    // Copyright (C) 2013  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_SCAN_fHOG_PYRAMID_Hh_
    #define DLIB_SCAN_fHOG_PYRAMID_Hh_

    #include "scan_fhog_pyramid_abstract.h"
    #include "../matrix.h"
    #include "../image_transforms.h"
    #include "../array.h"
    #include "../array2d.h"
    #include "object_detector.h"

    namespace dlib
    {

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

        class default_fhog_feature_extractor
        {
        public:
            inline rectangle image_to_feats (
                const rectangle& rect,
                int cell_size,
                int filter_rows_padding,
                int filter_cols_padding
            ) const
            {
                return image_to_fhog(rect, cell_size, filter_rows_padding, filter_cols_padding);
            }

            inline rectangle feats_to_image (
                const rectangle& rect,
                int cell_size,
                int filter_rows_padding,
                int filter_cols_padding
            ) const
            {
                return fhog_to_image(rect, cell_size, filter_rows_padding, filter_cols_padding);
            }

            template <
                typename image_type
                >
            void operator()(
                const image_type& img,
                dlib::array<array2d<float> >& hog,
                int cell_size,
                int filter_rows_padding,
                int filter_cols_padding
            ) const
            {
                extract_fhog_features(img,hog,cell_size,filter_rows_padding,filter_cols_padding);
            }

            inline unsigned long get_num_planes (
            ) const
            {
                return 31;
            }
        };

        inline void serialize   (const default_fhog_feature_extractor&, std::ostream&) {}
        inline void deserialize (default_fhog_feature_extractor&, std::istream&) {}

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

        template <
            typename Pyramid_type,
            typename Feature_extractor_type = default_fhog_feature_extractor
            >
        class scan_fhog_pyramid : noncopyable
        {

        public:

            typedef matrix<double,0,1> feature_vector_type;

            typedef Pyramid_type pyramid_type;
            typedef Feature_extractor_type feature_extractor_type;

            scan_fhog_pyramid (
            );

            explicit scan_fhog_pyramid (
                const feature_extractor_type& fe_
            );

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

            inline bool is_loaded_with_image (
            ) const;

            inline void copy_configuration (
                const scan_fhog_pyramid& item
            );

            void set_detection_window_size (
                unsigned long width,
                unsigned long height
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(width > 0 && height > 0,
                    "\t void scan_fhog_pyramid::set_detection_window_size()"
                    << "\n\t Invalid inputs were given to this function "
                    << "\n\t width:  " << width
                    << "\n\t height: " << height
                    << "\n\t this:   " << this
                    );

                window_width = width;
                window_height = height;
                feats.clear();
            }

            inline unsigned long get_detection_window_width (
            ) const { return window_width; }
            inline unsigned long get_detection_window_height (
            ) const { return window_height; }

            inline unsigned long get_num_detection_templates (
            ) const;

            inline unsigned long get_num_movable_components_per_detection_template (
            ) const;

            void set_padding (
                unsigned long new_padding
            )
            {
                padding = new_padding;
                feats.clear();
            }

            unsigned long get_padding (
            ) const { return padding; }

            void set_cell_size (
                unsigned long new_cell_size
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(new_cell_size > 0 ,
                    "\t void scan_fhog_pyramid::set_cell_size()"
                    << "\n\t You can't have zero sized fHOG cells. "
                    << "\n\t this: " << this
                    );

                cell_size = new_cell_size;
                feats.clear();
            }

            unsigned long get_cell_size (
            ) const { return cell_size; }

            inline long get_num_dimensions (
            ) const;

            unsigned long get_max_pyramid_levels (
            ) const;

            const feature_extractor_type& get_feature_extractor(
            ) const { return fe; }

            void set_max_pyramid_levels (
                unsigned long max_levels
            );

