// PCA wymaga poprawek ale zeby to sprawdzic musza byc wszystkie metody zrobione
// PCA to glowna metoda algorytmu eigenface. To ona 'rozbija' twarz na wektory
// i normalizuje dzieki czemu mozna je potem porownywac
// i szukac najmniejszego 'distance'
private void doPCA() {
int i;
CvTermCriteria calcLimit;
CvSize faceImgSize = new CvSize();
// set the number of eigenvalues to use
nEigens = nTrainFaces - 1;
// allocate the eigenvector images
faceImgSize.width(trainingFaceImgArr[0].width());
faceImgSize.height(trainingFaceImgArr[0].height());
eigenVectArr = new IplImage[nEigens];
for (i = 0; i < nEigens; i++) {
eigenVectArr[i] =
cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
}
// System.out.println(calcLimit);
// allocate the eigenvalue array
eigenValMat =
cvCreateMat(
1, // rows
nEigens, // cols
CV_32FC1); // type, 32-bit float, 1 channel
// allocate the averaged image
pAvgTrainImg =
cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
// set the PCA termination criterion
calcLimit =
cvTermCriteria(
CV_TERMCRIT_ITER, // type
nEigens, // max_iter
1); // epsilon
// compute average image, eigenvalues, and eigenvectors TU JEST PROBLEM
cvCalcEigenObjects(
nTrainFaces, // nObjects
trainingFaceImgArr, // input
eigenVectArr, // output
CV_EIGOBJ_NO_CALLBACK, // ioFlags
0, // ioBufSize
null, // userData
calcLimit,
pAvgTrainImg, // avg
eigenValMat.data_fl()); // eigVals
// normalizacja czyli odjecie avarage face
cvNormalize(
eigenValMat, // src (CvArr)
eigenValMat, // dst (CvArr)
1, // a
0, // b
CV_L1, // norm_type
null); // mask
System.out.println("PCA done.");
}
public void learn(String tmpImageDataOutput) {
File folder = new File(tmpImageDataOutput);
File[] listOfFiles = folder.listFiles();
// load image to trainingFaces
for (int i = 0; i < listOfFiles.length; i++) {
if (listOfFiles[i].isFile()) {
String file = listOfFiles[i].getName();
String filepath = tmpImageDataOutput + "/" + file;
IplImage tmpImage = cvLoadImage(filepath, CV_LOAD_IMAGE_GRAYSCALE);
if (tmpImage != null) {
trainingFaces.add(tmpImage);
}
}
}
int nfaces = trainingFaces.size();
System.out.println("total: " + nfaces);
// Does the Principal Component Analysis, finding the average image and the eigenfaces that
// represent any image in the given dataset.....so call PCA
// set the number of eigenvalues to use
nTrainFaces = nfaces;
nEigens = nTrainFaces - 1;
CvTermCriteria calcLimit;
CvSize faceImgSize = new CvSize();
faceImgSize.width(trainingFaces.get(0).width());
faceImgSize.height(trainingFaces.get(0).height());
personNumTruthMat = cvCreateMat(1, nfaces, CV_32SC1);
// rows ,nFaces, type, 32-bit unsigned, one channel
// initialize the person number matrix - for ease of debugging
for (int j1 = 0; j1 < nfaces; j1++) {
personNumTruthMat.put(0, j1, 0);
}
for (int i = 0; i < nEigens; i++) {
eigens.add(
cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1)); // channels)
}
eigenValMat = cvCreateMat(1, nEigens, CV_32FC1); // type, 32-bit float, 1 channel
// allocate the averaged image
pAvgTrainImg =
cvCreateImage(
faceImgSize, // size
IPL_DEPTH_32F, // depth
1); // channels
// set the PCA termination criterion
calcLimit =
cvTermCriteria(
CV_TERMCRIT_ITER, // type
nEigens, // max_iter
1); // epsilon
LOGGER.info("computing average image, eigenvalues and eigenvectors");
// compute average image, eigenvalues, and eigenvectors
eigenVectArr = eigens.toArray(new IplImage[eigens.size()]);
cvCalcEigenObjects(
nTrainFaces, // nObjects
trainingFaces.toArray(new IplImage[trainingFaces.size()]), // input
eigenVectArr, // the output is array... need to transfer to arrayList latter
CV_EIGOBJ_NO_CALLBACK, // ioFlags
0, // ioBufSize
null, // userData
calcLimit,
pAvgTrainImg, // avg
eigenValMat.data_fl()); // eigVals
// eigens = (ArrayList) Arrays.asList(eigenVectArr) ;
LOGGER.info("normalizing the eigenvectors");
cvNormalize(
eigenValMat, // src (CvArr)
eigenValMat, // dst (CvArr)
1, // a
0, // b
CV_L1, // norm_type
null); // mask
LOGGER.info("projecting the training images onto the PCA subspace");
// project the training images onto the PCA subspace
projectedTrainFaceMat =
cvCreateMat(
nTrainFaces, // rows
nEigens, // cols
CV_32FC1); // type, 32-bit float, 1 channel
// initialize the training face matrix - for ease of debugging
for (int i1 = 0; i1 < nTrainFaces; i1++) {
for (int j1 = 0; j1 < nEigens; j1++) {
projectedTrainFaceMat.put(i1, j1, 0.0);
}
}
LOGGER.info(
"created projectedTrainFaceMat with "
+ nTrainFaces
+ " (nTrainFaces) rows and "
+ nEigens
+ " (nEigens) columns");
/**
* nTrainFace should always > 5 !!!!!!!!!!!!!!!! if (nTrainFaces < 5) {
* LOGGER.info("projectedTrainFaceMat contents:\n" +
* oneChannelCvMatToString(projectedTrainFaceMat)); }
*/
final FloatPointer floatPointer = new FloatPointer(nEigens);
for (int i = 0; i < nTrainFaces; i++) {
cvEigenDecomposite(
trainingFaces.get(i), // obj
nEigens, // nEigObjs
eigenVectArr, // eigInput (Pointer)
0, // ioFlags
null, // userData (Pointer)
pAvgTrainImg, // avg
floatPointer); // coeffs (FloatPointer)
/*nTrainFace should always > 5 !!!!!!!!!!!!!!!!
if (nTrainFaces < 5) {
LOGGER.info("floatPointer: " + floatPointerToString(floatPointer));
}*/
for (int j1 = 0; j1 < nEigens; j1++) {
projectedTrainFaceMat.put(i, j1, floatPointer.get(j1));
}
}
/*nTrainFace should always > 5 !!!!!!!!!!!!!!!!
if (nTrainFaces < 5) {
LOGGER.info("projectedTrainFaceMat after cvEigenDecomposite:\n" + projectedTrainFaceMat);
}
*/
// store the recognition data as an xml file
// storeTrainingData();
// Save all the eigenvectors as images, so that they can be checked.
// storeEigenfaceImages();
}