OpencvEigenFace人脸识别算法详解

//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 <

string filename = string("path.txt");
 ifstream file(filename);
 if (!file) { 
    printf("could not load file"); 
  }
 vectorimages;
 vectorlabels;
 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
 }

Ptr 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

//重建人脸
 for (int i = min(10, vals.rows); i 

#include
#include
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vectorimages和标签vectorlabels
 string filename = string("path.txt");
 ifstream file(filename);
 if (!file) { printf("could not load file"); }
 vectorimages;
 vectorlabels;
 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 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