You can use ML Kit to detect and track objects in successive video frames.
When you pass an image to ML Kit, it detects up to five objects in the image along with the position of each object in the image. When detecting objects in video streams, each object has a unique ID that you can use to track the object from frame to frame. You can also optionally enable coarse object classification, which labels objects with broad category descriptions.
Try it out
- Play around with the sample app to see an example usage of this API.
- See the Material Design showcase app for an end-to-end implementation of this API.
Before you begin
- In your project-level
build.gradlefile, make sure to include Google's Maven repository in both yourbuildscriptandallprojectssections. - Add the dependencies for the ML Kit Android libraries to your module's
app-level gradle file, which is usually
app/build.gradle:dependencies { // ... implementation ' com . google . mlkit : object - detection : 17.0.2 ' }
1. Configure the object detector
To detect and track objects, first create an instance of ObjectDetector
and
optionally specify any detector settings that you want to change from the
default.
-
Configure the object detector for your use case with an
ObjectDetectorOptionsobject. You can change the following settings:Object Detector SettingsDetection modeSTREAM_MODE(default) |SINGLE_IMAGE_MODEIn
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, inSTREAM_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 returns the result after the object's bounding box is determined. If you also enable classification it returns the result after the bounding box and category label are both available. As a consequence, detection latency is potentially higher. Also, inSINGLE_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 objectsfalse(default) |trueWhether to detect and track up to five objects or only the most prominent object (default).
Classify objectsfalse(default) |trueWhether 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, and plants.
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.
- The detection of multiple objects from a static image.
To configure the API for these use cases:
Kotlin
// Live detection and tracking val options = ObjectDetectorOptions . Builder () . setDetectorMode ( ObjectDetectorOptions . STREAM_MODE ) . enableClassification () // Optional . build () // Multiple object detection in static images val options = ObjectDetectorOptions . Builder () . setDetectorMode ( ObjectDetectorOptions . SINGLE_IMAGE_MODE ) . enableMultipleObjects () . enableClassification () // Optional . build ()
Java
// Live detection and tracking ObjectDetectorOptions options = new ObjectDetectorOptions . Builder () . setDetectorMode ( ObjectDetectorOptions . STREAM_MODE ) . enableClassification () // Optional . build (); // Multiple object detection in static images ObjectDetectorOptions options = new ObjectDetectorOptions . Builder () . setDetectorMode ( ObjectDetectorOptions . SINGLE_IMAGE_MODE ) . enableMultipleObjects () . enableClassification () // Optional . build ();
-
Get an instance of
ObjectDetector:Kotlin
val objectDetector = ObjectDetection . getClient ( options )
Java
ObjectDetector objectDetector = ObjectDetection . getClient ( options );
2. Prepare the input image
To detect and track objects, pass images to theObjectDetector
instance's process()
method. The object detector runs directly from a Bitmap
, NV21 ByteBuffer
or a
YUV_420_888 media.Image
. Constructing an InputImage
from those sources
are recommended if you have direct access to one of them. If you construct
an InputImage
from other sources, we will handle the conversion
internally for you and it might be less efficient.
For each frame of video or image in a sequence, do the following:
You can create an InputImage
object from different sources, each is explained below.
Using a media.Image
To create an InputImage
object from a media.Image
object, such as when you capture an image from a
device's camera, pass the media.Image
object and the image's
rotation to InputImage.fromMediaImage()
.
If you use the CameraX
library, the OnImageCapturedListener
and ImageAnalysis.Analyzer
classes calculate the rotation value
for you.
