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

    #include "structural_svm_object_detection_problem_abstract.h"
    #include "../matrix.h"
    #include "structural_svm_problem_threaded.h"
    #include <sstream>
    #include "../string.h"
    #include "../array.h"
    #include "../image_processing/full_object_detection.h"
    #include "../image_processing/box_overlap_testing.h"

    namespace dlib
    {

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

        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
        {
        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_,
                const std::vector<std::vector<rectangle> >& ignore_,
                const test_box_overlap& ignore_overlap_tester_,
                unsigned long num_threads = 2
            ) :
                structural_svm_problem_threaded<matrix<double,0,1> >(num_threads),
                boxes_overlap(overlap_tester),
                images(images_),
                truth_object_detections(truth_object_detections_),
                ignore(ignore_),
                ignore_overlap_tester(ignore_overlap_tester_),
                match_eps(0.5),
                loss_per_false_alarm(1),
                loss_per_missed_target(1)
            {
    #ifdef ENABLE_ASSERTS
                // make sure requires clause is not broken
                DLIB_ASSERT(is_learning_problem(images_, truth_object_detections_) &&
                            ignore_.size() == images_.size() &&
                             scanner.get_num_detection_templates() > 0,
                    "\t structural_svm_object_detection_problem::structural_svm_object_detection_problem()"
                    << "\n\t Invalid inputs were given to this function "
                    << "\n\t scanner.get_num_detection_templates(): " << scanner.get_num_detection_templates()
                    << "\n\t is_learning_problem(images_,truth_object_detections_): " << is_learning_problem(images_,truth_object_detections_)
                    << "\n\t ignore.size(): " << ignore.size()
                    << "\n\t images.size(): " << images.size()
                    << "\n\t this: " << this
                    );
                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() == scanner.get_num_movable_components_per_detection_template(),
                            "\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 scanner.get_num_movable_components_per_detection_template(): " <<
                                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
                // The purpose of the max_num_dets member variable is to give us a reasonable
                // upper limit on the number of detections we can expect from a single image.
                // This is used in the separation_oracle to put a hard limit on the number of
                // detections we will consider.  We do this purely for computational reasons
                // since otherwise we can end up wasting large amounts of time on certain
                // pathological cases during optimization which ultimately do not influence the
                // result.  Therefore, we force the separation oracle to only consider the
                // max_num_dets strongest detections.
                max_num_dets = 0;
                for (unsigned long i = 0; i < truth_object_detections.size(); ++i)
                {
                    if (truth_object_detections[i].size() > max_num_dets)
                        max_num_dets = truth_object_detections[i].size();
                }
                max_num_dets = max_num_dets*3 + 10;

                initialize_scanners(scanner, num_threads);

                if (auto_overlap_tester)
                {
                    auto_configure_overlap_tester();
                }
            }

            test_box_overlap get_overlap_tester (
            ) const
            {
                return boxes_overlap;
            }

            void set_match_eps (
                double eps
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(0 < eps && eps < 1,
                    "\t void structural_svm_object_detection_problem::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_svm_object_detection_problem::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_svm_object_detection_problem::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;
            }

        private:

            void auto_configure_overlap_tester(
            )
            {
                std::vector<std::vector<rectangle> > mapped_rects(truth_object_detections.size());
                for (unsigned long i = 0; i < truth_object_detections.size(); ++i)
                {
                    mapped_rects[i].resize(truth_object_detections[i].size());
                    for (unsigned long j = 0; j < truth_object_detections[i].size(); ++j)
                    {
                        mapped_rects[i][j] = scanners[i].get_best_matching_rect(truth_object_detections[i][j].get_rect());
                    }
                }

                boxes_overlap = find_tight_overlap_tester(mapped_rects);
            }

            virtual long get_num_dimensions (
            ) const
            {
                return scanners[0].get_num_dimensions() +
                    1;// for threshold
            }

            virtual long get_num_samples (
            ) const
            {
                return images.size();
            }

            virtual void get_truth_joint_feature_vector (
                long idx,
                feature_vector_type& psi
            ) const
            {
                const image_scanner_type& scanner = scanners[idx];

                psi.set_size(get_num_dimensions());
                std::vector<rectangle> mapped_rects;

                psi = 0;
                for (unsigned long i = 0; i < truth_object_detections[idx].size(); ++i)
                {
                    mapped_rects.push_back(scanner.get_best_matching_rect(truth_object_detections[idx][i].get_rect()));
                    scanner.get_feature_vector(truth_object_detections[idx][i], psi);
                }
                psi(scanner.get_num_dimensions()) = -1.0*truth_object_detections[idx].size();

