Point Cloud Library (PCL) 1.13.0
color_gradient_modality.h
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37
38#pragma once
39
40#include <pcl/recognition/quantizable_modality.h>
41
42#include <pcl/pcl_base.h>
43#include <pcl/point_cloud.h>
44#include <pcl/point_types.h>
45#include <pcl/recognition/point_types.h>
46#include <pcl/filters/convolution.h>
47
48#include <list>
49
50namespace pcl
51{
52
53 /** \brief Modality based on max-RGB gradients.
54 * \author Stefan Holzer
55 */
56 template <typename PointInT>
58 : public QuantizableModality, public PCLBase<PointInT>
59 {
60 protected:
61 using PCLBase<PointInT>::input_;
62
63 /** \brief Candidate for a feature (used in feature extraction methods). */
64 struct Candidate
65 {
66 /** \brief The gradient. */
68
69 /** \brief The x-position. */
70 int x;
71 /** \brief The y-position. */
72 int y;
73
74 /** \brief Operator for comparing to candidates (by magnitude of the gradient).
75 * \param[in] rhs the candidate to compare with.
76 */
77 bool operator< (const Candidate & rhs) const
78 {
79 return (gradient.magnitude > rhs.gradient.magnitude);
80 }
81 };
82
83 public:
85
86 /** \brief Different methods for feature selection/extraction. */
88 {
90 MASK_BORDER_EQUALLY, // this gives templates most equally to the OpenCV implementation
92 };
93
94 /** \brief Constructor. */
96 /** \brief Destructor. */
98
99 /** \brief Sets the threshold for the gradient magnitude which is used when quantizing the data.
100 * Gradients with a smaller magnitude are ignored.
101 * \param[in] threshold the new gradient magnitude threshold.
102 */
103 inline void
104 setGradientMagnitudeThreshold (const float threshold)
105 {
106 gradient_magnitude_threshold_ = threshold;
107 }
108
109 /** \brief Sets the threshold for the gradient magnitude which is used for feature extraction.
110 * Gradients with a smaller magnitude are ignored.
111 * \param[in] threshold the new gradient magnitude threshold.
112 */
113 inline void
115 {
116 gradient_magnitude_threshold_feature_extraction_ = threshold;
117 }
118
119 /** \brief Sets the feature selection method.
120 * \param[in] method the feature selection method.
121 */
122 inline void
124 {
125 feature_selection_method_ = method;
126 }
127
128 /** \brief Sets the spreading size for spreading the quantized data. */
129 inline void
130 setSpreadingSize (const std::size_t spreading_size)
131 {
132 spreading_size_ = spreading_size;
133 }
134
135 /** \brief Sets whether variable feature numbers for feature extraction is enabled.
136 * \param[in] enabled enables/disables variable feature numbers for feature extraction.
137 */
138 inline void
139 setVariableFeatureNr (const bool enabled)
140 {
141 variable_feature_nr_ = enabled;
142 }
143
144 /** \brief Returns a reference to the internally computed quantized map. */
145 inline QuantizedMap &
146 getQuantizedMap () override
147 {
148 return (filtered_quantized_color_gradients_);
149 }
150
151 /** \brief Returns a reference to the internally computed spread quantized map. */
152 inline QuantizedMap &
154 {
155 return (spreaded_filtered_quantized_color_gradients_);
156 }
157
158 /** \brief Returns a point cloud containing the max-RGB gradients. */
161 {
162 return (color_gradients_);
163 }
164
165 /** \brief Extracts features from this modality within the specified mask.
166 * \param[in] mask defines the areas where features are searched in.
167 * \param[in] nr_features defines the number of features to be extracted
168 * (might be less if not sufficient information is present in the modality).
169 * \param[in] modalityIndex the index which is stored in the extracted features.
170 * \param[out] features the destination for the extracted features.
171 */
172 void
173 extractFeatures (const MaskMap & mask, std::size_t nr_features, std::size_t modalityIndex,
174 std::vector<QuantizedMultiModFeature> & features) const override;
175
176 /** \brief Extracts all possible features from the modality within the specified mask.
177 * \param[in] mask defines the areas where features are searched in.
178 * \param[in] nr_features IGNORED (TODO: remove this parameter).
179 * \param[in] modalityIndex the index which is stored in the extracted features.
180 * \param[out] features the destination for the extracted features.
181 */
182 void
183 extractAllFeatures (const MaskMap & mask, std::size_t nr_features, std::size_t modalityIndex,
184 std::vector<QuantizedMultiModFeature> & features) const override;
185
186 /** \brief Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
187 * \param cloud the const boost shared pointer to a PointCloud message
188 */
189 void
190 setInputCloud (const typename PointCloudIn::ConstPtr & cloud) override
191 {
192 input_ = cloud;
193 }
194
195 /** \brief Processes the input data (smoothing, computing gradients, quantizing, filtering, spreading). */
196 virtual void
198
199 /** \brief Processes the input data assuming that everything up to filtering is already done/available
200 * (so only spreading is performed). */
201 virtual void
203
204 protected:
205
206 /** \brief Computes the Gaussian kernel used for smoothing.
207 * \param[in] kernel_size the size of the Gaussian kernel.
208 * \param[in] sigma the sigma.
209 * \param[out] kernel_values the destination for the values of the kernel. */
210 void
211 computeGaussianKernel (const std::size_t kernel_size, const float sigma, std::vector <float> & kernel_values);
212
213 /** \brief Computes the max-RGB gradients for the specified cloud.
214 * \param[in] cloud the cloud for which the gradients are computed.
