The output schema moved to version 2, where each actor's "scenes" is a
list of {start, end, belief, route} objects rather than [t0, t1] pairs.
Both scorers still unpacked pairs and raised "too many values to unpack
(expected 2, got 4)" on current output. Read either form: an object's
start/end, or a two-element list. Fixes second_score.py (the optimizer's
per-second scorer) and sample_eval.py (the X-Ray/MovieNet presence eval).
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.
Bali was chosen because the TRECVID DVU set ships character mugshots, but
its reference crops are unusable at scale: median detected face 27 px
against a 69 px maximum, so every reference was upscaled 4x or more past
what the embedder was trained for (AR-011). A 66 px floor left 2 of 69
references; no threshold exists that both keeps the faces in distribution
and leaves enough of them to calibrate.
SuperHero is 69 px median and 241 px max. Its gallery builds at a 66 px
floor with 14 references over 5 characters, and calibrates on its own
(a=15.2867 b=-4.98633, 100% train accuracy) instead of borrowing constants.
Measured on the fused 17-minute film, one stream rather than per-scene
clips so presence windows cross real scene boundaries as SR-002 intends:
precision 1.00, recall 0.65, F1 0.79 — 13 true positives, 0 false
positives, 7 misses. Every out-of-gallery character was declined rather
than forced onto a nearest match. The misses are the short scenes (14 s,
38 s, 27 s), consistent with per-track accumulation needing sightings.
- build_gallery gains --min-face-px, filtering the *detected face* rather
than the crop. The DVU images are scene crops, not mugshots, so crop
dimensions say nothing about face scale. A poisoned reference is
permanent in a way a bad frame is not: it corrupts every future match
against that identity.
- scripts/fetch_dvu.sh fetches mugshots, scene graphs and segmentation for
any DVU film. NIST names the same film three different ways, so KG_DIR
and KG_FILE are overridable rather than derived. This exists as a script
because the first copy of this data was assembled ad hoc in /tmp and was
lost with it, taking the working gallery along.
- Replay fixtures move to the artifact registry: push/pull_artifacts.sh
gain a replay-fixtures target, and tests/fixtures/dumps/.gitignore keeps
them out of git. superhero.h5 is ~9 MB and regenerating it needs the
film, the models and a GPU — none of which CI has. The gallery ships
with the dumps, since a dump only replays against the gallery it was
produced with.
- AR-012 and AR-013 coverage is ported onto the new fixture rather than
dropped with the Bali cases: 12369 assertions, up from 7991, since the
film is an order of magnitude larger than the clips.
Suite: 15679 assertions, 101 test cases.
TRACES: AR-011, AR-012, AR-013 | VR-001, VR-005 | SR-002
sae_audio exposes the shipped signature to Python. It compiles
audio_signature.cpp directly against FFmpeg rather than linking
sae_gallery: the signature needs no model, no OpenCV and no HDF5, so a
module that dragged those in would make `import sae_audio` depend on a
GPU-capable build of a path that is pure CPU DSP.
The point of binding rather than porting is that a fingerprint is only
useful if every implementation agrees byte for byte. A numpy port would
be a third implementation, and the one nobody checks against the golden
vector.
VR-014 then recovers a known trim from real film audio rather than from
the synthetic tone: 40 random in-cap offsets, every one recovered to the
nearest frame, worst error 46 ms against a 500 ms budget — and 46 ms is
the quantisation floor, not a result, since offsets land on whole
92.88 ms frames.
The soft spot is tier labelling rather than accuracy. Sub-frame
misalignment drags the score down (0.94-0.99 near a frame boundary,
0.69-0.73 at half a frame), demoting 27 of 40 correct alignments to
`loose`. Allowing +/-1 frame of slack in the score fixes it: all 40 back
to `audio` at min 0.906, false matches unmoved at 0.12-0.16, for 81 ms
of the budget.
The module stops at the producer's edge. Sliding one signature against
another is the consumer's algorithm (server SPEC §3, and the jRay
plugin implements it), so a caller writing that slide in numpy is not
duplicating anything this repo owns.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
TRACES: IR-004, IR-005 | VR-014 | UT-105, UT-106, UT-107, UT-108 | SR-003
Deletes the Python ports of SCRFDDecoder, ArcFaceEmbedder, align_face,
enhance_for_retry and calibrate_gallery, and calls the shipped C++
instead. 297 lines removed, 108 added.
The ports existed because sae_embed only exposed embed(path), so a
caller could not embed a crop it had degraded. That gap is closed:
detect(), align_face(), enhance_for_retry(), embed_crop()/embed_crops()
and GalleryCalibration are bound now, so there is no longer a reason to
keep a second implementation of any of them.
The calibration is the one that mattered. A parallel copy of the sigmoid
is precisely where "always the calibrated probability, never a raw
cosine" (AR-024) breaks without anyone noticing — the copy goes on
returning plausible numbers after the original has moved. Scoring
through the binding makes the rule structural rather than remembered.
Verified against the committed run: same shape, FPI 0.0% at every size,
same operating point of 32 px. Absolute rates differ by 1-2 points
because this check sampled 100 actors / 574 crops against the original's
258 / 999, not because anything regressed.
Also: --providers and --batch are gone, since provider selection and
batching belong to the backend; embeds are chunked at its max_batch,
because the engine does not split an oversized request and a whole
gallery in one call asks CUDA for a multi-gigabyte buffer. DEDUP_SIM and
MIN_EMB_FOR_POSITIVE stay as mirrored constants — used only to report
the population the C++ fitted on, not to refit it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
TRACES: VR-005 | AR-024
Holds out one mugshot 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. Replaces AR-002's 66x66 px working estimate
with a measurement. Needs no video and no ground truth beyond the
mugshot cache already on disk.
LVFace-B over 258 actors, 999 gallery embeddings, threshold 0.754:
px 12 16 20 24 32 40 48+
TPI 6.6% 46.5% 81.8% 93.4% 98.1% 99.2% 99.2%
FPI is 0.000 at every size — a face too small to identify degrades to
unidentified, never to a wrong name. rank-1 holds at >=99.6% from 24 px
up, so what fails first is the calibrated probability crossing
threshold, not the ranking.
Two limits on reading this. FPI grows with the number of actors
competing, so 258 understates it against a production library. And
detection and alignment run on the native image with only the resulting
112x112 crop degraded, so landmark error at small face sizes is excluded
by construction and the curve is an upper bound — VR-010 measures the
same question end to end, and lands well above these numbers.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
TRACES: VR-005 | AR-002
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.
dtourolle
