You can use ML Kit to label objects recognized in an image, using either
an on-device model or a cloud model. See the
overview
to learn about the benefits of
each approach.
Before you begin
- If you haven't already,
add Firebase to your Android project
.
- Add the dependencies for the ML Kit Android libraries to your module
(app-level) Gradle file (usually
app/build.gradle
):
apply plugin: 'com.android.application'
apply plugin: 'com.google.gms.google-services'
dependencies {
// ...
implementation 'com.google.firebase:firebase-ml-vision:24.0.3'
implementation 'com.google.firebase:firebase-ml-vision-image-label-model:20.0.1'
}
-
Optional but recommended
: If you use the on-device API, configure your
app to automatically download the ML model to the device after your app is
installed from the Play Store.
To do so, add the following declaration to your app's
AndroidManifest.xml
file:
<application ...>
...
<meta-data
android:name="com.google.firebase.ml.vision.DEPENDENCIES"
android:value="label" />
<!-- To use multiple models: android:value="label,model2,model3" -->
</application>
If you do not enable install-time model downloads, the model will be
downloaded the first time you run the on-device detector. Requests you make
before the download has completed will produce no results.
-
If you want to use the Cloud-based model, and you have not already enabled
the Cloud-based APIs for your project, do so now:
- Open the
ML Kit
APIs page
of the Firebase console.
-
If you have not already upgraded your project to a Blaze pricing plan, click
Upgrade
to do so. (You will be prompted to upgrade only if your
project isn't on the Blaze plan.)
Only Blaze-level projects can use Cloud-based APIs.
- If Cloud-based APIs aren't already enabled, click
Enable Cloud-based
APIs
.
If you want to use only the on-device model, you can skip this step.
Now you are ready to label images using either an on-device model or a
cloud-based model.
Create a
FirebaseVisionImage
object from your image.
The image labeler runs fastest when you use a
Bitmap
or, if you use the
camera2 API, a JPEG-formatted
media.Image
, which are recommended when
possible.
-
To create a
FirebaseVisionImage
object from a
media.Image
object, such as when capturing an image from a
device's camera, pass the
media.Image
object and the image's
rotation to
FirebaseVisionImage.fromMediaImage()
.
If you use the
CameraX
library, the
OnImageCapturedListener
and
ImageAnalysis.Analyzer
classes calculate the rotation value
for you, so you just need to convert the rotation to one of ML Kit's
ROTATION_
constants before calling
FirebaseVisionImage.fromMediaImage()
:
Java
private class YourAnalyzer implements ImageAnalysis.Analyzer {
private int degreesToFirebaseRotation(int degrees) {
switch (degrees) {
case 0:
return FirebaseVisionImageMetadata.ROTATION_0;
case 90:
return FirebaseVisionImageMetadata.ROTATION_90;
case 180:
return FirebaseVisionImageMetadata.ROTATION_180;
case 270:
return FirebaseVisionImageMetadata.ROTATION_270;
default:
throw new IllegalArgumentException(
"Rotation must be 0, 90, 180, or 270.");
}
}
@Override
public void analyze(ImageProxy imageProxy, int degrees) {
if (imageProxy == null || imageProxy.getImage() == null) {
return;
}
Image mediaImage = imageProxy.getImage();
int rotation = degreesToFirebaseRotation(degrees);
FirebaseVisionImage image =
FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
// Pass image to an ML Kit Vision API
// ...
}
}
Kotlin+KTX
private class YourImageAnalyzer : ImageAnalysis.Analyzer {
private fun degreesToFirebaseRotation(degrees: Int): Int = when(degrees) {
0 -> FirebaseVisionImageMetadata.ROTATION_0
90 -> FirebaseVisionImageMetadata.ROTATION_90
180 -> FirebaseVisionImageMetadata.ROTATION_180
270 -> FirebaseVisionImageMetadata.ROTATION_270
else -> throw Exception("Rotation must be 0, 90, 180, or 270.")
}
override fun analyze(imageProxy: ImageProxy?, degrees: Int) {
val mediaImage = imageProxy?.image
val imageRotation = degreesToFirebaseRotation(degrees)
if (mediaImage != null) {
val image = FirebaseVisionImage.fromMediaImage(mediaImage, imageRotation)
// Pass image to an ML Kit Vision API
// ...
}
}
}
If you don't use a camera library that gives you the image's rotation, you
can calculate it from the device's rotation and the orientation of camera
sensor in the device:
Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray();
static {
ORIENTATIONS.append(Surface.ROTATION_0, 90);
ORIENTATIONS.append(Surface.ROTATION_90, 0);
ORIENTATIONS.append(Surface.ROTATION_180, 270);
ORIENTATIONS.append(Surface.ROTATION_270, 180);
}
/**
* Get the angle by which an image must be rotated given the device's current
* orientation.