215 */
216 void
218
219 /** \brief Computes the max-RGB gradients for the specified cloud using sobel.
220 * \param[in] cloud the cloud for which the gradients are computed.
221 */
222 void
224
225 /** \brief Quantizes the color gradients. */
226 void
228
229 /** \brief Filters the quantized gradients. */
230 void
232
233 /** \brief Erodes a mask.
234 * \param[in] mask_in the mask which will be eroded.
235 * \param[out] mask_out the destination for the eroded mask.
236 */
237 static void
238 erode (const pcl::MaskMap & mask_in, pcl::MaskMap & mask_out);
239
240 private:
241
242 /** \brief Determines whether variable numbers of features are extracted or not. */
243 bool variable_feature_nr_;
244
245 /** \brief Stores a smoothed version of the input cloud. */
246 pcl::PointCloud<pcl::RGB>::Ptr smoothed_input_;
247
248 /** \brief Defines which feature selection method is used. */
249 FeatureSelectionMethod feature_selection_method_;
250
251 /** \brief The threshold applied on the gradient magnitudes (for quantization). */
252 float gradient_magnitude_threshold_;
253 /** \brief The threshold applied on the gradient magnitudes for feature extraction. */
254 float gradient_magnitude_threshold_feature_extraction_;
255
256 /** \brief The point cloud which holds the max-RGB gradients. */
257 pcl::PointCloud<pcl::GradientXY> color_gradients_;
258
259 /** \brief The spreading size. */
260 std::size_t spreading_size_;
261
262 /** \brief The map which holds the quantized max-RGB gradients. */
263 pcl::QuantizedMap quantized_color_gradients_;
264 /** \brief The map which holds the filtered quantized data. */
265 pcl::QuantizedMap filtered_quantized_color_gradients_;
266 /** \brief The map which holds the spread quantized data. */
267 pcl::QuantizedMap spreaded_filtered_quantized_color_gradients_;
268
269 };
270
271}
272
273//////////////////////////////////////////////////////////////////////////////////////////////
274template <typename PointInT>
277 : variable_feature_nr_ (false)
278 , smoothed_input_ (new pcl::PointCloud<pcl::RGB> ())
279 , feature_selection_method_ (DISTANCE_MAGNITUDE_SCORE)
280 , gradient_magnitude_threshold_ (10.0f)
281 , gradient_magnitude_threshold_feature_extraction_ (55.0f)
282 , spreading_size_ (8)
283{
284}
285
286//////////////////////////////////////////////////////////////////////////////////////////////
287template <typename PointInT>
289~ColorGradientModality () = default;
290
291//////////////////////////////////////////////////////////////////////////////////////////////
292template <typename PointInT> void
294computeGaussianKernel (const std::size_t kernel_size, const float sigma, std::vector <float> & kernel_values)
295{
296 // code taken from OpenCV
297 const int n = int (kernel_size);
298 const int SMALL_GAUSSIAN_SIZE = 7;
299 static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
300 {
301 {1.f},
302 {0.25f, 0.5f, 0.25f},
303 {0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
304 {0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f}
305 };
306
307 const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ?
308 small_gaussian_tab[n>>1] : nullptr;
309
310 //CV_Assert( ktype == CV_32F || ktype == CV_64F );
311 /*Mat kernel(n, 1, ktype);*/
312 kernel_values.resize (n);
313 float* cf = &(kernel_values[0]);
314 //double* cd = (double*)kernel.data;
315
316 double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
317 double scale2X = -0.5/(sigmaX*sigmaX);
318 double sum = 0;
319
320 for( int i = 0; i < n; i++ )
321 {
322 double x = i - (n-1)*0.5;
323 double t = fixed_kernel ? double (fixed_kernel[i]) : std::exp (scale2X*x*x);
324
325 cf[i] = float (t);
326 sum += cf[i];
327 }
328
329 sum = 1./sum;
330 for ( int i = 0; i < n; i++ )
331 {
332 cf[i] = float (cf[i]*sum);
333 }
334}
335
336//////////////////////////////////////////////////////////////////////////////////////////////
337template <typename PointInT>
338void
341{
342 // compute gaussian kernel values
343 const std::size_t kernel_size = 7;
344 std::vector<float> kernel_values;
345 computeGaussianKernel (kernel_size, 0.0f, kernel_values);
346
347 // smooth input
349 Eigen::ArrayXf gaussian_kernel(kernel_size);
350 //gaussian_kernel << 1.f/16, 1.f/8, 3.f/16, 2.f/8, 3.f/16, 1.f/8, 1.f/16;
351 //gaussian_kernel << 16.f/1600.f, 32.f/1600.f, 64.f/1600.f, 128.f/1600.f, 256.f/1600.f, 128.f/1600.f, 64.f/1600.f, 32.f/1600.f, 16.f/1600.f;
352 gaussian_kernel << kernel_values[0], kernel_values[1], kernel_values[2], kernel_values[3], kernel_values[4], kernel_values[5], kernel_values[6];
353
355
356 const std::uint32_t width = input_->width;
357 const std::uint32_t height = input_->height;
358
359 rgb_input_->resize (width*height);
360 rgb_input_->width = width;
361 rgb_input_->height = height;
362 rgb_input_->is_dense = input_->is_dense;
363 for (std::size_t row_index = 0; row_index < height; ++row_index)
364 {
365 for (std::size_t col_index = 0; col_index < width; ++col_index)
366 {
367 (*rgb_input_) (col_index, row_index).r = (*input_) (col_index, row_index).r;
368 (*rgb_input_) (col_index, row_index).g = (*input_) (col_index, row_index).g;
