Image segmenters predict whether each pixel of an image is associated with a
certain class. This is in contrast to object detection, which detects objects in
rectangular regions, and image classification, which classifies the overall
image. See the [image segmentation overview
example](https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation)
for more information about image segmenters.

Use the Task Library `ImageSegmenter` API to deploy your custom image segmenters
or pretrained ones into your mobile apps.

## Key features of the ImageSegmenter API

- Input image processing, including rotation, resizing, and color space
  conversion.

- Label map locale.

- Two output types, category mask and confidence masks.

- Colored label for display purpose.

## Supported image segmenter models

The following models are guaranteed to be compatible with the `ImageSegmenter`
API.

- The [pretrained image segmentation models on TensorFlow
  Hub](https://www.kaggle.com/models?id=72,287,103,292,54,283).

- Custom models that meet the [model compatibility
  requirements](https://developers.google.com/edge/litert/libraries/task_library/image_segmenter#model-compatibility-requirements).

## Run inference in Java

See the [Image Segmentation reference
app](https://github.com/tensorflow/examples/tree/master/lite/examples/image_segmentation/android/)
for an example of how to use `ImageSegmenter` in an Android app.

### Step 1: Import Gradle dependency and other settings

Copy the `.tflite` model file to the assets directory of the Android module
where the model will be run. Specify that the file should not be compressed, and
add the TensorFlow Lite library to the module's `build.gradle` file:

    android {
        // Other settings

        // Specify tflite file should not be compressed for the app apk
        aaptOptions {
            noCompress "tflite"
        }
    }

    dependencies {
        // Other dependencies

        // Import the Task Vision Library dependency
        implementation 'org.tensorflow:tensorflow-lite-task-vision'
        // Import the GPU delegate plugin Library for GPU inference
        implementation 'org.tensorflow:tensorflow-lite-gpu-delegate-plugin'
    }

> [!NOTE]
> **Note:** starting from version 4.1 of the Android Gradle plugin, .tflite will be added to the noCompress list by default and the aaptOptions above is not needed anymore.

### Step 2: Using the model

    // Initialization
    ImageSegmenterOptions options =
        ImageSegmenterOptions.builder()
            .setBaseOptions(BaseOptions.builder().useGpu().build())
            .setOutputType(OutputType.CONFIDENCE_MASK)
            .build();
    ImageSegmenter imageSegmenter =
        ImageSegmenter.createFromFileAndOptions(context, modelFile, options);

    // Run inference
    List<Segmentation> results = imageSegmenter.segment(image);

See the [source code and
javadoc](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/java/src/java/org/tensorflow/lite/task/vision/segmenter/ImageSegmenter.java)
for more options to configure `ImageSegmenter`.

## Run inference in iOS

### Step 1: Install the dependencies

The Task Library supports installation using CocoaPods. Make sure that CocoaPods
is installed on your system. Please see the [CocoaPods installation
guide](https://guides.cocoapods.org/using/getting-started.html#getting-started)
for instructions.

Please see the [CocoaPods
guide](https://guides.cocoapods.org/using/using-cocoapods.html) for details on
adding pods to an Xcode project.

Add the `TensorFlowLiteTaskVision` pod in the Podfile.

    target 'MyAppWithTaskAPI' do
      use_frameworks!
      pod 'TensorFlowLiteTaskVision'
    end

Make sure that the `.tflite` model you will be using for inference is present in
your app bundle.

### Step 2: Using the model

#### Swift

    // Imports
    import TensorFlowLiteTaskVision

    // Initialization
    guard let modelPath = Bundle.main.path(forResource: "deeplabv3",
                                                ofType: "tflite") else { return }

    let options = ImageSegmenterOptions(modelPath: modelPath)

    // Configure any additional options:
    // options.outputType = OutputType.confidenceMasks

    let segmenter = try ImageSegmenter.segmenter(options: options)

    // Convert the input image to MLImage.
    // There are other sources for MLImage. For more details, please see:
    // https://developers.google.com/ml-kit/reference/ios/mlimage/api/reference/Classes/GMLImage
    guard let image = UIImage (named: "plane.jpg"), let mlImage = MLImage(image: image) else { return }

    // Run inference
    let segmentationResult = try segmenter.segment(mlImage: mlImage)

#### Objective-C

    // Imports
    #import <TensorFlowLiteTaskVision/TensorFlowLiteTaskVision.h>

    // Initialization
    NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"deeplabv3" ofType:@"tflite"];

    TFLImageSegmenterOptions *options =
        [[TFLImageSegmenterOptions alloc] initWithModelPath:modelPath];

    // Configure any additional options:
    // options.outputType = TFLOutputTypeConfidenceMasks;

    TFLImageSegmenter *segmenter = [TFLImageSegmenter imageSegmenterWithOptions:options
                                                                          error:nil];

    // Convert the input image to MLImage.
    UIImage *image = [UIImage imageNamed:@"plane.jpg"];

    // There are other sources for GMLImage. For more details, please see:
    // https://developers.google.com/ml-kit/reference/ios/mlimage/api/reference/Classes/GMLImage
    GMLImage *gmlImage = [[GMLImage alloc] initWithImage:image];

    // Run inference
    TFLSegmentationResult *segmentationResult =
        [segmenter segmentWithGMLImage:gmlImage error:nil];

See the [source
code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/ios/task/vision/sources/TFLImageSegmenter.h)
for more options to configure `TFLImageSegmenter`.

