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