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

    #include "scan_image_pyramid_abstract.h"
    #include "../matrix.h"
    #include "../geometry.h"
    #include "../image_processing.h"
    #include "../array2d.h"
    #include <vector>
    #include "full_object_detection.h"
    #include "../image_processing/generic_image.h"

    namespace dlib
    {

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        class scan_image_pyramid : noncopyable
        {

        public:

            typedef matrix<double,0,1> feature_vector_type;

            typedef Pyramid_type pyramid_type;
            typedef Feature_extractor_type feature_extractor_type;

            scan_image_pyramid (
            );

            template <
                typename image_type
                >
            void load (
                const image_type& img
            );

            inline bool is_loaded_with_image (
            ) const;

            inline void copy_configuration(
                const feature_extractor_type& fe
            );

            inline void copy_configuration (
                const scan_image_pyramid& item
            );

            const Feature_extractor_type& get_feature_extractor (
            ) const { return feats_config; }

            void add_detection_template (
                const rectangle& object_box,
                const std::vector<rectangle>& stationary_feature_extraction_regions,
                const std::vector<rectangle>& movable_feature_extraction_regions
            );

            void add_detection_template (
                const rectangle& object_box,
                const std::vector<rectangle>& stationary_feature_extraction_regions
            );

            inline unsigned long get_num_detection_templates (
            ) const;

            inline unsigned long get_num_movable_components_per_detection_template (
            ) const;

            inline unsigned long get_num_stationary_components_per_detection_template (
            ) const;

            inline unsigned long get_num_components_per_detection_template (
            ) const;

            inline long get_num_dimensions (
            ) const;

            unsigned long get_max_pyramid_levels (
            ) const;

            void set_max_pyramid_levels (
                unsigned long max_levels
            );

            inline unsigned long get_max_detections_per_template (
            ) const;

            void set_min_pyramid_layer_size (
                unsigned long width,
                unsigned long height
            );

            inline unsigned long get_min_pyramid_layer_width (
            ) const;

            inline unsigned long get_min_pyramid_layer_height (
            ) const;

            void set_max_detections_per_template (
                unsigned long max_dets
            );

            void detect (
                const feature_vector_type& w,
                std::vector<std::pair<double, rectangle> >& dets,
                const double thresh
            ) const;

            void get_feature_vector (
                const full_object_detection& obj,
                feature_vector_type& psi
            ) const;

            full_object_detection get_full_object_detection (
                const rectangle& rect,
                const feature_vector_type& w
            ) const;

            const rectangle get_best_matching_rect (
                const rectangle& rect
            ) const;

            template <typename T, typename U>
            friend void serialize (
                const scan_image_pyramid<T,U>& item,
                std::ostream& out
            );

            template <typename T, typename U>
            friend void deserialize (
                scan_image_pyramid<T,U>& item,
                std::istream& in
            );

        private:
            static bool compare_pair_rect (
                const std::pair<double, rectangle>& a,
                const std::pair<double, rectangle>& b
            )
            {
                return a.first < b.first;
            }

            struct detection_template
            {
                rectangle object_box; // always centered at (0,0)
                std::vector<rectangle> rects; // template with respect to (0,0)
                std::vector<rectangle> movable_rects;
            };

            friend void serialize(const detection_template& item, std::ostream& out)
            {
                int version = 1;
                serialize(version, out);
                serialize(item.object_box, out);
                serialize(item.rects, out);
                serialize(item.movable_rects, out);
            }
            friend void deserialize(detection_template& item, std::istream& in)
            {
                int version = 0;
                deserialize(version, in);
                if (version != 1)
                    throw serialization_error("Unexpected version found while deserializing a dlib::scan_image_pyramid::detection_template object.");

                deserialize(item.object_box, in);
                deserialize(item.rects, in);
                deserialize(item.movable_rects, in);
            }

            void get_mapped_rect_and_metadata (
                const unsigned long number_pyramid_levels,
                rectangle rect,
                rectangle& mapped_rect,
                detection_template& best_template,
                rectangle& object_box,
                unsigned long& best_level,
                unsigned long& detection_template_idx
            ) const;

            double get_match_score (
                rectangle r1,
                rectangle r2
            ) const
            {
                // make the rectangles overlap as much as possible before computing the match score.
                r1 = move_rect(r1, r2.tl_corner());
                return (r1.intersect(r2).area())/(double)(r1 + r2).area();
            }

            void test_coordinate_transforms()
            {
                for (long x = -10; x <= 10; x += 10)
                {
                    for (long y = -10; y <= 10; y += 10)
                    {
                        const rectangle rect = centered_rect(x,y,5,6);
                        rectangle a;

