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-rw-r--r-- | ml/dlib/examples/dnn_mmod_face_detection_ex.cpp | 114 |
1 files changed, 114 insertions, 0 deletions
diff --git a/ml/dlib/examples/dnn_mmod_face_detection_ex.cpp b/ml/dlib/examples/dnn_mmod_face_detection_ex.cpp new file mode 100644 index 000000000..3cdf4fcc7 --- /dev/null +++ b/ml/dlib/examples/dnn_mmod_face_detection_ex.cpp @@ -0,0 +1,114 @@ +// 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 face detector using dlib. The + example loads a pretrained model and uses it to find faces in images. The + CNN model is much more accurate than the HOG based model shown in the + face_detection_ex.cpp example, but takes much more computational power to + run, and is meant to be executed on a GPU to attain reasonable speed. For + example, on a NVIDIA Titan X GPU, this example program processes images at + about the same speed as face_detection_ex.cpp. + + Also, 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 + + + + TRAINING THE MODEL + Finally, users interested in how the face detector was trained should + read the dnn_mmod_ex.cpp example program. It should be noted that the + face detector used in this example uses a bigger training dataset and + larger CNN architecture than what is shown in dnn_mmod_ex.cpp, but + otherwise training is the same. If you compare the net_type statements + in this file and dnn_mmod_ex.cpp you will see that they are very similar + except that the number of parameters has been increased. + + Additionally, the following training parameters were different during + training: The following lines in dnn_mmod_ex.cpp were changed from + mmod_options options(face_boxes_train, 40,40); + trainer.set_iterations_without_progress_threshold(300); + to the following when training the model used in this example: + mmod_options options(face_boxes_train, 80,80); + trainer.set_iterations_without_progress_threshold(8000); + + Also, the random_cropper was left at its default settings, So we didn't + call these functions: + cropper.set_chip_dims(200, 200); + cropper.set_min_object_size(40,40); + + The training data used to create the model is also available at + http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz +*/ + + +#include <iostream> +#include <dlib/dnn.h> +#include <dlib/data_io.h> +#include <dlib/image_processing.h> +#include <dlib/gui_widgets.h> + + +using namespace std; +using namespace dlib; + +// ---------------------------------------------------------------------------------------- + +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<45,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(int argc, char** argv) try +{ + if (argc == 1) + { + cout << "Call this program like this:" << endl; + cout << "./dnn_mmod_face_detection_ex mmod_human_face_detector.dat faces/*.jpg" << endl; + cout << "\nYou can get the mmod_human_face_detector.dat file from:\n"; + cout << "http://dlib.net/files/mmod_human_face_detector.dat.bz2" << endl; + return 0; + } + + + net_type net; + deserialize(argv[1]) >> net; + + image_window win; + for (int i = 2; i < argc; ++i) + { + matrix<rgb_pixel> img; + load_image(img, argv[i]); + + // Upsampling the image will allow us to detect smaller faces but will cause the + // program to use more RAM and run longer. + while(img.size() < 1800*1800) + pyramid_up(img); + + // Note that you can process a bunch of images in a std::vector at once and it runs + // much faster, since this will form mini-batches of images and therefore get + // better parallelism out of your GPU hardware. However, all the images must be + // the same size. To avoid this requirement on images being the same size we + // process them individually in this example. + auto dets = net(img); + win.clear_overlay(); + win.set_image(img); + for (auto&& d : dets) + win.add_overlay(d); + + cout << "Hit enter to process the next image." << endl; + cin.get(); + } +} +catch(std::exception& e) +{ + cout << e.what() << endl; +} + + |