369 (*rgb_input_) (col_index, row_index).b = (*input_) (col_index, row_index).b;
370 }
371 }
372
373 convolution.setInputCloud (rgb_input_);
374 convolution.setKernel (gaussian_kernel);
375
376 convolution.convolve (*smoothed_input_);
377
378 // extract color gradients
379 computeMaxColorGradientsSobel (smoothed_input_);
380
381 // quantize gradients
382 quantizeColorGradients ();
383
384 // filter quantized gradients to get only dominants one + thresholding
385 filterQuantizedColorGradients ();
386
387 // spread filtered quantized gradients
388 //spreadFilteredQunatizedColorGradients ();
389 pcl::QuantizedMap::spreadQuantizedMap (filtered_quantized_color_gradients_,
390 spreaded_filtered_quantized_color_gradients_,
391 spreading_size_);
392}
393
394//////////////////////////////////////////////////////////////////////////////////////////////
395template <typename PointInT>
396void
399{
400 // spread filtered quantized gradients
401 //spreadFilteredQunatizedColorGradients ();
402 pcl::QuantizedMap::spreadQuantizedMap (filtered_quantized_color_gradients_,
403 spreaded_filtered_quantized_color_gradients_,
404 spreading_size_);
405}
406
407//////////////////////////////////////////////////////////////////////////////////////////////
408template <typename PointInT>
410extractFeatures (const MaskMap & mask, const std::size_t nr_features, const std::size_t modality_index,
411 std::vector<QuantizedMultiModFeature> & features) const
412{
413 const std::size_t width = mask.getWidth ();
414 const std::size_t height = mask.getHeight ();
415
416 std::list<Candidate> list1;
417 std::list<Candidate> list2;
418
419
420 if (feature_selection_method_ == DISTANCE_MAGNITUDE_SCORE)
421 {
422 for (std::size_t row_index = 0; row_index < height; ++row_index)
423 {
424 for (std::size_t col_index = 0; col_index < width; ++col_index)
425 {
426 if (mask (col_index, row_index) != 0)
427 {
428 const GradientXY & gradient = color_gradients_ (col_index, row_index);
429 if (gradient.magnitude > gradient_magnitude_threshold_feature_extraction_
430 && filtered_quantized_color_gradients_ (col_index, row_index) != 0)
431 {
432 Candidate candidate;
433 candidate.gradient = gradient;
434 candidate.x = static_cast<int> (col_index);
435 candidate.y = static_cast<int> (row_index);
436
437 list1.push_back (candidate);
438 }
439 }
440 }
441 }
442
443 list1.sort();
444
445 if (variable_feature_nr_)
446 {
447 list2.push_back (*(list1.begin ()));
448 //while (list2.size () != nr_features)
449 bool feature_selection_finished = false;
450 while (!feature_selection_finished)
451 {
452 float best_score = 0.0f;
453 auto best_iter = list1.end ();
454 for (auto iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
455 {
456 // find smallest distance
457 float smallest_distance = std::numeric_limits<float>::max ();
458 for (auto iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
459 {
460 const float dx = static_cast<float> (iter1->x) - static_cast<float> (iter2->x);
461 const float dy = static_cast<float> (iter1->y) - static_cast<float> (iter2->y);
462
463 const float distance = dx*dx + dy*dy;
464
465 if (distance < smallest_distance)
466 {
467 smallest_distance = distance;
468 }
469 }
470
471 const float score = smallest_distance * iter1->gradient.magnitude;
472
473 if (score > best_score)
474 {
475 best_score = score;
476 best_iter = iter1;
477 }
478 }
479
480
481 float min_min_sqr_distance = std::numeric_limits<float>::max ();
482 float max_min_sqr_distance = 0;
483 for (auto iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
484 {
485 float min_sqr_distance = std::numeric_limits<float>::max ();
486 for (auto iter3 = list2.begin (); iter3 != list2.end (); ++iter3)
487 {
488 if (iter2 == iter3)
489 continue;
490
491 const float dx = static_cast<float> (iter2->x) - static_cast<float> (iter3->x);
492 const float dy = static_cast<float> (iter2->y) - static_cast<float> (iter3->y);
493
494 const float sqr_distance = dx*dx + dy*dy;
495
496 if (sqr_distance < min_sqr_distance)
497 {
498 min_sqr_distance = sqr_distance;
499 }
500
501 //std::cerr << min_sqr_distance;
502 }
503 //std::cerr << std::endl;
504
505 // check current feature
506 {
507 const float dx = static_cast<float> (iter2->x) - static_cast<float> (best_iter->x);
508 const float dy = static_cast<float> (iter2->y) - static_cast<float> (best_iter->y);
509
510 const float sqr_distance = dx*dx + dy*dy;
511
512 if (sqr_distance < min_sqr_distance)
513 {
514 min_sqr_distance = sqr_distance;
515 }
516 }
517
518 if (min_sqr_distance < min_min_sqr_distance)
519 min_min_sqr_distance = min_sqr_distance;
520 if (min_sqr_distance > max_min_sqr_distance)
521 max_min_sqr_distance = min_sqr_distance;
522
523 //std::cerr << min_sqr_distance << ", " << min_min_sqr_distance << ", " << max_min_sqr_distance << std::endl;
524 }
525
526 if (best_iter != list1.end ())
527 {
528 //std::cerr << "feature_index: " << list2.size () << std::endl;
529 //std::cerr << "min_min_sqr_distance: " << min_min_sqr_distance << std::endl;
530 //std::cerr << "max_min_sqr_distance: " << max_min_sqr_distance << std::endl;
