133 lines
7.1 KiB
Markdown
133 lines
7.1 KiB
Markdown
# Plex for Photos — Self-Hosted Photo Library Architecture
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A self-built media server for your photo collection, running as another container in your homelab. Follows the same pattern as your existing study app: React/Vite frontend + Express/SQLite backend, files stay on disk, DB just indexes them.
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---
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## High-Level Flow
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```
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┌───────────────────┐ watches ┌──────────────────────┐
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│ Photo Library │ ─────────────────────▶ │ Ingestion Engine │
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│ (disk / NAS) │ │ (chokidar file watch)│
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└───────────────────┘ └───────────┬───────────┘
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│ extract EXIF,
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│ generate poster,
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│ compute pHash
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▼
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┌──────────────────────┐
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│ SQLite metadata DB │
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│ (photos, collections,│
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│ hashes, artwork) │
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└───────────┬───────────┘
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│
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▼
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┌──────────────────────┐
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│ Express API server │
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└───────────┬───────────┘
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│ REST/WS
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▼
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┌──────────────────────┐
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│ React/Vite frontend │
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│ (browse, collections,│
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│ discover, map) │
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└──────────────────────┘
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```
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Original files never move or duplicate — the DB just points to paths on disk. Posters (thumbnails) are the only generated/derived asset stored separately (for instant browsing and discovery views).
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---
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## Components
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### Ingestion Engine
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**Stack:** Node + chokidar (file watcher) or a scheduled scan job
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**On new file detected:**
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- **Extract EXIF** (date taken, GPS, camera model, orientation) — exifr or exiftool wrapper
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- **Generate posters** (e.g., sharp) at multiple sizes — grid poster + hero/preview size
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- **Compute a perceptual hash** (pHash via sharp + blockhash-core) for smart duplicate detection
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- **Insert a row** into SQLite: path, EXIF fields, poster path, hash, import timestamp
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Runs as a background process inside the same container, or a separate lightweight worker container if you want to isolate it from the API.
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---
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### Database (SQLite)
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**Core tables to start with:**
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| Table | Purpose |
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|-------|---------|
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| `photos` | `id, filepath, poster_path, date_taken, date_imported, camera_model, gps_lat, gps_lon, phash, width, height, filesize` |
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| `collections` | `id, name, created_at, description, poster_path` |
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| `photo_collections` (join) | `photo_id, collection_id` |
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| `tags` | `id, name` |
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| `photo_tags` (join) | `photo_id, tag_id` |
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Matches the SQLite pattern you're already using in the study app — no new infra to stand up.
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---
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### API Server (Express)
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- **GET /photos** — paginated library query (filter by date range, tag, collection)
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- **GET /photos/:id** — full detail + EXIF metadata
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- **GET /photos/similar** — grouped by similar pHash (within a distance threshold)
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- **POST /collections, POST /photos/:id/tags** — manual organization endpoints
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- **GET /photos/map** — GPS-tagged photos for map view
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- **Poster serving** — directly via endpoint or Nginx for static file efficiency
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---
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### Frontend (React/Vite)
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- **Browse view** — infinite scroll, grouped by day/month, lazy-loaded posters (like Plex's continuous feed)
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- **Collection/tag views** — same grid, filtered (like Plex collections for photos)
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- **Detail view** — full image, EXIF panel, tag editor, related photos
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- **Duplicate review** — side-by-side similar photos with keep/flag workflow (like Plex's duplicate management)
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- **Map view** — Leaflet/MapLibre pins from GPS EXIF, click to open photo
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- **Discovery/search** — date range, camera, tag filters, full-text search
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---
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## Smart Duplicate Detection (High Value — Do This Early)
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At import time, compute pHash for every photo. On a scheduled job (or on-demand from a "Find Similar" button), compare hashes pairwise within a similarity threshold (Hamming distance) to group near-duplicates — burst shots, slightly different crops, etc.
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Surface groups in a review UI rather than auto-deleting — you make the call, app just surfaces the candidates. This is much cheaper computationally than face recognition and gives you the real "cleanup" value people want from a photo manager.
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---
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## Face Grouping (Optional, Scope Carefully)
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If you want this eventually:
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**Options:** face-api.js (runs in Node, ONNX-based, no Python needed) or a Python microservice using face_recognition/dlib if you're OK adding a Python component.
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Store face embeddings + bounding boxes in a `faces` table, cluster embeddings to group "this looks like the same person" without needing named identities upfront — you label clusters after the fact.
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**This is the single biggest scope jump** — worth treating as a v2/v3 feature, not part of the initial build.
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---
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## Deployment Fit
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**New container** (`photo-library`), same pattern as your other homelab services:
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- Mounts your existing photo backup folder(s) read-only (ingestion engine doesn't need write access to originals) plus a writable volume for posters/DB
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- Nginx Proxy Manager route (photos.necloud.us), behind Authelia like your other private services
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- No external dependencies — everything runs locally against files already on disk
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---
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## Suggested Build Order
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1. **Ingestion engine + SQLite schema** — watch folder, extract EXIF, generate posters, populate DB (get this solid before touching UI)
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2. **Basic API + browse UI** — paginated grid, chronological browsing — this alone is already usable
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3. **Detail view + manual tagging/collections**
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4. **Smart duplicate detection + review UI** — high value, moderate effort
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5. **Search + filters**
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6. **Map view** (if your library has meaningful GPS data)
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7. **Face grouping** (only if you still want it after living with V1–V6 for a while)
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