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。
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。
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//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
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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
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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
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//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
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//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）
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//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：
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#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;} |
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_24946843/article/details/82876629
