hessian,id>
412  for(unsigned int i = 0; i<keypoints.size(); ++i)
413  {
414  //Keep track of the data, to be easier to manage the data in the next step
415  hessianMap.insert(std::pair<float, int>(fabs(keypoints[i].response), i));
416  }
417 
418  // Keep keypoints with highest response
419  removed = (int)hessianMap.size()-maxKeypoints;
420  std::multimap<float, int>::reverse_iterator iter = hessianMap.rbegin();
421  for(int k=0; k<maxKeypoints && iter!=hessianMap.rend(); ++k, ++iter)
422  {
423  inliers[iter->second] = true;
424  minimumHessian = iter->first;
425  }
426  }
427  ULOGGER_DEBUG("%d keypoints removed, (kept %d), minimum response=%f", removed, maxKeypoints, minimumHessian);
428  ULOGGER_DEBUG("filter keypoints time = %f s", timer.ticks());
429  }
430  else
431  {
432  ULOGGER_DEBUG("keeping all %d keypoints", (int)keypoints.size());
433  inliers.resize(keypoints.size(), true);
434  }
435 }
436 
437 void Feature2D::limitKeypoints(const std::vector<cv::KeyPoint> & keypoints, std::vector<bool> & inliers, int maxKeypoints, const cv::Size & imageSize, int gridRows, int gridCols, bool ssc)
438 {
439  if(maxKeypoints <= 0 || (int)keypoints.size() <= maxKeypoints)
440  {
441  inliers.resize(keypoints.size(), true);
442  return;
443  }
444  UASSERT(gridCols>=1 && gridRows >=1);
445  UASSERT(imageSize.height>gridRows && imageSize.width>gridCols);
446  int rowSize = imageSize.height / gridRows;
447  int colSize = imageSize.width / gridCols;
448  int maxKeypointsPerCell = maxKeypoints / (gridRows * gridCols);
449  std::vector<std::vector<cv::KeyPoint> > keypointsPerCell(gridRows * gridCols);
450  std::vector<std::vector<int> > indexesPerCell(gridRows * gridCols);
451  for(size_t i=0; i<keypoints.size(); ++i)
452  {
453  int cellRow = int(keypoints[i].pt.y)/rowSize;
454  int cellCol = int(keypoints[i].pt.x)/colSize;
455  UASSERT(cellRow >=0 && cellRow < gridRows);
456  UASSERT(cellCol >=0 && cellCol < gridCols);
457 
458  keypointsPerCell[cellRow*gridCols + cellCol].push_back(keypoints[i]);
459  indexesPerCell[cellRow*gridCols + cellCol].push_back(i);
460  }
461  inliers.resize(keypoints.size(), false);
462  for(size_t i=0; i<keypointsPerCell.size(); ++i)
463  {
464  std::vector<bool> inliersCell;
465  limitKeypoints(keypointsPerCell[i], inliersCell, maxKeypointsPerCell, cv::Size(colSize, rowSize), ssc);
466  for(size_t j=0; j<inliersCell.size(); ++j)
467  {
468  if(inliersCell[j])
469  {
470  inliers.at(indexesPerCell[i][j]) = true;
471  }
472  }
473  }
474 }
475 
476 cv::Rect Feature2D::computeRoi(const cv::Mat & image, const std::string & roiRatios)
477 {
478  return util2d::computeRoi(image, roiRatios);
479 }
480 
481 cv::Rect Feature2D::computeRoi(const cv::Mat & image, const std::vector<float> & roiRatios)
482 {
483  return util2d::computeRoi(image, roiRatios);
484 }
485 
487 // Feature2D
489 Feature2D::Feature2D(const ParametersMap & parameters) :
490  maxFeatures_(Parameters::defaultKpMaxFeatures()),
491  SSC_(Parameters::defaultKpSSC()),
492  _maxDepth(Parameters::defaultKpMaxDepth()),
493  _minDepth(Parameters::defaultKpMinDepth()),
494  _roiRatios(std::vector<float>(4, 0.0f)),
495  _subPixWinSize(Parameters::defaultKpSubPixWinSize()),
496  _subPixIterations(Parameters::defaultKpSubPixIterations()),
497  _subPixEps(Parameters::defaultKpSubPixEps()),
498  gridRows_(Parameters::defaultKpGridRows()),
499  gridCols_(Parameters::defaultKpGridCols())
500 {
501  _stereo = new Stereo(parameters);
502  this->parseParameters(parameters);
503 }
504 Feature2D::~Feature2D()
505 {
506  delete _stereo;
507 }
508 void Feature2D::parseParameters(const ParametersMap & parameters)
509 {
510  uInsert(parameters_, parameters);
511 
512  Parameters::parse(parameters, Parameters::kKpMaxFeatures(), maxFeatures_);
513  Parameters::parse(parameters, Parameters::kKpSSC(), SSC_);
514  Parameters::parse(parameters, Parameters::kKpMaxDepth(), _maxDepth);
515  Parameters::parse(parameters, Parameters::kKpMinDepth(), _minDepth);
516  Parameters::parse(parameters, Parameters::kKpSubPixWinSize(), _subPixWinSize);
517  Parameters::parse(parameters, Parameters::kKpSubPixIterations(), _subPixIterations);
518  Parameters::parse(parameters, Parameters::kKpSubPixEps(), _subPixEps);
519  Parameters::parse(parameters, Parameters::kKpGridRows(), gridRows_);
520  Parameters::parse(parameters, Parameters::kKpGridCols(), gridCols_);
521 
522  UASSERT(gridRows_ >= 1 && gridCols_>=1);
523 
524  // convert ROI from string to vector
525  ParametersMap::const_iterator iter;
526  if((iter=parameters.find(Parameters::kKpRoiRatios())) != parameters.end())
527  {
528  std::list<std::string> strValues = uSplit(iter->second, ' ');
529  if(strValues.size() != 4)
530  {
531  ULOGGER_ERROR("The number of values must be 4 (roi=\"%s\")", iter->second.c_str());
532  }
533  else
534  {
535  std::vector<float> tmpValues(4);
536  unsigned int i=0;
537  for(std::list<std::string>::iterator jter = strValues.begin(); jter!=strValues.end(); ++jter)
538  {
539  tmpValues[i] = uStr2Float(*jter);
540  ++i;
541  }
542 
543  if(tmpValues[0] >= 0 && tmpValues[0] < 1 && tmpValues[0] < 1.0f-tmpValues[1] &&
544  tmpValues[1] >= 0 && tmpValues[1] < 1 && tmpValues[1] < 1.0f-tmpValues[0] &&
545  tmpValues[2] >= 0 && tmpValues[2] < 1 && tmpValues[2] < 1.0f-tmpValues[3] &&
546  tmpValues[3] >= 0 && tmpValues[3] < 1 && tmpValues[3] < 1.0f-tmpValues[2])
547  {
548  _roiRatios = tmpValues;
549  }
550  else
551  {
552  ULOGGER_ERROR("The roi ratios are not valid (roi=\"%s\")", iter->second.c_str());
553  }
554  }
555  }
556 
557  //stereo
558  UASSERT(_stereo != 0);
559  if((iter=parameters.find(Parameters::kStereoOpticalFlow())) != parameters.end())
560  {
561  delete _stereo;
562  _stereo = Stereo::create(parameters_);
563  }
564  else
565  {
566  _stereo->parseParameters(parameters);
567  }
568 }
569 Feature2D * Feature2D::create(const ParametersMap & parameters)
570 {
571  int type = Parameters::defaultKpDetectorStrategy();
572  Parameters::parse(parameters, Parameters::kKpDetectorStrategy(), type);
573  return create((Feature2D::Type)type, parameters);
574 }
575 Feature2D * Feature2D::create(Feature2D::Type type, const ParametersMap & parameters)
576 {
577 
578 // NONFREE checks
579 #if CV_MAJOR_VERSION < 3 || (CV_MAJOR_VERSION == 4 && CV_MINOR_VERSION <= 3) || (CV_MAJOR_VERSION == 3 && (CV_MINOR_VERSION < 4 || (CV_MINOR_VERSION==4 && CV_SUBMINOR_VERSION<11)))
580 
581  #ifndef RTABMAP_NONFREE
582  if(type == Feature2D::kFeatureSurf || type == Feature2D::kFeatureSift || type == Feature2D::kFeatureSurfFreak || type == Feature2D::kFeatureSurfDaisy)
583  {
584  #if CV_MAJOR_VERSION < 3
585  UWARN("SURF and SIFT features cannot be used because OpenCV was not built with nonfree module. GFTT/ORB is used instead.");
586  #else
587  UWARN("SURF and SIFT features cannot be used because OpenCV was not built with xfeatures2d module. GFTT/ORB is used instead.");
588  #endif
589  type = Feature2D::kFeatureGfttOrb;
590  }
591  #endif
592 
593 #else // >= 4.4.0 >= 3.4.11
594 
595  #ifndef RTABMAP_NONFREE
596  if(type == Feature2D::kFeatureSurf)
597  {
598  UWARN("SURF features cannot be used because OpenCV was not built with nonfree module. SIFT is used instead.");
599  type = Feature2D::kFeatureSift;
600  }
601  else if(type == Feature2D::kFeatureSurfFreak || type == Feature2D::kFeatureSurfDaisy)
602  {
603  UWARN("SURF detector cannot be used because OpenCV was not built with nonfree module. GFTT/ORB is used instead.");
604  type = Feature2D::kFeatureGfttOrb;
605  }
606  #endif
607 
608 #endif // >= 4.4.0 >= 3.4.11
609 
610 #if !defined(HAVE_OPENCV_XFEATURES2D) && CV_MAJOR_VERSION >= 3
611  if(type == Feature2D::kFeatureFastBrief ||
612  type == Feature2D::kFeatureFastFreak ||
613  type == Feature2D::kFeatureGfttBrief ||
614  type == Feature2D::kFeatureGfttFreak ||
615  type == Feature2D::kFeatureSurfFreak ||
616  type == Feature2D::kFeatureGfttDaisy ||
617  type == Feature2D::kFeatureSurfDaisy)
618  {
619  UWARN("BRIEF, FREAK and DAISY features cannot be used because OpenCV was not built with xfeatures2d module. GFTT/ORB is used instead.");
620  type = Feature2D::kFeatureGfttOrb;
621  }
622 #elif CV_MAJOR_VERSION < 3
623  if(type == Feature2D::kFeatureKaze)
624  {
625  #ifdef RTABMAP_NONFREE
626  UWARN("KAZE detector/descriptor can be used only with OpenCV3. SURF is used instead.");
627  type = Feature2D::kFeatureSurf;
628  #else
629  UWARN("KAZE detector/descriptor can be used only with OpenCV3. GFTT/ORB is used instead.");
630  type = Feature2D::kFeatureGfttOrb;
631  #endif
632  }
633  if(type == Feature2D::kFeatureGfttDaisy || type == Feature2D::kFeatureSurfDaisy)
634  {
635  UWARN("DAISY detector/descriptor can be used only with OpenCV3. GFTT/BRIEF is used instead.");
636  type = Feature2D::kFeatureGfttBrief;
637  }
638 #endif
639 
640 
641 #ifndef RTABMAP_ORB_OCTREE
642  if(type == Feature2D::kFeatureOrbOctree)
643  {
644  UWARN("ORB OcTree feature cannot be used as RTAB-Map is not built with the option enabled. GFTT/ORB is used instead.");
645  type = Feature2D::kFeatureGfttOrb;
646  }
647 #endif
648 
649 #ifndef RTABMAP_TORCH
650  if(type == Feature2D::kFeatureSuperPointTorch)
651  {
652  UWARN("SupertPoint Torch feature cannot be used as RTAB-Map is not built with the option enabled. GFTT/ORB is used instead.");
