flutter_pose_detection 0.1.0

Hardware-accelerated pose detection using native ML frameworks (Apple Vision on iOS, TensorFlow Lite on Android). Detects 33 body landmarks in MediaPipe-compatible format.

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.1.0 - 2024-12-30

Added

• Initial release of NPU Pose Detection plugin
• iOS Support: Apple Vision Framework with Neural Engine acceleration
  • Automatic NPU/GPU acceleration on A12+ devices
  • VNDetectHumanBodyPoseRequest for 17 body landmarks
  • Landmark mapping to MediaPipe 33-point format
• Android Support: TensorFlow Lite with MoveNet Lightning model
  • GPU Delegate for hardware acceleration
  • NNAPI support for devices API 27-34
  • CPU fallback with XNNPack optimization
• Core Features:
  • Static image pose detection (detectPose, detectPoseFromFile)
  • Real-time camera frame processing (processFrame, startCameraDetection)
  • Video file analysis (analyzeVideo with progress tracking)
  • Configurable detection parameters (PoseDetectorConfig)
• Data Models:
  • Pose with 33 MediaPipe-compatible landmarks
  • PoseLandmark with normalized coordinates (0-1) and confidence scores
  • BoundingBox for detected person region
  • LandmarkType enum for easy landmark access
• Error Handling:
  • Typed DetectionError with error codes
  • Graceful fallback from NPU to GPU to CPU
• Example App:
  • Image detection demo with gallery picker
  • Real-time camera detection with pose overlay
  • Video analysis with progress UI