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Diffstat (limited to 'ml/dlib/dlib/svm/cross_validate_object_detection_trainer_abstract.h')
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diff --git a/ml/dlib/dlib/svm/cross_validate_object_detection_trainer_abstract.h b/ml/dlib/dlib/svm/cross_validate_object_detection_trainer_abstract.h deleted file mode 100644 index 575ed77fb..000000000 --- a/ml/dlib/dlib/svm/cross_validate_object_detection_trainer_abstract.h +++ /dev/null @@ -1,297 +0,0 @@ -// Copyright (C) 2011 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. -#undef DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_ABSTRACT_Hh_ -#ifdef DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_ABSTRACT_Hh_ - -#include <vector> -#include "../matrix.h" -#include "../geometry.h" -#include "../image_processing/full_object_detection_abstract.h" -#include "../dnn/layers_abstract.h" - -namespace dlib -{ - -// ---------------------------------------------------------------------------------------- - - template < - typename object_detector_type, - typename image_array_type - > - const matrix<double,1,3> test_object_detection_function ( - object_detector_type& detector, - const image_array_type& images, - const std::vector<std::vector<full_object_detection> >& truth_dets, - const std::vector<std::vector<rectangle> >& ignore, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - is_learning_problem(images,truth_dets) - - images.size() == ignore.size() - - object_detector_type == some kind of object detector function object - (e.g. object_detector) - - image_array_type must be an implementation of dlib/array/array_kernel_abstract.h - and it must contain objects which can be accepted by detector(). - ensures - - Tests the given detector against the supplied object detection problem and - returns the precision, recall, and average precision. Note that the task is - to predict, for each images[i], the set of object locations given by - truth_dets[i]. Additionally, any detections on image[i] that match a box in - ignore[i] are ignored. That is, detections matching a box in ignore[i] do - not count as a false alarm and similarly if any element of ignore[i] goes - undetected it does not count as a missed detection. So we say that ignore[i] - contains a set of boxes that we "don't care" if they are detected or not. - - In particular, returns a matrix M such that: - - M(0) == the precision of the detector object. This is a number - in the range [0,1] which measures the fraction of detector outputs - which correspond to a real target. A value of 1 means the detector - never produces any false alarms while a value of 0 means it only - produces false alarms. - - M(1) == the recall of the detector object. This is a number in the - range [0,1] which measures the fraction of targets found by the - detector. A value of 1 means the detector found all the targets - in truth_dets while a value of 0 means the detector didn't locate - any of the targets. - - M(2) == the average precision of the detector object. This is a number - in the range [0,1] which measures the overall quality of the detector. - We compute this by taking all the detections output by the detector and - ordering them in descending order of their detection scores. Then we use - the average_precision() routine to score the ranked listing and store the - output into M(2). - - This function considers a detector output D to match a rectangle T if and - only if overlap_tester(T,D) returns true. - - Note that you can use the adjust_threshold argument to raise or lower the - detection threshold. This value is passed into the identically named - argument to the detector object and therefore influences the number of - output detections. It can be useful, for example, to lower the detection - threshold because it results in more detections being output by the - detector, and therefore provides more information in the ranking, - possibly raising the average precision. - !*/ - - template < - typename object_detector_type, - typename image_array_type - > - const matrix<double,1,3> test_object_detection_function ( - object_detector_type& detector, - const image_array_type& images, - const std::vector<std::vector<rectangle> >& truth_dets, - const std::vector<std::vector<rectangle> >& ignore, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - All the requirements of the above test_object_detection_function() routine. - ensures - - converts all the rectangles in truth_dets into full_object_detection objects - via full_object_detection's rectangle constructor. Then invokes - test_object_detection_function() on the full_object_detections and returns - the results. - !*/ - - template < - typename object_detector_type, - typename image_array_type - > - const matrix<double,1,3> test_object_detection_function ( - object_detector_type& detector, - const image_array_type& images, - const std::vector<std::vector<rectangle> >& truth_dets, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - All the requirements of the above test_object_detection_function() routine. - ensures - - This function simply invokes test_object_detection_function() with all the - given arguments and an empty set of ignore rectangles and returns the results. - !*/ - - template < - typename object_detector_type, - typename image_array_type - > - const matrix<double,1,3> test_object_detection_function ( - object_detector_type& detector, - const image_array_type& images, - const std::vector<std::vector<full_object_detection> >& truth_dets, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - All the requirements of the above test_object_detection_function() routine. - ensures - - This function simply invokes test_object_detection_function() with all the - given arguments and an empty set of ignore rectangles and returns the results. - !