                        a = feats_config.image_to_feat_space(rect);
                        if (a.width()  > 10000000 || a.height() > 10000000 )
                        {
                            DLIB_CASSERT(false, "The image_to_feat_space() routine is outputting rectangles of an implausibly "
                                         << "\nlarge size.  This means there is probably a bug in your feature extractor.");
                        }
                        a = feats_config.feat_to_image_space(rect);
                        if (a.width()  > 10000000 || a.height() > 10000000 )
                        {
                            DLIB_CASSERT(false, "The feat_to_image_space() routine is outputting rectangles of an implausibly "
                                         << "\nlarge size.  This means there is probably a bug in your feature extractor.");
                        }
                    }
                }

            }

            feature_extractor_type feats_config; // just here to hold configuration.  use it to populate the feats elements.
            array<feature_extractor_type> feats;
            std::vector<detection_template> det_templates;
            unsigned long max_dets_per_template;
            unsigned long max_pyramid_levels;
            unsigned long min_pyramid_layer_width;
            unsigned long min_pyramid_layer_height;

        };

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

        template <typename T, typename U>
        void serialize (
            const scan_image_pyramid<T,U>& item,
            std::ostream& out
        )
        {
            int version = 3;
            serialize(version, out);
            serialize(item.feats_config, out);
            serialize(item.feats, out);
            serialize(item.det_templates, out);
            serialize(item.max_dets_per_template, out);
            serialize(item.max_pyramid_levels, out);
            serialize(item.min_pyramid_layer_width, out);
            serialize(item.min_pyramid_layer_height, out);
            serialize(item.get_num_dimensions(), out);
        }

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

        template <typename T, typename U>
        void deserialize (
            scan_image_pyramid<T,U>& item,
            std::istream& in
        )
        {
            int version = 0;
            deserialize(version, in);
            if (version != 3)
                throw serialization_error("Unsupported version found when deserializing a scan_image_pyramid object.");

            deserialize(item.feats_config, in);
            deserialize(item.feats, in);
            deserialize(item.det_templates, in);
            deserialize(item.max_dets_per_template, in);
            deserialize(item.max_pyramid_levels, in);
            deserialize(item.min_pyramid_layer_width, in);
            deserialize(item.min_pyramid_layer_height, in);

            // When developing some feature extractor, it's easy to accidentally change its
            // number of dimensions and then try to deserialize data from an older version of
            // your extractor into the current code.  This check is here to catch that kind of
            // user error.
            long dims;
            deserialize(dims, in);
            if (item.get_num_dimensions() != dims)
                throw serialization_error("Number of dimensions in serialized scan_image_pyramid doesn't match the expected number.");
        }

    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------
    //                         scan_image_pyramid member functions
    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        scan_image_pyramid (
        ) :
            max_dets_per_template(10000),
            max_pyramid_levels(1000),
            min_pyramid_layer_width(20),
            min_pyramid_layer_height(20)
        {
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        template <
            typename image_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        load (
            const image_type& img
        )
        {
            unsigned long levels = 0;
            rectangle rect = get_rect(img);

            // figure out how many pyramid levels we should be using based on the image size
            pyramid_type pyr;
            do
            {
                rect = pyr.rect_down(rect);
                ++levels;
            } while (rect.width() >= min_pyramid_layer_width && rect.height() >= min_pyramid_layer_height &&
                     levels < max_pyramid_levels);

            if (feats.max_size() < levels)
                feats.set_max_size(levels);
            feats.set_size(levels);

