Examples with FaceDetector com.google.android.gms.vision.face.FaceDetector used on opensource projects

Example 1 with FaceDetector

use of com.google.android.gms.vision.face.FaceDetector in project android-vision by googlesamples.

the class GooglyEyesActivity method createFaceDetector.

//==============================================================================================
// Detector
//==============================================================================================
/**
     * Creates the face detector and associated processing pipeline to support either front facing
     * mode or rear facing mode.  Checks if the detector is ready to use, and displays a low storage
     * warning if it was not possible to download the face library.
     */
@NonNull
private FaceDetector createFaceDetector(Context context) {
    // For both front facing and rear facing modes, the detector is initialized to do landmark
    // detection (to find the eyes), classification (to determine if the eyes are open), and
    // tracking.
    //
    // Use of "fast mode" enables faster detection for frontward faces, at the expense of not
    // attempting to detect faces at more varied angles (e.g., faces in profile).  Therefore,
    // faces that are turned too far won't be detected under fast mode.
    //
    // For front facing mode only, the detector will use the "prominent face only" setting,
    // which is optimized for tracking a single relatively large face.  This setting allows the
    // detector to take some shortcuts to make tracking faster, at the expense of not being able
    // to track multiple faces.
    //
    // Setting the minimum face size not only controls how large faces must be in order to be
    // detected, it also affects performance.  Since it takes longer to scan for smaller faces,
    // we increase the minimum face size for the rear facing mode a little bit in order to make
    // tracking faster (at the expense of missing smaller faces).  But this optimization is less
    // important for the front facing case, because when "prominent face only" is enabled, the
    // detector stops scanning for faces after it has found the first (large) face.
    FaceDetector detector = new FaceDetector.Builder(context).setLandmarkType(FaceDetector.ALL_LANDMARKS).setClassificationType(FaceDetector.ALL_CLASSIFICATIONS).setTrackingEnabled(true).setMode(FaceDetector.FAST_MODE).setProminentFaceOnly(mIsFrontFacing).setMinFaceSize(mIsFrontFacing ? 0.35f : 0.15f).build();
    Detector.Processor<Face> processor;
    if (mIsFrontFacing) {
        // For front facing mode, a single tracker instance is used with an associated focusing
        // processor.  This configuration allows the face detector to take some shortcuts to
        // speed up detection, in that it can quit after finding a single face and can assume
        // that the nextIrisPosition face position is usually relatively close to the last seen
        // face position.
        Tracker<Face> tracker = new GooglyFaceTracker(mGraphicOverlay);
        processor = new LargestFaceFocusingProcessor.Builder(detector, tracker).build();
    } else {
        // For rear facing mode, a factory is used to create per-face tracker instances.  A
        // tracker is created for each face and is maintained as long as the same face is
        // visible, enabling per-face state to be maintained over time.  This is used to store
        // the iris position and velocity for each face independently, simulating the motion of
        // the eyes of any number of faces over time.
        //
        // Both the front facing mode and the rear facing mode use the same tracker
        // implementation, avoiding the need for any additional code.  The only difference
        // between these cases is the choice of Processor: one that is specialized for tracking
        // a single face or one that can handle multiple faces.  Here, we use MultiProcessor,
        // which is a standard component of the mobile vision API for managing multiple items.
        MultiProcessor.Factory<Face> factory = new MultiProcessor.Factory<Face>() {

            @Override
            public Tracker<Face> create(Face face) {
                return new GooglyFaceTracker(mGraphicOverlay);
            }
        };
        processor = new MultiProcessor.Builder<>(factory).build();
    }
    detector.setProcessor(processor);
    if (!detector.isOperational()) {
        // Note: The first time that an app using face API is installed on a device, GMS will
        // download a native library to the device in order to do detection.  Usually this
        // completes before the app is run for the first time.  But if that download has not yet
        // completed, then the above call will not detect any faces.
        //
        // isOperational() can be used to check if the required native library is currently
        // available.  The detector will automatically become operational once the library
        // download completes on device.
        Log.w(TAG, "Face detector dependencies are not yet available.");
        // Check for low storage.  If there is low storage, the native library will not be
        // downloaded, so detection will not become operational.
        IntentFilter lowStorageFilter = new IntentFilter(Intent.ACTION_DEVICE_STORAGE_LOW);
        boolean hasLowStorage = registerReceiver(null, lowStorageFilter) != null;
        if (hasLowStorage) {
            Toast.makeText(this, R.string.low_storage_error, Toast.LENGTH_LONG).show();
            Log.w(TAG, getString(R.string.low_storage_error));
        }
    }
    return detector;
}

Example 2 with FaceDetector

use of com.google.android.gms.vision.face.FaceDetector in project HomeMirror by HannahMitt.

the class MoodModule method createCameraSource.

