Point Cloud Library (PCL) 1.14.0
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rf_face_utils.h
1/*
2 * fanellis_face_detector.h
3 *
4 * Created on: 22 Sep 2012
5 * Author: Aitor Aldoma
6 */
7
8#pragma once
9
10#include "pcl/recognition/face_detection/face_common.h"
11#include <pcl/ml/feature_handler.h>
12#include <pcl/ml/stats_estimator.h>
13#include <pcl/ml/branch_estimator.h>
14
15namespace pcl
16{
17 namespace face_detection
18 {
19 template<class FT, class DataSet, class ExampleIndex>
20 class FeatureHandlerDepthAverage: public pcl::FeatureHandler<FT, DataSet, ExampleIndex>
21 {
22
23 private:
24 int wsize_; //size of the window
25 int max_patch_size_; //max size of the smaller patches
26 int num_channels_; //the number of feature channels
27 float min_valid_small_patch_depth_; //percentage of valid depth in a small patch
28 public:
29
31 {
32 wsize_ = 80;
33 max_patch_size_ = 40;
34 num_channels_ = 1;
35 min_valid_small_patch_depth_ = 0.5f;
36 }
37
38 /** \brief Sets the size of the window to extract features.
39 * \param[in] w Window size.
40 */
41 void setWSize(int w)
42 {
43 wsize_ = w;
44 }
45
46 /** \brief Sets the number of channels a feature has (i.e. 1 - depth, 4 - depth + normals)
47 * \param[in] nf Number of channels.
48 */
49 void setNumChannels(int nf)
50 {
51 num_channels_ = nf;
52 }
53
54 /** \brief Create a set of random tests to evaluate examples.
55 * \param[in] w Number features to generate.
56 */
57 void setMaxPatchSize(int w)
58 {
59 max_patch_size_ = w;
60 }
61
62 /** \brief Create a set of random tests to evaluate examples.
63 * \param[in] num_of_features Number features to generated.
64 * \param[out] features Generated features.
65 */
66 /*void createRandomFeatures(const std::size_t num_of_features, std::vector<FT> & features)
67 {
68 srand (time(NULL));
69 int min_s = 10;
70 float range_d = 0.03f;
71 for (std::size_t i = 0; i < num_of_features; i++)
72 {
73 FT f;
74
75 f.row1_ = rand () % (wsize_ - max_patch_size_ - 1);
76 f.col1_ = rand () % (wsize_ / 2 - max_patch_size_ - 1);
77 f.wsizex1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));
78 f.wsizey1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));
79
80 f.row2_ = rand () % (wsize_ - max_patch_size_ - 1);
81 f.col2_ = wsize_ / 2 + rand () % (wsize_ / 2 - max_patch_size_ - 1);
82 f.wsizex2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));
83 f.wsizey2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));
84
85 f.used_ii_ = 0;
86 if(num_channels_ > 1)
87 f.used_ii_ = rand() % num_channels_;
88
89 f.threshold_ = -range_d + (rand () / static_cast<float> (RAND_MAX)) * (range_d * 2.f);
90 features.push_back (f);
91 }
92 }*/
93
94 void createRandomFeatures(const std::size_t num_of_features, std::vector<FT> & features) override
95 {
96 srand (static_cast<unsigned int>(time (nullptr)));
97 int min_s = 20;
98 float range_d = 0.05f;
99 float incr_d = 0.01f;
100
101 std::vector < FT > windows_and_functions;
102
103 for (std::size_t i = 0; i < num_of_features; i++)
104 {
105 FT f;
106
107 f.row1_ = rand () % (wsize_ - max_patch_size_ - 1);
108 f.col1_ = rand () % (wsize_ / 2 - max_patch_size_ - 1);
109 f.wsizex1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));
110 f.wsizey1_ = min_s + (rand () % (max_patch_size_ - min_s - 1));
111
112 f.row2_ = rand () % (wsize_ - max_patch_size_ - 1);
113 f.col2_ = wsize_ / 2 + rand () % (wsize_ / 2 - max_patch_size_ - 1);
114 f.wsizex2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));
115 f.wsizey2_ = min_s + (rand () % (max_patch_size_ - 1 - min_s));
116
117 f.used_ii_ = 0;
118 if (num_channels_ > 1)
119 f.used_ii_ = rand () % num_channels_;
120
121 windows_and_functions.push_back (f);
122 }
123
124 for (std::size_t i = 0; i < windows_and_functions.size (); i++)
125 {
126 FT f = windows_and_functions[i];
127 for (std::size_t j = 0; j <= 10; j++)
128 {
129 f.threshold_ = -range_d + static_cast<float> (j) * incr_d;
130 features.push_back (f);
131 }
132 }
133 }
134
135 /** \brief Evaluates a feature on the specified set of examples.
