    // 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 vehicle detector using dlib.  The
        example loads a pretrained model and uses it to find the front and rear ends
        of cars in an image.  The model used by this example was trained by the
        dnn_mmod_train_find_cars_ex.cpp example program on this dataset:
            http://dlib.net/files/data/dlib_front_and_rear_vehicles_v1.tar

        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.

        You can also see a video of this vehicle detector running on YouTube:
            https://www.youtube.com/watch?v=OHbJ7HhbG74
    */

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

    using namespace std;
    using namespace dlib;

    // The front and rear view vehicle detector network
    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<55,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() try
    {
        net_type net;
        shape_predictor sp;
        // You can get this file from http://dlib.net/files/mmod_front_and_rear_end_vehicle_detector.dat.bz2
        // This network was produced by the dnn_mmod_train_find_cars_ex.cpp example program.
        // As you can see, the file also includes a separately trained shape_predictor.  To see
        // a generic example of how to train those refer to train_shape_predictor_ex.cpp.
        deserialize("mmod_front_and_rear_end_vehicle_detector.dat") >> net >> sp;

        matrix<rgb_pixel> img;
        load_image(img, "../mmod_cars_test_image2.jpg");

        image_window win;
        win.set_image(img);

        // Run the detector on the image and show us the output.
        for (auto&& d : net(img))
        {
            // We use a shape_predictor to refine the exact shape and location of the detection
            // box.  This shape_predictor is trained to simply output the 4 corner points of
            // the box.  So all we do is make a rectangle that tightly contains those 4 points
            // and that rectangle is our refined detection position.
            auto fd = sp(img,d);
            rectangle rect;
            for (unsigned long j = 0; j < fd.num_parts(); ++j)
                rect += fd.part(j);

            if (d.label == "rear")
                win.add_overlay(rect, rgb_pixel(255,0,0), d.label);
            else
                win.add_overlay(rect, rgb_pixel(255,255,0), d.label);
        }

        cout << "Hit enter to end program" << endl;
        cin.get();
    }
    catch(image_load_error& e)
    {
        cout << e.what() << endl;
        cout << "The test image is located in the examples folder.  So you should run this program from a sub folder so that the relative path is correct." << endl;
    }
    catch(serialization_error& e)
    {
        cout << e.what() << endl;
        cout << "The correct model file can be obtained from: http://dlib.net/files/mmod_front_and_rear_end_vehicle_detector.dat.bz2" << endl;
    }
    catch(std::exception& e)
    {
        cout << e.what() << endl;
    }