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dtourolle/scene-actor-extraction:v1.0.0
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Author SHA1 Message Date
dtourolle e7dfd20a1a Merge branch 'feature/ci-images' into feature/opencv5 2026-08-04 14:53:24 +02:00
dtourolle 90072e48da feat(ci): CPU builder image for the unit-test workflow 
TRACES: DP-007 | PR-004

Pinned by tag rather than :latest, so a workflow run is reproducible against
the image it was written for.
 2026-08-04 14:53:12 +02:00
dtourolle 9a8a0d57af docs: SuperHero benchmark — how to reproduce it and what it scores 
Records the reference film end to end: fetching the annotations and mugshots,
fusing the scene clips into one stream, building the gallery at the 66 px face
floor, and the measured result (precision 1.00, recall 0.65, F1 0.79).

Three things are written down because each cost time to discover:

- Why Bali was withdrawn. Its reference crops have a median detected face of
  27 px against a 69 px maximum, so every reference was upscaled past what the
  embedder was trained for (AR-011). No threshold fixed it — at 66 px, 2 of 69
  references survived. Any accuracy figure recorded against Bali measures
  upscaling artifacts as much as the pipeline.
- Run it as one film. Per-scene clips defeat per-film gallery expansion
  (AR-019) and pay model load ten times over.
- Check you are on the GPU. ORT's CUDA provider fails to load here and falls
  back to CPU silently, so a build-ort timing is a CPU number wearing a GPU
  label — a 15x error whose only symptom is a number with no baseline.

TRACES: AR-011, AR-019 | VR-001, VR-005 | SR-002
 2026-08-04 14:32:33 +02:00
dtourolle 02fed7d328 chore: bump KPN to 454f72c (ignore generated ORT cache) 2026-08-04 14:08:13 +02:00
dtourolle 2e167c671f chore(models): track 2d106det and the larger SCRFD variants via LFS 
Detector variants used by the resolution and min-face studies. LFS per
.gitattributes, so the repo carries pointers rather than 20 MB of weights.
 2026-08-04 14:04:50 +02:00
dtourolle 51205d0ba2 chore(traces): put TRACES tags on their own line; regenerate the report 
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.
 2026-08-04 14:04:21 +02:00
dtourolle 546700f47e refactor(bench): SuperHero replaces Road to Bali as the reference film 
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
 2026-08-04 13:49:11 +02:00
dtourolleandClaude Opus 5 05a3142d27 fix(expansion): finish AR-018, retiring the last two expansion cosines 
AR-018 was marked Done while the promotion path still ran on the
constants it was meant to replace. track_gallery.hpp rejected a track
when buffer_spread (1 minus the minimum pairwise cosine) exceeded
expand_track_spread_max, and skipped a view when its raw gal_sim cleared
expand_novelty_sim. Both were bare cosines with no recorded EXCEPTION,
so both were defects under the AR-024 invariant rather than tagging gaps.

The calibrated band was real but unreachable. expand_band_lo/hi were
declared in Config and read nowhere, and set_band() had no callers, so
the gate always ran at the hardcoded 0.90/0.95 while --expand-novelty-sim
and --expand-spread-max stayed live flags.

The spread gate becomes store_coherence: the band's lower bound asked of
every pair in the store, in probability space, rather than a second
constant. admit() compares a newcomer only against its nearest existing
member, so a gradually drifting track chains A to B to C with every step
inside the band while A and C are strangers — the shape a track-ID
collision takes over a slow pan. The bound is re-asked pairwise before
anything reaches an actor's annex.

The novelty gate is deleted rather than converted. SPEC section AR-018
contrasts the band with expand_novelty_sim as the thing it replaces, and
AR-019 requires only that the band is satisfied. Novelty-seeking now
lives entirely in the eviction ordering, which ranks by similarity to the
actor's references instead of cutting at a constant, so there is nothing
left to tune but the two bounds.

BufEntry stored a raw cosine and the eviction loop compared two of them.
The map is monotonic so the ranking was never wrong, but it left a bare
cosine as a decision variable; it now stores the calibrated probability.

The [AR-018] Catch2 tag previously sat on the spread gate, reporting the
replaced mechanism as verification of its replacement. It now sits on the
band: both bounds asserted exactly, since they are inclusive and an
off-by-one there is invisible anywhere else; refusal counted on each
side; and the config bounds driven away from the shipped defaults so a
hardcoded fallback fails. The case that carries the invariant is "band
thresholds probability, not cosine" — under a calibration shifted by
0.10, cosine 0.84 is admitted and cosine 0.92 refused, the opposite of
their raw verdicts. A raw-cosine gate passes an identity-calibrated test
by accident and cannot pass that one. 15 cases, 38 assertions, passing.

scene_preview.cpp takes the flag rename because it would otherwise
reference deleted Config fields. It still does not compile, for reasons
predating this change: it also reads track_max_embed_dist and
track_max_frames_missing, retired by the earlier AR-024 tracker work, and
constructs FaceTrackerFunc with one argument where the registry and
calibration are now required.

Two notes for anyone reading the chain. The main.cpp flag rename and the
AR-018/AR-024 register rows landed in 35e7033, whose trailer names AR-004
only, so git log --grep=AR-018 will not surface them. And
docs/traceability.md is left uncommitted on purpose: regenerating it now
would bake in VR-013 rows for two experiment scripts that are not yet
committed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-018, AR-024 | SR-005
 2026-07-31 22:48:07 +02:00
dtourolleandClaude Opus 5 e327ab19e7 test: tag the untagged suites; correct two stale headers 
Four test files and one node header carried no TRACES tag, so the
requirements they verify read as implemented-but-unverified. Tagging a
test is what distinguishes the two.

test_calibration.cpp is AR-023; its three [report] cases verify GR-003
and are tagged separately, since the report is fitted from the same
distributions but is its own requirement. test_similarity.cpp is the CI
half of AR-026 — equivalence against hand-computed dot products, where
throughput at scale is AR-027 and cannot run on this host.
test_face_tracker.cpp is AR-007 and AR-008.

Two headers described code that no longer exists. face_aligner_node.hpp
still documented the RANSAC fit AR-005 replaced with an Umeyama
least-squares fit over all five points — not merely out of date but the
opposite of what the file does, and it reads as a rationale for
discarding the landmarks AR-030 measures. test_face_tracker.cpp still
described the park/revive branch AR-008 deleted, and the raw-cosine
cut_revive_sim that guarded it, which AR-024 retired.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-005, AR-007, AR-008, AR-023, AR-026, AR-030 | GR-003 | SR-001, SR-002
 2026-07-31 22:47:59 +02:00
dtourolle 35e703350f fix(kpn): park on full outputs; surface node exceptions 
Adopts the KPN backpressure fix (28e0667) and registers the application
error listener it exposes.

`push_blocking` parked a scheduler worker inside the push. Each ObjectNode
owns a private single-thread pool, so the parked thread was the only one
that could drain that node's own input: under sustained backpressure
frame_source, camera_pos, face_detector and face_aligner all slept in
nanosleep at once and the pipeline stopped. Nodes now hold the value,
release the worker, and resume on a channel space-callback.

main.cpp registers set_error_handler so a node that throws names itself
and its exception. Previously the exception was discarded at the node
boundary and survived only as "node 'x' stopped unexpectedly", which says
that a node died but not why — the missing detail that made this slow to
diagnose.

AR-004 drops from Done to Mostly. Two gaps are recorded rather than
claimed fixed: a hang surviving at roughly 1 run in 20 against a 300 s
timeout (down from every run failing), and FanoutNode still dropping on
overflow instead of parking, which sheds frames on the AR-010 scene join
precisely when the dense branch falls behind.

TRACES: AR-004 | SR-002
 2026-07-31 22:42:19 +02:00
dtourolleandClaude Opus 5 de2eb5fa5c docs: regenerate the traceability matrix for VR-014 
The committed matrix predated the audio-signature binding, so VR-014 and
the four UT tags in `test_audio_offset.py` were absent from it while being
present in the register — the one inconsistency a generated file is
supposed to make impossible.

VR-014 also needed an explicit tier row. The blanket `VR-* | Out of CI`
line is right about every other study and wrong about this one: its
fixture is committed and its signature is CPU-only DSP, so it is a test a
CI host can run rather than a measurement someone has to remember to
repeat. Left as an exception under the blanket rather than rewriting the
rule, because the rule still describes the other thirteen.

Coverage unchanged at 38/69; the gate reports no orphan tags.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: VR-014
 2026-07-31 17:02:45 +02:00
dtourolle 7b093eee92 Merge branch 'feature/quality-knee' into feature/opencv5 
# Conflicts:
#	docs/requirements.md
 2026-07-31 16:54:09 +02:00
dtourolle 890f1946bf Merge branch 'feature/dump-provenance' into feature/opencv5 2026-07-31 16:53:58 +02:00
dtourolleandClaude Opus 5 afca0524c9 docs: VR-013 and VR-014 results; AR-002 raised to 40px 
Records study results and the requirement change that follows from them.

VR-013 measures minimum face size end to end — gallery from one recording,
probes from another — rather than by degrading an already-aligned crop. Holding
90% of the plateau needs ~50 px that way against VR-005's ~22 px, the gap being
detection and landmark error rather than the embedder. AR-002 therefore takes
40 px, not 32: VR-005 isolates the embedder and is an upper bound, and 32 admits
faces in the falling region. FPI stayed 0.0% at every scale, and the ceiling is
cross-view rather than resolution.

VR-014 exercises audio-signature offset recovery on real film audio instead of
the synthetic golden tone. Forty 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 rather than a result, since offsets land on whole 92.88 ms
frames. The runtime/2 anchor is confirmed through head-trimmed files.

The soft spot VR-014 found is tier labelling, not accuracy: the score drops with
sub-frame misalignment, so 27 of 40 correct alignments were demoted to `loose`.
One frame of slack in the score restores all forty to `audio` with false matches
unmoved.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-002, VR-005, VR-013, VR-014 | SR-002, SR-003
 2026-07-31 16:53:58 +02:00
dtourolleandClaude Opus 5 d57e489e91 feat(audio): bind the v1 signature and validate offset recovery on real content 
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
 2026-07-31 16:52:11 +02:00
dtourolleandClaude Opus 5 0dbbe5f6a3 fix: belief accumulates across frames (lazy-OR), not once 
A track recognised on 318 of 385 frames was owned on none, so the truth file
named nobody while the matcher was accepting almost continuously.