*/ - -// ---------------------------------------------------------------------------------------- - - template < - typename SUBNET, - typename image_array_type - > - const matrix<double,1,3> test_object_detection_function ( - loss_mmod<SUBNET>& detector, - const image_array_type& images, - const std::vector<std::vector<mmod_rect>>& truth_dets, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0, - const test_box_overlap& overlaps_ignore_tester = test_box_overlap() - ); - /*! - requires - - is_learning_problem(images,truth_dets) - - image_array_type must be an implementation of dlib/array/array_kernel_abstract.h - and it must contain objects which can be accepted by detector(). - ensures - - This function is just like the test_object_detection_function() for - object_detector's except it runs on CNNs that use loss_mmod. - - Tests the given detector against the supplied object detection problem and - returns the precision, recall, and average precision. Note that the task is - to predict, for each images[i], the set of object locations, and their - corresponding labels, given by truth_dets[i]. Additionally, any detections - on image[i] that match a box in truth_dets[i] that are marked ignore are - ignored. That is, detections matching an ignore box, regardless of the - ignore box's label, do not count as a false alarm and similarly if any - ignored box in truth_dets goes undetected it does not count as a missed - detection. To test if a box overlaps an ignore box, we use overlaps_ignore_tester. - - In particular, returns a matrix M such that: - - M(0) == the precision of the detector object. This is a number - in the range [0,1] which measures the fraction of detector outputs - which correspond to a real target. A value of 1 means the detector - never produces any false alarms while a value of 0 means it only - produces false alarms. - - M(1) == the recall of the detector object. This is a number in the - range [0,1] which measures the fraction of targets found by the detector. - A value of 1 means the detector found all the non-ignore targets in - truth_dets while a value of 0 means the detector didn't locate any of the - targets. - - M(2) == the average precision of the detector object. This is a number - in the range [0,1] which measures the overall quality of the detector. - We compute this by taking all the detections output by the detector and - ordering them in descending order of their detection scores. Then we use - the average_precision() routine to score the ranked listing and store the - output into M(2). - - This function considers a detector output D to match a truth rectangle T if - and only if overlap_tester(T,D) returns true and the labels are identical strings. - - Note that you can use the adjust_threshold argument to raise or lower the - detection threshold. This value is passed into the identically named - argument to the detector object and therefore influences the number of - output detections. It can be useful, for example, to lower the detection - threshold because it results in more detections being output by the - detector, and therefore provides more information in the ranking, - possibly raising the average precision. - !*/ - -// ---------------------------------------------------------------------------------------- -// ---------------------------------------------------------------------------------------- -// ---------------------------------------------------------------------------------------- - - template < - typename trainer_type, - typename image_array_type - > - const matrix<double,1,3> cross_validate_object_detection_trainer ( - const trainer_type& trainer, - const image_array_type& images, - const std::vector<std::vector<full_object_detection> >& truth_dets, - const std::vector<std::vector<rectangle> >& ignore, - const long folds, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - is_learning_problem(images,truth_dets) - - images.size() == ignore.size() - - 1 < folds <= images.size() - - trainer_type == some kind of object detection trainer (e.g structural_object_detection_trainer) - - image_array_type must be an implementation of dlib/array/array_kernel_abstract.h - and it must contain objects which can be accepted by detector(). - - it is legal to call trainer.train(images, truth_dets) - ensures - - Performs k-fold cross-validation by using the given trainer to solve an - object detection problem for the given number of folds. Each fold is tested - using the output of the trainer and a matrix summarizing the results is - returned. The matrix contains the precision, recall, and average - precision of the trained detectors and is defined identically to the - test_object_detection_function() routine defined at the top of this file. - !*/ - - template < - typename trainer_type, - typename image_array_type - > - const matrix<double,1,3> cross_validate_object_detection_trainer ( - const trainer_type& trainer, - const image_array_type& images, - const std::vector<std::vector<rectangle> >& truth_dets, - const std::vector<std::vector<rectangle> >& ignore, - const long folds, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - all the requirements of the above cross_validate_object_detection_trainer() routine. - ensures - - converts all the rectangles in truth_dets into full_object_detection objects - via full_object_detection's rectangle constructor. Then invokes - cross_validate_object_detection_trainer() on the full_object_detections and - returns the results. - !*/ - - template < - typename trainer_type, - typename image_array_type - > - const matrix<double,1,3> cross_validate_object_detection_trainer ( - const trainer_type& trainer, - const image_array_type& images, - const std::vector<std::vector<rectangle> >& truth_dets, - const long folds, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - All the requirements of the above cross_validate_object_detection_trainer() routine. - ensures - - This function simply invokes cross_validate_object_detection_trainer() with all - the given arguments and an empty set of ignore rectangles and returns the results. - !*/ - - template < - typename trainer_type, - typename image_array_type - > - const matrix<double,1,3> cross_validate_object_detection_trainer ( - const trainer_type& trainer, - const image_array_type& images, - const std::vector<std::vector<full_object_detection> >& truth_dets, - const long folds, - const test_box_overlap& overlap_tester = test_box_overlap(), - const double adjust_threshold = 0 - ); - /*! - requires - - All the requirements of the above cross_validate_object_detection_trainer() routine. - ensures - - This function simply invokes cross_validate_object_detection_trainer() with all - the given arguments and an empty set of ignore rectangles and returns the results. - !*/ - -// ---------------------------------------------------------------------------------------- - -} - -#endif // DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_ABSTRACT_Hh_ - - |