Segmentation

RunAnywhere.segmentation.segment returns a per-pixel class mask.

final image = ImageInput.rawRgb(rgbBytes, width, height);
final result = await RunAnywhere.segmentation.segment(image);

for (final info in result.classes) {
  print('${info.label}: ${(info.fraction * 100).toStringAsFixed(1)}%');
}

Future<SegmentationResult> segment(
  ImageInput image, {
  SegmentationOptions? options,
});

The input must be ImageInput.rawRgb(data, width, height). Packed RGB8 is the only layout the segmentation ABI accepts, so ImageInput.file and ImageInput.bytes throw here. Decode the image yourself first — see Preparing raw RGB.

It also throws SDKException when the SDK is not initialized or no segmentation model is loaded. segment does not auto-load: load the model yourself with RunAnywhere.models.load(id).

SegmentationResult

Field	Type	Notes
`classMask`	`Uint16List`	One class id per pixel, row-major at source size
`width`	`int`	Mask width
`height`	`int`	Mask height
`classes`	`List<ClassInfo>`	Classes present in the mask
`diagnosticImage`	`Uint8List?`	Class-colour RGBA overlay, when requested

ClassInfo carries classId (the value used in the mask), label, pixelCount, and fraction, the share of the image the class covers. Read a single pixel’s class with classMask[y * width + x].

SegmentationOptions

final result = await RunAnywhere.segmentation.segment(
  image,
  options: const SegmentationOptions(includeDiagnosticImage: true),
);

final overlay = result.diagnosticImage;
if (overlay != null) {
  // overlay is RGBA at result.width x result.height
}

includeDiagnosticImage defaults to false. When true you also get a deterministic class-colour RGBA image, which is useful for debugging but costs extra bytes across the bridge.

Counting a class

final result = await RunAnywhere.segmentation.segment(image);

for (final info in result.classes) {
  if (info.label == 'person') {
    print('${info.pixelCount} person pixels');
  }
}

Preparing raw RGB

Any decoder works as long as you end up with three bytes per pixel and no padding.

import 'package:image/image.dart' as img;

ImageInput toRawRgb(Uint8List encoded) {
  final decoded = img.decodeImage(encoded)!;
  final rgb = Uint8List(decoded.width * decoded.height * 3);
  var i = 0;
  for (final pixel in decoded) {
    rgb[i++] = pixel.r.toInt();
    rgb[i++] = pixel.g.toInt();
    rgb[i++] = pixel.b.toInt();
  }
  return ImageInput.rawRgb(rgb, decoded.width, decoded.height);
}

Models

Segmentation models register under MODEL_CATEGORY_SEMANTIC_SEGMENTATION.

await RunAnywhere.models.register(
  ModelRegistration.url(
    id: 'deeplabv3-mobilenet',
    name: 'DeepLabV3 MobileNet',
    url: 'https://example.com/deeplabv3-mobilenet.onnx',
    framework: InferenceFramework.INFERENCE_FRAMEWORK_ONNX,
    category: ModelCategory.MODEL_CATEGORY_SEMANTIC_SEGMENTATION,
    memoryRequirementBytes: 45000000,
  ),
);

await RunAnywhere.models.load('deeplabv3-mobilenet');

Vision language

Describing images in words

Models

Registration and loading

Swift SDK

Kotlin SDK

React Native SDK

Flutter SDK

Web SDK

Electron SDK

SegmentationResult

SegmentationOptions

Counting a class

Preparing raw RGB

Models

See also

Vision language

Models

​SegmentationResult

​SegmentationOptions

​Counting a class

​Preparing raw RGB

​Models

​See also

Vision language

Models

SegmentationResult

SegmentationOptions

Counting a class

Preparing raw RGB

Models

See also