The correlation discount was an annihilator rather than an attenuator. Weight
was 1 - P(same view), so once a track had one stored view every later frame of
that same face scored ~0.01 and the belief stopped moving. One observation just
over the accept threshold is logit(0.78) ~ 1.27, under the ownership bar — hence
recognised always, owned never.

Two changes, in the order they were found.

Correlated evidence is now attenuated by effective sample size,
n_eff = n / (1 + (n-1)·rho), each frame contributing the marginal gain. That has
the right shape at both ends: uncorrelated evidence accumulates linearly, and a
held pose converges on 1/rho rather than growing without bound. A constant floor
was tried first and rejected — it grows linearly forever, so a long shot could
out-argue genuinely varied evidence purely by lasting longer.

Combination is now weighted lazy-OR: P = 1 - (1-P_old)·(1-p)^w, stored as
log(1-P) so the update is additive and precision stays where it matters as P
approaches 1. Each frame is new evidence that this track is that actor, and the
belief is the probability that at least one sighting was right. It converges
faster than summing log-odds at the same effective count — 2.98 vs 2.53 after
two observations at p=0.78 — which is what a real clip needs.

Note that summing log-odds was already a correct sequential Bayesian update:
the matcher fits with prior 0.5, so logit(p) IS the per-frame log-likelihood
ratio and the running sum carries the prior forward. It was not wrong, it was
slow. What blocked ownership was the discount, not the combination rule.

Also fixes a real correctness bug: the observation count lived on the
discounter, which is shared by every track, so tracks pooled into one effective
sample and each was discounted by how many others happened to be on screen. It
is now a per-track parameter.

The registry's frame scope holds its lock for its lifetime and the mutex is not
recursive, so calling observe() inside a scope self-deadlocks. The pipeline
never does — separate nodes — but the test did, and hung rather than failing.
Documented at the call site.

Verified end to end: the same clip that produced zero actors now identifies
Bing Crosby and Dorothy Lamour with belief 0.97.

Suite: 96 cases, 6142 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-025 | SR-002
 2026-07-31 16:51:08 +02:00
dtourolle 83f38a617e docs: the RANSAC aligner was a defect, measured 
AR-005 replaced cv::estimateAffinePartial2D(..., RANSAC, 3.0) with Umeyama
least squares over all five points — the estimator InsightFace aligns with, and
so the one the ArcFace/LVFace training crops were produced by.

The first note here assumed the two agree wherever RANSAC keeps all five points,
leaving a small divergence on non-frontal faces. Measured on 400 gallery
headshots with the model held fixed, that was wrong: the crops disagree by a
median 17 source px and 83.5% embed below cos 0.99 of their Umeyama counterpart.
A 4-DoF similarity is exactly determined by two points, so every minimal sample
fits its own pair perfectly and is scored on the other three; real landmarks sit
a median 2.74 canonical px from any similarity fit, so a landmark outside the
3 px band is the common case and RANSAC returns an under-determined transform.

How much that cost in accuracy is a separate question, and the honest answer is
less than those numbers suggest. Rebuilding the full gallery moved the
intra/inter separation the AR-023 calibration is fitted from by 0.583 to 0.590:
the old warp was wrong but self-consistent, gallery and probe both went through
it, and the embedder tolerates framing variation. The sharper evidence is
duplicate detection — the rebuild dropped 1614 near-duplicates against the
original build's ~100, because unstable two-point fits gave near-identical
images visibly different vectors. That instability, not a headline accuracy
delta, is what a tracker accumulating evidence across frames was paying for.

Also records the AR-030 residual's real-data floor: on the most cooperative
images the pipeline sees, it runs a median 2.74 px, so landmark noise occupies
the first few pixels and the synthetic foreshortening ladder is optimistic about
the low end. Any discount curve has to treat that range as uninformative rather
than as mild pose, and VR-012 must set thresholds against the measured
distribution.

Tests carry the tag they verify: the residual's roll/scale invariance and
monotonicity under foreshortening are what make it a pose measure rather than a
pose-and-everything-else measure.

TRACES: AR-005, AR-030 | SR-002
 2026-07-31 16:39:12 +02:00
dtourolleandClaude Opus 5 05f30c51fc feat(artifacts): push and pull the VR-013 corpus 
The cross-source study needs two 4K recordings and a hand-sorted set of
face crops, neither of which belongs in git. Adds an xsource target to
both artifact scripts.

Push uploads the clips as-is (already compressed) and zips labelling/.
Pull fetches both and regenerates frames with ffmpeg rather than
downloading them: ~320 MB of PNG that is deterministic from the clips.
The extraction settings are pinned in the script, not left to the
caller, because the manifests key on frame filenames and on detection
order within each frame — verify_labels.py runs afterwards and fails
loudly if they drift.

Pull refuses to overwrite an existing labelling/. It is human ground
truth: somebody looked at 167 crops and placed each one, and silently
replacing that with a remote copy would destroy the expensive half of
the study.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: VR-013
 2026-07-31 15:58:24 +02:00
dtourolle 402429dc2f Merge branch 'feature/gallery-report' into feature/opencv5 2026-07-31 15:31:07 +02:00
dtourolleandClaude Opus 5 ddb748eecb feat: gallery build report 
GR-003 — the calibration fit already computed per-actor dedup counts, how many
actors are eligible for positive pairs, and a 200-bin histogram of the intra and
inter distributions, then discarded all of it to stderr. Nothing persisted, so
nobody could audit whether a gallery was any good.

The report is written alongside the gallery at build time. That is the right
moment: the matcher fits the same sigmoid at analysis time, but by then the
answer is per-run and nobody is looking, whereas build time is when a gallery's
quality is actually decided.

What it surfaces, in order of usefulness:
- actors with no usable image — a silent recall ceiling, since the pipeline can
  never name them and nothing else says why
- actors below the positive-pair threshold — not broken, so nothing complains;
  they just quietly weaken every threshold downstream
- near-duplicate references removed, per actor and total
- the fitted calibration AND the two distributions behind it

That last one is the point. Every threshold in the pipeline is expressed in the
probability space this sigmoid defines, so if the distributions overlap heavily
the calibration is weak and every downstream decision inherits it — while the
gallery still looks fine from the outside.

The gallery-derived prior, intra/(intra+inter), is computed and reported but the
shipped default of 0.5 is deliberately left alone. The spec records these as
disagreeing; now the real value is visible, so the decision can be made on
evidence rather than argument.

Three tests: a zero-image actor is visible in the report, an under-referenced
actor is counted, and the report round-trips through JSON.

Suite: 95 cases, 6142 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: GR-003 | SR-001
 2026-07-31 15:30:46 +02:00
dtourolleandClaude Opus 5 a667caa313 feat: provenance attributes on the embedding dump 
VR-010 — a dump made with one detector/embedder pair was byte-indistinguishable
from one made with another, except for the two attributes GR-004 added. Replayed
against a gallery from a different model, cosine similarities are meaningless
but look entirely plausible. The register states the principle directly: a
fixture whose provenance is unknown is worse than no fixture, because it will be
trusted.

Sixteen attributes now record everything that determines the dump's content:
detector model and thresholds, min_face_px, max_faces, cut_threshold,
dense_scale, bbox_upscale, start/end, track_assoc_min_prob, and scene_detect.

scene_detect is the one that matters most. is_scene_boundary is all-zero both
when the detector found nothing and when it never ran, and those mean completely
different things to a consumer — without the flag they are indistinguishable.

No schema_version bump: new root attributes are additive and replay.py already
reads attributes with a default, so older dumps stay readable and the committed
fixtures — which predate this — still load.

Also corrects SCHEMA.md, which claimed bbox was already mapped to original
resolution at dump time. It is not; the upscale is applied downstream in the
matcher, after the dump tap. Harmless while dense_scale is 1 and silently wrong
otherwise, so bbox_upscale is now recorded and the doc says what the code does.

Verified end to end: all sixteen attributes present and correct on a freshly
generated dump.

Suite: 92 cases, 6136 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: VR-010, VR-001 | PR-002
 2026-07-31 15:24:06 +02:00
dtourolleandClaude Opus 5 d81fc59824 study(VR-013): cross-source identification probe over input resolution 
Gallery from one recording, probes from another, sweeping the probe's
input resolution end to end. VR-005 asked the same question over gallery
mugshots but degraded an already-aligned 112x112 crop with alignment held
perfect, so it isolates the embedder. Here the whole frame is downscaled
before the detector, so detection and landmark regression degrade with
it — which is most of the difference.

Corpus is two 4096x2160 clips of one shoot, four people, hand-sorted.
Ground truth is sorted by hand and gated by verify_labels.py; labels
carried down the scales geometrically by box position, never by
embedding similarity, which would keep only the faces the embedder
already gets right and drop the ones the sweep exists to find.

Findings, all scored through the production gallery sigmoid at
prob_threshold 0.754 — never a raw cosine:

- Holding 90% of the plateau needs ~50 px end to end, against VR-005's
  ~22 px. min_face_px at 40 looks right; 32 would admit faces in the
  falling region.
- FPI is 0.0% at every scale. Resolution loss goes entirely to TBI.
- The ceiling is cross-view, not resolution: everyone matches themselves
  within a recording (0.55-0.85) and collapses across two (0.14-0.45,
  threshold 0.335). Only the subject with frontal *gallery* references
  identified reliably, whatever their probe pose — so the lever is
  gallery pose coverage, not a better landmark source.
- Averaging SCRFD's overlapping detections instead of discarding them at
  NMS lifts cross-recording TPI 41% -> 49%, for one forward pass and no
  extra model.

Four identities and one shoot, so the shape is the result and the
absolute rates are not. Both clips contain all four people, so there is
no out-of-gallery class and the 10x-weighted out-of-cast misID is
untested here.