*/
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
private int getRotationCompensation(String cameraId, Activity activity, Context context)
throws CameraAccessException {
// Get the device's current rotation relative to its "native" orientation.
// Then, from the ORIENTATIONS table, look up the angle the image must be
// rotated to compensate for the device's rotation.
int deviceRotation = activity.getWindowManager().getDefaultDisplay().getRotation();
int rotationCompensation = ORIENTATIONS.get(deviceRotation);
// On most devices, the sensor orientation is 90 degrees, but for some
// devices it is 270 degrees. For devices with a sensor orientation of
// 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
CameraManager cameraManager = (CameraManager) context.getSystemService(CAMERA_SERVICE);
int sensorOrientation = cameraManager
.getCameraCharacteristics(cameraId)
.get(CameraCharacteristics.SENSOR_ORIENTATION);
rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360;
// Return the corresponding FirebaseVisionImageMetadata rotation value.
int result;
switch (rotationCompensation) {
case 0:
result = FirebaseVisionImageMetadata.ROTATION_0;
break;
case 90:
result = FirebaseVisionImageMetadata.ROTATION_90;
break;
case 180:
result = FirebaseVisionImageMetadata.ROTATION_180;
break;
case 270:
result = FirebaseVisionImageMetadata.ROTATION_270;
break;
default:
result = FirebaseVisionImageMetadata.ROTATION_0;
Log.e(TAG, "Bad rotation value: " + rotationCompensation);
}
return result;
}
Kotlin+KTX
private val ORIENTATIONS = SparseIntArray()
init {
ORIENTATIONS.append(Surface.ROTATION_0, 90)
ORIENTATIONS.append(Surface.ROTATION_90, 0)
ORIENTATIONS.append(Surface.ROTATION_180, 270)
ORIENTATIONS.append(Surface.ROTATION_270, 180)
}
/**
* Get the angle by which an image must be rotated given the device's current
* orientation.
*/
@RequiresApi(api = Build.VERSION_CODES.LOLLIPOP)
@Throws(CameraAccessException::class)
private fun getRotationCompensation(cameraId: String, activity: Activity, context: Context): Int {
// Get the device's current rotation relative to its "native" orientation.
// Then, from the ORIENTATIONS table, look up the angle the image must be
// rotated to compensate for the device's rotation.
val deviceRotation = activity.windowManager.defaultDisplay.rotation
var rotationCompensation = ORIENTATIONS.get(deviceRotation)
// On most devices, the sensor orientation is 90 degrees, but for some
// devices it is 270 degrees. For devices with a sensor orientation of
// 270, rotate the image an additional 180 ((270 + 270) % 360) degrees.
val cameraManager = context.getSystemService(CAMERA_SERVICE) as CameraManager
val sensorOrientation = cameraManager
.getCameraCharacteristics(cameraId)
.get(CameraCharacteristics.SENSOR_ORIENTATION)!!
rotationCompensation = (rotationCompensation + sensorOrientation + 270) % 360
// Return the corresponding FirebaseVisionImageMetadata rotation value.
val result: Int
when (rotationCompensation) {
0 -> result = FirebaseVisionImageMetadata.ROTATION_0
90 -> result = FirebaseVisionImageMetadata.ROTATION_90
180 -> result = FirebaseVisionImageMetadata.ROTATION_180
270 -> result = FirebaseVisionImageMetadata.ROTATION_270
else -> {
result = FirebaseVisionImageMetadata.ROTATION_0
Log.e(TAG, "Bad rotation value: $rotationCompensation")
}
}
return result
}
Then, pass the
media.Image
object and the
rotation value to
FirebaseVisionImage.fromMediaImage()
:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation);
Kotlin+KTX
val image = FirebaseVisionImage.fromMediaImage(mediaImage, rotation)
- To create a
FirebaseVisionImage
object from a file URI, pass
the app context and file URI to
FirebaseVisionImage.fromFilePath()
. This is useful when you
use an
ACTION_GET_CONTENT
intent to prompt the user to select
an image from their gallery app.
Java
FirebaseVisionImage image;
try {
image = FirebaseVisionImage.fromFilePath(context, uri);
} catch (IOException e) {
e.printStackTrace();
}
Kotlin+KTX
val image: FirebaseVisionImage
try {
image = FirebaseVisionImage.fromFilePath(context, uri)
} catch (e: IOException) {
e.printStackTrace()
}
- To create a
FirebaseVisionImage
object from a
ByteBuffer
or a byte array, first calculate the image
rotation as described above for
media.Image
input.
