Segmentation

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,
});

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
}

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

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

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);
}

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');