653  type = Feature2D::kFeatureGfttOrb;
654  }
655 #endif
656 
657  Feature2D * feature2D = 0;
658  switch(type)
659  {
660  case Feature2D::kFeatureSurf:
661  feature2D = new SURF(parameters);
662  break;
663  case Feature2D::kFeatureSift:
664  feature2D = new SIFT(parameters);
665  break;
666  case Feature2D::kFeatureOrb:
667  feature2D = new ORB(parameters);
668  break;
669  case Feature2D::kFeatureFastBrief:
670  feature2D = new FAST_BRIEF(parameters);
671  break;
672  case Feature2D::kFeatureFastFreak:
673  feature2D = new FAST_FREAK(parameters);
674  break;
675  case Feature2D::kFeatureGfttFreak:
676  feature2D = new GFTT_FREAK(parameters);
677  break;
678  case Feature2D::kFeatureGfttBrief:
679  feature2D = new GFTT_BRIEF(parameters);
680  break;
681  case Feature2D::kFeatureGfttOrb:
682  feature2D = new GFTT_ORB(parameters);
683  break;
684  case Feature2D::kFeatureBrisk:
685  feature2D = new BRISK(parameters);
686  break;
687  case Feature2D::kFeatureKaze:
688  feature2D = new KAZE(parameters);
689  break;
690  case Feature2D::kFeatureOrbOctree:
691  feature2D = new ORBOctree(parameters);
692  break;
693 #ifdef RTABMAP_TORCH
694  case Feature2D::kFeatureSuperPointTorch:
695  feature2D = new SuperPointTorch(parameters);
696  break;
697 #endif
698  case Feature2D::kFeatureSurfFreak:
699  feature2D = new SURF_FREAK(parameters);
700  break;
701  case Feature2D::kFeatureGfttDaisy:
702  feature2D = new GFTT_DAISY(parameters);
703  break;
704  case Feature2D::kFeatureSurfDaisy:
705  feature2D = new SURF_DAISY(parameters);
706  break;
707 #ifdef RTABMAP_PYTHON
708  case Feature2D::kFeaturePyDetector:
709  feature2D = new PyDetector(parameters);
710  break;
711 #endif
712 #ifdef RTABMAP_NONFREE
713  default:
714  feature2D = new SURF(parameters);
715  type = Feature2D::kFeatureSurf;
716  break;
717 #else
718  default:
719  feature2D = new ORB(parameters);
720  type = Feature2D::kFeatureGfttOrb;
721  break;
722 #endif
723 
724  }
725  return feature2D;
726 }
727 
728 std::vector<cv::KeyPoint> Feature2D::generateKeypoints(const cv::Mat & image, const cv::Mat & maskIn)
729 {
730  UASSERT(!image.empty());
731  UASSERT(image.type() == CV_8UC1);
732 
733  cv::Mat mask;
734  if(!maskIn.empty())
735  {
736  if(maskIn.type()==CV_16UC1 || maskIn.type() == CV_32FC1)
737  {
738  mask = cv::Mat::zeros(maskIn.rows, maskIn.cols, CV_8UC1);
739  for(int i=0; i<(int)mask.total(); ++i)
740  {
741  float value = 0.0f;
742  if(maskIn.type()==CV_16UC1)
743  {
744  if(((unsigned short*)maskIn.data)[i] > 0 &&
745  ((unsigned short*)maskIn.data)[i] < std::numeric_limits<unsigned short>::max())
746  {
747  value = float(((unsigned short*)maskIn.data)[i])*0.001f;
748  }
749  }
750  else
751  {
752  value = ((float*)maskIn.data)[i];
753  }
754 
755  if(value>_minDepth &&
756  (_maxDepth == 0.0f || value <= _maxDepth) &&
757  uIsFinite(value))
758  {
759  ((unsigned char*)mask.data)[i] = 255; // ORB uses 255 to handle pyramids
760  }
761  }
762  }
763  else if(maskIn.type()==CV_8UC1)
764  {
765  // assume a standard mask
766  mask = maskIn;
767  }
768  else
769  {
770  UERROR("Wrong mask type (%d)! Should be 8UC1, 16UC1 or 32FC1.", maskIn.type());
771  }
772  }
773 
774  UASSERT(mask.empty() || (mask.cols == image.cols && mask.rows == image.rows));
775 
776  std::vector<cv::KeyPoint> keypoints;
777  UTimer timer;
778  cv::Rect globalRoi = Feature2D::computeRoi(image, _roiRatios);
779  if(!(globalRoi.width && globalRoi.height))
780  {
781  globalRoi = cv::Rect(0,0,image.cols, image.rows);
782  }
783 
784  // Get keypoints
785  int rowSize = globalRoi.height / gridRows_;
786  int colSize = globalRoi.width / gridCols_;
787  int maxFeatures = maxFeatures_ / (gridRows_ * gridCols_);
788  for (int i = 0; i<gridRows_; ++i)
789  {
790  for (int j = 0; j<gridCols_; ++j)
791  {
792  cv::Rect roi(globalRoi.x + j*colSize, globalRoi.y + i*rowSize, colSize, rowSize);
793  std::vector<cv::KeyPoint> subKeypoints;
794  subKeypoints = this->generateKeypointsImpl(image, roi, mask);
795  if (this->getType() != Feature2D::Type::kFeaturePyDetector)
796  {
797  limitKeypoints(subKeypoints, maxFeatures, roi.size(), this->getSSC());
798  }
799  if(roi.x || roi.y)
800  {
801  // Adjust keypoint position to raw image
802  for(std::vector<cv::KeyPoint>::iterator iter=subKeypoints.begin(); iter!=subKeypoints.end(); ++iter)
803  {
804  iter->pt.x += roi.x;
805  iter->pt.y += roi.y;
806  }
807  }
808  keypoints.insert( keypoints.end(), subKeypoints.begin(), subKeypoints.end() );
809  }
810  }
811  UDEBUG("Keypoints extraction time = %f s, keypoints extracted = %d (grid=%dx%d, mask empty=%d)",
812  timer.ticks(), keypoints.size(), gridCols_, gridRows_, mask.empty()?1:0);
813 
814  if(keypoints.size() && _subPixWinSize > 0 && _subPixIterations > 0)
815  {
816  std::vector<cv::Point2f> corners;
817  cv::KeyPoint::convert(keypoints, corners);
818  cv::cornerSubPix( image, corners,
819  cv::Size( _subPixWinSize, _subPixWinSize ),
820  cv::Size( -1, -1 ),
821  cv::TermCriteria( CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, _subPixIterations, _subPixEps ) );
822 
823  for(unsigned int i=0;i<corners.size(); ++i)
824  {
825  keypoints[i].pt = corners[i];
826  }
827  UDEBUG("subpixel time = %f s", timer.ticks());
828  }
829 
830  return keypoints;
831 }
832 
833 cv::Mat Feature2D::generateDescriptors(
834  const cv::Mat & image,
835  std::vector<cv::KeyPoint> & keypoints) const
836 {
837  cv::Mat descriptors;
838  if(keypoints.size())
839  {
840  UASSERT(!image.empty());
841  UASSERT(image.type() == CV_8UC1);
842  descriptors = generateDescriptorsImpl(image, keypoints);
843  UASSERT_MSG(descriptors.rows == (int)keypoints.size(), uFormat("descriptors=%d, keypoints=%d", descriptors.rows, (int)keypoints.size()).c_str());
844  UDEBUG("Descriptors extracted = %d, remaining kpts=%d", descriptors.rows, (int)keypoints.size());
845  }
846  return descriptors;
847 }
848 
849 std::vector<cv::Point3f> Feature2D::generateKeypoints3D(
850  const SensorData & data,
851  const std::vector<cv::KeyPoint> & keypoints) const
852 {
853  std::vector<cv::Point3f> keypoints3D;
854  if(keypoints.size())
855  {
856  if(!data.rightRaw().empty() && !data.imageRaw().empty() &&
857  !data.stereoCameraModels().empty() &&
858  data.stereoCameraModels()[0].isValidForProjection())
859  {
860  //stereo
861  cv::Mat imageMono;
862  // convert to grayscale
863  if(data.imageRaw().channels() > 1)
864  {
865  cv::cvtColor(data.imageRaw(), imageMono, cv::COLOR_BGR2GRAY);
866  }
867  else
868  {
869  imageMono = data.imageRaw();
870  }
871 
872  std::vector<cv::Point2f> leftCorners;
873  cv::KeyPoint::convert(keypoints, leftCorners);
874 
875  std::vector<cv::Point2f> rightCorners;
876 
877  if(data.stereoCameraModels().size() == 1)
878  {
879  std::vector<unsigned char> status;
880  rightCorners = _stereo->computeCorrespondences(
881  imageMono,
882  data.rightRaw(),
883  leftCorners,
884  status);
885 
886  if(ULogger::level() >= ULogger::kWarning)
887  {
888  int rejected = 0;
889  for(size_t i=0; i<status.size(); ++i)
890  {
891  if(status[i]==0)
892  {
893  ++rejected;
894  }
895  }
896  if(rejected > (int)status.size()/2)
897  {
898  UWARN("A large number (%d/%d) of stereo correspondences are rejected! "
899  "Optical flow may have failed because images are not calibrated, "
900  "the background is too far (no disparity between the images), "
901  "maximum disparity may be too small (%f) or that exposure between "
902  "left and right images is too different.",
903  rejected,
904  (int)status.size(),
905  _stereo->maxDisparity());
906  }
907  }
908 
909  keypoints3D = util3d::generateKeypoints3DStereo(
910  leftCorners,
911  rightCorners,
912  data.stereoCameraModels()[0],
913  status,
914  _minDepth,
915  _maxDepth);
916  }
917  else
918  {
919  int subImageWith = imageMono.cols / data.stereoCameraModels().size();
920  UASSERT(imageMono.cols % subImageWith == 0);
921  std::vector<std::vector<cv::Point2f> > subLeftCorners(data.stereoCameraModels().size());
922  std::vector<std::vector<int> > subIndex(data.stereoCameraModels().size());
923  // Assign keypoints per camera
924  for(size_t i=0; i<leftCorners.size(); ++i)
925  {
926  int cameraIndex = int(leftCorners[i].x / subImageWith);
927  leftCorners[i].x -= cameraIndex*subImageWith;
928  subLeftCorners[cameraIndex].push_back(leftCorners[i]);
929  subIndex[cameraIndex].push_back(i);
930  }
931 
932  keypoints3D.resize(keypoints.size());
933  int total = 0;
934  int rejected = 0;
935  for(size_t i=0; i<data.stereoCameraModels().size(); ++i)
936  {
937  if(!subLeftCorners[i].empty())
938  {
939  std::vector<unsigned char> status;
940  rightCorners = _stereo->computeCorrespondences(
941  imageMono.colRange(cv::Range(subImageWith*i, subImageWith*(i+1))),
942  data.rightRaw().colRange(cv::Range(subImageWith*i, subImageWith*(i+1))),
943  subLeftCorners[i],
944  status);
945 
946  std::vector<cv::Point3f> subKeypoints3D = util3d::generateKeypoints3DStereo(
947  subLeftCorners[i],
948  rightCorners,
949  data.stereoCameraModels()[i],
950  status,
951  _minDepth,
952  _maxDepth);
953 
954  if(ULogger::level() >= ULogger::kWarning)
955  {
956  for(size_t i=0; i<status.size(); ++i)
957  {
958  if(status[i]==0)
959  {
