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

    #include "structural_object_detection_trainer_abstract.h"
    #include "../algs.h"
    #include "../optimization.h"
    #include "structural_svm_object_detection_problem.h"
    #include "../image_processing/object_detector.h"
    #include "../image_processing/box_overlap_testing.h"
    #include "../image_processing/full_object_detection.h"

    namespace dlib
    {

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

        template <
            typename image_scanner_type,
            typename svm_struct_prob_type
            >
        void configure_nuclear_norm_regularizer (
            const image_scanner_type&,
            svm_struct_prob_type&
        )
        {
            // does nothing by default.  Specific scanner types overload this function to do
            // whatever is appropriate.
        }

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

        template <
            typename image_scanner_type
            >
        class structural_object_detection_trainer : noncopyable
        {

        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_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(scanner_.get_num_detection_templates() > 0,
                    "\t structural_object_detection_trainer::structural_object_detection_trainer(scanner_)"
                    << "\n\t You can't have zero detection templates"
                    << "\n\t this: " << this
                    );

                C = 1;
                verbose = false;
                eps = 0.1;
                num_threads = 2;
                max_cache_size = 5;
                match_eps = 0.5;
                loss_per_missed_target = 1;
                loss_per_false_alarm = 1;

                scanner.copy_configuration(scanner_);

                auto_overlap_tester = true;
            }

            const image_scanner_type& get_scanner (
            ) const
            {
                return scanner;
            }

            bool auto_set_overlap_tester (
            ) const
            {
                return auto_overlap_tester;
            }

            void set_overlap_tester (
                const test_box_overlap& tester
            )
            {
                overlap_tester = tester;
                auto_overlap_tester = false;
            }

            test_box_overlap get_overlap_tester (
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(auto_set_overlap_tester() == false,
                    "\t test_box_overlap structural_object_detection_trainer::get_overlap_tester()"
                    << "\n\t You can't call this function if the overlap tester is generated dynamically."
                    << "\n\t this: " << this
                    );

                return overlap_tester;
            }

            void set_num_threads (
                unsigned long num
            )
            {
                num_threads = num;
            }

            unsigned long get_num_threads (
            ) const
            {
                return num_threads;
            }

            void set_epsilon (
                scalar_type eps_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(eps_ > 0,
                    "\t void structural_object_detection_trainer::set_epsilon()"
                    << "\n\t eps_ must be greater than 0"
                    << "\n\t eps_: " << eps_
                    << "\n\t this: " << this
                    );

                eps = eps_;
            }

            scalar_type get_epsilon (
            ) const { return eps; }

            void set_max_cache_size (
                unsigned long max_size
            )
            {
                max_cache_size = max_size;
            }

            unsigned long get_max_cache_size (
            ) const
            {
                return max_cache_size;
            }

            void be_verbose (
            )
            {
                verbose = true;
            }

            void be_quiet (
            )
            {
                verbose = false;
            }

            void set_oca (
                const oca& item
            )
            {
                solver = item;
            }

            const oca get_oca (
            ) const
            {
                return solver;
            }

            void set_c (
                scalar_type C_
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(C_ > 0,
                    "\t void structural_object_detection_trainer::set_c()"
                    << "\n\t C_ must be greater than 0"
                    << "\n\t C_:    " << C_
                    << "\n\t this: " << this
                    );

                C = C_;
            }

            scalar_type get_c (
            ) const
            {
                return C;
            }

            void set_match_eps (
                double eps
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0 < eps && eps < 1,
                    "\t void structural_object_detection_trainer::set_match_eps(eps)"
                    << "\n\t Invalid inputs were given to this function "
                    << "\n\t eps:  " << eps
                    << "\n\t this: " << this
                    );

                match_eps = eps;
            }

            double get_match_eps (
            ) const
            {
                return match_eps;
            }

            double get_loss_per_missed_target (
            ) const
            {
                return loss_per_missed_target;
            }

            void set_loss_per_missed_target (
                double loss
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(loss > 0,
                    "\t void structural_object_detection_trainer::set_loss_per_missed_target(loss)"
                    << "\n\t Invalid inputs were given to this function "
                    << "\n\t loss: " << loss
                    << "\n\t this: " << this
                    );

                loss_per_missed_target = loss;
            }

            double get_loss_per_false_alarm (
            ) const
            {
                return loss_per_false_alarm;
            }

            void set_loss_per_false_alarm (
                double loss
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(loss > 0,
                    "\t void structural_object_detection_trainer::set_loss_per_false_alarm(loss)"
                    << "\n\t Invalid inputs were given to this function "
                    << "\n\t loss: " << loss
                    << "\n\t this: " << this
                    );