            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 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(is_loaded_with_image() &&
                            w.size() >= get_num_dimensions(),
                    "\t void scan_fhog_pyramid::detect()"
                    << "\n\t Invalid inputs were given to this function "
                    << "\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
                    );

                fhog_filterbank temp = build_fhog_filterbank(w);
                detect(temp, dets, thresh);
            }

            class fhog_filterbank
            {
                friend class scan_fhog_pyramid;
            public:
                inline long get_num_dimensions() const
                {
                    unsigned long dims = 0;
                    for (unsigned long i = 0; i < filters.size(); ++i)
                    {
                        dims += filters[i].size();
                    }
                    return dims;
                }

                const std::vector<matrix<float> >& get_filters() const { return filters;}

                unsigned long num_separable_filters() const
                {
                    unsigned long num = 0;
                    for (unsigned long i = 0; i < row_filters.size(); ++i)
                    {
                        num += row_filters[i].size();
                    }
                    return num;
                }

                std::vector<matrix<float> > filters;
                std::vector<std::vector<matrix<float,0,1> > > row_filters, col_filters;
            };

            fhog_filterbank build_fhog_filterbank (
                const feature_vector_type& weights
            ) const
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(weights.size() >= get_num_dimensions(),
                    "\t fhog_filterbank scan_fhog_pyramid::build_fhog_filterbank()"
                    << "\n\t The number of weights isn't enough to fill out the filterbank. "
                    << "\n\t weights.size():       " << weights.size()
                    << "\n\t get_num_dimensions(): " << get_num_dimensions()
                    << "\n\t this: " << this
                    );

                fhog_filterbank temp;
                temp.filters.resize(fe.get_num_planes());
                temp.row_filters.resize(fe.get_num_planes());
                temp.col_filters.resize(fe.get_num_planes());

                // load filters from w
                unsigned long width, height;
                compute_fhog_window_size(width, height);
                const long size = width*height;
                for (unsigned long i = 0; i < temp.filters.size(); ++i)
                {
                    matrix<double> u,v,w,f;
                    f = reshape(rowm(weights, range(i*size, (i+1)*size-1)), height, width);
                    temp.filters[i] = matrix_cast<float>(f);

                    svd3(f, u,w,v);

                    matrix<double> w2 = w;
                    rsort_columns(u,w);
                    rsort_columns(v,w2);

                    double thresh = std::max(1e-4, max(w)*0.001);
                    w = round_zeros(w, thresh);

                    for (long j = 0; j < w.size(); ++j)
                    {
                        if (w(j) != 0)
                        {
                            temp.col_filters[i].push_back(matrix_cast<float>(colm(u,j)*std::sqrt(w(j))));
                            temp.row_filters[i].push_back(matrix_cast<float>(colm(v,j)*std::sqrt(w(j))));
                        }
                    }
                }

                return temp;
            }

            void detect (
                const fhog_filterbank& 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;

            double get_nuclear_norm_regularization_strength (
            ) const { return nuclear_norm_regularization_strength; }

            void set_nuclear_norm_regularization_strength (
                double strength
            )
            {
                // make sure requires clause is not broken
                DLIB_ASSERT(strength >= 0 ,
                    "\t void scan_fhog_pyramid::set_nuclear_norm_regularization_strength()"
                    << "\n\t You can't have a negative regularization strength."
                    << "\n\t strength: " << strength
                    << "\n\t this: " << this
                );

                nuclear_norm_regularization_strength = strength;
            }

            unsigned long get_fhog_window_width (
            ) const
            {
                unsigned long width, height;
                compute_fhog_window_size(width, height);
                return width;
            }

            unsigned long get_fhog_window_height (
            ) const
            {
                unsigned long width, height;
                compute_fhog_window_size(width, height);
                return height;
            }

