visual-cataloguer

Batch catalogue physical collections using visual dividers (QR codes) and automated image processing.

Website: retroverse-studios.github.io/visual-cataloguer · Downloads: latest release

The Problem

You have thousands of items (retro games, books, vinyl, trading cards) stored in boxes. You need them in a searchable database so you can find things and list them on eBay. Manual entry would take weeks.

The Solution

1. Print QR code dividers (one per storage location)
2. Photograph items one at a time, using dividers to mark location changes
3. Run viscatalog process ./photos --ai-identify
4. Search and browse your collection via CLI or web UI

Scope & Philosophy

This tool does one thing well: Process a folder of photographs into a searchable inventory database.

What it handles:

• One item per photograph (a "complete in box" game with box + manual + cartridge counts as one item)
• QR code dividers to track storage locations (BOX-1, SHELF-A3, etc.)
• Black frame images to end location sequences
• AI identification of items (title, platform, condition, completeness)
• Reference-quality images for verification (not eBay listing photos)

What it doesn't do:

• Multi-item segmentation (photographing 5 cartridges and splitting them)
• Image preprocessing (rotation, cropping, alignment)
• eBay-ready photo processing

The images stored are for verifying the database is correct, not for direct use in listings. When you're ready to list an item, you'll retrieve it from storage and take proper detailed photos.

Workflow

1. Prepare Dividers

Option A: QR codes (recommended) Print QR codes containing location IDs:

BOX-1    BOX-2    SHELF-A1    GARAGE-BIN-3

Option B: Hand-written/printed text Write the location ID on white paper. When AI mode is enabled, the tool will read text dividers automatically. Keep the paper clean - just the location text on white background.

Black frames: Put your hand over the lens to create a black image. This signals the end of a location sequence.

2. Photograph Your Collection

[QR: BOX-1] → [Item] → [Item] → [Item] → [BLACK] → [QR: BOX-2] → [Item] → ...

Rules:

• One item per photo
• Complete sets (box + game + manual) in one photo = one item
• QR code starts a new location
• Black frame ends the current location (optional, but helps with organization)
• Multiple cameras OK - images merge by EXIF timestamp

3. Process Images

# Default: Auto-detects AI (tries Ollama first, then Claude)
viscatalog process ./photos

# Force a specific provider
viscatalog process ./photos --provider ollama
viscatalog process ./photos --provider claude

# Offline mode: QR/OCR only, no AI
viscatalog process ./photos --offline

# Use a specific model
viscatalog process ./photos --provider ollama --model llava:13b

The tool auto-detects available AI providers: Ollama (free, local) is preferred, with Claude as fallback if ANTHROPIC_API_KEY is set.

4. Review & Correct

# View stats
viscatalog stats

# List items needing review
viscatalog list --unknown           # No title identified
viscatalog list --low-confidence    # AI uncertain
viscatalog list --needs-review      # Flagged for review

# View item details
viscatalog show 42

# Manually correct
viscatalog edit 42 --title "Super Mario Bros." --platform NES

# Re-run AI identification
viscatalog reidentify 42 --provider claude

5. Browse & Search

# Search
viscatalog search "zelda"

# List by location
viscatalog list --location BOX-1

# Web interface
viscatalog serve --port 8000
# Open http://localhost:8000

How It Works

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ Load Image  │────▶│     AI      │────▶│   Process   │
│ (ARW/JPG)   │     │  Classify   │     │ Accordingly │
└─────────────┘     └─────────────┘     └─────────────┘
                           │
           ┌───────────────┼───────────────┐
           ▼               ▼               ▼
     ┌──────────┐    ┌──────────┐    ┌──────────┐
     │ LOCATION │    │  BLACK   │    │   ITEM   │
     │ DIVIDER  │    │  FRAME   │    │          │
     └──────────┘    └──────────┘    └──────────┘
          │               │               │
          ▼               ▼               ▼
     Set current     Clear current    Store item
     location        location         in database

AI-First Architecture: A single AI call classifies the image type AND identifies item details. This handles QR codes, handwritten text, stylized logos, and Japanese text seamlessly.

