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.

<br />

This API uses an unbundled library that must be downloaded before use. See [this guide](https://developers.google.com/ml-kit/tips/installation-paths.md.txt) for more information.

## Try it out

- Play around with [the sample app](https://github.com/googlesamples/mlkit/tree/master/android/vision-quickstart) to see an example usage of this API.
- See the [Material Design showcase
  app](https://github.com/googlesamples/mlkit/tree/master/android/material-showcase) for an end-to-end implementation of this API.

## Before you begin

> [!NOTE]
> This API requires Android API level 23 or above. Make sure that your app's build file uses a `minSdkVersion` value of 23 or higher.

1. In your project-level `build.gradle` file, make sure to include Google's Maven repository in both your `buildscript` and `allprojects` sections.
2. 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.

1. Configure the object detector for your use case with an
   `ObjectDetectorOptions` 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 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, 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, 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

   ```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

   ```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();
   ```
2. Get an instance of `ObjectDetector`:

   ### Kotlin

   ```kotlin
   val objectDetector = ObjectDetection.getClient(options)
   ```

   ### Java

   ```java
   ObjectDetector objectDetector = ObjectDetection.getClient(options);
   ```

## 2. Prepare the input image

To detect and track objects, pass images to the `ObjectDetector` instance's `process()` method.

<br />

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`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
object from different sources, each is explained below.

### Using a `media.Image`


To create an [`InputImage`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
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](https://developer.android.com/training/camerax) library, the `OnImageCapturedListener` and
`ImageAnalysis.Analyzer` classes calculate the rotation value
for you.

### Kotlin

```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

```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

```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

```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

```kotlin
val image = InputImage.fromMediaImage(mediaImage, rotation)
```

### Java

```java
InputImage image = InputImage.fromMediaImage(mediaImage, rotation);
```

### Using a file URI

To create an [`InputImage`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
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

```kotlin
val image: InputImage
try {
    image = InputImage.fromFilePath(context, uri)
} catch (e: IOException) {
    e.printStackTrace()
}
```

### Java

```java
InputImage image;
try {
    image = InputImage.fromFilePath(context, uri);
} catch (IOException e) {
    e.printStackTrace();
}
```

### Using a `ByteBuffer` or `ByteArray`

To create an [`InputImage`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
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

```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

```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`](https://developers.google.com/android/reference/com/google/mlkit/vision/common/InputImage.md.txt)
object from a `Bitmap` object, make the following declaration:

### Kotlin

```kotlin
val image = InputImage.fromBitmap(bitmap, 0)
```

### Java

```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 the `process()` method:

<br />

### Kotlin

```kotlin
objectDetector.process(image)
    .addOnSuccessListener { detectedObjects ->
        // Task completed successfully
        // ...
    }
    .addOnFailureListener { e ->
        // Task failed with an exception
        // ...
    }
```

### Java

```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
                // ...
            }
        });
```
Note: If you are using the [`CameraX`](https://developer.android.com/jetpack/androidx/releases/camera) API, make sure to close the `ImageProxy` when finish using it, e.g., by adding an `OnCompleteListener` to the `Task` returned from the `process` method. See the [`VisionProcessorBase`](https://github.com/googlesamples/mlkit/blob/e949654bef09e97b237c7692349c52ee06e9fd9e/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/java/VisionProcessorBase.java#L178-L181) class in the quickstart sample app for an example.

## 4. Get information about detected objects

If the call to `process()` succeeds, a list of `DetectedObject`s is passed to
the success listener.

> [!NOTE]
> **Note:** In streaming mode, the object detector might need to process 30 or more frames, depending on device performance, before it detects the first object.

Each `DetectedObject` contains the following properties:
Bounding box A `Rect` that indicates the position of the object in the image. Tracking ID An integer that identifies the object across images. Null in SINGLE_IMAGE_MODE. Labels 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

```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

```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](https://github.com/googlesamples/mlkit/tree/master/android/material-showcase) and the
Material Design
[Patterns for machine learning-powered features](https://material.io/collections/machine-learning/) 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 [`Camera`](https://developer.android.com/reference/android/hardware/Camera) or [`camera2`](https://developer.android.com/reference/android/hardware/camera2/package-summary) API, throttle calls to the detector. If a new video frame becomes available while the detector is running, drop the frame. See the [`VisionProcessorBase`](https://github.com/googlesamples/mlkit/blob/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/kotlin/VisionProcessorBase.kt) class in the quickstart sample app for an example.
- If you use the [`CameraX`](https://developer.android.com/jetpack/androidx/releases/camera) API, be sure that backpressure strategy is set to its default value [`ImageAnalysis.STRATEGY_KEEP_ONLY_LATEST`](https://developer.android.com/reference/androidx/camera/core/ImageAnalysis#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 [`CameraSourcePreview`](https://github.com/googlesamples/mlkit/blob/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/CameraSourcePreview.java) and [`GraphicOverlay`](https://github.com/googlesamples/mlkit/blob/master/android/vision-quickstart/app/src/main/java/com/google/mlkit/vision/demo/GraphicOverlay.java) classes in the quickstart sample app for an example.
- 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.