960  ++rejected;
961  }
962  }
963  total+=status.size();
964  }
965 
966  UASSERT(subIndex[i].size() == subKeypoints3D.size());
967  for(size_t j=0; j<subKeypoints3D.size(); ++j)
968  {
969  keypoints3D[subIndex[i][j]] = subKeypoints3D[j];
970  }
971  }
972  }
973 
974  if(ULogger::level() >= ULogger::kWarning)
975  {
976  if(rejected > total/2)
977  {
978  UWARN("A large number (%d/%d) of stereo correspondences are rejected! "
979  "Optical flow may have failed because images are not calibrated, "
980  "the background is too far (no disparity between the images), "
981  "maximum disparity may be too small (%f) or that exposure between "
982  "left and right images is too different.",
983  rejected,
984  total,
985  _stereo->maxDisparity());
986  }
987  }
988  }
989  }
990  else if(!data.depthRaw().empty() && data.cameraModels().size())
991  {
992  keypoints3D = util3d::generateKeypoints3DDepth(
993  keypoints,
994  data.depthOrRightRaw(),
995  data.cameraModels(),
996  _minDepth,
997  _maxDepth);
998  }
999  }
1000 
1001  return keypoints3D;
1002 }
1003 
1005 //SURF
1007 SURF::SURF(const ParametersMap & parameters) :
1008  hessianThreshold_(Parameters::defaultSURFHessianThreshold()),
1009  nOctaves_(Parameters::defaultSURFOctaves()),
1010  nOctaveLayers_(Parameters::defaultSURFOctaveLayers()),
1011  extended_(Parameters::defaultSURFExtended()),
1012  upright_(Parameters::defaultSURFUpright()),
1013  gpuKeypointsRatio_(Parameters::defaultSURFGpuKeypointsRatio()),
1014  gpuVersion_(Parameters::defaultSURFGpuVersion())
1015 {
1016  parseParameters(parameters);
1017 }
1018 
1019 SURF::~SURF()
1020 {
1021 }
1022 
1023 void SURF::parseParameters(const ParametersMap & parameters)
1024 {
1025  Feature2D::parseParameters(parameters);
1026 
1027  Parameters::parse(parameters, Parameters::kSURFExtended(), extended_);
1028  Parameters::parse(parameters, Parameters::kSURFHessianThreshold(), hessianThreshold_);
1029  Parameters::parse(parameters, Parameters::kSURFOctaveLayers(), nOctaveLayers_);
1030  Parameters::parse(parameters, Parameters::kSURFOctaves(), nOctaves_);
1031  Parameters::parse(parameters, Parameters::kSURFUpright(), upright_);
1032  Parameters::parse(parameters, Parameters::kSURFGpuKeypointsRatio(), gpuKeypointsRatio_);
1033  Parameters::parse(parameters, Parameters::kSURFGpuVersion(), gpuVersion_);
1034 
1035 #ifdef RTABMAP_NONFREE
1036 #if CV_MAJOR_VERSION < 3
1037  if(gpuVersion_ && cv::gpu::getCudaEnabledDeviceCount() == 0)
1038  {
1039  UWARN("GPU version of SURF not available! Using CPU version instead...");
1040  gpuVersion_ = false;
1041  }
1042 #else
1043  if(gpuVersion_ && cv::cuda::getCudaEnabledDeviceCount() == 0)
1044  {
1045  UWARN("GPU version of SURF not available! Using CPU version instead...");
1046  gpuVersion_ = false;
1047  }
1048 #endif
1049  if(gpuVersion_)
1050  {
1051  _gpuSurf = cv::Ptr<CV_SURF_GPU>(new CV_SURF_GPU(hessianThreshold_, nOctaves_, nOctaveLayers_, extended_, gpuKeypointsRatio_, upright_));
1052  }
1053  else
1054  {
1055 #if CV_MAJOR_VERSION < 3
1056  _surf = cv::Ptr<CV_SURF>(new CV_SURF(hessianThreshold_, nOctaves_, nOctaveLayers_, extended_, upright_));
1057 #else
1058  _surf = CV_SURF::create(hessianThreshold_, nOctaves_, nOctaveLayers_, extended_, upright_);
1059 #endif
1060  }
1061 #else
1062  UWARN("RTAB-Map is not built with OpenCV nonfree module so SURF cannot be used!");
1063 #endif
1064 }
1065 
1066 std::vector<cv::KeyPoint> SURF::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
1067 {
1068  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1069  std::vector<cv::KeyPoint> keypoints;
1070 
1071 #ifdef RTABMAP_NONFREE
1072  cv::Mat imgRoi(image, roi);
1073  cv::Mat maskRoi;
1074  if(!mask.empty())
1075  {
1076  maskRoi = cv::Mat(mask, roi);
1077  }
1078  if(gpuVersion_)
1079  {
1080 #if CV_MAJOR_VERSION < 3
1081  cv::gpu::GpuMat imgGpu(imgRoi);
1082  cv::gpu::GpuMat maskGpu(maskRoi);
1083  (*_gpuSurf.obj)(imgGpu, maskGpu, keypoints);
1084 #else
1085  cv::cuda::GpuMat imgGpu(imgRoi);
1086  cv::cuda::GpuMat maskGpu(maskRoi);
1087  (*_gpuSurf.get())(imgGpu, maskGpu, keypoints);
1088 #endif
1089  }
1090  else
1091  {
1092  _surf->detect(imgRoi, keypoints, maskRoi);
1093  }
1094 #else
1095  UWARN("RTAB-Map is not built with OpenCV nonfree module so SURF cannot be used!");
1096 #endif
1097  return keypoints;
1098 }
1099 
1100 cv::Mat SURF::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1101 {
1102  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1103  cv::Mat descriptors;
1104 #ifdef RTABMAP_NONFREE
1105  if(gpuVersion_)
1106  {
1107 #if CV_MAJOR_VERSION < 3
1108  cv::gpu::GpuMat imgGpu(image);
1109  cv::gpu::GpuMat descriptorsGPU;
1110  (*_gpuSurf.obj)(imgGpu, cv::gpu::GpuMat(), keypoints, descriptorsGPU, true);
1111 #else
1112  cv::cuda::GpuMat imgGpu(image);
1113  cv::cuda::GpuMat descriptorsGPU;
1114  (*_gpuSurf.get())(imgGpu, cv::cuda::GpuMat(), keypoints, descriptorsGPU, true);
1115 #endif
1116 
1117  // Download descriptors
1118  if (descriptorsGPU.empty())
1119  descriptors = cv::Mat();
1120  else
1121  {
1122  UASSERT(descriptorsGPU.type() == CV_32F);
1123  descriptors = cv::Mat(descriptorsGPU.size(), CV_32F);
1124  descriptorsGPU.download(descriptors);
1125  }
1126  }
1127  else
1128  {
1129  _surf->compute(image, keypoints, descriptors);
1130  }
1131 #else
1132  UWARN("RTAB-Map is not built with OpenCV nonfree module so SURF cannot be used!");
1133 #endif
1134 
1135  return descriptors;
1136 }
1137 
1139 //SIFT
1141 SIFT::SIFT(const ParametersMap & parameters) :
1142  nOctaveLayers_(Parameters::defaultSIFTNOctaveLayers()),
1143  contrastThreshold_(Parameters::defaultSIFTContrastThreshold()),
1144  edgeThreshold_(Parameters::defaultSIFTEdgeThreshold()),
1145  sigma_(Parameters::defaultSIFTSigma()),
1146  rootSIFT_(Parameters::defaultSIFTRootSIFT())
1147 {
1148  parseParameters(parameters);
1149 }
1150 
1151 SIFT::~SIFT()
1152 {
1153 }
1154 
1155 void SIFT::parseParameters(const ParametersMap & parameters)
1156 {
1157  Feature2D::parseParameters(parameters);
1158 
1159  Parameters::parse(parameters, Parameters::kSIFTContrastThreshold(), contrastThreshold_);
1160  Parameters::parse(parameters, Parameters::kSIFTEdgeThreshold(), edgeThreshold_);
1161  Parameters::parse(parameters, Parameters::kSIFTNOctaveLayers(), nOctaveLayers_);
1162  Parameters::parse(parameters, Parameters::kSIFTSigma(), sigma_);
1163  Parameters::parse(parameters, Parameters::kSIFTRootSIFT(), rootSIFT_);
1164 
1165 #if CV_MAJOR_VERSION < 3 || (CV_MAJOR_VERSION == 4 && CV_MINOR_VERSION <= 3) || (CV_MAJOR_VERSION == 3 && (CV_MINOR_VERSION < 4 || (CV_MINOR_VERSION==4 && CV_SUBMINOR_VERSION<11)))
1166 #ifdef RTABMAP_NONFREE
1167 #if CV_MAJOR_VERSION < 3
1168  _sift = cv::Ptr<CV_SIFT>(new CV_SIFT(this->getMaxFeatures(), nOctaveLayers_, contrastThreshold_, edgeThreshold_, sigma_));
1169 #else
1170  _sift = CV_SIFT::create(this->getMaxFeatures(), nOctaveLayers_, contrastThreshold_, edgeThreshold_, sigma_);
1171 #endif
1172 #else
1173  UWARN("RTAB-Map is not built with OpenCV nonfree module so SIFT cannot be used!");
1174 #endif
1175 #else // >=4.4, >=3.4.11
1176  _sift = CV_SIFT::create(this->getMaxFeatures(), nOctaveLayers_, contrastThreshold_, edgeThreshold_, sigma_);
1177 #endif
1178 }
1179 
1180 std::vector<cv::KeyPoint> SIFT::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
1181 {
1182  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1183  std::vector<cv::KeyPoint> keypoints;
1184  cv::Mat imgRoi(image, roi);
1185  cv::Mat maskRoi;
1186  if(!mask.empty())
1187  {
1188  maskRoi = cv::Mat(mask, roi);
1189  }
1190 #if CV_MAJOR_VERSION < 3 || (CV_MAJOR_VERSION == 4 && CV_MINOR_VERSION <= 3) || (CV_MAJOR_VERSION == 3 && (CV_MINOR_VERSION < 4 || (CV_MINOR_VERSION==4 && CV_SUBMINOR_VERSION<11)))
1191 #ifdef RTABMAP_NONFREE
1192  _sift->detect(imgRoi, keypoints, maskRoi); // Opencv keypoints
1193 #else
1194  UWARN("RTAB-Map is not built with OpenCV nonfree module so SIFT cannot be used!");
1195 #endif
1196 #else // >=4.4, >=3.4.11
1197  _sift->detect(imgRoi, keypoints, maskRoi); // Opencv keypoints
1198 #endif
1199  return keypoints;
1200 }
1201 
1202 cv::Mat SIFT::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1203 {
1204  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1205  cv::Mat descriptors;
1206 #if CV_MAJOR_VERSION < 3 || (CV_MAJOR_VERSION == 4 && CV_MINOR_VERSION <= 3) || (CV_MAJOR_VERSION == 3 && (CV_MINOR_VERSION < 4 || (CV_MINOR_VERSION==4 && CV_SUBMINOR_VERSION<11)))
1207 #ifdef RTABMAP_NONFREE
1208  _sift->compute(image, keypoints, descriptors);
1209 #else
1210  UWARN("RTAB-Map is not built with OpenCV nonfree module so SIFT cannot be used!");
1211 #endif
1212 #else // >=4.4, >=3.4.11
1213  _sift->compute(image, keypoints, descriptors);
1214 #endif
1215  if( rootSIFT_ && !descriptors.empty())
1216  {
1217  UDEBUG("Performing RootSIFT...");
1218  // see http://www.pyimagesearch.com/2015/04/13/implementing-rootsift-in-python-and-opencv/
1219  // apply the Hellinger kernel by first L1-normalizing and taking the
1220  // square-root
1221  for(int i=0; i<descriptors.rows; ++i)
1222  {
1223  // By taking the L1 norm, followed by the square-root, we have
1224  // already L2 normalized the feature vector and further normalization
1225  // is not needed.
