#include "faceidcmp.h"

FaceIdCmp* FaceIdCmp::m_FaceIdCmp = nullptr;
FaceIdCmp::FaceIdCmp(QObject *parent) : QObject(parent)
{
//    deserialize("/TCrane/shape_predictor_68_face_landmarks.dat") >> predictor;
//    deserialize("/TCrane/dlib_face_recognition_resnet_model_v1.dat") >> net;

//    detector = get_frontal_face_detector();
//    vec = CreateDataset();
    connect(DataDictionary::GetInstance(),SIGNAL(PersonInfoChange()),\
            this,SLOT(BuildCmpLib()));
}

FaceIdCmp::~FaceIdCmp()
{

}

FaceIdCmp* FaceIdCmp::GetInstance()
{
    if(m_FaceIdCmp == nullptr){
        m_FaceIdCmp = new FaceIdCmp;
    }
    return m_FaceIdCmp;
}

bool FaceIdCmp::Comparison(cv::Mat frame1)
{
    qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
//    matrix<rgb_pixel> img;
//    cv::Mat frame = frame1.clone();
//    array2d< bgr_pixel> arrimg(frame.rows, frame.cols);
//    dlib::assign_image(img, cv_image<rgb_pixel>(frame));

//    std::vector<dlib::rectangle> dets_test = detector(img);
//    std::vector<matrix<float, 0, 1>> features;
//    std::vector<matrix<rgb_pixel>> faces;//定义存放截取人脸数据组
////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
//    for (auto const& det : dets_test)
//    {
//        auto shape = predictor(img, det);
//        matrix<rgb_pixel> face_chip;
//        extract_image_chip(img, get_face_chip_details(shape, 150, 0.25), face_chip);//截取人脸部分，并将大小调为150*150
//        faces.push_back(std::move(face_chip));
//    }
////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
//    std::vector<matrix<float, 0, 1>> face_descriptors = net(faces);//将150*150人脸图像载入Resnet残差网络，返回128D人脸特征存于face_descriptors
////    for (auto const& feature : face_descriptors)
////    {
////        features.push_back(feature);
////    }
////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
////    std::vector<int> face_id = GetLabelIndex(vec, face_descriptors);
//////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
////    string name = "";
////    for (int const& index : face_id)
////    {

////        if (index == -1)
////        {
////                //name = "other";
//////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
////            //CmpImg = Mat2QImage::Transform(CmpFrame);
////            QImage Img;
////            ComparisonResult(false,Img,CmpName);
////            qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
////            return false;
////        }
////        else
////        {
////            std::map<string,cv::Mat>::iterator it = files.begin();
////            for(int i = 0;i < index;i++,it++)
////                    ;
////            qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
//////            //CmpFrame = imread(it->first);
//////            CmpName = QString::fromStdString(it->second);
//////            CmpImg = Mat2QImage::Transform(CmpFrame);
////            CmpName = QString::fromStdString(it->first);
////            Mat Pic = it->second.clone();
////            CmpImg = Mat2QImage::Transform(Pic);
////            //CmpImg = ImgList[index];
//////            qDebug() <<  index;
////            ComparisonResult(true,CmpImg,CmpName);
//////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
////            return true;
////        }
////                //name = label[index];
////    }

//    int face_id = GetLabelIndex(vec, face_descriptors);
//    ComparisonScore((int)((1 - m_Score)*100));
//    if (face_id == -1 || face_id >= files.size())
//    {
//            //name = "other";
//        QImage Img;
//        ComparisonResult(false,Img,CmpName,CmpUuid);
//        return false;
//    }
//    else
//    {

//        std::map<string,cv::Mat>::iterator it = files.begin();
//        for(int i = 0;i < face_id;i++,it++)
//                ;
//        CmpUuid = QString::fromStdString(it->first);
//        CmpName = DataDictionary::GetInstance()->UuidSearchPersonInfo(CmpUuid).Name;
//        Mat Pic = it->second.clone();
//        CmpImg = Mat2QImage::Transform(Pic);
//        //CmpImg = ImgList[index];
//            qDebug() <<  face_id;
//        ComparisonResult(true,CmpImg,CmpName,CmpUuid);

//        return true;
//    }
////qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
//    QImage Img;
//    ComparisonResult(false,Img,CmpName,CmpUuid);
////    qDebug() << __func__ << " : " << __LINE__ << " :" << QDateTime::currentMSecsSinceEpoch();
    return false;
}


bool FaceIdCmp::Comparison1(cv::Mat frame)
{
//    matrix<rgb_pixel> img;
//    array2d< bgr_pixel> arrimg(frame.rows, frame.cols);
//    dlib::assign_image(img, cv_image<rgb_pixel>(frame));

