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

#include "../matrix.h"
#include "structural_svm_problem_threaded_abstract.h"
#include <sstream>
#include "../image_processing/full_object_detection_abstract.h"
#include "../image_processing/box_overlap_testing.h"

namespace dlib
{

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

    class impossible_labeling_error : public dlib::error 
    { 
        /*!
            WHAT THIS OBJECT REPRESENTS
                This is the exception thrown by the structural_svm_object_detection_problem
                when it detects that the image_scanner_type it is working with is incapable
                of representing the truth rectangles it has been asked to predict.  

                This kind of problem can happen when the test_box_overlap object indicates
                that two ground truth rectangles overlap and are therefore not allowed to
                both be output at the same time.  Or alternatively, if there are not enough
                detection templates to cover the variety of truth rectangle shapes.
        !*/
    };

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

    template <
        typename image_scanner_type,
        typename image_array_type 
        >
    class structural_svm_object_detection_problem : public structural_svm_problem_threaded<matrix<double,0,1> >,
                                                    noncopyable
    {
        /*!
            REQUIREMENTS ON image_scanner_type
                image_scanner_type must be an implementation of 
                dlib/image_processing/scan_image_pyramid_abstract.h

            REQUIREMENTS ON image_array_type
                image_array_type must be an implementation of dlib/array/array_kernel_abstract.h 
                and it must contain objects which can be accepted by image_scanner_type::load().

            WHAT THIS OBJECT REPRESENTS
                This object is a tool for learning the parameter vector needed to use
                a scan_image_pyramid object.  

                It learns the parameter vector by formulating the problem as a structural 
                SVM problem.  The general approach is similar to the method discussed in 
                Learning to Localize Objects with Structured Output Regression by 
                Matthew B. Blaschko and Christoph H. Lampert.  However, the method has 
                been extended to datasets with multiple, potentially overlapping, objects 
                per image and the measure of loss is different from what is described in 
                the paper.  

                In particular, the loss is measured as follows:
                    let FA == the number of false alarms produced by a labeling of an image.
                    let MT == the number of targets missed by a labeling of an image.  
                    Then the loss for a particular labeling is the quantity:
                        FA*get_loss_per_false_alarm() + MT*get_loss_per_missed_target()

                A detection is considered a false alarm if it doesn't match with any 
                of the ground truth rectangles or if it is a duplicate detection of a 
                truth rectangle.  Finally, for the purposes of calculating loss, a match 
                is determined using the following formula where rectangles A and B match 
                if and only if:
                    A.intersect(B).area()/(A+B).area() > get_match_eps()
        !*/
    public:

        structural_svm_object_detection_problem(
            const image_scanner_type& scanner,
            const test_box_overlap& overlap_tester,
            const bool auto_overlap_tester,
            const image_array_type& images,
            const std::vector<std::vector<full_object_detection> >& truth_object_detections,
            unsigned long num_threads = 2
        );
        /*!
            requires
                - is_learning_problem(images, truth_object_detections)
                - scanner.get_num_detection_templates() > 0
                - scanner.load(images[0]) must be a valid expression.
                - for all valid i, j:
                    - truth_object_detections[i][j].num_parts() == scanner.get_num_movable_components_per_detection_template() 
                    - all_parts_in_rect(truth_object_detections[i][j]) == true
            ensures
                - This object attempts to learn a mapping from the given images to the
                  object locations given in truth_object_detections.  In particular, it
                  attempts to learn to predict truth_object_detections[i] based on
                  images[i].  Or in other words, this object can be used to learn a
                  parameter vector, w, such that an object_detector declared as:
                    object_detector<image_scanner_type> detector(scanner,get_overlap_tester(),w)
                  results in a detector object which attempts to compute the locations of
                  all the objects in truth_object_detections.  So if you called
                  detector(images[i]) you would hopefully get a list of rectangles back
                  that had truth_object_detections[i].size() elements and contained exactly
                  the rectangles indicated by truth_object_detections[i].
                - if (auto_overlap_tester == true) then
                    - #get_overlap_tester() == a test_box_overlap object that is configured
                      using the find_tight_overlap_tester() routine and the contents of
                      truth_object_detections. 
                - else
                    - #get_overlap_tester() == overlap_tester
                - #get_match_eps() == 0.5
                - This object will use num_threads threads during the optimization 
                  procedure.  You should set this parameter equal to the number of 
                  available processing cores on your machine.
                - #get_loss_per_missed_target() == 1
                - #get_loss_per_false_alarm() == 1
        !*/

        test_box_overlap get_overlap_tester (
        ) const;
        /*!
            ensures
                - returns the overlap tester used by this object.  
        !*/

        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.  The precise formula for determining
                  if two rectangles match each other is the following, rectangles 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.
        !*/

        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.
        !*/

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

    };

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

}

#endif // DLIB_STRUCTURAL_SVM_ObJECT_DETECTION_PROBLEM_ABSTRACT_H__