Clips, frames, hand-sorted crops and results are gitignored and belong
in the artifact registry — the sorting is human ground truth and
expensive to redo.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: VR-013 | AR-002, AR-005, AR-024
 2026-07-31 15:20:18 +02:00
dtourolleandClaude Opus 5 50649c1f87 feat: expansion promotion gated on all three discontinuity signals 
AR-019 — promotion may only borrow same-identity evidence from a span where
identity is certain, so every discontinuity signal now clears the buffers rather
than just the histogram cut.

is_scene_boundary was already named in the gate but never set by anything, so
that half of it was dead until AR-010 gave it a producer. It now does what the
spec always said. The third signal, an identity contradiction, needs no code
here: AR-015 closes a track whose belief swapped, so it can no longer promote.

Ownership now comes from the registry rather than a second tally. TrackGallery
was computing its own plurality vote over accepted frames, which meant two
different answers to "who is this track" could coexist in one run — and the
expansion one ignored the Bayesian accumulation entirely, weighting thirty
near-identical looks the same as thirty distinct ones. The local tally survives
only as a fallback for callers with no registry attached, which is the unit
tests and the replay harness.

Suite: 92 cases, 6133 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-019, AR-010, AR-015 | SR-005
 2026-07-31 15:11:56 +02:00
dtourolleandClaude Opus 5 080581c050 feat: banded admission for the per-subject embedding store 
AR-018 — an embedding joins a track's store only if its similarity to something
already there falls inside a band, rather than merely being far from the gallery.

Above the upper bound it is redundant: another look at a pose the store already
covers, teaching the annex nothing while costing a slot a novel view could have
used. Below the lower bound it is suspect: within one track every face is the
same person by construction, so an embedding unlike everything else on the track
is evidence that construction failed — a track-ID collision or a bad detection.
Admitting it is exactly how an actor's annex gets poisoned with someone else's
face.

The old gate had only the upper half of that idea, expressed as a raw cosine
against the gallery. Both bounds are now calibrated probabilities (AR-024), so
the same number means the same thing here as in association and evidence
weighting rather than three different things.

This catches track-ID collisions EARLIER than the spread gate did — at the door
rather than at promotion — so the buffer never becomes two-person in the first
place. The spread gate stays as a second line for a track that drifts gradually
instead of jumping. The existing test was asserting the mechanism rather than
the outcome, so it was rewritten to assert what actually matters: whichever gate
fires, the outsider must not reach the annex.

Rejections are counted. A store that admits nothing is as broken as one that
admits everything, and neither is visible otherwise.

Band defaults 0.90-0.95 are working values pending VR-007; the two bounds fail in
opposite directions and must be swept separately.

Suite: 92 cases, 6133 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-018, AR-024 | SR-005
 2026-07-31 15:10:31 +02:00
dtourolleandClaude Opus 5 6da8ac2bdb perf: back the CPU similarity GEMM with OpenBLAS 
The CPU path was a scalar triple loop. It is the correctness oracle for the GPU
backends, but it is also what CI runs — there is no GPU on the N100 host — and
since AR-003 removed the per-frame face cap, a crowded frame now scores many
faces against a library-scale gallery. Scoring one face against 5000 embeddings
is 2.6 MFLOP; in scalar that does not hold up (AR-027).

S(g,f) viewed as row-major [n_faces x n_gallery] is exactly query * gallery^T,
so the loop nest collapses into a single cblas_sgemm.

OpenBLAS is optional in the build: found via pkg-config, and the scalar path
remains when it is absent so no hard dependency is added and the two can be
diffed when a similarity looks wrong. The configure step warns rather than
failing, since a developer without it should still get a working tree.

The test target links it too. Without that the suite compiles the scalar
fallback while the builder image ships CBLAS, so CI would be verifying a kernel
that is not the one running in production — the same class of mistake as testing
a path the gate never executes.

Recorded as required (not optional) in the DP-007 image, for the same reason.

Suite: 92 cases, 6136 assertions, with CBLAS compiled in.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-026, AR-027, DP-007 | SR-001
 2026-07-31 15:04:29 +02:00
dtourolleandClaude Opus 5 bbab5aed23 feat: join the decode butterfly so scene boundaries reach the face branch 
AR-010 — is_scene_boundary had no producer: SceneDetectorFunc was a terminal
sink writing scenes.json and never annotating the frames flowing to face
detection. The flag was permanently false, so the boundary half of AR-007's
frame-dependent association was dead code that a test could still exercise
synthetically and appear to verify.

The topology already forks after decode — dense frames to TransNetV2, sampled
frames to face detection — so this is a fork-join. SceneBoundaries is the join:
the detector publishes each window's verdict with a watermark, and an annotator
on the sampled branch stamps the flag.

The watermark is the part that matters. TransNetV2 buffers 100 frames before it
can score any of them, so at any instant it has an opinion up to some time T and
none after. Without recording T a consumer cannot tell "no boundary" from "not
scored yet", and those demand opposite behaviour — treating unscored frames as
boundary-free is exactly what makes a downstream check pass while verifying
nothing.

Buffering alone does not work, which was my first attempt. Channel depth creates
lag only when the consumer is slower, and the face branch runs four orders of
magnitude faster per frame than TransNetV2 (0.01ms vs 400ms), so its channels
drain instantly and no lag accumulates. Measured: 106 of 364 frames outran the
detector. The annotator therefore waits on the watermark explicitly. The
detector signals completion so the tail cannot deadlock, and publishes from
flush_remaining too — without that the final frames arrive with no verdict.

Boundaries are deduped on publish, matching what scenes.json does at write time.
A run of adjacent high-scoring frames is one boundary, not several; leaving them
raw made this view report 357 where the file said 13. Now the two agree exactly.

Frames past the detector's last scored window remain unverified and are counted
as such rather than silently marked boundary-free.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-007, AR-010 | SR-002
 2026-07-31 14:56:38 +02:00
dtourolleandClaude Opus 5 a2c699a844 docs: AR-010 is blocked on a design decision, not an implementation gap 
Making SceneDetectorFunc a pass-through does not work. TransNetV2 buffers 100
dense frames before it can score any of them and trusts only each window's
centre, so a boundary at time T is not known until roughly 3.3s after T at
30 fps. The face pipeline runs on a parallel branch and has long since passed T.
An association hint that arrives after the association is worthless.

Three options recorded with their costs: two-pass (correct, doubles the decode
that already dominates runtime), delaying the face branch (couples the two
branches' timing, which invites heisenbugs under backpressure), or leaving it
unwired.

Leaving it unwired costs less than it looks, which is what makes this a decision
rather than a defect. The redesign made cuts and boundaries do the same thing —
both say "spatial continuity is broken, associate on embedding" — so TransNetV2
adds nothing over the histogram except on transitions the histogram cannot see:
slow dissolves and fades. That gap is real but narrow.

Where TransNetV2 still earns its cost is AR-019, whose promotion gate wants a
span free of cuts and boundaries. A late answer is fine there, because promotion
happens on track confirmation rather than per frame — so it can be wired
offline against the collected boundary list, off the hot path entirely.

Recommendation: leave the association path on is_cut alone, wire boundaries into
AR-019, and revisit if dissolve-heavy material shows association failures.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-010, AR-019 | SR-002
 2026-07-31 14:27:05 +02:00
dtourolleandClaude Opus 5 dfb8f5801e feat: no fixed cap on faces per frame 
AR-003 — max_faces defaults to 0, meaning no cap. A fixed cap discards the
SMALLEST faces first, which are exactly the background cast X-Ray still credits
with scene membership, so the pipeline was systematically losing the people it
is supposed to find in crowded scenes.

This is only safe now that AR-004 landed. Previously an uncapped frame would
have pushed more work into channels that dropped on overflow, trading a visible
cap for silent loss. With backpressure the producer slows instead, so per-frame
cost is contained rather than discarded.

The matcher's kMaxFaces used to throw above 32, which made it an accidental
second cap. It sizes the similarity engine's preallocated buffer, so it bounds
memory rather than face count — the frame is now scored in batches of that size.
Memory stays bounded; faces do not.

Largest-first ordering is kept even without the cap, and the comment now says
why: the Hungarian solver tie-breaks on index order, so that ordering is
load-bearing for the replay determinism test rather than a leftover of the cap.

Verified end to end on a real clip: identical output to the capped run (385
frames, 693 faces), which is expected since that footage peaks at 4 faces per
frame — the point is the absence of a regression. The committed fixtures remain
byte-identical and valid.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-003 | SR-002
 2026-07-31 14:22:20 +02:00
dtourolleandClaude Opus 5 036f44fbdd fix: point the KPN submodule at the merged commit 
The recorded pointer was be6e922 — the backpressure fix as originally committed,
before it was rebased onto KPN master. That commit exists on no pushed branch,
so a fresh clone of this branch could not fetch the submodule at all.

Now 6595e6e, the same change on KPN master.

Worth noting for next time: rebasing a submodule commit after the superproject
has already recorded it silently invalidates the pointer. Nothing in the
superproject's status shows it, because the submodule working tree is clean and
at a valid commit — just not the one recorded.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-004 | SR-002
 2026-07-31 11:53:27 +02:00
dtourolleandClaude Opus 5 cd62d6452d test: replay the real tracker and registry from committed fixtures 
Tier T2 — composition rather than units. The registry tests construct awkward
states directly; these feed the pieces real 480x360 footage with the cuts, gaps
and crowded frames that synthetic input does not produce.

Six cases:
- fixture integrity: exact frame and face counts, contiguous face_offset, and
  the embedder identity each dump carries (GR-004). The counts are asserted
  exactly rather than approximately, which was impossible before AR-004 — what
  a lossy run dropped depended on timing.
- determinism: replaying a fixture twice gives identical track ids and windows.
  This is the property the whole fixture strategy rests on; without it every
  golden output derived from a fixture is unreliable and the CI replay tier is
  worthless.
- every face is assigned a track, and flush leaves nothing open — a track still
  live at EOF is a window that never reaches the output.
- windows are well-formed and inside the clip. A window ends at the last
  sighting, so it can never extend past the footage that produced it.
- a longer extinction window yields fewer, longer tracks. On the sparse fixture
  (140 faces over 385 frames) that is the difference the constant actually
  makes: absorbing a gap versus splitting a window.
- the cut-heavy fixture still contains cuts. This guards the corpus, not the
  code: a regeneration that produced cut-free fixtures would leave the
  association tests passing while silently testing nothing.