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

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

        private:
            inline void compute_fhog_window_size(
                unsigned long& width,
                unsigned long& height
            ) const
            {
                const rectangle rect = centered_rect(point(0,0),window_width,window_height);
                const rectangle temp = grow_rect(fe.image_to_feats(rect, cell_size, 1, 1), padding);
                width = temp.width();
                height = temp.height();
            }

            void get_mapped_rect_and_metadata (
                const unsigned long number_pyramid_levels,
                const rectangle& rect,
                rectangle& mapped_rect,
                rectangle& fhog_rect,
                unsigned long& best_level
            ) 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();
            }

            typedef array<array2d<float> > fhog_image;

            feature_extractor_type fe;
            array<fhog_image> feats;
            int cell_size;
            unsigned long padding;
            unsigned long window_width;
            unsigned long window_height;
            unsigned long max_pyramid_levels;
            unsigned long min_pyramid_layer_width;
            unsigned long min_pyramid_layer_height;
            double nuclear_norm_regularization_strength;

            void init()
            {
                cell_size = 8;
                padding = 1;
                window_width = 64;
                window_height = 64;
                max_pyramid_levels = 1000;
                min_pyramid_layer_width = 64;
                min_pyramid_layer_height = 64;
                nuclear_norm_regularization_strength = 0;
            }

        };

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

        namespace impl
        {
            template <typename fhog_filterbank>
            rectangle apply_filters_to_fhog (
                const fhog_filterbank& w,
                const array<array2d<float> >& feats,
                array2d<float>& saliency_image
            )
            {
                const unsigned long num_separable_filters = w.num_separable_filters();
                rectangle area;
                // use the separable filters if they would be faster than running the regular filters.
                if (num_separable_filters > w.filters.size()*std::min(w.filters[0].nr(),w.filters[0].nc())/3.0)
                {
                    area = spatially_filter_image(feats[0], saliency_image, w.filters[0]);
                    for (unsigned long i = 1; i < w.filters.size(); ++i)
                    {
                        // now we filter but the output adds to saliency_image rather than
                        // overwriting it.
                        spatially_filter_image(feats[i], saliency_image, w.filters[i], 1, false, true);
                    }
                }
                else
                {
                    saliency_image.clear();
                    array2d<float> scratch;

                    // find the first filter to apply
                    unsigned long i = 0;
                    while (i < w.row_filters.size() && w.row_filters[i].size() == 0)
                        ++i;

                    for (; i < w.row_filters.size(); ++i)
                    {
                        for (unsigned long j = 0; j < w.row_filters[i].size(); ++j)
                        {
                            if (saliency_image.size() == 0)
                                area = float_spatially_filter_image_separable(feats[i], saliency_image, w.row_filters[i][j], w.col_filters[i][j],scratch,false);
                            else
                                area = float_spatially_filter_image_separable(feats[i], saliency_image, w.row_filters[i][j], w.col_filters[i][j],scratch,true);
                        }
                    }
                    if (saliency_image.size() == 0)
                    {
                        saliency_image.set_size(feats[0].nr(), feats[0].nc());
                        assign_all_pixels(saliency_image, 0);
                    }
                }
                return area;
            }
        }

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

        template <typename T, typename U>
        void serialize (
            const scan_fhog_pyramid<T,U>& item,
            std::ostream& out
        )
        {
            int version = 1;
            serialize(version, out);
            serialize(item.fe, out);
            serialize(item.feats, out);
            serialize(item.cell_size, out);
            serialize(item.padding, out);
            serialize(item.window_width, out);
            serialize(item.window_height, out);
            serialize(item.max_pyramid_levels, out);
            serialize(item.min_pyramid_layer_width, out);
            serialize(item.min_pyramid_layer_height, out);
            serialize(item.nuclear_norm_regularization_strength, out);
            serialize(item.get_num_dimensions(), out);
        }

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

        template <typename T, typename U>
        void deserialize (
            scan_fhog_pyramid<T,U>& item,
            std::istream& in
        )
        {
            int version = 0;
            deserialize(version, in);
            if (version != 1)
                throw serialization_error("Unsupported version found when deserializing a scan_fhog_pyramid object.");

            deserialize(item.fe, in);
            deserialize(item.feats, in);
            deserialize(item.cell_size, in);
            deserialize(item.padding, in);
            deserialize(item.window_width, in);
            deserialize(item.window_height, in);
            deserialize(item.max_pyramid_levels, in);
            deserialize(item.min_pyramid_layer_width, in);
            deserialize(item.min_pyramid_layer_height, in);
            deserialize(item.nuclear_norm_regularization_strength, 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_fhog_pyramid doesn't match the expected number.");
        }