1226  descriptors.row(i) = descriptors.row(i) / cv::sum(descriptors.row(i))[0];
1227  cv::sqrt(descriptors.row(i), descriptors.row(i));
1228  }
1229  }
1230  return descriptors;
1231 }
1232 
1234 //ORB
1236 ORB::ORB(const ParametersMap & parameters) :
1237  scaleFactor_(Parameters::defaultORBScaleFactor()),
1238  nLevels_(Parameters::defaultORBNLevels()),
1239  edgeThreshold_(Parameters::defaultORBEdgeThreshold()),
1240  firstLevel_(Parameters::defaultORBFirstLevel()),
1241  WTA_K_(Parameters::defaultORBWTA_K()),
1242  scoreType_(Parameters::defaultORBScoreType()),
1243  patchSize_(Parameters::defaultORBPatchSize()),
1244  gpu_(Parameters::defaultORBGpu()),
1245  fastThreshold_(Parameters::defaultFASTThreshold()),
1246  nonmaxSuppresion_(Parameters::defaultFASTNonmaxSuppression())
1247 {
1248  parseParameters(parameters);
1249 }
1250 
1251 ORB::~ORB()
1252 {
1253 }
1254 
1255 void ORB::parseParameters(const ParametersMap & parameters)
1256 {
1257  Feature2D::parseParameters(parameters);
1258 
1259  Parameters::parse(parameters, Parameters::kORBScaleFactor(), scaleFactor_);
1260  Parameters::parse(parameters, Parameters::kORBNLevels(), nLevels_);
1261  Parameters::parse(parameters, Parameters::kORBEdgeThreshold(), edgeThreshold_);
1262  Parameters::parse(parameters, Parameters::kORBFirstLevel(), firstLevel_);
1263  Parameters::parse(parameters, Parameters::kORBWTA_K(), WTA_K_);
1264  Parameters::parse(parameters, Parameters::kORBScoreType(), scoreType_);
1265  Parameters::parse(parameters, Parameters::kORBPatchSize(), patchSize_);
1266  Parameters::parse(parameters, Parameters::kORBGpu(), gpu_);
1267 
1268  Parameters::parse(parameters, Parameters::kFASTThreshold(), fastThreshold_);
1269  Parameters::parse(parameters, Parameters::kFASTNonmaxSuppression(), nonmaxSuppresion_);
1270 
1271 #if CV_MAJOR_VERSION < 3
1272 #ifdef HAVE_OPENCV_GPU
1273  if(gpu_ && cv::gpu::getCudaEnabledDeviceCount() == 0)
1274  {
1275  UWARN("GPU version of ORB not available! Using CPU version instead...");
1276  gpu_ = false;
1277  }
1278 #else
1279  if(gpu_)
1280  {
1281  UWARN("GPU version of ORB not available (OpenCV not built with gpu/cuda module)! Using CPU version instead...");
1282  gpu_ = false;
1283  }
1284 #endif
1285 #else
1286 #ifndef HAVE_OPENCV_CUDAFEATURES2D
1287  if(gpu_)
1288  {
1289  UWARN("GPU version of ORB not available (OpenCV cudafeatures2d module)! Using CPU version instead...");
1290  gpu_ = false;
1291  }
1292 #endif
1293  if(gpu_ && cv::cuda::getCudaEnabledDeviceCount() == 0)
1294  {
1295  UWARN("GPU version of ORB not available (no GPU found)! Using CPU version instead...");
1296  gpu_ = false;
1297  }
1298 #endif
1299  if(gpu_)
1300  {
1301 #if CV_MAJOR_VERSION < 3
1302 #ifdef HAVE_OPENCV_GPU
1303  _gpuOrb = cv::Ptr<CV_ORB_GPU>(new CV_ORB_GPU(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_));
1304  _gpuOrb->setFastParams(fastThreshold_, nonmaxSuppresion_);
1305 #else
1306  UFATAL("not supposed to be here");
1307 #endif
1308 #else
1309 #ifdef HAVE_OPENCV_CUDAFEATURES2D
1310  _gpuOrb = CV_ORB_GPU::create(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_, fastThreshold_);
1311 #endif
1312 #endif
1313  }
1314  else
1315  {
1316 #if CV_MAJOR_VERSION < 3
1317  _orb = cv::Ptr<CV_ORB>(new CV_ORB(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_, parameters));
1318 #elif CV_MAJOR_VERSION > 3
1319  _orb = CV_ORB::create(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, (cv::ORB::ScoreType)scoreType_, patchSize_, fastThreshold_);
1320 #else
1321  _orb = CV_ORB::create(this->getMaxFeatures(), scaleFactor_, nLevels_, edgeThreshold_, firstLevel_, WTA_K_, scoreType_, patchSize_, fastThreshold_);
1322 #endif
1323  }
1324 }
1325 
1326 std::vector<cv::KeyPoint> ORB::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
1327 {
1328  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1329  std::vector<cv::KeyPoint> keypoints;
1330  cv::Mat imgRoi(image, roi);
1331  cv::Mat maskRoi;
1332  if(!mask.empty())
1333  {
1334  maskRoi = cv::Mat(mask, roi);
1335  }
1336 
1337  if(gpu_)
1338  {
1339 #if CV_MAJOR_VERSION < 3
1340 #ifdef HAVE_OPENCV_GPU
1341  cv::gpu::GpuMat imgGpu(imgRoi);
1342  cv::gpu::GpuMat maskGpu(maskRoi);
1343  (*_gpuOrb.obj)(imgGpu, maskGpu, keypoints);
1344 #else
1345  UERROR("Cannot use ORBGPU because OpenCV is not built with gpu module.");
1346 #endif
1347 #else
1348 #ifdef HAVE_OPENCV_CUDAFEATURES2D
1349  cv::cuda::GpuMat d_image(imgRoi);
1350  cv::cuda::GpuMat d_mask(maskRoi);
1351  try {
1352  _gpuOrb->detectAndCompute(d_image, d_mask, keypoints, cv::cuda::GpuMat(), false);
1353  } catch (cv::Exception& e) {
1354  const char* err_msg = e.what();
1355  UWARN("OpenCV exception caught: %s", err_msg);
1356  }
1357 #endif
1358 #endif
1359  }
1360  else
1361  {
1362  _orb->detect(imgRoi, keypoints, maskRoi);
1363  }
1364 
1365  return keypoints;
1366 }
1367 
1368 cv::Mat ORB::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1369 {
1370  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1371  cv::Mat descriptors;
1372  if(image.empty())
1373  {
1374  ULOGGER_ERROR("Image is null ?!?");
1375  return descriptors;
1376  }
1377  if(gpu_)
1378  {
1379 #if CV_MAJOR_VERSION < 3
1380 #ifdef HAVE_OPENCV_GPU
1381  cv::gpu::GpuMat imgGpu(image);
1382  cv::gpu::GpuMat descriptorsGPU;
1383  (*_gpuOrb.obj)(imgGpu, cv::gpu::GpuMat(), keypoints, descriptorsGPU);
1384  // Download descriptors
1385  if (descriptorsGPU.empty())
1386  descriptors = cv::Mat();
1387  else
1388  {
1389  UASSERT(descriptorsGPU.type() == CV_32F);
1390  descriptors = cv::Mat(descriptorsGPU.size(), CV_32F);
1391  descriptorsGPU.download(descriptors);
1392  }
1393 #else
1394  UERROR("GPU version of ORB not available (OpenCV not built with gpu/cuda module)! Using CPU version instead...");
1395 #endif
1396 #else
1397 #ifdef HAVE_OPENCV_CUDAFEATURES2D
1398  cv::cuda::GpuMat d_image(image);
1399  cv::cuda::GpuMat d_descriptors;
1400  try {
1401  _gpuOrb->detectAndCompute(d_image, cv::cuda::GpuMat(), keypoints, d_descriptors, true);
1402  } catch (cv::Exception& e) {
1403  const char* err_msg = e.what();
1404  UWARN("OpenCV exception caught: %s", err_msg);
1405  }
1406  // Download descriptors
1407  if (d_descriptors.empty())
1408  descriptors = cv::Mat();
1409  else
1410  {
1411  UASSERT(d_descriptors.type() == CV_32F || d_descriptors.type() == CV_8U);
1412  d_descriptors.download(descriptors);
1413  }
1414 #endif
1415 #endif
1416  }
1417  else
1418  {
1419  _orb->compute(image, keypoints, descriptors);
1420  }
1421 
1422  return descriptors;
1423 }
1424 
1426 //FAST
1428 FAST::FAST(const ParametersMap & parameters) :
1429  threshold_(Parameters::defaultFASTThreshold()),
1430  nonmaxSuppression_(Parameters::defaultFASTNonmaxSuppression()),
1431  gpu_(Parameters::defaultFASTGpu()),
1432  gpuKeypointsRatio_(Parameters::defaultFASTGpuKeypointsRatio()),
1433  minThreshold_(Parameters::defaultFASTMinThreshold()),
1434  maxThreshold_(Parameters::defaultFASTMaxThreshold()),
1435  gridRows_(Parameters::defaultFASTGridRows()),
1436  gridCols_(Parameters::defaultFASTGridCols()),
1437  fastCV_(Parameters::defaultFASTCV()),
1438  fastCVinit_(false),
1439  fastCVMaxFeatures_(10000),
1440  fastCVLastImageHeight_(0)
1441 {
1442 #ifdef RTABMAP_FASTCV
1443  char sVersion[128] = { 0 };
1444  fcvGetVersion(sVersion, 128);
1445  UINFO("fastcv version = %s", sVersion);
1446  int ix;
1447  if ((ix = fcvSetOperationMode(FASTCV_OP_PERFORMANCE)))
1448  {
1449  UERROR("fcvSetOperationMode return=%d, OpenCV FAST will be used instead!", ix);
1450  fastCV_ = 0;
1451  }
1452  else
1453  {
1454  fcvMemInit();
1455 
1456  if (!(fastCVCorners_ = (uint32_t*)fcvMemAlloc(fastCVMaxFeatures_ * sizeof(uint32_t) * 2, 16)) ||
1457  !(fastCVCornerScores_ = (uint32_t*)fcvMemAlloc( fastCVMaxFeatures_ * sizeof(uint32_t), 16 )))
1458  {
1459  UERROR("could not alloc fastcv mem, using opencv fast instead!");
1460 
1461  if (fastCVCorners_)
1462  {
1463  fcvMemFree(fastCVCorners_);
1464  fastCVCorners_ = NULL;
1465  }
1466  if (fastCVCornerScores_)
1467  {
1468  fcvMemFree(fastCVCornerScores_);
1469  fastCVCornerScores_ = NULL;
1470  }
1471  }
1472  else
1473  {
1474  fastCVinit_ = true;
1475  }
1476  }
1477  #endif
1478  parseParameters(parameters);
1479 }
1480 
1481 FAST::~FAST()
1482 {
1483 #ifdef RTABMAP_FASTCV
1484  if(fastCVinit_)
1485  {
1486  fcvMemDeInit();
1487 
1488  if (fastCVCorners_)
1489  fcvMemFree(fastCVCorners_);
1490  if (fastCVCornerScores_)
1491  fcvMemFree(fastCVCornerScores_);
1492  if (fastCVTempBuf_)
1493  fcvMemFree(fastCVTempBuf_);
1494  }
1495 #endif
1496 }
1497 
1498 void FAST::parseParameters(const ParametersMap & parameters)
1499 {
1500  Feature2D::parseParameters(parameters);
1501 
1502  Parameters::parse(parameters, Parameters::kFASTThreshold(), threshold_);
1503  Parameters::parse(parameters, Parameters::kFASTNonmaxSuppression(), nonmaxSuppression_);
1504  Parameters::parse(parameters, Parameters::kFASTGpu(), gpu_);
1505  Parameters::parse(parameters, Parameters::kFASTGpuKeypointsRatio(), gpuKeypointsRatio_);
1506 
1507  Parameters::parse(parameters, Parameters::kFASTMinThreshold(), minThreshold_);
1508  Parameters::parse(parameters, Parameters::kFASTMaxThreshold(), maxThreshold_);
1509  Parameters::parse(parameters, Parameters::kFASTGridRows(), gridRows_);
1510  Parameters::parse(parameters, Parameters::kFASTGridCols(), gridCols_);
1511 
1512  Parameters::parse(parameters, Parameters::kFASTCV(), fastCV_);
1513  UASSERT(fastCV_ == 0 || fastCV_ == 9 || fastCV_ == 10);
1514 
1515  UASSERT_MSG(threshold_ >= minThreshold_, uFormat("%d vs %d", threshold_, minThreshold_).c_str());
1516  UASSERT_MSG(threshold_ <= maxThreshold_, uFormat("%d vs %d", threshold_, maxThreshold_).c_str());
1517 
1518 #if CV_MAJOR_VERSION < 3
1519 #ifdef HAVE_OPENCV_GPU
1520  if(gpu_ && cv::gpu::getCudaEnabledDeviceCount() == 0)
1521  {
1522  UWARN("GPU version of FAST not available! Using CPU version instead...");
1523  gpu_ = false;
1524  }
1525 #else
1526  if(gpu_)
1527  {
1528  UWARN("GPU version of FAST not available (OpenCV not built with gpu/cuda module)! Using CPU version instead...");
1529  gpu_ = false;
1530  }
1531 #endif
1532 #else
1533 #ifdef HAVE_OPENCV_CUDAFEATURES2D
1534  if(gpu_ && cv::cuda::getCudaEnabledDeviceCount() == 0)
1535  {
1536  UWARN("GPU version of FAST not available! Using CPU version instead...");
1537  gpu_ = false;
1538  }
1539 #else
1540  if(gpu_)
1541  {
1542  UWARN("GPU version of FAST not available (OpenCV cudafeatures2d module)! Using CPU version instead...");
1543  gpu_ = false;
1544  }
1545 #endif
1546 #endif
1547  if(gpu_)
1548  {
1549 #if CV_MAJOR_VERSION < 3
1550 #ifdef HAVE_OPENCV_GPU
1551  _gpuFast = new CV_FAST_GPU(threshold_, nonmaxSuppression_, gpuKeypointsRatio_);
1552 #else
1553  UFATAL("not supposed to be here!");
1554 #endif
1555 #else
1556 #ifdef HAVE_OPENCV_CUDAFEATURES2D
1557  UFATAL("not implemented");
1558 #endif
1559 #endif
1560  }
1561  else
1562  {
1563 #if CV_MAJOR_VERSION < 3
1564  if(gridRows_ > 0 && gridCols_ > 0)
1565  {
1566  UDEBUG("grid max features = %d", this->getMaxFeatures());
1567  cv::Ptr<cv::FeatureDetector> fastAdjuster = cv::Ptr<cv::FastAdjuster>(new cv::FastAdjuster(threshold_, nonmaxSuppression_, minThreshold_, maxThreshold_));