                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
            {
                std::vector<std::vector<rectangle> > empty_ignore(images.size());
                return train_impl(images, truth_object_detections, empty_ignore, test_box_overlap());
            }

            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
            {
                return train_impl(images, truth_object_detections, ignore, ignore_overlap_tester);
            }

            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> > empty_ignore(images.size());
                return train(images, truth_object_detections, empty_ignore, test_box_overlap());
            }

            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
            {
                std::vector<std::vector<full_object_detection> > truth_dets(truth_object_detections.size());
                for (unsigned long i = 0; i < truth_object_detections.size(); ++i)
                {
                    for (unsigned long j = 0; j < truth_object_detections[i].size(); ++j)
                    {
                        truth_dets[i].push_back(full_object_detection(truth_object_detections[i][j]));
                    }
                }

                return train_impl(images, truth_dets, ignore, ignore_overlap_tester);
            }

        private:

            template <
                typename image_array_type
                >
            const trained_function_type train_impl (
                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
            ) const
            {
    #ifdef ENABLE_ASSERTS
                // make sure requires clause is not broken
                DLIB_ASSERT(is_learning_problem(images,truth_object_detections) == true && images.size() == ignore.size(),
                    "\t trained_function_type structural_object_detection_trainer::train()"
                    << "\n\t invalid inputs were given to this function"
                    << "\n\t images.size():      " << images.size()
                    << "\n\t ignore.size():      " << ignore.size()
                    << "\n\t truth_object_detections.size(): " << truth_object_detections.size()
                    << "\n\t is_learning_problem(images,truth_object_detections): " << is_learning_problem(images,truth_object_detections)
                    );
                for (unsigned long i = 0; i < truth_object_detections.size(); ++i)
                {
                    for (unsigned long j = 0; j < truth_object_detections[i].size(); ++j)
                    {
                        DLIB_ASSERT(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,
                            "\t trained_function_type structural_object_detection_trainer::train()"
                            << "\n\t invalid inputs were given to this function"
                            << "\n\t truth_object_detections["<<i<<"]["<<j<<"].num_parts():                " <<
                                truth_object_detections[i][j].num_parts()
                            << "\n\t get_scanner().get_num_movable_components_per_detection_template(): " <<
                                get_scanner().get_num_movable_components_per_detection_template()
                            << "\n\t all_parts_in_rect(truth_object_detections["<<i<<"]["<<j<<"]): " << all_parts_in_rect(truth_object_detections[i][j])
                        );
                    }
                }
    #endif

                structural_svm_object_detection_problem<image_scanner_type,image_array_type >
                    svm_prob(scanner, overlap_tester, auto_overlap_tester, images,
                        truth_object_detections, ignore, ignore_overlap_tester, num_threads);

                if (verbose)
                    svm_prob.be_verbose();

                svm_prob.set_c(C);
                svm_prob.set_epsilon(eps);
                svm_prob.set_max_cache_size(max_cache_size);
                svm_prob.set_match_eps(match_eps);
                svm_prob.set_loss_per_missed_target(loss_per_missed_target);
                svm_prob.set_loss_per_false_alarm(loss_per_false_alarm);
                configure_nuclear_norm_regularizer(scanner, svm_prob);
                matrix<double,0,1> w;

                // Run the optimizer to find the optimal w.
                solver(svm_prob,w);

                // report the results of the training.
                return object_detector<image_scanner_type>(scanner, svm_prob.get_overlap_tester(), w);
            }

            image_scanner_type scanner;
            test_box_overlap overlap_tester;

            double C;
            oca solver;
            double eps;
            double match_eps;
            bool verbose;
            unsigned long num_threads;
            unsigned long max_cache_size;
            double loss_per_missed_target;
            double loss_per_false_alarm;
            bool auto_overlap_tester;

        };

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

    }

    #endif // DLIB_STRUCTURAL_OBJECT_DETECTION_TRAiNER_H__