# dlib C++ Library - dnn_mmod_dog_hipsterizer.cpp

    // 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 dog face detector using dlib.  The
        example loads a pretrained model and uses it to find dog faces in images.
        We also use the dlib::shape_predictor to find the location of the eyes and
        nose and then draw glasses and a mustache onto each dog found :)

        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 dog face detector was trained should
            read the dnn_mmod_ex.cpp example program.  It should be noted that the
            dog 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/CU_dogs_fully_labeled.tar.gz

            Lastly, the shape_predictor was trained with default settings except we
            used the following non-default settings: cascade depth=20, tree
            depth=5, padding=0.2
    */

    #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 < 3)
        {
            cout << "Call this program like this:" << endl;
            cout << "./dnn_mmod_dog_hipsterizer mmod_dog_hipsterizer.dat faces/dogs.jpg" << endl;
            cout << "\nYou can get the mmod_dog_hipsterizer.dat file from:\n";
            cout << "http://dlib.net/files/mmod_dog_hipsterizer.dat.bz2" << endl;
            return 0;
        }

        // load the models as well as glasses and mustache.
        net_type net;
        shape_predictor sp;
        matrix<rgb_alpha_pixel> glasses, mustache;
        deserialize(argv[1]) >> net >> sp >> glasses >> mustache;
        pyramid_up(glasses);
        pyramid_up(mustache);

        image_window win1(glasses);
        image_window win2(mustache);
        image_window win_wireframe, win_hipster;

        // Now process each image, find dogs, and hipsterize them by drawing glasses and a
        // mustache on each dog :)
        for (int i = 2; i < argc; ++i)
        {
            matrix<rgb_pixel> img;
            load_image(img, argv[i]);

            // Upsampling the image will allow us to find smaller dog faces but will use more
            // computational resources.
            //pyramid_up(img);

            auto dets = net(img);
            win_wireframe.clear_overlay();
            win_wireframe.set_image(img);
            // We will also draw a wireframe on each dog's face so you can see where the
            // shape_predictor is identifying face landmarks.
            std::vector<image_window::overlay_line> lines;
            for (auto&& d : dets)
            {
                // get the landmarks for this dog's face
                auto shape = sp(img, d.rect);

                const rgb_pixel color(0,255,0);
                auto top  = shape.part(0);
                auto lear = shape.part(1);
                auto leye = shape.part(2);
                auto nose = shape.part(3);
                auto rear = shape.part(4);
                auto reye = shape.part(5);

                // The locations of the left and right ends of the mustache.
                auto lmustache = 1.3*(leye-reye)/2 + nose;
                auto rmustache = 1.3*(reye-leye)/2 + nose;

                // Draw the glasses onto the image.
                std::vector<point> from = {2*point(176,36), 2*point(59,35)}, to = {leye, reye};
                auto tform = find_similarity_transform(from, to);
                for (long r = 0; r < glasses.nr(); ++r)
                {
                    for (long c = 0; c < glasses.nc(); ++c)
                    {
                        point p = tform(point(c,r));
                        if (get_rect(img).contains(p))
                            assign_pixel(img(p.y(),p.x()), glasses(r,c));
                    }
                }

                // Draw the mustache onto the image right under the dog's nose.
                auto mrect = get_rect(mustache);
                from = {mrect.tl_corner(), mrect.tr_corner()};
                to = {rmustache, lmustache};
                tform = find_similarity_transform(from, to);
                for (long r = 0; r < mustache.nr(); ++r)
                {
                    for (long c = 0; c < mustache.nc(); ++c)
                    {
                        point p = tform(point(c,r));
                        if (get_rect(img).contains(p))
                            assign_pixel(img(p.y(),p.x()), mustache(r,c));
                    }
                }

                // Record the lines needed for the face wire frame.
                lines.push_back(image_window::overlay_line(leye, nose, color));
                lines.push_back(image_window::overlay_line(nose, reye, color));
                lines.push_back(image_window::overlay_line(reye, leye, color));
                lines.push_back(image_window::overlay_line(reye, rear, color));
                lines.push_back(image_window::overlay_line(rear, top, color));
                lines.push_back(image_window::overlay_line(top, lear,  color));
                lines.push_back(image_window::overlay_line(lear, leye,  color));
            }

            win_wireframe.add_overlay(lines);
            win_hipster.set_image(img);

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