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97 lines
3.6 KiB
97 lines
3.6 KiB
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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/*
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This example shows how to run a CNN based vehicle detector using dlib. The
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example loads a pretrained model and uses it to find the front and rear ends
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of cars in an image. The model used by this example was trained by the
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dnn_mmod_train_find_cars_ex.cpp example program on this dataset:
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http://dlib.net/files/data/dlib_front_and_rear_vehicles_v1.tar
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Users who are just learning about dlib's deep learning API should read
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the dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp examples to learn
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how the API works. For an introduction to the object detection method you
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should read dnn_mmod_ex.cpp.
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You can also see a video of this vehicle detector running on YouTube:
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https://www.youtube.com/watch?v=OHbJ7HhbG74
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*/
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#include <iostream>
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#include <dlib/dnn.h>
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#include <dlib/image_io.h>
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#include <dlib/gui_widgets.h>
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#include <dlib/image_processing.h>
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using namespace std;
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using namespace dlib;
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// The rear view vehicle detector network
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template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
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template <long num_filters, typename SUBNET> using con5 = con<num_filters,5,5,1,1,SUBNET>;
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template <typename SUBNET> using downsampler = relu<affine<con5d<32, relu<affine<con5d<32, relu<affine<con5d<16,SUBNET>>>>>>>>>;
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template <typename SUBNET> using rcon5 = relu<affine<con5<55,SUBNET>>>;
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using net_type = loss_mmod<con<1,9,9,1,1,rcon5<rcon5<rcon5<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
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// ----------------------------------------------------------------------------------------
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int main() try
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{
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net_type net;
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shape_predictor sp;
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// You can get this file from http://dlib.net/files/mmod_front_and_rear_end_vehicle_detector.dat.bz2
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// This network was produced by the dnn_mmod_train_find_cars_ex.cpp example program.
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// As you can see, the file also includes a separately trained shape_predictor. To see
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// a generic example of how to train those refer to train_shape_predictor_ex.cpp.
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deserialize("mmod_front_and_rear_end_vehicle_detector.dat") >> net >> sp;
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matrix<rgb_pixel> img;
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load_image(img, "../mmod_cars_test_image2.jpg");
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image_window win;
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win.set_image(img);
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// Run the detector on the image and show us the output.
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for (auto&& d : net(img))
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{
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// We use a shape_predictor to refine the exact shape and location of the detection
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// box. This shape_predictor is trained to simply output the 4 corner points of
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// the box. So all we do is make a rectangle that tightly contains those 4 points
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// and that rectangle is our refined detection position.
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auto fd = sp(img,d);
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rectangle rect;
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for (unsigned long j = 0; j < fd.num_parts(); ++j)
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rect += fd.part(j);
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if (d.label == "rear")
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win.add_overlay(rect, rgb_pixel(255,0,0), d.label);
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else
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win.add_overlay(rect, rgb_pixel(255,255,0), d.label);
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}
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cout << "Hit enter to end program" << endl;
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cin.get();
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}
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catch(image_load_error& e)
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{
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cout << e.what() << endl;
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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;
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}
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catch(serialization_error& e)
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{
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cout << e.what() << endl;
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cout << "The correct model file can be obtained from: http://dlib.net/files/mmod_front_and_rear_end_vehicle_detector.dat.bz2" << endl;
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}
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catch(std::exception& e)
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{
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cout << e.what() << endl;
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}
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