weebi_barcode_dart 1.0.2

Core barcode detection library using embedded YOLO neural network. Provides low-level FFI interface to Rust-based barcode detection with support for multiple image formats and high accuracy detection.

Weebi Barcode Dart

Core barcode detection library using embedded YOLO neural network. Provides low-level FFI interface to Rust-based barcode detection with support for multiple image formats and high accuracy detection.

Features

• 🎯 YOLO-based Detection: High-accuracy barcode detection using embedded neural network
• 🖼️ Multiple Image Formats: Support for PNG, JPEG, YUV420, BGRA8888, and generic YUV
• 🚀 Super-Resolution: Optional image enhancement for better detection accuracy
• 🏗️ Cross-Platform: Windows, macOS, Linux, Android, and iOS support
• 💻 Pure Dart: Framework-agnostic, can be used in any Dart/Flutter project
• 🔧 Low-Level FFI: Direct interface to native Rust library for maximum performance

Installation

Add this to your package's pubspec.yaml:

dependencies:
  weebi_barcode_dart: ^1.0.0

Quick Start

import 'package:weebi_barcode_dart/weebi_barcode_dart.dart';
import 'dart:io';
import 'dart:typed_data';

void main() async {
  // Initialize the SDK with your model file
  bool initialized = await BarcodeDetector.initialize('assets/best.rten');
  if (!initialized) {
    print('Failed to initialize barcode detector');
    return;
  }

  // Read an image file
  final imageFile = File('barcode_image.jpg');
  final imageBytes = await imageFile.readAsBytes();

  // Process the image
  try {
    final results = await BarcodeDetector.processImage(
      format: ImageFormat.jpeg,
      bytes: imageBytes,
    );

    for (final result in results) {
      print('Found ${result.format}: ${result.text}');
      if (result.bounds != null) {
        print('Location: ${result.bounds}');
      }
    }
  } catch (e) {
    print('Error processing image: $e');
  }
}

Advanced Usage

YUV420 Processing (Camera Integration)

For camera applications, you can process YUV420 data directly for better performance:

final results = await BarcodeDetector.processYuv420Image(
  yPlane: yPlaneData,
  uPlane: uPlaneData,
  vPlane: vPlaneData,
  width: imageWidth,
  height: imageHeight,
  uvRowStride: uvRowStride,
  uvPixelStride: uvPixelStride,
  useSuperResolution: true,
);

Image Format Support

The library supports multiple image formats:

// PNG images
await BarcodeDetector.processImage(
  format: ImageFormat.png,
  bytes: pngBytes,
);

// JPEG images  
await BarcodeDetector.processImage(
  format: ImageFormat.jpeg,
  bytes: jpegBytes,
);

// BGRA8888 format (with dimensions)
await BarcodeDetector.processImage(
  format: ImageFormat.bgra8888,
  bytes: bgra8888Bytes,
  width: imageWidth,
  height: imageHeight,
  bytesPerRow: bytesPerRow,
);

Platform Setup

Windows

# pubspec.yaml
flutter:
  assets:
    - assets/best.rten
    - windows/rust_barcode_lib.dll

Android

# pubspec.yaml  
flutter:
  assets:
    - assets/best.rten
    - android/jniLibs/

iOS/macOS

# pubspec.yaml
flutter:
  assets:
    - assets/best.rten
    - ios/lib_rust_barcode_lib.dylib  # iOS
    - macos/lib_rust_barcode_lib.dylib  # macOS

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues and questions:

• 📝 GitHub Issues
• 📧 Contact: [e-mail]