1568  _fast = cv::Ptr<cv::FeatureDetector>(new cv::GridAdaptedFeatureDetector(fastAdjuster, this->getMaxFeatures(), gridRows_, gridCols_));
1569  }
1570  else
1571  {
1572  if(gridRows_ > 0)
1573  {
1574  UWARN("Parameter \"%s\" is set (value=%d) but not \"%s\"! Grid adaptor will not be added.",
1575  Parameters::kFASTGridRows().c_str(), gridRows_, Parameters::kFASTGridCols().c_str());
1576  }
1577  else if(gridCols_ > 0)
1578  {
1579  UWARN("Parameter \"%s\" is set (value=%d) but not \"%s\"! Grid adaptor will not be added.",
1580  Parameters::kFASTGridCols().c_str(), gridCols_, Parameters::kFASTGridRows().c_str());
1581  }
1582  _fast = cv::Ptr<cv::FeatureDetector>(new CV_FAST(threshold_, nonmaxSuppression_));
1583  }
1584 #else
1585  _fast = CV_FAST::create(threshold_, nonmaxSuppression_);
1586 #endif
1587  }
1588 }
1589 
1590 std::vector<cv::KeyPoint> FAST::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
1591 {
1592  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1593  std::vector<cv::KeyPoint> keypoints;
1594 
1595 #ifdef RTABMAP_FASTCV
1596  if(fastCV_>0)
1597  {
1598  // Note: mask not supported, it should be the inverse of the current mask used (0=where to extract)
1599  uint32_t nCorners = 0;
1600 
1601  UASSERT(fastCVCorners_ != NULL && fastCVCornerScores_ != NULL);
1602  if (nonmaxSuppression_)
1603  {
1604  if(fastCVTempBuf_==NULL || (fastCVTempBuf_!= NULL && fastCVLastImageHeight_!= image.rows))
1605  {
1606  if (fastCVTempBuf_)
1607  {
1608  fcvMemFree(fastCVTempBuf_);
1609  fastCVTempBuf_ = NULL;
1610  }
1611  if(!(fastCVTempBuf_ = (uint32_t*)fcvMemAlloc( (3*fastCVMaxFeatures_+image.rows+1)*4, 16 )))
1612  {
1613  UERROR("could not alloc fastcv mem for temp buf (%s=true)", Parameters::kFASTNonmaxSuppression().c_str());
1614  fastCVLastImageHeight_ = 0;
1615  return keypoints;
1616  }
1617  fastCVLastImageHeight_ = image.rows;
1618  }
1619  }
1620 
1621  // image.data should be 128 bits aligned
1622  UDEBUG("%dx%d (step=%d) thr=%d maxFeatures=%d", image.cols, image.rows, image.step1(), threshold_, fastCVMaxFeatures_);
1623  if(fastCV_ == 10)
1624  {
1625  fcvCornerFast10Scoreu8(image.data, image.cols, image.rows, 0, threshold_, 0, fastCVCorners_, fastCVCornerScores_, fastCVMaxFeatures_, &nCorners, nonmaxSuppression_?1:0, fastCVTempBuf_);
1626  }
1627  else
1628  {
1629  fcvCornerFast9Scoreu8_v2(image.data, image.cols, image.rows, image.step1(), threshold_, 0, fastCVCorners_, fastCVCornerScores_, fastCVMaxFeatures_, &nCorners, nonmaxSuppression_?1:0, fastCVTempBuf_);
1630  }
1631  UDEBUG("number of corners found = %d:", nCorners);
1632  keypoints.resize(nCorners);
1633  for (uint32_t i = 0; i < nCorners; i++)
1634  {
1635  keypoints[i].pt.x = fastCVCorners_[i * 2];
1636  keypoints[i].pt.y = fastCVCorners_[(i * 2) + 1];
1637  keypoints[i].size = 3;
1638  keypoints[i].response = fastCVCornerScores_[i];
1639  }
1640 
1641  if(this->getMaxFeatures() > 0)
1642  {
1643  this->limitKeypoints(keypoints, this->getMaxFeatures());
1644  }
1645  return keypoints;
1646  }
1647 #endif
1648 
1649  if(fastCV_>0)
1650  {
1651  UWARN( "RTAB-Map is not built with FastCV support. OpenCV's FAST is used instead. "
1652  "Please set %s to 0. This message will only appear once.",
1653  Parameters::kFASTCV().c_str());
1654  fastCV_ = 0;
1655  }
1656 
1657  cv::Mat imgRoi(image, roi);
1658  cv::Mat maskRoi;
1659  if(!mask.empty())
1660  {
1661  maskRoi = cv::Mat(mask, roi);
1662  }
1663  if(gpu_)
1664  {
1665 #if CV_MAJOR_VERSION < 3
1666 #ifdef HAVE_OPENCV_GPU
1667  cv::gpu::GpuMat imgGpu(imgRoi);
1668  cv::gpu::GpuMat maskGpu(maskRoi);
1669  (*_gpuFast.obj)(imgGpu, maskGpu, keypoints);
1670 #else
1671  UERROR("Cannot use FAST GPU because OpenCV is not built with gpu module.");
1672 #endif
1673 #else
1674 #ifdef HAVE_OPENCV_CUDAFEATURES2D
1675  UFATAL("not implemented");
1676 #endif
1677 #endif
1678  }
1679  else
1680  {
1681  _fast->detect(imgRoi, keypoints, maskRoi); // Opencv keypoints
1682  }
1683  return keypoints;
1684 }
1685 
1687 //FAST-BRIEF
1689 FAST_BRIEF::FAST_BRIEF(const ParametersMap & parameters) :
1690  FAST(parameters),
1691  bytes_(Parameters::defaultBRIEFBytes())
1692 {
1693  parseParameters(parameters);
1694 }
1695 
1696 FAST_BRIEF::~FAST_BRIEF()
1697 {
1698 }
1699 
1700 void FAST_BRIEF::parseParameters(const ParametersMap & parameters)
1701 {
1702  FAST::parseParameters(parameters);
1703 
1704  Parameters::parse(parameters, Parameters::kBRIEFBytes(), bytes_);
1705 #if CV_MAJOR_VERSION < 3
1706  _brief = cv::Ptr<CV_BRIEF>(new CV_BRIEF(bytes_));
1707 #else
1708 #ifdef HAVE_OPENCV_XFEATURES2D
1709  _brief = CV_BRIEF::create(bytes_);
1710 #else
1711  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Brief cannot be used!");
1712 #endif
1713 #endif
1714 }
1715 
1716 cv::Mat FAST_BRIEF::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1717 {
1718  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1719  cv::Mat descriptors;
1720 #if CV_MAJOR_VERSION < 3
1721  _brief->compute(image, keypoints, descriptors);
1722 #else
1723 #ifdef HAVE_OPENCV_XFEATURES2D
1724  _brief->compute(image, keypoints, descriptors);
1725 #else
1726  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Brief cannot be used!");
1727 #endif
1728 #endif
1729  return descriptors;
1730 }
1731 
1733 //FAST-FREAK
1735 FAST_FREAK::FAST_FREAK(const ParametersMap & parameters) :
1736  FAST(parameters),
1737  orientationNormalized_(Parameters::defaultFREAKOrientationNormalized()),
1738  scaleNormalized_(Parameters::defaultFREAKScaleNormalized()),
1739  patternScale_(Parameters::defaultFREAKPatternScale()),
1740  nOctaves_(Parameters::defaultFREAKNOctaves())
1741 {
1742  parseParameters(parameters);
1743 }
1744 
1745 FAST_FREAK::~FAST_FREAK()
1746 {
1747 }
1748 
1749 void FAST_FREAK::parseParameters(const ParametersMap & parameters)
1750 {
1751  FAST::parseParameters(parameters);
1752 
1753  Parameters::parse(parameters, Parameters::kFREAKOrientationNormalized(), orientationNormalized_);
1754  Parameters::parse(parameters, Parameters::kFREAKScaleNormalized(), scaleNormalized_);
1755  Parameters::parse(parameters, Parameters::kFREAKPatternScale(), patternScale_);
1756  Parameters::parse(parameters, Parameters::kFREAKNOctaves(), nOctaves_);
1757 
1758 #if CV_MAJOR_VERSION < 3
1759  _freak = cv::Ptr<CV_FREAK>(new CV_FREAK(orientationNormalized_, scaleNormalized_, patternScale_, nOctaves_));
1760 #else
1761 #ifdef HAVE_OPENCV_XFEATURES2D
1762  _freak = CV_FREAK::create(orientationNormalized_, scaleNormalized_, patternScale_, nOctaves_);
1763 #else
1764  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Freak cannot be used!");
1765 #endif
1766 #endif
1767 }
1768 
1769 cv::Mat FAST_FREAK::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1770 {
1771  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1772  cv::Mat descriptors;
1773 #if CV_MAJOR_VERSION < 3
1774  _freak->compute(image, keypoints, descriptors);
1775 #else
1776 #ifdef HAVE_OPENCV_XFEATURES2D
1777  _freak->compute(image, keypoints, descriptors);
1778 #else
1779  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Freak cannot be used!");
1780 #endif
1781 #endif
1782  return descriptors;
1783 }
1784 
1786 //GFTT
1788 GFTT::GFTT(const ParametersMap & parameters) :
1789  _qualityLevel(Parameters::defaultGFTTQualityLevel()),
1790  _minDistance(Parameters::defaultGFTTMinDistance()),
1791  _blockSize(Parameters::defaultGFTTBlockSize()),
1792  _useHarrisDetector(Parameters::defaultGFTTUseHarrisDetector()),
1793  _k(Parameters::defaultGFTTK())
1794 {
1795  parseParameters(parameters);
1796 }
1797 
1798 GFTT::~GFTT()
1799 {
1800 }
1801 
1802 void GFTT::parseParameters(const ParametersMap & parameters)
1803 {
1804  Feature2D::parseParameters(parameters);
1805 
1806  Parameters::parse(parameters, Parameters::kGFTTQualityLevel(), _qualityLevel);
1807  Parameters::parse(parameters, Parameters::kGFTTMinDistance(), _minDistance);
1808  Parameters::parse(parameters, Parameters::kGFTTBlockSize(), _blockSize);
1809  Parameters::parse(parameters, Parameters::kGFTTUseHarrisDetector(), _useHarrisDetector);
1810  Parameters::parse(parameters, Parameters::kGFTTK(), _k);
1811 
1812 #if CV_MAJOR_VERSION < 3
1813  _gftt = cv::Ptr<CV_GFTT>(new CV_GFTT(this->getMaxFeatures(), _qualityLevel, _minDistance, _blockSize, _useHarrisDetector ,_k));
1814 #else
1815  _gftt = CV_GFTT::create(this->getMaxFeatures(), _qualityLevel, _minDistance, _blockSize, _useHarrisDetector ,_k);
1816 #endif
1817 }
1818 
1819 std::vector<cv::KeyPoint> GFTT::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
1820 {
1821  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1822  std::vector<cv::KeyPoint> keypoints;
1823  cv::Mat imgRoi(image, roi);
1824  cv::Mat maskRoi;
1825  if(!mask.empty())
1826  {
1827  maskRoi = cv::Mat(mask, roi);
1828  }
1829  _gftt->detect(imgRoi, keypoints, maskRoi); // Opencv keypoints
1830  return keypoints;
1831 }
1832 
1834 //FAST-BRIEF
1836 GFTT_BRIEF::GFTT_BRIEF(const ParametersMap & parameters) :
1837  GFTT(parameters),
1838  bytes_(Parameters::defaultBRIEFBytes())
1839 {
1840  parseParameters(parameters);
1841 }
1842 
1843 GFTT_BRIEF::~GFTT_BRIEF()
1844 {
1845 }
1846 
1847 void GFTT_BRIEF::parseParameters(const ParametersMap & parameters)
1848 {
1849  GFTT::parseParameters(parameters);
1850 
1851  Parameters::parse(parameters, Parameters::kBRIEFBytes(), bytes_);
1852 #if CV_MAJOR_VERSION < 3
1853  _brief = cv::Ptr<CV_BRIEF>(new CV_BRIEF(bytes_));
1854 #else
1855 #ifdef HAVE_OPENCV_XFEATURES2D
1856  _brief = CV_BRIEF::create(bytes_);
1857 #else
1858  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Brief cannot be used!");
1859 #endif
1860 #endif
1861 }
1862 
1863 cv::Mat GFTT_BRIEF::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1864 {
1865  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1866  cv::Mat descriptors;
1867 #if CV_MAJOR_VERSION < 3
1868  _brief->compute(image, keypoints, descriptors);
1869 #else
1870 #ifdef HAVE_OPENCV_XFEATURES2D
1871  _brief->compute(image, keypoints, descriptors);
1872 #else
1873  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Brief cannot be used!");
1874 #endif
1875 #endif
1876  return descriptors;
1877 }
1878 
1880 //GFTT-FREAK
1882 GFTT_FREAK::GFTT_FREAK(const ParametersMap & parameters) :
1883  GFTT(parameters),
1884  orientationNormalized_(Parameters::defaultFREAKOrientationNormalized()),
1885  scaleNormalized_(Parameters::defaultFREAKScaleNormalized()),
1886  patternScale_(Parameters::defaultFREAKPatternScale()),
1887  nOctaves_(Parameters::defaultFREAKNOctaves())
1888 {
1889  parseParameters(parameters);
1890 }
1891 
1892 GFTT_FREAK::~GFTT_FREAK()
1893 {
1894 }
1895 
1896 void GFTT_FREAK::parseParameters(const ParametersMap & parameters)
1897 {
1898  GFTT::parseParameters(parameters);
1899 
1900  Parameters::parse(parameters, Parameters::kFREAKOrientationNormalized(), orientationNormalized_);
1901  Parameters::parse(parameters, Parameters::kFREAKScaleNormalized(), scaleNormalized_);
1902  Parameters::parse(parameters, Parameters::kFREAKPatternScale(), patternScale_);
1903  Parameters::parse(parameters, Parameters::kFREAKNOctaves(), nOctaves_);
1904 
1905 #if CV_MAJOR_VERSION < 3
1906  _freak = cv::Ptr<CV_FREAK>(new CV_FREAK(orientationNormalized_, scaleNormalized_, patternScale_, nOctaves_));
1907 #else
