dtourolle
f891e579c5
The parser reads a tag up to end of line, so `# TRACES: GR-004 | SR-001 — prose` swallowed the prose into the tag and the row went unmatched. Splitting the comment leaves the tag greppable by the same pattern as the code tags and the commit trailers, which is the point of the house format. Mechanical throughout; no logic touched. The regenerated report reflects this session's new tags: 137 -> 148 found, and one more tagged-but-unexecuted, which is the SuperHero accuracy assertion that is documented but not yet a test.
scripts/validation — per-scene actor-presence eval
Validates the pipeline's per-scene "who's on screen" output against external
ground truth, offline. Annealing (anneal_sec) means an actor's presence is only
defined after the whole file is merged into [start,end] windows, so we cannot
score live: process → write the pipeline JSON → sample timepoints → compare
predicted vs ground-truth presence sets → micro-sum TP/FP/FN → precision/recall/F1.
Ground-truth sources
| Source | Semantics | Fair to a face pipeline? | What it measures |
|---|---|---|---|
| MovieNet-PS | on-screen face presence per shot | yes — like-for-like | recognition accuracy |
| Amazon X-Ray (Zenodo) | cast-in-scene (incl. off-camera / non-speaking) | no — penalizes by design | coverage ceiling; recall gap = actors we structurally can't see |
- MovieNet is the honest recognition number.
- X-Ray is an upper bound: its recall gap tells you how much presence is off-camera cast a face detector can never reach — not a pipeline error.
X-Ray dataset: Zenodo DOI 10.5281/zenodo.17659734 (CC-BY-4.0). Per movie it ships
people.csv, scenes.csv, people_in_scenes.csv.
Usage
# against Amazon X-Ray CSVs for one title
python scripts/validation/sample_eval.py \
--pred "Scene in a Mall.json" \
--xray /data/xray/<movie_dir> \
--gallery gallery_arcface_w600k_r50.json \
--step 1.0
# against MovieNet-PS for one title
python scripts/validation/sample_eval.py \
--pred out.json \
--movienet /data/movienet --split Train_app10 --title tt0032138 \
--gallery gallery_arcface_w600k_r50.json
Sampling modes
--step Sregular grid every S s (default 1.0) — time-weighted headline number.--random NN uniform-random timepoints (for confidence intervals).--scene-anchoredone timepoint per GT scene midpoint — the literal X-Ray "did I get this scene's cast right?" question; neutralizes long-scene bias.
Ground truth is compared raw (annealing is not applied to GT).
Matching & masking
Identity is provider-agnostic (identity.py): each actor is the set of every key
we can derive — imdb:nm…, tmdb:…, jf:…, name:<normalized>. Predicted and GT
actors match iff their key-sets intersect, so an output carrying only tmdb/jellyfin
ids still joins X-Ray's nm ids via the normalized-name fallback.
Scoring is masked to gallery ∩ GT: a GT actor absent from the gallery is
ignored (not an FN), so we measure pipeline accuracy, not gallery coverage. Without
--gallery the mask falls back to GT ∩ pred keys. --no-mask disables it.
Exact id join via the tmdb→imdb crosswalk (recommended)
The gallery/pipeline output key actors by TMDB id (no nm…), while X-Ray and
MovieNet key on IMDb. They only overlap on the fuzzy name: key by default.
Build a cached tmdb→imdb table once and pass it with --crosswalk to turn the
name join into an exact id join:
# one-time: resolve every gallery tmdb id via TMDB /person/{id}/external_ids
python scripts/validation/tmdb_imdb_map.py \
--gallery gallery_arcface_w600k_r50.json \
--out scripts/validation/tmdb_imdb.json # TMDB_API_KEY from env/.env
# then score with exact ids
python scripts/validation/sample_eval.py --pred out.json --xray <dir> \
--gallery gallery_arcface_w600k_r50.json \
--crosswalk scripts/validation/tmdb_imdb.json
The table caches nulls (tmdb ids TMDB has no IMDb id for) and checkpoints, so a
re-run only resolves new ids. TMDB is authoritative for this crosswalk — there is
no clean free bulk tmdb_person ↔ nm file, so we query the API once and cache.
Minimum face size (VR-005)
min_face_size.py is a separate, self-contained study: it needs no video and no
ground truth, only the gallery mugshot cache. It holds out one image per actor,
degrades that probe to each candidate face size and matches it against a gallery
held at native resolution, reporting TPI/FPI per size — the measurement that
replaces AR-002's 66×66 px estimate.
python scripts/validation/min_face_size.py \
--images images --gallery gallery_lvface.h5 \
--arcface models/LVFace-B_Glint360K.onnx \
--actors 100 --out experiments/results/vr005_min_face_size
FPI grows with the number of actors competing, so a 100-actor run understates it
against a library of thousands: read FPI as relative across sizes, not as an
absolute rate. Re-run per --arcface model to see whether min_face_px should be
one constant or scale with the embedder (GR-004).
Files
sample_eval.py— CLI scorer.ground_truth.py—XRayGroundTruth,MovieNetGroundTruthloaders.identity.py— provider-agnostic match keys.tmdb_imdb_map.py— build/consult the cachedtmdb→imdbcrosswalk.min_face_size.py— VR-005 probe-size sweep (see above).test_sample_eval.py— self-contained tests (python scripts/validation/test_sample_eval.py).