openCV学习笔记（十六） —— 人脸识别 —— 程序 —— PCA特征脸Eigenfaces、重建人脸

此程序主要来源于F:\personal\data\openCV\opencv_contrib_3.4.1\modules\face\samples\facerec_eigenfaces.cpp

流程

读人脸数据文件
创建特征脸识别与训练
识别人脸
输出中间平均脸、特征脸、重建结果

程序

/*
    此程序主要来源于F:\personal\data\openCV\opencv_contrib_3.4.1\modules\face\samples\facerec_eigenfaces.cpp
*/
#include <opencv2/opencv.hpp>
#include <opencv2/face.hpp>
//#include <fstream>
//#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::face;
Mat normal(Mat& src, Mat& dst)
{
    if (src.channels() == 1)
    {
        normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
    }
    else
    {
        normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
    }
    return dst;
}
void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';')
{
    std::ifstream file(filename.c_str(), ifstream::in);
    if (!file) {
        string error_message = "No valid input file was given, please check the given filename.";
        CV_Error(Error::StsBadArg, error_message);
    }
    string line, path, classlabel;
    while (getline(file, line)) {
        stringstream liness(line);
        getline(liness, path, separator);
        getline(liness, classlabel);
        if (!path.empty() && !classlabel.empty()) {
            images.push_back(imread(path, 0));
            labels.push_back(atoi(classlabel.c_str()));
        }
    }
}
int main()
{
    string filename = ".\\face_recg\\face\\at.txt";
    vector<Mat> images;
    vector<int> labels;
    //从文件中读取图像保存到向量中
    try {
        read_csv(filename, images, labels);
    }
    catch (Exception& e)
    {
        cerr << "Error opening file \n" << filename << "\". Reason: " << e.msg << endl;
        exit(1);
    }
    if (images.size() <= 1)
    {
        cerr << "图像太少，请至少添加两个图片！" << endl;
        exit(1);
    }
    //从第一个图像获取高度，在后面的代码里面需要用到它来重塑原来的图片大小
    int height = images[0].rows;
    int width = images[0].cols;
    printf("height: %d, width: %d\n", height, width);
    //最后一个人为测试样本
    Mat testSample = images[images.size() - 1];
    int testLabel = labels[labels.size() - 1];
    images.pop_back();
    labels.pop_back();
    //训练
    Ptr<EigenFaceRecognizer> model = EigenFaceRecognizer::create();
    model->train(images, labels);
    //识别
    int predictedLabel = model->predict(testSample);
    printf("actual label: %d, predict label: %d\n", testLabel, predictedLabel);
    //获得特征值，特征向量，均值，平均脸
    Mat eigenValues = model->getEigenValues();
    Mat eigenVector = model->getEigenVectors();
    Mat mean = model->getMean();
    Mat meanFace = mean.reshape(1, height);
    Mat dst;
    dst = normal(meanFace, dst);
    imshow("MeanFace", dst);
    //特征脸
    for (int i = 0; i < min(10, eigenVector.cols); i++)
    {
        Mat ev = eigenVector.col(i).clone();
        Mat eigenFace = ev.reshape(1, height);
        Mat grayScale;
        grayScale = normal(eigenFace, grayScale);
        Mat colorFace;
        applyColorMap(grayScale, colorFace, COLORMAP_BONE);
        imshow(format("eigenFace_%d", i), colorFace);
    }
    //重建人脸
    for (int i = min(10, eigenVector.cols); i < min(300, eigenVector.cols); i += 15)
    {
        Mat evs = Mat(eigenVector, Range::all(), Range(0, i));
        Mat projection = LDA::subspaceProject(evs, mean, images[0].reshape(1, 1));
        Mat reconstruction = LDA::subspaceReconstruct(evs, mean, projection);
        Mat result = reconstruction.reshape(1, height);
        reconstruction = normal(result, reconstruction);
        imshow(format("reco_face_%d", i), reconstruction);
    }
    waitKey(0);
    destroyAllWindows();
    return 0;
}