Driving the functors directly rather than through a KPN network is deliberate —
no threads, no channels, no scheduling, so the same input gives the same output.

Suite: 86 cases, 6106 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-004, AR-012, AR-013, VR-001, VR-002 | SR-002
 2026-07-31 11:23:17 +02:00
dtourolleandClaude Opus 5 c36885de73 fix: dropped frames fail the run instead of printing a footer 
A drop was reported to stderr and the process exited 0, so a run that discarded
320 frames "succeeded" and produced a truth file that looked complete. The
output in that case is a claim about footage that was never analysed, and
nothing in the file says so.

Now exits 2 and says why. Distinct from 1 (node crash) because the failures are
different: a crash produced no output, a drop produced output that cannot be
trusted.

This is also the regression test for AR-004 that otherwise did not exist. The
backpressure fix is one line in the KPN submodule — easy to lose in an update —
and with data pushes blocking, a drop can no longer occur on the data path. So
any drop now means either that fix regressed or a channel was disabled mid-run,
and both are worth stopping for.

Verified: a clean run still exits 0.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-004 | SR-002
 2026-07-31 10:45:39 +02:00
dtourolleandClaude Opus 5 b5c7d4f6d9 test: committed replay fixtures from the public-domain corpus 
Five HDF5 embedding dumps from bali/ — Road to Bali (1952) — 3.6 MB total,
generated at 5 fps with a 32 px minimum face. CI never calls a model, so
inference happens on a GPU host and CI replays these as data; everything
downstream of embedding is cheap CPU maths.

Public domain is the reason this corpus rather than a convenient one: derived
fixtures can be committed, where anything cut from a copyrighted title could not
live in the repository at all.

The set covers distinct behaviours rather than being five of the same thing:
bali_28 has 9 cuts, so it exercises shot/reverse-shot association (AR-007);
bali_46 is sparse at 140 faces over 385 frames, so it exercises gaps and
extinction (AR-013); bali_13 is the busiest at 4 faces per frame; bali_31 is
short at 29s. All five recorded zero drops.

Both pinned parameters are consequences of measurements, not defaults: 5 fps
because 1 fps over a 77s clip is 77 frames, too thin for an extinction window
measured in tens of seconds; 32 px because that is the VR-005 floor, and the
corpus is 480x360 so a stricter value would reject most of what is there.

make_fixtures.sh regenerates them. Reproducibility is the requirement — a
fixture whose provenance is unknown is worse than none, because it will be
trusted. These are byte-reproducible only because of AR-004: before node
outputs blocked rather than dropped, the same command produced different dumps
run to run.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: VR-001 | PR-002
 2026-07-31 10:41:51 +02:00
dtourolleandClaude Opus 5 09a4650fd9 feat: backpressure — the pipeline slows instead of losing frames 
Picks up the KPN fix: node data outputs block on a full channel rather than
dropping. Sentinels stay out-of-band, so EOF can always overtake a stalled data
path and the hold-and-wait deadlock that comment warns about is not reachable.

Verified on a 77s clip at 5 fps, which should yield 385 sampled frames:

  before  65 written, 320 dropped, 29s, two runs differ
  after   385 written, 0 dropped, 17s, two runs byte-identical

The determinism is the part that matters. Golden fixtures were impossible while
what got dropped depended on timing; VR-001 fixture generation is unblocked by
this, and so is the CI replay strategy that depends on it.

Faster rather than slower, which is worth recording because the intuition runs
the other way: a dropped frame has already cost its decode, and the overflow
exception cost more still.

AR-004 is not fully closed. Channel capacity remains a count of items, while a
face carries a 112x112 crop and a 512-float embedding — so a crowded frame
occupies far more memory per slot than a sparse one. Bounding by bytes in
flight is the remaining half, and it matters once max_faces is removed (AR-003).

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-004, VR-001 | SR-002
 2026-07-31 10:35:29 +02:00
dtourolleandClaude Opus 5 b98372bad8 docs: AR-004 is a KPN change, with the measurement behind it 
Backpressure cannot be implemented in this repository. Every node output in KPN
uses the dropping push() (pool_node.hpp:404 and :710, plus branch, fanout and
interrupt_node). A lossless push_blocking() already exists on both Channel and
OutputPort — "wait for the consumer to drain instead of dropping; the producer
just runs slower" — and nothing calls it. The fix is a per-channel policy or a
network default in KPN, and this pipeline should select lossless: a dropped
frame here does not degrade a result, it silently changes one.

Measured rather than inferred. One 77s clip at 5 fps should yield ~385 sampled
frames. On CPU it produced 49, ending at 51s, with 285 dropped at camera_pos
and 51 at face_aligner. Rebuilt with CUDA the same clip ran in 29s and reached
EOF correctly, and still dropped 320 at camera_pos, yielding 65. Faster
hardware moves where the queue backs up; it does not change what happens when
it does — which is why this is a correctness requirement rather than a
throughput one.

Raising channel capacity is therefore a stopgap: it lowers the probability of
overflow without changing the behaviour on overflow, and the failure it hides
is silent corruption of the output.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-003, AR-004, VR-001 | SR-002
 2026-07-31 10:29:53 +02:00
dtourolleandClaude Opus 5 e0f9c95689 docs: minimum face size is 32px, measured (VR-005) 
Replaces the 66px working estimate with the sweep result. 258 probes degraded
to each size and matched against a native-resolution gallery: 16px 46.5% TPI,
24px 93.4%, 32px 98.1%, 40px 99.2%, flat to 112px. The knee is 24-32 and 32
sits within about a point of the ceiling.

The estimate was roughly twice too strict. At 66px a large share of usable
faces would have been discarded, and on 480x360 sources most of them — which
is exactly the resolution of the fixture corpus.

The more useful finding: false identification was 0.0 at every size, including
12px. Small faces fail by becoming unidentified, never by being attributed to
the wrong actor. That asymmetry is what makes a low threshold safe — the cost
of admitting a marginal face is a miss, not a false claim.

Caveat recorded rather than assumed: FPI grows with gallery size, so 258 actors
understates it against a full library. Treat 0.0 as an observation at this
scale, not a property.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-002, VR-005 | SR-002, PR-002
 2026-07-31 10:22:38 +02:00
dtourolleandClaude Opus 5 9e4cdc4efc docs: bali fixture corpus, and AR-004 blocks reproducible fixtures 
Records `bali/` — five ~77s clips of Road to Bali (1952) — as the fixture
source. Public domain, which is the point rather than a convenience: derived
fixtures can be committed, where anything cut from a copyrighted title could
not live in the repository at all.

Makes explicit what the tier table only implied: CI never calls a model. Not a
preference — the embedder measures ~930 ms/frame on the CPU provider, so a 77s
clip at 5 fps is six minutes of inference. Every model invocation happens
locally and CI consumes the result as data, which is what makes the T1/T2 split
load-bearing rather than stylistic.

Two properties of the corpus to design around: 480x360 puts many faces below
the AR-002 66px minimum, so generation must set and record --min-face-px; and
77s at 1 fps is too thin to exercise an extinction window measured in tens of
seconds, so fixtures want 5 fps.

The finding that matters: a trial dump produced 49 frames of an expected ~385,
stopping at 51s of 77s, with 285 frames dropped at camera_pos and 51 at
face_aligner on channel overflow. Channels drop rather than block, and what
drops depends on timing, so the same command twice can yield different dumps.
Golden fixtures cannot be built on that — AR-004 is a prerequisite for VR-001
fixtures, not just a throughput concern for crowd scenes.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-004, VR-001 | PR-002
 2026-07-31 10:19:10 +02:00
dtourolleandClaude Opus 5 08941540cb feat: presence windows come from registry claims (schema_version 2) 
The sink no longer reconstructs presence from per-frame detections. A reaped
track already IS a window — [first_seen, last_seen] of a track an actor owned —
so it is pushed straight to the aggregator when it dies and written out as-is.

AR-012 completed end to end. The annealing pass is deleted, not disabled:
anneal_sec existed only to bridge gaps between isolated accepted frames, and a
track that survives its own gaps leaves it nothing to do. The field is REMOVED
from the output rather than zeroed — a field naming a mechanism the pipeline no
longer has is actively misleading to anyone reading a manifest, and would
outlive everyone who remembers why it reads 0.

IR-002 — schema_version 2, matching jRay/SPEC.md JR-002. Windows become objects
carrying `belief` and `route` rather than bare float pairs, so a consumer can
caveat or filter instead of treating every window as equally certain. The new
`extraction` block carries `extinction_sec` (the successor to anneal_sec, and
what a consumer actually needs to interpret a window) and `gallery_scope` —
global vs limited being the strongest single quality signal when two manifests
compete for one cut, since identical gallery_size can mean very different
recall.

AR-016 wired: a pre-write hook flushes the registry with the last timestamp
seen, so tracks still live at EOF are emitted. A film ends with faces on screen
and those tracks have not timed out; without this the closing scene's cast is
silently dropped, which reads as a recognition miss rather than a bookkeeping
bug.

IR-003 stays In Progress deliberately: the sink now writes after the flush, but
the deferred re-identification pass (AR-020) does not exist yet, so output is
still final at EOF rather than after it.

This is a BREAKING format change and part of the coordinated SR-003 bump — it
must ship together with the jRay reader and the server's acceptance of the new
shape, not ahead of them.

Suite: 80 cases, 3250 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-012, AR-016, IR-002, IR-003 | SR-002, SR-003
 2026-07-31 10:10:51 +02:00
dtourolleandClaude Opus 5 fe29d014da feat: identity evidence reaches the registry 
Closes the link that made AR-012 inert: the tracker was maintaining registry
state, but nothing called observe(), so no belief accumulated, no track was ever
owned, and no presence claim could be emitted. Tracking worked and presence did
not.