Kotlin
private class YourImageAnalyzer : ImageAnalysis . Analyzer { override fun analyze ( imageProxy : ImageProxy ) { val mediaImage = imageProxy . image if ( mediaImage != null ) { val image = InputImage . fromMediaImage ( mediaImage , imageProxy . imageInfo . rotationDegrees ) // Pass image to an ML Kit Vision API // ... } } }
Java
private class YourAnalyzer implements ImageAnalysis . Analyzer { @Override public void analyze ( ImageProxy imageProxy ) { Image mediaImage = imageProxy . getImage (); if ( mediaImage != null ) { InputImage image = InputImage . fromMediaImage ( mediaImage , imageProxy . getImageInfo (). getRotationDegrees ()); // Pass image to an ML Kit Vision API // ... } } }
If you don't use a camera library that gives you the image's rotation degree, you can calculate it from the device's rotation degree and the orientation of camera sensor in the device:
Kotlin
private val ORIENTATIONS = SparseIntArray () init { ORIENTATIONS . append ( Surface . ROTATION_0 , 0 ) ORIENTATIONS . append ( Surface . ROTATION_90 , 90 ) ORIENTATIONS . append ( Surface . ROTATION_180 , 180 ) ORIENTATIONS . append ( Surface . ROTATION_270 , 270 ) } /** * 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 , isFrontFacing : Boolean ): 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 ) // Get the device's sensor orientation. val cameraManager = activity . getSystemService ( CAMERA_SERVICE ) as CameraManager val sensorOrientation = cameraManager . getCameraCharacteristics ( cameraId ) . get ( CameraCharacteristics . SENSOR_ORIENTATION ) !! if ( isFrontFacing ) { rotationCompensation = ( sensorOrientation + rotationCompensation ) % 360 } else { // back-facing rotationCompensation = ( sensorOrientation - rotationCompensation + 360 ) % 360 } return rotationCompensation }
Java
private static final SparseIntArray ORIENTATIONS = new SparseIntArray (); static { ORIENTATIONS . append ( Surface . ROTATION_0 , 0 ); ORIENTATIONS . append ( Surface . ROTATION_90 , 90 ); ORIENTATIONS . append ( Surface . ROTATION_180 , 180 ); ORIENTATIONS . append ( Surface . ROTATION_270 , 270 ); } /** * 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 , boolean isFrontFacing ) 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 ); // Get the device's sensor orientation. CameraManager cameraManager = ( CameraManager ) activity . getSystemService ( CAMERA_SERVICE ); int sensorOrientation = cameraManager . getCameraCharacteristics ( cameraId ) . get ( CameraCharacteristics . SENSOR_ORIENTATION ); if ( isFrontFacing ) { rotationCompensation = ( sensorOrientation + rotationCompensation ) % 360 ; } else { // back-facing rotationCompensation = ( sensorOrientation - rotationCompensation + 360 ) % 360 ; } return rotationCompensation ; }
Then, pass the media.Image
object and the
rotation degree value to InputImage.fromMediaImage()
:
Kotlin
val image = InputImage . fromMediaImage ( mediaImage , rotation )
Java
InputImage image = InputImage . fromMediaImage ( mediaImage , rotation );
Using a file URI
To create an InputImage
object from a file URI, pass the app context and file URI to InputImage.fromFilePath()
. This is useful when you
use an ACTION_GET_CONTENT
intent to prompt the user to select
an image from their gallery app.
Kotlin
val image : InputImage try { image = InputImage . fromFilePath ( context , uri ) } catch ( e : IOException ) { e . printStackTrace () }
Java
InputImage image ; try { image = InputImage . fromFilePath ( context , uri ); } catch ( IOException e ) { e . printStackTrace (); }
Using a ByteBuffer
or ByteArray
To create an InputImage
object from a ByteBuffer
or a ByteArray
, first calculate the image
rotation degree as previously described for media.Image
input.
Then, create the InputImage
object with the buffer or array, together with image's
height, width, color encoding format, and rotation degree:
Kotlin
val image = InputImage . fromByteBuffer ( byteBuffer , /* image width */ 480 , /* image height */ 360 , rotationDegrees , InputImage . IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 ) // Or: val image = InputImage . fromByteArray ( byteArray , /* image width */ 480 , /* image height */ 360 , rotationDegrees , InputImage . IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 )
Java
InputImage image = InputImage . fromByteBuffer ( byteBuffer , /* image width */ 480 , /* image height */ 360 , rotationDegrees , InputImage . IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 ); // Or: InputImage image = InputImage . fromByteArray ( byteArray , /* image width */ 480 , /* image height */ 360 , rotation , InputImage . IMAGE_FORMAT_NV21 // or IMAGE_FORMAT_YV12 );
Using a Bitmap
To create an InputImage
object from a Bitmap
object, make the following declaration:
Kotlin
val image = InputImage . fromBitmap ( bitmap , 0 )
Java
InputImage image = InputImage . fromBitmap ( bitmap , rotationDegree );
The image is represented by a Bitmap
object together with rotation degrees.
