Interactive image segmentation guide for Android

The MediaPipe Interactive Image Segmenter task takes a location in an image, estimates the boundaries of an object at that location, and returns the segmentation for the object as image data. These instructions show you how to use the Interactive Image Segmenter with Android apps. The code example described in these instructions is available on GitHub. For more information about the capabilities, models, and configuration options of this task, see the Overview.

Code example

The MediaPipe Tasks code example is a simple implementation of a Interactive Image Segmenter app for Android. The example works with images selected from the device gallery.

You can use the app as a starting point for your own Android app, or refer to it when modifying an existing app. The Interactive Image Segmenter example code is hosted on GitHub.

Download the code

The following instructions show you how to create a local copy of the example code using the git command line tool.

To download the example code:

  1. Clone the git repository using the following command:
    git clone
  2. Optionally, configure your git instance to use sparse checkout, so you have only the files for the Interactive Image Segmenter example app:
    cd mediapipe
    git sparse-checkout init --cone
    git sparse-checkout set examples/interactive_segmentation/android

After creating a local version of the example code, you can import the project into Android Studio and run the app. For instructions, see the Setup Guide for Android.

Key components

The following files contain the crucial code for this image segmentation example application:


This section describes key steps for setting up your development environment and code projects to use Interactive Image Segmenter. For general information on setting up your development environment for using MediaPipe tasks, including platform version requirements, see the Setup guide for Android.


Interactive Image Segmenter uses the library. Add this dependency to the build.gradle file of your Android app development project. Import the required dependencies with the following code:

dependencies {
    implementation ''


The MediaPipe Interactive Image Segmenter task requires a trained model that is compatible with this task. For more information on available trained models for Interactive Image Segmenter, see the task overview Models section.

Select and download the model, and then store it within your project directory:


Use the BaseOptions.Builder.setModelAssetPath() method to specify the path used by the model. This method is shown in the code example in the next section.

In the Interactive Image Segmenter example code, the model is defined in the InteractiveSegmenterHelper.kt class in the setupInteractiveSegmenter() function.

Create the task

You can use the createFromOptions function to create the task. The createFromOptions function accepts configuration options including mask output types. For more information on configuration options, see Configuration Overview.

InteractiveSegmenterOptions options =
    .setResultListener((result, inputImage) -> {
         // Process the segmentation result here.
    .setErrorListener((result, inputImage) -> {
         // Process the segmentation errors here.
    })    .build();
interactivesegmenter = InteractiveSegmenter.createFromOptions(context, options);

For a more detailed example of setting up this task, see the InteractiveSegmenterHelper class setupInteractiveSegmenter() function.

Configuration options

This task has the following configuration options for Android apps:

Option Name Description Value Range Default Value
outputCategoryMask If set to True, the output includes a segmentation mask as a uint8 image, where each pixel value indicates if the pixel is part of the object located at the area of interest. {True, False} False
outputConfidenceMasks If set to True, the output includes a segmentation mask as a float value image, where each float value represents the confidence that the pixel is part of the object located at the area of interest. {True, False} True
displayNamesLocale Sets the language of labels to use for display names provided in the metadata of the task's model, if available. Default is en for English. You can add localized labels to the metadata of a custom model using the TensorFlow Lite Metadata Writer API Locale code en
errorListener Sets an optional error listener. N/A Not set

Prepare data

Interactive Image Segmenter works with images, and the task handles the data input preprocessing, including resizing, rotation and value normalization. You need to convert the input image to a object before passing it to the task.


// Load an image on the user’s device as a Bitmap object using BitmapFactory.

// Convert an Android’s Bitmap object to a MediaPipe’s Image object.
Image mpImage = new BitmapImageBuilder(bitmap).build();

In the Interactive Image Segmenter example code, the data preparation is handled in the InteractiveSegmenterHelper class by the segment() function.

Run the task

Call the segment function to run the prediction and generate segments. The Interactive Image Segmenter task returns the identified segment regions within the input image.

RegionOfInterest roi = RegionOfInterest.create(
        normX * it.width,
        normY * it.height

ImageSegmenterResult segmenterResult = interactivesegmenter.segment(image, roi);

In the Interactive Image Segmenter example code, the segment functions are defined in the InteractiveSegmenterHelper.kt file.

Handle and display results

Upon running inference, the Interactive Image Segmenter task returns an ImageSegmenterResult object which contains the results of the segmentation task. The content of the output may include a category mask, confidence mask, or both, depending on what you set when you configured the task.

The following sections further explain the output data from this task:

Category mask

The following images show a visualization of the task output for a category value mask with a point area of interest indicated. Each pixel is a uint8 value indicating if the pixel is part of the object located at the area of interest. The black and white circle on the second image indicates the selected area of interest.

Original image and category mask output. Source image from the Pascal VOC 2012 dataset.

Confidence mask

The output for a confidence mask contains float values between [0, 1] for each image input channel. Higher values indicate a higher confidence that the image pixel is part of the object located at the area of interest.