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

            // build our feature pyramid
            feats[0].load(img);
            if (feats.size() > 1)
            {
                image_type temp1, temp2;
                pyr(img, temp1);
                feats[1].load(temp1);
                swap(temp1,temp2);

                for (unsigned long i = 2; i < feats.size(); ++i)
                {
                    pyr(temp2, temp1);
                    feats[i].load(temp1);
                    swap(temp1,temp2);
                }
            }

        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_max_detections_per_template (
        ) const
        {
            return max_dets_per_template;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        set_max_detections_per_template (
            unsigned long max_dets
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(max_dets > 0 ,
                "\t void scan_image_pyramid::set_max_detections_per_template()"
                << "\n\t The max number of possible detections can't be zero. "
                << "\n\t max_dets: " << max_dets
                << "\n\t this: " << this
                );

            max_dets_per_template = max_dets;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        bool scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        is_loaded_with_image (
        ) const
        {
            return feats.size() != 0;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        copy_configuration(
            const feature_extractor_type& fe
        )
        {
            test_coordinate_transforms();
            feats_config.copy_configuration(fe);
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        copy_configuration (
            const scan_image_pyramid& item
        )
        {
            feats_config.copy_configuration(item.feats_config);
            det_templates = item.det_templates;
            max_dets_per_template = item.max_dets_per_template;
            max_pyramid_levels = item.max_pyramid_levels;
            min_pyramid_layer_width = item.min_pyramid_layer_width;
            min_pyramid_layer_height = item.min_pyramid_layer_height;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        add_detection_template (
            const rectangle& object_box,
            const std::vector<rectangle>& stationary_feature_extraction_regions,
            const std::vector<rectangle>& movable_feature_extraction_regions
        )
        {
    #ifdef ENABLE_ASSERTS
            // make sure requires clause is not broken
            DLIB_ASSERT((get_num_detection_templates() == 0 ||
                            (get_num_stationary_components_per_detection_template() == stationary_feature_extraction_regions.size() &&
                            get_num_movable_components_per_detection_template() == movable_feature_extraction_regions.size())) &&
                            center(object_box) == point(0,0),
                "\t void scan_image_pyramid::add_detection_template()"
                << "\n\t The number of rects in this new detection template doesn't match "
                << "\n\t the number in previous detection templates."
                << "\n\t get_num_stationary_components_per_detection_template(): " << get_num_stationary_components_per_detection_template()
                << "\n\t stationary_feature_extraction_regions.size():           " << stationary_feature_extraction_regions.size()
                << "\n\t get_num_movable_components_per_detection_template():    " << get_num_movable_components_per_detection_template()
                << "\n\t movable_feature_extraction_regions.size():              " << movable_feature_extraction_regions.size()
                << "\n\t this: " << this
                );

            for (unsigned long i = 0; i < movable_feature_extraction_regions.size(); ++i)
            {
                DLIB_ASSERT(center(movable_feature_extraction_regions[i]) == point(0,0),
                            "Invalid inputs were given to this function."
                            << "\n\t center(movable_feature_extraction_regions["<<i<<"]): " << center(movable_feature_extraction_regions[i])
                            << "\n\t this: " << this
                );
            }
    #endif

            detection_template temp;
            temp.object_box = object_box;
            temp.rects = stationary_feature_extraction_regions;
            temp.movable_rects = movable_feature_extraction_regions;
            det_templates.push_back(temp);
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        add_detection_template (
            const rectangle& object_box,
            const std::vector<rectangle>& stationary_feature_extraction_regions
        )
        {
            // an empty set of movable feature regions
            const std::vector<rectangle> movable_feature_extraction_regions;
            add_detection_template(object_box, stationary_feature_extraction_regions,
                                   movable_feature_extraction_regions);
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_num_detection_templates (
        ) const
        {
            return det_templates.size();
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_num_stationary_components_per_detection_template (
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 ,
                "\t unsigned long scan_image_pyramid::get_num_stationary_components_per_detection_template()"
                << "\n\t You need to give some detection templates before calling this function. "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t this: " << this
                );

