OpenCV人脸检测与图像处理实例

本文将详细介绍如何利用OpenCV库实现人脸检测和图像处理功能。通过本文的步骤指导，您可以轻松完成目标检测和图像分析任务。

开发环境配置

在开始编写代码之前，确保您的开发环境已正确配置。以下是必要的软件版本：

操作系统：Windows 10或更高版本

OpenCV库：安装最新版本的OpenCV，确保支持所需功能

开发工具：Visual Studio Code或其他支持C++的开发工具

项目文件结构

创建一个新的项目文件夹，文件结构如下：

project├── src│   └── main.cpp└── build    └── Debug        └── main.exe

代码实现

#include 
   
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     #include 
     
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          using namespace cv;using namespace std;void detectAndDraw(Mat& img, CascadeClassifier& cascade, CascadeClassifier& nestedCascade, double scale, bool tryflip) { vector
          
            faces, faces2; 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) }; Mat gray, smallImg(cvRound(img.rows / scale), cvRound(img.cols / scale), CV_8UC1); cvtColor(img, gray, CV_BGR2GRAY); resize(gray, smallImg, smallImg.size(), 0, 0, INTER_LINEAR); equalizeHist(smallImg, smallImg); cascade.detectMultiScale(smallImg, faces, 1.1, 2, 0, Size(30, 30)); if (tryflip) { flip(smallImg, smallImg, 1); cascade.detectMultiScale(smallImg, faces2, 1.1, 2, 0, Size(30, 30)); for (auto r : faces2.begin(), auto rEnd : faces2.end()) { faces.push_back(Rect(smallImg.cols - r->x - r->width, r->y, r->width, r->height)); } } for (auto r : faces.begin(), auto rEnd : faces.end()) { int i++; Point center; Scalar color = colors[i % 8]; double aspect_ratio = 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); int radius = cvRound((r.width + r.height) * 0.25 * scale); circle(img, center, radius, color, 3, 8, 0); } else { 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); } Mat smallImgROI = smallImg(r); if (!nestedCascade.empty()) { vector
           
             nestedObjects; nestedCascade.detectMultiScale(smallImgROI, nestedObjects, 1.1, 2, 0, Size(30, 30)); for (auto nr : nestedObjects.begin(), auto nrEnd : nestedObjects.end()) { center.x = cvRound((r.x + nr.x + nr.width * 0.5) * scale); center.y = cvRound((r.y + nr.y + nr.height * 0.5) * scale); int radius = cvRound((nr.width + nr.height) * 0.25 * scale); circle(img, center, radius, color, 3, 8, 0); } } } imshow("识别结果", img);}int main() { CascadeClassifier cascade, nestedCascade; string cascade_path = "D:/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml"; string nested_path = "D:/opencv/sources/data/haarcascades/haarcascade_eye.xml"; VideoCapture cap(0); if (!cap.isOpened()) { return -1; } Mat frame; namedWindow("输入图像", WINDOW_AUTOSIZE); while (true) { cap >> frame; detectAndDraw(frame, cascade, nestedCascade, 2, 0); if (waitKey(30) >= 0) { break; } } return 0;}