    // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    /*
        Instance segmentation using the PASCAL VOC2012 dataset.

        Instance segmentation sort-of combines object detection with semantic
        segmentation. While each dog, for example, is detected separately,
        the output is not only a bounding-box but a more accurate, per-pixel
        mask.

        For introductions to object detection and semantic segmentation, you
        can have a look at dnn_mmod_ex.cpp and dnn_semantic_segmentation.h,
        respectively.

        Instructions how to run the example:
        1. Download the PASCAL VOC2012 data, and untar it somewhere.
           http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
        2. Build the dnn_instance_segmentation_train_ex example program.
        3. Run:
           ./dnn_instance_segmentation_train_ex /path/to/VOC2012
        4. Wait while the network is being trained.
        5. Build the dnn_instance_segmentation_ex example program.
        6. Run:
           ./dnn_instance_segmentation_ex /path/to/VOC2012-or-other-images

        An alternative to steps 2-4 above is to download a pre-trained network
        from here: http://dlib.net/files/instance_segmentation_voc2012net_v2.dnn

        It would be a good idea to become familiar with dlib's DNN tooling before reading this
        example.  So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
        before reading this example program.
    */

    #ifndef DLIB_DNn_INSTANCE_SEGMENTATION_EX_H_
    #define DLIB_DNn_INSTANCE_SEGMENTATION_EX_H_

    #include <dlib/dnn.h>

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

    namespace {
        // Segmentation will be performed using patches having this size.
        constexpr int seg_dim = 227;
    }

    dlib::rectangle get_cropping_rect(const dlib::rectangle& rectangle)
    {
        DLIB_ASSERT(!rectangle.is_empty());

        const auto center_point = dlib::center(rectangle);
        const auto max_dim = std::max(rectangle.width(), rectangle.height());
        const auto d = static_cast<long>(std::round(max_dim / 2.0 * 1.5)); // add +50%

        return dlib::rectangle(
            center_point.x() - d,
            center_point.y() - d,
            center_point.x() + d,
            center_point.y() + d
        );
    }

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

    // The object detection network.
    // Adapted from dnn_mmod_train_find_cars_ex.cpp and friends.

    template <long num_filters, typename SUBNET> using con5d = dlib::con<num_filters,5,5,2,2,SUBNET>;
    template <long num_filters, typename SUBNET> using con5  = dlib::con<num_filters,5,5,1,1,SUBNET>;

    template <typename SUBNET> using bdownsampler = dlib::relu<dlib::bn_con<con5d<128,dlib::relu<dlib::bn_con<con5d<128,dlib::relu<dlib::bn_con<con5d<32,SUBNET>>>>>>>>>;
    template <typename SUBNET> using adownsampler = dlib::relu<dlib::affine<con5d<128,dlib::relu<dlib::affine<con5d<128,dlib::relu<dlib::affine<con5d<32,SUBNET>>>>>>>>>;

    template <typename SUBNET> using brcon5 = dlib::relu<dlib::bn_con<con5<256,SUBNET>>>;
    template <typename SUBNET> using arcon5 = dlib::relu<dlib::affine<con5<256,SUBNET>>>;

    using det_bnet_type = dlib::loss_mmod<dlib::con<1,9,9,1,1,brcon5<brcon5<brcon5<bdownsampler<dlib::input_rgb_image_pyramid<dlib::pyramid_down<6>>>>>>>>;
    using det_anet_type = dlib::loss_mmod<dlib::con<1,9,9,1,1,arcon5<arcon5<arcon5<adownsampler<dlib::input_rgb_image_pyramid<dlib::pyramid_down<6>>>>>>>>;

    // The segmentation network.
    // For the time being, this is very much copy-paste from dnn_semantic_segmentation.h, although the network is made narrower (smaller feature maps).

    template <int N, template <typename> class BN, int stride, typename SUBNET>
    using block = BN<dlib::con<N,3,3,1,1,dlib::relu<BN<dlib::con<N,3,3,stride,stride,SUBNET>>>>>;

    template <int N, template <typename> class BN, int stride, typename SUBNET>
    using blockt = BN<dlib::cont<N,3,3,1,1,dlib::relu<BN<dlib::cont<N,3,3,stride,stride,SUBNET>>>>>;

    template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
    using residual = dlib::add_prev1<block<N,BN,1,dlib::tag1<SUBNET>>>;

    template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
    using residual_down = dlib::add_prev2<dlib::avg_pool<2,2,2,2,dlib::skip1<dlib::tag2<block<N,BN,2,dlib::tag1<SUBNET>>>>>>;

    template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
    using residual_up = dlib::add_prev2<dlib::cont<N,2,2,2,2,dlib::skip1<dlib::tag2<blockt<N,BN,2,dlib::tag1<SUBNET>>>>>>;

    template <int N, typename SUBNET> using res       = dlib::relu<residual<block,N,dlib::bn_con,SUBNET>>;
    template <int N, typename SUBNET> using ares      = dlib::relu<residual<block,N,dlib::affine,SUBNET>>;
    template <int N, typename SUBNET> using res_down  = dlib::relu<residual_down<block,N,dlib::bn_con,SUBNET>>;
    template <int N, typename SUBNET> using ares_down = dlib::relu<residual_down<block,N,dlib::affine,SUBNET>>;
    template <int N, typename SUBNET> using res_up    = dlib::relu<residual_up<block,N,dlib::bn_con,SUBNET>>;
    template <int N, typename SUBNET> using ares_up   = dlib::relu<residual_up<block,N,dlib::affine,SUBNET>>;