    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------
    //                         scan_fhog_pyramid member functions
    // ----------------------------------------------------------------------------------------
    // ----------------------------------------------------------------------------------------

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        scan_fhog_pyramid (
        )
        {
            init();
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        scan_fhog_pyramid (
            const feature_extractor_type& fe_
        )
        {
            init();
            fe = fe_;
        }

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

        namespace impl
        {
            template <
                typename pyramid_type,
                typename image_type,
                typename feature_extractor_type
                >
            void create_fhog_pyramid (
                const image_type& img,
                const feature_extractor_type& fe,
                array<array<array2d<float> > >& feats,
                int cell_size,
                int filter_rows_padding,
                int filter_cols_padding,
                unsigned long min_pyramid_layer_width,
                unsigned long min_pyramid_layer_height,
                unsigned long max_pyramid_levels
            )
            {
                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);

                // build our feature pyramid
                fe(img, feats[0], cell_size,filter_rows_padding,filter_cols_padding);
                DLIB_ASSERT(feats[0].size() == fe.get_num_planes(),
                    "Invalid feature extractor used with dlib::scan_fhog_pyramid.  The output does not have the \n"
                    "indicated number of planes.");

                if (feats.size() > 1)
                {
                    typedef typename image_traits<image_type>::pixel_type pixel_type;
                    array2d<pixel_type> temp1, temp2;
                    pyr(img, temp1);
                    fe(temp1, feats[1], cell_size,filter_rows_padding,filter_cols_padding);
                    swap(temp1,temp2);

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

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        template <
            typename image_type
            >
        void scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        load (
            const image_type& img
        )
        {
            unsigned long width, height;
            compute_fhog_window_size(width,height);
            impl::create_fhog_pyramid<Pyramid_type>(img, fe, feats, cell_size, height,
                width, min_pyramid_layer_width, min_pyramid_layer_height,
                max_pyramid_levels);
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        bool scan_fhog_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_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        copy_configuration (
            const scan_fhog_pyramid& item
        )
        {
            cell_size = item.cell_size;
            padding = item.padding;
            window_width = item.window_width;
            window_height = item.window_height;
            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;
            nuclear_norm_regularization_strength = item.nuclear_norm_regularization_strength;
            fe = item.fe;
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        unsigned long scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_num_detection_templates (
        ) const
        {
            return 1;
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        unsigned long scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_num_movable_components_per_detection_template (
        ) const
        {
            return 0;
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        long scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_num_dimensions (
        ) const
        {
            unsigned long width, height;
            compute_fhog_window_size(width,height);
            return width*height*fe.get_num_planes();
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        unsigned long scan_fhog_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_fhog_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_fhog_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;
        }

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

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

            template <
                typename pyramid_type,
                typename feature_extractor_type,
                typename fhog_filterbank
                >
            void detect_from_fhog_pyramid (
                const array<array<array2d<float> > >& feats,
                const feature_extractor_type& fe,
                const fhog_filterbank& w,
                const double thresh,
                const unsigned long det_box_height,
                const unsigned long det_box_width,
                const int cell_size,
                const int filter_rows_padding,
                const int filter_cols_padding,
                std::vector<std::pair<double, rectangle> >& dets
            )
            {
                dets.clear();

                array2d<float> saliency_image;
                pyramid_type pyr;

                // for all pyramid levels
                for (unsigned long l = 0; l < feats.size(); ++l)
                {
                    const rectangle area = apply_filters_to_fhog(w, feats[l], saliency_image);

