static Mat formatImagesForPCA(const vector<Mat> &data)
{
    Mat dst(static_cast<int>(data.size()), data[0].rows*data[0].cols, CV_32F);
    for (unsigned int i = 0; i < data.size(); i++)
    {
        Mat image_row = data[i].clone().reshape(1, 1);
        Mat row_i = dst.row(i);
        image_row.convertTo(row_i, CV_32F);
    }
    return dst;
}

static Mat toGrayscale(InputArray _src) {
    Mat src = _src.getMat();
    // only allow one channel
    if (src.channels() != 1) {
        CV_Error(CV_StsBadArg, "Only Matrices with one channel are supported");
    }
    // create and return normalized image
    Mat dst;
    cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
    return dst;
}

void main(int argc, char** argv)
{
    Mat src1 = imread("F:\\cha_classification\\train\\c0.jpg");
    Mat src2 = imread("F:\\cha_classification\\train\\c1.jpg");
    Mat src3 = imread("F:\\cha_classification\\train\\c2.jpg");

    Mat g1, g2, g3;
    vector<Mat> gray;
    
    cvtColor(src1, g1, CV_RGB2GRAY);
    gray.push_back(g1);
    cvtColor(src2, g2, CV_RGB2GRAY);
    gray.push_back(g2);
    cvtColor(src3, g3, CV_RGB2GRAY);
    gray.push_back(g3);

    imshow("g1", g1);
    //waitKey(0);

    Mat data = formatImagesForPCA(gray);//data矩阵大小为：3*（18*18）//  cout << pcaSet << endl;  
    PCA pca(data, cv::Mat(), CV_PCA_DATA_AS_ROW, 0.99);
    //PCA pca(data, Mat(), 200 );
    cout << pca.eigenvalues << endl;
    cout << endl;
    //cout << pca.eigenvectors << endl;


    // 将PCA降维的效果用第一张图片来显示出来。
    Mat point = pca.project(data.row(0)); // project into the eigenspace, thus the image becomes a "point"
    Mat reconstruction = pca.backProject(point); // re-create the image from the "point"
    reconstruction = reconstruction.reshape(gray[0].channels(), gray[0].rows); // reshape from a row vector into image shape
    reconstruction = toGrayscale(reconstruction); // re-scale for displaying purposes
    namedWindow("r", WINDOW_NORMAL);
    imshow("r", reconstruction);

    waitKey(0);
    //return 0;
}