# Labeling Tool — Backend

API + data model for the bounding-box labeling tool. See CLAUDE.md §3.2 for the
full requirements this is built against.

**Status:** core data model + CRUD API implemented (sets, hierarchical classes,
frames, labels, per-frame/per-set status), frame image serving, bulk frame
ingest from a recording session, and dataset version promotion (see
`labeling/frontend/` for the UI). **Not yet implemented:** auth/login (see
below) or the active-learning auto-label workflow.

## Stack

- **FastAPI** + **SQLAlchemy** (2.0), **SQLite** by default (`./labeling.db`),
  swappable via the `DATABASE_URL` env var (e.g. to Postgres later without code
  changes — one Postgres-compatible ORM).
- **Multi-user, no auth yet:** labels/status changes take a plain `created_by` /
  `updated_by` username string, resolved via get-or-create (`users.py`). There's no
  login flow — attribution only, since there's no UI yet that would need real auth.

## Data model

- `LabelSet` — a label set (`Enemy`, `Items`, `Traps`, ...), one per detector model.
- `MainClass` / `SubClass` — the per-set Main → Sub class hierarchy (e.g. `Enemy` →
  `Bat`, `Snake`), created ad hoc via the API, no migration needed to add classes.
- `Frame` — one labelable image, identified by `(session_name, frame_index)` —
  matches the recording tool's frame-extraction output 1:1.
- `Label` — one bounding box (`x, y, width, height` in pixel space), scoped to a
  frame + set + sub-class.
- `FrameSetStatus` — per-frame, per-set label state (`unlabeled` / `auto_labeled` /
  `reviewed`).
- `DatasetVersion` / `DatasetVersionFrame` / `DatasetVersionLabel` — a named,
  immutable snapshot of a set's frames+labels as of promotion time (`enemy-v1`,
  `enemy-v2`, ...). Labels stay live-editable at any time; a promoted version
  copies the label data at that moment, so it stays reproducible regardless of
  later edits or deletions to the live labels.

Frame images are served from `FRAMES_ROOT` (env var, default `./frames`) at
`/images/<image_path>`; `Frame.image_path` is always relative to that root.

## Setup

```
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
```

## Run

```
spelunkai-labeling-backend
# or: uvicorn spelunkai_labeling_backend.main:create_app --factory --reload
```

Interactive API docs at `http://127.0.0.1:8000/docs` once running.

## Bulk-ingesting a recording session

After a session's extracted frames (`recording/`'s `extract-frames` output) have
been copied/rsynced onto this machine under `FRAMES_ROOT`, register them all in
one shot (writes directly to the database, no server needs to be running):

```
spelunkai-labeling-backend ingest-session \
  --frames-dir /path/under/FRAMES_ROOT/run01_frames \
  --session-name run01
```

`--image-path-prefix` defaults to the frames directory's own name (here
`run01_frames`) — override it if the directory was copied under a different name.
Safe to re-run: frames already registered for that session (by frame index) are
skipped. `--width`/`--height` default to 1280x720 (Spelunky Classic HD's fixed
capture resolution).

## Promoting a dataset version

Once frames are marked `reviewed` (see the frontend, or `PUT
/frames/{id}/sets/{id}/status`), freeze them into a named, reproducible
version:

```
POST /sets/{set_id}/dataset-versions
{"name": "enemy-v1", "description": "first reviewed batch"}
```

Defaults to every currently-`reviewed` frame in that set; pass an explicit
`frame_ids` list to promote a different selection instead. Fetch the frozen
result (what training will eventually consume) via `GET
/dataset-versions/{id}` — it returns each frame plus the exact labels that
existed at promotion time, unaffected by any later edits to the live labels.

## Testing

```
pytest
```

Each test gets a fully isolated app + SQLite file via `create_app(database_url=...)`
(see `tests/conftest.py`) — no shared state between tests, no real server needed.