The matcher now feeds every scored face to the registry as a calibrated
posterior plus its embedding. Deliberately every scored face, not only the ones
clearing prob_threshold: a run of near-misses for one actor is evidence, and
discarding it would leave ownership depending on the per-frame threshold this
redesign exists to stop relying on. The registry discounts for correlation and
decides ownership from the accumulated posterior (AR-025).

The registry is an optional dependency of the matcher. Without one it behaves
exactly as before, which keeps the replay harness and the unit tests working
unchanged rather than forcing every caller to construct a registry it does not
need.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-012, AR-025 | SR-002
 2026-07-31 10:05:59 +02:00
dtourolleandClaude Opus 5 be5f67fa96 feat: tracker on the registry — one pool, calibrated, frame-dependent 
Merges feature/tracker-registry. See e9aea3f for the detail; in summary the
tracker no longer owns track state, association weights embedding over position
whenever position is uninformative, and every similarity comparison is a
calibrated probability.

Four constants retired: track_max_embed_dist, cut_revive_sim,
cut_inactive_max_frames, track_max_frames_missing.

Suite: 80 cases, 3250 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-007, AR-008, AR-024 | SR-002
 2026-07-31 10:01:31 +02:00
dtourolleandClaude Opus 5 e9aea3fc41 feat: tracker owns no state; association is frame-dependent and calibrated 
Three requirements land together because they cannot be separated. The
cross-cut revival branch was the only user of cut_revive_sim, so retiring that
raw cosine forces the pool collapse, and collapsing the pool removes the only
caller of the constant. Splitting them would have produced an intermediate
commit whose only purpose was to be split.

AR-008 — FaceTrackerFunc no longer keeps its own tracks_/inactive_ maps; it
holds a shared_ptr<TrackRegistry> and operates on it directly. Two parallel
copies of track state could disagree, and every divergence would surface as a
wrong presence window with nothing to indicate it. There is now ONE candidate
pool: last_seen alone says whether IoU is meaningful. The park/revive path is
deleted outright — matching a dormant track is ordinary inter-frame
association, and continuity falls out of the embedding comparison the tracker
already did rather than being a mechanism of its own.

AR-007 — track_alpha becomes the base weight for ordinary frames only.
Association drops to embedding-only when position carries no information:
on is_cut or is_scene_boundary, because the viewpoint changed, and for a
dormant track, because time has passed since its box was last valid. The second
case matters as much as the first and had no equivalent before.

AR-024 — association cost is a calibrated probability, never a raw cosine. The
tracker takes the calibration belonging to the active embedder, the same
function object EvidenceDiscounter uses. track_max_embed_dist becomes
track_assoc_min_prob, which means the same thing for every model, gallery and
face size, where a bare cosine threshold did not.

Retired: track_max_embed_dist, cut_revive_sim, cut_inactive_max_frames, and
track_max_frames_missing — the last superseded by the registry's extinction
window. That one is worth naming: a frame count silently changed meaning with
sample_fps, so the same configuration behaved differently at 1 fps and 5 fps.
Extinction is in seconds and lives in one place.

Tests rewritten rather than deleted. The old cases asserted revival by raw
cosine; the same behaviours are now asserted through the registry — a face lost
across a cut and re-associated is the SAME track, one unbroken window, and a
face returning past the extinction window is not. Added the case AR-007 exists
for: two people swap screen positions across a cut while keeping their faces,
and identity must follow the embedding rather than the box.

Suite: 80 cases, 3250 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-007, AR-008, AR-024 | SR-002
 2026-07-31 09:58:27 +02:00
dtourolleandClaude Opus 5 b7c96641a9 docs: no exact tier — the file-hash tier was withdrawn 
A stale reference to 'the runtime/exact tiers' as the fallback for media too
short to carry an audio signature. The exact tier keyed on a file hash and was
withdrawn on legal grounds: it fingerprinted the individual copy a user holds
rather than the cut the timings describe.

The pipeline never emitted a video_hash, so nothing in the code changes — but a
spec that still names a withdrawn tier is what makes the withdrawal look like an
oversight to the next reader, which is exactly how it nearly got re-added.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-07-31 09:55:50 +02:00
dtourolleandClaude Opus 5 843852e19c feat: registry owns correlation discounting (AR-024, AR-025) 
Moves two responsibilities inside the registry that callers should never have
been trusted with.

AR-025 — per-frame evidence is discounted for correlation by the registry
itself, via EvidenceDiscounter. Log-odds accumulation is only valid for
independent observations, and consecutive frames of one track are anything but:
near-identical pose, lighting and expression. Accumulated naively, thirty frames
of the same face at the same angle drive the posterior to certainty on what is
effectively one measurement.

Each observation is weighted by how much it adds — a view already contributed
counts for ~nothing, a genuinely new pose counts in full. This reuses the
novelty judgement gallery expansion already makes rather than inventing a second
one. The discounter is a separate class the registry holds, so it stays testable
and swappable, but it is a constructor argument rather than an option: there is
no correct way to accumulate without it.

AR-024 — observe() takes a calibrated probability and converts to log-odds
internally. A caller can no longer hand it a raw cosine, which would have been
silently wrong rather than obviously so. Retiring the remaining raw-cosine
constants in the tracker is still open.

DeadTrack now reports effective_obs alongside observations: the raw count and
the evidence that actually counted. A large gap between them is a track the
camera stared at, and worth seeing.

Three tests, one of which is the point: two tracks given the same number of
observations at the same posterior, one repeating a single view and one seeing
eight distinct ones, must not end up equally confident. Without discounting they
would be identical.

Fixed a test that asserted a belief swap on tied evidence. A tie leaves
ownership where it is — a challenger must out-accumulate the incumbent, since
one contrary observation is noise. The original test passed only because it fed
raw log-odds directly.

Suite: 78 cases, 3245 assertions. Coverage 20/63 to 22/63.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-024, AR-025 | SR-002
 2026-07-31 09:15:44 +02:00
dtourolleandClaude Opus 5 f0c7126f80 feat: TrackRegistry — presence follows track extent 
The spine of the redesign. Presence is now the extent of a track an actor owns,
[first_seen, last_seen], rather than the subset of frames in which recognition
happened to succeed. An actor recognised only at the end of a long track is
present for all of it, which is what the scene-scoped ground truth actually
records.

AR-013 — `last_seen` as an optional carries the entire liveness state: unset
means on screen, set means went off at that timestamp and still revivable,
reaped means emitted and erased. No missing-frame counter, no expired flag. It
subsumes the tracker's existing two-pool split, so there is no separate revival
path — matching a dormant track is ordinary inter-frame association.

The asymmetry is the point: interior gaps are claimed, the trailing cool-down is
not. A face lost and re-associated within the timeout never closed its track, so
the gap is presence — someone briefly occluded has not left the scene. But a
track that dies ends at its last sighting, never at the death time. That is
precisely the over-claim the retired extinction_sec keep-alive produced, where
presence ran on into the closing credits.

AR-014 — a belief swap A→B closes the track and opens a successor at the swap
frame. Not a correction: two non-twins both clearing the threshold on one face
is not realistic, whereas a track_id carried across a viewpoint change onto a
different person is. Treating it as a swap-and-continue would emit one window
blending two people; treating it as a boundary yields two that are each right.

AR-015 — two live tracks owned by one actor means at least one is wrong, since a
person cannot be in two places at once. A reverse index catches it on the update
that causes it rather than by scanning. This makes identity a third cut
detector, independent of the histogram and TransNetV2 and firing where those
failed.

AR-016 — flush() closes tracks still live at EOF. Without it a film ending
mid-shot silently drops its closing cast, which presents as a recognition miss
rather than a bookkeeping bug.

Reaping hands the dead track to the aggregator and erases it, so the registry
holds only live tracks and its size is bounded by concurrent on-screen faces
rather than growing with the film.

Locking: a frame's association pass is atomic as a unit via FrameScope, since
per-call locking would let another thread observe a half-updated frame.
owner() reads tally and verdict under one lock — separately, a track could be
both unowned and owned within a single promotion decision. A vote for an
already-reaped track is dropped and counted, because a nonzero count means the
timeout is shorter than the matcher's lag.

11 unit tests, driven directly against the registry with no network and no
fixture — the awkward cases are constructed rather than hunted for. Suite: 75
cases, 3236 assertions. Coverage 14/63 to 20/63.

Not yet wired into FaceTrackerFunc; that is AR-007/AR-008.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-012, AR-013, AR-014, AR-015, AR-016, AR-017 | SR-002
 2026-07-31 09:08:39 +02:00
dtourolleandClaude Opus 5 b35d49c772 docs: tag the implemented core with its requirement IDs 
Adds TRACES tags to code that already satisfies a Done requirement, so coverage
reflects what exists rather than starting from zero:

AR-001 face detection, AR-005 ArcFace alignment, AR-023 calibration fit,
DP-001/DP-002 the single analysis core behind the CLI, IR-001 truth-file
emission, IR-006 the Jellyfin round trip, GR-001/GR-002 gallery build and
incremental merge, VR-001 the embedding dump, VR-002 replay through the real
nodes, VR-003 per-second scoring.

Only Done requirements are tagged. A tag on Planned work would inflate coverage
with fiction that looks plausible — the same failure family as a gate that
cannot fail, and harder to spot.

GR-005 (gallery never leaves the instance) stays untagged deliberately: it is a
prohibition satisfied by the absence of an egress path, so there is no unit that
decides it. Same shape as PR-005 in the system spec, which has no software row
for the same reason. A goal held only by prohibitions cannot be verified by
pointing at code.

Coverage 5/63 to 14/63. The three VR tags are reported as tagged-but-unexecuted
and excluded from the numerator, since their tier cannot run on the CI host —
tagging deliberately cannot raise the number on its own.

Suite still 64 cases, 3199 assertions.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-001, AR-005, AR-023, DP-001, DP-002, IR-001, IR-006, GR-001, GR-002, VR-001, VR-002, VR-003
 2026-07-30 21:20:27 +02:00
dtourolleandClaude Opus 5 62396fce75 docs: record why exclude_dirs stays unset, refresh matrix 
The tool's defaults already exclude `vendor`, which covers the submodule at
scripts/vendor/jray-project. Setting the key explicitly is a trap worth
documenting: it REPLACES the defaults rather than extending them, and matching
is on path components rather than prefixes — so ["scripts/vendor"] matches
nothing while silently dropping __pycache__, node_modules, build and the rest.
Verified: the submodule's source is not scanned, and the only vendored path in
the report is the system spec it reads for PR/SR orphan checking.

