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运行程序。该程序会自动打开摄像头，识别并定位摄像头前的人脸以及眼睛部位。
输入q或者Q，退出程序。
或进行文本中所包含的图片路径 或 单个图片进行检测，自行修改代码即可

配置环境项目，debug
解决error C4996: ‘fopen’: This function or variable may be unsafe. Consider using fopen_s instead…
参考配置

确保lib，dll，头文件都配置正确


也可将dll文件添加到系统环境变量path里，便无需移动了，最简单
后续单独打包exe，再额外将exe和所需的dll放一起

导出项目模板，以后直接使用这个项目模板创建新项目




如果程序运行需要提供命令行参数，可在此处设置
等效于在cmd里面运行

OpenCV源代码在此路径查找

添加现有项，将cpp文件直接添加进来，无需再新建cpp文件了


将所需的data数据放在.cpp文件下

数据集在opencv的sources文件夹下，必需，一定要正确放置data文件，避免报错

两者的路径不一致，非必需，可自行修改选择图片，仅用于测试

修改一下代码，对默认的lema图片检测如下所示
源代码，自行添加了注释，未修改源代码

#include "opencv2/objdetect.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/videoio.hpp"
#include <iostream>using namespace std;
using namespace cv;static void help(const char **argv) {cout << "\nThis program demonstrates the use of cv::CascadeClassifier class to detect objects (Face + eyes). You can use Haar or LBP features.\n""This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.\n""It's most known use is for faces.\n""Usage:\n"<< argv[0]<< "   [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]\n""   [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]\n""   [--scale=<image scale greater or equal to 1, try 1.3 for example>]\n""   [--try-flip]\n""   [filename|camera_index]\n\n""example:\n"<< argv[0]<< " --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n""During execution:\n\tHit any key to quit.\n""\tUsing OpenCV version " << CV_VERSION << "\n" << endl;
}void detectAndDraw(Mat &img, CascadeClassifier &cascade,CascadeClassifier &nestedCascade,double scale, bool tryflip);string cascadeName;
string nestedCascadeName;int main(int argc, const char **argv) {VideoCapture capture;Mat frame, image;string inputName;bool tryflip;CascadeClassifier cascade, nestedCascade;double scale;cv::CommandLineParser parser(argc, argv,"{help h||}""{cascade|data/haarcascades/haarcascade_frontalface_alt.xml|}""{nested-cascade|data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}""{scale|1|}{try-flip||}{@filename||}");if (parser.has("help")) {help(argv);return 0;}cascadeName = parser.get<string>("cascade");nestedCascadeName = parser.get<string>("nested-cascade");scale = parser.get<double>("scale");if (scale < 1)scale = 1;tryflip = parser.has("try-flip");inputName = parser.get<string>("@filename");if (!parser.check()) {parser.printErrors();return 0;}if (!nestedCascade.load(samples::findFileOrKeep(nestedCascadeName)))cerr << "WARNING: Could not load classifier cascade for nested objects" << endl;if (!cascade.load(samples::findFile(cascadeName))) {cerr << "ERROR: Could not load classifier cascade" << endl;help(argv);return -1;}//如果 inputName 为空或是一个单个数字字符，则尝试打开对应索引的摄像头。if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1)) {//奥比中光的gemini 2接入，1可以使用，0和2无法使用，3则是笔记本内置摄像头//不接入设备，0是笔记本内置摄像头//如果 inputName 为空，则默认使用摄像头0；否则，将 inputName 的第一个字符转换为整数作为摄像头索引。int camera = inputName.empty() ? 0 : inputName[0] - '0';if (!capture.open(camera)) {cout << "Capture from camera #" << camera << " didn't work" << endl;return 1;}} else if (!inputName.empty()) {//如果 inputName 不为空且不是一个单个数字字符，则尝试读取指定的图像文件。image = imread(samples::findFileOrKeep(inputName), IMREAD_COLOR);if (image.empty()) {if (!capture.open(samples::findFileOrKeep(inputName))) {cout << "Could not read " << inputName << endl;return 1;}}} else {//读取默认的图像文件 lena.jpg。