Add the core labeling data model (label sets, ad-hoc Main->Sub class
hierarchy, frames, bounding-box labels, per-frame/per-set label
status) behind a FastAPI app, with SQLite as the default swappable
DATABASE_URL. Multi-user support is attribution-only for now
(get-or-create by username, no login flow yet). Dataset
versioning/promotion is intentionally deferred - it needs its own
design pass around snapshot semantics.
Each test gets a fully isolated app+DB via create_app(database_url=...)
rather than relying on process-global state. 13/13 tests pass; also
verified live end-to-end against a running uvicorn instance.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Slice a recorded session's lossless video into individual frame PNGs
(ffmpeg -vsync 0, no drop/dup) via a new `extract-frames` subcommand,
with frame count cross-checked against the manifest and input log so
any capture-rate drift surfaces immediately instead of silently
misaligning frames and logged input later. Includes a real end-to-end
test against an ffmpeg-generated synthetic video (skipped when ffmpeg
isn't installed, e.g. bare WSL).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Add ffmpeg/x11grab-based video capture (libx264rgb, qp=0, true lossless
RGB) and an evdev-based keyboard state logger, orchestrated by a single
frame-tick loop so each JSONL input row lines up 1:1 with its video
frame. Unit-tested with fake keyboard/video components (no real device
or ffmpeg needed); real hardware capture still needs validation on the
recording PC.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Add top-level README, .gitignore, and per-component directories
(recording, labeling backend/frontend, training, inference, control)
with READMEs and pyproject.toml/package skeletons per CLAUDE.md §2-3.
No implementation yet, just structure to build against.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