/**
     * Creates and starts the camera.  Note that this uses a higher resolution in comparison
     * to other detection examples to enable the barcode detector to detect small barcodes
     * at long distances.
     */
private void createCameraSource() {
    Context context = mContextWeakReference.get();
    FaceDetector detector = new FaceDetector.Builder(context).setClassificationType(FaceDetector.ALL_CLASSIFICATIONS).build();
    detector.setProcessor(new Detector.Processor<Face>() {

        @Override
        public void release() {
        }

        @Override
        public void receiveDetections(final Detector.Detections<Face> detections) {
            final SparseArray<Face> detectedItems = detections.getDetectedItems();
            if (detectedItems.size() != 0) {
                final int key = detectedItems.keyAt(0);
                final Face face = detectedItems.get(key);
                final float isSmilingProbability = face.getIsSmilingProbability();
                String feedback = getFeedbackForSmileProbability(isSmilingProbability);
                mCallBacks.onShouldGivePositiveAffirmation(feedback);
            }
        }
    });
    if (!detector.isOperational()) {
        // Note: The first time that an app using face API is installed on a device, GMS will
        // download a native library to the device in order to do detection.  Usually this
        // completes before the app is run for the first time.  But if that download has not yet
        // completed, then the above call will not detect any faces.
        //
        // isOperational() can be used to check if the required native library is currently
        // available.  The detector will automatically become operational once the library
        // download completes on device.
        Log.w(TAG, "Face detector dependencies are not yet available.");
    }
    try {
        mCameraSource = new CameraSource.Builder(context, detector).setRequestedPreviewSize(640, 480).setFacing(CameraSource.CAMERA_FACING_FRONT).setRequestedFps(30.0f).build();
        mCameraSource.start();
    } catch (IOException | RuntimeException e) {
        Log.e(TAG, "Something went horribly wrong, with your face.", e);
    }
}

Example 3 with FaceDetector

use of com.google.android.gms.vision.face.FaceDetector in project android-vision by googlesamples.

the class FaceTrackerActivity method createCameraSource.

/**
     * Creates and starts the camera.  Note that this uses a higher resolution in comparison
     * to other detection examples to enable the barcode detector to detect small barcodes
     * at long distances.
     */
private void createCameraSource() {
    Context context = getApplicationContext();
    FaceDetector detector = new FaceDetector.Builder(context).setClassificationType(FaceDetector.ALL_CLASSIFICATIONS).build();
    detector.setProcessor(new MultiProcessor.Builder<>(new GraphicFaceTrackerFactory()).build());
    if (!detector.isOperational()) {
        // Note: The first time that an app using face API is installed on a device, GMS will
        // download a native library to the device in order to do detection.  Usually this
        // completes before the app is run for the first time.  But if that download has not yet
        // completed, then the above call will not detect any faces.
        //
        // isOperational() can be used to check if the required native library is currently
        // available.  The detector will automatically become operational once the library
        // download completes on device.
        Log.w(TAG, "Face detector dependencies are not yet available.");
    }
    mCameraSource = new CameraSource.Builder(context, detector).setRequestedPreviewSize(640, 480).setFacing(CameraSource.CAMERA_FACING_BACK).setRequestedFps(30.0f).build();
}

Example 4 with FaceDetector

use of com.google.android.gms.vision.face.FaceDetector in project android-vision by googlesamples.

the class MultiTrackerActivity method createCameraSource.