//    std::vector<dlib::rectangle> dets_test = detector(img);
//    std::vector<matrix<float, 0, 1>> features;
//    std::vector<matrix<rgb_pixel>> faces;//定义存放截取人脸数据组

//    for (auto const& det : dets_test)
//    {
//        auto shape = predictor(img, det);
//        matrix<rgb_pixel> face_chip;
//        extract_image_chip(img, get_face_chip_details(shape, 150, 0.25), face_chip);//截取人脸部分，并将大小调为150*150
//        faces.push_back(std::move(face_chip));
//    }

//    std::vector<matrix<float, 0, 1>> face_descriptors = net(faces);//将150*150人脸图像载入Resnet残差网络，返回128D人脸特征存于face_descriptors
//    for (auto const& feature : face_descriptors)
//    {
//        features.push_back(feature);
//    }

////    std::vector<int> face_id = GetLabelIndex(vec, features);

////    string name = "";
////    for (int const& index : face_id)
////    {

////        if (index == -1)
////        {
////                //name = "other";

////            return false;
////        }
////        else
////        {
////            std::map<string,cv::Mat>::iterator it = files.begin();
////            for(int i = 0;i < index;i++,it++)
////                    ;
////            return true;
////        }
////    }

//    int face_id = GetLabelIndex(vec, features);
//    if (face_id == -1)
//    {
//            //name = "other";

//        return false;
//    }
//    else
//    {
//        std::map<string,cv::Mat>::iterator it = files.begin();
//        for(int i = 0;i < face_id;i++,it++)
//                ;
//        return true;
//    }

    return false;
}

void FaceIdCmp::BuildCmpLib()
{
//    vec = CreateDataset();
}

//std::vector<matrix<float, 0, 1>> FaceIdCmp::CreateDataset()
//{
//        std::vector<matrix<float, 0, 1>> vec;
//        //getFiles(FACE_ROOT_DIR"/face_data", files);

//        getFiles(files);
//        if(files.empty())
//        {
//            std::cout<< "No pic files found in "<< std::endl;
//            return vec;
//        }

//        for(std::map<string, cv::Mat>::iterator it = files.begin(); it != files.end(); it++  )
//        {

//            matrix<rgb_pixel> img;
//            //cv::Mat image = cv::imread(it->first);
//            cv::Mat image = it->second;
//            if(image.empty())
//            {
//                cout << it->first << " pic format is error" << endl;
//                continue;
//            }
//            dlib::assign_image(img, cv_image<rgb_pixel>(image));
//            std::vector<matrix<float, 0, 1>> features = GetFaceFeature(img);
//            if (features.size() < 1)
//                cout << "There is no face" << endl;
//            else if (features.size() > 1)
//                cout << "There is to many face" << endl;
//            else
//                vec.push_back(features[0]);
//        }
//        return vec;
//}

//int FaceIdCmp::GetLabelIndex(std::vector<matrix<float, 0, 1>> features, std::vector<matrix<float, 0, 1>> predict_value)
//{
//    float v =((float)DataDictionary::GetInstance()->GetMisData(MIS_FACEID_CMP_VALUE).toInt()/100);
//    float min_dis = 1 - v;
//    int face_id = -1;
//    m_Score = 1.0;
//    qDebug() << "min_dis : " << min_dis;
//    if(predict_value.size() > 0)
//    {
//        for (int j = 0; j < features.size(); j++)
//        {
//            float dis = (float)length(predict_value[0] - features[j]);
//            if (min_dis > dis)
//            {
//                    min_dis = dis;
//                    face_id = j;
//                    qDebug() << "dis : " << dis;
//            }
//            if(m_Score > dis)
//            {
//                m_Score = dis;
//            }
//        }
//    }

//    return face_id;
//}
////void FaceIdCmp::getFiles(string path, std::map<string, cv::Mat> &files)
void FaceIdCmp::getFiles(std::map<string, cv::Mat> &files)
{
        DIR *dir;
        struct dirent *ptr;
        char base[1000];

        files.clear();
        ImgList.clear();
//        if(path[path.length()-1] != '/')
//            path = path + "/";

//        if((dir = opendir(path.c_str())) == NULL)
//        {
//            cout<<"open the dir: "<< path <<"error!" <<endl;
//            return;
//        }


//        while((ptr=readdir(dir)) !=NULL )
//        {
//            ///current dir OR parrent dir
//            if(strcmp(ptr->d_name,".")==0 || strcmp(ptr->d_name,"..")==0)
//                    continue;
//            else if(ptr->d_type == 8) //file
//            {
//                string fn(ptr->d_name);
//                string name;
//                name = fn.substr(0, fn.find_last_of("."));