136 * \param[in] feature The feature to evaluate.
137 * \param[in] data_set The data set on which the feature is evaluated.
138 * \param[in] examples The set of examples of the data set the feature is evaluated on.
139 * \param[out] results The destination for the results of the feature evaluation.
140 * \param[out] flags Flags that are supplied together with the results.
141 */
142 void evaluateFeature(const FT & feature, DataSet & data_set, std::vector<ExampleIndex> & examples, std::vector<float> & results,
143 std::vector<unsigned char> & flags) const override
144 {
145 results.resize (examples.size ());
146 for (std::size_t i = 0; i < examples.size (); i++)
147 {
148 evaluateFeature (feature, data_set, examples[i], results[i], flags[i]);
149 }
150 }
151
152 /** \brief Evaluates a feature on the specified example.
153 * \param[in] feature The feature to evaluate.
154 * \param[in] data_set The data set on which the feature is evaluated.
155 * \param[in] example The example of the data set the feature is evaluated on.
156 * \param[out] result The destination for the result of the feature evaluation.
157 * \param[out] flag Flags that are supplied together with the results.
158 */
159 void evaluateFeature(const FT & feature, DataSet & data_set, const ExampleIndex & example, float & result, unsigned char & flag) const override
160 {
161 TrainingExample te = data_set[example];
162 int el_f1 = te.iimages_[feature.used_ii_]->getFiniteElementsCount (te.col_ + feature.col1_, te.row_ + feature.row1_, feature.wsizex1_,
163 feature.wsizey1_);
164 int el_f2 = te.iimages_[feature.used_ii_]->getFiniteElementsCount (te.col_ + feature.col2_, te.row_ + feature.row2_, feature.wsizex2_,
165 feature.wsizey2_);
166
167 float sum_f1 = static_cast<float>(te.iimages_[feature.used_ii_]->getFirstOrderSum (te.col_ + feature.col1_, te.row_ + feature.row1_, feature.wsizex1_, feature.wsizey1_));
168 float sum_f2 = static_cast<float>(te.iimages_[feature.used_ii_]->getFirstOrderSum (te.col_ + feature.col2_, te.row_ + feature.row2_, feature.wsizex2_, feature.wsizey2_));
169
170 float f = min_valid_small_patch_depth_;
171 if (el_f1 == 0 || el_f2 == 0 || (el_f1 <= static_cast<int> (f * static_cast<float>(feature.wsizex1_ * feature.wsizey1_)))
172 || (el_f2 <= static_cast<int> (f * static_cast<float>(feature.wsizex2_ * feature.wsizey2_))))
173 {
174 result = static_cast<float> (pcl_round (static_cast<float>(rand ()) / static_cast<float> (RAND_MAX)));
175 flag = 1;
176 } else
177 {
178 result = static_cast<float> ((sum_f1 / static_cast<float>(el_f1) - sum_f2 / static_cast<float>(el_f2)) > feature.threshold_);
179 flag = 0;
180 }
181
182 }
183
184 /** \brief Generates evaluation code for the specified feature and writes it to the specified stream.
185 */
186 // param[in] feature The feature for which code is generated.
187 // param[out] stream The destination for the code.
188 void generateCodeForEvaluation(const FT &/*feature*/, ::std::ostream &/*stream*/) const override
189 {
190
191 }
192 };
193
194 /** \brief Statistics estimator for regression trees which optimizes information gain and pose parameters error. */
195 template<class LabelDataType, class NodeType, class DataSet, class ExampleIndex>
196 class PoseClassRegressionVarianceStatsEstimator: public pcl::StatsEstimator<LabelDataType, NodeType, DataSet, ExampleIndex>
197 {
198
199 public:
200 /** \brief Constructor. */
202 branch_estimator_ (branch_estimator)
203 {
204 }
205
206 /** \brief Destructor. */
208
209 /** \brief Returns the number of branches the corresponding tree has. */
210 inline std::size_t getNumOfBranches() const override
211 {
212 return branch_estimator_->getNumOfBranches ();
213 }
214
215 /** \brief Returns the label of the specified node.