/**
     * Creates and starts the camera.  Note that this uses a higher resolution in comparison
     * to other detection examples to enable the barcode detector to detect small barcodes
     * at long distances.
     */
private void createCameraSource() {
    Context context = getApplicationContext();
    // A face detector is created to track faces.  An associated multi-processor instance
    // is set to receive the face detection results, track the faces, and maintain graphics for
    // each face on screen.  The factory is used by the multi-processor to create a separate
    // tracker instance for each face.
    FaceDetector faceDetector = new FaceDetector.Builder(context).build();
    FaceTrackerFactory faceFactory = new FaceTrackerFactory(mGraphicOverlay);
    faceDetector.setProcessor(new MultiProcessor.Builder<>(faceFactory).build());
    // A barcode detector is created to track barcodes.  An associated multi-processor instance
    // is set to receive the barcode detection results, track the barcodes, and maintain
    // graphics for each barcode on screen.  The factory is used by the multi-processor to
    // create a separate tracker instance for each barcode.
    BarcodeDetector barcodeDetector = new BarcodeDetector.Builder(context).build();
    BarcodeTrackerFactory barcodeFactory = new BarcodeTrackerFactory(mGraphicOverlay);
    barcodeDetector.setProcessor(new MultiProcessor.Builder<>(barcodeFactory).build());
    // A multi-detector groups the two detectors together as one detector.  All images received
    // by this detector from the camera will be sent to each of the underlying detectors, which
    // will each do face and barcode detection, respectively.  The detection results from each
    // are then sent to associated tracker instances which maintain per-item graphics on the
    // screen.
    MultiDetector multiDetector = new MultiDetector.Builder().add(faceDetector).add(barcodeDetector).build();
    if (!multiDetector.isOperational()) {
        // Note: The first time that an app using the barcode or face API is installed on a
        // device, GMS will download a native libraries to the device in order to do detection.
        // Usually this completes before the app is run for the first time.  But if that
        // download has not yet completed, then the above call will not detect any barcodes
        // and/or faces.
        //
        // isOperational() can be used to check if the required native libraries are currently
        // available.  The detectors will automatically become operational once the library
        // downloads complete on device.
        Log.w(TAG, "Detector dependencies are not yet available.");
        // Check for low storage.  If there is low storage, the native library will not be
        // downloaded, so detection will not become operational.
        IntentFilter lowstorageFilter = new IntentFilter(Intent.ACTION_DEVICE_STORAGE_LOW);
        boolean hasLowStorage = registerReceiver(null, lowstorageFilter) != null;
        if (hasLowStorage) {
            Toast.makeText(this, R.string.low_storage_error, Toast.LENGTH_LONG).show();
            Log.w(TAG, getString(R.string.low_storage_error));
        }
    }
    // Creates and starts the camera.  Note that this uses a higher resolution in comparison
    // to other detection examples to enable the barcode detector to detect small barcodes
    // at long distances.
    mCameraSource = new CameraSource.Builder(getApplicationContext(), multiDetector).setFacing(CameraSource.CAMERA_FACING_BACK).setRequestedPreviewSize(1600, 1024).setRequestedFps(15.0f).build();
}

Example 5 with FaceDetector

use of com.google.android.gms.vision.face.FaceDetector in project android-vision by googlesamples.

the class GooglyEyesActivity method createCameraSource.

//==============================================================================================
// Camera Source
//==============================================================================================
/**
     * Creates the face detector and the camera.
     */
private void createCameraSource() {
    Context context = getApplicationContext();
    FaceDetector detector = createFaceDetector(context);
    int facing = CameraSource.CAMERA_FACING_FRONT;
    if (!mIsFrontFacing) {
        facing = CameraSource.CAMERA_FACING_BACK;
    }
    // The camera source is initialized to use either the front or rear facing camera.  We use a
    // relatively low resolution for the camera preview, since this is sufficient for this app
    // and the face detector will run faster at lower camera resolutions.
    //
    // However, note that there is a speed/accuracy trade-off with respect to choosing the
    // camera resolution.  The face detector will run faster with lower camera resolutions,
    // but may miss smaller faces, landmarks, or may not correctly detect eyes open/closed in
    // comparison to using higher camera resolutions.  If you have any of these issues, you may
    // want to increase the resolution.
    mCameraSource = new CameraSource.Builder(context, detector).setFacing(facing).setRequestedPreviewSize(320, 240).setRequestedFps(60.0f).setAutoFocusEnabled(true).build();
}