// 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"

namespace dlib
{

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

    template <
        typename object_detector_type,
        typename image_array_type
        >
    const matrix<double,1,2> test_object_detection_function (
        object_detector_type& detector,
        const image_array_type& images,
        const std::vector<std::vector<rectangle> >& truth_rects,
        const double overlap_eps = 0.5
    );
    /*!
        requires
            - is_learning_problem(images,truth_rects)
            - 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 and recall.  Note that the task is to predict, 
              for each images[i], the set of object locations given by truth_rects[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 measure the fraction of targets found by the
                  detector.  A value of 1 means the detector found all the targets
                  in truth_rects while a value of 0 means the detector didn't locate
                  any of the targets.
                - 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
    !*/

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

    template <
        typename trainer_type,
        typename image_array_type
        >
    const matrix<double,1,2> cross_validate_object_detection_trainer (
        const trainer_type& trainer,
        const image_array_type& images,
        const std::vector<std::vector<rectangle> >& truth_rects,
        const long folds,
        const double overlap_eps = 0.5
    );
    /*!
        requires
            - is_learning_problem(images,truth_rects)
            - 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().
        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 and recall of the trained 
              detectors and is defined identically to the test_object_detection_function()
              routine defined at the top of this file.
    !*/

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

}

#endif // DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_ABSTRACT_H__