216 * \param[in] node The node which label is returned.
217 */
218 inline LabelDataType getLabelOfNode(NodeType & node) const override
219 {
220 return node.value;
221 }
222
223 /** \brief Computes the covariance matrix for translation offsets.
224 * \param[in] data_set The corresponding data set.
225 * \param[in] examples A set of examples from the dataset.
226 * \param[out] covariance_matrix The covariance matrix.
227 * \param[out] centroid The mean of the data.
228 */
229 inline unsigned int computeMeanAndCovarianceOffset(DataSet & data_set, std::vector<ExampleIndex> & examples, Eigen::Matrix3d & covariance_matrix,
230 Eigen::Vector3d & centroid) const
231 {
232 Eigen::Matrix<double, 1, 9, Eigen::RowMajor> accu = Eigen::Matrix<double, 1, 9, Eigen::RowMajor>::Zero ();
233 auto point_count = static_cast<unsigned int> (examples.size ());
234
235 for (std::size_t i = 0; i < point_count; ++i)
236 {
237 TrainingExample te = data_set[examples[i]];
238 accu[0] += te.trans_[0] * te.trans_[0];
239 accu[1] += te.trans_[0] * te.trans_[1];
240 accu[2] += te.trans_[0] * te.trans_[2];
241 accu[3] += te.trans_[1] * te.trans_[1];
242 accu[4] += te.trans_[1] * te.trans_[2];
243 accu[5] += te.trans_[2] * te.trans_[2];
244 accu[6] += te.trans_[0];
245 accu[7] += te.trans_[1];
246 accu[8] += te.trans_[2];
247 }
248
249 if (point_count != 0)
250 {
251 accu /= static_cast<double> (point_count);
252 centroid.head<3> ().matrix () = accu.tail<3> ();
253 covariance_matrix.coeffRef (0) = accu[0] - accu[6] * accu[6];
254 covariance_matrix.coeffRef (1) = accu[1] - accu[6] * accu[7];
255 covariance_matrix.coeffRef (2) = accu[2] - accu[6] * accu[8];
256 covariance_matrix.coeffRef (4) = accu[3] - accu[7] * accu[7];
257 covariance_matrix.coeffRef (5) = accu[4] - accu[7] * accu[8];
258 covariance_matrix.coeffRef (8) = accu[5] - accu[8] * accu[8];
259 covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);
260 covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);
261 covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);
262 }
263
264 return point_count;
265 }
266
267 /** \brief Computes the covariance matrix for rotation values.
268 * \param[in] data_set The corresponding data set.
269 * \param[in] examples A set of examples from the dataset.
270 * \param[out] covariance_matrix The covariance matrix.
271 * \param[out] centroid The mean of the data.
272 */
273 inline unsigned int computeMeanAndCovarianceAngles(DataSet & data_set, std::vector<ExampleIndex> & examples, Eigen::Matrix3d & covariance_matrix,
274 Eigen::Vector3d & centroid) const
275 {
276 Eigen::Matrix<double, 1, 9, Eigen::RowMajor> accu = Eigen::Matrix<double, 1, 9, Eigen::RowMajor>::Zero ();
277 auto point_count = static_cast<unsigned int> (examples.size ());
278
279 for (std::size_t i = 0; i < point_count; ++i)
280 {
281 TrainingExample te = data_set[examples[i]];
282 accu[0] += te.rot_[0] * te.rot_[0];
283 accu[1] += te.rot_[0] * te.rot_[1];
284 accu[2] += te.rot_[0] * te.rot_[2];
285 accu[3] += te.rot_[1] * te.rot_[1];
286 accu[4] += te.rot_[1] * te.rot_[2];
287 accu[5] += te.rot_[2] * te.rot_[2];
288 accu[6] += te.rot_[0];
289 accu[7] += te.rot_[1];
290 accu[8] += te.rot_[2];
291 }
292
293 if (point_count != 0)
294 {
295 accu /= static_cast<double> (point_count);
296 centroid.head<3> ().matrix () = accu.tail<3> ();
297 covariance_matrix.coeffRef (0) = accu[0] - accu[6] * accu[6];
298 covariance_matrix.coeffRef (1) = accu[1] - accu[6] * accu[7];
299 covariance_matrix.coeffRef (2) = accu[2] - accu[6] * accu[8];
300 covariance_matrix.coeffRef (4) = accu[3] - accu[7] * accu[7];
301 covariance_matrix.coeffRef (5) = accu[4] - accu[7] * accu[8];
302 covariance_matrix.coeffRef (8) = accu[5] - accu[8] * accu[8];
303 covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);
304 covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);
305 covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);
306 }
307
308 return point_count;
309 }
310
311 /** \brief Computes the information gain obtained by the specified threshold.
