dlib 02 dlib人脸关键点检测器训练

01 dlib人脸检测器原始数据获取

68个关键点的训练数据集(1.7GB)：http://dlib.net/files/data/ibug_300W_large_face_landmark_dataset.tar.gz
194个关键点的数据集(需要)：http://stackoverflow.com/questions/36711905/dlib-train-shape-predictor-ex-cpp?answertab=votes#tab-top

02 训练代码

dlib中examples中的代码。
dlib/examples/train_shape_predictor_ex.cpp

// dlib/examples/train_shape_predictor_ex.cpp
#include 
#include 
#include 

using namespace dlib;
using namespace std;

std::vector<std::vector<double> > get_interocular_distances (
    const std::vector<std::vector >& objects
);

int main(int argc, char** argv)
{
    try
    {
        if (argc != 2)
        {
            cout << "Give the path to the examples/faces directory as the argument to this" << endl;
            cout << "program.  For example, if you are in the examples folder then execute " << endl;
            cout << "this program by running: " << endl;
            cout << "   ./train_shape_predictor_ex faces" << endl;
            cout << endl;
            return 0;
        }
        const std::string faces_directory = argv[1];
        dlib::arrayunsigned char> > images_train, images_test;
        std::vector<std::vector > faces_train, faces_test;
        // 1. 载入训练集，测试集
        // load_image_dataset(images_train, faces_train, faces_directory+"/training_with_face_landmarks.xml");
        // load_image_dataset(images_test, faces_test, faces_directory+"/testing_with_face_landmarks.xml");
        // 68个点的训练数据集:http://dlib.net/files/data/ibug_300W_large_face_landmark_dataset.tar.gz
        load_image_dataset(images_train, faces_train, faces_directory + "/labels_ibug_300W_train.xml");
        load_image_dataset(images_test, faces_test, faces_directory + "/labels_ibug_300W_test.xml");

        shape_predictor_trainer trainer;

        // 测试中调节了 tree_depth参数：2,4, 5, 10
        // 测试机器为8核，set_num_threads使用8，训练时cpu:70%
        trainer.set_oversampling_amount(300);
        trainer.set_nu(0.05);
        trainer.set_tree_depth(5);
        trainer.set_num_threads(8);
        trainer.be_verbose();
        // 训练
        shape_predictor sp = trainer.train(images_train, faces_train);

        cout << "mean training error: "<< 
            test_shape_predictor(sp, images_train, faces_train, get_interocular_distances(faces_train)) << endl;

        cout << "mean testing error:  "<< 
            test_shape_predictor(sp, images_test, faces_test, get_interocular_distances(faces_test)) << endl;
        // 保存模型
        serialize("sp.dat") << sp;
        std::string str;
        std::cin >> str;
    }
    catch (exception& e)
    {
        cout << "\nexception thrown!" << endl;
        cout << e.what() << endl;
    }
}

double interocular_distance (
    const full_object_detection& det
)
{
    dlib::vector<double,2> l, r;
    double cnt = 0;

    for (unsigned long i = 36; i <= 41; ++i) 
    {
        l += det.part(i);
        ++cnt;
    }
    l /= cnt;

    cnt = 0;
    for (unsigned long i = 42; i <= 47; ++i) 
    {
        r += det.part(i);
        ++cnt;
    }
    r /= cnt;

    return length(l-r);
}

std::vector<std::vector<double> > get_interocular_distances (
    const std::vector<std::vector >& objects
)
{
    std::vector<std::vector<double> > temp(objects.size());
    for (unsigned long i = 0; i < objects.size(); ++i)
    {
        for (unsigned long j = 0; j < objects[i].size(); ++j)
        {
            temp[i].push_back(interocular_distance(objects[i][j]));
        }
    }
    return temp;
}

03 训练结果

194关键点训练情况：
tree_depth=2，num_threads=2，
Release版本训练时间 5+小时
Debug版本训练时间 148+小时(训练一定要使用Release版本)
tree_depth=2,sp.dat=44.6MB，占用内存最大11GB
tree_depth=10,sp.data=11GB

68关键点训练结果：
tree_depth=2，num_threads=8，CPU:70% 内存：20+GB 6+小时 sp.data=15.8MB
tree_depth=4，num_threads=8，CPU:70% 内存：20+GB 12+小时 sp.data=63.3MB
tree_depth=5，num_threads=8，CPU:70% 内存：20+GB 16+小时 sp.data=126MB

tree_depth=5，num_threads=8
mean training error: 0.0479476
mean testing error:  0.0586204

04 测试效果

使用dlib中examples中的代码测试。
dlib/examples/face_landmark_detection_ex.cpp
也可食用dlib提供的训练模型：
http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2

原图：

检测效果图：

05 参考

http://blog.csdn.net/jcx1314/article/details/65937839
http://blog.csdn.net/elaine_bao/article/details/53054533