PhotoPrism is a self-hosted photo management server that indexes, organizes, and serves personal photo and video collections. In production, it extracts EXIF/XMP/IPTC metadata, generates thumbnails and previews, transcodes videos, performs AI-powered object/face recognition and NSFW detection, and provides a web UI for browsing, searching, sharing, and editing media. It also supports WebDAV access, OAuth2/OIDC authentication, and multi-instance clustering. The architecture is a monolithic Go application with a REST API (Gin framework) and a Vue.js frontend. It uses a relational database (MariaDB/MySQL or SQLite) via GORM. Background workers handle indexing, face recognition, place detection, and cleanup. It integrates with external tools: FFmpeg for video transcoding, libvips for image processing, TensorFlow/ONNX for AI models, and exiftool for metadata extraction. The frontend communicates via REST and WebSocket. Key entry points include the CLI (cmd/photoprism/photoprism.go) with subcommands for import, index, search, and user management, and the API layer (internal/api) exposing endpoints for photos, albums, labels, faces, places, and vision. The indexing pipeline scans configured directories, extracts metadata, generates thumbnails, runs AI models, and stores results in the database. The system also supports background sync with remote WebDAV services and backup/restore. External dependencies include FFmpeg, exiftool, libvips, TensorFlow, ONNX Runtime, Darktable, RawTherapee, yt-dlp, and various Go libraries. It can optionally integrate with OpenAI/Ollama for vision captioning. The codebase is large and mature, with extensive utility packages and a well-organized module structure.
rclip is a command-line semantic photo search tool that indexes local image collections using a CLIP model (OpenCLIP ViT-B/32-256) and supports natural-language, image-to-image, and combined queries with boolean operations. The core architecture is monolithic: a Python package with modules for model inference (ONNX/CoreML), SQLite-based feature caching, image preprocessing, and a Textual terminal user interface for interactive browsing. It is fully offline and privacy-preserving, supporting a wide range of image formats including HEIC, raw (CR2, DNG, ARW), and standard web formats. The tool is distributed via PyPI, Snap, AppImage, Homebrew, and Windows MSI, with careful compliance management for third-party licenses, codecs, and model artifacts. It includes an extensive test suite (unit and end-to-end), CI/CD via GitHub Actions, and a compliance toolkit that enforces license policies during builds. The codebase is well-structured and demonstrates good engineering practices, though it relies on a pinned version of textual-image that uses a private API, introducing some technical drift risk.
Photonix is a self-hosted, full-stack photo management application with AI-powered classification and search. The backend is a Django monolith exposing a GraphQL API, with background workers for importing, thumbnail generation, raw processing, and ML classification (color, objects, faces, location, style, events). The frontend is a React 19 single-page app built with TanStack Router, Apollo Client, Zustand state management, and a dark-themed shadcn/ui component library. The system supports multi-user libraries, invitations, map view, albums, and fine-grained search with facets. The architecture relies on a PostgreSQL database, Redis for task coordination and locks, and supervisor to manage numerous background processes. While the project shows solid engineering and a modern tech stack, it carries some technical debt from vendored ML models, TensorFlow 1.x compatibility, and hardcoded download URLs, and is explicitly not yet feature-complete.
This repository provides Fujifilm camera profiles for use with raw images from digital cameras, specifically matching Adobe Camera Raw/Lightroom profiles for Fuji X-cameras. It includes DCP profiles, 3D LUTs (.cube files), PNG HaldCLUTs, and XML tables for various film simulations (Provia, Velvia, Astia, etc.). The system is designed to be used with linear camera profiles in applications like Adobe Camera Raw. The repository contains no executable code or software—it is purely a collection of data files (LUTs, profiles, and documentation) for color grading and film simulation. The DCP example file is a binary profile that would require dcptool to modify. The README provides usage instructions but the actual functionality depends on external tools like dcptool and compatible RAW processors.