    // Copyright (C) 2015  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_CORRELATION_TrACKER_H_
    #define DLIB_CORRELATION_TrACKER_H_

    #include "correlation_tracker_abstract.h"
    #include "../geometry.h"
    #include "../matrix.h"
    #include "../array2d.h"
    #include "../image_transforms/assign_image.h"

    namespace dlib
    {

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

        class correlation_tracker
        {
        public:

            explicit correlation_tracker (unsigned long filter_size = 6,
                unsigned long num_scale_levels = 5,
                unsigned long scale_window_size = 23,
                double regularizer_space = 0.001,
                double nu_space = 0.025,
                double regularizer_scale = 0.001,
                double nu_scale = 0.025,
                double scale_pyramid_alpha = 1.020
            )
                : filter_size(1 << filter_size), num_scale_levels(1 << num_scale_levels),
                scale_window_size(scale_window_size),
                regularizer_space(regularizer_space), nu_space(nu_space),
                regularizer_scale(regularizer_scale), nu_scale(nu_scale),
                scale_pyramid_alpha(scale_pyramid_alpha)
            {
                // Create the cosine mask used for space filtering.
                mask = make_cosine_mask();

                // Create the cosine mask used for the scale filtering.
                scale_cos_mask.resize(get_num_scale_levels());
                const long max_level = get_num_scale_levels()/2;
                for (unsigned long k = 0; k < get_num_scale_levels(); ++k)
                {
                    double dist = std::abs((double)k-max_level)/max_level*pi/2;
                    dist = std::min(dist, pi/2);
                    scale_cos_mask[k] = std::cos(dist);
                }
            }

            template <typename image_type>
            void start_track (
                const image_type& img,
                const drectangle& p
            )
            {
                DLIB_CASSERT(p.is_empty() == false,
                    "\t void correlation_tracker::start_track()"
                    << "\n\t You can't give an empty rectangle."
                );

                B.set_size(0,0);

                point_transform_affine tform = inv(make_chip(img, p, F));
                for (unsigned long i = 0; i < F.size(); ++i)
                    fft_inplace(F[i]);
                make_target_location_image(tform(center(p)), G);
                A.resize(F.size());
                for (unsigned long i = 0; i < F.size(); ++i)
                {
                    A[i] = pointwise_multiply(G, F[i]);
                    B += squared(real(F[i]))+squared(imag(F[i]));
                }

                position = p;

                // now do the scale space stuff
                make_scale_space(img, Fs);
                for (unsigned long i = 0; i < Fs.size(); ++i)
                    fft_inplace(Fs[i]);
                make_scale_target_location_image(get_num_scale_levels()/2, Gs);
                Bs.set_size(0);
                As.resize(Fs.size());
                for (unsigned long i = 0; i < Fs.size(); ++i)
                {
                    As[i] = pointwise_multiply(Gs, Fs[i]);
                    Bs += squared(real(Fs[i]))+squared(imag(Fs[i]));
                }
            }

            unsigned long get_filter_size (
            ) const { return filter_size; }

            unsigned long get_num_scale_levels(
            ) const { return num_scale_levels; }

            unsigned long get_scale_window_size (
            ) const { return scale_window_size; }

            double get_regularizer_space (
            ) const { return regularizer_space; }
            inline double get_nu_space (
            ) const { return nu_space;}

            double get_regularizer_scale (
            ) const { return regularizer_scale; }
            double get_nu_scale (
            ) const { return nu_scale;}

            drectangle get_position (
            ) const
            {
                return position;
            }

            double get_scale_pyramid_alpha (
            ) const { return scale_pyramid_alpha; }

            template <typename image_type>
            double update_noscale(
                const image_type& img,
                const drectangle& guess
            )
            {
                DLIB_CASSERT(get_position().is_empty() == false,
                    "\t double correlation_tracker::update()"
                    << "\n\t You must call start_track() first before calling update()."
                );

                const point_transform_affine tform = make_chip(img, guess, F);
                for (unsigned long i = 0; i < F.size(); ++i)
                    fft_inplace(F[i]);

                // use the current filter to predict the object's location
                G = 0;
                for (unsigned long i = 0; i < F.size(); ++i)
                    G += pointwise_multiply(F[i],conj(A[i]));
                G = pointwise_multiply(G, reciprocal(B+get_regularizer_space()));
                ifft_inplace(G);
                const dlib::vector<double,2> pp = max_point_interpolated(real(G));

