opencv学习笔记六十五：EigenFace人脸识别算法

简要：

EigenFace是基于PCA降维的人脸识别算法，PCA是使整体数据降维后的方差最大，没有考虑降维后类间的变化。 它是将图像每一个像素当作一维特征，然后用SVM或其它机器学习算法进行训练。但这样维数太多，根本无法计算。我这里用的是ORL人脸数据库，英国剑桥实验室拍摄的，有40位志愿者的人脸，在不同表情不同光照下每位志愿者拍摄10张，共有400张图片，大小为112*92，所以如果把每个像素当做特征拿来训练的话，一张人脸就有10304维特征，这么高维的数据根本无法处理。所以需要先对数据进行降维，去掉一些冗余的特征。PCA降维在我的这篇博客中有详细推导https://blog.csdn.net/qq_24946843/article/details/81775368。

 第一步：将ORL人脸图片的地址统一放在一个文件里，等会通过对该文件操作，将图片全部加载进来。

//ofstream一般对文件进行读写操作，ifstream一般对文件进行读操作
ofstream file;
	file.open("path.txt");//新建并打开文件
	char str[50] = {};
	for (int i = 1; i <= 40; i++) {
		for (int j = 1; j <= 10; j++) {	
			sprintf_s(str, "orl_faces/s%d/%d.pgm;%d", i, j, i);//将数字转换成字符
			file << str << endl;//写入
		}		
	}

得到路劲文件如下图所示：

 第二步：读入模型需要输入的数据，即用来训练的图像vector<Mat>images和标签vector<int>labels

string filename = string("path.txt");
	ifstream file(filename);
	if (!file) { 
        printf("could not load file"); 
    }
	vector<Mat>images;
	vector<int>labels;
	char separator = ';';
	string line,path, classlabel;
	while (getline(file,line)) {
		stringstream lines(line);
		getline(lines, path, separator);
		getline(lines, classlabel);
		images.push_back(imread(path, 0));
		labels.push_back(atoi(classlabel.c_str()));//atoi(ASCLL to int)将字符串转换为整数型
	}

第三步：加载、训练、预测模型

Ptr<BasicFaceRecognizer> model = EigenFaceRecognizer::create();
	model->train(images, labels);
	int predictedLabel = model->predict(testSample);
	printf("actual label:%d,predict label :%d\n", testLabel, predictedLabel);

补充：

1、显示平均脸

//计算特征值特征向量及平均值
	Mat vals = model->getEigenValues();//89*1
	printf("%d,%d\n", vals.rows, vals.cols);
	Mat vecs = model->getEigenVectors();//10324*89
	printf("%d,%d\n", vecs.rows, vecs.cols);
	Mat mean = model->getMean();//1*10304
	printf("%d,%d\n", mean.rows, mean.cols);

	//显示平均脸
	Mat meanFace = mean.reshape(1, height);//第一个参数为通道数，第二个参数为多少行
	normalize(meanFace, meanFace, 0, 255, NORM_MINMAX, CV_8UC1);
	imshow("Mean Face", meanFace);

2、显示前部分特征脸

//显示特征脸
	for (int i = 0; i<min(10, vals.rows); i++) {
		Mat feature_vec = vecs.col(i).clone();
		Mat feature_face= feature_vec.reshape(1, height);	
		normalize(feature_face, feature_face, 0, 255, NORM_MINMAX, CV_8UC1);	
		Mat colorface;
		applyColorMap(feature_face, colorface, COLORMAP_BONE);
		
		sprintf_s(win_title, "eigenface%d", i);
		imshow(win_title, colorface);
	}

3、对第一张人脸在特征向量空间进行人脸重建（分别基于前10,20,30,40,50,60个特征向量进行人脸重建）

//重建人脸
	for (int i = min(10, vals.rows); i <min(61, vals.rows); i+=10) {
		Mat vecs_space = Mat(vecs, Range::all(), Range(0, i));
		Mat projection = LDA::subspaceProject(vecs_space, mean, images[0].reshape(1, 1));//投影到子空间
		Mat reconstruction = LDA::subspaceReconstruct(vecs_space, mean, projection);//重建
		Mat result = reconstruction.reshape(1, height);
		normalize(result, result, 0, 255, NORM_MINMAX, CV_8UC1);
		//char wintitle[40] = {};
		sprintf_s(win_title, "recon face %d", i);
		imshow(win_title, result);
	}