                    // now search the saliency image for any detections
                    for (long r = area.top(); r <= area.bottom(); ++r)
                    {
                        for (long c = area.left(); c <= area.right(); ++c)
                        {
                            // if we found a detection
                            if (saliency_image[r][c] >= thresh)
                            {
                                rectangle rect = fe.feats_to_image(centered_rect(point(c,r),det_box_width,det_box_height),
                                    cell_size, filter_rows_padding, filter_cols_padding);
                                rect = pyr.rect_up(rect, l);
                                dets.push_back(std::make_pair(saliency_image[r][c], rect));
                            }
                        }
                    }
                }

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

            inline bool overlaps_any_box (
                const test_box_overlap& tester,
                const std::vector<rect_detection>& rects,
                const rect_detection& rect
            )
            {
                for (unsigned long i = 0; i < rects.size(); ++i)
                {
                    // Only compare detections from the same detector.  That is, we don't want
                    // the output of one detector to stop on the output of another detector.
                    if (rects[i].weight_index == rect.weight_index && tester(rects[i].rect, rect.rect))
                        return true;
                }
                return false;
            }

        }

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

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

            unsigned long width, height;
            compute_fhog_window_size(width,height);

            impl::detect_from_fhog_pyramid<pyramid_type>(feats, fe, w, thresh,
                height-2*padding, width-2*padding, cell_size, height, width, dets);
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        const rectangle scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_best_matching_rect (
            const rectangle& rect
        ) const
        {
            rectangle mapped_rect, fhog_rect;
            unsigned long best_level;
            get_mapped_rect_and_metadata(max_pyramid_levels, rect, mapped_rect, fhog_rect, best_level);
            return mapped_rect;
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        void scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_mapped_rect_and_metadata (
            const unsigned long number_pyramid_levels,
            const rectangle& rect,
            rectangle& mapped_rect,
            rectangle& fhog_rect,
            unsigned long& best_level
        ) const
        {
            pyramid_type pyr;
            best_level = 0;
            double best_match_score = -1;

            unsigned long width, height;
            compute_fhog_window_size(width,height);

            // Figure out the pyramid level which best matches rect against our detection
            // window.
            for (unsigned long l = 0; l < number_pyramid_levels; ++l)
            {
                const rectangle rect_fhog_space = fe.image_to_feats(pyr.rect_down(rect,l), cell_size, height,width);

                const rectangle win_image_space = pyr.rect_up(fe.feats_to_image(centered_rect(center(rect_fhog_space),width-2*padding,height-2*padding), cell_size, height,width), l);

                const double match_score = get_match_score(win_image_space, rect);
                if (match_score > best_match_score)
                {
                    best_match_score = match_score;
                    best_level = l;
                    fhog_rect = centered_rect(center(rect_fhog_space), width, height);
                }

                if (rect_fhog_space.area() <= 1)
                    break;
            }
            mapped_rect = pyr.rect_up(fe.feats_to_image(shrink_rect(fhog_rect,padding), cell_size,height,width),best_level);
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        full_object_detection scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_full_object_detection (
            const rectangle& rect,
            const feature_vector_type&
        ) const
        {
            return full_object_detection(rect);
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        void scan_fhog_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(is_loaded_with_image() &&
                        psi.size() >= get_num_dimensions() &&
                        obj.num_parts() == 0,
                "\t void scan_fhog_pyramid::get_feature_vector()"
                << "\n\t Invalid inputs were given to this function "
                << "\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 obj.num_parts():                            " << obj.num_parts()
                << "\n\t this: " << this
                );

            rectangle mapped_rect;
            unsigned long best_level;
            rectangle fhog_rect;
            get_mapped_rect_and_metadata(feats.size(), obj.get_rect(), mapped_rect, fhog_rect, best_level);