//                string p = path + string(ptr->d_name);
//                files.insert(pair<string, string>(p, name));
//            }
//            else if(ptr->d_type == 10)    ///link file
//            {}
//            else if(ptr->d_type == 4)    ///dir
//            {}
//        }

//        closedir(dir);

        QList<PersonInfo_t> List = DataDictionary::GetInstance()->GetPersonList();
        for(int i = 0;i < List.size();i++)
        {
            QImage Img = QImage::fromData(QByteArray::fromBase64(List[i].Pic.toLocal8Bit()));
            ImgList.append(Img);
            files.insert(pair<string, cv::Mat>(List[i].Uuid.toStdString(), QImage2Mat::Transform(Img)));
        }
        return ;
}

//std::vector<matrix<float, 0, 1>> FaceIdCmp::GetFaceFeature(matrix<rgb_pixel> img)
//{
//        std::vector<matrix<float, 0, 1>> features;
//        std::vector<dlib::rectangle> dets = detector(img);  //用dlib自带的人脸检测器检测人脸，然后将人脸位置大小信息存放到dets中
//        std::vector<matrix<rgb_pixel>> faces;//定义存放截取人脸数据组
//        for (auto const& det : dets)
//        {
//            auto shape = predictor(img, det);
//            matrix<rgb_pixel> face_chip;
//            extract_image_chip(img, get_face_chip_details(shape, 150, 0.25), face_chip);//截取人脸部分，并将大小调为150*150
//            faces.push_back(std::move(face_chip));
//        }

//        std::vector<matrix<float, 0, 1>> face_descriptors = net(faces);//将150*150人脸图像载入Resnet残差网络，返回128D人脸特征存于face_descriptors
////        for (auto const& feature : face_descriptors)
////        {
////            features.push_back(feature);
////        }

//        return face_descriptors;
//}

//std::vector<dlib::rectangle> FaceIdCmp::GetFaceArea(cv::Mat &frame)
//{
//    cv::Mat dst;

//    //提取灰度图
//    cv::cvtColor(frame, dst, CV_BGR2GRAY);

////    //加载dlib的人脸识别器
////    dlib::frontal_face_detector detector = dlib::get_frontal_face_detector();

//    //加载人脸形状探测器
////    dlib::shape_predictor sp;
////    dlib::deserialize("./shape_predictor_68_face_landmarks.dat") >> sp;

//    //Mat转化为dlib的matrix
////    dlib::array2d<dlib::bgr_pixel> dimg;
////    dlib::assign_image(dimg, dlib::cv_image<uchar>(dst));

////    //获取一系列人脸所在区域
////    std::vector<dlib::rectangle> dets = detector(dimg);
////    std::cout << "Number of faces detected: " << dets.size() << std::endl;
////    return dets;
//}

bool FaceIdCmp::FaceIdDetection(cv::Mat frame)
{
//    std::vector<dlib::rectangle> dets = GetFaceArea(frame);

//    if (dets.size() == 0)
//        return false;

    return true;
}

bool FaceIdCmp::FaceIdAreaMark(cv::Mat &frame)
{
//    std::vector<dlib::rectangle> dets = GetFaceArea(frame);

//    if (dets.size() == 0)
//        return false;

//    cv::Mat dst;
//    cv::cvtColor(frame, dst, CV_BGR2GRAY);
//    dlib::array2d<dlib::bgr_pixel> dimg;
//    dlib::assign_image(dimg, dlib::cv_image<uchar>(dst));


//    //获取人脸特征点分布
//    std::vector<dlib::full_object_detection> shapes;
//    int i = 0;
//    for(i = 0; i < dets.size(); i++)
//    {
//        dlib::full_object_detection shape = predictor(dimg, dets[i]); //获取指定一个区域的人脸形状
//        shapes.push_back(shape);
//    }

//    //指出每个检测到的人脸的位置
//    for(i=0; i<dets.size(); i++)
//    {
//        //画出人脸所在区域
//        cv::Rect r;
//        r.x = dets[i].left();
//        r.y = dets[i].top();
//        r.width = dets[i].width();
//        r.height = dets[i].height();
//        cv::rectangle(frame, r, cv::Scalar(0, 0, 255), 1, 1, 0);
//    }

    return true;

}

//bool FaceIdCmp::SaveFaceIdInfo(QString Name,cv::Mat &frame)
//{
//    QString FileName = QString(FACE_ROOT_DIR"/face_data") + "/" + Name + ".jpg";

//    QFile File(Name);
//    if(File.exists())
//    {
//        return false;
//    }
//    if(imwrite(FileName.toStdString(),frame))
//    {
//        BuildCmpLib();
//        return true;
//    }
//    else
//    {
//        return false;
//    }
//}