You can use ML Kit to detect and track objects across frames of video.
When you pass ML Kit images, ML Kit returns, for each image, a list of
up to five detected objects and their position in the image. When detecting
objects in video streams, every object has an ID that you can use to track the
object across images. You can also optionally enable coarse object
classification, which labels objects with broad category descriptions.
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-object-detection-model:19.0.6'
}
To start detecting and tracking objects, first create an instance of
FirebaseVisionObjectDetector
, optionally specifying any detector settings you
want to change from the default.
Configure the object detector for your use case with a
FirebaseVisionObjectDetectorOptions
object. You can change the following
settings:
Object Detector Settings
|
Detection mode
|
STREAM_MODE
(default) |
SINGLE_IMAGE_MODE
In
STREAM_MODE
(default), the object detector runs
with low latency, but might produce incomplete results (such as
unspecified bounding boxes or category labels) on the first few
invocations of the detector. Also, in
STREAM_MODE
,
the detector assigns tracking IDs to objects, which you can use to
track objects across frames. Use this mode when you want to track
objects, or when low latency is important, such as when processing
video streams in real time.
In
SINGLE_IMAGE_MODE
, the object detector waits
until a detected object's bounding box and (if you enabled
classification) category label are available before returning a
result. As a consequence, detection latency is potentially higher.
Also, in
SINGLE_IMAGE_MODE
, tracking IDs are not
assigned. Use this mode if latency isn't critical and you don't
want to deal with partial results.
|
Detect and track multiple objects
|
false
(default) |
true
Whether to detect and track up to five objects or only the most
prominent object (default).
|
Classify objects
|
false
(default) |
true
Whether or not to classify detected objects into coarse categories.
When enabled, the object detector classifies objects into the
following categories: fashion goods, food, home goods,
places, plants, and unknown.
|
The object detection and tracking API is optimized for these two core use
cases:
- Live detection and tracking of the most prominent object in the camera
viewfinder
- Detection of multiple objects from a static image
To configure the API for these use cases:
Java
// Live detection and tracking
FirebaseVisionObjectDetectorOptions options =
new FirebaseVisionObjectDetectorOptions.Builder()
.setDetectorMode(FirebaseVisionObjectDetectorOptions.STREAM_MODE)
.enableClassification() // Optional
.build();
// Multiple object detection in static images
FirebaseVisionObjectDetectorOptions options =
new FirebaseVisionObjectDetectorOptions.Builder()
.setDetectorMode(FirebaseVisionObjectDetectorOptions.SINGLE_IMAGE_MODE)
.enableMultipleObjects()
.enableClassification() // Optional
.build();
Kotlin+KTX
// Live detection and tracking
val options = FirebaseVisionObjectDetectorOptions.Builder()
.setDetectorMode(FirebaseVisionObjectDetectorOptions.STREAM_MODE)
.enableClassification() // Optional
.build()
// Multiple object detection in static images
val options = FirebaseVisionObjectDetectorOptions.Builder()
.setDetectorMode(FirebaseVisionObjectDetectorOptions.SINGLE_IMAGE_MODE)
.enableMultipleObjects()
.enableClassification() // Optional
.build()
Get an instance of
FirebaseVisionObjectDetector
:
Java
FirebaseVisionObjectDetector objectDetector =
FirebaseVision.getInstance().getOnDeviceObjectDetector();
// Or, to change the default settings:
FirebaseVisionObjectDetector objectDetector =
FirebaseVision.getInstance().getOnDeviceObjectDetector(options);
Kotlin+KTX
val objectDetector = FirebaseVision.getInstance().getOnDeviceObjectDetector()
// Or, to change the default settings:
val objectDetector = FirebaseVision.getInstance().getOnDeviceObjectDetector(options)
2. Run the object detector
To detect and track objects, pass images to the
FirebaseVisionObjectDetector
instance's
processImage()
method.
For each frame of video or image in a sequence, do the following:
Create a
FirebaseVisionImage
object from your image.
-
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.
Pass the image to the
processImage()
method:
Java
objectDetector.processImage(image)
.addOnSuccessListener(
new OnSuccessListener<List<FirebaseVisionObject>>() {
@Override
public void onSuccess(List<FirebaseVisionObject> detectedObjects) {
// Task completed successfully
// ...
}
})
.addOnFailureListener(
new OnFailureListener() {
@Override
public void onFailure(@NonNull Exception e) {
// Task failed with an exception
// ...
}
});
Kotlin+KTX
objectDetector.processImage(image)
.addOnSuccessListener { detectedObjects ->
// Task completed successfully
// ...
}
.addOnFailureListener { e ->
// Task failed with an exception
// ...
}
If the call to
processImage()
succeeds, a list of
FirebaseVisionObject
s
is passed to the success listener.
Each
FirebaseVisionObject
contains the following properties:
Bounding box
|
A
Rect
indicating the position of the object in the
image.
|
Tracking ID
|
An integer that identifies the object across images. Null in
SINGLE_IMAGE_MODE.
|
Category
|
The coarse category of the object. If the object detector doesn't
have classification enabled, this is always
FirebaseVisionObject.CATEGORY_UNKNOWN
.
|
Confidence
|
The confidence value of the object classification. If the object
detector doesn't have classification enabled, or the object is
classified as unknown, this is
null
.
|
Java
// The list of detected objects contains one item if multiple object detection wasn't enabled.
for (FirebaseVisionObject obj : detectedObjects) {
Integer id = obj.getTrackingId();
Rect bounds = obj.getBoundingBox();
// If classification was enabled:
int category = obj.getClassificationCategory();
Float confidence = obj.getClassificationConfidence();
}
Kotlin+KTX
// The list of detected objects contains one item if multiple object detection wasn't enabled.
for (obj in detectedObjects) {
val id = obj.trackingId // A number that identifies the object across images
val bounds = obj.boundingBox // The object's position in the image
// If classification was enabled:
val category = obj.classificationCategory
val confidence = obj.classificationConfidence
}