private IplImage[] loadFaceImgArray(String filename) {
IplImage[] faceImgArr;
BufferedReader imgListFile;
String imgFilename;
int iFace = 0;
int nFaces = 0;
int i;
try {
imgListFile = new BufferedReader(new FileReader(filename));
while (true) {
final String line = imgListFile.readLine();
if (line == null || line.isEmpty()) break;
nFaces++;
}
System.out.println("loadingImgArray-> liczba plikow: " + nFaces);
imgListFile = new BufferedReader(new FileReader(filename));
faceImgArr = new IplImage[nFaces];
personNumTruthMat =
cvCreateMat(
1, // rows
nFaces, // cols
CV_32SC1 // type 32 bit unsigned 1 ch
);
// initialize for debugging
for (int j1 = 0; j1 < nFaces; j1++) {
personNumTruthMat.put(0, j1, 0);
}
personNames.clear();
nPersons = 0;
for (iFace = 0; iFace < nFaces; iFace++) {
String personName;
String sPersonName;
int personNumber;
// reading person number
final String line = imgListFile.readLine();
if (line.isEmpty()) {
break;
}
final String[] tokens = line.split(" ");
personNumber = Integer.parseInt(tokens[0]);
personName = tokens[1];
imgFilename = tokens[2];
sPersonName = personName;
System.out.println(
"Got: " + iFace + " " + personNumber + " " + personName + " " + imgFilename);
// check if a new person is beaing loaded
if (personNumber > nPersons) {
// allocate memory
personNames.add(sPersonName);
nPersons = personNumber;
System.out.println(
"Added new person"
+ sPersonName
+ " ->nPersons = "
+ nPersons
+ " ["
+ personNames.size()
+ "] ");
}
// keep the data
personNumTruthMat.put(
0, // i
iFace, // j
personNumber); // v
// load the face img
faceImgArr[iFace] =
cvLoadImage(
imgFilename, // filename
CV_LOAD_IMAGE_GRAYSCALE // is color
);
if (faceImgArr[iFace] == null) {
throw new RuntimeException("Can't load image from " + imgFilename);
}
}
imgListFile.close();
} catch (IOException ex) {
throw new RuntimeException(ex);
}
System.out.println(
"Data loaded from" + filename + "': (" + nFaces + " images of " + nPersons + " people).");
return faceImgArr;
}
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();
}