Then, create a
FirebaseVisionImageMetadata
object
that contains the image's height, width, color encoding format,
and rotation:
Java
FirebaseVisionImageMetadata metadata = new FirebaseVisionImageMetadata.Builder()
.setWidth(480) // 480x360 is typically sufficient for
.setHeight(360) // image recognition
.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
.setRotation(rotation)
.build();
Kotlin+KTX
val metadata = FirebaseVisionImageMetadata.Builder()
.setWidth(480) // 480x360 is typically sufficient for
.setHeight(360) // image recognition
.setFormat(FirebaseVisionImageMetadata.IMAGE_FORMAT_NV21)
.setRotation(rotation)
.build()
Use the buffer or array, and the metadata object, to create a
FirebaseVisionImage
object:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromByteBuffer(buffer, metadata);
// Or: FirebaseVisionImage image = FirebaseVisionImage.fromByteArray(byteArray, metadata);
Kotlin+KTX
val image = FirebaseVisionImage.fromByteBuffer(buffer, metadata)
// Or: val image = FirebaseVisionImage.fromByteArray(byteArray, metadata)
- To create a
FirebaseVisionImage
object from a
Bitmap
object:
Java
FirebaseVisionImage image = FirebaseVisionImage.fromBitmap(bitmap);
Kotlin+KTX
val image = FirebaseVisionImage.fromBitmap(bitmap)
The image represented by the
Bitmap
object must
be upright, with no additional rotation required.
To label objects in an image, pass the
FirebaseVisionImage
object to the
FirebaseVisionImageLabeler
's
processImage
method.
First, get an instance of
FirebaseVisionImageLabeler
.
If you want to use the on-device image labeler:
Java
FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
.getOnDeviceImageLabeler();
// Or, to set the minimum confidence required:
// FirebaseVisionOnDeviceImageLabelerOptions options =
// new FirebaseVisionOnDeviceImageLabelerOptions.Builder()
// .setConfidenceThreshold(0.7f)
// .build();
// FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
// .getOnDeviceImageLabeler(options);
Kotlin+KTX
val labeler = FirebaseVision.getInstance().getOnDeviceImageLabeler()
// Or, to set the minimum confidence required:
// val options = FirebaseVisionOnDeviceImageLabelerOptions.Builder()
// .setConfidenceThreshold(0.7f)
// .build()
// val labeler = FirebaseVision.getInstance().getOnDeviceImageLabeler(options)
If you want to use the cloud image labeler:
Java
FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
.getCloudImageLabeler();
// Or, to set the minimum confidence required:
// FirebaseVisionCloudImageLabelerOptions options =
// new FirebaseVisionCloudImageLabelerOptions.Builder()
// .setConfidenceThreshold(0.7f)
// .build();
// FirebaseVisionImageLabeler labeler = FirebaseVision.getInstance()
// .getCloudImageLabeler(options);
Kotlin+KTX
val labeler = FirebaseVision.getInstance().getCloudImageLabeler()
// Or, to set the minimum confidence required:
// val options = FirebaseVisionCloudImageLabelerOptions.Builder()
// .setConfidenceThreshold(0.7f)
// .build()
// val labeler = FirebaseVision.getInstance().getCloudImageLabeler(options)
Then, pass the image to the
processImage()
method:
Java
labeler.processImage(image)
.addOnSuccessListener(new OnSuccessListener<List<FirebaseVisionImageLabel>>() {
@Override
public void onSuccess(List<FirebaseVisionImageLabel> labels) {
// Task completed successfully
// ...
}
})
.addOnFailureListener(new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
// Task failed with an exception
// ...
}
});
Kotlin+KTX
labeler.processImage(image)
.addOnSuccessListener { labels ->
// Task completed successfully
// ...
}
.addOnFailureListener { e ->
// Task failed with an exception
// ...
}
If the image labeling operation succeeds, a list of
FirebaseVisionImageLabel
objects will be passed to the
success listener. Each
FirebaseVisionImageLabel
object represents something
that was labeled in the image. For each label, you can get the label's text
description, its
Knowledge Graph entity ID
(if available), and the confidence score of the match. For example:
Java
for (FirebaseVisionImageLabel label: labels) {
String text = label.getText();
String entityId = label.getEntityId();
float confidence = label.getConfidence();
}
Kotlin+KTX
for (label in labels) {
val text = label.text
val entityId = label.entityId
val confidence = label.confidence
}
If you want to label images in a real-time application, follow these
guidelines to achieve the best framerates:
- Throttle calls to the image labeler. If a new video frame becomes
available while the image labeler is running, drop the frame.
- If you are using the output of the image labeler to overlay graphics on
the input image, first get the result from ML Kit, then render the image
and overlay in a single step. By doing so, you render to the display surface
only once for each input frame.
-
If you use the Camera2 API, capture images in
ImageFormat.YUV_420_888
format.
If you use the older Camera API, capture images in
ImageFormat.NV21
format.
Next steps