完整代码如下：

#include<opencv2\opencv.hpp>
#include<opencv2\face.hpp>
using namespace cv;
using namespace face;
using namespace std;
char win_title[40] = {};

int main(int arc, char** argv) { 
	namedWindow("input",CV_WINDOW_AUTOSIZE);

	//读入模型需要输入的数据，用来训练的图像vector<Mat>images和标签vector<int>labels
	string filename = string("path.txt");
	ifstream file(filename);
	if (!file) { printf("could not load file"); }
	vector<Mat>images;
	vector<int>labels;
	char separator = ';';
	string line,path, classlabel;
	while (getline(file,line)) {
		stringstream lines(line);
		getline(lines, path, separator);
		getline(lines, classlabel);
		//printf("%d\n", atoi(classlabel.c_str()));
		images.push_back(imread(path, 0));
		labels.push_back(atoi(classlabel.c_str()));//atoi(ASCLL to int)将字符串转换为整数型
	}
	int height = images[0].rows;
	int width = images[0].cols;
	printf("height:%d,width:%d\n", height, width);
	//将最后一个样本作为测试样本
	Mat testSample = images[images.size() - 1];
	int testLabel = labels[labels.size() - 1];
	//删除列表末尾的元素
	images.pop_back();
	labels.pop_back();
	
	//加载，训练，预测
	Ptr<BasicFaceRecognizer> model = EigenFaceRecognizer::create();
	model->train(images, labels);
	int predictedLabel = model->predict(testSample);
	printf("actual label:%d,predict label :%d\n", testLabel, predictedLabel);

	//计算特征值特征向量及平均值
	Mat vals = model->getEigenValues();//89*1
	printf("%d,%d\n", vals.rows, vals.cols);
	Mat vecs = model->getEigenVectors();//10324*89
	printf("%d,%d\n", vecs.rows, vecs.cols);
	Mat mean = model->getMean();//1*10304
	printf("%d,%d\n", mean.rows, mean.cols);

	//显示平均脸
	Mat meanFace = mean.reshape(1, height);//第一个参数为通道数，第二个参数为多少行
	normalize(meanFace, meanFace, 0, 255, NORM_MINMAX, CV_8UC1);
	imshow("Mean Face", meanFace);

	//显示特征脸
	for (int i = 0; i<min(10, vals.rows); i++) {
		Mat feature_vec = vecs.col(i).clone();
		Mat feature_face= feature_vec.reshape(1, height);	
		normalize(feature_face, feature_face, 0, 255, NORM_MINMAX, CV_8UC1);	
		Mat colorface;
		applyColorMap(feature_face, colorface, COLORMAP_BONE);
		
		sprintf_s(win_title, "eigenface%d", i);
		imshow(win_title, colorface);
	}

	//重建人脸
	for (int i = min(10, vals.rows); i <min(61, vals.rows); i+=10) {
		Mat vecs_space = Mat(vecs, Range::all(), Range(0, i));
		Mat projection = LDA::subspaceProject(vecs_space, mean, images[0].reshape(1, 1));
		Mat reconstruction = LDA::subspaceReconstruct(vecs_space, mean, projection);
		Mat result = reconstruction.reshape(1, height);
		normalize(result, result, 0, 255, NORM_MINMAX, CV_8UC1);
		//char wintitle[40] = {};
		sprintf_s(win_title, "recon face %d", i);
		imshow(win_title, result);
	}

	waitKey(0);
	return 0;
}