image = imread(samples::findFile("lena.jpg"), IMREAD_COLOR);if (image.empty()) {cout << "Couldn't read lena.jpg" << endl;return 1;}}if (capture.isOpened()) {cout << "Video capturing has been started ..." << endl;/*从视频捕获设备读取帧。对每一帧进行人脸和眼睛的检测。在图像上绘制检测结果。持续运行，直到帧为空或用户按下退出键。（Esc、'q'或'Q'）*/for (;;) {capture >> frame;if (frame.empty())break;Mat frame1 = frame.clone();detectAndDraw(frame1, cascade, nestedCascade, scale, tryflip);char c = (char)waitKey(10);if (c == 27 || c == 'q' || c == 'Q')break;}} else {cout << "Detecting face(s) in " << inputName << endl;if (!image.empty()) {detectAndDraw(image, cascade, nestedCascade, scale, tryflip);waitKey(0);} else if (!inputName.empty()) {/* assume it is a text file containing thelist of the image filenames to be processed - one per line *///如果已经加载了单张图像，则直接进行人脸检测并等待用户按键。//如果输入是一个包含图像文件列表的文本文件，则逐行读取文件内容，尝试读取每行指定的图像文件，并进行人脸检测。如果读取失败，则输出错误信息。//在检测每张图像后，等待用户按键，如果用户按下特定键（Esc、'q'或'Q'），则退出处理流程。FILE *f = fopen(inputName.c_str(), "rt");if (f) {char buf[1000 + 1];while (fgets(buf, 1000, f)) {//计算字符串buf的长度，然后从字符串末尾向前遍历，直到遇到非空白字符为止。最后，将字符串的结束符\0放在最后一个非空白字符之后，从而去除了行尾的空白字符。int len = (int)strlen(buf);while (len > 0 && isspace(buf[len - 1]))len--;buf[len] = '\0';cout << "file " << buf << endl;image = imread(buf, IMREAD_COLOR);if (!image.empty()) {detectAndDraw(image, cascade, nestedCascade, scale, tryflip);char c = (char)waitKey(0);if (c == 27 || c == 'q' || c == 'Q')break;} else {cerr << "Aw snap, couldn't read image " << buf << endl;}}fclose(f);}}}return 0;
}void detectAndDraw(Mat &img, CascadeClassifier &cascade,CascadeClassifier &nestedCascade,double scale, bool tryflip) {/*t：用于计时。faces 和 faces2：存储检测到的人脸矩形区域。colors：用于绘制检测结果的颜色数组。gray 和 smallImg：用于存储灰度图像和缩放后的图像。*/double t = 0;vector<Rect> faces, faces2;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;/*将输入图像转换为灰度图像。计算缩放比例 fx。将灰度图像缩放到指定比例。对缩放后的图像进行直方图均衡化，以提高对比度。*/cvtColor(img, gray, COLOR_BGR2GRAY);double fx = 1 / scale;resize(gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT);equalizeHist(smallImg, smallImg);//记录检测开始时间。//    使用主分类器 cascade 检测人脸，结果存储在 faces 中。//    如果 tryflip 为真，则翻转图像并再次检测人脸，结果存储在 faces2 中，并将 faces2 中的结果转换为原始图像坐标系后添加到 faces 中。//    计算并输出检测时间。t = (double)getTickCount();cascade.detectMultiScale(smallImg, faces,1.1, 2, 0//|CASCADE_FIND_BIGGEST_OBJECT//|CASCADE_DO_ROUGH_SEARCH| CASCADE_SCALE_IMAGE,Size(30, 30));if (tryflip) {flip(smallImg, smallImg, 1);cascade.detectMultiScale(smallImg, faces2,1.1, 2, 0//|CASCADE_FIND_BIGGEST_OBJECT//|CASCADE_DO_ROUGH_SEARCH| 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));}}t = (double)getTickCount() - t;printf("detection time = %g ms\n", t * 1000 / getTickFrequency());/*遍历检测到的人脸区域。计算人脸区域的宽高比，如果宽高比在合理范围内，则绘制圆形标记；否则绘制矩形标记。如果嵌套分类器不为空，则使用嵌套分类器检测眼睛，并在检测到的眼睛位置绘制圆形标记。显示检测结果图像。*/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;double aspect_ratio = (double)r.width / r.height;if (0.75 < aspect_ratio && aspect_ratio < 1.3) {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);} elserectangle(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);if (nestedCascade.empty())continue;smallImgROI = smallImg(r);nestedCascade.detectMultiScale(smallImgROI, nestedObjects,1.1, 2, 0//|CASCADE_FIND_BIGGEST_OBJECT//|CASCADE_DO_ROUGH_SEARCH//|CASCADE_DO_CANNY_PRUNING| CASCADE_SCALE_IMAGE,Size(30, 30));for (size_t j = 0; j < nestedObjects.size(); j++) {Rect nr = nestedObjects[j];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("result", img);
}