1908 #ifdef HAVE_OPENCV_XFEATURES2D
1909  _freak = CV_FREAK::create(orientationNormalized_, scaleNormalized_, patternScale_, nOctaves_);
1910 #else
1911  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Freak cannot be used!");
1912 #endif
1913 #endif
1914 }
1915 
1916 cv::Mat GFTT_FREAK::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1917 {
1918  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1919  cv::Mat descriptors;
1920 #if CV_MAJOR_VERSION < 3
1921  _freak->compute(image, keypoints, descriptors);
1922 #else
1923 #ifdef HAVE_OPENCV_XFEATURES2D
1924  _freak->compute(image, keypoints, descriptors);
1925 #else
1926  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Freak cannot be used!");
1927 #endif
1928 #endif
1929  return descriptors;
1930 }
1931 
1933 //SURF-FREAK
1935 SURF_FREAK::SURF_FREAK(const ParametersMap & parameters) :
1936  SURF(parameters),
1937  orientationNormalized_(Parameters::defaultFREAKOrientationNormalized()),
1938  scaleNormalized_(Parameters::defaultFREAKScaleNormalized()),
1939  patternScale_(Parameters::defaultFREAKPatternScale()),
1940  nOctaves_(Parameters::defaultFREAKNOctaves())
1941 {
1942  parseParameters(parameters);
1943 }
1944 
1945 SURF_FREAK::~SURF_FREAK()
1946 {
1947 }
1948 
1949 void SURF_FREAK::parseParameters(const ParametersMap & parameters)
1950 {
1951  SURF::parseParameters(parameters);
1952 
1953  Parameters::parse(parameters, Parameters::kFREAKOrientationNormalized(), orientationNormalized_);
1954  Parameters::parse(parameters, Parameters::kFREAKScaleNormalized(), scaleNormalized_);
1955  Parameters::parse(parameters, Parameters::kFREAKPatternScale(), patternScale_);
1956  Parameters::parse(parameters, Parameters::kFREAKNOctaves(), nOctaves_);
1957 
1958 #if CV_MAJOR_VERSION < 3
1959  _freak = cv::Ptr<CV_FREAK>(new CV_FREAK(orientationNormalized_, scaleNormalized_, patternScale_, nOctaves_));
1960 #else
1961 #ifdef HAVE_OPENCV_XFEATURES2D
1962  _freak = CV_FREAK::create(orientationNormalized_, scaleNormalized_, patternScale_, nOctaves_);
1963 #else
1964  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Freak cannot be used!");
1965 #endif
1966 #endif
1967 }
1968 
1969 cv::Mat SURF_FREAK::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
1970 {
1971  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
1972  cv::Mat descriptors;
1973 #if CV_MAJOR_VERSION < 3
1974  _freak->compute(image, keypoints, descriptors);
1975 #else
1976 #ifdef HAVE_OPENCV_XFEATURES2D
1977  _freak->compute(image, keypoints, descriptors);
1978 #else
1979  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so Freak cannot be used!");
1980 #endif
1981 #endif
1982  return descriptors;
1983 }
1984 
1986 //GFTT-ORB
1988 GFTT_ORB::GFTT_ORB(const ParametersMap & parameters) :
1989  GFTT(parameters),
1990  _orb(parameters)
1991 {
1992  parseParameters(parameters);
1993 }
1994 
1995 GFTT_ORB::~GFTT_ORB()
1996 {
1997 }
1998 
1999 void GFTT_ORB::parseParameters(const ParametersMap & parameters)
2000 {
2001  GFTT::parseParameters(parameters);
2002  _orb.parseParameters(parameters);
2003 }
2004 
2005 cv::Mat GFTT_ORB::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2006 {
2007  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2008  return _orb.generateDescriptors(image, keypoints);
2009 }
2010 
2012 //BRISK
2014 BRISK::BRISK(const ParametersMap & parameters) :
2015  thresh_(Parameters::defaultBRISKThresh()),
2016  octaves_(Parameters::defaultBRISKOctaves()),
2017  patternScale_(Parameters::defaultBRISKPatternScale())
2018 {
2019  parseParameters(parameters);
2020 }
2021 
2022 BRISK::~BRISK()
2023 {
2024 }
2025 
2026 void BRISK::parseParameters(const ParametersMap & parameters)
2027 {
2028  Feature2D::parseParameters(parameters);
2029 
2030  Parameters::parse(parameters, Parameters::kBRISKThresh(), thresh_);
2031  Parameters::parse(parameters, Parameters::kBRISKOctaves(), octaves_);
2032  Parameters::parse(parameters, Parameters::kBRISKPatternScale(), patternScale_);
2033 
2034 #if CV_MAJOR_VERSION < 3
2035  brisk_ = cv::Ptr<CV_BRISK>(new CV_BRISK(thresh_, octaves_, patternScale_));
2036 #else
2037  brisk_ = CV_BRISK::create(thresh_, octaves_, patternScale_);
2038 #endif
2039 }
2040 
2041 std::vector<cv::KeyPoint> BRISK::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
2042 {
2043  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2044  std::vector<cv::KeyPoint> keypoints;
2045  cv::Mat imgRoi(image, roi);
2046  cv::Mat maskRoi;
2047  if(!mask.empty())
2048  {
2049  maskRoi = cv::Mat(mask, roi);
2050  }
2051  brisk_->detect(imgRoi, keypoints, maskRoi); // Opencv keypoints
2052  return keypoints;
2053 }
2054 
2055 cv::Mat BRISK::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2056 {
2057  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2058  cv::Mat descriptors;
2059  brisk_->compute(image, keypoints, descriptors);
2060  return descriptors;
2061 }
2062 
2064 //KAZE
2066 KAZE::KAZE(const ParametersMap & parameters) :
2067  extended_(Parameters::defaultKAZEExtended()),
2068  upright_(Parameters::defaultKAZEUpright()),
2069  threshold_(Parameters::defaultKAZEThreshold()),
2070  nOctaves_(Parameters::defaultKAZENOctaves()),
2071  nOctaveLayers_(Parameters::defaultKAZENOctaveLayers()),
2072  diffusivity_(Parameters::defaultKAZEDiffusivity())
2073 {
2074  parseParameters(parameters);
2075 }
2076 
2077 KAZE::~KAZE()
2078 {
2079 }
2080 
2081 void KAZE::parseParameters(const ParametersMap & parameters)
2082 {
2083  Feature2D::parseParameters(parameters);
2084 
2085  Parameters::parse(parameters, Parameters::kKAZEExtended(), extended_);
2086  Parameters::parse(parameters, Parameters::kKAZEUpright(), upright_);
2087  Parameters::parse(parameters, Parameters::kKAZEThreshold(), threshold_);
2088  Parameters::parse(parameters, Parameters::kKAZENOctaves(), nOctaves_);
2089  Parameters::parse(parameters, Parameters::kKAZENOctaveLayers(), nOctaveLayers_);
2090  Parameters::parse(parameters, Parameters::kKAZEDiffusivity(), diffusivity_);
2091 
2092 #if CV_MAJOR_VERSION > 3
2093  kaze_ = cv::KAZE::create(extended_, upright_, threshold_, nOctaves_, nOctaveLayers_, (cv::KAZE::DiffusivityType)diffusivity_);
2094 #elif CV_MAJOR_VERSION > 2
2095  kaze_ = cv::KAZE::create(extended_, upright_, threshold_, nOctaves_, nOctaveLayers_, diffusivity_);
2096 #else
2097  UWARN("RTAB-Map is not built with OpenCV3 so Kaze feature cannot be used!");
2098 #endif
2099 }
2100 
2101 std::vector<cv::KeyPoint> KAZE::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
2102 {
2103  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2104  std::vector<cv::KeyPoint> keypoints;
2105 #if CV_MAJOR_VERSION > 2
2106  cv::Mat imgRoi(image, roi);
2107  cv::Mat maskRoi;
2108  if (!mask.empty())
2109  {
2110  maskRoi = cv::Mat(mask, roi);
2111  }
2112  kaze_->detect(imgRoi, keypoints, maskRoi); // Opencv keypoints
2113 #else
2114  UWARN("RTAB-Map is not built with OpenCV3 so Kaze feature cannot be used!");
2115 #endif
2116  return keypoints;
2117 }
2118 
2119 cv::Mat KAZE::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2120 {
2121  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2122  cv::Mat descriptors;
2123 #if CV_MAJOR_VERSION > 2
2124  kaze_->compute(image, keypoints, descriptors);
2125 #else
2126  UWARN("RTAB-Map is not built with OpenCV3 so Kaze feature cannot be used!");
2127 #endif
2128  return descriptors;
2129 }
2130 
2132 //ORBOctree
2134 ORBOctree::ORBOctree(const ParametersMap & parameters) :
2135  scaleFactor_(Parameters::defaultORBScaleFactor()),
2136  nLevels_(Parameters::defaultORBNLevels()),
2137  patchSize_(Parameters::defaultORBPatchSize()),
2138  edgeThreshold_(Parameters::defaultORBEdgeThreshold()),
2139  fastThreshold_(Parameters::defaultFASTThreshold()),
2140  fastMinThreshold_(Parameters::defaultFASTMinThreshold())
2141 {
2142  parseParameters(parameters);
2143 }
2144 
2145 ORBOctree::~ORBOctree()
2146 {
2147 }
2148 
2149 void ORBOctree::parseParameters(const ParametersMap & parameters)
2150 {
2151  Feature2D::parseParameters(parameters);
2152 
2153  Parameters::parse(parameters, Parameters::kORBScaleFactor(), scaleFactor_);
2154  Parameters::parse(parameters, Parameters::kORBNLevels(), nLevels_);
2155  Parameters::parse(parameters, Parameters::kORBPatchSize(), patchSize_);
2156  Parameters::parse(parameters, Parameters::kORBEdgeThreshold(), edgeThreshold_);
2157 
2158  Parameters::parse(parameters, Parameters::kFASTThreshold(), fastThreshold_);
2159  Parameters::parse(parameters, Parameters::kFASTMinThreshold(), fastMinThreshold_);
2160 
2161 #ifdef RTABMAP_ORB_OCTREE
2162  _orb = cv::Ptr<ORBextractor>(new ORBextractor(this->getMaxFeatures(), scaleFactor_, nLevels_, fastThreshold_, fastMinThreshold_, patchSize_, edgeThreshold_));
2163 #else
2164  UWARN("RTAB-Map is not built with ORB OcTree option enabled so ORB OcTree feature cannot be used!");
2165 #endif
2166 }
2167 
2168 std::vector<cv::KeyPoint> ORBOctree::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
2169 {
2170  std::vector<cv::KeyPoint> keypoints;
2171  descriptors_ = cv::Mat();
2172 #ifdef RTABMAP_ORB_OCTREE
2173  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2174  cv::Mat imgRoi(image, roi);
2175  cv::Mat maskRoi;
2176  if(!mask.empty())
2177  {
2178  maskRoi = cv::Mat(mask, roi);
2179  }
2180 
2181  (*_orb)(imgRoi, maskRoi, keypoints, descriptors_);
2182 
2183  // OrbOctree ignores the mask, so we have to apply it manually here
2184  if(!keypoints.empty() && !maskRoi.empty())
2185  {
2186  std::vector<cv::KeyPoint> validKeypoints;
2187  validKeypoints.reserve(keypoints.size());
2188  cv::Mat validDescriptors;
2189  for(size_t i=0; i<keypoints.size(); ++i)
2190  {
2191  if(maskRoi.at<unsigned char>(keypoints[i].pt.y+roi.y, keypoints[i].pt.x+roi.x) != 0)
2192  {
2193  validKeypoints.push_back(keypoints[i]);
2194  validDescriptors.push_back(descriptors_.row(i));
2195  }
2196  }
2197  keypoints = validKeypoints;
2198  descriptors_ = validDescriptors;
2199  }
2200 
2201  if((int)keypoints.size() > this->getMaxFeatures())
2202  {
2203  limitKeypoints(keypoints, descriptors_, this->getMaxFeatures(), roi.size(), this->getSSC());
2204  }
2205 #else
2206  UWARN("RTAB-Map is not built with ORB OcTree option enabled so ORB OcTree feature cannot be used!");
2207 #endif
2208  return keypoints;
2209 }
2210 
2211 cv::Mat ORBOctree::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2212 {
2213 #ifdef RTABMAP_ORB_OCTREE
2214  UASSERT_MSG((int)keypoints.size() == descriptors_.rows, uFormat("keypoints=%d descriptors=%d", (int)keypoints.size(), descriptors_.rows).c_str());
2215 #else
2216  UWARN("RTAB-Map is not built with ORB OcTree option enabled so ORB OcTree feature cannot be used!");
2217 #endif
2218  return descriptors_;
2219 }
2220 
2222 //SuperPointTorch
2224 SuperPointTorch::SuperPointTorch(const ParametersMap & parameters) :
2225  path_(Parameters::defaultSuperPointModelPath()),
2226  threshold_(Parameters::defaultSuperPointThreshold()),
2227  nms_(Parameters::defaultSuperPointNMS()),
2228  minDistance_(Parameters::defaultSuperPointNMSRadius()),
2229  cuda_(Parameters::defaultSuperPointCuda())
2230 {
2231  parseParameters(parameters);
2232 }
2233 
2234 SuperPointTorch::~SuperPointTorch()
2235 {
2236 }
2237 
2238 void SuperPointTorch::parseParameters(const ParametersMap & parameters)
