PCA算法：从一组照片中获取特征脸（特征向量）

#include "stdafx.h"
#include <string>
#include <opencv2\opencv.hpp> 
using namespace std;
using namespace cv;int _tmain(int argc, _TCHAR* argv[])
{//获取了mean_faceint num_sample = 38;int norm_row = 64, norm_col = 56;int num;Mat imgs = loadImages(num);Mat mean_face = Mat(norm_row, norm_col, CV_8UC1);vector<int> mean_face_total;mean_face_total.resize(norm_row * norm_col);for (int i = 0; i < num; i++){for (int j = 0; j < norm_row * norm_col; j++){mean_face_total.at(j) += imgs.at<uchar>(i * norm_row * norm_col + j);//eigen_face_total[j] += imgs.at<uchar>(i * 192 * 168 + j);}}for (int j = 0; j < norm_row * norm_col; j++){mean_face.at<uchar>(j) = (uchar)(mean_face_total.at(j) / num);}imwrite("C:/Users/dhj555/Desktop/YelaFaces/eigen_face/0001.jpg", mean_face);imshow("eigen_face", mean_face);//1、初始化数据CvMat* pData = cvCreateMat(num_sample, norm_row * norm_col, CV_32FC1);CvMat* pMean = cvCreateMat(1, norm_row * norm_col, CV_32FC1);//每个数标志一个特征值CvMat* pEigVals = cvCreateMat(1, min(num_sample, norm_row * norm_col), CV_32FC1);//每行表示一个特征向量CvMat* pEigVecs = cvCreateMat(min(num_sample, norm_row * norm_col), norm_row * norm_col, CV_32FC1);for (int i = 0; i < num_sample; i++){for (int j = 0; j < norm_row * norm_col; j++)cvmSet(pData, i, j, imgs.at<uchar>(i * norm_row * norm_col + j));}//2、PCA处理cvCalcPCA(pData, pMean, pEigVals, pEigVecs, CV_PCA_DATA_AS_ROW);//3、选出前P个特征向量(主成份),然后投影,结果保存在pResult中，pResult中包含了P个系数//CvMat* pResult = cvCreateMat(num_sample, 20, CV_32FC1);//cvProjectPCA(pData, pMean, pEigVecs, pResult);//4、重构, 结果保存在pRecon中//CvMat* pRecon = cvCreateMat(num_sample, norm_row*norm_col, CV_32FC1);//cvBackProjectPCA(pResult, pMean, pEigVecs, pRecon);//5、显示重构的图像//Mat mRecon = Mat(pRecon);//4、显示与保存特征向量for (int i = 0; i < min(num_sample, norm_row * norm_col); i++){float min = LLONG_MAX, max = LLONG_MIN, span = 0.0;for (int index = 0; index < norm_row*norm_col; index++){float d = cvmGet(pEigVecs, i, index);if (d>max)max = d;if (d < min)min = d;}span = max - min;Mat eigen_face = Mat(norm_row, norm_col, CV_8UC1);for (int index = 0; index < norm_row*norm_col; index++){float d = cvmGet(pEigVecs, i, index);eigen_face.at<uchar>(index) = (d - min) / span * 255.0;}char buffer[128];sprintf_s(buffer, "C:/Users/dhj555/Desktop/YelaFaces/eigen_face/001/1-000%d.jpg", i);string imgPath(buffer);imshow(imgPath, eigen_face);printf("%d st:\t%f\n", i, cvmGet(pEigVals, 0, i));imwrite(imgPath, eigen_face);}
}

三、实验结果