参考https://blog.csdn.net/li_wen01/article/details/78369751 

/*=============================================================================#     FileName: facecheck.cpp#         Desc: detect faces and eyes by opencv ,and then cut the face#       Author: Licaibiao#      Version:#   LastChange: 2017-10-31#      History:=============================================================================*/#include <opencv2/objdetect/objdetect.hpp>#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <iostream>#include <stdio.h>using namespace std;using namespace cv;#define CASCADENAME			"C:\\Users\\ThinkPad\\Downloads\\opencv\\sources\\data\\haarcascades_cuda\\haarcascade_frontalface_alt2.xml"#define NESTEDCASCADENAME	"C:\\Users\\ThinkPad\\Downloads\\opencv\\sources\\data\\haarcascades_cuda\\haarcascade_eye_tree_eyeglasses.xml"#define FACEPHOTO_FACENAME  "C:\\Users\\ThinkPad\\Pictures\\lena_detec.jpg"#define DETECT_IMAGE		"C:\\Users\\ThinkPad\\Pictures\\lena.jpg"void detectAndDraw(Mat& img, CascadeClassifier& cascade,CascadeClassifier& nestedCascade, double scale, bool tryflip){double t = 0;vector<Rect> faces, faces2;/* 定义七种颜色用于人脸标记 */const static Scalar colors[] ={Scalar(255,0,0),Scalar(255,128,0),Scalar(255,255,0),Scalar(0,255,0),Scalar(0,128,255),Scalar(0,255,255),Scalar(0,0,255),Scalar(255,0,255)};Mat gray, smallImg;/* 因为用的是类haar特征，所以都是基于灰度图像的，这里要转换成灰度图像 */cvtColor(img, gray, COLOR_BGR2GRAY);/* 将图片缩小，加快检测速度 */double fx = 1 / scale;/* 将尺寸缩小到1/scale, 用线性插值 */resize(gray, smallImg, Size(), fx, fx, INTER_LINEAR);/* 直方图均衡 */equalizeHist(smallImg, smallImg);/* 用来计算算法执行时间 */t = (double)getTickCount();/*人脸检测smallImg：输入的原图faces	：表示检测到的人脸目标序列1.1		：每次图像尺寸减小的比例为1.12		：每一个目标至少要被检测到3次才算是真的目标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) 目标的最大最小尺寸*/cascade.detectMultiScale(smallImg, faces, 1.1, 2, CASCADE_SCALE_IMAGE, Size(30, 30));if (tryflip){flip(smallImg, smallImg, 1);cascade.detectMultiScale(smallImg, faces2, 1.1, 2, 0 | CASCADE_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));}}/* 相减为算法执行的时间 */t = (double)getTickCount() - t;printf("detection time = %g ms\n", t * 1000 / getTickFrequency());for (size_t i = 0; i < faces.size(); i++){Rect r = faces[i];Mat smallImgROI;vector<Rect> nestedObjects;Point center;Scalar color = colors[i % 8];int radius;/* 人脸长宽比例，在0.75-1.3 间画圆，其他范围画矩形 */double aspect_ratio = (double)r.width / r.height;if (0.75 < aspect_ratio && aspect_ratio < 1.3){/*还原原来尺寸 计算圆心和圆半径 */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);/* 画出人脸检测区域 画圆 */circle(img, center, radius, color, 3, 8, 0);}else{/* 画出检测区域，画矩形 */rectangle(img, Point(cvRound(r.x*scale), cvRound(r.y*scale)),Point(cvRound((r.x + r.width - 1)*scale), cvRound((r.y + r.height - 1)*scale)), color, 3, 8, 0);}/* 检测到人眼，在人脸上画出人眼 */if (nestedCascade.empty()){continue;}smallImgROI = smallImg(r);/* 人眼检测 */nestedCascade.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, CASCADE_SCALE_IMAGE, Size(30, 30));for (size_t j = 0; j < nestedObjects.size(); j++){Rect nr = nestedObjects[j];/*还原原来尺寸 计算圆心和圆半径 */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);/* 画出人眼检测区域 画圆*/circle(img, center, radius, color, 3, 8, 0);}}/* 显示图像 img */imshow("result", img);}int main(int argc, const char** argv){Mat frame, image;bool tryflip=0;CascadeClassifier cascade, nestedCascade;double scale = 1.3;/* 加载分类器 */if (!nestedCascade.load(NESTEDCASCADENAME)){cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;}if (!cascade.load(CASCADENAME)){cerr << "ERROR: Could not load classifier cascade" << endl;return -1;}/* 加载图片 */image = imread(DETECT_IMAGE, 1);if (image.empty()){cout << "Couldn't read iamge" << DETECT_IMAGE << endl;}cout << "Detecting face(s) in " << DETECT_IMAGE << endl;/* 检测人脸及眼睛并画出检测到的区域 */if (!image.empty()){detectAndDraw(image, cascade, nestedCascade, scale, tryflip);waitKey(0);}return 0;}