/**
* Find the most likely person based on a detection. Returns the index, and stores the confidence
* value into pConfidence.
*
* @param projectedTestFace the projected test face
* @param pConfidencePointer a pointer containing the confidence value
* @param iTestFace the test face index
* @return the index
*/
private int findNearestNeighbor(float projectedTestFace[], FloatPointer pConfidencePointer) {
double leastDistSq = Double.MAX_VALUE;
int i = 0;
int iTrain = 0;
int iNearest = 0;
LOGGER.info("................");
LOGGER.info("find nearest neighbor from " + nTrainFaces + " training faces");
for (iTrain = 0; iTrain < nTrainFaces; iTrain++) {
// LOGGER.info("considering training face " + (iTrain + 1));
double distSq = 0;
for (i = 0; i < nEigens; i++) {
// LOGGER.debug(" projected test face distance from eigenface " + (i + 1) + " is " +
// projectedTestFace[i]);
float projectedTrainFaceDistance = (float) projectedTrainFaceMat.get(iTrain, i);
float d_i = projectedTestFace[i] - projectedTrainFaceDistance;
distSq +=
d_i
* d_i; // / eigenValMat.data_fl().get(i); // Mahalanobis distance (might give
// better results than Eucalidean distance)
// if (iTrain < 5) {
// LOGGER.info(" ** projected training face " + (iTrain + 1) + " distance from
// eigenface " + (i + 1) + " is " + projectedTrainFaceDistance);
// LOGGER.info(" distance between them " + d_i);
// LOGGER.info(" distance squared " + distSq);
// }
}
if (distSq < leastDistSq) {
leastDistSq = distSq;
iNearest = iTrain;
LOGGER.info(
" training face "
+ (iTrain + 1)
+ " is the new best match, least squared distance: "
+ leastDistSq);
}
}
// Return the confidence level based on the Euclidean distance,
// so that similar images should give a confidence between 0.5 to 1.0,
// and very different images should give a confidence between 0.0 to 0.5.
float pConfidence =
(float) (1.0f - Math.sqrt(leastDistSq / (float) (nTrainFaces * nEigens)) / 255.0f);
pConfidencePointer.put(pConfidence);
LOGGER.info(
"training face " + (iNearest + 1) + " is the final best match, confidence " + pConfidence);
return iNearest;
}
/* From a normalized image file to recognized if match current trained data.
*/
public void recognize(String tmpDetectImageOutput, String trainedOutput, String personName) {
File folder = new File(tmpDetectImageOutput);
File[] listOfFiles = folder.listFiles();
ArrayList<String> testFaceFileNames = new ArrayList<String>();
String answer = "";
// load image to testFaces array list
for (int i = 0; i < listOfFiles.length; i++) {
if (listOfFiles[i].isFile()) {
String file = listOfFiles[i].getName();
String filepath = tmpDetectImageOutput + "/" + file;
IplImage tmpImage = cvLoadImage(filepath, CV_LOAD_IMAGE_GRAYSCALE);
if (tmpImage != null) {
testFaces.add(tmpImage);
testFaceFileNames.add(filepath);
}
}
}
CvMat trainPersonNumMat = loadTrainingData(trainedOutput, personName);
// int ntestfaces = testFaces.size() ;
int nTestFaces = testFaces.size();
LOGGER.info(trainedOutput + "/" + personName + ".xml");
System.out.println("total: " + nTestFaces + " to be tested latter...");
personNumTruthMat = cvCreateMat(1, nTestFaces, CV_32SC1); // type, 32-
float[] projectedTestFace = new float[nEigens];
float confidence = 0.0f;
int nCorrect = 0;
int nWrong = 0;
double timeFaceRecognizeStart = (double) cvGetTickCount(); // supposedly to record the timing??
for (int i = 0; i < nTestFaces; i++) {
int iNearest;
int nearest;
int truth;
// project the test image onto the PCA subspace
LOGGER.info("before find decomposite..");
cvEigenDecomposite(
testFaces.get(i),
nEigens, // nEigObjs
eigenVectArr, // eigInput (Pointer)
0, // ioFlags
null, // userData
pAvgTrainImg, // avg
projectedTestFace); // coeffs
// LOGGER.info("projectedTestFace\n" + floatArrayToString(projectedTestFace));
final FloatPointer pConfidence = new FloatPointer(confidence);
LOGGER.info("before find nearest...");
iNearest = findNearestNeighbor(projectedTestFace, new FloatPointer(pConfidence));
confidence = pConfidence.get();
truth = personNumTruthMat.data_i().get(i);
nearest = trainPersonNumMat.data_i().get(iNearest);
if (nearest == truth) {
answer = "Correct";
nCorrect++;
} else {
answer = "WRONG!";
nWrong++;
}
LOGGER.info(testFaceFileNames.get(i));
LOGGER.info(
"nearest = "
+ nearest
+ ", Truth = "
+ truth
+ " ("
+ answer
+ "). Confidence = "
+ confidence);
}
}
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();
}