opencv学习之路(41)人脸识别

Posted 进击的小猴子

tags:

篇首语:本文由小常识网(cha138.com)小编为大家整理,主要介绍了opencv学习之路(41)人脸识别相关的知识,希望对你有一定的参考价值。

一、人脸检测并采集个人图像

//take_photo.cpp
#include<opencv2/opencv.hpp>
using namespace cv;
using namespace std;

void take_photo() {
    VideoCapture cap(0); //打开摄像头
    if (!cap.isOpened())
        return;

    //加载级联检测器
    CascadeClassifier cascade;
    cascade.load("F:/opencv3.2/Release_x64/etc/haarcascades/haarcascade_frontalface_alt_tree.xml");

    Mat frame;
    vector<Rect>faces;
    int count = 0;
    while (cap.read(frame))        //相当于cap >> frame
    {
        cascade.detectMultiScale(frame, faces, 1.1, 1, 0, Size(100, 100), Size(400, 400));//检测是否有人脸
        for (int i = 0; i < faces.size(); i++)
        {
            if (count % 10 == 0) {    //每10帧保存一次人脸图像
                Mat dst;
                resize(frame(faces[i]), dst, Size(92, 112));//设置人脸图像大小
                cvtColor(dst, dst, COLOR_BGR2GRAY);//转为灰度图节省计算
                imwrite(format("att_faces/s41/pic%d.jpg", count / 10), dst);
            }
            rectangle(frame, faces[i], Scalar(0, 255, 0));
        }
        imshow("video", frame);

        //按下任意键退出摄像头(waitkey在本系统环境下默认为255),或者是保存了20张人脸图片后,退出
        if (waitKey(50) != 255 || count / 10>20)
            break;
        count++;
    }
    cap.release();
    destroyAllWindows();//关闭所有窗口
}

 运行程序,打开摄像头后会自动保存人脸图像,头不要晃动,表情变化即可,对于不合适的照片还需进行筛选。

 二、基于特征脸算法的人脸识别

//face_recognition.cpp
#include <opencv2/opencv.hpp>
#include <opencv2/face.hpp>

using namespace cv;
using namespace cv::face;
using namespace std;

double face_recognition() {
    //读取文件,转换为数据流
    string filename = string("at.txt");
    ifstream file(filename.c_str(), ifstream::in);
    if (!file)
        cout << "error" << endl;

    string line, path, classlabel;
    vector<Mat>image;
    vector<int>labels;
    char separator = \';\';
    while (getline(file, line))
    {
        stringstream liness(line);
        getline(liness, path, separator);
        getline(liness, classlabel);
        if (!path.empty() && !classlabel.empty())
        {
            //cout << "path:" << path<< endl;
            image.push_back(imread(path, 0));
            labels.push_back(atoi(classlabel.c_str()));
        }
    }
    if (image.size() < 1 || labels.size() < 1)
        cout << "invalid image path..." << endl;

    //最后一个人为测试样本
    Mat testSample = image[image.size() - 1];
    int testLabel = labels[labels.size() - 1];
    image.pop_back();
    labels.pop_back();

    //EigenFace算法的模型训练
    Ptr<BasicFaceRecognizer>model = createEigenFaceRecognizer();
    model->train(image, labels);

    //对样本进行识别
    int predictLabel = model->predict(testSample);
    cout << "actual label:" << testLabel << ",predict label:" << predictLabel << endl;

    //加载级联检测器
    CascadeClassifier cascade;
    cascade.load("haarcascade_frontalface_alt_tree.xml");//识别时用alt_tree分类器,宁可漏检也不误检

    //打开摄像头
    VideoCapture cap(0);
    if (!cap.isOpened())
        cout << "error...";

    Mat frame;
    vector<Rect>faces;
    int correct = 0, total = 0;
    while (cap.read(frame))        //相当于cap >> frame,读取摄像头的每一帧
    {
        cascade.detectMultiScale(frame, faces, 1.1, 1, 0, Size(80, 100), Size(380, 400));//检测是否有人脸
        for (int i = 0; i < faces.size(); i++)
        {
            Mat roi = frame(faces[i]);
            cvtColor(roi, roi, COLOR_BGR2GRAY);
            resize(roi, testSample, testSample.size());
            int label = model->predict(testSample);
            rectangle(frame, faces[i], Scalar(0, 255, 0));
            if (label == 41)
            {
                putText(frame, "ZhangChunFu", faces[i].tl(), FONT_HERSHEY_COMPLEX, 1.0, Scalar(0, 0, 255));
                correct++;
            }
            else
                putText(frame, format("%d", label), faces[i].tl(), CV_FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 0, 255));
        }
        total++;
        imshow("人脸识别——MR.Zhang", frame);
        if (waitKey(50) == 27)
            break;
    }

    cap.release();
    destroyAllWindows();//关闭所有窗口
    waitKey(0);

    double rate = (1.0*correct) / total;
    return rate;//返回正确率
}
//main.cpp
#include <opencv2/opencv.hpp>
#include <opencv2/face.hpp>
#include <iostream>

using namespace cv;
using namespace cv::face;
using namespace std;

void take_photo();
double face_recognition();

void main() {
    int flag;
    double rate;
    cout << "欢迎使用人脸识别系统(1代表录入人脸,2代表识别人脸),请输入您的选择:" << endl;
    cin >> flag;
    cout << "请稍等片刻……"<<endl;
    switch (flag)
    {
    case 1:take_photo();
        return;
    case 2:
        rate=face_recognition();
        break;
    default:
        break;
    }
    cout << "识别率:" << rate << endl;
    system("pause");

}

 

以上是关于opencv学习之路(41)人脸识别的主要内容,如果未能解决你的问题,请参考以下文章

OpenCV实践之路——人脸识别之三识别自己的脸

实现人脸识别性别之路---opencv

OpenCV实践之路——人脸识别之二模型训练

OpenCV实践之路——人脸识别之一数据收集和预处理

Linux学习OpenCV+ROS 实现人脸识别(Ubantu16.04)

[OpenCV实战]2 人脸识别算法对比