## Run inference in Python

### Step 1: Install the pip package

    pip install tflite-support

### Step 2: Using the model

    # Imports
    from tflite_support.task import vision
    from tflite_support.task import core
    from tflite_support.task import processor

    # Initialization
    base_options = core.BaseOptions(file_name=model_path)
    segmentation_options = processor.SegmentationOptions(
        output_type=processor.SegmentationOptions.output_type.CATEGORY_MASK)
    options = vision.ImageSegmenterOptions(base_options=base_options, segmentation_options=segmentation_options)
    segmenter = vision.ImageSegmenter.create_from_options(options)

    # Alternatively, you can create an image segmenter in the following manner:
    # segmenter = vision.ImageSegmenter.create_from_file(model_path)

    # Run inference
    image_file = vision.TensorImage.create_from_file(image_path)
    segmentation_result = segmenter.segment(image_file)

See the [source
code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/python/task/vision/image_segmenter.py)
for more options to configure `ImageSegmenter`.

## Run inference in C++

    // Initialization
    ImageSegmenterOptions options;
    options.mutable_base_options()->mutable_model_file()->set_file_name(model_path);
    std::unique_ptr<ImageSegmenter> image_segmenter = ImageSegmenter::CreateFromOptions(options).value();

    // Create input frame_buffer from your inputs, `image_data` and `image_dimension`.
    // See more information here: tensorflow_lite_support/cc/task/vision/utils/frame_buffer_common_utils.h
    std::unique_ptr<FrameBuffer> frame_buffer = CreateFromRgbRawBuffer(
          image_data, image_dimension);

    // Run inference
    const SegmentationResult result = image_segmenter->Segment(*frame_buffer).value();

See the [source
code](https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/cc/task/vision/image_segmenter.h)
for more options to configure `ImageSegmenter`.

## Example results

Here is an example of the segmentation results of
[deeplab_v3](https://www.kaggle.com/models/tensorflow/deeplabv3/tfLite/metadata/1),
a generic segmentation model available on TensorFlow Hub.

![plane](https://developers.google.com/static/edge/litert/libraries/task_library/images/plane.jpg)

    Color Legend:
     (r: 000, g: 000, b: 000):
      index       : 0
      class name  : background
     (r: 128, g: 000, b: 000):
      index       : 1
      class name  : aeroplane

    # (omitting multiple lines for conciseness) ...

     (r: 128, g: 192, b: 000):
      index       : 19
      class name  : train
     (r: 000, g: 064, b: 128):
      index       : 20
      class name  : tv
    Tip: use a color picker on the output PNG file to inspect the output mask with
    this legend.

The segmentation category mask should looks like:

![segmentation-output](https://developers.google.com/static/edge/litert/libraries/task_library/images/segmentation-output.png)

Try out the simple [CLI demo tool for
ImageSegmenter](https://github.com/tensorflow/tflite-support/tree/master/tensorflow_lite_support/examples/task/vision/desktop#image-segmenter)
with your own model and test data.

## Model compatibility requirements

The `ImageSegmenter` API expects a TFLite model with mandatory [TFLite Model
Metadata](https://developers.google.com/edge/litert/conversion/tensorflow/metadata). See examples of creating
metadata for image segmenters using the [TensorFlow Lite Metadata Writer
API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial#image_segmenters).

- Input image tensor (kTfLiteUInt8/kTfLiteFloat32)

  - image input of size `[batch x height x width x channels]`.
  - batch inference is not supported (`batch` is required to be 1).
  - only RGB inputs are supported (`channels` is required to be 3).
  - if type is kTfLiteFloat32, NormalizationOptions are required to be attached to the metadata for input normalization.
- Output masks tensor: (kTfLiteUInt8/kTfLiteFloat32)

  - tensor of size `[batch x mask_height x mask_width x num_classes]`, where `batch` is required to be 1, `mask_width` and `mask_height` are the dimensions of the segmentation masks produced by the model, and `num_classes` is the number of classes supported by the model.
  - optional (but recommended) label map(s) can be attached as AssociatedFile-s with type TENSOR_AXIS_LABELS, containing one label per line. The first such AssociatedFile (if any) is used to fill the `label` field (named as `class_name` in C++) of the results. The `display_name` field is filled from the AssociatedFile (if any) whose locale matches the `display_names_locale` field of the `ImageSegmenterOptions` used at creation time ("en" by default, i.e. English). If none of these are available, only the `index` field of the results will be filled.