hand_detection_tflite 0.0.2

Hand & landmark detection using on-device TFLite models.

hand_detection_tflite

Flutter implementation of Google's MediaPipe hand detection and landmark models using TensorFlow Lite. Completely local: no remote API, just pure on-device, offline detection.

Hand Detection with 21-Point Landmarks

[Hand detection example]

Features

• On-device hand detection, runs fully offline
• 21-point hand landmarks with 3D depth information (x, y, z coordinates)
• Handedness detection (left/right hand)
• Gesture recognition: closed fist, open palm, pointing up, thumbs down, thumbs up, victory, I love you
• All coordinates are in absolute pixel coordinates
• Truly cross-platform: compatible with Android, iOS, macOS, Windows, and Linux
• Native OpenCV preprocessing (resize/letterbox/crops) for optimized throughput
• The example app illustrates how to detect and render results on images

Quick Start

import 'dart:io';
import 'package:hand_detection_tflite/hand_detection_tflite.dart';

Future main() async {
  HandDetector detector = HandDetector();
  await detector.initialize();

  final imageBytes = await File('path/to/image.jpg').readAsBytes();
  List<Hand> hands = await detector.detect(imageBytes);

  for (final hand in hands) {
    final boundingBox = hand.boundingBox;
    final handedness = hand.handedness;

    if (hand.hasLandmarks) {
      final wrist = hand.getLandmark(HandLandmarkType.wrist);
      final indexTip = hand.getLandmark(HandLandmarkType.indexFingerTip);
      print('Wrist: (${wrist?.x}, ${wrist?.y})');
    }
  }

  await detector.dispose();
}

Performance

XNNPACK can be enabled for 2-5x CPU speedup via SIMD vectorization (NEON on ARM, AVX on x86):

final detector = HandDetector(
  performanceConfig: PerformanceConfig.xnnpack(),
);
await detector.initialize();

Advanced: Direct Mat Input

For live camera streams, you can bypass image encoding/decoding entirely by using detectOnMat():

import 'package:hand_detection_tflite/hand_detection_tflite.dart';

Future<void> processFrame(cv.Mat frame) async {
  final detector = HandDetector();
  await detector.initialize();

  // Direct Mat input - fastest for video streams
  final hands = await detector.detectOnMat(frame);

  frame.dispose(); // always dispose Mats after use
  await detector.dispose();
}

When to use detectOnMat():

• Live camera streams where frames are already in memory
• When you need to preprocess images with OpenCV before detection
• Maximum throughput scenarios (avoids JPEG encode/decode overhead)

For all other cases, use the standard detect() method with image bytes.

Bounding Boxes

The boundingBox property returns a BoundingBox object representing the hand bounding box in absolute pixel coordinates.

Accessing Bounding Box

final BoundingBox boundingBox = hand.boundingBox;

// Access edges
final double left = boundingBox.left;
final double top = boundingBox.top;
final double right = boundingBox.right;
final double bottom = boundingBox.bottom;

// Calculate dimensions
final double width = boundingBox.right - boundingBox.left;
final double height = boundingBox.bottom - boundingBox.top;

print('Box: ($left, $top) to ($right, $bottom)');
print('Size: $width x $height');

Hand Landmarks (21-Point)

The landmarks property returns a list of 21 HandLandmark objects representing key points on the detected hand. Each landmark has 3D coordinates (x, y, z) and a visibility score.

21 Hand Landmarks

Index	Landmark	Description
0	wrist	Wrist
1-4	thumbCMC, thumbMCP, thumbIP, thumbTip	Thumb joints and tip
5-8	indexFingerMCP, indexFingerPIP, indexFingerDIP, indexFingerTip	Index finger
9-12	middleFingerMCP, middleFingerPIP, middleFingerDIP, middleFingerTip	Middle finger
13-16	ringFingerMCP, ringFingerPIP, ringFingerDIP, ringFingerTip	Ring finger
17-20	pinkyMCP, pinkyPIP, pinkyDIP, pinkyTip	Pinky finger

Accessing Landmarks

final Hand hand = hands.first;

// Access specific landmarks by type
final wrist = hand.getLandmark(HandLandmarkType.wrist);
final indexTip = hand.getLandmark(HandLandmarkType.indexFingerTip);
final thumbTip = hand.getLandmark(HandLandmarkType.thumbTip);

if (wrist != null) {
  print('Wrist: (${wrist.x}, ${wrist.y}, ${wrist.z})');
  print('Visibility: ${wrist.visibility}');
}

