flutter_pose_detection 0.3.1

Hardware-accelerated pose detection using MediaPipe PoseLandmarker with GPU acceleration. Detects 33 body landmarks.

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.

0.3.1 - 2026-01-02

Changed

• Updated README with comprehensive API documentation
• Added Configuration, Camera Stream, Video Analysis, and Angle Calculation sections
• Fixed Quick Start code examples (MediaPipeLandmarkType → LandmarkType)
• Added API Reference table with all methods and properties
• Improved MediaPipe 33 Landmarks documentation with structured table

Fixed

• Corrected library documentation (Vision Framework → MediaPipe PoseLandmarker)
• Fixed Pose class comment (17 landmarks → 33 landmarks)

0.3.0 - 2026-01-01

Changed

• BREAKING: Migrated from LiteRT/MoveNet to MediaPipe PoseLandmarker API
• Android: Uses official MediaPipe Tasks Vision library with GPU delegate
  • pose_landmarker_lite.task model for 33-landmark detection
  • Automatic GPU → CPU fallback
• iOS: Uses TFLite with CoreML/Metal delegates for ANE/GPU acceleration
  • pose_detector.tflite + pose_landmarks_detector.tflite 2-stage pipeline
  • Automatic ANE → GPU → CPU fallback

Removed

• LiteRT/MoveNet fallback code (simplified architecture)
• LiteRtPoseDetector.swift - MediaPipe only
• movenet_lightning.tflite, movenet_thunder.tflite - replaced by MediaPipe models
• hrnetpose_w8a8.tflite, HRNetPose.mlpackage - no longer needed

Fixed

• iOS main thread blocking during initialization (moved to background thread)
• iOS coordinate normalization (pixel space 0-256 → normalized 0-1)
• Android model loading with ByteBuffer instead of file path

0.2.2 - 2025-12-31

Fixed

• Critical: Fixed pose coordinate mismatch with video aspect ratio
  • Added letterboxing (padding) during image preprocessing to maintain original aspect ratio
  • Previously, images were stretched to 192x256 input size, causing coordinate distortion
  • Coordinates now correctly map back to original image dimensions
• Android: Implemented letterboxBitmap() and transformKeypointsFromLetterbox() in LiteRtPoseDetector.kt
• iOS: Implemented letterboxAndExtractRGB() and transformKeypointsFromLetterbox() in CoreMLPoseDetector.swift

0.2.1 - 2025-12-31

Fixed

• Android: Updated QNN SDK from 2.34.0 to 2.41.0
  • Fixes NoSuchMethodError: getBackendType()I crash on Snapdragon 8 Elite
  • Resolves native JNI method signature mismatch with newer Android firmware

Added

• Android: ChipsetDetector for runtime SoC detection (Snapdragon, Exynos, Tensor, MediaTek)
• iOS: CoreMLPoseDetector for native Core ML inference with ANE optimization
  • Direct MLModel API usage (no Vision framework overhead)
  • ImageNet normalization for HRNet models

0.2.0 - 2024-12-31

Changed

• BREAKING: Migrated from TensorFlow Lite to LiteRT 2.1.0 (Google's successor to TFLite)
• Android: New LiteRtPoseDetector using CompiledModel API with NPU/GPU/CPU fallback
  • Replaces TFLitePoseDetector and DelegateFactory
  • Uses Accelerator.NPU, Accelerator.GPU for hardware acceleration
  • Automatic fallback chain: NPU → GPU → CPU
• iOS: New LiteRtPoseDetector using TensorFlowLiteSwift 2.14.0
  • Replaces VisionPoseDetector with MoveNet-based detection
  • CoreML delegate for Neural Engine (A12+), Metal delegate for GPU
  • Automatic fallback chain: Neural Engine → Metal → CPU
• Both Platforms: Now use same MoveNet Lightning/Thunder models for consistent results
  • Cross-platform landmark positions match within 5% variance
  • Lightning (192x192) for fast mode, Thunder (256x256) for accurate mode

Removed

• TFLitePoseDetector.kt - replaced by LiteRtPoseDetector.kt
• DelegateFactory.kt - accelerator selection now handled by CompiledModel API
• VisionPoseDetector.swift - replaced by LiteRtPoseDetector.swift

Fixed

• Consistent 17-landmark COCO format output across platforms
• Proper resource cleanup on dispose()

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