OpenCV结合V4l2实现人脸检测以及人脸识别

该Demo的基础是之前的OpenCV人脸检测以及V4l2摄像头驱动程序，在此基础上添加人脸识别功能，由于需要鉴别特定人脸，因此需要进行训练生成xml格式的级联分类器，然后加载此分类器即可实现特定人脸的识别。

说明

本Demo使用的是基于LBPH的FaceRecongnizer，思路为首先构造LBPHFaceRecognizer，然后进行训练并将训练结果保存以便下次使用，最后传入图像进行鉴别，每张图片会对应一个label，如果鉴别结果与某张图片吻合，就会返回该图片的下标，通过下标与label数组即可确定识别结果，思路清晰，不过这种鉴别算法已经是上古时代的了，以后打算使用神经网络实现人脸识别。

代码

需要下载Qt工程（点我下载）

#include "facerecognize.h"

FaceRecognize::FaceRecognize()
{
LBPHRecog = createLBPHFaceRecognizer(1, 8 ,3, 3, 50);//构造LBPH人脸识别类的对象并初始化
faceDetect.load("/usr/local/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml");
}

void FaceRecognize::loadLBPHState()
{
if(access("./customCascade/face_lbph.xml", F_OK) == 0)
LBPHRecog->load("./customCascade/face_lbph.xml");//加载LBPH识别器的状态，避免重复训练
else
train();

}

void FaceRecognize::train()
{
Mat src;
string pic;
for(int i = 0; i<filesName.size(); i++)
{
pic = filesName[i];
src = imread(pic);
cvtColor(src, src, CV_RGB2GRAY);
trainPic.push_back(src);
labelsInfo.insert(make_pair(i, filesName[i]));
labels.push_back(i);
}
//LBPHRecog->setLabelsInfo(labelsInfo);
LBPHRecog->train(trainPic, labels);         //LBP人脸识别训练函数
LBPHRecog->save("./customCascade/face_lbph.xml");           //保存LBPH识别器的状态

}

void FaceRecognize::printdir(const char *dir, int depth)
{
cout<<"start find file name"<<endl;
string filepath = dir;
DIR *dp;
struct dirent *entry;
struct stat statbuf;
if((dp = opendir(dir)) == NULL) {
fprintf(stderr,"cannot open directory: %s\n", dir);
return;
}
chdir(dir);
while((entry = readdir(dp)) != NULL) {
lstat(entry->d_name,&statbuf);
if(S_ISDIR(statbuf.st_mode)) {

if(strcmp(".",entry->d_name) == 0 ||
strcmp("..",entry->d_name) == 0)
continue;
//printf("%*s%s/\n",depth,"",entry->d_name);

//printdir(entry->d_name,depth+4);//如果需要递归，可以加上这句话
}
else
{
cout<<"file path is"<<filepath + entry->d_name<<endl;
filesName.push_back(filepath + entry->d_name);
}
}
chdir("..");
closedir(dp);
}

void FaceRecognize::getFilesName(string path)//获取path目录中的所有图片作为训练资源
{
//filesName.push_back("./trainPic/WangRui1.jpg");
printdir(path.c_str(), 0);
for(int i=0; i<filesName.size(); i++)
cout<<filesName[i]<<endl;

}

Mat FaceRecognize::recognizeAndDraw(Mat frame)
{
//flip(frame, frame, 1);
recogPic = Mat::zeros(200, 200, frame.type());
double t = (double)getTickCount();//检测的时间
faces = FaceDetector(frame);//获取该帧中检测到的脸部的具体位置
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<t<<endl;
DrawFace(frame, faces);//给脸部画上矩形框

for(size_t i = 0; i<faces.size(); i++)
{
facePic = frame(Rect(faces[i]));
resize(facePic, recogPic, recogPic.size());
cvtColor(recogPic, recogPic, CV_RGB2GRAY);
//equalizeHist(recogPic, recogPic);
result = LBPHRecog->predict(recogPic);//进行识别
//将识别结果显示在实时画面上
if(result ==-1)
putText(frame, "unknow", Point(faces[i].x, faces[i].y), 3, 0.5, Scalar(0, 255, 255), 1);
else
putText(frame, filesName[result], Point(faces[i].x, faces[i].y), 3, 0.5, Scalar(0, 255, 255), 1);
}
return frame;

}

vector<Rect> FaceRecognize::FaceDetector(Mat img)
{
Mat src = Mat::zeros(240, 320, img.type());
vector<Rect> faces;
cvtColor(img, img, CV_RGB2GRAY);
resize(img, src, src.size());
faceDetect.detectMultiScale(src, faces, 1.2, 6, 0,Size(30,30));
for(int i=0; i<faces.size(); i++)
{
faces[i].x=faces[i].x*2;
faces[i].y=faces[i].y*2;
faces[i].width=faces[i].width*2;
faces[i].height=faces[i].height*2;
}
return faces;
}

void FaceRecognize::DrawFace(Mat img, vector<Rect> faces)
{
for(size_t num = 0; num<faces.size(); num++)
{
rectangle(img, Point(faces[num].x, faces[num].y),
Point(faces[num].x + faces[num].width, faces[num].y + faces[num].height), Scalar(0, 255, 0), 1, 8);
}
}