$ show EFPrefix/EFQRCode
EFQRCode is a pure-Swift, cross-platform Apple framework for generating stylized and highly customizable QR codes, and for recognizing QR codes from images. The core production code exposes two primary entry points: `EFQRCode.Generator` and `EFQRCode.Recognizer`. The generator accepts text/data input, encodes it using the external `QRCodeSwift` package, maps the resulting matrix through a strategy-based style hierarchy (`EFQRCodeStyleBasic`, `EFQRCodeStyleBubble`, `EFQRCodeStyleLine`, `EFQRCodeStyle25D`, `EFQRCodeStyleImage`, etc.), and produces SVG representations which are then rasterized by `SwiftDraw` into static images, PDFs, GIFs, APNGs, or AVFoundation-backed video files. The recognizer uses CoreImage-based detection with grayscale fallback and result caching. The library is heavily documented, supports iOS, macOS, tvOS, watchOS, and visionOS, and is distributed through SPM, CocoaPods, and Carthage. The `Examples/` directory contains demo apps for each platform that act as integration-style usage references, but these are example clients rather than the core deliverable. This is definitively an application/library repository; there are no data pipelines, notebooks, or research artifacts.
The LLM identified 3 files worth highlighting for reuse:
Contains the main `EFQRCode.Generator` API and, notably, the `reconcileFrameImages` algorithm for synchronizing multiple animated image sequences with different frame counts and durations using LCM/continued-fraction-based rational approximation. This is a non-obvious, self-contained animation-compositing technique that could be extracted for any multi-layer animated asset pipeline, not just QR codes. The file also demonstrates parallel frame rendering with an `OperationQueue` and synchronized error cancellation.
Implements pixel-level grayscale sampling with gamma adjustment, contrast/exposure tuning, and per-pixel probability mapping to drive image-resampled QR code modules. The underlying `getGrayPointList` style sampling logic is a self-contained image-to-grid luminance mapping algorithm that could be reused in other stylized raster-to-module rendering contexts, such as generative art or data-driven image mosaics.
Provides a clean set of pure CoreGraphics image utilities including grayscale conversion, resizing, cropping, and PNG data serialization. These operations are platform-independent, have no internal coupling to QR-specific logic, and could be extracted into a standalone image-processing extension for any Swift project targeting Apple platforms or cross-platform CoreGraphics environments.