531
532 if (min_min_sqr_distance < 50)
533 {
534 feature_selection_finished = true;
535 break;
536 }
537
538 list2.push_back (*best_iter);
539 }
540 }
541 }
542 else
543 {
544 if (list1.size () <= nr_features)
545 {
546 for (auto iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
547 {
549
550 feature.x = iter1->x;
551 feature.y = iter1->y;
552 feature.modality_index = modality_index;
553 feature.quantized_value = filtered_quantized_color_gradients_ (iter1->x, iter1->y);
554
555 features.push_back (feature);
556 }
557 return;
558 }
559
560 list2.push_back (*(list1.begin ()));
561 while (list2.size () != nr_features)
562 {
563 float best_score = 0.0f;
564 auto best_iter = list1.end ();
565 for (auto iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
566 {
567 // find smallest distance
568 float smallest_distance = std::numeric_limits<float>::max ();
569 for (auto iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
570 {
571 const float dx = static_cast<float> (iter1->x) - static_cast<float> (iter2->x);
572 const float dy = static_cast<float> (iter1->y) - static_cast<float> (iter2->y);
573
574 const float distance = dx*dx + dy*dy;
575
576 if (distance < smallest_distance)
577 {
578 smallest_distance = distance;
579 }
580 }
581
582 const float score = smallest_distance * iter1->gradient.magnitude;
583
584 if (score > best_score)
585 {
586 best_score = score;
587 best_iter = iter1;
588 }
589 }
590
591 if (best_iter != list1.end ())
592 {
593 list2.push_back (*best_iter);
594 }
595 else
596 {
597 break;
598 }
599 }
600 }
601 }
602 else if (feature_selection_method_ == MASK_BORDER_HIGH_GRADIENTS || feature_selection_method_ == MASK_BORDER_EQUALLY)
603 {
604 MaskMap eroded_mask;
605 erode (mask, eroded_mask);
606
607 auto diff_mask = MaskMap::getDifferenceMask (mask, eroded_mask);
608
609 for (std::size_t row_index = 0; row_index < height; ++row_index)
610 {
611 for (std::size_t col_index = 0; col_index < width; ++col_index)
612 {
613 if (diff_mask (col_index, row_index) != 0)
614 {
615 const GradientXY & gradient = color_gradients_ (col_index, row_index);
616 if ((feature_selection_method_ == MASK_BORDER_EQUALLY || gradient.magnitude > gradient_magnitude_threshold_feature_extraction_)
617 && filtered_quantized_color_gradients_ (col_index, row_index) != 0)
618 {
619 Candidate candidate;
620 candidate.gradient = gradient;
621 candidate.x = static_cast<int> (col_index);
622 candidate.y = static_cast<int> (row_index);
623
624 list1.push_back (candidate);
625 }
626 }
627 }
628 }
629
630 list1.sort();
631
632 if (list1.size () <= nr_features)
633 {
634 for (auto iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
635 {
637
638 feature.x = iter1->x;
639 feature.y = iter1->y;
640 feature.modality_index = modality_index;
641 feature.quantized_value = filtered_quantized_color_gradients_ (iter1->x, iter1->y);
642
643 features.push_back (feature);
644 }
645 return;
646 }
647
648 std::size_t distance = list1.size () / nr_features + 1; // ???
649 while (list2.size () != nr_features)
650 {
651 const std::size_t sqr_distance = distance*distance;
652 for (auto iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
653 {
654 bool candidate_accepted = true;
655
656 for (auto iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
657 {
658 const int dx = iter1->x - iter2->x;
659 const int dy = iter1->y - iter2->y;
660 const unsigned int tmp_distance = dx*dx + dy*dy;
661
662 //if (tmp_distance < distance)
663 if (tmp_distance < sqr_distance)
664 {
665 candidate_accepted = false;
666 break;
667 }
668 }
669
670 if (candidate_accepted)
671 list2.push_back (*iter1);
672
673 if (list2.size () == nr_features)
674 break;
675 }
676 --distance;
677 }
678 }
679
680 for (auto iter2 = list2.begin (); iter2 != list2.end (); ++iter2)
681 {
683
684 feature.x = iter2->x;
685 feature.y = iter2->y;
686 feature.modality_index = modality_index;
687 feature.quantized_value = filtered_quantized_color_gradients_ (iter2->x, iter2->y);
688
689 features.push_back (feature);
690 }
691}
692
693//////////////////////////////////////////////////////////////////////////////////////////////
694template <typename PointInT> void
696extractAllFeatures (const MaskMap & mask, const std::size_t, const std::size_t modality_index,
697 std::vector<QuantizedMultiModFeature> & features) const
698{
699 const std::size_t width = mask.getWidth ();
700 const std::size_t height = mask.getHeight ();
701
702 std::list<Candidate> list1;
703 std::list<Candidate> list2;
704
705
706 for (std::size_t row_index = 0; row_index < height; ++row_index)
707 {
708 for (std::size_t col_index = 0; col_index < width; ++col_index)
709 {
710 if (mask (col_index, row_index) != 0)
711 {
712 const GradientXY & gradient = color_gradients_ (col_index, row_index);
713 if (gradient.magnitude > gradient_magnitude_threshold_feature_extraction_
714 && filtered_quantized_color_gradients_ (col_index, row_index) != 0)
715 {
716 Candidate candidate;
717 candidate.gradient = gradient;
718 candidate.x = static_cast<int> (col_index);
719 candidate.y = static_cast<int> (row_index);