                // check if any of the boxes overlap.  If they do then it is impossible for
                // us to learn to correctly classify this sample
                for (unsigned long i = 0; i < mapped_rects.size(); ++i)
                {
                    for (unsigned long j = i+1; j < mapped_rects.size(); ++j)
                    {
                        if (boxes_overlap(mapped_rects[i], mapped_rects[j]))
                        {
                            const double area_overlap = mapped_rects[i].intersect(mapped_rects[j]).area();
                            const double match_amount = area_overlap/(double)( mapped_rects[i]+mapped_rects[j]).area();
                            const double overlap_amount = area_overlap/std::min(mapped_rects[i].area(),mapped_rects[j].area());

                            std::ostringstream sout;
                            sout << "An impossible set of object labels was detected. This is happening because ";
                            sout << "the truth labels for an image contain rectangles which overlap according to the ";
                            sout << "test_box_overlap object supplied for non-max suppression.  To resolve this, you ";
                            sout << "either need to relax the test_box_overlap object so it doesn't mark these rectangles as ";
                            sout << "overlapping or adjust the truth rectangles in your training dataset. ";

                            // make sure the above string fits nicely into a command prompt window.
                            std::string temp = sout.str();
                            sout.str(""); sout << wrap_string(temp,0,0) << std::endl << std::endl;

                            sout << "image index: "<< idx << std::endl;
                            sout << "The offending rectangles are:\n";
                            sout << "rect1: "<< mapped_rects[i] << std::endl;
                            sout << "rect2: "<< mapped_rects[j] << std::endl;
                            sout << "match amount:   " << match_amount << std::endl;
                            sout << "overlap amount: " << overlap_amount << std::endl;
                            throw dlib::impossible_labeling_error(sout.str());
                        }
                    }
                }

                // make sure the mapped rectangles are within match_eps of the
                // truth rectangles.
                for (unsigned long i = 0; i < mapped_rects.size(); ++i)
                {
                    const double area = (truth_object_detections[idx][i].get_rect().intersect(mapped_rects[i])).area();
                    const double total_area = (truth_object_detections[idx][i].get_rect() + mapped_rects[i]).area();
                    if (area/total_area <= match_eps)
                    {
                        std::ostringstream sout;
                        sout << "An impossible set of object labels was detected.  This is happening because ";
                        sout << "none of the object locations checked by the supplied image scanner is a close ";
                        sout << "enough match to one of the truth boxes in your training dataset.  To resolve this ";
                        sout << "you need to either lower the match_eps, adjust the settings of the image scanner ";
                        sout << "so that it is capable of hitting this truth box, or adjust the offending truth rectangle so it ";
                        sout << "can be matched by the current image scanner.  Also, if you ";
                        sout << "are using the scan_fhog_pyramid object then you could try using a finer image pyramid.  ";
                        sout << "Additionally, the scan_fhog_pyramid scans a fixed aspect ratio box across the image when it ";
                        sout << "searches for objects.  So if you are getting this error and you are using the scan_fhog_pyramid, ";
                        sout << "it's very likely the problem is that your training dataset contains truth rectangles of widely ";
                        sout << "varying aspect ratios.  The solution is to make sure your training boxes all have about the same aspect ratio. ";

                        // make sure the above string fits nicely into a command prompt window.
                        std::string temp = sout.str();
                        sout.str(""); sout << wrap_string(temp,0,0) << std::endl << std::endl;