            return det_templates[0].rects.size();
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_num_movable_components_per_detection_template (
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 ,
                "\t unsigned long scan_image_pyramid::get_num_movable_components_per_detection_template()"
                << "\n\t You need to give some detection templates before calling this function. "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t this: " << this
                );

            return det_templates[0].movable_rects.size();
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_num_components_per_detection_template (
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 ,
                "\t unsigned long scan_image_pyramid::get_num_components_per_detection_template()"
                << "\n\t You need to give some detection templates before calling this function. "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t this: " << this
                );

            return get_num_movable_components_per_detection_template() +
                   get_num_stationary_components_per_detection_template();
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_num_dimensions (
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 ,
                "\t long scan_image_pyramid::get_num_dimensions()"
                << "\n\t You need to give some detection templates before calling this function. "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t this: " << this
                );

            return feats_config.get_num_dimensions()*get_num_components_per_detection_template() + get_num_detection_templates();
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_max_pyramid_levels (
        ) const
        {
            return max_pyramid_levels;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        set_max_pyramid_levels (
            unsigned long max_levels
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(max_levels > 0 ,
                "\t void scan_image_pyramid::set_max_pyramid_levels()"
                << "\n\t You can't have zero levels. "
                << "\n\t max_levels: " << max_levels
                << "\n\t this: " << this
                );

            max_pyramid_levels = max_levels;

        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        detect (
            const feature_vector_type& w,
            std::vector<std::pair<double, rectangle> >& dets,
            const double thresh
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 &&
                        is_loaded_with_image() &&
                        w.size() >= get_num_dimensions(),
                "\t void scan_image_pyramid::detect()"
                << "\n\t Invalid inputs were given to this function "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t is_loaded_with_image(): " << is_loaded_with_image()
                << "\n\t w.size():               " << w.size()
                << "\n\t get_num_dimensions():   " << get_num_dimensions()
                << "\n\t this: " << this
                );

            dets.clear();

            array<array2d<double> > saliency_images;
            saliency_images.set_max_size(get_num_components_per_detection_template());
            saliency_images.set_size(get_num_components_per_detection_template());
            std::vector<std::pair<unsigned int,rectangle> > stationary_region_rects(get_num_stationary_components_per_detection_template());
            std::vector<std::pair<unsigned int,rectangle> > movable_region_rects(get_num_movable_components_per_detection_template());
            pyramid_type pyr;
            std::vector<std::pair<double, point> > point_dets;

            // for all pyramid levels
            for (unsigned long l = 0; l < feats.size(); ++l)
            {
                for (unsigned long i = 0; i < saliency_images.size(); ++i)
                {
                    saliency_images[i].set_size(feats[l].nr(), feats[l].nc());
                    const unsigned long offset = get_num_detection_templates() + feats_config.get_num_dimensions()*i;

                    // build saliency images for pyramid level l
                    for (long r = 0; r < feats[l].nr(); ++r)
                    {
                        for (long c = 0; c < feats[l].nc(); ++c)
                        {
                            const typename feature_extractor_type::descriptor_type& descriptor = feats[l](r,c);

                            double sum = 0;
                            for (unsigned long k = 0; k < descriptor.size(); ++k)
                            {
                                sum += w(descriptor[k].first + offset)*descriptor[k].second;
                            }
                            saliency_images[i][r][c] = sum;
                        }
                    }
                }

                // now search the saliency images
                for (unsigned long i = 0; i < det_templates.size(); ++i)
                {
                    const point offset = -feats[l].image_to_feat_space(point(0,0));
                    for (unsigned long j = 0; j < stationary_region_rects.size(); ++j)
                    {
                        stationary_region_rects[j] = std::make_pair(j, translate_rect(feats[l].image_to_feat_space(det_templates[i].rects[j]),offset));
                    }
                    for (unsigned long j = 0; j < movable_region_rects.size(); ++j)
                    {
                        // Scale the size of the movable rectangle but make sure its center
                        // stays at point(0,0).
                        const rectangle temp = feats[l].image_to_feat_space(det_templates[i].movable_rects[j]);
                        movable_region_rects[j] = std::make_pair(j+stationary_region_rects.size(),
                                                                 centered_rect(point(0,0),temp.width(), temp.height()));
                    }

