IrisLandmark class

Estimates dense iris keypoints within cropped eye regions and lets callers derive a robust iris center (with fallback if inference fails).

The underlying TFLite model (iris_landmark.tflite) is sourced from Google's MediaPipe framework. See the official model card for architecture details, training data, and intended use cases: https://mediapipe.page.link/iris-mc (local copy: doc/model_cards/iris_landmark_model_card.pdf)

Properties

hashCode int
The hash code for this object.
no setterinherited
inputHeight int
The model input height in pixels.
no setter
inputWidth int
The model input width in pixels.
no setter
runtimeType Type
A representation of the runtime type of the object.
no setterinherited

Methods

call(Mat eyeCrop, {Float32List? buffer}) Future<List<List<double>>>
Predicts iris and eye contour landmarks from a cv.Mat eye crop.
dispose() → void
Releases all TensorFlow Lite resources held by this model.
noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toString() String
A string representation of this object.
inherited

Operators

operator ==(Object other) bool
The equality operator.
inherited

Static Methods

callWithIsolate(Uint8List eyeCropBytes, String modelPath) Future<List<List<double>>>
Runs iris detection in a separate isolate for non-blocking inference.
create({InterpreterOptions? options, PerformanceConfig? performanceConfig}) Future<IrisLandmark>
Creates and initializes an iris landmark model instance.
createCompiledFromBuffer(Uint8List modelBytes) Future<IrisLandmark>
Creates an iris landmark model backed by LiteRT CompiledModel.
createFromBuffer(Uint8List modelBytes, {PerformanceConfig? performanceConfig}) Future<IrisLandmark>
Creates an iris landmark model from pre-loaded model bytes.
createFromFile(String modelPath, {InterpreterOptions? options, PerformanceConfig? performanceConfig}) Future<IrisLandmark>
Creates and initializes an iris landmark model from a custom file path.