            long i = 0;
            for (unsigned long ii = 0; ii < feats[best_level].size(); ++ii)
            {
                const rectangle rect = get_rect(feats[best_level][0]);
                for (long r = fhog_rect.top(); r <= fhog_rect.bottom(); ++r)
                {
                    for (long c = fhog_rect.left(); c <= fhog_rect.right(); ++c)
                    {
                        if (rect.contains(c,r))
                            psi(i) += feats[best_level][ii][r][c];
                        ++i;
                    }
                }
            }
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        void scan_fhog_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_fhog_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_fhog_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_fhog_pyramid<Pyramid_type,feature_extractor_type>::
        get_min_pyramid_layer_height (
        ) const
        {
            return min_pyramid_layer_height;
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        matrix<unsigned char> draw_fhog (
            const object_detector<scan_fhog_pyramid<Pyramid_type,feature_extractor_type> >& detector,
            const unsigned long weight_index = 0,
            const long cell_draw_size = 15
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(weight_index < detector.num_detectors(),
                "\t matrix draw_fhog()"
                << "\n\t Invalid arguments were given to this function. "
                << "\n\t weight_index:             " << weight_index
                << "\n\t detector.num_detectors(): " << detector.num_detectors()
                );
            DLIB_ASSERT(cell_draw_size > 0 && detector.get_w(weight_index).size() >= detector.get_scanner().get_num_dimensions(),
                "\t matrix draw_fhog()"
                << "\n\t Invalid arguments were given to this function. "
                << "\n\t cell_draw_size:                              " << cell_draw_size
                << "\n\t weight_index:                                " << weight_index
                << "\n\t detector.get_w(weight_index).size():         " << detector.get_w(weight_index).size()
                << "\n\t detector.get_scanner().get_num_dimensions(): " << detector.get_scanner().get_num_dimensions()
                );

            typename scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::fhog_filterbank fb = detector.get_scanner().build_fhog_filterbank(detector.get_w(weight_index));
            return draw_fhog(fb.get_filters(),cell_draw_size);
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        unsigned long num_separable_filters (
            const object_detector<scan_fhog_pyramid<Pyramid_type,feature_extractor_type> >& detector,
            const unsigned long weight_index = 0
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(weight_index < detector.num_detectors(),
                "\t unsigned long num_separable_filters()"
                << "\n\t Invalid arguments were given to this function. "
                << "\n\t weight_index:             " << weight_index
                << "\n\t detector.num_detectors(): " << detector.num_detectors()
                );
            DLIB_ASSERT(detector.get_w(weight_index).size() >= detector.get_scanner().get_num_dimensions() ,
                "\t unsigned long num_separable_filters()"
                << "\n\t Invalid arguments were given to this function. "
                << "\n\t detector.get_w(weight_index).size():         " << detector.get_w(weight_index).size()
                << "\n\t detector.get_scanner().get_num_dimensions(): " << detector.get_scanner().get_num_dimensions()
                );

            typename scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::fhog_filterbank fb = detector.get_scanner().build_fhog_filterbank(detector.get_w(weight_index));
            return fb.num_separable_filters();
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        object_detector<scan_fhog_pyramid<Pyramid_type,feature_extractor_type> > threshold_filter_singular_values (
            const object_detector<scan_fhog_pyramid<Pyramid_type,feature_extractor_type> >& detector,
            double thresh,
            const unsigned long weight_index = 0
        )
        {
            // make sure requires clause is not broken
            DLIB_ASSERT(thresh >= 0 ,
                "\t object_detector threshold_filter_singular_values()"
                << "\n\t Invalid inputs were given to this function."
                << "\n\t thresh: " << thresh
            );

            DLIB_ASSERT(weight_index < detector.num_detectors(),
                "\t object_detector threshold_filter_singular_values()"
                << "\n\t Invalid arguments were given to this function. "
                << "\n\t weight_index:             " << weight_index
                << "\n\t detector.num_detectors(): " << detector.num_detectors()
                );
            DLIB_ASSERT(detector.get_w(weight_index).size() >= detector.get_scanner().get_num_dimensions() ,
                "\t object_detector threshold_filter_singular_values()"
                << "\n\t Invalid arguments were given to this function. "
                << "\n\t detector.get_w(weight_index).size():         " << detector.get_w(weight_index).size()
                << "\n\t detector.get_scanner().get_num_dimensions(): " << detector.get_scanner().get_num_dimensions()
                );