                // Compute the peak to side lobe ratio.
                const point p = pp;
                running_stats<double> rs;
                const rectangle peak = centered_rect(p, 8,8);
                for (long r = 0; r < G.nr(); ++r)
                {
                    for (long c = 0; c < G.nc(); ++c)
                    {
                        if (!peak.contains(point(c,r)))
                            rs.add(G(r,c).real());
                    }
                }
                const double psr = (G(p.y(),p.x()).real()-rs.mean())/rs.stddev();

                // update the position of the object
                position = translate_rect(guess, tform(pp)-center(guess));

                // now update the position filters
                make_target_location_image(pp, G);
                B *= (1-get_nu_space());
                for (unsigned long i = 0; i < F.size(); ++i)
                {
                    A[i] = get_nu_space()*pointwise_multiply(G, F[i]) + (1-get_nu_space())*A[i];
                    B += get_nu_space()*(squared(real(F[i]))+squared(imag(F[i])));
                }

                return psr;
            }

            template <typename image_type>
            double update (
                const image_type& img,
                const drectangle& guess
            )
            {
                double psr = update_noscale(img, guess);

                // Now predict the scale change
                make_scale_space(img, Fs);
                for (unsigned long i = 0; i < Fs.size(); ++i)
                    fft_inplace(Fs[i]);
                Gs = 0;
                for (unsigned long i = 0; i < Fs.size(); ++i)
                    Gs += pointwise_multiply(Fs[i],conj(As[i]));
                Gs = pointwise_multiply(Gs, reciprocal(Bs+get_regularizer_scale()));
                ifft_inplace(Gs);
                const double pos = max_point_interpolated(real(Gs)).y();

                // update the rectangle's scale
                position *= std::pow(get_scale_pyramid_alpha(), pos-(double)get_num_scale_levels()/2);

                // Now update the scale filters
                make_scale_target_location_image(pos, Gs);
                Bs *= (1-get_nu_scale());
                for (unsigned long i = 0; i < Fs.size(); ++i)
                {
                    As[i] = get_nu_scale()*pointwise_multiply(Gs, Fs[i]) + (1-get_nu_scale())*As[i];
                    Bs += get_nu_scale()*(squared(real(Fs[i]))+squared(imag(Fs[i])));
                }

                return psr;
            }

            template <typename image_type>
            double update_noscale (
                const image_type& img
            )
            {
                return update_noscale(img, get_position());
            }

            template <typename image_type>
            double update(
                const image_type& img
                )
            {
                return update(img, get_position());
            }

        private:

            template <typename image_type>
            void make_scale_space(
                const image_type& img,
                std::vector<matrix<std::complex<double>,0,1> >& Fs
            ) const
            {
                typedef typename image_traits<image_type>::pixel_type pixel_type;

                // Make an image pyramid and put it into the chips array.
                const long chip_size = get_scale_window_size();
                drectangle ppp = position*std::pow(get_scale_pyramid_alpha(), -(double)get_num_scale_levels()/2);
                dlib::array<array2d<pixel_type> > chips;
                std::vector<dlib::vector<double,2> > from_points, to_points;
                from_points.push_back(point(0,0));
                from_points.push_back(point(chip_size-1,0));
                from_points.push_back(point(chip_size-1,chip_size-1));
                for (unsigned long i = 0; i < get_num_scale_levels(); ++i)
                {
                    array2d<pixel_type> chip(chip_size,chip_size);

                    // pull box into chip
                    to_points.clear();
                    to_points.push_back(ppp.tl_corner());
                    to_points.push_back(ppp.tr_corner());
                    to_points.push_back(ppp.br_corner());
                    transform_image(img,chip,interpolate_bilinear(),find_affine_transform(from_points, to_points));

                    chips.push_back(chip);
                    ppp *= get_scale_pyramid_alpha();
                }

                // extract HOG for each chip
                dlib::array<dlib::array<array2d<float> > > hogs(chips.size());
                for (unsigned long i = 0; i < chips.size(); ++i)
                {
                    extract_fhog_features(chips[i], hogs[i], 4);
                    hogs[i].resize(32);
                    assign_image(hogs[i][31], chips[i]);
                    assign_image(hogs[i][31], mat(hogs[i][31])/255.0);
                }

