ch.zhaw.facerecognitionlibrary.Helpers.MatName Java Examples
The following examples show how to use
ch.zhaw.facerecognitionlibrary.Helpers.MatName.
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Example #1
| Source File: Eigenfaces.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 6 votes |
|
public void saveToFile(){
FileHelper fh = new FileHelper();
fh.saveIntegerList(labelList, fh.createLabelFile(fh.EIGENFACES_PATH, "train"));
fh.saveLabelMapToFile(fh.EIGENFACES_PATH, labelMap, "train");
MatName mOmega = new MatName("Omega", Omega);
MatName mPsi = new MatName("Psi", Psi);
MatName mEigVectors = new MatName("eigVectors", eigVectors);
// Save Phi for tSNE
MatName mPhi = new MatName("Phi", Phi);
List<MatName> listMat = new ArrayList<MatName>();
listMat.add(mOmega);
listMat.add(mPsi);
listMat.add(mEigVectors);
listMat.add(mPhi);
fh.saveMatListToXml(listMat, fh.EIGENFACES_PATH, filename);
}
Example #2
| Source File: Eigenfaces.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 6 votes |
|
public void loadFromFile(){
FileHelper fh = new FileHelper();
MatName mOmega = new MatName("Omega", Omega);
MatName mPsi = new MatName("Psi", Psi);
MatName mEigVectors = new MatName("eigVectors", eigVectors);
List<MatName> listMat = new ArrayList<MatName>();
listMat.add(mOmega);
listMat.add(mPsi);
listMat.add(mEigVectors);
listMat = fh.getMatListFromXml(listMat, fh.EIGENFACES_PATH, filename);
for (MatName mat : listMat){
switch (mat.getName()){
case "Omega":
Omega = mat.getMat();
break;
case "Psi":
Psi = mat.getMat();
break;
case "eigVectors":
eigVectors = mat.getMat();
break;
}
}
labelList = fh.loadIntegerList(fh.createLabelFile(fh.EIGENFACES_PATH, "train"));
labelMap = fh.getLabelMapFromFile(fh.EIGENFACES_PATH);
}
Example #3
| Source File: StudentImageCollectionActivity.java From ml-authentication with Apache License 2.0 | 5 votes |
|
/**
* Stores all the buffered StudentImages to the file system and database
*/
private synchronized void storeStudentImages(){
new Thread(new Runnable() {
@Override
public void run() {
StudentImageCollectionEvent studentImageCollectionEvent = new StudentImageCollectionEvent();
studentImageCollectionEvent.setTime(Calendar.getInstance());
studentImageCollectionEvent.setDevice(device);
Long studentImageCollectionEventId = studentImageCollectionEventDao.insert(studentImageCollectionEvent);
for(int i=0; i<studentImages.size(); i++){
MatName matName = new MatName(Integer.toString(i), studentImages.get(i));
FileHelper fileHelper = new FileHelper();
String wholeFolderPath = StudentHelper.getStudentImageDirectory() + "/" + device.getDeviceId() + "/" + Long.toString(studentImageCollectionEventId);
new File(wholeFolderPath).mkdirs();
fileHelper.saveMatToImage(matName, wholeFolderPath + "/");
String imageUrl = wholeFolderPath + "/" + Integer.toString(i) + ".png";
StudentImage studentImage = new StudentImage();
studentImage.setTimeCollected(Calendar.getInstance());
studentImage.setImageFileUrl(imageUrl);
studentImage.setStudentImageCollectionEvent(studentImageCollectionEvent);
studentImageDao.insert(studentImage);
}
Log.i(getClass().getName(), "storeStudentImages has finished successfully.");
// Initiate background job for face recognition training
BootReceiver.scheduleFaceRecognitionTranining(getApplicationContext());
}
}).start();
}
Example #4
| Source File: KNearestNeighbor.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 5 votes |
|
@Override
public void saveTestData() {
MatName mTestList = new MatName("TestList", testList);
List<MatName> listMat = new ArrayList<>();
listMat.add(mTestList);
fh.saveMatListToXml(listMat, fh.KNN_PATH, testFile);
fh.saveIntegerList(labelListTest, fh.createLabelFile(fh.KNN_PATH, "test"));
fh.saveLabelMapToFile(fh.KNN_PATH, labelMapTest, "test");
}
Example #5
| Source File: KNearestNeighbor.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 5 votes |
|
@Override
public void loadFromFile() {
MatName mtrainingList = new MatName("TrainingList", trainingList);
List<MatName> listMat = new ArrayList<MatName>();
listMat.add(mtrainingList);
labelList = fh.loadIntegerList(fh.createLabelFile(fh.KNN_PATH, "train"));
labelMap = fh.getLabelMapFromFile(fh.KNN_PATH);
trainingList = fh.getMatListFromXml(listMat, fh.KNN_PATH, trainingFile).get(0).getMat();
labels = new Mat(labelList.size(), 1, CvType.CV_8UC1);
for (int i=0; i<labelList.size(); i++) {
Integer label = labelList.get(i);
