    // Copyright (C) 2011  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_STRUCTURAL_OBJECT_DETECTION_TRAiNER_H_ABSTRACT__
    #ifdef DLIB_STRUCTURAL_OBJECT_DETECTION_TRAiNER_H_ABSTRACT__

    #include "structural_svm_object_detection_problem_abstract.h"
    #include "../image_processing/object_detector_abstract.h"
    #include "../image_processing/box_overlap_testing_abstract.h"
    #include "../image_processing/full_object_detection_abstract.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        template <
            typename image_scanner_type
            >
        class structural_object_detection_trainer : noncopyable
        {
            /*!
                REQUIREMENTS ON image_scanner_type
                    image_scanner_type must be an implementation of
                    dlib/image_processing/scan_fhog_pyramid_abstract.h or
                    dlib/image_processing/scan_image_custom_abstract.h or
                    dlib/image_processing/scan_image_pyramid_abstract.h or
                    dlib/image_processing/scan_image_boxes_abstract.h

                WHAT THIS OBJECT REPRESENTS
                    This object is a tool for learning to detect objects in images based on a
                    set of labeled images. The training procedure produces an object_detector
                    which can be used to predict the locations of objects in new images.

                    Note that this is just a convenience wrapper around the structural_svm_object_detection_problem
                    to make it look similar to all the other trainers in dlib.
            !*/

        public:
            typedef double scalar_type;
            typedef default_memory_manager mem_manager_type;
            typedef object_detector<image_scanner_type> trained_function_type;

            explicit structural_object_detection_trainer (
                const image_scanner_type& scanner
            );
            /*!
                requires
                    - scanner.get_num_detection_templates() > 0
                ensures
                    - #get_c() == 1
                    - this object isn't verbose
                    - #get_epsilon() == 0.1
                    - #get_num_threads() == 2
                    - #get_max_cache_size() == 5
                    - #get_match_eps() == 0.5
                    - #get_loss_per_missed_target() == 1
                    - #get_loss_per_false_alarm() == 1
                    - This object will attempt to learn a model for the given
                      scanner object when train() is called.
                    - #get_scanner() == scanner
                      (note that only the "configuration" of scanner is copied.
                      I.e. the copy is done using copy_configuration())
                    - #auto_set_overlap_tester() == true
            !*/

            const image_scanner_type& get_scanner (
            ) const;
            /*!
                ensures
                    - returns the image scanner used by this object.
            !*/

            bool auto_set_overlap_tester (
            ) const;
            /*!
                ensures
                    - if (this object will automatically determine an appropriate
                      state for the overlap tester used for non-max suppression.) then
                        - returns true
                        - In this case, it is determined using the find_tight_overlap_tester()
                          routine based on the truth_object_detections given to the
                          structural_object_detection_trainer::train() method.
                    - else
                        - returns false
            !*/

            void set_overlap_tester (
                const test_box_overlap& tester
            );
            /*!
                ensures
                    - #get_overlap_tester() == tester
                    - #auto_set_overlap_tester() == false
            !*/

            test_box_overlap get_overlap_tester (
            ) const;
            /*!
                requires
                    - auto_set_overlap_tester() == false
                ensures
                    - returns the overlap tester object which will be used to perform non-max suppression.
                      In particular, this function returns the overlap tester which will populate the
                      object_detector returned by train().
            !*/

            void set_num_threads (
                unsigned long num
            );
            /*!
                ensures
                    - #get_num_threads() == num
            !*/

            unsigned long get_num_threads (
            ) const;
            /*!
                ensures
                    - returns the number of threads used during training.  You should
                      usually set this equal to the number of processing cores on your
                      machine.
            !*/

            void set_epsilon (
                scalar_type eps
            );
            /*!
                requires
                    - eps > 0
                ensures
                    - #get_epsilon() == eps
            !*/

