使用dlib基于CNN(卷积神经网络)的人脸检测器来检测人脸
基于机器学习CNN方法来检测人脸比之前介绍的效率要慢很多
需要先下载一个训练好的模型数据:
// dlib_cnn_facedetect.cpp: 定义控制台应用程序的入口点。 // #include "stdafx.h" #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; } }