720
721 list1.push_back (candidate);
722 }
723 }
724 }
725 }
726
727 list1.sort();
728
729 for (auto iter1 = list1.begin (); iter1 != list1.end (); ++iter1)
730 {
732
733 feature.x = iter1->x;
734 feature.y = iter1->y;
735 feature.modality_index = modality_index;
736 feature.quantized_value = filtered_quantized_color_gradients_ (iter1->x, iter1->y);
737
738 features.push_back (feature);
739 }
740}
741
742//////////////////////////////////////////////////////////////////////////////////////////////
743template <typename PointInT>
744void
747{
748 const int width = cloud->width;
749 const int height = cloud->height;
750
751 color_gradients_.resize (width*height);
752 color_gradients_.width = width;
753 color_gradients_.height = height;
754
755 const float pi = std::tan (1.0f) * 2;
756 for (int row_index = 0; row_index < height-2; ++row_index)
757 {
758 for (int col_index = 0; col_index < width-2; ++col_index)
759 {
760 const int index0 = row_index*width+col_index;
761 const int index_c = row_index*width+col_index+2;
762 const int index_r = (row_index+2)*width+col_index;
763
764 //const int index_d = (row_index+1)*width+col_index+1;
765
766 const unsigned char r0 = (*cloud)[index0].r;
767 const unsigned char g0 = (*cloud)[index0].g;
768 const unsigned char b0 = (*cloud)[index0].b;
769
770 const unsigned char r_c = (*cloud)[index_c].r;
771 const unsigned char g_c = (*cloud)[index_c].g;
772 const unsigned char b_c = (*cloud)[index_c].b;
773
774 const unsigned char r_r = (*cloud)[index_r].r;
775 const unsigned char g_r = (*cloud)[index_r].g;
776 const unsigned char b_r = (*cloud)[index_r].b;
777
778 const float r_dx = static_cast<float> (r_c) - static_cast<float> (r0);
779 const float g_dx = static_cast<float> (g_c) - static_cast<float> (g0);
780 const float b_dx = static_cast<float> (b_c) - static_cast<float> (b0);
781
782 const float r_dy = static_cast<float> (r_r) - static_cast<float> (r0);
783 const float g_dy = static_cast<float> (g_r) - static_cast<float> (g0);
784 const float b_dy = static_cast<float> (b_r) - static_cast<float> (b0);
785
786 const float sqr_mag_r = r_dx*r_dx + r_dy*r_dy;
787 const float sqr_mag_g = g_dx*g_dx + g_dy*g_dy;
788 const float sqr_mag_b = b_dx*b_dx + b_dy*b_dy;
789
790 if (sqr_mag_r > sqr_mag_g && sqr_mag_r > sqr_mag_b)
791 {
792 GradientXY gradient;
793 gradient.magnitude = std::sqrt (sqr_mag_r);
794 gradient.angle = std::atan2 (r_dy, r_dx) * 180.0f / pi;
795 gradient.x = static_cast<float> (col_index);
796 gradient.y = static_cast<float> (row_index);
797
798 color_gradients_ (col_index+1, row_index+1) = gradient;
799 }
800 else if (sqr_mag_g > sqr_mag_b)
801 {
802 GradientXY gradient;
803 gradient.magnitude = std::sqrt (sqr_mag_g);
804 gradient.angle = std::atan2 (g_dy, g_dx) * 180.0f / pi;
805 gradient.x = static_cast<float> (col_index);
806 gradient.y = static_cast<float> (row_index);
807
808 color_gradients_ (col_index+1, row_index+1) = gradient;
809 }
810 else
811 {
812 GradientXY gradient;
813 gradient.magnitude = std::sqrt (sqr_mag_b);
814 gradient.angle = std::atan2 (b_dy, b_dx) * 180.0f / pi;
815 gradient.x = static_cast<float> (col_index);
816 gradient.y = static_cast<float> (row_index);
817
818 color_gradients_ (col_index+1, row_index+1) = gradient;
819 }
820
821 assert (color_gradients_ (col_index+1, row_index+1).angle >= -180 &&
822 color_gradients_ (col_index+1, row_index+1).angle <= 180);
823 }
824 }
825
826 return;
827}
828
829//////////////////////////////////////////////////////////////////////////////////////////////
830template <typename PointInT>
831void
834{
835 const int width = cloud->width;
836 const int height = cloud->height;
837
838 color_gradients_.resize (width*height);
839 color_gradients_.width = width;
840 color_gradients_.height = height;
841
842 const float pi = tanf (1.0f) * 2.0f;
843 for (int row_index = 1; row_index < height-1; ++row_index)
844 {
845 for (int col_index = 1; col_index < width-1; ++col_index)
846 {
847 const int r7 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index-1)].r);
848 const int g7 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index-1)].g);
849 const int b7 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index-1)].b);
850 const int r8 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index)].r);
851 const int g8 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index)].g);
852 const int b8 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index)].b);
853 const int r9 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index+1)].r);
854 const int g9 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index+1)].g);
855 const int b9 = static_cast<int> ((*cloud)[(row_index-1)*width + (col_index+1)].b);
856 const int r4 = static_cast<int> ((*cloud)[(row_index)*width + (col_index-1)].r);
857 const int g4 = static_cast<int> ((*cloud)[(row_index)*width + (col_index-1)].g);
858 const int b4 = static_cast<int> ((*cloud)[(row_index)*width + (col_index-1)].b);
859 const int r6 = static_cast<int> ((*cloud)[(row_index)*width + (col_index+1)].r);
860 const int g6 = static_cast<int> ((*cloud)[(row_index)*width + (col_index+1)].g);
