opencv人脸识别-蒲公英云

opencv人脸识别

opencv人脸识别：

首先我们来简单的讲一下人脸识别流程：这里采用的是vs2017+opencv3.3扩展库

1、进行人脸检测

2、识别器训练与分类

3、人脸检测完和识别器进行预测

4、圈出人脸并在人脸上面显示人名

注：这里用的库比较多是opencv扩展库中的，官方的库会报错(因为没有相应的EigenFaceRecognizer类模板无法进行识别器的训练分类) 可以参考上一篇博客如何编译opencv扩展库还要注意的是不同版本的识别器创建的写法会不同，最好百度查一下api（这个也是搞了我蛮久的）

Code：

//face_recog_from_video.cpp 定义控制台应用程序的入口点。
#include "pch.h" //预编译 不同版本不同  
#include "opencv2/opencv.hpp"
#include "opencv2/face.hpp"
#include <stdio.h>
#include<iostream>
using namespace std;
using namespace cv;
using namespace cv::face;
/** Function Headers */
void detectAndDisplay(Mat frame);
/** Global variables */
String face_cascade_name = "G:\\opencv\\opencv+\\opencvmake\\install\\etc\\haarcascades\\haarcascade_frontalface_alt.xml";
CascadeClassifier face_cascade;
String window_name = "Capture - Face Recognition";
string face1_path = "D:\\xue.jpg";//1
string face2_path = "D:\\zxy.jpg";//2
string face3_path = "D:\\zly.jpg";//3
string face4_path = "D:\\my.jpg";//我->4号
Ptr<EigenFaceRecognizer> modelPCA = EigenFaceRecognizer::create();// 创建特征脸模型  EigenFaceRecognizer
/** @function main */
int main(int argc, const char** argv)
{
    vector<Mat> images;
    images.push_back(imread(face1_path, CV_LOAD_IMAGE_GRAYSCALE));
    images.push_back(imread(face2_path, CV_LOAD_IMAGE_GRAYSCALE));
    images.push_back(imread(face3_path, CV_LOAD_IMAGE_GRAYSCALE));
    images.push_back(imread(face4_path, CV_LOAD_IMAGE_GRAYSCALE));
    vector<int> labels(4);
    for (int i = 1;i <= 4;i++)
        labels[i - 1] = i;
    //训练
    modelPCA->train(images, labels);
    VideoCapture capture;
    Mat frame;
    //-- 1. Load the cascades
    if (!face_cascade.load(face_cascade_name)) { printf("--(!)Error loading face cascade\n"); return -1; };
    //-- 2. Read the video stream
    capture.open(0); //打开摄像头
    if (!capture.isOpened()) { printf("--(!)Error opening video capture\n"); return -1; }
    while (capture.read(frame)) //读取帧
    {
        if (frame.empty())
        {
            printf(" --(!) No captured frame -- Break!");
            break;
        }
        //-- 3. Apply the classifier to the frame
        detectAndDisplay(frame);
        if (waitKey(10) == 'k') { break; } // escape  
    }
    return 0;
}
/** @function detectAndDisplay */
void detectAndDisplay(Mat frame)
{
    std::vector<Rect> faces;
    Mat frame_gray;
    cvtColor(frame, frame_gray, COLOR_BGR2GRAY);  //BGR 转化为灰度图
    equalizeHist(frame_gray, frame_gray);   //直方图均衡化
    //-- Detect faces
    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(60, 60));
    for (size_t i = 0; i < faces.size(); i++)
    {
        Point center(faces[i].x + faces[i].width / 2, faces[i].y + faces[i].height / 2); // 人脸中心坐标
        ellipse(frame, center, Size(faces[i].width / 2, faces[i].height / 2), 0, 0, 360, Scalar(255, 0, 255), 4, 8, 0); // 椭圆
        Mat faceROI = frame_gray(faces[i]);
        Mat face_test;
        // 调整大小为92*112  
        resize(faceROI, face_test, Size(92, 112));
        imwrite("lisa.pgm", face_test);
        double confidence;
        int predictPCA;
        modelPCA->predict(face_test, predictPCA, confidence);
        cout << "the predict result is " << predictPCA << endl << "confidence is " << confidence << endl;
        if (predictPCA == 4)
        {
            putText(frame, "Hello lzw", Point(faces[i].x, faces[i].y), FONT_HERSHEY_SIMPLEX, 1.5, Scalar(0, 0, 255), 2);
        }
    }
    //-- Show what you got
    imshow(window_name, frame);
}