                // Now copy the hog features into the Fs outputs and also apply the cosine
                // windowing.
                Fs.resize(hogs[0].size()*hogs[0][0].size());
                unsigned long i = 0;
                for (long r = 0; r < hogs[0][0].nr(); ++r)
                {
                    for (long c = 0; c < hogs[0][0].nc(); ++c)
                    {
                        for (unsigned long j = 0; j < hogs[0].size(); ++j)
                        {
                            Fs[i].set_size(hogs.size());
                            for (unsigned long k = 0; k < hogs.size(); ++k)
                            {
                                Fs[i](k) = hogs[k][j][r][c]*scale_cos_mask[k];
                            }
                            ++i;
                        }
                    }
                }
            }

            template <typename image_type>
            point_transform_affine make_chip (
                const image_type& img,
                drectangle p,
                std::vector<matrix<std::complex<double> > >& chip
            ) const
            {
                typedef typename image_traits<image_type>::pixel_type pixel_type;
                array2d<pixel_type> temp;
                const double padding = 1.4;
                const chip_details details(p*padding, chip_dims(get_filter_size(), get_filter_size()));
                extract_image_chip(img, details, temp);

                chip.resize(32);
                dlib::array<array2d<float> > hog;
                extract_fhog_features(temp, hog, 1, 3,3 );
                for (unsigned long i = 0; i < hog.size(); ++i)
                    assign_image(chip[i], pointwise_multiply(matrix_cast<double>(mat(hog[i])), mask));

                assign_image(chip[31], temp);
                assign_image(chip[31], pointwise_multiply(mat(chip[31]), mask)/255.0);

                return inv(get_mapping_to_chip(details));
            }

            void make_target_location_image (
                const dlib::vector<double,2>& p,
                matrix<std::complex<double> >& g
            ) const
            {
                g.set_size(get_filter_size(), get_filter_size());
                g = 0;
                rectangle area = centered_rect(p, 21,21).intersect(get_rect(g));
                for (long r = area.top(); r <= area.bottom(); ++r)
                {
                    for (long c = area.left(); c <= area.right(); ++c)
                    {
                        double dist = length(point(c,r)-p);
                        g(r,c) = std::exp(-dist/3.0);
                    }
                }
                fft_inplace(g);
                g = conj(g);
            }

            void make_scale_target_location_image (
                const double scale,
                matrix<std::complex<double>,0,1>& g
            ) const
            {
                g.set_size(get_num_scale_levels());
                for (long i = 0; i < g.size(); ++i)
                {
                    double dist = std::pow((i-scale),2.0);
                    g(i) = std::exp(-dist/1.000);
                }
                fft_inplace(g);
                g = conj(g);
            }

            matrix<double> make_cosine_mask (
            ) const
            {
                const long size = get_filter_size();
                matrix<double> temp(size,size);
                point cent = center(get_rect(temp));
                for (long r = 0; r < temp.nr(); ++r)
                {
                    for (long c = 0; c < temp.nc(); ++c)
                    {
                        point delta = point(c,r)-cent;
                        double dist = length(delta)/(size/2.0)*(pi/2);
                        dist = std::min(dist*1.0, pi/2);

                        temp(r,c) = std::cos(dist);
                    }
                }
                return temp;
            }

            std::vector<matrix<std::complex<double> > > A, F;
            matrix<double> B;

            std::vector<matrix<std::complex<double>,0,1> > As, Fs;
            matrix<double,0,1> Bs;
            drectangle position;

            matrix<double> mask;
            std::vector<double> scale_cos_mask;

            // G and Gs do not logically contribute to the state of this object.  They are
            // here just so we can void reallocating them over and over.
            matrix<std::complex<double> > G;
            matrix<std::complex<double>,0,1> Gs;

            unsigned long filter_size;
            unsigned long num_scale_levels;
            unsigned long scale_window_size;
            double regularizer_space;
            double nu_space;
            double regularizer_scale;
            double nu_scale;
            double scale_pyramid_alpha;
        };
    }

    #endif // DLIB_CORRELATION_TrACKER_H_