861 const int b6 = static_cast<int> ((*cloud)[(row_index)*width + (col_index+1)].b);
862 const int r1 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index-1)].r);
863 const int g1 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index-1)].g);
864 const int b1 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index-1)].b);
865 const int r2 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index)].r);
866 const int g2 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index)].g);
867 const int b2 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index)].b);
868 const int r3 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index+1)].r);
869 const int g3 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index+1)].g);
870 const int b3 = static_cast<int> ((*cloud)[(row_index+1)*width + (col_index+1)].b);
871
872 //const int r_tmp1 = - r7 + r3;
873 //const int r_tmp2 = - r1 + r9;
874 //const int g_tmp1 = - g7 + g3;
875 //const int g_tmp2 = - g1 + g9;
876 //const int b_tmp1 = - b7 + b3;
877 //const int b_tmp2 = - b1 + b9;
878 ////const int gx = - r7 - (r4<<2) - r1 + r3 + (r6<<2) + r9;
879 ////const int gy = - r7 - (r8<<2) - r9 + r1 + (r2<<2) + r3;
880 //const int r_dx = r_tmp1 + r_tmp2 - (r4<<2) + (r6<<2);
881 //const int r_dy = r_tmp1 - r_tmp2 - (r8<<2) + (r2<<2);
882 //const int g_dx = g_tmp1 + g_tmp2 - (g4<<2) + (g6<<2);
883 //const int g_dy = g_tmp1 - g_tmp2 - (g8<<2) + (g2<<2);
884 //const int b_dx = b_tmp1 + b_tmp2 - (b4<<2) + (b6<<2);
885 //const int b_dy = b_tmp1 - b_tmp2 - (b8<<2) + (b2<<2);
886
887 //const int r_tmp1 = - r7 + r3;
888 //const int r_tmp2 = - r1 + r9;
889 //const int g_tmp1 = - g7 + g3;
890 //const int g_tmp2 = - g1 + g9;
891 //const int b_tmp1 = - b7 + b3;
892 //const int b_tmp2 = - b1 + b9;
893 //const int gx = - r7 - (r4<<2) - r1 + r3 + (r6<<2) + r9;
894 //const int gy = - r7 - (r8<<2) - r9 + r1 + (r2<<2) + r3;
895 const int r_dx = r9 + 2*r6 + r3 - (r7 + 2*r4 + r1);
896 const int r_dy = r1 + 2*r2 + r3 - (r7 + 2*r8 + r9);
897 const int g_dx = g9 + 2*g6 + g3 - (g7 + 2*g4 + g1);
898 const int g_dy = g1 + 2*g2 + g3 - (g7 + 2*g8 + g9);
899 const int b_dx = b9 + 2*b6 + b3 - (b7 + 2*b4 + b1);
900 const int b_dy = b1 + 2*b2 + b3 - (b7 + 2*b8 + b9);
901
902 const int sqr_mag_r = r_dx*r_dx + r_dy*r_dy;
903 const int sqr_mag_g = g_dx*g_dx + g_dy*g_dy;
904 const int sqr_mag_b = b_dx*b_dx + b_dy*b_dy;
905
906 if (sqr_mag_r > sqr_mag_g && sqr_mag_r > sqr_mag_b)
907 {
908 GradientXY gradient;
909 gradient.magnitude = std::sqrt (static_cast<float> (sqr_mag_r));
910 gradient.angle = std::atan2 (static_cast<float> (r_dy), static_cast<float> (r_dx)) * 180.0f / pi;
911 if (gradient.angle < -180.0f) gradient.angle += 360.0f;
912 if (gradient.angle >= 180.0f) gradient.angle -= 360.0f;
913 gradient.x = static_cast<float> (col_index);
914 gradient.y = static_cast<float> (row_index);
915
916 color_gradients_ (col_index, row_index) = gradient;
917 }
918 else if (sqr_mag_g > sqr_mag_b)
919 {
920 GradientXY gradient;
921 gradient.magnitude = std::sqrt (static_cast<float> (sqr_mag_g));
922 gradient.angle = std::atan2 (static_cast<float> (g_dy), static_cast<float> (g_dx)) * 180.0f / pi;
923 if (gradient.angle < -180.0f) gradient.angle += 360.0f;
924 if (gradient.angle >= 180.0f) gradient.angle -= 360.0f;
925 gradient.x = static_cast<float> (col_index);
926 gradient.y = static_cast<float> (row_index);
927
928 color_gradients_ (col_index, row_index) = gradient;
929 }
930 else
931 {
932 GradientXY gradient;
933 gradient.magnitude = std::sqrt (static_cast<float> (sqr_mag_b));
934 gradient.angle = std::atan2 (static_cast<float> (b_dy), static_cast<float> (b_dx)) * 180.0f / pi;
935 if (gradient.angle < -180.0f) gradient.angle += 360.0f;
936 if (gradient.angle >= 180.0f) gradient.angle -= 360.0f;
937 gradient.x = static_cast<float> (col_index);
938 gradient.y = static_cast<float> (row_index);
939
940 color_gradients_ (col_index, row_index) = gradient;
941 }
942
943 assert (color_gradients_ (col_index, row_index).angle >= -180 &&
944 color_gradients_ (col_index, row_index).angle <= 180);
945 }
946 }
947
948 return;
949}
950
951//////////////////////////////////////////////////////////////////////////////////////////////
952template <typename PointInT>
953void
956{
957 //std::cerr << "quantize this, bastard!!!" << std::endl;
958
959 //unsigned char quantization_map[16] = {0,1,2,3,4,5,6,7,0,1,2,3,4,5,6,7};
960 //unsigned char quantization_map[16] = {1,2,3,4,5,6,7,8,1,2,3,4,5,6,7,8};
961
962 //for (float angle = 0.0f; angle < 360.0f; angle += 1.0f)
963 //{
964 // const int quantized_value = quantization_map[static_cast<int> (angle * angleScale)];
965 // std::cerr << angle << ": " << quantized_value << std::endl;
966 //}
967
968
969 const std::size_t width = input_->width;
970 const std::size_t height = input_->height;
971
972 quantized_color_gradients_.resize (width, height);
973
974 const float angleScale = 16.0f/360.0f;
975
976 //float min_angle = std::numeric_limits<float>::max ();
977 //float max_angle = -std::numeric_limits<float>::max ();