312 * \param[in] data_set The data set corresponding to the supplied result data.
313 * \param[in] examples The examples used for extracting the supplied result data.
314 * \param[in] label_data The label data corresponding to the specified examples.
315 * \param[in] results The results computed using the specified examples.
316 * \param[in] flags The flags corresponding to the results.
317 * \param[in] threshold The threshold for which the information gain is computed.
318 */
319 float computeInformationGain(DataSet & data_set, std::vector<ExampleIndex> & examples, std::vector<LabelDataType> & label_data,
320 std::vector<float> & results, std::vector<unsigned char> & flags, const float threshold) const override
321 {
322 const std::size_t num_of_examples = examples.size ();
323 const std::size_t num_of_branches = getNumOfBranches ();
324
325 // compute variance
326 std::vector < LabelDataType > sums (num_of_branches + 1, 0.f);
327 std::vector < LabelDataType > sqr_sums (num_of_branches + 1, 0.f);
328 std::vector < std::size_t > branch_element_count (num_of_branches + 1, 0.f);
329
330 for (std::size_t branch_index = 0; branch_index < num_of_branches; ++branch_index)
331 {
332 branch_element_count[branch_index] = 1;
333 ++branch_element_count[num_of_branches];
334 }
335
336 for (std::size_t example_index = 0; example_index < num_of_examples; ++example_index)
337 {
338 unsigned char branch_index;
339 computeBranchIndex (results[example_index], flags[example_index], threshold, branch_index);
340
341 LabelDataType label = label_data[example_index];
342
343 ++branch_element_count[branch_index];
344 ++branch_element_count[num_of_branches];
345
346 sums[branch_index] += label;
347 sums[num_of_branches] += label;
348 }
349
350 std::vector<float> hp (num_of_branches + 1, 0.f);
351 for (std::size_t branch_index = 0; branch_index < (num_of_branches + 1); ++branch_index)
352 {
353 float pf = sums[branch_index] / static_cast<float> (branch_element_count[branch_index]);
354 float pnf = (static_cast<LabelDataType>(branch_element_count[branch_index]) - sums[branch_index] + 1.f)
355 / static_cast<LabelDataType> (branch_element_count[branch_index]);
356 hp[branch_index] -= static_cast<float>(pf * std::log (pf) + pnf * std::log (pnf));
357 }
358
359 //use mean of the examples as purity
360 float purity = sums[num_of_branches] / static_cast<LabelDataType>(branch_element_count[num_of_branches]);
361 float tp = 0.8f;
362
363 if (purity >= tp)
364 {
365 //compute covariance matrices from translation offsets and angles for the whole set and children
366 //consider only positive examples...
367 std::vector < std::size_t > branch_element_count (num_of_branches + 1, 0);
368 std::vector < std::vector<ExampleIndex> > positive_examples;
369 positive_examples.resize (num_of_branches + 1);
370
371 for (std::size_t example_index = 0; example_index < num_of_examples; ++example_index)
372 {
373 unsigned char branch_index;
374 computeBranchIndex (results[example_index], flags[example_index], threshold, branch_index);
375
376 LabelDataType label = label_data[example_index];
377
378 if (label == 1 /*&& !flags[example_index]*/)
379 {
380 ++branch_element_count[branch_index];
381 ++branch_element_count[num_of_branches];
382
383 positive_examples[branch_index].push_back (examples[example_index]);
384 positive_examples[num_of_branches].push_back (examples[example_index]);
385 }
386 }
387
388 //compute covariance from offsets and angles for all branches
389 std::vector < Eigen::Matrix3d > offset_covariances;
390 std::vector < Eigen::Matrix3d > angle_covariances;
391
392 std::vector < Eigen::Vector3d > offset_centroids;
393 std::vector < Eigen::Vector3d > angle_centroids;
394
395 offset_covariances.resize (num_of_branches + 1);
396 angle_covariances.resize (num_of_branches + 1);
397 offset_centroids.resize (num_of_branches + 1);
398 angle_centroids.resize (num_of_branches + 1);
399
400 for (std::size_t branch_index = 0; branch_index < (num_of_branches + 1); ++branch_index)
401 {
402 computeMeanAndCovarianceOffset (data_set, positive_examples[branch_index], offset_covariances[branch_index],
403 offset_centroids[branch_index]);
404 computeMeanAndCovarianceAngles (data_set, positive_examples[branch_index], angle_covariances[branch_index],
405 angle_centroids[branch_index]);
406 }
407
408 //update information_gain