            const unsigned long width = detector.get_scanner().get_fhog_window_width();
            const unsigned long height = detector.get_scanner().get_fhog_window_height();
            const long num_planes = detector.get_scanner().get_feature_extractor().get_num_planes();
            const long size = width*height;

            std::vector<matrix<double,0,1> > detector_weights;
            for (unsigned long j = 0; j < detector.num_detectors(); ++j)
            {
                matrix<double,0,1> weights = detector.get_w(j);

                if (j == weight_index)
                {
                    matrix<double> u,v,w,f;
                    for (long i = 0; i < num_planes; ++i)
                    {
                        f = reshape(rowm(weights, range(i*size, (i+1)*size-1)), height, width);

                        svd3(f, u,w,v);
                        const double scaled_thresh = std::max(1e-3, max(w)*thresh);
                        w = round_zeros(w, scaled_thresh);
                        f = u*diagm(w)*trans(v);

                        set_rowm(weights,range(i*size, (i+1)*size-1)) = reshape_to_column_vector(f);
                    }
                }
                detector_weights.push_back(weights);
            }

            return object_detector<scan_fhog_pyramid<Pyramid_type,feature_extractor_type> >(detector.get_scanner(),
                                                                     detector.get_overlap_tester(),
                                                                     detector_weights);
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type,
            typename svm_struct_prob_type
            >
        void configure_nuclear_norm_regularizer (
            const scan_fhog_pyramid<Pyramid_type,feature_extractor_type>& scanner,
            svm_struct_prob_type& prob
        )
        {
            const double strength = scanner.get_nuclear_norm_regularization_strength();
            const long num_planes = scanner.get_feature_extractor().get_num_planes();
            if (strength != 0)
            {
                const unsigned long width = scanner.get_fhog_window_width();
                const unsigned long height = scanner.get_fhog_window_height();
                for (long i = 0; i < num_planes; ++i)
                {
                    prob.add_nuclear_norm_regularizer(i*width*height, height, width, strength);
                }
                prob.set_cache_based_epsilon(0.001);
            }
        }

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

        template <
            typename Pyramid_type,
            typename feature_extractor_type
            >
        struct processed_weight_vector<scan_fhog_pyramid<Pyramid_type,feature_extractor_type> >
        {
            processed_weight_vector(){}

            typedef matrix<double,0,1> feature_vector_type;
            typedef typename scan_fhog_pyramid<Pyramid_type,feature_extractor_type>::fhog_filterbank fhog_filterbank;

            void init (
                const scan_fhog_pyramid<Pyramid_type,feature_extractor_type>& scanner
            )
            {
                fb = scanner.build_fhog_filterbank(w);
            }

            const fhog_filterbank& get_detect_argument() const { return fb; }

            feature_vector_type w;
            fhog_filterbank fb;

        };

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

        template <
            typename pyramid_type,
            typename image_type
            >
        void evaluate_detectors (
            const std::vector<object_detector<scan_fhog_pyramid<pyramid_type> > >& detectors,
            const image_type& img,
            std::vector<rect_detection>& dets,
            const double adjust_threshold = 0
        )
        {
            typedef scan_fhog_pyramid<pyramid_type> scanner_type;

            dets.clear();
            if (detectors.size() == 0)
                return;

            const unsigned long cell_size = detectors[0].get_scanner().get_cell_size();

            // Find the maximum sized filters and also most extreme pyramiding settings used.
            unsigned long max_filter_width = 0;
            unsigned long max_filter_height = 0;
            unsigned long min_pyramid_layer_width = std::numeric_limits<unsigned long>::max();
            unsigned long min_pyramid_layer_height = std::numeric_limits<unsigned long>::max();
            unsigned long max_pyramid_levels = 0;
            bool all_cell_sizes_the_same = true;
            for (unsigned long i = 0; i < detectors.size(); ++i)
            {
                const scanner_type& scanner = detectors[i].get_scanner();
                max_filter_width = std::max(max_filter_width, scanner.get_fhog_window_width());
                max_filter_height = std::max(max_filter_height, scanner.get_fhog_window_height());
                max_pyramid_levels = std::max(max_pyramid_levels, scanner.get_max_pyramid_levels());
                min_pyramid_layer_width = std::min(min_pyramid_layer_width, scanner.get_min_pyramid_layer_width());
                min_pyramid_layer_height = std::min(min_pyramid_layer_height, scanner.get_min_pyramid_layer_height());
                if (cell_size != scanner.get_cell_size())
                    all_cell_sizes_the_same = false;
            }