Coverage after the merges: 5/63, 0 orphans. Every tag names a real requirement,
and nothing claims a requirement that is still Planned.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-07-30 21:16:15 +02:00
dtourolleandClaude Opus 5 908d166173 feat: bind galleries to the embedder that built them 
GR-004 — a gallery built with one embedding model is meaningless with another.
Cosine similarities across models are garbage but look entirely plausible, so
this fails silently and expensively; every measurement taken against a
mismatched pair would have been quietly wrong.

The stamp is the model basename plus a SHA-256 of its bytes, with embed_dim as
a cheap extra guard. The hash decides and the name explains, because neither
works alone: a name is a promise rather than a fact — models get re-exported in
place under an unchanged filename, which is exactly the case where the weights
differ and nothing else does — while a bare hash mismatch tells an operator
nothing actionable.

Mismatch is fatal in every mode with no bypass. Unstamped only warns, because
unstamped is unknown rather than known-bad, and an error firing on every legacy
gallery trains people to reach for the bypass reflexively. scripts/stamp_gallery.py
binds an existing gallery in place with no re-embedding, so the warning is a
migration step rather than a permanent state; --require-gallery-stamp promotes
it to an error once a site has migrated.

Two gaps found that would have defeated the requirement outright:

- Embedding dumps carried no stamp, so a replay — which has no live embedder —
  had nothing to check the gallery against. Dumps now carry embedder_model and
  embedder_sha256 as root attributes. Additive; schema_version stays 1. This is
  the same gap the dump audit identified independently.
- --merge produced one file holding two embedding spaces, which no later check
  can untangle. Merge paths now verify before writing.

The stamp also survives identity_matcher's calibration write-back, which would
otherwise have stripped it on the first analysis run — the check would have
worked exactly once.

Conflicts resolved additively: both branches appended a source to sae_gallery
and to the test target, and both edited the GR-004 register row.

Merged suite: 64 cases, 3199 assertions, passing on CPU with no GPU.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: GR-004, VR-001 | SR-001
 2026-07-30 19:04:07 +02:00
dtourolleandClaude Opus 5 662a469870 feat: v1 content-derived audio signature 
Implements the server spec §3 construction: a 120 s window centred on the media
midpoint, downmixed and resampled to mono 11025 Hz, a 4096/1024 Hann STFT, 32
log-spaced bands over 300-3000 Hz, one byte per frame carrying a 5-bit peak band
and a 2-bit energy class, base64 with a v1: prefix.

IR-004 — the signature itself, in src/audio_signature.{hpp,cpp}. Decode reuses
the already-linked FFmpeg libraries; libswresample was missing from ffmpeg_libs
and is added. The FFT is written out rather than taken from a library: the
output must be bit-identical against a separate C# implementation, so a
dependency whose version can change the numerics is a liability.

IR-005 — a golden fixture at tests/fixtures/audio/, verified against an
independent Python implementation producing identical bytes. FLAC rather than
WAV because 120 s of 11025 Hz PCM is 2.6 MB and does not compress in git; both
decode to identical samples. The PCM checksum is asserted separately from the
signature so a codec-level divergence is distinguishable from a DSP one.

IR-007 — media under 120 s emits no signature at all, since the centred window
underflows. The rule must be identical in both producers or signatures never
match on exactly the short items most likely to be misidentified.

IR-008 — the v1: prefix is emitted and honoured, so a future change to the DSP
chain is detectable rather than silently non-matching.

Six parameters the spec left undefined had to be pinned to reproduce a byte
stream at all: periodic Hann, band value as the mean of linear magnitudes, ties
to the lowest band, the energy-class definition and its thresholds, byte layout,
and the base64 alphabet. These are now normative in the server spec — left only
in a C++ header, the C# side would have guessed and diverged.

Not yet emitted into the truth file; that is the coordinated schema_version bump
under IR-002/IR-003.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: IR-004, IR-005, IR-007, IR-008 | SR-003
 2026-07-30 19:00:08 +02:00
dtourolle 28e3bd9496 docs: generated traceability matrix 
Committed rather than ignored, matching house precedent: coverage becomes
visible to anyone browsing the repo, and its movement over time is real history
worth having in the log.
 2026-07-30 18:59:06 +02:00
dtourolleandClaude Opus 5 d9aaf8fa4e build: consume the shared traceability tooling via submodule 
jray-project is added at scripts/vendor/jray-project and the extractor is used
from there. Only two files are repo-local: traceability.toml, which carries
everything repo-specific, and the CI workflow that invokes the vendored gate.

The extractor is deliberately NOT copied in. One implementation, parameterised
by config — a second copy would drift from the first, and the tool already
proves it works unchanged against all three registers.

Enables system_spec so PR/SR orphan checking runs: previously uncheckable,
because the system spec lived outside every component's checkout.

Gate is green at 0.0% of 63 requirements, which is correct — nothing is tagged
yet. Eleven are flagged unverifiable on this CI host and excluded from the
numerator rather than counted.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-07-30 18:58:40 +02:00
dtourolleandClaude Opus 5 a2ebdc4cdd docs: GR-004 done — gallery/embedder binding 
Stamp is model basename + SHA-256 + embed_dim: the hash decides, the name
explains. A name alone is a promise rather than a fact — models get re-exported
in place under an unchanged filename, which is exactly the case where weights
differ and nothing else does. A hash alone is unactionable in an error message.

Mismatch is fatal in every mode with no bypass. Unstamped only warns, because
unstamped is unknown rather than known-bad, and an error that fires on every
legacy gallery trains people to reach for the bypass. A migration script binds
existing galleries in place with no re-embedding, so warn is not permanent.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: GR-004 | SR-001
 2026-07-30 18:37:07 +02:00
Claude 7db40f430d GR-004: bind galleries to the embedder that built them 
A gallery is only valid for the embedder that produced its vectors. Cosine
similarities across models are meaningless but *look* plausible, so the mistake
is silent and every measurement taken afterwards is suspect. Stamp the embedder
identity into the gallery at build; verify it at every load.

The stamp is the model file's basename plus the SHA-256 of its bytes (plus
embed_dim). The hash decides, the name explains. A name alone is a promise
rather than a fact — models get re-exported and overwritten in place under an
unchanged filename, which is exactly the case where the weights differ and
nothing else does. A hash alone is correct but unactionable in an error message.
SHA-256 is derived from the artefact, needs no registry kept current, and costs
~0.1s for a 250MB ONNX, memoised per process.

Mismatch is a hard error in every mode, with no bypass, naming both sides.

Unstamped legacy galleries warn loudly and proceed: unknown is not known-bad,
and hard-failing every pre-existing gallery would turn the check into something
people disable rather than trust. --require-gallery-stamp (or
SAE_REQUIRE_GALLERY_STAMP=1, which propagates to subprocesses) promotes that to
a hard error — the mode measurement work should run in. scripts/stamp_gallery.py
re-binds an existing gallery with no re-embedding, so "warn" is a cheap state to
leave rather than a permanent one.

Embedding dumps carry the same stamp: a replay has no live embedder, so the dump
is the embedder as far as the gallery is concerned. Derived galleries inherit
their source's stamp; --merge and the JSON gallery merge check before writing,
since one file holding two embedding spaces cannot be untangled afterwards.

Verified in: scene_analyze, scene_preview, the sae_kpn matcher binding,
replay.py, optimize.py (once per film at startup, before the first evaluation),
movienet_eval.py and both merge paths.

Stamp logic lives in src/gallery/embedder_stamp.{hpp,cpp} and its Python twin
scripts/sae_stamp.py, kept dependency-light so replay subprocesses do not pay
sae_gallery's requests/Pillow import to ask whether two models match.

Tests: 12 new cases in test_gallery_store.cpp covering the comparison logic,
both round trips, and the SHA-256 vectors that guarantee the C++ and hashlib
stamps agree. No ONNX or GPU required.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-07-30 18:35:46 +02:00
dtourolleandClaude Opus 5 020306c94f docs: builder images and per-backend release binaries (DP-008) 
Adds the build/deploy story that was missing: containerised builder images for
cpu / cuda / rocm, and release jobs producing prebuilt binaries so a first
install need not compile.

States explicitly that this does not reverse DP-005. That requirement rejects
Docker as a *runtime* — GPU passthrough is fragile and exists only because of
the container. Using it as a *build* environment is the opposite case, and lets
one machine produce binaries for backends it cannot itself run. Build in a
container, run natively.

Two things deliberately cannot ship, and the installer must not imply otherwise:
TensorRT engines are GPU-architecture and TRT-version specific, so
build_trt_engines.sh still runs on the target; and models are ~725 MB in LFS,
orthogonal to the binary.

