opencv提高之人脸检测

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/keith_bb/article/details/71172926

上一篇介绍了cascade分类器的训练，这里给出训练好的xml文件进行人脸检测的代码(由于眼睛发炎不能长时间看电脑屏幕，以后补详细介绍)

#include <iostream>
#include <opencv2\core\core.hpp>
#include <opencv2\highgui\highgui.hpp>
#include <opencv2\imgproc\imgproc.hpp>
#include <opencv2\objdetect\objdetect.hpp>

using namespace std;
using namespace cv;

//定义全局变量
string face_cascade_name = "haarcascade_frontalface_alt.xml";
//string face_cascade_name = "haarcascade_frontalface_default.xml";
string face_detect_txt = "face";
CascadeClassifier face_cascade;     //创建分类器对象

int main()
{
    Mat srcImage, grayImage;
    srcImage = imread("bigBang.jpg");
    //判断图像是否加载成功
    if (srcImage.empty())
    {
        cout << "图像加载失败...";
        return -1;
    }

    //加载级联分类器
    if (!face_cascade.load(face_cascade_name))
    {
        cout << "分类文件加载失败...";
        return -1;
    }

    //定义存放检测到人脸的矢量
    vector<Rect> faces;
    cvtColor(srcImage, grayImage, COLOR_BGR2GRAY);      //转换为灰度图
    equalizeHist(grayImage, grayImage);                 //直方图均衡化

    //多尺寸检测人脸
    face_cascade.detectMultiScale(grayImage, faces, 1.1, 2, 0 | CV_HAAR_SCALE_IMAGE, Size(100, 100));

    //以矩形绘制检测到的人脸
    for (int i = 0; i < faces.size(); i++)
    {
        rectangle(srcImage, Point(faces[i].x, faces[i].y), Point(faces[i].x + faces[i].width, faces[i].y + faces[i].height), Scalar(0, 0, 255), 2, 8, 0);
        putText(srcImage, face_detect_txt, Point(faces[i].x, faces[i].y - 2), FONT_HERSHEY_SIMPLEX, 0.7, Scalar(255, 0, 0), 2, 8, false);
    }

    imshow("face", srcImage);
    waitKey(0);

    return 0;

}

运行结果