    // 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_H__
    #ifdef DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_ABSTRACT_H__

    #include <vector>
    #include "../matrix.h"
    #include "../geometry.h"
    #include "../image_processing/full_object_detection_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 double overlap_eps = 0.5,
            const double adjust_threshold = 0
        );
        /*!
            requires
                - is_learning_problem(images,truth_dets)
                - 0 < overlap_eps <= 1
                - 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].
                - 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).
                    - The rule for deciding if a detector output, D, matches a truth rectangle,
                      T, is the following:
                        T and R match if and only if: T.intersect(R).area()/(T+R).area() > overlap_eps
                    - 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 double overlap_eps = 0.5,
            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 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 double overlap_eps = 0.5,
            const double adjust_threshold = 0
        );
        /*!
            requires
                - is_learning_problem(images,truth_dets)
                - 0 < overlap_eps <= 1
                - 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 long folds,
            const double overlap_eps = 0.5,
            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.
        !*/
    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_ABSTRACT_H__