#ifndef MY_DLIB_H
#define MY_DLIB_H

#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/dnn.h>
#include <opencv2/opencv.hpp>

#include<QVector>

using namespace dlib;

/*
 * my_dlib类 包含所有对dlib库的操作包括：
 *  1，生成csv文件
 *  2，读取cvs文件内容
 *
*/

/*
 * # 要读取人脸图像文件的路径
    path_images_from_camera = "D:/QT_file/opencv_test/face_camera/data"

    # Dlib 正向人脸检测器
    detector = dlib.get_frontal_face_detector()

    # Dlib 人脸预测器
    predictor = dlib.shape_predictor("./model/shape_predictor_68_face_landmarks.dat")

    # Dlib 人脸识别模型
    # Face recognition model, the object maps human faces into 128D vectors
      face_rec = dlib.face_recognition_model_v1("./model/dlib_face_recognition_resnet_model_v1.dat")
*/


// anet_type的声明
//----------------------------------------------------------------
template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual = dlib::add_prev1<block<N, BN, 1, dlib::tag1<SUBNET>>>;

template <template <int, template<typename>class, int, typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual_down = dlib::add_prev2<dlib::avg_pool<2, 2, 2, 2, dlib::skip1<dlib::tag2<block<N, BN, 2, dlib::tag1<SUBNET>>>>>>;

template <int N, template <typename> class BN, int stride, typename SUBNET>
using block = BN<dlib::con<N, 3, 3, 1, 1, dlib::relu<BN<dlib::con<N, 3, 3, stride, stride, SUBNET>>>>>;

template <int N, typename SUBNET> using ares = dlib::relu<residual<block, N, dlib::affine, SUBNET>>;
template <int N, typename SUBNET> using ares_down = dlib::relu<residual_down<block, N, dlib::affine, SUBNET>>;

template <typename SUBNET> using alevel0 = ares_down<256, SUBNET>;
template <typename SUBNET> using alevel1 = ares<256, ares<256, ares_down<256, SUBNET>>>;
template <typename SUBNET> using alevel2 = ares<128, ares<128, ares_down<128, SUBNET>>>;
template <typename SUBNET> using alevel3 = ares<64, ares<64, ares<64, ares_down<64, SUBNET>>>>;
template <typename SUBNET> using alevel4 = ares<32, ares<32, ares<32, SUBNET>>>;

using anet_type = dlib::loss_metric<dlib::fc_no_bias<128, dlib::avg_pool_everything<
                                                  alevel0<
                                                      alevel1<
                                                          alevel2<
                                                              alevel3<
                                                                  alevel4<
                                                                      dlib::max_pool<3, 3, 2, 2, dlib::relu<dlib::affine<dlib::con<32, 7, 7, 2, 2,
                                                                                                           dlib::input_rgb_image_sized<150>
                                                                                                           >>>>>>>>>>>>;
//----------------------------------------------------------------




//一张人脸的信息
typedef struct FaceDescribe_S {

    cv::Rect rect;              //矩形框
    std::string name = "";      //人脸姓名
    float similarity = 0.f;
    std::vector<cv::Point> marks;       //脸部标记点
    dlib::matrix<dlib::rgb_pixel> face;     //脸部图片
    dlib::matrix<float, 0, 1> desc;      //脸部数据
}FaceDescribe;
typedef std::vector<FaceDescribe> FaceDescribes;




class my_dlib
{
public:
    my_dlib();

    //读取所有人脸数据，并保存为csv
    void read_all_file();            //设置为静态，在input_face里面使用

//    //将计算出来的数据保存到csv中
//    bool save_to_csv(std::vector<std::vector<matrix<float, 0, 1>>>&);

    //Mat转化为 faceDesc
    bool read_imag_of_Name(const QString);


    //接受人脸mat 返回是否是指定人脸的姓名
    QString recognition_face(const cv::Mat &cap);


private:
    QVector<QVector<float>> data_csv;       //存放读取的csv
    //std::vector<matrix<float, 0, 1>> vec;        //定义一个向量组，用于存放每一个人脸的编码

    QVector<QString> folder_name;           //存放人脸文件夹名

    //三个模型
    anet_type net;  //model
    dlib::frontal_face_detector detector ;
    dlib::shape_predictor sp;     // landmarks


};




#endif // MY_DLIB_H