RawCull

Swift Concurrency in RawCull

Thu, 26 Mar 2026 00:00:00 +0000

Swift Concurrency in RawCull

A summarized document about Concurrency in RawCull.

1 Why Concurrency Matters in RawCull

RawCull is a macOS photo-culling application that works with Sony A1 ARW raw files. A single RAW file from the A1 can be 50–80 MB. When you open a folder with hundreds of shots, the app must scan metadata, extract embedded JPEG previews, decode thumbnails, and manage a multi-gigabyte in-memory cache — all while keeping the UI perfectly fluid and responsive at 60 fps. Without concurrency that would be impossible.

Sharpness Scoring

Wed, 25 Mar 2026 00:00:00 +0000

Updated Sharpness Scoring in RawCull

This document describes how RawCull computes sharpness scores, what each parameter does, and a boundary-value test procedure for validating parameter behaviour. This applies to version 1.3.7 of RawCull

How the Scoring Pipeline Works

flowchart TD
 A([ARW File]) --> B[Thumbnail decode\nthumbnailMaxPixelSize]
 A --> C[Saliency detection\nApple Vision]

 B --> D[Gaussian pre-blur\npreBlurRadius × isoFactor × resFactor]
 D --> E[Laplacian Metal kernel\nfocusLaplacian]
 E --> F[Amplify\nenergyMultiplier]
 F --> G[Border suppression\nborderInsetFraction]
 G --> H[Render to Float32 buffer]

 H --> I[Full-frame samples\nborder-inset region]
 C -->|bounding box ≥ 3% area| J[Salient-region samples\nVision bounding box]
 H --> J

 I --> K[p95 winsorized tail score\nfull-frame f]
 J --> L[p95 winsorized tail score\nsubject s]

 K --> M{Fusion}
 L --> M
 C -->|area| M

 M --> N[finalScore]
 N --> O([Badge: score / maxScore × 100])

 style A fill:#2d2d2d,color:#fff
 style O fill:#2d2d2d,color:#fff

Each ARW file goes through the following stages in detail:

Threads

Wed, 25 Mar 2026 00:00:00 +0000

Concurrency Monitoring — ScanFiles

Analysis

The cooperative thread pool

Swift’s runtime manages a pool of threads capped at roughly the number of CPU cores. Apple Silicon M-series typically has 8–12 performance cores. The output shows a peak of ~10–11 concurrent tasks, which matches this ceiling. This is intentional — the runtime will not spin up 21 threads for 21 tasks because context-switching that many threads would cost more than the parallelism gains.

Security & Privacy

Mon, 23 Mar 2026 00:00:00 +0000

Security & Privacy

RawCull is built from the ground up with macOS security and user privacy as first-class concerns. The following is a precise and detailed account of every security mechanism the application employs, derived directly from the source code and project configuration.

App Sandbox

RawCull runs inside the macOS App Sandbox (com.apple.security.app-sandbox = true), enforced by the operating system. The entitlements file contains only this single declaration — no additional entitlements are granted. This means:

Memory Cache

Tue, 17 Mar 2026 00:00:00 +0000

Cache System — RawCull

RawCull uses a three-layer cache to avoid repeated RAW decoding. Decoding an ARW file on demand is expensive — the three-layer approach ensures that most requests are served from RAM or disk rather than from source.

Layers (fastest to slowest):

Memory cache — NSCache<NSURL, DiscardableThumbnail> in RAM
Disk cache — JPEG files on disk in ~/Library/Caches/no.blogspot.RawCull/Thumbnails/
Source decode — CGImageSourceCreateThumbnailAtIndex from the ARW file

The same cache stack is shared by two paths: the bulk preload flow (ScanAndCreateThumbnails) and on-demand per-file requests (RequestThumbnail).

Thumbnails

Tue, 17 Mar 2026 00:00:00 +0000

Thumbnails and Previews — RawCull

RawCull handles Sony ARW files through two distinct image paths:

Generated thumbnails — fast, sized-down previews for browsing and culling, extracted with ImageIO and cached in RAM and on disk
Embedded JPEG previews — full-resolution embedded JPEGs extracted from the ARW binary for high-quality inspection and export

Both paths integrate with the shared three-layer cache system: RAM → disk → source decode.