409 std::vector<float> hr (num_of_branches + 1, 0.f);
410 for (std::size_t branch_index = 0; branch_index < (num_of_branches + 1); ++branch_index)
411 {
412 hr[branch_index] = static_cast<float>(0.5f * std::log (std::pow (2 * M_PI, 3)
413 * offset_covariances[branch_index].determinant ())
414 + 0.5f * std::log (std::pow (2 * M_PI, 3)
415 * angle_covariances[branch_index].determinant ()));
416 }
417
418 for (std::size_t branch_index = 0; branch_index < (num_of_branches + 1); ++branch_index)
419 {
420 hp[branch_index] += std::max (sums[branch_index] / static_cast<float> (branch_element_count[branch_index]) - tp, 0.f) * hr[branch_index];
421 }
422 }
423
424 float information_gain = hp[num_of_branches + 1];
425 for (std::size_t branch_index = 0; branch_index < (num_of_branches); ++branch_index)
426 {
427 information_gain -= static_cast<float> (branch_element_count[branch_index]) / static_cast<float> (branch_element_count[num_of_branches])
428 * hp[branch_index];
429 }
430
431 return information_gain;
432 }
433
434 /** \brief Computes the branch indices for all supplied results.
435 * \param[in] results The results the branch indices will be computed for.
436 * \param[in] flags The flags corresponding to the specified results.
437 * \param[in] threshold The threshold used to compute the branch indices.
438 * \param[out] branch_indices The destination for the computed branch indices.
439 */
440 void computeBranchIndices(std::vector<float> & results, std::vector<unsigned char> & flags, const float threshold,
441 std::vector<unsigned char> & branch_indices) const override
442 {
443 const std::size_t num_of_results = results.size ();
444
445 branch_indices.resize (num_of_results);
446 for (std::size_t result_index = 0; result_index < num_of_results; ++result_index)
447 {
448 unsigned char branch_index;
449 computeBranchIndex (results[result_index], flags[result_index], threshold, branch_index);
450 branch_indices[result_index] = branch_index;
451 }
452 }
453
454 /** \brief Computes the branch index for the specified result.
455 * \param[in] result The result the branch index will be computed for.
456 * \param[in] flag The flag corresponding to the specified result.
457 * \param[in] threshold The threshold used to compute the branch index.
458 * \param[out] branch_index The destination for the computed branch index.
459 */
460 inline void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char & branch_index) const override
461 {
462 branch_estimator_->computeBranchIndex (result, flag, threshold, branch_index);
463 }
464
465 /** \brief Computes and sets the statistics for a node.
466 * \param[in] data_set The data set which is evaluated.
467 * \param[in] examples The examples which define which parts of the data set are used for evaluation.
468 * \param[in] label_data The label_data corresponding to the examples.
469 * \param[out] node The destination node for the statistics.
470 */
471 void computeAndSetNodeStats(DataSet & data_set, std::vector<ExampleIndex> & examples, std::vector<LabelDataType> & label_data, NodeType & node) const override
472 {
473 const std::size_t num_of_examples = examples.size ();
474
475 LabelDataType sum = 0.0f;
476 LabelDataType sqr_sum = 0.0f;
477 for (std::size_t example_index = 0; example_index < num_of_examples; ++example_index)
478 {
479 const LabelDataType label = label_data[example_index];
480
481 sum += label;
482 sqr_sum += label * label;
483 }
484
485 sum /= static_cast<float>(num_of_examples);
486 sqr_sum /= static_cast<float>(num_of_examples);
487
488 const float variance = sqr_sum - sum * sum;
489
490 node.value = sum;
491 node.variance = variance;
492
493 //set node stats regarding pose regression
494 std::vector < ExampleIndex > positive_examples;
495
496 for (std::size_t example_index = 0; example_index < num_of_examples; ++example_index)
497 {
498 LabelDataType label = label_data[example_index];
499
500 if (label == 1)
501 positive_examples.push_back (examples[example_index]);
502
503 }
504
505 //compute covariance from offsets and angles
506 computeMeanAndCovarianceOffset (data_set, positive_examples, node.covariance_trans_, node.trans_mean_);
507 computeMeanAndCovarianceAngles (data_set, positive_examples, node.covariance_rot_, node.rot_mean_);
508 }
509
510 /** \brief Generates code for branch index computation.