// Fill shorter labels with 0s
labels.put(i, 0, label);
}
labels.convertTo(labels, CvType.CV_32F);
PreferencesHelper preferencesHelper = new PreferencesHelper(context);
k = preferencesHelper.getK();
knn = KNearest.create();
knn.setIsClassifier(true);
knn.train(trainingList, 0,labels);
}
Example #6
| Source File: Eigenfaces.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 5 votes |
|
@Override
public void saveTestData() {
FileHelper fh = new FileHelper();
fh.saveIntegerList(labelListTest, fh.createLabelFile(fh.EIGENFACES_PATH, "test"));
fh.saveLabelMapToFile(fh.EIGENFACES_PATH, labelMapTest, "test");
MatName mTestList = new MatName("TestList", testList);
List<MatName> listMat = new ArrayList<>();
listMat.add(mTestList);
fh.saveMatListToXml(listMat, fh.EIGENFACES_PATH, "testlist.xml");
}
Example #7
| Source File: StudentImageCollectionActivity.java From ml-authentication with Apache License 2.0 | 5 votes |
|
/**
* Stores all the buffered StudentImages to the file system and database
*/
private synchronized void storeStudentImages(){
new Thread(new Runnable() {
@Override
public void run() {
StudentImageCollectionEvent studentImageCollectionEvent = new StudentImageCollectionEvent();
studentImageCollectionEvent.setTime(Calendar.getInstance());
studentImageCollectionEvent.setDevice(device);
Long studentImageCollectionEventId = studentImageCollectionEventDao.insert(studentImageCollectionEvent);
for(int i=0; i<studentImages.size(); i++){
MatName matName = new MatName(Integer.toString(i), studentImages.get(i));
FileHelper fileHelper = new FileHelper();
String wholeFolderPath = StudentHelper.getStudentImageDirectory() + "/" + device.getDeviceId() + "/" + Long.toString(studentImageCollectionEventId);
new File(wholeFolderPath).mkdirs();
fileHelper.saveMatToImage(matName, wholeFolderPath + "/");
String imageUrl = wholeFolderPath + "/" + Integer.toString(i) + ".png";
StudentImage studentImage = new StudentImage();
studentImage.setTimeCollected(Calendar.getInstance());
studentImage.setImageFileUrl(imageUrl);
studentImage.setStudentImageCollectionEvent(studentImageCollectionEvent);
studentImageDao.insert(studentImage);
}
Log.i(getClass().getName(), "storeStudentImages has finished successfully.");
// Initiate background job for face recognition training
BootReceiver.scheduleFaceRecognitionTranining(getApplicationContext());
}
}).start();
}
Example #8
| Source File: DetectionTestActivity.java From Android-Face-Recognition-with-Deep-Learning-Test-Framework with Apache License 2.0 | 4 votes |
|
@Override
protected void onResume() {
super.onResume();
final Handler handler = new Handler(Looper.getMainLooper());
thread = new Thread(new Runnable() {
public void run() {
if(!Thread.currentThread().isInterrupted()){
PreProcessorFactory ppF = new PreProcessorFactory(getApplicationContext());
FileHelper fileHelper = new FileHelper();
File[] detectionFolders = fileHelper.getDetectionTestList();
if (detectionFolders.length > 0) {
// total and matches are used to calculate the accuracy afterwards
int total = 0;
int matches = 0;
List<String> results = new ArrayList<>();
results.add("Expected Name;Expected File;Result");
Date time_start = new Date();
for (File folder : detectionFolders) {
File[] files = folder.listFiles();
int counter = 1;
for (File file : files) {
if (FileHelper.isFileAnImage(file)) {
Mat imgRgba = Imgcodecs.imread(file.getAbsolutePath());
Imgproc.cvtColor(imgRgba, imgRgba, Imgproc.COLOR_BGRA2RGBA);
List<Mat> images = ppF.getProcessedImage(imgRgba, PreProcessorFactory.PreprocessingMode.DETECTION);
Rect[] faces = ppF.getFacesForRecognition();
String result = "";
if (faces == null || faces.length == 0) {
result = RESULT_NEGATIVE;
} else {
result = RESULT_POSITIVE;
faces = MatOperation.rotateFaces(imgRgba, faces, ppF.getAngleForRecognition());
for(int i = 0; i<faces.length; i++){
MatOperation.drawRectangleAndLabelOnPreview(images.get(0), faces[i], "", false);
}
}
// Save images
String[] tokens = file.getName().split("\\.");
String filename = tokens[0];
for (int i=0; i<images.size();i++){
MatName m = new MatName(filename + "_" + (i + 1), images.get(i));
fileHelper.saveMatToImage(m, FileHelper.RESULTS_PATH + "/" + time_start.toString() + "/");
}
tokens = file.getParent().split("/");
final String name = tokens[tokens.length - 1];
results.add(name + ";" + file.getName() + ";" + result);
total++;
