• OpenCV例程实现人脸检测


          前段时间看的OpenCV,其实有很多的例子程序,参考代码值得我们学习,对图像特征提取三大法宝:HOG特征,LBP特征,Haar特征有一定了解后。

    对本文中的例子程序刚开始没有调通,今晚上调通了,试了试效果还可以,还需要深入理解。值得大家动手试试,还是很有成就感的,虽然是现成的例子.......

    环境:OpenCV3.1+VS2013+WIN10

    /*!
     * file Capture.cpp
     *
     * author ranjiewen
     * date 十一月 2016
     *
     *  http://www.cnblogs.com/tanfy/p/5552270.html
    
        解析opencv自带人脸识别源码(……/opencv-3.1.0/samples/cpp/facedetect.cpp)
     */
    #include "opencv2/objdetect.hpp"
    #include "opencv2/highgui.hpp"
    #include "opencv2/imgproc.hpp"
    #include <iostream>
    
    using namespace std;
    using namespace cv;
    
    static void help()
    {
        cout << "
    This program demonstrates the cascade recognizer. Now you can use Haar or LBP features.
    "
            "This classifier can recognize many kinds of rigid objects, once the appropriate classifier is trained.
    "
            "It's most known use is for faces.
    "
            "Usage:
    "
            "./facedetect [--cascade=<cascade_path> this is the primary trained classifier such as frontal face]
    "
            "   [--nested-cascade[=nested_cascade_path this an optional secondary classifier such as eyes]]
    "
            "   [--scale=<image scale greater or equal to 1, try 1.3 for example>]
    "
            "   [--try-flip]
    "
            "   [filename|camera_index]
    
    "
            "see facedetect.cmd for one call:
    "
            "./facedetect --cascade="../../data/haarcascades/haarcascade_frontalface_alt.xml" --nested-cascade="../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml" --scale=1.3
    
    "
            "During execution:
    	Hit any key to quit.
    "
            "	Using OpenCV version " << CV_VERSION << "
    " << 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是Opencv中做人脸检测的时候的一个级联分类器,现在有两种选择:一是使用老版本的CvHaarClassifierCascade函数,一是使用新版本的CascadeClassifier类。老版本的分类器只支持类Haar特征,而新版本的分类器既可以使用Haar,也可以使用LBP特征。
        CascadeClassifier cascade, nestedCascade;
        double scale;
    
        cv::CommandLineParser parser(argc, argv,
            "{help h||}"
            "{cascade|D:/opencv/sources/data/haarcascades/haarcascade_frontalface_alt.xml|}"   //默认路径实在安装路径下sample,修改了路径,以便加载load成功
            "{nested-cascade|D:/opencv/sources/data/haarcascades/haarcascade_eye_tree_eyeglasses.xml|}"  //修改路径
            "{scale|1|}{try-flip||}{@filename||}" //文件为空时,设置摄像头,实时检测人脸
            );
        if (parser.has("help"))
        {
            help();
            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");
        std::cout << inputName << std::endl;  // test
        if (!parser.check())
        {
            parser.printErrors();
            return 0;
        }
    
        // 加载模型
        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;
            help();
            return -1;
        }
        // 读取摄像头
        // isdigit检测字符是否为阿拉伯数字 
        if (inputName.empty() || (isdigit(inputName[0]) && inputName.size() == 1))
        {
            int c = inputName.empty() ? 0 : inputName[0] - '0';
            // 此处若系统在虚拟机上,需在虚拟机中设置接管摄像头:虚拟机(M)-> 可移动设备 -> 摄像头名称 -> 连接(断开与主机连接)
            if (!capture.open(c))
                cout << "Capture from camera #" << c << " didn't work" << endl;
            else {
                capture.set(CV_CAP_PROP_FRAME_WIDTH, 640);
                capture.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
            }
        }
        else if (inputName.size())
        {
            image = imread(inputName, 1);
            if (image.empty())
            {
                if (!capture.open(inputName))
                    cout << "Could not read " << inputName << endl;
            }
        }
        else
        {
            image = imread("../data/lena.jpg", 1);
            if (image.empty()) cout << "Couldn't read ../data/lena.jpg" << endl;
        }
    
        if (capture.isOpened())
        {
            cout << "Video capturing has been started ..." << endl;
    
    
            for (;;)
            {
                std::cout << "capturing..." << std::endl;  // test
                capture >> frame;
                if (frame.empty())
                    break;
    