978 for (std::size_t row_index = 0; row_index < height; ++row_index)
979 {
980 for (std::size_t col_index = 0; col_index < width; ++col_index)
981 {
982 if (color_gradients_ (col_index, row_index).magnitude < gradient_magnitude_threshold_)
983 {
984 quantized_color_gradients_ (col_index, row_index) = 0;
985 continue;
986 }
987
988 const float angle = 11.25f + color_gradients_ (col_index, row_index).angle + 180.0f;
989 const int quantized_value = (static_cast<int> (angle * angleScale)) & 7;
990 quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> (quantized_value + 1);
991
992 //const float angle = color_gradients_ (col_index, row_index).angle + 180.0f;
993
994 //min_angle = std::min (min_angle, angle);
995 //max_angle = std::max (max_angle, angle);
996
997 //if (angle < 0.0f || angle >= 360.0f)
998 //{
999 // std::cerr << "angle shitty: " << angle << std::endl;
1000 //}
1001
1002 //const int quantized_value = quantization_map[static_cast<int> (angle * angleScale)];
1003 //quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> (quantized_value);
1004
1005 //assert (0 <= quantized_value && quantized_value < 16);
1006 //quantized_color_gradients_ (col_index, row_index) = quantization_map[quantized_value];
1007 //quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> ((quantized_value & 7) + 1); // = (quantized_value % 8) + 1
1008 }
1009 }
1010
1011 //std::cerr << ">>>>> " << min_angle << ", " << max_angle << std::endl;
1012}
1013
1014//////////////////////////////////////////////////////////////////////////////////////////////
1015template <typename PointInT>
1016void
1019{
1020 const std::size_t width = input_->width;
1021 const std::size_t height = input_->height;
1022
1023 filtered_quantized_color_gradients_.resize (width, height);
1024
1025 // filter data
1026 for (std::size_t row_index = 1; row_index < height-1; ++row_index)
1027 {
1028 for (std::size_t col_index = 1; col_index < width-1; ++col_index)
1029 {
1030 unsigned char histogram[9] = {0,0,0,0,0,0,0,0,0};
1031
1032 {
1033 const unsigned char * data_ptr = quantized_color_gradients_.getData () + (row_index-1)*width+col_index-1;
1034 assert (data_ptr[0] < 9 && data_ptr[1] < 9 && data_ptr[2] < 9);
1035 ++histogram[data_ptr[0]];
1036 ++histogram[data_ptr[1]];
1037 ++histogram[data_ptr[2]];
1038 }
1039 {
1040 const unsigned char * data_ptr = quantized_color_gradients_.getData () + row_index*width+col_index-1;
1041 assert (data_ptr[0] < 9 && data_ptr[1] < 9 && data_ptr[2] < 9);
1042 ++histogram[data_ptr[0]];
1043 ++histogram[data_ptr[1]];
1044 ++histogram[data_ptr[2]];
1045 }
1046 {
1047 const unsigned char * data_ptr = quantized_color_gradients_.getData () + (row_index+1)*width+col_index-1;
1048 assert (data_ptr[0] < 9 && data_ptr[1] < 9 && data_ptr[2] < 9);
1049 ++histogram[data_ptr[0]];
1050 ++histogram[data_ptr[1]];
1051 ++histogram[data_ptr[2]];
1052 }
1053
1054 unsigned char max_hist_value = 0;
1055 int max_hist_index = -1;
1056
1057 // for (int i = 0; i < 8; ++i)
1058 // {
1059 // if (max_hist_value < histogram[i+1])
1060 // {
1061 // max_hist_index = i;
1062 // max_hist_value = histogram[i+1]
1063 // }
1064 // }
1065 // Unrolled for performance optimization:
1066 if (max_hist_value < histogram[1]) {max_hist_index = 0; max_hist_value = histogram[1];}
1067 if (max_hist_value < histogram[2]) {max_hist_index = 1; max_hist_value = histogram[2];}
1068 if (max_hist_value < histogram[3]) {max_hist_index = 2; max_hist_value = histogram[3];}
1069 if (max_hist_value < histogram[4]) {max_hist_index = 3; max_hist_value = histogram[4];}
1070 if (max_hist_value < histogram[5]) {max_hist_index = 4; max_hist_value = histogram[5];}
1071 if (max_hist_value < histogram[6]) {max_hist_index = 5; max_hist_value = histogram[6];}
1072 if (max_hist_value < histogram[7]) {max_hist_index = 6; max_hist_value = histogram[7];}
1073 if (max_hist_value < histogram[8]) {max_hist_index = 7; max_hist_value = histogram[8];}
1074
1075 if (max_hist_index != -1 && max_hist_value >= 5)
1076 filtered_quantized_color_gradients_ (col_index, row_index) = static_cast<unsigned char> (0x1 << max_hist_index);
1077 else
1078 filtered_quantized_color_gradients_ (col_index, row_index) = 0;
1079
1080 }
1081 }
1082}
1083
1084//////////////////////////////////////////////////////////////////////////////////////////////
1085template <typename PointInT>
1086void
1088erode (const pcl::MaskMap & mask_in,
1089 pcl::MaskMap & mask_out)
1090{
1091 const std::size_t width = mask_in.getWidth ();
1092 const std::size_t height = mask_in.getHeight ();
1093
1094 mask_out.resize (width, height);
1095
1096 for (std::size_t row_index = 1; row_index < height-1; ++row_index)
1097 {
1098 for (std::size_t col_index = 1; col_index < width-1; ++col_index)
1099 {
1100 if (mask_in (col_index, row_index-1) == 0 ||
1101 mask_in (col_index-1, row_index) == 0 ||
1102 mask_in (col_index+1, row_index) == 0 ||
1103 mask_in (col_index, row_index+1) == 0)
1104 {
1105 mask_out (col_index, row_index) = 0;
1106 }
1107 else
1108 {
1109 mask_out (col_index, row_index) = 255;
1110 }
1111 }
1112 }
1113}
Modality based on max-RGB gradients.