            const scalar_type get_epsilon (
            ) const;
            /*!
                ensures
                    - returns the error epsilon that determines when training should stop.
                      Smaller values may result in a more accurate solution but take longer
                      to train.  You can think of this epsilon value as saying "solve the
                      optimization problem until the average loss per sample is within epsilon
                      of its optimal value".
            !*/

            void set_max_cache_size (
                unsigned long max_size
            );
            /*!
                ensures
                    - #get_max_cache_size() == max_size
            !*/

            unsigned long get_max_cache_size (
            ) const;
            /*!
                ensures
                    - During training, this object basically runs the object detector on
                      each image, over and over.  To speed this up, it is possible to cache
                      the results of these detector invocations.  This function returns the
                      number of cache elements per training sample kept in the cache.  Note
                      that a value of 0 means caching is not used at all.  Note also that
                      each cache element takes up about sizeof(double)*scanner.get_num_dimensions()
                      memory (where scanner is the scanner given to this object's constructor).
            !*/

            void be_verbose (
            );
            /*!
                ensures
                    - This object will print status messages to standard out so that a
                      user can observe the progress of the algorithm.
            !*/

            void be_quiet (
            );
            /*!
                ensures
                    - this object will not print anything to standard out
            !*/

            void set_oca (
                const oca& item
            );
            /*!
                ensures
                    - #get_oca() == item
            !*/

            const oca get_oca (
            ) const;
            /*!
                ensures
                    - returns a copy of the optimizer used to solve the structural SVM problem.
            !*/

            void set_c (
                scalar_type C
            );
            /*!
                requires
                    - C > 0
                ensures
                    - #get_c() = C
            !*/

            const scalar_type get_c (
            ) const;
            /*!
                ensures
                    - returns the SVM regularization parameter.  It is the parameter
                      that determines the trade-off between trying to fit the training
                      data (i.e. minimize the loss) or allowing more errors but hopefully
                      improving the generalization of the resulting detector.  Larger
                      values encourage exact fitting while smaller values of C may encourage
                      better generalization.
            !*/

            void set_match_eps (
                double eps
            );
            /*!
                requires
                    - 0 < eps < 1
                ensures
                    - #get_match_eps() == eps
            !*/

            double get_match_eps (
            ) const;
            /*!
                ensures
                    - returns the amount of alignment necessary for a detection to be considered
                      as matching with a ground truth rectangle.  If it doesn't match then
                      it is considered to be a false alarm.  To define this precisely, let
                      A and B be two rectangles, then A and B match if and only if:
                        A.intersect(B).area()/(A+B).area() > get_match_eps()
            !*/

            double get_loss_per_missed_target (
            ) const;
            /*!
                ensures
                    - returns the amount of loss experienced for failing to detect one of the
                      targets.  If you care more about finding targets than having a low false
                      alarm rate then you can increase this value.
            !*/

            void set_loss_per_missed_target (
                double loss
            );
            /*!
                requires
                    - loss > 0
                ensures
                    - #get_loss_per_missed_target() == loss
            !*/

            double get_loss_per_false_alarm (
            ) const;
            /*!
                ensures
                    - returns the amount of loss experienced for emitting a false alarm detection.
                      Or in other words, the loss for generating a detection that doesn't correspond
                      to one of the truth rectangles.  If you care more about having a low false
                      alarm rate than finding all the targets then you can increase this value.
            !*/

            void set_loss_per_false_alarm (
                double loss
            );
            /*!
                requires
                    - loss > 0
                ensures
                    - #get_loss_per_false_alarm() == loss
            !*/

            template <
                typename image_array_type
                >
            const trained_function_type train (
                const image_array_type& images,
                const std::vector<std::vector<full_object_detection> >& truth_object_detections
            ) const;
            /*!
                requires
                    - is_learning_problem(images, truth_object_detections) == true
                    - it must be valid to pass images[0] into the image_scanner_type::load() method.
                      (also, image_array_type must be an implementation of dlib/array/array_kernel_abstract.h)
                    - for all valid i, j:
                        - truth_object_detections[i][j].num_parts() == get_scanner().get_num_movable_components_per_detection_template()
                        - all_parts_in_rect(truth_object_detections[i][j]) == true
                ensures
                    - Uses the structural_svm_object_detection_problem to train an object_detector
                      on the given images and truth_object_detections.
                    - returns a function F with the following properties:
                        - F(new_image) == A prediction of what objects are present in new_image.  This
                          is a set of rectangles indicating their positions.
            !*/