                    // Scale the object box into the feature extraction image, but keeping it
                    // centered at point(0,0).
                    rectangle scaled_object_box = feats[l].image_to_feat_space(det_templates[i].object_box);
                    scaled_object_box = centered_rect(point(0,0),scaled_object_box.width(), scaled_object_box.height());

                    // Each detection template gets its own special threshold in addition to
                    // the global detection threshold.  This allows us to model the fact that
                    // some detection templates might be more prone to false alarming or since
                    // their size is different naturally require a larger or smaller threshold
                    // (since they integrate over a larger or smaller region of the image).
                    const double template_specific_thresh = w(i);

                    scan_image_movable_parts(point_dets, saliency_images, scaled_object_box,
                                             stationary_region_rects, movable_region_rects,
                                             thresh+template_specific_thresh, max_dets_per_template);

                    // convert all the point detections into rectangles at the original image scale and coordinate system
                    for (unsigned long j = 0; j < point_dets.size(); ++j)
                    {
                        const double score = point_dets[j].first-template_specific_thresh;
                        point p = point_dets[j].second;
                        p = feats[l].feat_to_image_space(p);
                        rectangle rect = translate_rect(det_templates[i].object_box, p);
                        rect = pyr.rect_up(rect, l);

                        dets.push_back(std::make_pair(score, rect));
                    }
                }
            }

            std::sort(dets.rbegin(), dets.rend(), compare_pair_rect);
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        const rectangle scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_best_matching_rect (
            const rectangle& rect
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 ,
                "\t const rectangle scan_image_pyramid::get_best_matching_rect()"
                << "\n\t Invalid inputs were given to this function "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t this: " << this
                );

            rectangle mapped_rect, object_box;
            detection_template best_template;
            unsigned long best_level, junk;
            get_mapped_rect_and_metadata(max_pyramid_levels, rect, mapped_rect, best_template, object_box, best_level, junk);
            return mapped_rect;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_mapped_rect_and_metadata (
            const unsigned long number_pyramid_levels,
            rectangle rect,
            rectangle& mapped_rect,
            detection_template& best_template,
            rectangle& object_box,
            unsigned long& best_level,
            unsigned long& detection_template_idx
        ) const
        {
            pyramid_type pyr;
            // Figure out the pyramid level which best matches rect against one of our
            // detection template object boxes.
            best_level = 0;
            double best_match_score = -1;

            // Find the best matching detection template for rect
            for (unsigned long l = 0; l < number_pyramid_levels; ++l)
            {
                const rectangle temp = pyr.rect_down(rect,l);
                if (temp.area() <= 1)
                    break;

                // At this pyramid level, what matches best?
                for (unsigned long t = 0; t < det_templates.size(); ++t)
                {
                    const double match_score = get_match_score(det_templates[t].object_box, temp);
                    if (match_score > best_match_score)
                    {
                        best_match_score = match_score;
                        best_level = l;
                        best_template = det_templates[t];
                        detection_template_idx = t;
                    }
                }
            }

            // Now we translate best_template into the right spot (it should be centered at the location
            // determined by rect) and convert it into the feature image coordinate system.
            rect = pyr.rect_down(rect,best_level);
            const point offset = -feats_config.image_to_feat_space(point(0,0));
            const point origin = feats_config.image_to_feat_space(center(rect)) + offset;
            for (unsigned long k = 0; k < best_template.rects.size(); ++k)
            {
                rectangle temp = best_template.rects[k];
                temp = feats_config.image_to_feat_space(temp);
                temp = translate_rect(temp, origin);
                best_template.rects[k] = temp;
            }
            for (unsigned long k = 0; k < best_template.movable_rects.size(); ++k)
            {
                rectangle temp = best_template.movable_rects[k];
                temp = feats_config.image_to_feat_space(temp);
                temp = centered_rect(point(0,0), temp.width(), temp.height());
                best_template.movable_rects[k] = temp;
            }

            const rectangle scaled_object_box = feats_config.image_to_feat_space(best_template.object_box);
            object_box = centered_rect(origin-offset, scaled_object_box.width(), scaled_object_box.height());