1. Thumbnail Sizes and Settings

All thumbnail dimensions are configurable via SettingsViewModel and persisted to ~/Library/Application Support/RawCull/settings.json.

Culling Photos

Sun, 01 Feb 2026 00:00:00 +0000

RawCull is not a tool for image editing; it is solely intended for selecting and copying selected images for editing by dedicated software such as DxO PL, Lightroom, and others.

A photo culling application is specialized software designed to assist photographers in efficiently reviewing, organizing, and selecting the most suitable images from a photo shoot. RawCull supports Focus Peaking and Focus Points.

Persistent Marking and Session Recovery

RawCull automatically saves your marking decisions as you work, allowing you to pause and resume your culling session at any time. Your marks, ratings, and review status are saved to disk, so if you need to continue later, simply reopen the same folder and your progress will be restored.

Sharpness Scoring

Wed, 25 Mar 2026 00:00:00 +0000

Sharpness Scoring

RawCull can automatically rank every image in a catalog by sharpness, so the sharpest frames rise to the top and out-of-focus or motion-blurred shots sink to the bottom. This page explains what the score means, how to run it, and how to interpret the results.

Overview

When you click Sharpness, RawCull analyses each RAW file using a multi-step pipeline:

A small thumbnail is extracted directly from the ARW file — fast, no full RAW decode needed.
Apple Vision locates the main subject in the frame and, when classification is enabled, identifies what that subject is (bird, person, animal, etc.). A salient region is accepted when it covers more than 3 % of the frame or when Vision’s confidence is ≥ 0.9 — the high-confidence path means a small but clearly identifiable subject (a bird on a stick, a distant raptor) is never discarded for being too small.
A Gaussian pre-blur smooths the image to suppress high-ISO noise before edge detection. The radius adapts automatically: it scales proportionally with √(ISO/400) to compensate for noise at high ISO values, and with √(imageWidth/512) to maintain consistent spatial-frequency selectivity on larger images. Both factors are capped at 3×.
A Laplacian edge-detection kernel measures per-pixel sharpness energy across the entire frame. The outer 4 % of each border is excluded from the full-frame score to prevent Gaussian edge artifacts from inflating the result.
The edge response is amplified, then scored with a robust tail score — the mean of values above the 95th percentile, clipped at the 99.5th — so a single sharp feather edge cannot carry a blurry frame.
Two scores are computed separately: one for the full frame (minus the border strip) and one for the salient subject region. They are fused as 25 % full-frame + 75 % subject region. If no salient region is found, the full-frame score is used directly. A small proportional bonus is applied for subjects that fill more of the frame.
All scores are normalised against the p90 of the catalog: that anchor becomes 100, and every other frame is expressed as a percentage of it. Using p90 rather than the maximum prevents a single noise spike from collapsing the rest of the scale.

Because scores are relative to your catalog, a score of 60 does not mean the image is soft — it means it is 60 % as sharp as the sharpest typical frame in that session. Opening a catalog of sharper images will recalibrate the scale.

Focus Peaking

Wed, 11 Mar 2026 00:00:00 +0000

Focus Peaking: The Laplacian of Gaussian Method

Note: To obtain the most optimal results in Focus Peak view (extract), it is recommended to extract the JPG file from the ARW file. While it is possible to view Focus Peek by creating thumbnails, the mask is more effective when applied to JPGs. To access Focus Peek, double-click on the image in main view. In the Settings menu, Thumbnails tab, disable the “Use Thumbnail for Zoom” to use JPGs as base for Focus Peak.

Settings

Fri, 20 Feb 2026 00:00:00 +0000

Settings view

RawCull provides settings to customize your culling experience and optimize performance:

Cache Management: Control thumbnail cache location and size to balance performance with disk space usage
Thumbnail Quality: Adjust thumbnail generation settings to optimize preview quality and loading speed
Memory Settings: Configure memory allocation for handling large photo catalogs efficiently

Access these settings via RawCull → Settings to fine-tune RawCull for your specific needs and system configuration. The cache statistics present the memory hit-rate when viewing photos.

Focus Points

Mon, 02 Mar 2026 00:00:00 +0000

RawCull facilitates native extraction of Focus Points from Sony A1 ARW files. Users have the option to activate or deactivate the Focus Points feature in both the primary thumbnail view and the extracted zoom views.

