用于面部检测的OpenCV C++程序

该程序使用OpenCV库检测来自网络摄像头的实时流中或本地计算机中存储的视频文件中的人脸。该程序实时检测人脸并对其进行跟踪。它使用相同的预训练XML分类器。该程序中使用的分类器具有在其中训练的面部特征。可以使用不同的分类器来检测不同的对象。
运行程序的要求：
1)必须在本地计算机上安装OpenCV。
2)必须在执行程序之前提供分类器XML文件的路径。这些XML文件可在OpenCV目录“ opencv / data / haarcascades”中找到。
3)在capture.open(0)中使用0播放网络摄像头。
4)为了在本地视频中进行检测，请提供视频的路径。(capture.open(“ path_to_video”))。执行：

// CPP program to detects face in a video
  
// Include required header files from OpenCV directory
#include "/usr/local/include/opencv2/objdetect.hpp"
#include "/usr/local/include/opencv2/highgui.hpp"
#include "/usr/local/include/opencv2/imgproc.hpp"
#include 
  
using namespace std;
using namespace cv;
  
// Function for Face Detection
void detectAndDraw( Mat& img, CascadeClassifier& cascade, 
                CascadeClassifier& nestedCascade, double scale );
string cascadeName, nestedCascadeName;
  
int main( int argc, const char** argv )
{
    // VideoCapture class for playing video for which faces to be detected
    VideoCapture capture; 
    Mat frame, image;
  
    // PreDefined trained XML classifiers with facial features
    CascadeClassifier cascade, nestedCascade; 
    double scale=1;
  
    // Load classifiers from "opencv/data/haarcascades" directory 
    nestedCascade.load( "../../haarcascade_eye_tree_eyeglasses.xml" ) ;
  
    // Change path before execution 
    cascade.load( "../../haarcascade_frontalcatface.xml" ) ; 
  
    // Start Video..1) 0 for WebCam 2) "Path to Video" for a Local Video
    capture.open(0); 
    if( capture.isOpened() )
    {
        // Capture frames from video and detect faces
        cout << "Face Detection Started...." << endl;
        while(1)
        {
            capture >> frame;
            if( frame.empty() )
                break;
            Mat frame1 = frame.clone();
            detectAndDraw( frame1, cascade, nestedCascade, scale ); 
            char c = (char)waitKey(10);
          
            // Press q to exit from window
            if( c == 27 || c == 'q' || c == 'Q' ) 
                break;
        }
    }
    else
        cout<<"Could not Open Camera";
    return 0;
}
  
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
                    CascadeClassifier& nestedCascade,
                    double scale)
{
    vector faces, faces2;
    Mat gray, smallImg;
  
    cvtColor( img, gray, COLOR_BGR2GRAY ); // Convert to Gray Scale
    double fx = 1 / scale;
  
    // Resize the Grayscale Image 
    resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR ); 
    equalizeHist( smallImg, smallImg );
  
    // Detect faces of different sizes using cascade classifier 
    cascade.detectMultiScale( smallImg, faces, 1.1, 
                            2, 0|CASCADE_SCALE_IMAGE, Size(30, 30) );
  
    // Draw circles around the faces
    for ( size_t i = 0; i < faces.size(); i++ )
    {
        Rect r = faces[i];
        Mat smallImgROI;
        vector nestedObjects;
        Point center;
        Scalar color = Scalar(255, 0, 0); // Color for Drawing tool
        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 );
        }
        else
            rectangle( img, cvPoint(cvRound(r.x*scale), cvRound(r.y*scale)),
                    cvPoint(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 );
          
        // Detection of eyes int the input image
        nestedCascade.detectMultiScale( smallImgROI, nestedObjects, 1.1, 2,
                                        0|CASCADE_SCALE_IMAGE, Size(30, 30) ); 
          
        // Draw circles around eyes
        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 );
        }
    }
  
    // Show Processed Image with detected faces
    imshow( "Face Detection", img ); 
}

输出：

人脸检测

下一篇：用于面部检测的Opencv Python程序
参考：

1)http://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html
2)http://docs.opencv.org/2.4/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html

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