Optimizer (scripts/optimizer/): replay.py runs the real C++ tracker/matcher/
scene_tracker chain over a dumped-embeddings HDF5 via sae_kpn, so a threshold
sweep never re-decodes video or re-embeds faces. optimize.py drives scipy's
differential_evolution over the knob space, with DE-level parallelism
(multiple population candidates evaluated concurrently via a ThreadPoolExecutor)
on top of per-film replay parallelism. second_score.py is the per-second X-Ray
scoring metric (TPI/FPI/FN, out-of-cast misID weighted 10x, fair recall masked
to gallery-known cast) that superseded an earlier scene-union metric.
dump_error_frames.py / dump_scene_montage.py extract annotated video frames
(bounding boxes, TPI/FPI/FN captions, onscreen-vs-offscreen split) for visual
review of a replay against ground truth. Gallery utilities: cast_restrict.py,
gallery_membership.py, fetch_missing_actors.py, reembed_gallery.py.
scripts/validation/: X-Ray ground-truth loading and provider-agnostic identity
matching (identity.py's keys_for — an actor is the union of every id we can
derive, since pipeline output and ground truth don't share one id space).
scripts/artifacts/: push/pull scripts for the Gitea generic package registry —
galleries, montage frames, and experiment data (manifests/trajectories/results)
are pushed there instead of committed, since none are needed to run the app,
only benchmarks. Versioned by git short-SHA.
scripts/docs/: MkDocs site build (build_site.sh) and the calibration-curve
comparison chart (calibration_chart.py, matplotlib, reads each gallery's
embedded calibration).
Gallery-building scripts (make_jellyfin_gallery.py, make_gallery.py,
filter_gallery.py, run_from_jellyfin.py, movienet_eval.py, movienet_prep.py,
sae_gallery.py) updated to read/write HDF5 galleries exclusively, matching the
engine-side format switch. run_from_jellyfin.py and the optimizer no longer
carry movie source paths in shared manifests (some source filenames include
scene-release tags) — resolved locally via a gitignored file-lut.json instead.
scene_gap_hist.py scans scene_analyze output JSONs and, for every actor,
computes the gap (next_scene_start - prev_scene_end) between consecutive
scenes, emitting a text histogram of the distribution. Used to inform the
anneal_sec default.
movienet_eval: replace the per-element dot() with numpy — actor references are
loaded once as an ndarray and scored with a single matmul, keeping a
whole-library gallery fast.
movienet_prep: count and report frames referenced by annotations but absent
from Image.zip instead of skipping them silently.
Add two cameo hunters that flag actors recognised in a title but absent from
its cast:
- cameo_jellyfin.py — pure-Jellyfin cast-membership check (no id cross-walk)
- cameo_hunt.py — TMDB filmography check (actor's combined_credits)
run_from_jellyfin.py now stamps the analysed title's Jellyfin item GUID into
the output JSON as top-level 'jellyfin_item_id' (scene_analyze can't know it),
which cameo_jellyfin.py uses to look up the cast in Jellyfin's own id space.
Document that field in the result-sink output schema header.
Consolidate copy-pasted logic across the gallery/run scripts into shared
modules:
- sae_env.py — zero-dependency .env loader (populates os.environ)
- sae_tmdb.py — TMDB API helpers (tmdb_get, person images, id lookups)
- sae_jellyfin.py— Jellyfin API helpers (jf_get, id/URL normalisation)
- sae_gallery.py — image download + gallery.json writing
make_gallery, make_jellyfin_gallery and filter_gallery now import these
instead of carrying their own near-identical copies.
dtourolle