[置顶] OpenCv人脸识别

OpenCv

在进行人脸识别时候，为了达到效果，我们使用OpenCv的分类器。进行对图片进行识别。

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/objdetect/objdetect.hpp>

using namespace cv;
using namespace std;

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

int main()
{
//VideoCapture cap(0); //打开默认摄像头
//if(!cap.isOpened())
//{
// return -1;
//}
    Mat frame;
    Mat edges;

    CascadeClassifier cascade, nestedCascade;
bool stop = false;
//训练好的文件名称，放置在可执行文件同目录下
    cascade.load("E:\\OpenFile\\OpenCv\\opencv\\build\\etc\\haarcascades\\haarcascade_frontalface_alt.xml");
    nestedCascade.load("E:\\OpenFile\\OpenCv\\opencv\\build\\etc\\haarcascades\\haarcascade_eye.xml");
    frame = imread("E:\\GitHubSample\\101.jpg");
    detectAndDraw(frame, cascade, nestedCascade, 2, 0);
    waitKey(6000);
//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;
//建立用于存放人脸的向量容器
vector<Rect> faces, faces2;
//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 / scale), cvRound(img.cols / scale), CV_8UC1);
//转成灰度图像，Harr特征基于灰度图
    cvtColor(img, gray, CV_BGR2GRAY);
    imshow("灰度", gray);
//改变图像大小，使用双线性差值
    resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR);
    imshow("缩小尺寸", smallImg);
//变换后的图像进行直方图均值化处理
    equalizeHist(smallImg, smallImg);
    imshow("直方图均值处理", 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
    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，翻转图像继续检测
if (tryflip)
    {
        flip(smallImg, smallImg, 1);
        imshow("反转图像", 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)
        {
//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)*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);
//同样方法检测人眼
        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("识别结果", img);
}

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效果图：