mirror of https://github.com/davisking/dlib.git
97 lines
3.6 KiB
C++
97 lines
3.6 KiB
C++
// 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;
|
|
}
|
|
|
|
|
|
|
|
|