                Mat frame1 = frame.clone();
                std::cout << "Start to detect..." << std::endl;  // test
                detectAndDraw(frame1, cascade, nestedCascade, scale, tryflip);
    
                int c = 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 the
                list of the image filenames to be processed - one per line */
                FILE* f = fopen(inputName.c_str(), "rt");
                if (f)
                {
                    char buf[1000 + 1];
                    while (fgets(buf, 1000, f))
                    {
                        int len = (int)strlen(buf), c;
                        while (len > 0 && isspace(buf[len - 1]))
                            len--;
                        buf[len] = '';
                        cout << "file " << buf << endl;
                        image = imread(buf, 1);
                        if (!image.empty())
                        {
                            detectAndDraw(image, cascade, nestedCascade, scale, tryflip);
                            c = 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)
    {
        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);
        equalizeHist(smallImg, smallImg);
    
        t = (double)cvGetTickCount();
        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)cvGetTickCount() - t;
        printf("detection time = %g ms
    ", t / ((double)cvGetTickFrequency()*1000.));
        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, 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);
            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);
    }
    
    
    
    
    /*****************************************************
    * file Capture.cpp
    * date 2016/11/10 0:22
    * author ranjiewen
    * contact: ranjiewen@outlook.com
    * 问题描述:
    http://www.cnblogs.com/lingshaohu/archive/2011/12/16/2290017.html
    
    * 问题分析:
    可以存avi,但是不能打开,待改善
    *****************************************************/
    
    //#include <iostream>
    //#include <opencv2/opencv.hpp>
    //using namespace cv;;
    //using namespace std;
    //int main()
    //{
    //    CvCapture* capture = cvCaptureFromCAM(-1);
    //    CvVideoWriter* video = NULL;
    //    IplImage* frame = NULL;
    //    int n;
    //    if (!capture) //如果不能打开摄像头给出警告
    //    {
    //        cout << "Can not open the camera." << endl;
    //        return -1;
    //    }
    //    else
    //    {
    //        frame = cvQueryFrame(capture); //首先取得摄像头中的一帧
    //        video = cvCreateVideoWriter("camera.avi", CV_FOURCC('X', 'V', 'I', 'D'), 25,
    //            cvSize(frame->width, frame->height)); //创建CvVideoWriter对象并分配空间
    //        //保存的文件名为camera.avi,编码要在运行程序时选择,大小就是摄像头视频的大小,帧频率是32
    //        if (video) //如果能创建CvVideoWriter对象则表明成功
    //        {
    //            cout << "VideoWriter has created." << endl;
    //        }
    //
    //        cvNamedWindow("Camera Video", 1); //新建一个窗口
    //        int i = 0;
    //        while (i <= 300) // 让它循环200次自动停止录取
    //        {
    //            frame = cvQueryFrame(capture); //从CvCapture中获得一帧
    //            if (!frame)
    //            {
    //                cout << "Can not get frame from the capture." << endl;
    //                break;
    //            }
    //            n = cvWriteFrame(video, frame); //判断是否写入成功,如果返回的是1,表示写入成功
    //            cout << n << endl;
    //            cvShowImage("Camera Video", frame); //显示视频内容的图片
    //            i++;
    //            if (cvWaitKey(2) > 0)
    //                break; //有其他键盘响应,则退出
    //        }
    //
    //        cvReleaseVideoWriter(&video);
    //        cvReleaseCapture(&capture);
    //        cvDestroyWindow("Camera Video");
    //    }
    //    return 0;
    //}

    这是调用摄像头动态检测人脸的程序,实验结果:

    控制台输出:

    ***** VIDEOINPUT LIBRARY - 0.1995 - TFW07 *****
    
    
    SETUP: Setting up device 0
    SETUP: Integrated Camera
    SETUP: Couldn't find preview pin using SmartTee
    SETUP: Default Format is set to 640x480
    SETUP: trying specified format RGB24 @ 640x480
    SETUP: trying format RGB24 @ 640x480
    SETUP: trying format RGB32 @ 640x480
    SETUP: trying format RGB555 @ 640x480
    SETUP: trying format RGB565 @ 640x480
    SETUP: trying format YUY2 @ 640x480
    SETUP: Capture callback set
    SETUP: Device is setup and ready to capture.
    
    Video capturing has been started ...
    capturing...
    Start to detect...
    detection time = 486.754 ms
    capturing...
    Start to detect...
    detection time = 444.236 ms
    capturing...
    Start to detect...
    detection time = 441.649 ms
    capturing...
    Start to detect...
    detection time = 447.361 ms
    capturing...
    Start to detect...
    detection time = 427.589 ms
    capturing...
    Start to detect...
    detection time = 453.187 ms
    capturing...
    Start to detect...
    d
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  • 原文地址:https://www.cnblogs.com/ranjiewen/p/6059932.html
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