dlib/examples/dnn_mmod_find_cars2_ex.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 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 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;
}