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