// Iterate through all landmarks
for (final landmark in hand.landmarks) {
  print('${landmark.type.name}: (${landmark.x}, ${landmark.y})');
}

Drawing Hand Skeleton

Use the handLandmarkConnections constant to draw the hand skeleton:

import 'package:hand_detection_tflite/hand_detection_tflite.dart';

// Draw skeleton connections
for (final connection in handLandmarkConnections) {
  final start = hand.getLandmark(connection[0]);
  final end = hand.getLandmark(connection[1]);

  if (start != null && end != null) {
    canvas.drawLine(
      Offset(start.x, start.y),
      Offset(end.x, end.y),
      paint,
    );
  }
}

Handedness

The handedness property indicates whether the detected hand is a left or right hand:

final Hand hand = hands.first;

if (hand.handedness == Handedness.left) {
  print('Left hand detected');
} else if (hand.handedness == Handedness.right) {
  print('Right hand detected');
}

Gesture Recognition

Enable gesture recognition to classify hand poses into 7 gestures:

[Gesture detection example]

Gesture	Description
closedFist	Closed fist
openPalm	Open palm
pointingUp	Index finger pointing up
thumbDown	Thumbs down
thumbUp	Thumbs up
victory	Victory / peace sign
iLoveYou	"I love you" sign

Enabling Gestures

final detector = HandDetector(
  enableGestures: true,
  gestureMinConfidence: 0.5, // optional, default 0.5
);
await detector.initialize();

final hands = await detector.detect(imageBytes);
for (final hand in hands) {
  if (hand.hasGesture) {
    print('Gesture: ${hand.gesture!.type.name}');
    print('Confidence: ${hand.gesture!.confidence}');
  }
}

Gesture recognition uses a two-stage pipeline (gesture embedder + classifier) and requires HandMode.boxesAndLandmarks (the default mode).

Detection Modes

This package supports two detection modes:

Mode	Features	Speed
boxesAndLandmarks (default)	Bounding boxes + 21 landmarks + handedness	Standard
boxes	Bounding boxes only	Faster

Code Examples

// Full mode (default): bounding boxes + 21 landmarks + handedness
final detector = HandDetector(
  mode: HandMode.boxesAndLandmarks,
);

// Fast mode: bounding boxes only
final detector = HandDetector(
  mode: HandMode.boxes,
);

Configuration Options

The HandDetector constructor accepts several configuration options:

final detector = HandDetector(
  mode: HandMode.boxesAndLandmarks,      // Detection mode
  landmarkModel: HandLandmarkModel.full, // Landmark model variant
  detectorConf: 0.6,                     // Palm detection confidence (0.0-1.0)
  maxDetections: 10,                     // Maximum hands to detect
  minLandmarkScore: 0.5,                 // Minimum landmark confidence (0.0-1.0)
  interpreterPoolSize: 1,                // TFLite interpreter pool size
  performanceConfig: PerformanceConfig.xnnpack(), // Performance optimization
  enableGestures: false,                 // Enable gesture recognition
  gestureMinConfidence: 0.5,             // Minimum gesture confidence (0.0-1.0)
);

Live Camera Detection

For real-time hand detection with a camera feed, use detectOnMat() to avoid repeated JPEG encode/decode overhead:

import 'package:camera/camera.dart';
import 'package:hand_detection_tflite/hand_detection_tflite.dart';

HandDetector detector = HandDetector(
  performanceConfig: PerformanceConfig.xnnpack(),
);
await detector.initialize();

final cameras = await availableCameras();
CameraController camera = CameraController(cameras.first, ResolutionPreset.medium);
await camera.initialize();

camera.startImageStream((CameraImage image) async {
  // Convert CameraImage (YUV420) directly to cv.Mat (BGR)
  final cv.Mat mat = convertCameraImageToMat(image); // see example app

  // Detect hands using Mat for maximum performance
  List<Hand> hands = await detector.detectOnMat(mat);

  // Always dispose Mat after use
  mat.dispose();

  // Process hands...
});

Key points:

• Use detectOnMat() instead of detect() to bypass JPEG encoding/decoding
• Convert YUV420 camera frames directly to BGR Mat format
• Always call mat.dispose() after detection
• Use HandMode.boxes for fastest real-time performance

See the full example app for complete implementation.

Example

The sample code from the pub.dev example tab includes a Flutter app that paints detections onto an image: bounding boxes and 21-point hand landmarks with skeleton connections.