            template <
                typename image_array_type
                >
            const trained_function_type train (
                const image_array_type& images,
                const std::vector<std::vector<rectangle> >& truth_object_detections
            ) const;
            /*!
                requires
                    - is_learning_problem(images, truth_object_detections) == true
                    - it must be valid to pass images[0] into the image_scanner_type::load() method.
                      (also, image_array_type must be an implementation of dlib/array/array_kernel_abstract.h)
                    - get_scanner().get_num_movable_components_per_detection_template() == 0
                ensures
                    - This function is identical to the above train(), except that it converts
                      each element of truth_object_detections into a full_object_detection by
                      passing it to full_object_detection's constructor taking only a rectangle.
                      Therefore, this version of train() is a convenience function for for the
                      case where you don't have any movable components of the detection templates.
            !*/

            template <
                typename image_array_type
                >
            const trained_function_type train (
                const image_array_type& images,
                const std::vector<std::vector<full_object_detection> >& truth_object_detections,
                const std::vector<std::vector<rectangle> >& ignore,
                const test_box_overlap& ignore_overlap_tester = test_box_overlap()
            ) const;
            /*!
                requires
                    - is_learning_problem(images, truth_object_detections) == true
                    - it must be valid to pass images[0] into the image_scanner_type::load() method.
                      (also, image_array_type must be an implementation of dlib/array/array_kernel_abstract.h)
                    - ignore.size() == images.size()
                    - for all valid i, j:
                        - truth_object_detections[i][j].num_parts() == get_scanner().get_num_movable_components_per_detection_template()
                        - all_parts_in_rect(truth_object_detections[i][j]) == true
                ensures
                    - Uses the structural_svm_object_detection_problem to train an object_detector
                      on the given images and truth_object_detections.
                    - for all valid i:
                        - Within images[i] any detections that match against a rectangle in
                          ignore[i], according to ignore_overlap_tester, are ignored.  That is,
                          the optimizer doesn't care if the detector outputs a detection that
                          matches any of the ignore rectangles or if it fails to output a
                          detection for an ignore rectangle.  Therefore, if there are objects
                          in your dataset that you are unsure if you want to detect or otherwise
                          don't care if the detector gets or doesn't then you can mark them
                          with ignore rectangles and the optimizer will simply ignore them.
                    - returns a function F with the following properties:
                        - F(new_image) == A prediction of what objects are present in new_image.  This
                          is a set of rectangles indicating their positions.
            !*/

            template <
                typename image_array_type
                >
            const trained_function_type train (
                const image_array_type& images,
                const std::vector<std::vector<rectangle> >& truth_object_detections,
                const std::vector<std::vector<rectangle> >& ignore,
                const test_box_overlap& ignore_overlap_tester = test_box_overlap()
            ) const;
            /*!
                requires
                    - is_learning_problem(images, truth_object_detections) == true
                    - ignore.size() == images.size()
                    - it must be valid to pass images[0] into the image_scanner_type::load() method.
                      (also, image_array_type must be an implementation of dlib/array/array_kernel_abstract.h)
                    - get_scanner().get_num_movable_components_per_detection_template() == 0
                ensures
                    - This function is identical to the above train(), except that it converts
                      each element of truth_object_detections into a full_object_detection by
                      passing it to full_object_detection's constructor taking only a rectangle.
                      Therefore, this version of train() is a convenience function for for the
                      case where you don't have any movable components of the detection templates.
            !*/
        };

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_STRUCTURAL_OBJECT_DETECTION_TRAiNER_H_ABSTRACT__