Pca.rar_PCA 降维_pca

// eigenface.c, by Robin Hewitt, 2007 // edited by Xin QIao, AUG 2007 // // Example program showing how to implement eigenface with OpenCV // Usage: // // First, you need some face images. I used the ORL face database. // You can download it for free at // www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html // // List the training and test face images you want to use in the // input files train.txt and test.txt. (Example input files are provided // in the download.) To use these input files exactly as provided, unzip // the ORL face database, and place train.txt, test.txt, and eigenface.exe // at the root of the unzipped database. // // To run the learning phase of eigenface, enter // eigenface train // at the command prompt. To run the recognition phase, enter // eigenface test #include <stdio.h> #include <string.h> #include <math.h> #include "cv.h" #include "cvaux.h" #include "highgui.h" //// Global variables IplImage ** faceImgArr = 0; // array of face images CvMat * personNumTruthMat = 0; // array of person numbers int nTrainFaces = 0; // the number of training images int nEigens = 0; // the number of eigenvalues IplImage * pAvgTrainImg = 0; // the average image IplImage ** eigenVectArr = 0; // eigenvectors CvMat * eigenValMat = 0; // eigenvalues CvMat * projectedTrainFaceMat = 0; // projected training faces //// Function prototypes void learn(); void recognize(); void doPCA(); void storeTrainingData(); int loadTrainingData(CvMat ** pTrainPersonNumMat); double * findNearestNeighbor(float * projectedTestFace); int loadFaceImgArray(char * filename); void printUsage(); ////////////////////////////////// // main() // int main( int argc, char** argv ) { // validate that an input was specified if( argc != 2 ) { printUsage(); return -1; } if( !strcmp(argv[1], "train") ) learn(); else if( !strcmp(argv[1], "test") ) recognize(); else { printf("Unknown command: %s\n", argv[1]); printUsage(); } return 0; } ////////////////////////////////// // learn() // void learn() { int i, j, k, offset; // load training data nTrainFaces = loadFaceImgArray("E:\\test\\train.txt"); if( nTrainFaces < 2 ) { fprintf(stderr, "Need 2 or more training faces\n" "Input file contains only %d\n", nTrainFaces); return; } // do PCA on the training faces doPCA(); // project the training images onto the PCA subspace projectedTrainFaceMat = cvCreateMat( nTrainFaces, nEigens, CV_32FC1 ); offset = projectedTrainFaceMat->step / sizeof(float); for(i=0; i<nTrainFaces; i++) { //int offset = i * nEigens; cvEigenDecomposite( (IplImage *)faceImgArr, nEigens, eigenVectArr, 0, 0, pAvgTrainImg, //projectedTrainFaceMat->data.fl + i*nEigens); projectedTrainFaceMat->data.fl + i*offset); } // store the projectedTrainFaceMat as TrainFace.txt FILE * TrainfaceFile = 0; if( TrainfaceFile = fopen("TrainFace.txt", "w") ) { for(j = 0 ; j < nTrainFaces ; j++){ fprintf(TrainfaceFile,"%d ", j); for(k = 0; k < nEigens ; k++){ fprintf(TrainfaceFile, " %d : %f ", k, (projectedTrainFaceMat->data.fl + j*offset)[k] ); } fprintf(TrainfaceFile," -1 : ? \n"); } } // store the recognition data as an xml file storeTrainingData(); } ////////////////////////////////// // recognize() // void recognize() { int i, j=0, nTestFaces = 0; // the number of test images CvMat * trainPersonNumMat = 0; // the person numbers during training float * projectedTestFace = 0; // load test images and ground truth for person number nTestFaces = loadFaceImgArray("test.txt"); printf("%d test faces loaded\n", nTestFaces); // load the saved training data if( !loadTrainingData( &trainPersonNumMat ) ) return; // project the test images onto the PCA subspace projectedTestFace = (float *)cvAlloc( nEigens*sizeof(float) ); double * sim = (double *)cvAlloc( nEigens*sizeof(double) );; for(i=0; i<nTestFaces; i++) { int iNearest=0, k; //int star[nTrainFaces]; // project the test image onto the PCA subspace cvEigenDecomposite( (IplImage *)faceImgArr, nEigens, eigenVectArr, 0, 0, pAvgTrainImg, projectedTestFace); // store the projectedTestFace as TestFace.txt FILE * TestfaceFile = 0; if( TestfaceFile = fopen("TestFace.txt", "w") ) { fprintf(TestfaceFile,"%d ", 0); for(k = 0; k < nEigens ; k++){ fprintf(TestfaceFile, " %d : %f ", k, projectedTestFace[k] ); } fprintf(TestfaceFile," -1 : ? \n"); } sim = findNearestNeighbor(projectedTestFace); //truth = personNumTruthMat->data.i; //nearest = trainPersonNumMat->data.i[iNearest]; /* //sort int order[nTrainFaces]; order[0] = iNearest; double temp[nTrainFaces]; for ( j = 0; j < nTrainFaces; j++){ temp[j] = sim[j]; } for ( k = 0; k < nTrainFaces; k++){ //记录temp中最小元素的index int result=0; double leastSim = DBL_MAX; //找到temp中最小元素的index，记录入result for ( j = 0; j < nTrainFaces; j++){ if( temp[j] < leastSim ){ leastSim = temp[j]; result = j; } } order = result; //将temp中找到的最小元素置为无穷大 temp[result] = DBL_MAX; //} */ printf("%d test image : \n",i+1); for ( k = 0; k < nTrainFaces; k++) printf("star : %d, simility : %f\n", k, sim[ k ]); //printf("nearest = %d, Truth = %d\n\n", nearest, truth); } } ////////////////////////////////// // loadTrainingData() // int loadTrainingData(CvMat ** pTrainPersonNumMat) { CvFileStorage * fileStorage; int i; // create a file-storage interface fileStorage = cvOpenFileStorage( "./data/facedata.xml", 0, CV_STORAGE_READ ); if( !fileStorage ) { fprintf(stderr, "Can't open facedata.xml\n"); return 0; } nEigens = cvReadIntByName(fileStorage, 0, "nEigens", 0); nTrainFaces = cvReadIntByName(fileStorage, 0, "nTrainFaces", 0); *pTrainPersonNumMat = (CvMat *)cvReadByName(fileStorage, 0, "trainPersonNumMat", 0); eigenValMat = (CvMat *)cvReadByName(fileStorage, 0, "eigenValMat", 0); projectedTrainFaceMat = (CvMat *)cvReadByName(fileStorage, 0, "projectedTrainFaceMat", 0); pAvgTrainImg = (IplImage *)cvReadByName(fileStorage, 0, "avgTrainImg", 0); eigenVectArr = (IplImage **)cvAlloc(nTrainFaces*sizeof(IplImage *)); for(i=0; i<nEigens; i++) { char varname[200]; sprintf( varname, "eigenVect_%d", i ); eigenVectArr = (IplImage **)cvReadByName(fileStorage, 0, varname, 0); } // release the file-storage interface cvReleaseFileStorage( &fileStorage ); return 1; } ////////////////////////////////// // storeTrainingData() // void storeTrainingData() { CvFileStorage * fileStorage; int i;