#include "facerecognizerstrategy.h"
#include <QDebug>
FaceRecognizerStrategy::FaceRecognizerStrategy()
{
    m_cascade.load("lbpcascade_frontalface.xml");
    //m_nestedCascade.load("haarcascade_eye_tree_eyeglasses.xml");
    m_nestedCascade.load("haarcascade_eye.xml");
    m_scale = 2.0;
}

FaceRecognizerStrategy::~FaceRecognizerStrategy()
{

}

Mat FaceRecognizerStrategy::detect(Mat img)
{
    int i = 0;
    double t = 0;
    //建立用于存放人脸的向量容器
    vector<Rect> faces;
    //定义一些颜色，用来标示不同的人脸
    const static Scalar colors[] =  { CV_RGB(0,0,255),
        CV_RGB(0,128,255),
        CV_RGB(0,255,255),
        CV_RGB(0,255,0),
        CV_RGB(255,128,0),
        CV_RGB(255,255,0),
        CV_RGB(255,0,0),
        CV_RGB(255,0,255)} ;
    //建立缩小的图片，加快检测速度
    //nt cvRound (double value) 对一个double型的数进行四舍五入，并返回一个整型数！
    Mat gray, smallImg( cvRound (img.rows/m_scale), cvRound(img.cols/m_scale), CV_8UC1 );
    //转成灰度图像，Harr特征基于灰度图
    cvtColor( img, gray, CV_BGR2GRAY );
    //改变图像大小，使用双线性差值
    resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
    //变换后的图像进行直方图均值化处理
    equalizeHist( smallImg, smallImg );

    //程序开始和结束插入此函数获取时间，经过计算求得算法执行时间
    t = (double)cvGetTickCount();
    //检测人脸
    //detectMultiScale函数中smallImg表示的是要检测的输入图像为smallImg，faces表示检测到的人脸目标序列，1.1表示
    //每次图像尺寸减小的比例为1.1，2表示每一个目标至少要被检测到3次才算是真的目标(因为周围的像素和不同的窗口大
    //小都可以检测到人脸),CV_HAAR_SCALE_IMAGE表示不是缩放分类器来检测，而是缩放图像，Size(30, 30)为目标的
    //最小最大尺寸
    m_cascade.detectMultiScale( smallImg, faces,
        1.1, 3, 0
        //|CV_HAAR_FIND_BIGGEST_OBJECT
        //|CV_HAAR_DO_ROUGH_SEARCH
        |CV_HAAR_SCALE_IMAGE
        ,
        Size(30, 30));
    t = (double)cvGetTickCount() - t;
    qDebug( "detection time = %g ms\n", t/((double)cvGetTickFrequency()*1000.) );
    for( vector<Rect>::const_iterator r = faces.begin(); r != faces.end(); r++, i++ )
    {
        Mat smallImgROI;
        vector<Rect> nestedObjects;
        Point center;
        Scalar color = colors[i%8];
        int radius;

        double aspect_ratio = (double)r->width/r->height;
        if( 0.75 < aspect_ratio && aspect_ratio < 1.3 )
        {
            //标示人脸时在缩小之前的图像上标示，所以这里根据缩放比例换算回去
            center.x = cvRound((r->x + r->width*0.5)*m_scale);
            center.y = cvRound((r->y + r->height*0.5)*m_scale);
            radius = cvRound((r->width + r->height)*0.25*m_scale);
            circle( img, center, radius, color, 3, 8, 0 );
        }
        else
            rectangle( img, cvPoint(cvRound(r->x*m_scale), cvRound(r->y*m_scale)),
                       cvPoint(cvRound((r->x + r->width-1)*m_scale), cvRound((r->y + r->height-1)*m_scale)),
                       color, 3, 8, 0);
        if( m_nestedCascade.empty() )
            continue;
        smallImgROI = smallImg(*r);
        //同样方法检测人眼
        qDebug("finding");
        m_nestedCascade.detectMultiScale( smallImgROI, nestedObjects,
            1.1, 2, 0
            //|CV_HAAR_FIND_BIGGEST_OBJECT
            //|CV_HAAR_DO_ROUGH_SEARCH
            //|CV_HAAR_DO_CANNY_PRUNING
            |CV_HAAR_SCALE_IMAGE
            ,
            Size(10, 10) );
        for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
        {
            qDebug("find eyes");
            center.x = cvRound((r->x + nr->x + nr->width*0.5)*m_scale);
            center.y = cvRound((r->y + nr->y + nr->height*0.5)*m_scale);
            radius = cvRound((nr->width + nr->height)*0.25*m_scale);
            circle( img, center, radius, color, 3, 8, 0 );
        }
    }
    return img;
}