Focus Point off:

Focus Point on:

Both the Focus Point and the Focus Peak can be applied simultaneously.

Version 1.4.0

Tue, 07 Apr 2026 00:00:00 +0000

Version 1.4.0 - April 7, 2026

RawCull supports a broader range of Sony FullFrame camera bodies. See table below for what is tested now.

RawCull Changes Summary (v1.3.5 → v1.4.0)

Sharpness scoring was significantly upgraded: added saliency-aware scoring, optional subject classification, subject-based filtering, configurable scoring parameters, and improved normalization/progress handling.
New UI controls and views were added around scoring and stats:
- new Scoring Parameters sheet
- new Scan Summary/Statistics sheet
- toolbar toggles for score badge and saliency badge
- expanded culling/rating status display (including per-star counts).
EXIF/metadata support expanded: now includes RAW compression type, size class (L/M/S), and pixel dimensions; these are surfaced in inspector UI.
Sony compatibility work: maker-note parser now supports more Sony bodies and adds robust diagnostics/full-file fallback for focus metadata extraction.
Performance/state management improvements: rating/tag caching for O(1) lookups, better cancellation behavior in scoring, memory warning refinements (soft/full), and various cleanup/refactors.
Persistence/settings updates: settings now store badge visibility toggles and decode safely with defaults.
Testing expanded with a large new ARW body compatibility diagnostic test suite plus concurrency/sendability test adjustments.

ARW body compatibility diagnostic

The following Sony bodies successfully extract EXIF, focus points, sharpness, and saliency, except for the ILCE-7RM5, which failed to extract saliency on one of its three files. The ILCE-1M2 is the only body tested across all three Sony RAW size variants (S/M/L). All files use compressed RAW, and every body achieves full-resolution L-size output, ranging from 12.4 MP (ILCE-1M2 S-crop) to 60.2 MP (ILCE-7RM5). The ILCE-7M5 and ILCE-7RM5 are the next bodies to focus on, but I depend on test ARW files to properly test them before officially concluding support for these two bodies.

Version 1.4.1

Tue, 07 Apr 2026 00:00:00 +0000

Version 1.4.1 - April 7, 2026

RawCull supports a broader range of Sony FullFrame camera bodies. See table below for what is tested now.

RawCull Changes Summary (v1.4.0 → v1.4.1)

Version 1.4.1 solves an issue with Sony A7V (ILCE-7M5) Compressed ARW file.

ARW body compatibility diagnostic

Version 1.3.5

Sat, 04 Apr 2026 00:00:00 +0000

Version 1.3.5 - April 4, 2026

April 4, 2026: It has been observed that RawCull supports a broader range of Sony FullFrame camera bodies. I have conducted tests on ARW files for the Sony A7RV and the newly released Sony A7V. After Easter, I plan to acquire additional full-frame ARW files from Sony A bodies to conduct further tests. Upon the completion of these tests, I will compile a comprehensive list of the Sony bodies that RawCull supports.

Version 1.3.3

Thu, 02 Apr 2026 00:00:00 +0000

Version 1.3.3 - April 2, 2026

RawCull is developed for culling Sony A1 mkI and mkII ARW files. However, I have recently tested RawCull on Sony 7RV and the new Sony 7V, and it appears that all functions are functioning correctly on these models as well.

In this release, RawCull supports culling by color. Please refer to the Culling section in the documentation for further details. Additionally, updates have been made to the Focus Mask and pre-calibration in Sharpness Scoring.

Version 1.2.8

Mon, 30 Mar 2026 00:00:00 +0000

Version 1.2.8 - March 30, 2026

RawCull is developed for culling Sony A1 mkI and mkII ARW files. However, I have recently tested RawCull on Sony 7RV and the new Sony 7V, and it appears that all functions are functioning correctly on these models as well.

Code cleanup and several minor bugs were resolved. Additionally, a focus issue was addressed, eliminating the need for two clicks on either vertical or horizontal thumbnail row before utilizing arrow keys for navigation and tagging. Now, a single click suffices to achieve the desired focus. Furthermore, the tag command was modified to a single keypress of t, enabling tagging or detagging functionality.

