Detect objects in images with an AutoML-trained model on Android

After you train your own model using AutoML Vision Edge, you can use it in your app to detect objects in images.

There are two ways to integrate models trained from AutoML Vision Edge: You can bundle the model by putting it inside your app’s asset folder, or you can dynamically download it from Firebase.

Before you begin

1. If you want to download a model, make sure you add Firebase to your Android project, if you have not already done so. This is not required when you bundle the model.

2. Add the dependencies for the TensorFlow Lite Task library to your module's app-level gradle file, which is usually app/build.gradle:

   For bundling a model with your app:

   dependencies {
     // ...
     // Object detection with a bundled Auto ML model
     implementation 'org.tensorflow:tensorflow-lite-task-vision:0.0.0-nightly-SNAPSHOT'
   }
   

   For dynamically downloading a model from Firebase, also add the Firebase ML dependency:

   dependencies {
     // ...
     // Object detection with an Auto ML model deployed to Firebase
     implementation platform('com.google.firebase:firebase-bom:26.1.1')
     implementation 'com.google.firebase:firebase-ml-model-interpreter'
   
     implementation 'org.tensorflow:tensorflow-lite-task-vision:0.0.0-nightly'
   }
   

1. Load the model

Configure a local model source

To bundle the model with your app:

1. Extract the model from the zip archive you downloaded from the Google Cloud console.
2. Include your model in your app package:
   1. If you don't have an assets folder in your project, create one by right-clicking the app/ folder, then clicking New > Folder > Assets Folder.
   2. Copy your tflite model file with embedded metadata to the assets folder.

3. Add the following to your app's build.gradle file to ensure Gradle doesn’t compress the model file when building the app:

   android {
       // ...
       aaptOptions {
           noCompress "tflite"
       }
   }
   

   The model file will be included in the app package and available as a raw asset.

Configure a Firebase-hosted model source

To use the remotely-hosted model, create a RemoteModel object, specifying the name you assigned the model when you published it:

// Specify the name you assigned when you deployed the model.
FirebaseCustomRemoteModel remoteModel =
        new FirebaseCustomRemoteModel.Builder("your_model").build();

// Specify the name you assigned when you deployed the model.
val remoteModel =
    FirebaseCustomRemoteModel.Builder("your_model_name").build()

Then, start the model download task, specifying the conditions under which you want to allow downloading. If the model isn't on the device, or if a newer version of the model is available, the task will asynchronously download the model from Firebase:

DownloadConditions downloadConditions = new DownloadConditions.Builder()
        .requireWifi()
        .build();
RemoteModelManager.getInstance().download(remoteModel, downloadConditions)
        .addOnSuccessListener(new OnSuccessListener<Void>() {
            @Override
            public void onSuccess(@NonNull Task<Void> task) {
                // Success.
            }
        });

val downloadConditions = DownloadConditions.Builder()
    .requireWifi()
    .build()
RemoteModelManager.getInstance().download(remoteModel, downloadConditions)
    .addOnSuccessListener {
        // Success.
    }

Many apps start the download task in their initialization code, but you can do so at any point before you need to use the model.

Create an object detector from your model

After you configure your model sources, create a ObjectDetector object from one of them.

If you only have a locally-bundled model, just create an object detector from your model file and configure the confidence score threshold you want to require (see Evaluate your model):

// Initialization
ObjectDetectorOptions options = ObjectDetectorOptions.builder()
    .setScoreThreshold(0)  // Evaluate your model in the Google Cloud console
                           // to determine an appropriate value.
    .build();
ObjectDetector objectDetector = ObjectDetector.createFromFileAndOptions(context, modelFile, options);

// Initialization
val options = ObjectDetectorOptions.builder()
    .setScoreThreshold(0)  // Evaluate your model in the Google Cloud console
                           // to determine an appropriate value.
    .build()
val objectDetector = ObjectDetector.createFromFileAndOptions(context, modelFile, options)

If you have a remotely-hosted model, you will have to check that it has been downloaded before you run it. You can check the status of the model download task using the model manager's isModelDownloaded() method.

Although you only have to confirm this before running the object detector, if you have both a remotely-hosted model and a locally-bundled model, it might make sense to perform this check when instantiating the object detector: create an object detector from the remote model if it's been downloaded, and from the local model otherwise.

FirebaseModelManager.getInstance().isModelDownloaded(remoteModel)
        .addOnSuccessListener(new OnSuccessListener<Boolean>() {
            @Override
            public void onSuccess(Boolean isDownloaded) {
            }
        });

FirebaseModelManager.getInstance().isModelDownloaded(remoteModel)
        .addOnSuccessListener { success ->

        }

If you only have a remotely-hosted model, you should disable model-related functionality—for example, grey-out or hide part of your UI—until you confirm the model has been downloaded. You can do so by attaching a listener to the model manager's download() method.

Once you know your model has been downloaded, create an object detector from the model file:

FirebaseModelManager.getInstance().getLatestModelFile(remoteModel)
        .addOnCompleteListener(new OnCompleteListener<File>() {
            @Override
            public void onComplete(@NonNull Task<File> task) {
                File modelFile = task.getResult();
                if (modelFile != null) {
                    ObjectDetectorOptions options = ObjectDetectorOptions.builder()
                            .setScoreThreshold(0)
                            .build();
                    objectDetector = ObjectDetector.createFromFileAndOptions(
                            getApplicationContext(), modelFile.getPath(), options);
                }
            }
        });

FirebaseModelManager.getInstance().getLatestModelFile(remoteModel)
        .addOnSuccessListener { modelFile ->
            val options = ObjectDetectorOptions.builder()
                    .setScoreThreshold(0f)
                    .build()
            objectDetector = ObjectDetector.createFromFileAndOptions(
                    applicationContext, modelFile.path, options)
        }

2. Prepare the input image

Then, for each image you want to label, create a TensorImage object from your image. You can create a TensorImage object from a Bitmap using the fromBitmap method:

TensorImage image = TensorImage.fromBitmap(bitmap);

val image = TensorImage.fromBitmap(bitmap)

If your image data isn't in a Bitmap, you can load a pixel array as shown in the TensorFlow Lite docs.

3. Run the object detector

To detect objects in an image, pass the TensorImage object to the ObjectDetector's detect() method.

List<Detection> results = objectDetector.detect(image);

val results = objectDetector.detect(image)

4. Get information about labeled objects

If the object detection operation succeeds, it returns a list of Detection objects. Each Detection object represents something that was detected in the image. You can get each object's bounding box and its labels.

For example:

for (Detection result : results) {
    RectF bounds = result.getBoundingBox();
    List<Category> labels = result.getCategories();
}

for (result in results) {
    val bounds = result.getBoundingBox()
    val labels = result.getCategories()
}

Tips to improve real-time performance

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. See the VisionProcessorBase class in the quickstart sample app for an example.
• If you are using the output of the image labeler to overlay graphics on the input image, first get the result, 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. See the CameraSourcePreview and GraphicOverlay 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.