The base image is chosen by the OLDEST glibc to be supported, not by
convenience — a binary built in a container runs against the host's glibc, and
getting this wrong fails at load with GLIBC_2.xx not found. Accelerator runtimes
have the same shape of problem, so each image documents its compatible
CUDA/ROCm range and the installer checks it rather than discovering a mismatch
at first inference.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: DP-005, DP-007, DP-008 | PR-004
 2026-07-30 18:35:10 +02:00
dtourolleandClaude Opus 5 2919ed68d1 docs: audit findings, verification tiers, CI image, artifact storage 
Corrections from the dump audit and the completed agent work:

- AR-010 is not started, not in progress: is_scene_boundary has no producer
  anywhere. SceneDetectorFunc is a terminal sink writing scenes.json and never
  annotates the frame, so the field is permanently false and the dump column a
  constant 0. A replay test of the frame-dependent track_alpha would pass
  vacuously — the worst failure mode for a verification gate.
- T1 (functor-level) becomes the primary verification tier, not T2. KPN node
  functors are plain callables constructed outside the network, so a node is
  tested by calling operator() with hand-built inputs. That removes four
  hazards at once: fixture provenance, replay-from-frame-0, cross-test state
  leakage, and replay-harness nondeterminism. It also means a dead upstream
  producer no longer blocks testing its consumer.
- VR-010 (dump provenance) and VR-011 (replay harness rewrite) added. A dump
  made with LVFace is currently byte-indistinguishable from one made with
  w600k-R50 — the GR-004 problem again, in the dump.
- Four requirements had no verification tier at all; the traceability gate
  found them.
- DP-007: CI builder image, CPU-only, pinned by tag in the Gitea container
  registry. Corpus fixtures go to the package registry rather than LFS: LFS is
  pulled on clone and would tax every developer for data only CI reads.
- IR-004/005/007/008 marked done; the v1 DSP parameters they had to pin are
  now normative in the server spec.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

TRACES: AR-010, DP-007, IR-004, IR-005, IR-007, IR-008, VR-001, VR-010, VR-011 | SR-002, SR-003
 2026-07-30 18:33:32 +02:00
dtourolleandClaude Opus 5 45ef7c1916 Add the v1 audio signature to the pipeline (IR-004, IR-005, IR-007, IR-008) 
Implements the content-derived spectral-peak signature from
JRay-public-server/SPEC.md §3 so a truth file is self-identifying: 120 s
window centred on the media midpoint, mono at 11025 Hz, 4096/1024 Hann
STFT, 32 log-spaced bins over 300-3000 Hz, one byte per frame (5-bit peak
band + 2-bit energy class), base64, `v1:` prefix.

Audio decode is a second stream from the FFmpeg libraries the pipeline
already links for video; libswresample is added to the existing
ffmpeg_libs interface target. The FFT is written out rather than pulled
from a library for the same reason the plugin vendors one: the output has
to be bit-identical across two languages, so a dependency whose version
could change the numerics is a liability.

The server spec fixes the geometry but not enough to reproduce a byte
stream — Hann periodicity, band aggregation, the energy-class definition,
tie-breaking and the base64 alphabet are all unconstrained by it. Those
are pinned in audio_signature.hpp and mirrored in the golden fixture, so
the plugin can be implemented from the fixture alone.

IR-005: tests/fixtures/audio/ carries a deterministic 120 s tone (FLAC —
lossless, so identical PCM to the WAV make_fixture.py emits, and 3.5x
smaller in git) plus the signature it must produce, the decoded-PCM
checksum and the full parameter contract. That directory is the artefact
shared with the plugin repo; the PCM checksum is separate from the
signature so a codec-level difference is distinguishable from a DSP one.

IR-007: media under 120 s emits no signature. Same for a file with no
audio stream or one that will not open — UR-9 is an enhancement and must
never be able to break a fetch.

Verified against an independent Python reference implementation: same
bytes. All 32 bands and all 4 energy classes appear in the golden vector,
and the window-centring test wraps the fixture in 90 s of silence either
side and requires the golden value back.

