Android でカスタム分類モデルを使用してオブジェクトを検出、追跡、分類する

// Live detection and tracking
val customObjectDetectorOptions =
        CustomObjectDetectorOptions.Builder(localModel)
        .setDetectorMode(CustomObjectDetectorOptions.STREAM_MODE)
        .enableClassification()
        .setClassificationConfidenceThreshold(0.5f)
        .setMaxPerObjectLabelCount(3)
        .build()

// Multiple object detection in static images
val customObjectDetectorOptions =
        CustomObjectDetectorOptions.Builder(localModel)
        .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE)
        .enableMultipleObjects()
        .enableClassification()
        .setClassificationConfidenceThreshold(0.5f)
        .setMaxPerObjectLabelCount(3)
        .build()

val objectDetector =
        ObjectDetection.getClient(customObjectDetectorOptions)

// Live detection and tracking
CustomObjectDetectorOptions customObjectDetectorOptions =
        new CustomObjectDetectorOptions.Builder(localModel)
                .setDetectorMode(CustomObjectDetectorOptions.STREAM_MODE)
                .enableClassification()
                .setClassificationConfidenceThreshold(0.5f)
                .setMaxPerObjectLabelCount(3)
                .build();

// Multiple object detection in static images
CustomObjectDetectorOptions customObjectDetectorOptions =
        new CustomObjectDetectorOptions.Builder(localModel)
                .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE)
                .enableMultipleObjects()
                .enableClassification()
                .setClassificationConfidenceThreshold(0.5f)
                .setMaxPerObjectLabelCount(3)
                .build();

ObjectDetector objectDetector =
    ObjectDetection.getClient(customObjectDetectorOptions);

val modelFile = File(context.cacheDir, "my_remote_model.tflite")

val model = if (modelFile.exists()) {
    // Use the downloaded model if available
    LocalModel.Builder().setAbsoluteFilePath(modelFile.absolutePath).build()
} else {
    // Fall back to the bundled model
    LocalModel.Builder().setAssetFilePath("model.tflite").build()
}

val customObjectDetectorOptions =
        CustomObjectDetectorOptions.Builder(model)
        .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE)
        .enableClassification()
        .setClassificationConfidenceThreshold(0.5f)
        .setMaxPerObjectLabelCount(3)
        .build()

val objectDetector =
        ObjectDetection.getClient(customObjectDetectorOptions)

File modelFile = new File(context.getCacheDir(), "my_remote_model.tflite");

LocalModel model;
if (modelFile.exists()) {
    // Use the downloaded model if available
    model = new LocalModel.Builder().setAbsoluteFilePath(modelFile.getAbsolutePath()).build();
} else {
    // Fall back to the bundled model
    model = new LocalModel.Builder().setAssetFilePath("model.tflite").build();
}

CustomObjectDetectorOptions customObjectDetectorOptions =
        new CustomObjectDetectorOptions.Builder(model)
                .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE)
                .enableClassification()
                .setClassificationConfidenceThreshold(0.5f)
                .setMaxPerObjectLabelCount(3)
                .build();

ObjectDetector objectDetector =
        ObjectDetection.getClient(customObjectDetectorOptions);

val localFile = File(context.cacheDir, "my_remote_model.tflite")
if (localFile.exists()) {
    // Model is already cached, initialize immediately
    initializeDetector(localFile)
} else {
    // Model is not yet available, show loading UI and start download
    showLoadingUI()
    val storage = Firebase.storage
    val modelRef = storage.getReferenceFromUrl("gs://YOUR_BUCKET/path/to/model.tflite")
    modelRef.getFile(localFile)
        .addOnSuccessListener {
            // Download complete, initialize the detector
            hideLoadingUI()
            initializeDetector(localFile)
        }
        .addOnFailureListener {
            // Handle download error
            showErrorUI()
        }
}

private fun initializeDetector(modelFile: File) {
    val localModel = LocalModel.Builder().setAbsoluteFilePath(modelFile.absolutePath).build()
    val customObjectDetectorOptions = CustomObjectDetectorOptions.Builder(localModel)
            .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE)
            .enableClassification()
            .build()
    val objectDetector = ObjectDetection.getClient(customObjectDetectorOptions)
    // Enable ML-related UI features here
    enableMLFeatures(objectDetector)
}

File localFile = new File(context.getCacheDir(), "my_remote_model.tflite");
if (localFile.exists()) {
    // Model is already cached, initialize immediately
    initializeDetector(localFile);
} else {
    // Model is not yet available, show loading UI and start download
    showLoadingUI();
    FirebaseStorage storage = FirebaseStorage.getInstance();
    StorageReference modelRef = storage.getReferenceFromUrl("gs://YOUR_BUCKET/path/to/model.tflite");
    modelRef.getFile(localFile)
        .addOnSuccessListener(new OnSuccessListener<FileDownloadTask.TaskSnapshot>() {
            @Override
            public void onSuccess(FileDownloadTask.TaskSnapshot taskSnapshot) {
                // Download complete, initialize the detector
                hideLoadingUI();
                initializeDetector(localFile);
            }
        })
        .addOnFailureListener(new OnFailureListener() {
            @Override
            public void onFailure(@NonNull Exception exception) {
                // Handle download error
                showErrorUI();
            }
        });
}

private void initializeDetector(File modelFile) {
    LocalModel localModel = new LocalModel.Builder().setAbsoluteFilePath(modelFile.getAbsolutePath()).build();
    CustomObjectDetectorOptions customObjectDetectorOptions =
            new CustomObjectDetectorOptions.Builder(localModel)
                    .setDetectorMode(CustomObjectDetectorOptions.SINGLE_IMAGE_MODE)
                    .enableClassification()
                    .build();
    ObjectDetector objectDetector = ObjectDetection.getClient(customObjectDetectorOptions);
    // Enable ML-related UI features here
    enableMLFeatures(objectDetector);
}

objectDetector
    .process(image)
    .addOnFailureListener(e -> {...})
    .addOnSuccessListener(results -> {
        for (detectedObject in results) {
          // ...
        }
    });

objectDetector
    .process(image)
    .addOnFailureListener(e -> {...})
    .addOnSuccessListener(results -> {
        for (DetectedObject detectedObject : results) {
          // ...
        }
    });

// The list of detected objects contains one item if multiple
// object detection wasn't enabled.
for (detectedObject in results) {
    val boundingBox = detectedObject.boundingBox
    val trackingId = detectedObject.trackingId
    for (label in detectedObject.labels) {
      val text = label.text
      val index = label.index
      val confidence = label.confidence
    }
}

// The list of detected objects contains one item if multiple
// object detection wasn't enabled.
for (DetectedObject detectedObject : results) {
  Rect boundingBox = detectedObject.getBoundingBox();
  Integer trackingId = detectedObject.getTrackingId();
  for (Label label : detectedObject.getLabels()) {
    String text = label.getText();
    int index = label.getIndex();
    float confidence = label.getConfidence();
  }
}