                        sout << "image index              "<< idx << std::endl;
                        sout << "match_eps:               "<< match_eps << std::endl;
                        sout << "best possible match:     "<< area/total_area << std::endl;
                        sout << "truth rect:              "<< truth_object_detections[idx][i].get_rect() << std::endl;
                        sout << "truth rect width/height: "<< truth_object_detections[idx][i].get_rect().width()/(double)truth_object_detections[idx][i].get_rect().height() << std::endl;
                        sout << "truth rect area:         "<< truth_object_detections[idx][i].get_rect().area() << std::endl;
                        sout << "nearest detection template rect:              "<< mapped_rects[i] << std::endl;
                        sout << "nearest detection template rect width/height: "<< mapped_rects[i].width()/(double)mapped_rects[i].height() << std::endl;
                        sout << "nearest detection template rect area:         "<< mapped_rects[i].area() << std::endl;
                        throw dlib::impossible_labeling_error(sout.str());
                    }

                }
            }

            virtual void separation_oracle (
                const long idx,
                const matrix_type& current_solution,
                scalar_type& loss,
                feature_vector_type& psi
            ) const
            {
                const image_scanner_type& scanner = scanners[idx];

                std::vector<std::pair<double, rectangle> > dets;
                const double thresh = current_solution(scanner.get_num_dimensions());

                scanner.detect(current_solution, dets, thresh-loss_per_false_alarm);

                // The loss will measure the number of incorrect detections.  A detection is
                // incorrect if it doesn't hit a truth rectangle or if it is a duplicate detection
                // on a truth rectangle.
                loss = truth_object_detections[idx].size()*loss_per_missed_target;

                // Measure the loss augmented score for the detections which hit a truth rect.
                std::vector<double> truth_score_hits(truth_object_detections[idx].size(), 0);

                // keep track of which truth boxes we have hit so far.
                std::vector<bool> hit_truth_table(truth_object_detections[idx].size(), false);

                std::vector<rectangle> final_dets;
                // The point of this loop is to fill out the truth_score_hits array.
                for (unsigned long i = 0; i < dets.size() && final_dets.size() < max_num_dets; ++i)
                {
                    if (overlaps_any_box(boxes_overlap, final_dets, dets[i].second))
                        continue;

                    const std::pair<double,unsigned int> truth = find_best_match(truth_object_detections[idx], dets[i].second);

                    final_dets.push_back(dets[i].second);

                    const double truth_match = truth.first;
                    // if hit truth rect
                    if (truth_match > match_eps)
                    {
                        // if this is the first time we have seen a detect which hit truth_object_detections[idx][truth.second]
                        const double score = dets[i].first - thresh;
                        if (hit_truth_table[truth.second] == false)
                        {
                            hit_truth_table[truth.second] = true;
                            truth_score_hits[truth.second] += score;
                        }
                        else
                        {
                            truth_score_hits[truth.second] += score + loss_per_false_alarm;
                        }
                    }
                }

                hit_truth_table.assign(hit_truth_table.size(), false);

                final_dets.clear();
    #ifdef ENABLE_ASSERTS
                double total_score = 0;
    #endif
                // Now figure out which detections jointly maximize the loss and detection score sum.  We
                // need to take into account the fact that allowing a true detection in the output, while
                // initially reducing the loss, may allow us to increase the loss later with many duplicate
                // detections.
                for (unsigned long i = 0; i < dets.size() && final_dets.size() < max_num_dets; ++i)
                {
                    if (overlaps_any_box(boxes_overlap, final_dets, dets[i].second))
                        continue;

                    const std::pair<double,unsigned int> truth = find_best_match(truth_object_detections[idx], dets[i].second);

