dlib C++ Library - 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 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;
}