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-rw-r--r-- | ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp | 96 |
1 files changed, 96 insertions, 0 deletions
diff --git a/ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp b/ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp new file mode 100644 index 000000000..b9fffbba0 --- /dev/null +++ b/ml/dlib/examples/dnn_mmod_find_cars2_ex.cpp @@ -0,0 +1,96 @@ +// 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; +} + + + + |