source: proiecte/PPPP/eigenface2/pca_p.c @ 131

// eigenface.c, by Robin Hewitt, 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 "cv.h"
#include "cvaux.h"
#include "highgui.h"
#include "eigenface.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

IplImage ** eigenPics      = 0; // eigenvectors
char eig_name[20];

//// Function prototypes
void learn();
void recognize();
void doPCA();
void storeTrainingData();
int  loadTrainingData(CvMat ** pTrainPersonNumMat);
int  findNearestNeighbor(float * projectedTestFace);
int  loadFaceImgArray(char * filename);
void convertToUImage(IplImage *fImg, IplImage *uImg);

//////////////////////////////////
// learn()
//
void learn(char * filename)
{
        int i, offset;

        // load training data
        nTrainFaces = loadFaceImgArray(filename);
        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(
                        faceImgArr[i],
                        nEigens,
                        eigenVectArr,
                        0, 0,
                        pAvgTrainImg,
                        //projectedTrainFaceMat->data.fl + i*nEigens);
                        projectedTrainFaceMat->data.fl + i*offset);
                //~ calcDecomp(
                        //~ faceImgArr[i],
                        //~ nEigens,
                        //~ eigenVectArr,
                        //~ pAvgTrainImg,
                        //~ //projectedTrainFaceMat->data.fl + i*nEigens);
                        //~ projectedTrainFaceMat->data.fl + i*offset);
        }
        
        // store the recognition data as an xml file
        storeTrainingData();
}


//////////////////////////////////
// recognize()
//
void recognize(IplImage *faceImg)
{
        int i, nTestFaces  = 0;         // the number of test images
        CvMat * trainPersonNumMat = 0;  // the person numbers during training
        float * projectedTestFace = 0;
        int iNearest, nearest, truth;

        // 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) );

        // project the test image onto the PCA subspace
        cvEigenDecomposite(
                faceImg,
                nEigens,
                eigenVectArr,
                0, 0,
                pAvgTrainImg,
                projectedTestFace);
        //~ calcDecomp(
                //~ faceImg,
                //~ nEigens,
                //~ eigenVectArr,
                //~ pAvgTrainImg,
                //~ projectedTestFace);

        iNearest = findNearestNeighbor(projectedTestFace);
        nearest  = trainPersonNumMat->data.i[iNearest];

        printf("nearest = %d\n", nearest);
}


//////////////////////////////////
// loadTrainingData()
//
int loadTrainingData(CvMat ** pTrainPersonNumMat)
{
        CvFileStorage * fileStorage;
        int i;

        // create a file-storage interface
        fileStorage = cvOpenFileStorage( "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[i] = (IplImage *)cvReadByName(fileStorage, 0, varname, 0);
        }

        // release the file-storage interface
        cvReleaseFileStorage( &fileStorage );

        return 1;
}


//////////////////////////////////
// storeTrainingData()
//
void storeTrainingData()
{
        CvFileStorage * fileStorage;
        int i;

        // create a file-storage interface
        fileStorage = cvOpenFileStorage( "facedata.xml", 0, CV_STORAGE_WRITE );

        // store all the data
        cvWriteInt( fileStorage, "nEigens", nEigens );
        cvWriteInt( fileStorage, "nTrainFaces", nTrainFaces );
        cvWrite(fileStorage, "trainPersonNumMat", personNumTruthMat, cvAttrList(0,0));
        cvWrite(fileStorage, "eigenValMat", eigenValMat, cvAttrList(0,0));
        cvWrite(fileStorage, "projectedTrainFaceMat", projectedTrainFaceMat, cvAttrList(0,0));
        cvWrite(fileStorage, "avgTrainImg", pAvgTrainImg, cvAttrList(0,0));
        for(i=0; i<nEigens; i++)
        {
                char varname[200];
                sprintf( varname, "eigenVect_%d", i );
                cvWrite(fileStorage, varname, eigenVectArr[i], cvAttrList(0,0));
        }

        // release the file-storage interface
        cvReleaseFileStorage( &fileStorage );
}


//////////////////////////////////
// findNearestNeighbor()
//
int findNearestNeighbor(float * projectedTestFace)
{
        //double leastDistSq = 1e12;
        double leastDistSq = DBL_MAX;
        int i, iTrain, iNearest = 0;

        for(iTrain=0; iTrain<nTrainFaces; iTrain++)
        {
                double distSq=0;

                for(i=0; i<nEigens; i++)
                {
                        float d_i =
                                projectedTestFace[i] -
                                projectedTrainFaceMat->data.fl[iTrain*nEigens + i];
                        distSq += d_i*d_i / eigenValMat->data.fl[i];  // Mahalanobis
                        //distSq += d_i*d_i; // Euclidean
                }

                if(distSq < leastDistSq)
                {
                        leastDistSq = distSq;
                        iNearest = iTrain;
                }
        }

        return iNearest;
}


//////////////////////////////////
// doPCA()
//
void doPCA()
{
        int i;
        CvTermCriteria calcLimit;
        CvSize faceImgSize;

