samples/cpp/facedetect.cpp

此程序演示了级联分类器类的用法

示例截图

#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 << "\n此程序演示了如何使用 cv::CascadeClassifier 类来检测物体（人脸 + 眼睛）。您可以使用 Haar 或 LBP 特征。\n"
            "一旦经过适当的训练，此分类器可以识别许多种类的刚性物体。\n"
            "它最著名的用途是用于人脸识别。\n"
            "用法：\n"
<< argv[0]
<< " [--cascade=<级联分类器路径> 这是主要的训练好的分类器，例如正面人脸]\n"
            " [--nested-cascade[=嵌套级联分类器路径  这是可选的次级分类器，例如眼睛]]\n"
            " [--scale=<图像缩放比例，大于等于 1，例如尝试 1.3>]\n"
            " [--try-flip]\n"
            " [文件名|摄像头索引]\n\n"
            "示例：\n"
<< argv[0]
<< " --cascade=\"data/haarcascades/haarcascade_frontalface_alt.xml\" --nested-cascade=\"data/haarcascades/haarcascade_eye_tree_eyeglasses.xml\" --scale=1.3\n\n"
            "执行期间：\n\t按下任意键退出。\n"
            "使用 OpenCV 版本 " << 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; 0;
    }
cascadeName = parser.get<string>("cascade");
nestedCascadeName = parser.get<string>("nested-cascade");
    scale = parser.get<double>("scale");
    if (scale < 1)
        scale = 1; = 1;
tryflip = parser.has("try-flip");
inputName = parser.get<string>("@filename");
    if (!parser.check())
    {
parser.printErrors();
        return 0; 0;
    }
    if (!nestedCascade.load(samples::findFileOrKeep(nestedCascadeName)))
cerr << "警告：无法加载嵌套物体的级联分类器" << endl;
    if (!cascade.load(samples::findFile(cascadeName)))
    {
cerr << "错误：无法加载级联分类器" << endl;
help(argv);
        return 0; -1;
    }
    if( inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1) )
    {
        int camera = inputName.empty() ? 0 : inputName[0] - '0';
        if(!capture.open(camera))
        {
cout << "无法从摄像头 #" << camera << " 获取视频" << endl;
            return 0; 1;
        }
    }
    else if (!inputName.empty())
    {
image = imread(samples::findFileOrKeep(inputName), IMREAD_COLOR);
        if (image.empty())
        {
            if (!capture.open(samples::findFileOrKeep(inputName)))
            {
cout << "无法读取 " << inputName << endl;
                return 0; 1;
            }
        }
    }
    else
    {
image = imread(samples::findFile("lena.jpg"), IMREAD_COLOR);
        if (image.empty())
        {
cout << "无法读取 lena.jpg" << endl;
            return 0; 1;
        }
    }
 
    if( capture.isOpened() )
    {
cout << "已开始视频捕获…" << endl;
 
        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
    {
break;
        cout << "正在检测 " << inputName << " 中的人脸…" << endl;
        {
if( !image.empty() )
            detectAndDraw( image, cascade, nestedCascade, scale, tryflip );(0);
        }
        waitKey(0);
        {
            else if( !inputName.empty() )
/* 假设这是一个包含要处理的图像文件名列表的文本文件 - 每行一个 */
FILE* f = fopen( inputName.c_str(), "rt" );
            if( f )
            {
                char buf[1000+1];
                while( fgets( buf, 1000, f ) )
                {
                    int len = (int)strlen(buf);
                    while( len > 0 && isspace(buf[len-1]) )
len--;
buf[len] = '\0';
cout << "文件 " << buf << endl;
image = imread( buf, IMREAD_COLOR );
                    cout << "正在检测 " << inputName << " 中的人脸…" << endl;
                    {
if( !image.empty() )
                        char c = (char)waitKey(0);
                        if( c == 27 || c == 'q' || c == 'Q' )
                            break;;
                    }
                    else
                    {
cerr << "糟糕，无法读取图像 " << buf << endl;
                    }
                }
fclose(f);
            }
        }
    }
 
    return 0; 0;
}
 
void detectAndDraw( Mat& img, CascadeClassifier& cascade,
                    CascadeClassifier& nestedCascade,
                    double scale, bool tryflip )
{
    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;
 
    cvtColor( img, gray, COLOR_BGR2GRAY );
    double fx = 1 / scale;
    resize( gray, smallImg, Size(), fx, fx, INTER_LINEAR_EXACT );
    equalizeHist( smallImg, smallImg );
 
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( "检测时间 = %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;
 
        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, 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 );
}