#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <iostream>
using namespace cv;
using namespace std;

void detectAndDraw( Mat& img, CascadeClassifier& cascade,
          CascadeClassifier& nestedCascade,
          double scale, bool tryflip );

int main()
{
  CascadeClassifier cascade, nestedCascade;
  bool stop = false;
  cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
  nestedCascade.load("/usr/share/opencv/haarcascades/haarcascade_eye.xml");
  // frame = imread("renlian.jpg");
  VideoCapture cap(0);  //打開默認(rèn)攝像頭
  if(!cap.isOpened())
  {
    return -1;
  }
  Mat frame;
  Mat edges;
while(!stop)
{
cap>>frame;
 detectAndDraw( frame, cascade, nestedCascade,2,0 );
 if(waitKey(30) >=0)
 stop = true;
 imshow("cam",frame);
}
  //CascadeClassifier cascade, nestedCascade;
  // bool stop = false;
  //訓(xùn)練好的文件名稱，放置在可執(zhí)行文件同目錄下
  // cascade.load("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml");
//  nestedCascade.load("/usr/share/opencv/haarcascades/aarcascade_eye.xml");
//  frame = imread("renlian.jpg");
//  detectAndDraw( frame, cascade, nestedCascade,2,0 );
  // waitKey();
  //while(!stop)
  //{
  //  cap>>frame;
  //  detectAndDraw( frame, cascade, nestedCascade,2,0 );
    if(waitKey(30) >=0)
   stop = true;
  //}
  return 0;
}
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
          CascadeClassifier& nestedCascade,
          double scale, bool tryflip )
{
  int i = 0;
  double t = 0;
  //建立用于存放人臉的向量容器
  vector<Rect> faces, faces2;
  //定義一些顏色，用來標(biāo)示不同的人臉
  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)} ;
  //建立縮小的圖片，加快檢測速度
  //nt cvRound (double value) 對一個double型的數(shù)進(jìn)行四舍五入，并返回一個整型數(shù)！
  Mat gray, smallImg( cvRound (img.rows/scale), cvRound(img.cols/scale), CV_8UC1 );
  //轉(zhuǎn)成灰度圖像，Harr特征基于灰度圖
  cvtColor( img, gray, CV_BGR2GRAY );
  // imshow("灰度",gray);
  //改變圖像大小，使用雙線性差值
  resize( gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR );
 // imshow("縮小尺寸",smallImg);
  //變換后的圖像進(jìn)行直方圖均值化處理
  equalizeHist( smallImg, smallImg );
  //imshow("直方圖均值處理",smallImg);
  //程序開始和結(jié)束插入此函數(shù)獲取時間，經(jīng)過計算求得算法執(zhí)行時間
  t = (double)cvGetTickCount();
  //檢測人臉
  //detectMultiScale函數(shù)中smallImg表示的是要檢測的輸入圖像為smallImg，faces表示檢測到的人臉目標(biāo)序列，1.1表示
  //每次圖像尺寸減小的比例為1.1，2表示每一個目標(biāo)至少要被檢測到3次才算是真的目標(biāo)(因為周圍的像素和不同的窗口大
  //小都可以檢測到人臉),CV_HAAR_SCALE_IMAGE表示不是縮放分類器來檢測，而是縮放圖像，Size(30, 30)為目標(biāo)的
  //最小最大尺寸
  cascade.detectMultiScale( smallImg, faces,
    1.1, 2, 0
    //|CV_HAAR_FIND_BIGGEST_OBJECT
    //|CV_HAAR_DO_ROUGH_SEARCH
    |CV_HAAR_SCALE_IMAGE
    ,Size(30, 30));
  //如果使能，翻轉(zhuǎn)圖像繼續(xù)檢測
  if( tryflip )
  {
    flip(smallImg, smallImg, 1);
  //  imshow("反轉(zhuǎn)圖像",smallImg);
    cascade.detectMultiScale( smallImg, faces2,
      1.1, 2, 0
      //|CV_HAAR_FIND_BIGGEST_OBJECT
      //|CV_HAAR_DO_ROUGH_SEARCH
      |CV_HAAR_SCALE_IMAGE
      ,Size(30, 30) );
    for( vector<Rect>::const_iterator r = faces2.begin(); r != faces2.end(); r++ )
    {
      faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height));
    }
  }
  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 )
    {
      //標(biāo)示人臉時在縮小之前的圖像上標(biāo)示，所以這里根據(jù)縮放比例換算回去
      center.x = cvRound((r->x + r->width*0.5)*scale);
      center.y = cvRound((r->y + r->height*0.5)*scale);
      radius = cvRound((r->width + r->height)*0.25*scale);
      circle( img, center, radius, color, 3, 8, 0 );
    }
    else
      rectangle( img, cvPoint(cvRound(r->x*scale), cvRound(r->y*scale)),
      cvPoint(cvRound((r->x + r->width-1)*scale), cvRound((r->y + r->height-1)*scale)),
      color, 3, 8, 0);
    if( nestedCascade.empty() )
      continue;
    smallImgROI = smallImg(*r);
    //同樣方法檢測人眼
    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(30, 30) );
    for( vector<Rect>::const_iterator nr = nestedObjects.begin(); nr != nestedObjects.end(); nr++ )
    {
      center.x = cvRound((r->x + nr->x + nr->width*0.5)*scale);
      center.y = cvRound((r->y + nr->y + nr->height*0.5)*scale);
      radius = cvRound((nr->width + nr->height)*0.25*scale);
      circle( img, center, radius, color, 3, 8, 0 );
    }
  }
  // imshow( "識別結(jié)果", img );
}