        // set the number of eigenvalues to use
        nEigens = nTrainFaces-1;

        // allocate the eigenvector images
        faceImgSize.width  = faceImgArr[0]->width;
        faceImgSize.height = faceImgArr[0]->height;
        eigenVectArr = (IplImage**)cvAlloc(sizeof(IplImage*) * nEigens);
        eigenPics = (IplImage**)cvAlloc(sizeof(IplImage*) * nEigens);
        for(i=0; i<nEigens; i++) {
                eigenVectArr[i] = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1);
                eigenPics[i] = cvCreateImage(faceImgSize, IPL_DEPTH_8U, 1);
        }
        
        // allocate the eigenvalue array
        eigenValMat = cvCreateMat( 1, nEigens, CV_32FC1 );

        // allocate the averaged image
        pAvgTrainImg = cvCreateImage(faceImgSize, IPL_DEPTH_32F, 1);

        // set the PCA termination criterion
        calcLimit = cvTermCriteria( CV_TERMCRIT_ITER, nEigens, 1);

        // compute average image, eigenvalues, and eigenvectors
        //~ cvCalcEigenObjects(
                //~ nTrainFaces,
                //~ (void*)faceImgArr,
                //~ (void*)eigenVectArr,
                //~ CV_EIGOBJ_NO_CALLBACK,
                //~ 0,
                //~ 0,
                //~ &calcLimit,
                //~ pAvgTrainImg,
                //~ eigenValMat->data.fl);
        calcEigenFaces(
                nTrainFaces,
                faceImgArr,
                eigenVectArr,
                nEigens,
                pAvgTrainImg,
                eigenValMat->data.fl);

        cvNormalize(eigenValMat, eigenValMat, 1, 0, CV_L1, 0);
        
        for(i=0; i<nEigens; i++)
        {       
                convertToUImage(eigenVectArr[i], eigenPics[i]);
                sprintf(eig_name,"eigen_%d.jpg", i);
                cvSaveImage(eig_name,eigenPics[i]);
        }
}


//////////////////////////////////
// loadFaceImgArray()
//
int loadFaceImgArray(char * filename)
{
        FILE * imgListFile = 0;
        char imgFilename[512];
        int iFace, nFaces=0;


        // open the input file
        if( !(imgListFile = fopen(filename, "r")) )
        {
                fprintf(stderr, "Can\'t open file %s\n", filename);
                return 0;
        }

        // count the number of faces
        while( fgets(imgFilename, 512, imgListFile) ) ++nFaces;
        rewind(imgListFile);

        // allocate the face-image array and person number matrix
        faceImgArr        = (IplImage **)cvAlloc( nFaces*sizeof(IplImage *) );
        personNumTruthMat = cvCreateMat( 1, nFaces, CV_32SC1 );

        // store the face images in an array
        for(iFace=0; iFace<nFaces; iFace++)
        {
                // read person number and name of image file
                fscanf(imgListFile,
                        "%d %s", personNumTruthMat->data.i+iFace, imgFilename);
                // load the face image
                faceImgArr[iFace] = cvLoadImage(imgFilename, CV_LOAD_IMAGE_GRAYSCALE);

                if( !faceImgArr[iFace] )
                {
                        fprintf(stderr, "Can\'t load image from %s\n", imgFilename);
                        return 0;
                }
        }

        fclose(imgListFile);

        return nFaces;
}

void convertToUImage(IplImage *fImg, IplImage *uImg) {
        int i;
        float *bf;
        uchar *bu;
        CvSize size;
        
        cvGetImageRawData(fImg, (uchar**)&bf, NULL, &size);
        cvGetImageRawData(uImg, (uchar**)&bu, NULL, NULL);
        
        // Find the Maximum and Minimum of the pixel values
        float max, min;
        max = min = 0.0;
        for(i=0; i< size.width * size.height; i++) {
                if(max < bf[i])
                        max = bf[i];
                if(min > bf[i])
                        min = bf[i];
        }

        // Normalize the eigenface values between 0 and 255
        for(i = 0; i< size.width * size.height; i++) {
                bu[i] = (uchar)(( 255 * (( bf[i] - min)/ (max- min)) ));
        }
}

//////////////////////////////////
// printUsage()
//
void printUsage()
{
        printf("Usage: eigenface <command>\n",
               "  Valid commands are\n"
               "    train\n"
               "    test\n");
}