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

    #include "cross_validate_object_detection_trainer_abstract.h"
    #include <vector>
    #include "../matrix.h"
    #include "svm.h"
    #include "../geometry.h"
    #include "../image_processing/full_object_detection.h"
    #include "../statistics.h"

    namespace dlib
    {

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

        namespace impl
        {
            inline unsigned long number_of_truth_hits (
                const std::vector<full_object_detection>& truth_boxes,
                const std::vector<std::pair<double,rectangle> >& boxes,
                const double overlap_eps,
                std::vector<std::pair<double,bool> >& all_dets,
                unsigned long& missing_detections
            )
            /*!
                requires
                    - 0 < overlap_eps <= 1
                ensures
                    - returns the number of elements in truth_boxes which are overlapped by an
                      element of boxes.  In this context, two boxes, A and B, overlap if and only if
                      the following quantity is greater than overlap_eps:
                        A.intersect(B).area()/(A+B).area()
                    - No element of boxes is allowed to account for more than one element of truth_boxes.
                    - The returned number is in the range [0,truth_boxes.size()]
                    - Adds the score for each box from boxes into all_dets and labels each with
                      a bool indicating if it hit a truth box.  Also adds the number of truth
                      boxes which didn't have any hits into missing_detections.
            !*/
            {
                if (boxes.size() == 0)
                {
                    missing_detections += truth_boxes.size();
                    return 0;
                }

                unsigned long count = 0;
                std::vector<bool> used(boxes.size(),false);
                for (unsigned long i = 0; i < truth_boxes.size(); ++i)
                {
                    bool found_match = false;
                    // Find the first box that hits truth_boxes[i]
                    for (unsigned long j = 0; j < boxes.size(); ++j)
                    {
                        if (used[j])
                            continue;

                        const double overlap = truth_boxes[i].get_rect().intersect(boxes[j].second).area() /
                                                    (double)(truth_boxes[i].get_rect()+boxes[j].second).area();

                        if (overlap >= overlap_eps)
                        {
                            used[j] = true;
                            ++count;
                            found_match = true;
                            break;
                        }
                    }

                    if (!found_match)
                        ++missing_detections;
                }

                for (unsigned long i = 0; i < boxes.size(); ++i)
                {
                    all_dets.push_back(std::make_pair(boxes[i].first, used[i]));
                }

                return count;
            }

        }

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

        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
        )
        {
            // make sure requires clause is not broken
            DLIB_CASSERT( is_learning_problem(images,truth_dets) == true &&
                         0 < overlap_eps && overlap_eps <= 1,
                        "\t matrix test_object_detection_function()"
                        << "\n\t invalid inputs were given to this function"
                        << "\n\t is_learning_problem(images,truth_dets): " << is_learning_problem(images,truth_dets)
                        << "\n\t overlap_eps: "<< overlap_eps
                        );

            double correct_hits = 0;
            double total_hits = 0;
            double total_true_targets = 0;

            std::vector<std::pair<double,bool> > all_dets;
            unsigned long missing_detections = 0;

            for (unsigned long i = 0; i < images.size(); ++i)
            {
                std::vector<std::pair<double,rectangle> > hits;
                detector(images[i], hits, adjust_threshold);

                total_hits += hits.size();
                correct_hits += impl::number_of_truth_hits(truth_dets[i], hits, overlap_eps, all_dets, missing_detections);
                total_true_targets += truth_dets[i].size();
            }

            std::sort(all_dets.rbegin(), all_dets.rend());

            double precision, recall;

            if (total_hits == 0)
                precision = 1;
            else
                precision = correct_hits / total_hits;

            if (total_true_targets == 0)
                recall = 1;
            else
                recall = correct_hits / total_true_targets;

            matrix<double, 1, 3> res;
            res = precision, recall, average_precision(all_dets, missing_detections);
            return res;
        }

        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
        )
        {
            // convert into a list of regular rectangles.
            std::vector<std::vector<full_object_detection> > rects(truth_dets.size());
            for (unsigned long i = 0; i < truth_dets.size(); ++i)
            {
                for (unsigned long j = 0; j < truth_dets[i].size(); ++j)
                {
                    rects[i].push_back(full_object_detection(truth_dets[i][j]));
                }
            }

            return test_object_detection_function(detector, images, rects, overlap_eps, adjust_threshold);
        }