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

    template <typename SUBNET> using res16 = res<16,SUBNET>;
    template <typename SUBNET> using res24 = res<24,SUBNET>;
    template <typename SUBNET> using res32 = res<32,SUBNET>;
    template <typename SUBNET> using res48 = res<48,SUBNET>;
    template <typename SUBNET> using ares16 = ares<16,SUBNET>;
    template <typename SUBNET> using ares24 = ares<24,SUBNET>;
    template <typename SUBNET> using ares32 = ares<32,SUBNET>;
    template <typename SUBNET> using ares48 = ares<48,SUBNET>;

    template <typename SUBNET> using level1 = dlib::repeat<2,res16,res<16,SUBNET>>;
    template <typename SUBNET> using level2 = dlib::repeat<2,res24,res_down<24,SUBNET>>;
    template <typename SUBNET> using level3 = dlib::repeat<2,res32,res_down<32,SUBNET>>;
    template <typename SUBNET> using level4 = dlib::repeat<2,res48,res_down<48,SUBNET>>;

    template <typename SUBNET> using alevel1 = dlib::repeat<2,ares16,ares<16,SUBNET>>;
    template <typename SUBNET> using alevel2 = dlib::repeat<2,ares24,ares_down<24,SUBNET>>;
    template <typename SUBNET> using alevel3 = dlib::repeat<2,ares32,ares_down<32,SUBNET>>;
    template <typename SUBNET> using alevel4 = dlib::repeat<2,ares48,ares_down<48,SUBNET>>;

    template <typename SUBNET> using level1t = dlib::repeat<2,res16,res_up<16,SUBNET>>;
    template <typename SUBNET> using level2t = dlib::repeat<2,res24,res_up<24,SUBNET>>;
    template <typename SUBNET> using level3t = dlib::repeat<2,res32,res_up<32,SUBNET>>;
    template <typename SUBNET> using level4t = dlib::repeat<2,res48,res_up<48,SUBNET>>;

    template <typename SUBNET> using alevel1t = dlib::repeat<2,ares16,ares_up<16,SUBNET>>;
    template <typename SUBNET> using alevel2t = dlib::repeat<2,ares24,ares_up<24,SUBNET>>;
    template <typename SUBNET> using alevel3t = dlib::repeat<2,ares32,ares_up<32,SUBNET>>;
    template <typename SUBNET> using alevel4t = dlib::repeat<2,ares48,ares_up<48,SUBNET>>;

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

    template <
        template<typename> class TAGGED,
        template<typename> class PREV_RESIZED,
        typename SUBNET
    >
    using resize_and_concat = dlib::add_layer<
                              dlib::concat_<TAGGED,PREV_RESIZED>,
                              PREV_RESIZED<dlib::resize_prev_to_tagged<TAGGED,SUBNET>>>;

    template <typename SUBNET> using utag1 = dlib::add_tag_layer<2100+1,SUBNET>;
    template <typename SUBNET> using utag2 = dlib::add_tag_layer<2100+2,SUBNET>;
    template <typename SUBNET> using utag3 = dlib::add_tag_layer<2100+3,SUBNET>;
    template <typename SUBNET> using utag4 = dlib::add_tag_layer<2100+4,SUBNET>;

    template <typename SUBNET> using utag1_ = dlib::add_tag_layer<2110+1,SUBNET>;
    template <typename SUBNET> using utag2_ = dlib::add_tag_layer<2110+2,SUBNET>;
    template <typename SUBNET> using utag3_ = dlib::add_tag_layer<2110+3,SUBNET>;
    template <typename SUBNET> using utag4_ = dlib::add_tag_layer<2110+4,SUBNET>;

    template <typename SUBNET> using concat_utag1 = resize_and_concat<utag1,utag1_,SUBNET>;
    template <typename SUBNET> using concat_utag2 = resize_and_concat<utag2,utag2_,SUBNET>;
    template <typename SUBNET> using concat_utag3 = resize_and_concat<utag3,utag3_,SUBNET>;
    template <typename SUBNET> using concat_utag4 = resize_and_concat<utag4,utag4_,SUBNET>;

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

    static const char* instance_segmentation_net_filename = "instance_segmentation_voc2012net_v2.dnn";

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

    // training network type
    using seg_bnet_type = dlib::loss_binary_log_per_pixel<
                                  dlib::cont<1,1,1,1,1,
                                  dlib::relu<dlib::bn_con<dlib::cont<16,7,7,2,2,
                                  concat_utag1<level1t<
                                  concat_utag2<level2t<
                                  concat_utag3<level3t<
                                  concat_utag4<level4t<
                                  level4<utag4<
                                  level3<utag3<
                                  level2<utag2<
                                  level1<dlib::max_pool<3,3,2,2,utag1<
                                  dlib::relu<dlib::bn_con<dlib::con<16,7,7,2,2,
                                  dlib::input<dlib::matrix<dlib::rgb_pixel>>
                                  >>>>>>>>>>>>>>>>>>>>>>>>>;

    // testing network type (replaced batch normalization with fixed affine transforms)
    using seg_anet_type = dlib::loss_binary_log_per_pixel<
                                  dlib::cont<1,1,1,1,1,
                                  dlib::relu<dlib::affine<dlib::cont<16,7,7,2,2,
                                  concat_utag1<alevel1t<
                                  concat_utag2<alevel2t<
                                  concat_utag3<alevel3t<
                                  concat_utag4<alevel4t<
                                  alevel4<utag4<
                                  alevel3<utag3<
                                  alevel2<utag2<
                                  alevel1<dlib::max_pool<3,3,2,2,utag1<
                                  dlib::relu<dlib::affine<dlib::con<16,7,7,2,2,
                                  dlib::input<dlib::matrix<dlib::rgb_pixel>>
                                  >>>>>>>>>>>>>>>>>>>>>>>>>;

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

    #endif // DLIB_DNn_INSTANCE_SEGMENTATION_EX_H_