PCA.rar_PCA特征 c++

#include "opencv2/core/core.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/contrib/contrib.hpp" #include <iostream> #include <fstream> #include <sstream> using namespace cv; using namespace std; //把图像归一化为0-255，便于显示 Mat norm_0_255(const Mat& src) { Mat dst; switch(src.channels()) { case 1: cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1); break; case 3: cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3); break; default: src.copyTo(dst); break; } return dst; } //转化给定的图像为行矩阵 Mat asRowMatrix(const vector<Mat>& src, int rtype, double alpha = 1, double beta = 0) { //样本数量 size_t n = src.size(); //如果没有样本，返回空矩阵 if(n == 0) return Mat(); //样本的维数 size_t d = src[0].total(); Mat data(n, d, rtype); //拷贝数据 for(int i = 0; i < n; i++) { if(src[i].empty()) { string error_message = format("Image number %d was empty, please check your input data.", i); CV_Error(CV_StsBadArg, error_message); } // 确保数据能被reshape if(src[i].total() != d) { string error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src[i].total()); CV_Error(CV_StsBadArg, error_message); } Mat xi = data.row(i); //转化为1行，n列的格式 if(src[i].isContinuous()) { src[i].reshape(1, 1).convertTo(xi, rtype, alpha, beta); } else { src[i].clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta); } } return data; } int main(int argc, const char *argv[]) { vector<Mat> db; string prefix = "../att_faces/"; db.push_back(imread(prefix + "s1/1.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/2.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/3.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/4.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/5.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/6.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/7.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/8.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/9.pgm", IMREAD_GRAYSCALE)); db.push_back(imread(prefix + "s1/10.pgm", IMREAD_GRAYSCALE)); // Build a matrix with the observations in row: Mat data = asRowMatrix(db, CV_32FC1); // PCA算法保持5主成分分量 int num_components = 5; //执行pca算法 PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, num_components); //copy pca算法结果 Mat mean = pca.mean.clone(); Mat eigenvalues = pca.eigenvalues.clone(); Mat eigenvectors = pca.eigenvectors.clone(); //均值脸 imshow("avg", norm_0_255(mean.reshape(1, db[0].rows))); //五个特征脸 imshow("pc1", norm_0_255(pca.eigenvectors.row(0)).reshape(1, db[0].rows)); imshow("pc2", norm_0_255(pca.eigenvectors.row(1)).reshape(1, db[0].rows)); imshow("pc3", norm_0_255(pca.eigenvectors.row(2)).reshape(1, db[0].rows)); imshow("pc4", norm_0_255(pca.eigenvectors.row(3)).reshape(1, db[0].rows)); imshow("pc5", norm_0_255(pca.eigenvectors.row(4)).reshape(1, db[0].rows)); while(1) waitKey(0); // Success! return 0; }