    // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    /*
        This example shows how to run a CNN based face detector using dlib.  The
        example loads a pretrained model and uses it to find faces in images.  The
        CNN model is much more accurate than the HOG based model shown in the
        face_detection_ex.cpp example, but takes much more computational power to
        run, and is meant to be executed on a GPU to attain reasonable speed.  For
        example, on a NVIDIA Titan X GPU, this example program processes images at
        about the same speed as face_detection_ex.cpp.

        Also, users who are just learning about dlib's deep learning API should read
        the dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp examples to learn
        how the API works.  For an introduction to the object detection method you
        should read dnn_mmod_ex.cpp

        TRAINING THE MODEL
            Finally, users interested in how the face detector was trained should
            read the dnn_mmod_ex.cpp example program.  It should be noted that the
            face detector used in this example uses a bigger training dataset and
            larger CNN architecture than what is shown in dnn_mmod_ex.cpp, but
            otherwise training is the same.  If you compare the net_type statements
            in this file and dnn_mmod_ex.cpp you will see that they are very similar
            except that the number of parameters has been increased.

            Additionally, the following training parameters were different during
            training: The following lines in dnn_mmod_ex.cpp were changed from
                mmod_options options(face_boxes_train, 40,40);
                trainer.set_iterations_without_progress_threshold(300);
            to the following when training the model used in this example:
                mmod_options options(face_boxes_train, 80,80);
                trainer.set_iterations_without_progress_threshold(8000);

            Also, the random_cropper was left at its default settings,  So we didn't
            call these functions:
                cropper.set_chip_dims(200, 200);
                cropper.set_min_object_size(40,40);

            The training data used to create the model is also available at
            http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz
    */

    #include <iostream>
    #include <dlib/dnn.h>
    #include <dlib/data_io.h>
    #include <dlib/image_processing.h>
    #include <dlib/gui_widgets.h>

    using namespace std;
    using namespace dlib;

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

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

    template <typename SUBNET> using downsampler  = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
    template <typename SUBNET> using rcon5  = relu<affine<con5<45,SUBNET>>>;

    using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;

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

    int main(int argc, char** argv) try
    {
        if (argc == 1)
        {
            cout << "Call this program like this:" << endl;
            cout << "./dnn_mmod_face_detection_ex mmod_human_face_detector.dat faces/*.jpg" << endl;
            cout << "\nYou can get the mmod_human_face_detector.dat file from:\n";
            cout << "http://dlib.net/files/mmod_human_face_detector.dat.bz2" << endl;
            return 0;
        }

        net_type net;
        deserialize(argv[1]) >> net;

        image_window win;
        for (int i = 2; i < argc; ++i)
        {
            matrix<rgb_pixel> img;
            load_image(img, argv[i]);

            // Upsampling the image will allow us to detect smaller faces but will cause the
            // program to use more RAM and run longer.
            while(img.size() < 1800*1800)
                pyramid_up(img);

            // Note that you can process a bunch of images in a std::vector at once and it runs
            // much faster, since this will form mini-batches of images and therefore get
            // better parallelism out of your GPU hardware.  However, all the images must be
            // the same size.  To avoid this requirement on images being the same size we
            // process them individually in this example.
            auto dets = net(img);
            win.clear_overlay();
            win.set_image(img);
            for (auto&& d : dets)
                win.add_overlay(d);

            cout << "Hit enter to process the next image." << endl;
            cin.get();
        }
    }
    catch(std::exception& e)
    {
        cout << e.what() << endl;
    }