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：

地址点击下载

// 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;
    }

}