void filterQuantizedColorGradients()
Filters the quantized gradients.
static void erode(const pcl::MaskMap &mask_in, pcl::MaskMap &mask_out)
Erodes a mask.
void extractAllFeatures(const MaskMap &mask, std::size_t nr_features, std::size_t modalityIndex, std::vector< QuantizedMultiModFeature > &features) const override
Extracts all possible features from the modality within the specified mask.
FeatureSelectionMethod
Different methods for feature selection/extraction.
void setGradientMagnitudeThresholdForFeatureExtraction(const float threshold)
Sets the threshold for the gradient magnitude which is used for feature extraction.
void computeMaxColorGradientsSobel(const typename pcl::PointCloud< pcl::RGB >::ConstPtr &cloud)
Computes the max-RGB gradients for the specified cloud using sobel.
void setVariableFeatureNr(const bool enabled)
Sets whether variable feature numbers for feature extraction is enabled.
void setSpreadingSize(const std::size_t spreading_size)
Sets the spreading size for spreading the quantized data.
void computeMaxColorGradients(const typename pcl::PointCloud< pcl::RGB >::ConstPtr &cloud)
Computes the max-RGB gradients for the specified cloud.
void setFeatureSelectionMethod(const FeatureSelectionMethod method)
Sets the feature selection method.
QuantizedMap & getQuantizedMap() override
Returns a reference to the internally computed quantized map.
virtual void processInputDataFromFiltered()
Processes the input data assuming that everything up to filtering is already done/available (so only ...
virtual void processInputData()
Processes the input data (smoothing, computing gradients, quantizing, filtering, spreading).
void quantizeColorGradients()
Quantizes the color gradients.
void setInputCloud(const typename PointCloudIn::ConstPtr &cloud) override
Provide a pointer to the input dataset (overwrites the PCLBase::setInputCloud method)
QuantizedMap & getSpreadedQuantizedMap() override
Returns a reference to the internally computed spread quantized map.
~ColorGradientModality() override
Destructor.
pcl::PointCloud< pcl::GradientXY > & getMaxColorGradients()
Returns a point cloud containing the max-RGB gradients.
void extractFeatures(const MaskMap &mask, std::size_t nr_features, std::size_t modalityIndex, std::vector< QuantizedMultiModFeature > &features) const override
Extracts features from this modality within the specified mask.
void computeGaussianKernel(const std::size_t kernel_size, const float sigma, std::vector< float > &kernel_values)
Computes the Gaussian kernel used for smoothing.
void setGradientMagnitudeThreshold(const float threshold)
Sets the threshold for the gradient magnitude which is used when quantizing the data.
std::size_t getWidth() const
Definition: mask_map.h:57
void resize(std::size_t width, std::size_t height)
std::size_t getHeight() const
Definition: mask_map.h:63
static PCL_NODISCARD MaskMap getDifferenceMask(const MaskMap &mask0, const MaskMap &mask1)
PCL base class.
Definition: pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:147
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:462
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointInT > > ConstPtr
Definition: point_cloud.h:414
Interface for a quantizable modality.
static void spreadQuantizedMap(const QuantizedMap &input_map, QuantizedMap &output_map, std::size_t spreading_size)
Convolution is a mathematical operation on two functions f and g, producing a third function that is ...
Definition: convolution.h:73
void setInputCloud(const PointCloudInConstPtr &cloud)
Provide a pointer to the input dataset.
Definition: convolution.h:99
void convolve(const Eigen::ArrayXf &h_kernel, const Eigen::ArrayXf &v_kernel, PointCloudOut &output)
Convolve point cloud with an horizontal kernel along rows then vertical kernel along columns : convol...
void setKernel(const Eigen::ArrayXf &kernel)
Set convolving kernel.
Definition: convolution.h:104
Defines all the PCL implemented PointT point type structures.
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
Candidate for a feature (used in feature extraction methods).
bool operator<(const Candidate &rhs) const
Operator for comparing to candidates (by magnitude of the gradient).
A point structure representing Euclidean xyz coordinates, and the intensity value.
Definition: point_types.h:53
Feature that defines a position and quantized value in a specific modality.
std::size_t modality_index
the index of the corresponding modality.
unsigned char quantized_value
the quantized value attached to the feature.
A structure representing RGB color information.