flutter_pose_detection 0.3.0

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

Flutter Pose Detection

Hardware-accelerated pose detection Flutter plugin using MediaPipe PoseLandmarker with GPU acceleration.

Features

• MediaPipe PoseLandmarker: Official Google pose detection API
• 33 Landmarks: Full body tracking including hands and feet
• Cross-Platform: iOS (CoreML/Metal) and Android (GPU Delegate)
• Hardware Acceleration: Automatic GPU fallback to CPU
• Real-time: Camera frame processing with FPS tracking

Performance

Device	Chipset	Acceleration	Inference Time
Galaxy S24 Ultra	Snapdragon 8 Elite	GPU	~15ms
iPhone 15 Pro	A17 Pro	ANE (CoreML)	~12ms
Pixel 8	Tensor G3	GPU	~18ms

Platform Support

Platform	ML Framework	Model	Acceleration
iOS 14+	TFLite + CoreML	MediaPipe Pose	Neural Engine → GPU → CPU
Android API 24+	MediaPipe Tasks	PoseLandmarker Lite	GPU → CPU

Installation

dependencies:
  flutter_pose_detection: ^0.3.0

iOS Setup

Update ios/Podfile:

platform :ios, '14.0'

Add camera permission to ios/Runner/Info.plist:

<key>NSCameraUsageDescription</key>
<string>Camera access is needed for pose detection</string>

Android Setup

Update android/app/build.gradle:

android {
    defaultConfig {
        minSdkVersion 24
    }
}

Add camera permission to android/app/src/main/AndroidManifest.xml:

<uses-permission android:name="android.permission.CAMERA" />

Quick Start

import 'package:flutter_pose_detection/flutter_pose_detection.dart';

// Create and initialize detector
final detector = NpuPoseDetector();
final result = await detector.initialize();
print('Running on: ${result.accelerationMode}'); // GPU or CPU

// Detect pose from image
final imageBytes = await File('image.jpg').readAsBytes();
final poseResult = await detector.detectPose(imageBytes);

print('Inference time: ${poseResult.processingTimeMs}ms');

if (poseResult.hasPoses) {
  final pose = poseResult.firstPose!;
  print('Detected ${pose.landmarks.length} landmarks');

  // Access specific landmarks (MediaPipe 33-point format)
  final nose = pose.getLandmark(MediaPipeLandmarkType.nose);
  final leftShoulder = pose.getLandmark(MediaPipeLandmarkType.leftShoulder);
  print('Nose at (${nose.x}, ${nose.y})');
}

// Clean up
detector.dispose();

MediaPipe 33 Landmarks

0: nose
1-6: eyes (inner, center, outer)
7-8: ears
9-10: mouth corners
11-12: shoulders
13-14: elbows
15-16: wrists
17-22: hands (pinky, index, thumb)
23-24: hips
25-26: knees
27-28: ankles
29-30: heels
31-32: foot index

Model Architecture

This plugin uses MediaPipe PoseLandmarker (2-stage pipeline):

Stage	Model	Input Size	Output
1. Person Detection	pose_detector	224x224	Bounding box
2. Landmark Detection	pose_landmarks_detector	256x256	33 landmarks (x, y, z, visibility)

Documentation

• API Reference
• CHANGELOG

License

MIT License - see LICENSE