Yomu

Pure Dart QR Code & Barcode Reader Library

Yomu is a zero-dependency pure Dart implementation of a QR code and barcode reader library. It works in any Dart environment including Flutter, Dart CLI applications, and server-side Dart.

✨ Why Yomu?

📦 Zero Dependencies: No external package dependencies. Keep your app's dependency graph clean.
🎯 Pure Dart: No C++/Native code. Works instantly on Web (Wasm/JS), Desktop, and Mobile without build issues.
🚀 High Performance: Full HD in ~2.4ms, 4K in ~4.0ms on M4 MacBook Air (AOT). Fast enough for real-time scanning.
🛡️ Robust & Tested: Comprehensive test coverage. Tested against hundreds of distorted, noisy, and unevenly lit images.

🚀 Quick Start

QR Code + All Barcodes

import 'package:yomu/yomu.dart';

void main() {
  // Create a YomuImage container
  final image = YomuImage.rgba(
    bytes: imageBytes,
    width: 300,
    height: 300,
  );

  // Decode QR codes and all barcode formats
  final result = Yomu.all.decode(image);
  print('Decoded: ${result.text}');
}

QR Code Only

// For QR code only scanning
final result = Yomu.qrOnly.decode(YomuImage.rgba(
  bytes: imageBytes,
  width: width,
  height: height,
));

📖 API Reference

`Yomu` Class

The main entry point class.

Constructor / Static	Description
`Yomu.all`	QR codes + all barcode formats
`Yomu.qrOnly`	QR codes only
`Yomu.barcodeOnly`	1D barcodes only
`Yomu.responsive`	All formats, `DecodeEffort.balanced`
`Yomu.realtime`	All formats, `DecodeEffort.fast` (per-frame)
`Yomu({enableQRCode, barcodeScanner})`	Custom configuration

Method	Description
`decode()`	Decode the first QR code or barcode in an image
`decodeAll()`	Detect and decode all QR codes in an image

Detection vs Latency (`DecodeEffort`)

Retries only run on images the fast path cannot decode, so successful scans are never slowed down by this setting. What it trades is detection on hard inputs against latency on inputs holding no code at all — which is every frame of a camera preview pointed at nothing.

The levels split where the cost actually jumps: between stages that reuse the binarized image already in hand and stages that rebuild it from the source pixels.

`effort`	Retries	Detection¹	Blank frame²	Textured frame²
`DecodeEffort.fast`	none	167/201, 83.1%	1.41ms	2.58ms
`DecodeEffort.balanced`	corner grid search, despeckle, tolerant finder	188/201, 93.5%	1.36ms	17.98ms
`DecodeEffort.thorough`	+ full-resolution retry, alternate binarization thresholds	192/201, 95.5%	7.89ms	51.30ms

¹ Fixture corpus. ² Full HD frame containing no code, AOT. A textured frame costs more at every level because noise produces false finder patterns, so each stage has candidates to rule out rather than nothing to look at.

Pick by use case:

Single images (photos, uploaded pictures): keep the default thorough. A slower failure is better than a missed code.
Camera streams that can spend ~18ms on a bad frame: Yomu.responsive (balanced). It recovers 21 of the 25 codes thorough adds over fast, for a third of the cost on a textured frame.
Real-time preview: Yomu.realtime (fast). Frames without a code fail as fast as possible; a code missed on one frame is caught on a later one.

The older tryHarder: bool parameter still works — false maps to fast, true to thorough — but it is deprecated in favour of effort.

`YomuImage` Class

A platform-agnostic container for image data.

Factory	Description
`YomuImage.rgba()`	Create from RGBA bytes (4 bytes/pixel)
`YomuImage.bgra()`	Create from BGRA bytes (4 bytes/pixel)
`YomuImage.grayscale()`	Create from grayscale bytes (1 byte/pixel)
`YomuImage.yuv420()`	Create from Y-plane of YUV420 camera image

🔧 Support Status

Supported Image Classes

Yomu targets modern capture sources: printed codes, on-screen codes, and ordinary camera scans. Instead of relying on era-specific photo corpora, the test fixtures are generated to bracket the capability boundary of each distortion axis from both sides (see scripts/generate_stress_qr.py):

Distortion axis	Decodes	Does not decode
Salt & pepper noise	25%	30%
Low-light (Gaussian) noise	σ=120²	σ=170²
Gray dirt occlusion	30%	35%
Gaussian blur	radius 5.0	radius 6.0
Perspective (top squeeze)	0.3	0.4
Perspective (side squeeze)	0.6	— (saturates)
JPEG artifacts	quality 1	— (no boundary)
Specular glare	full saturation	— (EC absorbs it)
Screen moire	amplitude 0.8	amplitude 0.9
Composite casual scan¹	blur 5.5	blur 6.0

¹ Mild perspective (0.2) + lighting gradient + blur. Each component alone is well inside its single-axis boundary.

² The only probabilistic axis. Each σ draws one noise field, so a fixture near the transition reports its own draw rather than the decoder's limit. Measured decode rate over 20 independent draws per σ: 110→100%, 120→100%, 130→75%, 140→70%, 150→50%, 160→40%, 170→5%, 180→5%. The rungs are taken from the flat ends so neither side depends on a lucky draw; the real transition is around σ=150.

Degradations outside these definable classes — arbitrary surface curvature, finder patterns cut out of the frame, damage beyond the error-correction capacity — are out of scope; that long tail is the domain of ML-based detectors.

Encoding Modes (QR)

Mode	Support
Numeric	✅
Alphanumeric	✅
Byte (Latin-1/UTF-8)	✅
Kanji	✅
ECI	❌ Not Supported

1D Barcode Support

Format	Support	Description
EAN-13	✅	International retail (includes JAN)
EAN-8	✅	Small products
UPC-A	✅	North American retail
Code 128	✅	Logistics, high-density
Code 39	✅	Industrial, alphanumeric
ITF	✅	Interleaved 2 of 5, logistics
Codabar	✅	Libraries, blood banks

🎯 Performance

Run the benchmark suite:

uv run scripts/benchmark_runner.py

Standard Images (≤1000px)

Environment: M4 MacBook Air (2024), AOT Compiled

Mode	Avg Decode Time
AOT	~0.55ms
JIT	~0.72ms

Large Images (Fused Downsampling)

Images >1MP are automatically processed with a fused conversion step for optimal performance.

Resolution	Avg Decode Time
4K (3840×2160)	~4.0ms
Full HD (1920×1080)	~2.4ms

License

MIT License - see LICENSE for details.