Visual Studio 2017下使用OpenCV做的人脸识别

环境OpenCV3.2，Debug64

#include <opencv2/opencv.hpp>
#include <iostream>

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<Rect> faces;

vector<Rect> 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;
}