3. Process the image
Pass the image to theprocess()
method: Kotlin
objectDetector . process ( image ) . addOnSuccessListener { detectedObjects -> // Task completed successfully // ... } . addOnFailureListener { e -> // Task failed with an exception // ... }
Java
objectDetector . process ( image ) . addOnSuccessListener ( new OnSuccessListener<List<DetectedObject> > () { @Override public void onSuccess ( List<DetectedObject> detectedObjects ) { // Task completed successfully // ... } }) . addOnFailureListener ( new OnFailureListener () { @Override public void onFailure ( @NonNull Exception e ) { // Task failed with an exception // ... } });
4. Get information about detected objects
If the call to process()
succeeds, a list of DetectedObject
s is passed to
the success listener.
Each DetectedObject
contains the following properties:
Rect
that indicates the position of the object in the
image.| Label description | The label's text description. It will be one of the String
constants defined in PredefinedCategory
. |
| Label index | The label's index among all the labels supported by the
classifier. It will be one of the integer constants defined
in PredefinedCategory
. |
| Label confidence | The confidence value of the object classification. |
Kotlin
for ( detectedObject in detectedObjects ) { val boundingBox = detectedObject . boundingBox val trackingId = detectedObject . trackingId for ( label in detectedObject . labels ) { val text = label . text if ( PredefinedCategory . FOOD == text ) { ... } val index = label . index if ( PredefinedCategory . FOOD_INDEX == index ) { ... } val confidence = label . confidence } }
Java
// The list of detected objects contains one item if multiple // object detection wasn't enabled. for ( DetectedObject detectedObject : detectedObjects ) { Rect boundingBox = detectedObject . getBoundingBox (); Integer trackingId = detectedObject . getTrackingId (); for ( Label label : detectedObject . getLabels ()) { String text = label . getText (); if ( PredefinedCategory . FOOD . equals ( text )) { ... } int index = label . getIndex (); if ( PredefinedCategory . FOOD_INDEX == index ) { ... } float confidence = label . getConfidence (); } }
Ensuring a great user experience
For the best user experience, follow these guidelines in your app:
- Successful object detection depends on the object's visual complexity. In order to be detected, objects with a small number of visual features might need to take up a larger part of the image. You should provide users with guidance on capturing input that works well with the kind of objects you want to detect.
- When you use classification, if you want to detect objects that don't fall cleanly into the supported categories, implement special handling for unknown objects.
Also, check out the ML Kit Material Design showcase app and the Material Design Patterns for machine learning-powered features collection.
Improving performance
If you want to use object detection in a real-time application, follow these guidelines to achieve the best framerates:
-
When you use streaming mode in a real-time application, don't use multiple object detection, as most devices won't be able to produce adequate framerates.
-
Disable classification if you don't need it.
- If you use the
Cameraorcamera2API, throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame. See theVisionProcessorBaseclass in the quickstart sample app for an example. - If you use the
CameraXAPI, be sure that backpressure strategy is set to its default valueImageAnalysis.STRATEGY_KEEP_ONLY_LATEST. This guarantees only one image will be delivered for analysis at a time. If more images are produced when the analyzer is busy, they will be dropped automatically and not queued for delivery. Once the image being analyzed is closed by calling ImageProxy.close(), the next latest image will be delivered. - If you use the output of the detector to overlay graphics on
the input image, first get the result from ML Kit, then render the image
and overlay in a single step. This renders to the display surface
only once for each input frame. See the
CameraSourcePreviewandGraphicOverlayclasses in the quickstart sample app for an example. - If you use the Camera2 API, capture images in
ImageFormat.YUV_420_888format. If you use the older Camera API, capture images inImageFormat.NV21format.
