pose_detection 3.1.1

pose_detection: ^3.1.1 copied to clipboard

• Update documentation and dartdocs

Web: LiteRT.js (WebGPU + WASM fallback) is now the default web runtime.

• useLiteRt now defaults to true so no opt-in is required. Pass useLiteRt: false to use the legacy tflite-js path. liteRtAccelerator (String, default 'auto') controls the backend: 'auto' and 'webgpu' request WebGPU with WASM fallback when WebGPU compile fails, while 'wasm' opts out of GPU. On browsers without WebGPU support, 'auto' falls back to WASM transparently. Both runtimes load from CDN on first use with no web/index.html changes required; see configureLiteRtLoader in flutter_litert for self-hosting options.
• Update flutter_litert -> 2.5.2

• Update flutter_litert -> 2.5.0

• Update flutter_litert -> 2.4.1

Breaking:

• PoseDetector configuration moves from the constructor to initialize(). PoseDetector({mode: ..., landmarkModel: ..., ...}) → PoseDetector() + await detector.initialize(mode: ..., landmarkModel: ..., ...). Matches FaceDetector's shape. PoseDetector.create({...}) continues to accept the same named params unchanged.

• PoseDetector.detectFromMat signature changed. detectFromMat(cv.Mat, {required int imageWidth, required int imageHeight}) is now detectFromMat(cv.Mat). Dimensions are read from the Mat directly. Existing callers must drop the imageWidth and imageHeight named arguments.

• detect(...) no longer swallows exceptions. Undecodable native image bytes now throw FormatException (matching FaceDetector and HandDetector behaviour) rather than silently returning an empty list. Wrap detect(...) in a try/catch if your callsite depended on the previous silent-failure behaviour. On web, decode failure still returns an empty list because browser image decode failure does not throw through this API.

• On native platforms, inference now runs in a dedicated background isolate, keeping the UI thread free. Previously, native inference ran on the calling thread.

• Add PoseDetector.create({...}) one-step factory (mirrors FaceDetector.create and HandDetector.create).

• Add detectFromFilepath(String path): reads the file and delegates to detect.

• Add detectFromMatBytes(Uint8List, {required int width, required int height, int matType}) zero-copy fast path via TransferableTypedData.

• Add initializeFromBuffers({required Uint8List yoloBytes, required Uint8List landmarkBytes}) for callers that load model bytes independently of Flutter's asset system.

• Add isReady getter as an alias for isInitialized.

• Add a top-level Live Camera Detection section to the README, modelled on face_detection_tflite's packYuv420 + native cv.cvtColor pattern, and remove the orphan assets/models/pose_detection.tflite left over from the pre-YOLOv8n scaffold.

• Expand flutter_litert re-exports through the pose_detection barrel to match face_detection_tflite: tensor helpers (createNHWCTensor4D, fillNHWC4D, allocTensorShape, flattenDynamicTensor), math helpers (sigmoid, sigmoidClipped, clamp01, clip), letterbox helpers (computeLetterboxParams, LetterboxParams), BGR→RGB byte helpers (bgrBytesToRgbFloat32, bgrBytesToSignedFloat32), and PerformanceMode. Consumers no longer need a direct flutter_litert import for these.

• Add public PoseDetector.modelVersion and PoseDetector.modelVersionFor(...) APIs for downstream cache invalidation.

• Fix live camera in the example app on Android (previously detections were sideways and unreliable):
  • Apply rotation to raw landscape camera frames before detection so the pose detector sees upright people. _rotationFlagForFrame handles all four device orientations (portrait up/down, landscape left/right) via a combined sensorOrientation + DeviceOrientation formula.
  • Mirror the overlay on Android front camera to match CameraPreview's auto-mirrored preview texture.
  • Replace the per-pixel Dart YUV loop with flutter_litert's shared packYuv420 helper + native cv.cvtColor on mobile (iOS NV12, Android NV21 / I420).
  • Replace the per-pixel Dart BGRA→BGR / RGBA→BGR loop with native cv.cvtColor on desktop (macOS / Linux).
• Align example app live-camera layout with face_detection_tflite: Material+Row top bar (replaces AppBar), flip-camera button, FPS + detection-time display, rotating top bar in landscape with safe-area padding, and a settings popup housing pose-specific controls (landmark-model chips: Lite / Full / Heavy).
• Re-export packYuv420, YuvPlane, YuvLayout, and PackedYuv from flutter_litert through the pose_detection barrel.
• Update flutter_litert to ^2.2.0.

• Update flutter_litert -> 2.1.0

• Update flutter_litert to 2.0.13

• Update flutter_litert -> 2.0.12

• Update flutter_litert 2.0.10 -> 2.0.11

• Fixed Metal GPU delegate producing inconsistent detection counts on iOS

• Update documentation

• Update flutter_litert 2.0.8 -> 2.0.10

• Enable auto hardware acceleration by default (XNNPACK on all native platforms, Metal GPU on iOS)
• Update flutter_litert 2.0.6 -> 2.0.8

• Update flutter_litert 2.0.5 -> 2.0.6

• Fix Xcode build warnings by declaring PrivacyInfo.xcprivacy as a resource bundle in iOS and macOS podspecs

Breaking: Point now uses double coordinates. BoundingBox is now a 4-corner Point-based type.

• Use shared Point and BoundingBox from flutter_litert 2.0.0
• toPixel() now returns full-precision double coordinates (was truncating to int)
• Extract PersonDetectorBase shared between native and web detectors
• Simplify model classes and detector implementations
• Remove integration tests from unit test suite
• Remove dead test helpers (test_config.dart)

• Update camera_desktop 1.0.1 -> 1.0.3

• Update flutter_litert -> 1.2.0
• Refactor to use flutter_litert shared utilities (InterpreterFactory, InterpreterPool, PerformanceConfig)

• Update opencv_dart 2.1.0 -> 2.2.1
• Update flutter_litert 1.0.2 -> 1.0.3

• Update flutter_litert 1.0.1 -> 1.0.2

• Update documentation

• Update flutter_litert to 1.0.1, camera to 0.12.0

• Update flutter_litert to 0.2.2

• Initial release
• Person detection using YOLOv8
• Pose landmark detection with MediaPipe Pose
• Support for lite, full, and heavy models
• Box-only and full landmark detection modes