ImageLabeling

AI-generated Key Takeaways

ImageLabeling is the entry point for image labeling tasks in ML Kit, providing access to an ImageLabeler instance.
You create an ImageLabeler using ImageLabeling.getClient() with specified options.
To detect labels, you process an InputImage (created from a Bitmap, ByteBuffer, etc.) with the ImageLabeler.
Remember to call ImageLabeler.close() when finished to release resources.

public class ImageLabeling extends Object

Entry point to get an ImageLabeler for finding ImageLabels in a supplied image.

An image labeler is created via getClient(ImageLabelerOptionsBase).

Example:

 ImageLabeler imageLabeler = ImageLabeling.getClient(options);

To perform label detection in an image, you first need to create an instance of InputImage from a Bitmap, ByteBuffer, etc. See InputImage documentation for more details. For example, the code below creates an InputImage from a Bitmap.

 InputImage image = InputImage.fromBitmap(bitmap, rotationDegrees);

Then the code below can detect labels in the supplied InputImage.

 Task<List<ImageLabel>> task = imageLabeler.process(image);
 task.addOnSuccessListener(...).addOnFailureListener(...);

Public Method Summary

static ImageLabeler

getClient(ImageLabelerOptionsBase options)

Gets an new instance of ImageLabeler that labels a supplied image.

Inherited Method Summary

From class java.lang.Object

Object	clone()
boolean	equals(Object arg0)
void	finalize()
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

Public Methods

public static ImageLabeler getClient (ImageLabelerOptionsBase options)