Scan and Thumbnial Pipeline

Wed, 25 Mar 2026 00:00:00 +0000

RawCull — Scan and Thumbnail Pipeline

This document describes the complete execution flow from the moment a user opens a catalog folder to the point where all thumbnails are visible in the grid. It covers the actors involved, the data flow between them, the concurrency model, five performance bugs that were found and fixed, and the measured results on a real catalog of 809 Sony A1 ARW files stored on an external 800 MB/s SSD.

Sony MakerNote Parser

Wed, 25 Mar 2026 00:00:00 +0000

Sony MakerNote Parser — Focus Point Extraction

Files covered:

RawCull/Enum/SonyMakerNoteParser.swift

RawCull/Model/ViewModels/FocusPointsModel.swift

RawCull/Actors/ScanFiles.swift

RawCull/Views/FocusPoints/FocusOverlayView.swift

Overview

RawCull extracts autofocus (AF) focus point coordinates directly from Sony ARW raw files without requiring any external tools such as exiftool. The parser targets the Sony A1 (ILCE-1) and A1 Mark II (ILCE-1M2) cameras.

The focus location is stored inside the Sony proprietary MakerNote block embedded in the EXIF data of every ARW file. The SonyMakerNoteParser struct navigates the binary TIFF structure to locate and decode this data.

Concurrency model

Tue, 17 Mar 2026 00:00:00 +0000

Concurrency Model — RawCull

Files covered:

RawCull/Model/ViewModels/RawCullViewModel.swift

RawCull/Views/RawCullSidebarMainView/extension+RawCullView.swift

RawCull/Actors/ScanFiles.swift

RawCull/Actors/ScanAndCreateThumbnails.swift

RawCull/Actors/ExtractAndSaveJPGs.swift

RawCull/Actors/ThumbnailLoader.swift

RawCull/Actors/RequestThumbnail.swift

RawCull/Actors/SharedMemoryCache.swift

RawCull/Actors/DiskCacheManager.swift

RawCull/Actors/SaveJPGImage.swift

RawCull/Enum/SonyThumbnailExtractor.swift

RawCull/Enum/JPGSonyARWExtractor.swift

Overview

RawCull uses Swift structured concurrency (async/await, Task, TaskGroup, and actor) across four primary flows:

Flow	Entry point	Core actor(s)	Purpose
Catalog scan	`RawCullViewModel.handleSourceChange(url:)`	`ScanFiles`	Scan ARW files, extract metadata, load focus points
Thumbnail preload	`RawCullViewModel.handleSourceChange(url:)`	`ScanAndCreateThumbnails`	Bulk-populate the thumbnail cache for a selected catalog
JPG extraction	`extension+RawCullView.extractAllJPGS()`	`ExtractAndSaveJPGs`	Extract embedded JPEG previews and save to disk
On-demand thumbnails	UI grid + detail views	`ThumbnailLoader`, `RequestThumbnail`	Rate-limited, cached per-file thumbnail retrieval

The two long-running operations (thumbnail preload and JPG extraction) share a two-level task pattern:

Synchronous Code

Thu, 19 Feb 2026 00:00:00 +0000

A Guide to Handling Heavy Synchronous Code in Swift Concurrency

This post explains why CPU-intensive synchronous code (such as image decoding via ImageIO) must be dispatched off the Swift Concurrency thread pool, and shows the correct patterns RawCull uses to do so.

`DispatchQueue.global(qos:)` — QoS Levels Compared

The key difference is priority and resource allocation by the system.

`.userInitiated`

Priority: High (just below .userInteractive)
Use case: Work the user directly triggered and is actively waiting for — e.g., loading a document they tapped, parsing data to display a screen
Expected duration: Near-instantaneous to a few seconds
System behavior: Gets more CPU time and higher thread priority — the system treats this as urgent
Energy impact: Higher

`.utility`

Priority: Low-medium
Use case: Long-running work the user is aware of but not blocked by — e.g., downloading files, importing data, periodic syncs, progress-bar tasks
Expected duration: Seconds to minutes
System behavior: Balanced CPU/energy trade-off; the system throttles this more aggressively under load or low battery
Energy impact: Lower (system may apply energy efficiency optimizations)

Quick Comparison

	`.userInitiated`	`.utility`
Priority	High	Low-medium
User waiting?	Yes, directly	Aware but not blocked
Duration	< a few seconds	Seconds to minutes
CPU allocation	Aggressive	Conservative
Battery impact	Higher	Lower
Thread pool	Higher-priority threads	Lower-priority threads

Rule of thumb

// User tapped "Load" and is staring at a spinner → userInitiated
DispatchQueue.global(qos: .userInitiated).async {
 let data = loadCriticalData()
}

// Background sync / download with a progress bar → utility
DispatchQueue.global(qos: .utility).async {
 downloadLargeFile()
}

If you use .userInitiated for everything, you waste battery and CPU on non-urgent work. If you use .utility for user-blocking tasks, the UI will feel sluggish because the system may deprioritize the work.