            // The input rectangle was mapped to one of the detection templates.  Reverse the process
            // to figure out what the mapped rectangle is in the original input space.
            mapped_rect = translate_rect(best_template.object_box, feats_config.feat_to_image_space(origin-offset));
            mapped_rect = pyr.rect_up(mapped_rect, best_level);
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        full_object_detection scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_full_object_detection (
            const rectangle& rect,
            const feature_vector_type& w
        ) const
        {
            // fill in movable part positions.

            rectangle mapped_rect;
            detection_template best_template;
            unsigned long best_level, junk;
            rectangle object_box;
            get_mapped_rect_and_metadata(feats.size(), rect, mapped_rect, best_template, object_box, best_level, junk);

            Pyramid_type pyr;

            array2d<double> saliency_image, sum_img;

            double total_temp_score = 0;
            // convert into feature space.
            object_box = object_box.intersect(get_rect(feats[best_level]));

            std::vector<point> movable_parts;
            movable_parts.reserve(get_num_movable_components_per_detection_template());
            for (unsigned long i = 0; i < get_num_movable_components_per_detection_template(); ++i)
            {
                // make the saliency_image for the ith movable part.

                const rectangle part_rect = best_template.movable_rects[i];
                const rectangle area = grow_rect(object_box,
                                                 part_rect.width()/2,
                                                 part_rect.height()/2).intersect(get_rect(feats[best_level]));

                saliency_image.set_size(area.height(), area.width());
                const unsigned long offset = get_num_detection_templates() + feats_config.get_num_dimensions()*(i+get_num_stationary_components_per_detection_template());

                // build saliency image for pyramid level best_level
                for (long r = area.top(); r <= area.bottom(); ++r)
                {
                    for (long c = area.left(); c <= area.right(); ++c)
                    {
                        const typename feature_extractor_type::descriptor_type& descriptor = feats[best_level](r,c);

                        double sum = 0;
                        for (unsigned long k = 0; k < descriptor.size(); ++k)
                        {
                            sum += w(descriptor[k].first + offset)*descriptor[k].second;
                        }
                        saliency_image[r-area.top()][c-area.left()] = sum;
                    }
                }

                sum_img.set_size(saliency_image.nr(), saliency_image.nc());
                sum_filter_assign(saliency_image, sum_img, part_rect);
                // Figure out where the maximizer is in sum_img.  Note that we
                // only look in the part of sum_img that corresponds to a location inside
                // object_box.
                rectangle valid_area = get_rect(sum_img);
                valid_area.left()   += object_box.left()   - area.left();
                valid_area.top()    += object_box.top()    - area.top();
                valid_area.right()  += object_box.right()  - area.right();
                valid_area.bottom() += object_box.bottom() - area.bottom();
                double max_val = 0;
                point max_loc;
                for (long r = valid_area.top(); r <= valid_area.bottom(); ++r)
                {
                    for (long c = valid_area.left(); c <= valid_area.right(); ++c)
                    {
                        if (sum_img[r][c] > max_val)
                        {
                            //if (object_box.contains(point(c,r) + area.tl_corner()))
                            {
                                max_loc = point(c,r);
                                max_val = sum_img[r][c];
                            }
                        }
                    }
                }

                if (max_val <= 0)
                {
                    max_loc = OBJECT_PART_NOT_PRESENT;
                }
                else
                {
                    total_temp_score += max_val;
                    // convert max_loc back into feature image space from our cropped image.
                    max_loc += area.tl_corner();

