opencv 人脸识别（二）训练和识别

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发布于 2022-09-05 12:26:06

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发布于 2022-09-05 12:26:06

大家好，又见面了，我是你们的朋友全栈君。

上一篇中我们对训练数据做了一些预处理，检测出人脸并保存在\pic\color\x文件夹下（x=1,2,3,…类别号），本文做训练和识别。为了识别，首先将人脸训练数据转为灰度、对齐、归一化，再放入分类器（EigenFaceRecognizer）,最后用训练出的model进行predict。

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环境：vs2010+opencv 2.4.6.0

特征：eigenface

Input：一个人脸数据库，15个人，每人20个样本（左右）。

Output：人脸检测，并识别出每张检测到的人脸。

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1. 为训练数据预处理（转为灰度、对齐、归一化）

转为灰度和对齐是后面做训练时EigenFaceRecognizer的要求；
归一化是防止光照带来的影响

在上一篇的 2.2 Prehelper.cpp文件中加入函数

void resizeandtogray(char* dir,int k, vector<Mat> &images, vector<int> &labels, vector<Mat> &testimages, vector<int> &testlabels);

void resizeandtogray(char* dir,int K, vector<Mat> &images, vector<int> &labels,	vector<Mat> &testimages, vector<int> &testlabels){	IplImage* standard = cvLoadImage("D:\\privacy\\picture\\photo\\2.jpg",CV_LOAD_IMAGE_GRAYSCALE);	string cur_dir;	char id[5];	int i,j;	for(int i=1; i<=K; i++)	{		cur_dir = dir;		cur_dir.append("gray\\");			_itoa(i,id,10);		cur_dir.append(id);		const char* dd = cur_dir.c_str();		CStatDir statdir;		if (!statdir.SetInitDir(dd))		{			puts("Dir not exist");			return;		}		cout<<"Processing samples in Class "<<i<<endl;		vector<char*>file_vec = statdir.BeginBrowseFilenames("*.*");		for (j=0;j<file_vec.size();j++)		{			IplImage* cur_img = cvLoadImage(file_vec[j],CV_LOAD_IMAGE_GRAYSCALE);			cvResize(cur_img,standard,CV_INTER_AREA);			Mat cur_mat = cvarrToMat(standard,true),des_mat;			cv::normalize(cur_mat,des_mat,0, 255, NORM_MINMAX, CV_8UC1);			cvSaveImage(file_vec[j],cvCloneImage(&(IplImage) des_mat));			if(j!=file_vec.size())			{					images.push_back(des_mat);					labels.push_back(i);			}			else			{				testimages.push_back(des_mat);				testlabels.push_back(i);			}		}		cout<<file_vec.size()<<" images."<<endl;	}}

并在main中调用：

int main( )
{
	CvCapture* capture = 0;
	Mat frame, frameCopy, image;
	string inputName;	
	int mode;

	char dir[256] = "D:\\Courses\\CV\\Face_recognition\\pic\\"; 
	//preprocess_trainingdata(dir,K); //face_detection and extract to file
	vector<Mat> images,testimages;
	vector<int> labels,testlabels;
	resizeandtogray(dir,K,images,labels,testimages,testlabels); //togray, normalize and resize
	
	system("pause");
	return 0;
}

2. 训练

有了vector<Mat> images,testimages; vector<int> labels,testlabels; 可以开始训练了，我们采用EigenFaceRecognizer建模。

在Prehelper.cpp中加入函数

Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,vector<Mat> testimages, vector<int> testlabels)；

Ptr<FaceRecognizer> Recognition(vector<Mat> images, vector<int> labels,	vector<Mat> testimages, vector<int> testlabels){	Ptr<FaceRecognizer> model = createEigenFaceRecognizer(10);//10 Principal components	cout<<"train"<<endl;	model->train(images,labels);	int i,acc=0,predict_l;	for (i=0;i<testimages.size();i++)	{		predict_l = model->predict(testimages[i]);		if(predict_l != testlabels[i])		{			cout<<"An error in recognition: sample "<<i+1<<", predict "<<				predict_l<<", groundtruth "<<testlabels[i]<<endl;			imshow("error 1",testimages[i]);			waitKey();		}		else			acc++;	}	cout<<"Recognition Rate: "<<acc*1.0/testimages.size()<<endl;	return model;}

Recognization()输出分错的样本和正确率，最后返回建模结果Ptr<FaceRecognizer> model

主函数改为：

int main( )
{
	CvCapture* capture = 0;
	Mat frame, frameCopy, image;
	string inputName;	
	int mode;

	char dir[256] = "D:\\Courses\\CV\\Face_recognition\\pic\\"; 
	//preprocess_trainingdata(dir,K); //face_detection and extract to file
	vector<Mat> images,testimages;
	vector<int> labels,testlabels;
	//togray, normalize and resize; load to images,labels,testimages,testlabels
	resizeandtogray(dir,K,images,labels,testimages,testlabels); 
	//recognition
	Ptr<FaceRecognizer> model = Recognition(images,labels,testimages,testlabels);
	char* dirmodel = new char [256];
	strcpy(dirmodel,dir); strcat(dirmodel,"model.out");
	FILE* f = fopen(dirmodel,"w");
	fwrite(model,sizeof(model),1,f);
	system("pause");
	return 0;
}

最终结果：一个错分样本，正确率93.3%