Add a framework-free HTML/CSS/JS labeling UI: set/session/status
filters, a frame browser, drag-to-draw/move/resize bounding boxes with
a per-set class picker, per-frame status control, and Left/Right frame
navigation. No login - a locally cached username is sent for
attribution only, matching the backend's get-or-create user model.
Backend additions the frontend needed: serve frame images from a
configurable FRAMES_ROOT via a /images static mount, permissive CORS
(internal tool, not publicly exposed), and a GET /sets/{id}/frames
endpoint returning frames joined with their per-set label status
(defaulting missing rows to unlabeled) for the frame browser.
Verified the full call chain end-to-end against a running backend +
static frontend server (set/class creation, frame ingest, image
serving, label CRUD, status updates, CORS preflight) - every field
name the JS reads matches the API responses. 18/18 backend tests
pass. Not yet verified: actual interactive browser use (no browser
tooling available here) - try drag-to-draw/resize locally.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
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>