                    // now convert from feature space to image space.
                    max_loc = feats[best_level].feat_to_image_space(max_loc);
                    max_loc = pyr.point_up(max_loc, best_level);
                    max_loc = nearest_point(rect, max_loc);
                }

                movable_parts.push_back(max_loc);
            }

            return full_object_detection(rect, movable_parts);
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_feature_vector (
            const full_object_detection& obj,
            feature_vector_type& psi
        ) const
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(get_num_detection_templates() > 0 &&
                        is_loaded_with_image() &&
                        psi.size() >= get_num_dimensions() &&
                        obj.num_parts() == get_num_movable_components_per_detection_template(),
                "\t void scan_image_pyramid::get_feature_vector()"
                << "\n\t Invalid inputs were given to this function "
                << "\n\t get_num_detection_templates(): " << get_num_detection_templates()
                << "\n\t is_loaded_with_image(): " << is_loaded_with_image()
                << "\n\t psi.size():             " << psi.size()
                << "\n\t get_num_dimensions():   " << get_num_dimensions()
                << "\n\t get_num_movable_components_per_detection_template(): " << get_num_movable_components_per_detection_template()
                << "\n\t obj.num_parts():                            " << obj.num_parts()
                << "\n\t this: " << this
                );
            DLIB_ASSERT(all_parts_in_rect(obj),
                "\t void scan_image_pyramid::get_feature_vector()"
                << "\n\t Invalid inputs were given to this function "
                << "\n\t obj.get_rect(): " << obj.get_rect()
                << "\n\t this: " << this
            );

            rectangle mapped_rect;
            detection_template best_template;
            unsigned long best_level, detection_template_idx;
            rectangle object_box;
            get_mapped_rect_and_metadata(feats.size(), obj.get_rect(), mapped_rect, best_template, object_box, best_level, detection_template_idx);

            psi(detection_template_idx) -= 1;

            Pyramid_type pyr;

            // put the movable rects at the places indicated by obj.
            std::vector<rectangle> rects = best_template.rects;
            for (unsigned long i = 0; i < obj.num_parts(); ++i)
            {
                if (obj.part(i) != OBJECT_PART_NOT_PRESENT)
                {
                    // map from the original image to scaled feature space.
                    point loc = feats[best_level].image_to_feat_space(pyr.point_down(obj.part(i), best_level));
                    // Make sure the movable part always stays within the object_box.
                    // Otherwise it would be at a place that the detect() function can never
                    // look.
                    loc = nearest_point(object_box, loc);
                    rects.push_back(translate_rect(best_template.movable_rects[i], loc));
                }
                else
                {
                    // add an empty rectangle since this part wasn't observed.
                    rects.push_back(rectangle());
                }
            }

            // pull features out of all the boxes in rects.
            for (unsigned long j = 0; j < rects.size(); ++j)
            {
                const rectangle rect = rects[j].intersect(get_rect(feats[best_level]));
                const unsigned long template_region_id = j;
                const unsigned long offset = get_num_detection_templates() + feats_config.get_num_dimensions()*template_region_id;
                for (long r = rect.top(); r <= rect.bottom(); ++r)
                {
                    for (long c = rect.left(); c <= rect.right(); ++c)
                    {
                        const typename feature_extractor_type::descriptor_type& descriptor = feats[best_level](r,c);
                        for (unsigned long k = 0; k < descriptor.size(); ++k)
                        {
                            psi(descriptor[k].first + offset) += descriptor[k].second;
                        }
                    }
                }
            }

        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        void scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        set_min_pyramid_layer_size (
            unsigned long width,
            unsigned long height
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(width > 0 && height > 0 ,
                "\t void scan_image_pyramid::set_min_pyramid_layer_size()"
                << "\n\t These sizes can't be zero. "
                << "\n\t width:  " << width
                << "\n\t height: " << height
                << "\n\t this:   " << this
                );

            min_pyramid_layer_width = width;
            min_pyramid_layer_height = height;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_min_pyramid_layer_width (
        ) const
        {
            return min_pyramid_layer_width;
        }

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type
            >
        unsigned long scan_image_pyramid<Pyramid_type,Feature_extractor_type>::
        get_min_pyramid_layer_height (
        ) const
        {
            return min_pyramid_layer_height;
        }

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

    }

    #endif // DLIB_SCAN_IMaGE_PYRAMID_Hh_