Integrate image segmenters

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 introduction of 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 model 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.

• Custom models that meet the model compatibility requirements.

Run inference in Java

See the Image Segmentation reference app 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:0.0.0-nightly'
}

Step 2: Using the model

// Initialization
ImageSegmenterOptions options = ImageSegmenterOptions.builder().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 for more options to configure ImageSegmenter.

Run inference in C++

Note: we are working on improving the usability of the C++ Task Library, such as providing prebuilt binaries and creating user-friendly workflows to build from source code. The C++ API may be subject to change.

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

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

See the source code for more options to configure ImageSegmenter.

Example results

Here is an example of the segmentation results of deeplab_v3, a generic segmentation model available on TensorFlow Hub.

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:

Try out the simple CLI demo tool for ImageSegmenter with your own model and test data.

Model compatibility requirements

The ImageSegmenter API expects a TFLite model with mandatory TFLite Model Metadata.

• 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.