2239 {
2240  Feature2D::parseParameters(parameters);
2241 
2242  std::string previousPath = path_;
2243 #ifdef RTABMAP_TORCH
2244  bool previousCuda = cuda_;
2245 #endif
2246  Parameters::parse(parameters, Parameters::kSuperPointModelPath(), path_);
2247  Parameters::parse(parameters, Parameters::kSuperPointThreshold(), threshold_);
2248  Parameters::parse(parameters, Parameters::kSuperPointNMS(), nms_);
2249  Parameters::parse(parameters, Parameters::kSuperPointNMSRadius(), minDistance_);
2250  Parameters::parse(parameters, Parameters::kSuperPointCuda(), cuda_);
2251 
2252 #ifdef RTABMAP_TORCH
2253  if(superPoint_.get() == 0 || path_.compare(previousPath) != 0 || previousCuda != cuda_)
2254  {
2255  superPoint_ = cv::Ptr<SPDetector>(new SPDetector(path_, threshold_, nms_, minDistance_, cuda_));
2256  }
2257  else
2258  {
2259  superPoint_->setThreshold(threshold_);
2260  superPoint_->SetNMS(nms_);
2261  superPoint_->setMinDistance(minDistance_);
2262  }
2263 #else
2264  UWARN("RTAB-Map is not built with Torch support so SuperPoint Torch feature cannot be used!");
2265 #endif
2266 }
2267 
2268 std::vector<cv::KeyPoint> SuperPointTorch::generateKeypointsImpl(const cv::Mat & image, const cv::Rect & roi, const cv::Mat & mask)
2269 {
2270 #ifdef RTABMAP_TORCH
2271  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2272  if(roi.x!=0 || roi.y !=0)
2273  {
2274  UERROR("SuperPoint: Not supporting ROI (%d,%d,%d,%d). Make sure %s, %s, %s, %s, %s, %s are all set to default values.",
2275  roi.x, roi.y, roi.width, roi.height,
2276  Parameters::kKpRoiRatios().c_str(),
2277  Parameters::kVisRoiRatios().c_str(),
2278  Parameters::kVisGridRows().c_str(),
2279  Parameters::kVisGridCols().c_str(),
2280  Parameters::kKpGridRows().c_str(),
2281  Parameters::kKpGridCols().c_str());
2282  return std::vector<cv::KeyPoint>();
2283  }
2284  return superPoint_->detect(image, mask);
2285 #else
2286  UWARN("RTAB-Map is not built with Torch support so SuperPoint Torch feature cannot be used!");
2287  return std::vector<cv::KeyPoint>();
2288 #endif
2289 }
2290 
2291 cv::Mat SuperPointTorch::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2292 {
2293 #ifdef RTABMAP_TORCH
2294  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2295  return superPoint_->compute(keypoints);
2296 #else
2297  UWARN("RTAB-Map is not built with Torch support so SuperPoint Torch feature cannot be used!");
2298  return cv::Mat();
2299 #endif
2300 }
2301 
2302 
2304 //GFTT-DAISY
2306 GFTT_DAISY::GFTT_DAISY(const ParametersMap & parameters) :
2307  GFTT(parameters),
2308  orientationNormalized_(Parameters::defaultFREAKOrientationNormalized()),
2309  scaleNormalized_(Parameters::defaultFREAKScaleNormalized()),
2310  patternScale_(Parameters::defaultFREAKPatternScale()),
2311  nOctaves_(Parameters::defaultFREAKNOctaves())
2312 {
2313  parseParameters(parameters);
2314 }
2315 
2316 GFTT_DAISY::~GFTT_DAISY()
2317 {
2318 }
2319 
2320 void GFTT_DAISY::parseParameters(const ParametersMap & parameters)
2321 {
2322  GFTT::parseParameters(parameters);
2323 
2324  Parameters::parse(parameters, Parameters::kFREAKOrientationNormalized(), orientationNormalized_);
2325  Parameters::parse(parameters, Parameters::kFREAKScaleNormalized(), scaleNormalized_);
2326  Parameters::parse(parameters, Parameters::kFREAKPatternScale(), patternScale_);
2327  Parameters::parse(parameters, Parameters::kFREAKNOctaves(), nOctaves_);
2328 
2329 #ifdef HAVE_OPENCV_XFEATURES2D
2330  _daisy = CV_DAISY::create();
2331 #else
2332  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so DAISY cannot be used!");
2333 #endif
2334 }
2335 
2336 cv::Mat GFTT_DAISY::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2337 {
2338  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2339  cv::Mat descriptors;
2340 #ifdef HAVE_OPENCV_XFEATURES2D
2341  _daisy->compute(image, keypoints, descriptors);
2342 #else
2343  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so DAISY cannot be used!");
2344 #endif
2345  return descriptors;
2346 }
2347 
2349 //SURF-DAISY
2351 SURF_DAISY::SURF_DAISY(const ParametersMap & parameters) :
2352  SURF(parameters),
2353  orientationNormalized_(Parameters::defaultFREAKOrientationNormalized()),
2354  scaleNormalized_(Parameters::defaultFREAKScaleNormalized()),
2355  patternScale_(Parameters::defaultFREAKPatternScale()),
2356  nOctaves_(Parameters::defaultFREAKNOctaves())
2357 {
2358  parseParameters(parameters);
2359 }
2360 
2361 SURF_DAISY::~SURF_DAISY()
2362 {
2363 }
2364 
2365 void SURF_DAISY::parseParameters(const ParametersMap & parameters)
2366 {
2367  SURF::parseParameters(parameters);
2368 
2369  Parameters::parse(parameters, Parameters::kFREAKOrientationNormalized(), orientationNormalized_);
2370  Parameters::parse(parameters, Parameters::kFREAKScaleNormalized(), scaleNormalized_);
2371  Parameters::parse(parameters, Parameters::kFREAKPatternScale(), patternScale_);
2372  Parameters::parse(parameters, Parameters::kFREAKNOctaves(), nOctaves_);
2373 
2374 #ifdef HAVE_OPENCV_XFEATURES2D
2375  _daisy = CV_DAISY::create();
2376 #else
2377  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so DAISY cannot be used!");
2378 #endif
2379 }
2380 
2381 cv::Mat SURF_DAISY::generateDescriptorsImpl(const cv::Mat & image, std::vector<cv::KeyPoint> & keypoints) const
2382 {
2383  UASSERT(!image.empty() && image.channels() == 1 && image.depth() == CV_8U);
2384  cv::Mat descriptors;
2385 #ifdef HAVE_OPENCV_XFEATURES2D
2386  _daisy->compute(image, keypoints, descriptors);
2387 #else
2388  UWARN("RTAB-Map is not built with OpenCV xfeatures2d module so DAISY cannot be used!");
2389 #endif
2390  return descriptors;
2391 }
2392 
2393 }
rtabmap::SensorData
Definition: SensorData.h:51
int
int
rtabmap::Feature2D::filterKeypointsByDisparity
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Definition: Features2d.cpp:204
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Definition: Features2d.h:403
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Definition: Features2d.cpp:1368
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Definition: Features2d.h:446
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Definition: Features2d.cpp:1066
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Definition: Features2d.cpp:1819
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Definition: util2d.cpp:1113
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Definition: Features2d.h:465
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Definition: Features2d.cpp:569
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Definition: Features2d.h:578
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Definition: Features2d.cpp:2005
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Definition: Features2d.h:355
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Definition: Features2d.cpp:504
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Definition: Features2d.cpp:85
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Definition: Features2d.h:329
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Definition: Features2d.h:549
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@ kFeaturePyDetector
Definition: Features2d.h:124
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Definition: Features2d.cpp:1481
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Definition: Features2d.h:625
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Definition: Features2d.h:383
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uint32_t * fastCVCornerScores_
Definition: Features2d.h:362
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SURF_FREAK(const ParametersMap &parameters=ParametersMap())
Definition: Features2d.cpp:1935
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Definition: Features2d.h:562
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ORB _orb
Definition: Features2d.h:509
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Definition: Features2d.cpp:2022
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Definition: Features2d.h:115
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Definition: Features2d.h:370
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Definition: Features2d.h:267
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Definition: Features2d.h:426
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Definition: Features2d.cpp:1892
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Definition: Features2d.h:334
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Definition: Features2d.h:468
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Definition: Features2d.h:601
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cv::Ptr< ORBextractor > _orb
Definition: Features2d.h:583
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Definition: Features2d.h:53
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Definition: Features2d.h:112
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Definition: Features2d.h:353
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SURF(const ParametersMap &parameters=ParametersMap())
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Definition: Features2d.h:323
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virtual void parseParameters(const ParametersMap &parameters)
Definition: Features2d.cpp:1255
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Definition: Features2d.h:492
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Definition: Features2d.h:268
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ORBextractor.h
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Definition: Features2d.cpp:2306
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std::vector< cv::Point3f > RTABMAP_CORE_EXPORT generateKeypoints3DStereo(const std::vector< cv::Point2f > &leftCorners, const std::vector< cv::Point2f > &rightCorners, const StereoCameraModel &model, const std::vector< unsigned char > &mask=std::vector< unsigned char >(), float minDepth=0, float maxDepth=0)
Definition: util3d_features.cpp:158
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:1969
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std::vector< cv::Point3f > RTABMAP_CORE_EXPORT generateKeypoints3DDepth(const std::vector< cv::KeyPoint > &keypoints, const cv::Mat &depth, const CameraModel &cameraModel, float minDepth=0, float maxDepth=0)
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virtual void parseParameters(const ParametersMap &parameters)
Definition: Features2d.cpp:1949
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virtual std::vector< cv::KeyPoint > generateKeypointsImpl(const cv::Mat &image, const cv::Rect &roi, const cv::Mat &mask=cv::Mat())
Definition: Features2d.cpp:2041
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Definition: Stereo.h:38
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Definition: Features2d.h:317
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cv::Ptr< CV_BRIEF > _brief
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Definition: Features2d.h:252
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Definition: Features2d.h:551
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Index threshold_
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Definition: Features2d.h:402