Security-Scoped URLs

Thu, 05 Feb 2026 00:00:00 +0000

Security-scoped URLs are a cornerstone of macOS App Sandbox security. RawCull uses them to gain persistent, user-approved access to source and destination folders while remaining fully sandbox-compliant. This article walks through exactly how the implementation works, tracing the code from user interaction through to file operations.

What Are Security-Scoped URLs?

A security-scoped URL is a special file URL that carries a cryptographic capability granted by macOS, representing explicit user consent to access a specific file or folder. Without it, a sandboxed app cannot read or write anything outside its own container.

Compiling RawCull

Sun, 01 Feb 2026 00:00:00 +0000

Overview

There are three methods to compile RawCull: one without an Apple Developer account and two with an Apple Developer account. Regardless of the method used, it is straightforward to compile RawCull, as it is not dependent on any third-party code or library.

The easiest method is by using the included Makefile. The default make in /usr/bin/make does the job.

Compile by make

If you have an Apple Developer account, you should open the RawCull project and replace the Signing & Capabilities section with your own Apple Developer ID before using make and the procedure outlined below.

About

Mon, 01 Jan 0001 00:00:00 +0000

About RawCull

Hello!

If you have ideas, feedback, or suggestions about RawCull, please reach out to me at thomeven@gmail.com. I’d love to hear from you.

Background

I started RsyncOSX in August 2016 as a way to learn Swift, and after eight years of releases, it was archived in August 2024. RsyncUI, built with SwiftUI, began in late 2020 and continues to be actively developed. RawCull represents my latest venture into building specialized macOS applications with modern Swift technologies.

RawCull

Swift Concurrency in RawCull

Swift Concurrency in RawCull

1 Why Concurrency Matters in RawCull

Sharpness Scoring

Updated Sharpness Scoring in RawCull

How the Scoring Pipeline Works

Threads

Concurrency Monitoring — ScanFiles

Analysis

The cooperative thread pool

Security & Privacy

Security & Privacy

App Sandbox

Memory Cache

Cache System — RawCull

Thumbnails

Thumbnails and Previews — RawCull

1. Thumbnail Sizes and Settings

Culling Photos

Persistent Marking and Session Recovery

Sharpness Scoring

Sharpness Scoring

Overview

Focus Peaking

Focus Peaking: The Laplacian of Gaussian Method

Settings

Settings view

Focus Points

Version 1.4.0

Version 1.4.0 - April 7, 2026

RawCull Changes Summary (v1.3.5 → v1.4.0)

ARW body compatibility diagnostic

Version 1.4.1

Version 1.4.1 - April 7, 2026

RawCull Changes Summary (v1.4.0 → v1.4.1)

ARW body compatibility diagnostic

Version 1.3.5

Version 1.3.5 - April 4, 2026

Version 1.3.3

Version 1.3.3 - April 2, 2026

Version 1.2.8

Version 1.2.8 - March 30, 2026

Scan and Thumbnial Pipeline

RawCull — Scan and Thumbnail Pipeline

Sony MakerNote Parser

Sony MakerNote Parser — Focus Point Extraction

Overview

Concurrency model

Concurrency Model — RawCull

Overview

Synchronous Code

A Guide to Handling Heavy Synchronous Code in Swift Concurrency

DispatchQueue.global(qos:) — QoS Levels Compared

.userInitiated

.utility

Quick Comparison

Rule of thumb

Security-Scoped URLs

What Are Security-Scoped URLs?

Compiling RawCull

Overview

Compile by make

About

About RawCull

About RawCull

Background

`DispatchQueue.global(qos:)` — QoS Levels Compared

`.userInitiated`

`.utility`