Visual Studio 2017下使用OpenCV做的人脸识别

环境OpenCV3.2，Debug64

效果图：

#include 
#include 

using namespace cv;
using namespace std;

CascadeClassifier face_cascader;
CascadeClassifier eye_cascader;
//记得是/而不是\
//位置确定，在opencv中找到这两个文件，
//haarcascade_frontalface_alt.xml
//haarcascade_eye.xml
//可以使用相对路径，可以使用绝对路径
String facefile = "D:/opencv/build/etc/haarcascades/haarcascade_frontalface_alt.xml";
String eyefile = "D:/opencv/build/etc/haarcascades/haarcascade_eye.xml";

int main(int argc, char** argv) {
    if (!face_cascader.load(facefile)) {//载入xml
        printf("could not load face feature data...\n");
        return -1;
    }
    if (!eye_cascader.load(eyefile)) {//载入xml
        printf("could not load eye feature data...\n");
        return -1;
    }
    //创建窗体
    namedWindow("camera-demo", CV_WINDOW_AUTOSIZE);
    //打开摄像头
    VideoCapture capture(0);

    Mat frame;

    Mat gray;

    vector faces;

    vector eyes;

    while (capture.read(frame)) {//实时检测
        //去色
        cvtColor(frame, gray, COLOR_BGR2GRAY);

        equalizeHist(gray, gray);

        face_cascader.detectMultiScale(gray, faces, 1.2, 3, 0, Size(30, 30));

        for (size_t t = 0; t < faces.size(); t++) {
            Rect roi;
            roi.x = faces[static_cast<int>(t)].x;
            roi.y = faces[static_cast<int>(t)].y;
            roi.width = faces[static_cast<int>(t)].width;
            roi.height = faces[static_cast<int>(t)].height / 2;
            Mat faceROI = frame(roi);
            eye_cascader.detectMultiScale(faceROI, eyes, 1.2, 3, 0, Size(20, 20));
            for (size_t k = 0; k < eyes.size(); k++) {
                Rect rect;
                rect.x = faces[static_cast<int>(t)].x + eyes[k].x;
                rect.y = faces[static_cast<int>(t)].y + eyes[k].y;
                rect.width = eyes[k].width;
                rect.height = eyes[k].height;
                //识别出眼眶
                rectangle(frame, rect, Scalar(0, 255, 0), 2, 8, 0);
                Point center;
                center.x = rect.x + rect.width / 2;
                center.y = rect.y + rect.height / 2;
                //识别出瞳孔
                circle(frame, center, 5, Scalar(0, 255, 255), -1, 8);
            }
            //识别出人脸
            rectangle(frame, faces[static_cast<int>(t)], Scalar(0, 0, 255), 2, 8, 0);
        }
        //输出实时图像
        imshow("camera-demo", frame);
        //等待键盘响应
        char c = waitKey(30);
        if (c == 27) {
            break;
        }
    }
    waitKey(0);
    return 0;
}

识别出瞳孔，眼眶，还有人脸。