                    const double truth_match = truth.first;
                    if (truth_match > match_eps)
                    {
                        if (truth_score_hits[truth.second] > loss_per_missed_target)
                        {
                            if (!hit_truth_table[truth.second])
                            {
                                hit_truth_table[truth.second] = true;
                                final_dets.push_back(dets[i].second);
    #ifdef ENABLE_ASSERTS
                                total_score += dets[i].first;
    #endif
                                loss -= loss_per_missed_target;
                            }
                            else
                            {
                                final_dets.push_back(dets[i].second);
    #ifdef ENABLE_ASSERTS
                                total_score += dets[i].first;
    #endif
                                loss += loss_per_false_alarm;
                            }
                        }
                    }
                    else if (!overlaps_ignore_box(idx,dets[i].second))
                    {
                        // didn't hit anything
                        final_dets.push_back(dets[i].second);
    #ifdef ENABLE_ASSERTS
                        total_score += dets[i].first;
    #endif
                        loss += loss_per_false_alarm;
                    }
                }

                psi.set_size(get_num_dimensions());
                psi = 0;
                for (unsigned long i = 0; i < final_dets.size(); ++i)
                    scanner.get_feature_vector(scanner.get_full_object_detection(final_dets[i], current_solution), psi);

    #ifdef ENABLE_ASSERTS
                const double psi_score = dot(psi, current_solution);
                DLIB_CASSERT(std::abs(psi_score-total_score) <= 1e-4 * std::max(1.0,std::max(std::abs(psi_score),std::abs(total_score))),
                            "\t The get_feature_vector() and detect() methods of image_scanner_type are not in sync."
                            << "\n\t The relative error is too large to be attributed to rounding error."
                            << "\n\t error:       " << std::abs(psi_score-total_score)
                            << "\n\t psi_score:   " << psi_score
                            << "\n\t total_score: " << total_score
                );
    #endif

                psi(scanner.get_num_dimensions()) = -1.0*final_dets.size();
            }

            bool overlaps_ignore_box (
                const long idx,
                const dlib::rectangle& rect
            ) const
            {
                for (unsigned long i = 0; i < ignore[idx].size(); ++i)
                {
                    if (ignore_overlap_tester(ignore[idx][i], rect))
                        return true;
                }
                return false;
            }

            std::pair<double,unsigned int> find_best_match(
                const std::vector<full_object_detection>& boxes,
                const rectangle rect
            ) const
            /*!
                ensures
                    - determines which rectangle in boxes matches rect the most and
                      returns the amount of this match.  Specifically, the match is
                      a number O with the following properties:
                        - 0 <= O <= 1
                        - Let R be the maximum matching rectangle in boxes, then
                          O == (R.intersect(rect)).area() / (R + rect).area()
                        - O == 0 if there is no match with any rectangle.
            !*/
            {
                double match = 0;
                unsigned int best_idx = 0;
                for (unsigned long i = 0; i < boxes.size(); ++i)
                {

                    const unsigned long area = rect.intersect(boxes[i].get_rect()).area();
                    if (area != 0)
                    {
                        const double new_match = area / static_cast<double>((rect + boxes[i].get_rect()).area());
                        if (new_match > match)
                        {
                            match = new_match;
                            best_idx = i;
                        }
                    }
                }

                return std::make_pair(match,best_idx);
            }

            struct init_scanners_helper
            {
                init_scanners_helper (
                    array<image_scanner_type>& scanners_,
                    const image_array_type& images_
                ) :
                    scanners(scanners_),
                    images(images_)
                {}

                array<image_scanner_type>& scanners;
                const image_array_type& images;

                void operator() (long i ) const
                {
                    scanners[i].load(images[i]);
                }
            };

            void initialize_scanners (
                const image_scanner_type& scanner,
                unsigned long num_threads
            )
            {
                scanners.set_max_size(images.size());
                scanners.set_size(images.size());

                for (unsigned long i = 0; i < scanners.size(); ++i)
                    scanners[i].copy_configuration(scanner);

                // now load the images into all the scanners
                parallel_for(num_threads, 0, scanners.size(), init_scanners_helper(scanners, images));
            }

            test_box_overlap boxes_overlap;

            mutable array<image_scanner_type> scanners;

            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;

            unsigned long max_num_dets;
            double match_eps;
            double loss_per_false_alarm;
            double loss_per_missed_target;
        };

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

    }

    #endif // DLIB_STRUCTURAL_SVM_ObJECT_DETECTION_PROBLEM_Hh_