if (name.equals(result)) {
matches++;
}
// Update screen to show the progress
final int counterPost = counter;
final int filesLength = files.length;
progress.post(new Runnable() {
@Override
public void run() {
progress.append("Image " + counterPost + " of " + filesLength + " from " + name + "\n");
}
});
counter++;
}
}
}
Date time_end = new Date();
long duration = time_end.getTime() - time_start.getTime();
int durationPerImage = (int) duration / total;
double accuracy = (double) matches / (double) total;
Map<String, ?> printMap = PreferenceManager.getDefaultSharedPreferences(getApplicationContext()).getAll();
fileHelper.saveResultsToFile(printMap, accuracy, durationPerImage, results);
final Intent intent = new Intent(getApplicationContext(), MainActivity.class);
intent.setFlags(Intent.FLAG_ACTIVITY_CLEAR_TOP);
intent.putExtra("accuracy", accuracy);
handler.post(new Runnable() {
@Override
public void run() {
startActivity(intent);
}
});
}
} else {
Thread.currentThread().interrupt();
}
}
});
thread.start();
}
Example #9
| Source File: TrainingActivity.java From Android-Face-Recognition-with-Deep-Learning-Test-Framework with Apache License 2.0 | 4 votes |
|
@Override
public void onResume()
{
super.onResume();
final Handler handler = new Handler(Looper.getMainLooper());
thread = new Thread(new Runnable() {
public void run() {
if(!Thread.currentThread().isInterrupted()){
PreProcessorFactory ppF = new PreProcessorFactory(getApplicationContext());
PreferencesHelper preferencesHelper = new PreferencesHelper(getApplicationContext());
String algorithm = preferencesHelper.getClassificationMethod();
FileHelper fileHelper = new FileHelper();
fileHelper.createDataFolderIfNotExsiting();
final File[] persons = fileHelper.getTrainingList();
if (persons.length > 0) {
Recognition rec = RecognitionFactory.getRecognitionAlgorithm(getApplicationContext(), Recognition.TRAINING, algorithm);
for (File person : persons) {
if (person.isDirectory()){
File[] files = person.listFiles();
int counter = 1;
for (File file : files) {
if (FileHelper.isFileAnImage(file)){
Mat imgRgb = Imgcodecs.imread(file.getAbsolutePath());
Imgproc.cvtColor(imgRgb, imgRgb, Imgproc.COLOR_BGRA2RGBA);
Mat processedImage = new Mat();
imgRgb.copyTo(processedImage);
List<Mat> images = ppF.getProcessedImage(processedImage, PreProcessorFactory.PreprocessingMode.RECOGNITION);
if (images == null || images.size() > 1) {
// More than 1 face detected --> cannot use this file for training
continue;
} else {
processedImage = images.get(0);
}
if (processedImage.empty()) {
continue;
}
// The last token is the name --> Folder name = Person name
String[] tokens = file.getParent().split("/");
final String name = tokens[tokens.length - 1];
MatName m = new MatName("processedImage", processedImage);
fileHelper.saveMatToImage(m, FileHelper.DATA_PATH);
rec.addImage(processedImage, name, false);
// fileHelper.saveCroppedImage(imgRgb, ppF, file, name, counter);
// Update screen to show the progress
final int counterPost = counter;
final int filesLength = files.length;
progress.post(new Runnable() {
@Override
public void run() {
progress.append("Image " + counterPost + " of " + filesLength + " from " + name + " imported.\n");
}
});
counter++;
}
}
}
}
final Intent intent = new Intent(getApplicationContext(), MainActivity.class);
intent.setFlags(Intent.FLAG_ACTIVITY_CLEAR_TOP);
if (rec.train()) {
intent.putExtra("training", "Training successful");
} else {
intent.putExtra("training", "Training failed");
}
handler.post(new Runnable() {
@Override
public void run() {
startActivity(intent);
}
});
} else {
Thread.currentThread().interrupt();
}
}
}
});
thread.start();
}
Example #10
| Source File: Caffe.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 4 votes |
|
private String saveMatToImage(Mat img){
MatName m = new MatName("caffe_vector", img);
return fh.saveMatToImage(m, fh.CAFFE_PATH);
}
Example #11
| Source File: KNearestNeighbor.java From Android-Face-Recognition-with-Deep-Learning-Library with Apache License 2.0 | 3 votes |
|
@Override
public void saveToFile() {
fh.saveIntegerList(labelList, fh.createLabelFile(fh.KNN_PATH, "train"));
fh.saveLabelMapToFile(fh.KNN_PATH, labelMap, "train");
MatName mtrainingList = new MatName("TrainingList", trainingList);
List<MatName> listMat = new ArrayList<MatName>();
listMat.add(mtrainingList);
fh.saveMatListToXml(listMat, fh.KNN_PATH, trainingFile);
}