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

        namespace impl
        {
            template <
                typename array_type
                >
            struct array_subset_helper
            {
                typedef typename array_type::mem_manager_type mem_manager_type;

                array_subset_helper (
                    const array_type& array_,
                    const std::vector<unsigned long>& idx_set_
                ) :
                    array(array_),
                    idx_set(idx_set_)
                {
                }

                unsigned long size() const { return idx_set.size(); }

                typedef typename array_type::type type;
                const type& operator[] (
                    unsigned long idx
                ) const { return array[idx_set[idx]]; }

            private:
                const array_type& array;
                const std::vector<unsigned long>& idx_set;
            };

            template <
                typename T
                >
            const matrix_op<op_array_to_mat<array_subset_helper<T> > > mat (
                const array_subset_helper<T>& m
            )
            {
                typedef op_array_to_mat<array_subset_helper<T> > op;
                return matrix_op<op>(op(m));
            }

        }

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

        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
        )
        {
            // make sure requires clause is not broken
            DLIB_CASSERT( is_learning_problem(images,truth_dets) == true &&
                         0 < overlap_eps && overlap_eps <= 1 &&
                         1 < folds && folds <= static_cast<long>(images.size()),
                        "\t matrix cross_validate_object_detection_trainer()"
                        << "\n\t invalid inputs were given to this function"
                        << "\n\t is_learning_problem(images,truth_dets): " << is_learning_problem(images,truth_dets)
                        << "\n\t overlap_eps: "<< overlap_eps
                        << "\n\t folds: "<< folds
                        );

            double correct_hits = 0;
            double total_hits = 0;
            double total_true_targets = 0;

            const long test_size = images.size()/folds;

            std::vector<std::pair<double,bool> > all_dets;
            unsigned long missing_detections = 0;
            unsigned long test_idx = 0;
            for (long iter = 0; iter < folds; ++iter)
            {
                std::vector<unsigned long> train_idx_set;
                std::vector<unsigned long> test_idx_set;

                for (long i = 0; i < test_size; ++i)
                    test_idx_set.push_back(test_idx++);

                unsigned long train_idx = test_idx%images.size();
                std::vector<std::vector<full_object_detection> > training_rects;
                for (unsigned long i = 0; i < images.size()-test_size; ++i)
                {
                    training_rects.push_back(truth_dets[train_idx]);
                    train_idx_set.push_back(train_idx);
                    train_idx = (train_idx+1)%images.size();
                }

                impl::array_subset_helper<image_array_type> array_subset(images, train_idx_set);
                typename trainer_type::trained_function_type detector = trainer.train(array_subset, training_rects);
                for (unsigned long i = 0; i < test_idx_set.size(); ++i)
                {
                    std::vector<std::pair<double,rectangle> > hits;
                    detector(images[test_idx_set[i]], hits, adjust_threshold);

                    total_hits += hits.size();
                    correct_hits += impl::number_of_truth_hits(truth_dets[test_idx_set[i]], hits, overlap_eps, all_dets, missing_detections);
                    total_true_targets += truth_dets[test_idx_set[i]].size();
                }

            }

            std::sort(all_dets.rbegin(), all_dets.rend());

            double precision, recall;

            if (total_hits == 0)
                precision = 1;
            else
                precision = correct_hits / total_hits;

            if (total_true_targets == 0)
                recall = 1;
            else
                recall = correct_hits / total_true_targets;

            matrix<double, 1, 3> res;
            res = precision, recall, average_precision(all_dets, missing_detections);
            return res;
        }

        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
        )
        {
            // convert into a list of regular rectangles.
            std::vector<std::vector<full_object_detection> > dets(truth_dets.size());
            for (unsigned long i = 0; i < truth_dets.size(); ++i)
            {
                for (unsigned long j = 0; j < truth_dets[i].size(); ++j)
                {
                    dets[i].push_back(full_object_detection(truth_dets[i][j]));
                }
            }

            return cross_validate_object_detection_trainer(trainer, images, dets, folds, overlap_eps, adjust_threshold);
        }

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

    }

    #endif // DLIB_CROSS_VALIDATE_OBJECT_DETECTION_TRaINER_H__