            std::vector<rect_detection> dets_accum;
            // Do to the HOG feature extraction to make the fhog pyramid.  Again, note that we
            // are making a pyramid that will work with any of the detectors.  But only if all
            // the cell sizes are the same.  If they aren't then we have to calculate the
            // pyramid for each detector individually.
            array<array<array2d<float> > > feats;
            if (all_cell_sizes_the_same)
            {
                impl::create_fhog_pyramid<pyramid_type>(img,
                    detectors[0].get_scanner().get_feature_extractor(), feats, cell_size,
                    max_filter_height, max_filter_width, min_pyramid_layer_width,
                    min_pyramid_layer_height, max_pyramid_levels);
            }

            std::vector<std::pair<double, rectangle> > temp_dets;
            for (unsigned long i = 0; i < detectors.size(); ++i)
            {
                const scanner_type& scanner = detectors[i].get_scanner();
                if (!all_cell_sizes_the_same)
                {
                    impl::create_fhog_pyramid<pyramid_type>(img,
                        scanner.get_feature_extractor(), feats, scanner.get_cell_size(),
                        max_filter_height, max_filter_width, min_pyramid_layer_width,
                        min_pyramid_layer_height, max_pyramid_levels);
                }

                const unsigned long det_box_width  = scanner.get_fhog_window_width()  - 2*scanner.get_padding();
                const unsigned long det_box_height = scanner.get_fhog_window_height() - 2*scanner.get_padding();
                // A single detector object might itself have multiple weight vectors in it. So
                // we need to evaluate all of them.
                for (unsigned d = 0; d < detectors[i].num_detectors(); ++d)
                {
                    const double thresh = detectors[i].get_processed_w(d).w(scanner.get_num_dimensions());

                    impl::detect_from_fhog_pyramid<pyramid_type>(feats, scanner.get_feature_extractor(),
                        detectors[i].get_processed_w(d).get_detect_argument(), thresh+adjust_threshold,
                        det_box_height, det_box_width, cell_size, max_filter_height,
                        max_filter_width, temp_dets);

                    for (unsigned long j = 0; j < temp_dets.size(); ++j)
                    {
                        rect_detection temp;
                        temp.detection_confidence = temp_dets[j].first-thresh;
                        temp.weight_index = i;
                        temp.rect = temp_dets[j].second;
                        dets_accum.push_back(temp);
                    }
                }
            }

            // Do non-max suppression
            if (detectors.size() > 1)
                std::sort(dets_accum.rbegin(), dets_accum.rend());
            for (unsigned long i = 0; i < dets_accum.size(); ++i)
            {
                const test_box_overlap tester = detectors[dets_accum[i].weight_index].get_overlap_tester();
                if (impl::overlaps_any_box(tester, dets, dets_accum[i]))
                    continue;

                dets.push_back(dets_accum[i]);
            }
        }

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

        template <
            typename Pyramid_type,
            typename image_type
            >
        std::vector<rectangle> evaluate_detectors (
            const std::vector<object_detector<scan_fhog_pyramid<Pyramid_type> > >& detectors,
            const image_type& img,
            const double adjust_threshold = 0
        )
        {
            std::vector<rectangle> out_dets;
            std::vector<rect_detection> dets;
            evaluate_detectors(detectors, img, dets, adjust_threshold);
            out_dets.reserve(dets.size());
            for (unsigned long i = 0; i < dets.size(); ++i)
                out_dets.push_back(dets[i].rect);
            return out_dets;
        }

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

    }

    #endif // DLIB_SCAN_fHOG_PYRAMID_Hh_