511 * \param[out] stream The destination for the generated code.
512 */
513 // param[in] node The node for which code is generated.
514 void generateCodeForBranchIndexComputation(NodeType & /*node*/, std::ostream & stream) const override
515 {
516 stream << "ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)";
517 }
518
519 /** \brief Generates code for label output.
520 * \param[out] stream The destination for the generated code.
521 */
522 // param[in] node The node for which code is generated.
523 void generateCodeForOutput(NodeType & /*node*/, std::ostream & stream) const override
524 {
525 stream << "ERROR: RegressionVarianceStatsEstimator does not implement generateCodeForBranchIndex(...)";
526 }
527
528 private:
529 /** \brief The branch estimator. */
530 pcl::BranchEstimator * branch_estimator_;
531 };
532 }
533}
Interface for branch estimators.
virtual std::size_t getNumOfBranches() const =0
Returns the number of branches the corresponding tree has.
virtual void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &branch_index) const =0
Computes the branch index for the specified result.
Utility class interface which is used for creating and evaluating features.
Class interface for gathering statistics for decision tree learning.
void setWSize(int w)
Sets the size of the window to extract features.
void setMaxPatchSize(int w)
Create a set of random tests to evaluate examples.
void createRandomFeatures(const std::size_t num_of_features, std::vector< FT > &features) override
Create a set of random tests to evaluate examples.
void generateCodeForEvaluation(const FT &, ::std::ostream &) const override
Generates evaluation code for the specified feature and writes it to the specified stream.
void evaluateFeature(const FT &feature, DataSet &data_set, const ExampleIndex &example, float &result, unsigned char &flag) const override
Evaluates a feature on the specified example.
void setNumChannels(int nf)
Sets the number of channels a feature has (i.e.
void evaluateFeature(const FT &feature, DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< float > &results, std::vector< unsigned char > &flags) const override
Evaluates a feature on the specified set of examples.
Statistics estimator for regression trees which optimizes information gain and pose parameters error.
void computeAndSetNodeStats(DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< LabelDataType > &label_data, NodeType &node) const override
Computes and sets the statistics for a node.
void computeBranchIndices(std::vector< float > &results, std::vector< unsigned char > &flags, const float threshold, std::vector< unsigned char > &branch_indices) const override
Computes the branch indices for all supplied results.
unsigned int computeMeanAndCovarianceAngles(DataSet &data_set, std::vector< ExampleIndex > &examples, Eigen::Matrix3d &covariance_matrix, Eigen::Vector3d &centroid) const
Computes the covariance matrix for rotation values.
LabelDataType getLabelOfNode(NodeType &node) const override
Returns the label of the specified node.
PoseClassRegressionVarianceStatsEstimator(BranchEstimator *branch_estimator)
Constructor.
~PoseClassRegressionVarianceStatsEstimator() override=default
Destructor.
unsigned int computeMeanAndCovarianceOffset(DataSet &data_set, std::vector< ExampleIndex > &examples, Eigen::Matrix3d &covariance_matrix, Eigen::Vector3d &centroid) const
Computes the covariance matrix for translation offsets.
void generateCodeForBranchIndexComputation(NodeType &, std::ostream &stream) const override
Generates code for branch index computation.
void computeBranchIndex(const float result, const unsigned char flag, const float threshold, unsigned char &branch_index) const override
Computes the branch index for the specified result.
void generateCodeForOutput(NodeType &, std::ostream &stream) const override
Generates code for label output.
float computeInformationGain(DataSet &data_set, std::vector< ExampleIndex > &examples, std::vector< LabelDataType > &label_data, std::vector< float > &results, std::vector< unsigned char > &flags, const float threshold) const override
Computes the information gain obtained by the specified threshold.
std::size_t getNumOfBranches() const override
Returns the number of branches the corresponding tree has.
std::vector< pcl::IntegralImage2D< float, 1 >::Ptr > iimages_
Definition face_common.h:16
__inline double pcl_round(double number)
Win32 doesn't seem to have rounding functions.
Definition pcl_macros.h:239
#define M_PI
Definition pcl_macros.h:201