AI Identification

By default, the tool uses vision AI (Ollama) to classify and identify items:

Supports:

• Claude (default) - requires ANTHROPIC_API_KEY environment variable
• Ollama - local, free, use --ai-provider ollama

Features

• Multi-camera support: Merges photos from multiple cameras by EXIF timestamp
• RAW file support: Sony ARW, Canon CR2/CR3, Nikon NEF, and more via rawpy
• QR code detection: OpenCV-based with OCR fallback
• SQLite database: Single portable file with images stored as BLOBs
• Resume capability: SHA256 deduplication skips already-processed files
• Web interface: Browse, search, edit, and manage your collection
• REST API: Integrate with other tools
• Duplicate divider handling: Multiple QR codes or black frames in a row are handled gracefully

Installation

Three ways to run it, from easiest to most flexible:

Desktop app (no Python or Docker required)

Download the build for your platform from the latest release: macOS (Apple Silicon or Intel), Windows, or Linux. Unzip and run — your collection database is stored in your user data folder automatically.

The binaries are unsigned, so the first launch needs a manual approval:

• macOS: right-click the app → Open → Open (Gatekeeper warning)
• Windows: SmartScreen → "More info" → "Run anyway"

Note: the desktop build does not bundle Tesseract, so offline (--offline) OCR mode is unavailable — AI identification (Claude or Ollama) works normally.

Docker

curl -O https://raw.githubusercontent.com/retroverse-studios/visual-cataloguer/main/docker-compose.yml
docker compose up -d   # web UI on http://localhost:8000, database in ./data/

Python package

# From PyPI
pip install visual-cataloguer

# With web interface
pip install visual-cataloguer[web]

# With the desktop window (viscatalog desktop)
pip install visual-cataloguer[desktop]

# Or with uv
uv pip install visual-cataloguer[web]

System dependencies:

• Tesseract OCR: brew install tesseract (macOS) or apt install tesseract-ocr (Linux)

For AI identification:

• Claude: Set ANTHROPIC_API_KEY environment variable
• Ollama: Install from ollama.ai and run ollama pull llava

CLI Reference

# Processing (auto-detects AI provider)
viscatalog process <input-dir> [--provider auto|ollama|claude] [--model MODEL] [--offline]

# Viewing
viscatalog stats
viscatalog list [--location LOC] [--platform PLAT] [--unknown] [--low-confidence] [--needs-review]
viscatalog search <query>
viscatalog show <item-id> [--json]

# Editing
viscatalog edit <item-id> [--title T] [--platform P] [--completeness C] [--notes N]
viscatalog reidentify <item-id> [--provider P] [--model M] [--image PATH]
viscatalog review <item-id> [--done | --flag --reason R]

# Export
viscatalog export <output-dir>                    # Export images by location
viscatalog export ./data.csv --format csv         # Export metadata as CSV
viscatalog export ./data.json --format json       # Export metadata as JSON
viscatalog export ./images --include-metadata     # Images + JSON sidecar files

# Price research (recent eBay sold listings)
viscatalog price <item-id> [--keywords "override terms"]

# Web server
viscatalog serve [--port 8000] [--host 0.0.0.0]

# Desktop window (stores DB in your user data folder)
viscatalog desktop [--no-gui] [--database PATH]

API Endpoints

# Items
GET    /api/items                    # List items (with filters)
GET    /api/items/{id}               # Get item
PATCH  /api/items/{id}               # Update item
DELETE /api/items/{id}               # Delete item
POST   /api/items/{id}/reidentify    # Re-run AI identification
POST   /api/items/{id}/price-research # Estimate value from recent eBay sold listings
GET    /api/items/{id}/price-history  # Stored price research snapshots

# Images
GET    /api/items/{id}/images        # List item images
GET    /api/items/{id}/image/thumb   # Get thumbnail
GET    /api/items/{id}/image/full    # Get full image
POST   /api/items/{id}/images        # Upload image

# Other
GET    /api/locations                # List locations
GET    /api/search?q=query           # Search items
GET    /api/stats                    # Collection statistics

Full OpenAPI docs at http://localhost:8000/docs

Database Schema

SQLite database with tables:

• items - Catalogued items with metadata
• item_images - Images stored as JPEG BLOBs (one-to-many with items)
• locations - Storage locations (BOX-1, SHELF-A3, etc.)
• processing_log - Tracks processed files for resume

Development

git clone https://github.com/retroverse-studios/visual-cataloguer.git
cd visual-cataloguer
uv sync --extra web --extra dev

# Run tests
uv run pytest

# Type checking
uv run mypy cataloguer

# Linting
uv run ruff check cataloguer

License

MIT License - see LICENSE