bookmark/README.md

# Bookmark

A camera-driven book intake and checkout inventory system, built for
[Books in Hand](https://git.opensourcesolarpunk.com/books-in-hand), a
nonprofit that keeps books out of landfills and in people's hands.

Bookmark replaces manual book logging with a guided, camera-based workflow:
a volunteer places a book on a marked platform, the system photographs the
front and back, sorts it into a size category for tax/reporting purposes,
and later backfills the catalogue with title, author, and ISBN — all
running on local hardware, no cloud dependency.

This project is fully open source and is being built and donated as
volunteer/client work — it isn't part of any commercial product line.

## Status

**Phase 1 (intake station) is built, unit-tested, and ready for real-hardware validation.** Phase 2 (batch OCR/ISBN backfill) and Phase 3 (checkout station) are planned for later.

| Phase | What it does | Status |
|---|---|---|
| 1 — Intake station | Camera capture, guided front/back flow, deterministic size bucketing, CSV catalogue | Built, unit-tested — pending real-hardware validation |
| 2 — Batch OCR/ISBN backfill | Barcode decode + local vision-LLM fallback to fill in title/author/ISBN | Planned |
| 3 — Checkout station | Track books leaving inventory, reusing the intake identification pipeline | Planned |

## How it works (Phase 1)

1. A downward-facing webcam looks at a fixed platform with printed
   nested size-boundary markers (small / medium / large).
2. A volunteer centers the book; capture fires automatically or on a
   button press (configurable).
3. The system photographs the front, determines size — **deterministically**,
   by checking which physical markers are covered, not by any model
   inference step — then prompts the volunteer to flip the book and
   photographs the back.
4. Every book gets a row in a CSV catalogue: images, size bucket, and a
   status that later pipeline stages (OCR backfill, checkout) update.

Full design rationale lives in `AGENTS.md` (also readable as `CLAUDE.md` —
same file, symlinked for compatibility with different AI coding tools).

## Why this matters for a nonprofit

- **No cloud costs, no vendor lock-in.** Everything runs on donated
  hardware; no per-book or per-scan fees.
- **Deterministic where it counts.** Size-bucket counts feed Books in
  Hand's year-end tax and 501(c)(3) reporting — that number is computed
  from physical measurements, not inferred by a model, so it can't drift
  or hallucinate.
- **Volunteer-friendly.** The intake flow is designed to be usable by
  volunteers with no technical background — guided steps, big buttons,
  no login required.

## Development

Requires Python 3.11+.

### Setup

1. Create and activate a virtual environment:
   ```bash
   python3 -m venv .venv
   .venv/bin/pip install -e ".[dev]"
   ```

2. Mount the USB webcam over the capture platform. See the design spec
   (`circuitforge-plans/books-in-hand/superpowers/specs/2026-07-13-inventory-system-design.md`)
   for physical setup details: nested size-boundary markers, guide box.

3. Run calibration once per physical install, with the platform empty:
   ```bash
   .venv/bin/python calibrate.py <camera_device_index> reference.jpg
   ```
   This captures a reference image of the empty platform for marker detection.

4. Copy `config.example.json` to `config.json` and fill in:
   - `markers`: pixel coordinates (from the reference image)
   - `guide_box`: capture region boundaries
   - `trigger_strategy`: one of `manual`, `motion_stop`, or `stable_centered`

5. Run the backend:
   ```bash
   .venv/bin/python run.py config.json
   ```

6. Open `http://<machine-ip>:8000` in a kiosk-mode browser on the station's monitor.

### Running tests

```bash
.venv/bin/pytest -v
```

All tests run without requiring real camera hardware — they use synthetic
NumPy frames and mocked `cv2.VideoCapture`.

### End-to-end smoke test (manual, requires real hardware)

This checklist is for whoever installs this system on real hardware. It
verifies that the intake workflow functions end-to-end.

1. Boot the station per Setup above with `trigger_strategy: "manual"`.
2. Place a book on the platform, click "Capture" — confirm the status
   advances to "Front captured — flip it over" and a new row appears in
   `catalogue.csv` with a `size_bucket` value and populated
   `front_wide_path` / `front_cropped_path` columns pointing at real files
   under `captures/<book_id>/`.
3. Flip the book, click "Capture" again — confirm status advances to
   "Book complete!" and the same row now has `back_wide_path` /
   `back_cropped_path` populated.
4. Click "Next Book" — confirm status returns to "Waiting for book..." and
   the next capture gets a different `book_id`.
5. Repeat with a small book and a large book — confirm `size_bucket`
   differs correctly between them, matching which physical markers each
   book actually covers.
6. Switch `trigger_strategy` to `motion_stop` in `config.json`, restart,
   and confirm placing a book and holding it still triggers capture
   without pressing the button.
7. Switch to `stable_centered` and repeat, confirming capture fires once
   the book is centered and steady in the guide box.

## License

MIT — see `LICENSE`.