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Definition: Features2d.h:50
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virtual std::vector< cv::KeyPoint > generateKeypointsImpl(const cv::Mat &image, const cv::Rect &roi, const cv::Mat &mask=cv::Mat())
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Definition: Features2d.h:56
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Definition: Features2d.cpp:271
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Definition: Features2d.h:360
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Definition: Features2d.h:243
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Definition: ORBextractor.h:53
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Definition: Features2d.h:266
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Definition: Features2d.h:296
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Definition: Features2d.h:330
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@ kFeatureSift
Definition: Features2d.h:110
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:1769
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Definition: Features2d.h:351
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ULOGGER_DEBUG
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Definition: Features2d.h:528
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Definition: Features2d.cpp:833
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Definition: Features2d.h:113
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:1863
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Definition: Parameters.cpp:500
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Definition: Features2d.cpp:2077
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Definition: Features2d.h:117
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Definition: Features2d.h:527
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Definition: Features2d.cpp:1896
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Definition: Features2d.h:318
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virtual std::vector< cv::KeyPoint > generateKeypointsImpl(const cv::Mat &image, const cv::Rect &roi, const cv::Mat &mask=cv::Mat())=0
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Definition: Features2d.cpp:1100
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Definition: Features2d.h:275
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Definition: Features2d.cpp:1836
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Definition: Features2d.cpp:476
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Orb.h
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Definition: Orb.h:58
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Definition: Features2d.h:245
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Definition: Features2d.cpp:1251
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Definition: Features2d.h:389
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Definition: Features2d.cpp:1995
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SURF_DAISY(const ParametersMap &parameters=ParametersMap())
Definition: Features2d.cpp:2351
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Definition: Features2d.h:626
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Definition: Features2d.h:324
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Definition: Features2d.h:123
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Definition: Features2d.cpp:1749
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Definition: Features2d.cpp:2361
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data
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Definition: Features2d.h:405
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Definition: Stereo.cpp:61
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Definition: Features2d.cpp:1945
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Definition: Features2d.h:350
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Definition: Features2d.h:111
Features2d.h
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:2381
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Definition: Features2d.h:554
j
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Definition: PyDetector.h:20
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Definition: Features2d.cpp:1151
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Definition: Features2d.h:322
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Definition: Features2d.h:246
rtabmap::Feature2D::generateDescriptorsImpl
virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const =0
rtabmap::GFTT_DAISY::generateDescriptorsImpl
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Definition: Features2d.cpp:2336
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Definition: Features2d.cpp:1141
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Definition: Features2d.cpp:1689
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Definition: Features2d.h:121
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:2119
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Definition: Features2d.h:279
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Definition: Features2d.cpp:1788
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virtual std::vector< cv::KeyPoint > generateKeypointsImpl(const cv::Mat &image, const cv::Rect &roi, const cv::Mat &mask=cv::Mat())
Definition: Features2d.cpp:2268
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Definition: util3d.cpp:3327
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Definition: Features2d.cpp:1988
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Definition: Features2d.h:326
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Definition: Features2d.h:240
rtabmap::Feature2D::_stereo
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Definition: Features2d.h:248
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Definition: Features2d.h:627
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Definition: Features2d.cpp:508
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Definition: Features2d.h:356
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Definition: Features2d.h:588
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Definition: Features2d.h:120
CV_BRIEF
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Definition: Features2d.h:55
rtabmap::GFTT_ORB
Definition: Features2d.h:496
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Definition: Features2d.h:605
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virtual std::vector< cv::KeyPoint > generateKeypointsImpl(const cv::Mat &image, const cv::Rect &roi, const cv::Mat &mask=cv::Mat())
Definition: Features2d.cpp:1180
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Definition: Features2d.h:531
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Definition: Features2d.h:365
rtabmap::ORBOctree::~ORBOctree
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Definition: Features2d.cpp:2145
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Definition: Features2d.cpp:2234
UASSERT
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d
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rtabmap::FAST_FREAK::~FAST_FREAK
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Definition: Features2d.cpp:1745
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Definition: Features2d.h:106
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Definition: Features2d.h:269
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Definition: Features2d.h:603
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Definition: Features2d.h:606
rtabmap::Feature2D::Type
Type
Definition: Features2d.h:108
x
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rtabmap::KAZE::parseParameters
virtual void parseParameters(const ParametersMap &parameters)
Definition: Features2d.cpp:2081
rtabmap::GFTT_DAISY::parseParameters
virtual void parseParameters(const ParametersMap &parameters)
Definition: Features2d.cpp:2320
rtabmap::SIFT::rootSIFT_
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Definition: Features2d.h:297
rtabmap::Feature2D::getType
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Stereo.h
rtabmap::BRISK::patternScale_
float patternScale_
Definition: Features2d.h:529
rtabmap::GFTT_DAISY::orientationNormalized_
bool orientationNormalized_
Definition: Features2d.h:624
rtabmap::GFTT_BRIEF::parseParameters
virtual void parseParameters(const ParametersMap &parameters)
Definition: Features2d.cpp:1847
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Definition: Features2d.h:635
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Definition: Features2d.h:352
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Definition: Parameters.h:170
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float patternScale_
Definition: Features2d.h:404
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Definition: Features2d.h:604
rtabmap::SuperPointTorch::SuperPointTorch
SuperPointTorch(const ParametersMap &parameters=ParametersMap())
Definition: Features2d.cpp:2224
rtabmap::GFTT_FREAK::GFTT_FREAK
GFTT_FREAK(const ParametersMap &parameters=ParametersMap())
Definition: Features2d.cpp:1882
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Definition: Features2d.h:299
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Definition: Features2d.h:349
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Definition: Features2d.h:116
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Definition: Features2d.h:294
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:1916
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Definition: Features2d.cpp:1155
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Definition: Features2d.h:239
UASSERT_MSG
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rtabmap::SURF_FREAK::scaleNormalized_
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Definition: Features2d.h:488
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:2291
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Definition: SuperPoint.h:49
rtabmap::GFTT_BRIEF
Definition: Features2d.h:433
f
Point2(* f)(const Point3 &, OptionalJacobian< 2, 3 >)
uFormat
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Definition: UConversion.cpp:365
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float patternScale_
Definition: Features2d.h:650
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Definition: Features2d.h:241
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virtual cv::Mat generateDescriptorsImpl(const cv::Mat &image, std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:1202
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Array< double, 1, 3 > e(1./3., 0.5, 2.)
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Definition: Features2d.cpp:1802
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std::vector< cv::Point3f > generateKeypoints3D(const SensorData &data, const std::vector< cv::KeyPoint > &keypoints) const
Definition: Features2d.cpp:849
ULogger.h
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Definition: Features2d.h:114
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rtabmap
Author(s): Mathieu Labbe
autogenerated on Mon Jul 1 2024 02:42:27