Not wired into the truth-file output yet — that is the schema_version
bump under IR-002/IR-003 and is deliberately out of scope here.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
 2026-07-30 18:25:37 +02:00
102 changed files with 11809 additions and 602 deletions
Expand all files Collapse all files
.gitea/workflows/traceability-check.yml
+145
View File
@@ -0,0 +1,145 @@
name: Traceability Validation
# Mirrors JellyTau's .gitea/workflows/traceability-check.yml. The extractor is
# stdlib Python, so there is no toolchain install step and no jq.
#
# This workflow is component-agnostic: every repo-specific setting - which ID
# prefixes count, which file suffixes are source, which directories to scan,
# the threshold - lives in traceability.toml at the repo root, and the same
# extractor is shared by all three JRay components. Copying this file into
# another component needs no edits.
#
# NOTE: the runner here is an Intel N100 with no discrete GPU. This job is only
# ever static analysis of source comments plus markdown parsing, so it is cheap;
# the requirements it reports as "tagged but unexecuted" are the ones that need
# a GPU host, and they are deliberately never counted as covered.
on:
push:
branches:
- main
- master
- develop
pull_request:
branches:
- main
- master
- develop
jobs:
validate-traces:
runs-on: linux/amd64
name: Check requirement traces
steps:
- name: Checkout repository
uses: actions/checkout@v4
with:
fetch-depth: 0
submodules: recursive
- name: Check Python is available
run: |
set -e
command -v python3 >/dev/null 2>&1 || {
echo "python3 is missing from the runner image."
echo "The traceability tooling is stdlib-only Python;"
echo "3.9+ with CLI flags, 3.11+ to read traceability.toml."
exit 1
}
python3 --version
# The gate's own arithmetic is the thing being trusted, so its tests run
# before it does. JellyTau's gate was believed for months while it was
# dividing by frozen literals; untested gate logic is how that happens.
- name: Test the extractor
run: python3 scripts/vendor/jray-project/scripts/traceability/test_extract_traces.py
# Threshold policy and every other repo-specific setting live in
# traceability.toml, not here, so local runs and CI runs cannot disagree
# about what "passing" means. Denominators come from docs/requirements.md
# at run time and are never hardcoded -- in this file or anywhere else.
#
# A misconfigured run (zero requirements parsed, zero files scanned) is a
# hard failure rather than a plausible-looking 0%.
- name: Traceability gate
run: sh scripts/vendor/jray-project/scripts/traceability/traceability-gate.sh
- name: Check modified files for traces
if: github.event_name == 'pull_request'
run: |
set -e
echo "Checking modified sources for TRACES tags..."
# The extensions come from the report the gate just wrote, which got
# them from traceability.toml. Restating them here would be a second
# place for the source-file definition to live, and the two would
# drift the first time a language is added.
PATTERN=$(python3 -c "
import json, re, sys
suffixes = json.load(open('traces-report.json'))['config']['sourceSuffixes']
print('(' + '|'.join(re.escape(s) + '\$' for s in suffixes) + ')')
")
echo "Source suffixes from traceability.toml: $PATTERN"
CHANGED=$(git diff --name-only "origin/${{ github.base_ref }}...HEAD" \
| grep -E "$PATTERN" || true)
if [ -z "$CHANGED" ]; then
echo "No source files changed."
exit 0
fi
echo "Changed files:"
echo "$CHANGED" | sed 's/^/ /'
echo ""
# Advisory by design: not every file implements a requirement, and a
# tag on every function is noise that rots faster than it helps
# (CLAUDE.md: tag the unit that decides). This step exists to prompt,
# not to block. The blocking checks are in the gate step above.
#
# Piped into the loop rather than a here-string, and `case` rather
# than `[[ == ]]`, so this works under dash as well as bash. The loop
# body runs in a subshell, so misses are recorded in a file.
MISSING=$(mktemp)
echo "$CHANGED" | while IFS= read -r file; do
case "$file" in
*/test_*.py|*_test.py|*Tests.cs|tests/*|*/tests/*) continue ;;
esac
[ -f "$file" ] || continue
if ! grep -q 'TRACES:' "$file"; then
echo " no TRACES tag: $file"
echo "$file" >> "$MISSING"
fi
done
COUNT=$(wc -l < "$MISSING" | tr -d ' ')
rm -f "$MISSING"
if [ "$COUNT" -gt 0 ]; then
echo ""
echo "$COUNT changed file(s) carry no requirement tag."
echo "Format: // TRACES: AR-012, AR-013 | SR-002"
echo " (pipe separates requirement types, comma separates IDs)"
echo "A deliberate invariant exception is tagged separately:"
echo " // EXCEPTION: AR-024 <reason>"
echo "See CLAUDE.md and SPEC.md section 6."
fi
- name: Report summary
if: always()
run: |
echo "Traceability matrix: docs/traceability.md"
echo ""
head -40 docs/traceability.md || true
- name: Save reports
if: always()
uses: actions/upload-artifact@v3
with:
name: traceability-reports
path: |
traces-report.json
docs/traceability.md
retention-days: 30
.gitignore
+4
View File
@@ -19,6 +19,10 @@ compile_commands.json
# coverage). Regenerate with scripts/docs/run_holdout_all_models.py and
# scripts/docs/gallery_coverage_per_film.py.
!docs_data/*.json
# Exception: test fixtures are inputs, not build output. The audio golden
# vector (IR-005) is shared verbatim with the jRay plugin repo, so it has to be
# tracked. Regenerate the media with tests/fixtures/audio/make_fixture.py.
!tests/fixtures/**
# Video files
*.mp4
*.mkv
.gitmodules
+3
View File
@@ -2,3 +2,6 @@
path = external/KPN
url = https://gitea.tourolle.paris/dtourolle/KPN.git
branch = master
[submodule "jray-project"]
path = scripts/vendor/jray-project
url = git@gitea.tourolle.paris:dtourolle/jray-project.git
CMakeLists.txt
+46 -8
View File
@@ -152,6 +152,24 @@ if(SAE_GEMM_BACKEND STREQUAL "CPU")
set_target_properties(gemm_backend PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_include_directories(gemm_backend PRIVATE src)
target_compile_definitions(gemm_backend PRIVATE SAE_GEMM_CPU)
# AR-026/AR-027: back the CPU path with OpenBLAS when present. Optional, so
# the build gains no hard dependency — but without it the fallback is a
# scalar loop, which does not hold up against a library-scale gallery, and
# the CPU path is exactly what CI (no GPU) and the cpu builder image use.
find_package(PkgConfig QUIET)
if(PkgConfig_FOUND)
pkg_check_modules(OPENBLAS QUIET openblas)
endif()
if(OPENBLAS_FOUND)
message(STATUS "GEMM backend: CPU + OpenBLAS ${OPENBLAS_VERSION}")
target_compile_definitions(gemm_backend PRIVATE SAE_GEMM_CBLAS)
target_include_directories(gemm_backend PRIVATE ${OPENBLAS_INCLUDE_DIRS})
target_link_libraries(gemm_backend PRIVATE ${OPENBLAS_LINK_LIBRARIES})
else()
message(WARNING "GEMM backend: CPU scalar fallback — OpenBLAS not found. "
"Correct, but slow on a large gallery (AR-027).")
endif()
elseif(SAE_GEMM_BACKEND STREQUAL "CUDA")
find_library(CUBLAS_LIB cublas
HINTS /opt/cuda/targets/x86_64-linux/lib /opt/cuda/lib64
@@ -196,23 +214,31 @@ endif()
# FFmpeg (hwaccel video decode: CUDA/VAAPI, runtime-detected + swscale colour
# conversion). Hwaccel support is built into libavcodec/libavutil; no extra
# libraries are needed here.
# libswresample is the audio side of the same dependency — downmix + resample
# for the audio signature (IR-004, src/audio_signature.cpp). Not a new project
# dependency: it ships with the libav* set already required above.
find_package(PkgConfig REQUIRED)
pkg_check_modules(AVFORMAT REQUIRED libavformat)
pkg_check_modules(AVCODEC REQUIRED libavcodec)
pkg_check_modules(AVUTIL REQUIRED libavutil)
pkg_check_modules(SWSCALE REQUIRED libswscale)
pkg_check_modules(AVFORMAT REQUIRED libavformat)
pkg_check_modules(AVCODEC REQUIRED libavcodec)
pkg_check_modules(AVUTIL REQUIRED libavutil)
pkg_check_modules(SWSCALE REQUIRED libswscale)
pkg_check_modules(SWRESAMPLE REQUIRED libswresample)
add_library(ffmpeg_libs INTERFACE)
target_compile_options(ffmpeg_libs INTERFACE
${AVFORMAT_CFLAGS_OTHER} ${AVCODEC_CFLAGS_OTHER}
${AVUTIL_CFLAGS_OTHER} ${SWSCALE_CFLAGS_OTHER})
${AVUTIL_CFLAGS_OTHER} ${SWSCALE_CFLAGS_OTHER}
${SWRESAMPLE_CFLAGS_OTHER})
target_include_directories(ffmpeg_libs INTERFACE
${AVFORMAT_INCLUDE_DIRS} ${AVCODEC_INCLUDE_DIRS}
${AVUTIL_INCLUDE_DIRS} ${SWSCALE_INCLUDE_DIRS})
${AVUTIL_INCLUDE_DIRS} ${SWSCALE_INCLUDE_DIRS}
${SWRESAMPLE_INCLUDE_DIRS})
target_link_libraries(ffmpeg_libs INTERFACE
${AVFORMAT_LIBRARIES} ${AVCODEC_LIBRARIES}
${AVUTIL_LIBRARIES} ${SWSCALE_LIBRARIES})
message(STATUS "FFmpeg: avformat=${AVFORMAT_VERSION} avcodec=${AVCODEC_VERSION}")
${AVUTIL_LIBRARIES} ${SWSCALE_LIBRARIES}
${SWRESAMPLE_LIBRARIES})
message(STATUS "FFmpeg: avformat=${AVFORMAT_VERSION} avcodec=${AVCODEC_VERSION} "
"swresample=${SWRESAMPLE_VERSION}")
# nlohmann/json (gallery + output serialisation)
include(FetchContent)
@@ -249,6 +275,8 @@ find_package(HDF5 REQUIRED COMPONENTS CXX)
add_library(sae_gallery STATIC
src/gallery/gallery_store.cpp
src/gallery/gallery_builder.cpp
src/audio_signature.cpp # IR-004 — content-derived audio signature
src/gallery/embedder_stamp.cpp # GR-004 — gallery/embedder binding
)
set_target_properties(sae_gallery PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_include_directories(sae_gallery PUBLIC src ${HDF5_INCLUDE_DIRS})
@@ -280,6 +308,16 @@ target_link_libraries(sae_embed PRIVATE sae_gallery)
nanobind_add_module(sae_kpn src/kpn_bindings.cpp)
target_link_libraries(sae_kpn PRIVATE sae_gallery)
# ── sae_audio — Python module: the v1 audio signature (IR-004) ────────────────
# Compiles audio_signature.cpp directly and links only FFmpeg, rather than
# linking sae_gallery: the signature needs no model, no OpenCV and no HDF5, and
# a module that dragged all three in would make `import sae_audio` depend on a
# GPU-capable build of a repo whose audio path is pure CPU DSP. tests/ compiles
# the same source the same way, for the same reason.
nanobind_add_module(sae_audio src/audio_bindings.cpp src/audio_signature.cpp)
target_include_directories(sae_audio PRIVATE src)
target_link_libraries(sae_audio PRIVATE ffmpeg_libs)
# HDF5 already found above (before sae_gallery); vars HDF5_CXX_LIBRARIES / _INCLUDE_DIRS
# are reused by scene_analyze / dump_embeddings below.
Dockerfile.builder-cpu
+342
View File
@@ -0,0 +1,342 @@
# sae-builder-cpu — the CI build image
#
# TRACES: DP-007 | PR-004
#
# Build/push: scripts/ci/build_builder_image.sh --push
# Consumed by: .gitea/workflows/unit-tests.yml (pinned by tag, never :latest)
# Docs: docs/ci-image.md
#
# This is the CPU corner of the DP-008 builder matrix and the DP-007 CI image at
# the same time — one artifact, two uses. The CUDA and ROCm siblings differ only
# in the accelerator stack layered on top of this dependency set.
#
# CI runs on an Intel N100 with no discrete GPU. Everything here is chosen so
# that `-DSAE_INFERENCE_BACKEND=ORT -DSAE_GEMM_BACKEND=CPU -DSAE_BUILD_TESTS=ON`
# configures, builds and runs without a GPU, without a model, and without
# reaching GitHub.
# ─── Base image ──────────────────────────────────────────────────────────────
#
# Chosen for the OLDEST glibc to be supported, not for recency. A binary built
# in a container runs against the *host's* glibc; glibc is backward compatible
# but not forward, so the build base sets the floor for every machine DP-008's
# binaries can ever run on. Building on a newer base than the oldest supported
# host produces the classic `GLIBC_2.xx not found` failure at load time.
#
# Debian 12 "bookworm" = glibc 2.36 (Aug 2022). What that floor covers:
#
# Distro glibc Covered?
# Arch / CachyOS (rolling) 2.41+ yes
# Fedora 37 and later 2.36+ yes ← DP-005's targets are Fedora+Arch
# Debian 12 / 13 2.36+ yes
# Ubuntu 24.04 LTS 2.39 yes
# Ubuntu 22.04 LTS 2.35 NO
# RHEL / Rocky / Alma 9 2.34 NO
# Debian 11 2.31 NO
#
# The three misses are accepted deliberately: DP-005 puts Debian/Ubuntu out of
# installer scope and names Fedora + Arch as the supported distros, and every
# supported Fedora is 2.36 or newer. Going lower costs the toolchain rather than
# buying reach — Debian 11 ships GCC 10 (incomplete C++20) and Python 3.9, which
# has no `tomllib` and therefore cannot read the traceability gate's
# traceability.toml.
#
# Escape hatch, recorded now so it is not rediscovered under pressure: if the
# floor must drop to glibc 2.28 (RHEL 8 / manylinux_2_28 — the same baseline the
# ONNX Runtime and PyTorch wheels target), the move is a Rocky 8 base plus
# gcc-toolset-13, and OpenCV/FFmpeg/HDF5 all leave apt for source or
# EPEL/RPM Fusion. That is a different image, not a flag on this one.
#
# Not a glibc problem but worth stating: the binaries this image produces also
# link OpenCV, FFmpeg and HDF5 shared objects by soname. Making a *portable*
# release binary (DP-008) is a separate question from the glibc floor, and is
# answered by static linking or bundling, not by the base image.
FROM debian:12-slim
# Pins. Every version this image installs from source is an ARG so a rebuild is
# a one-line diff and `docker history` records what a given tag actually holds.
#
# ORT 1.28.0 and OpenCV 5.0.0 match the developer machine, so CI and local
# builds exercise the same libraries rather than merely similar ones.
# Catch2 / nlohmann_json / nanobind match the FetchContent pins in
# CMakeLists.txt:248 and tests/CMakeLists.txt:12 exactly — a vendored copy at a
# different version would be a silent divergence, not a convenience.
ARG ORT_VERSION=1.28.0
ARG OPENCV_VERSION=5.0.0
ARG CATCH2_VERSION=v3.5.3
ARG NLOHMANN_JSON_VERSION=v3.11.3
ARG NANOBIND_VERSION=v2.4.0
# Stamped so a build can prove which image it ran in, and so a green tick can be
# traced back to a specific dependency set. See the "Confirm the builder image"
# step in .gitea/workflows/unit-tests.yml.
ARG IMAGE_TAG=dev
ENV SAE_BUILDER=cpu \
SAE_BUILDER_VERSION=${IMAGE_TAG} \
SAE_ORT_VERSION=${ORT_VERSION} \
SAE_OPENCV_VERSION=${OPENCV_VERSION} \
DEBIAN_FRONTEND=noninteractive
# ─── System dependencies ─────────────────────────────────────────────────────
#
# One layer, ordered by why it is here rather than alphabetically.
RUN apt-get update && apt-get install -y --no-install-recommends \
# Toolchain. bookworm's default gcc is 12.2 — enough for the C++20 the
# project sets unconditionally (CMakeLists.txt:4). cmake is 3.25, above the
# 3.21 minimum. Ninja because the N100 has four cores and every second of
# build scheduling shows.
build-essential \
cmake \
ninja-build \
pkg-config \
git \
ca-certificates \
curl \
# Gitea's act_runner executes JS actions (actions/checkout, upload-artifact)
# with the `node` found *inside* the container. Without this the job cannot
# even check the repository out. Same reason as the kpnpp-builder image.
nodejs \
# HDF5 with the C++ API: galleries are HDF5-native and it is also the VR-001
# dump format. find_package(HDF5 COMPONENTS CXX) at CMakeLists.txt:273.
libhdf5-dev \
# FFmpeg decode. swresample is on this list deliberately: the audio
# signature (IR-004) downmixes to mono and resamples to 11025 Hz, and
# tests/test_audio_signature.cpp decodes the golden FLAC fixture, so the
# test build needs it as much as the main build does.
libavformat-dev \
libavcodec-dev \
libavutil-dev \
libswscale-dev \
libswresample-dev \
# OpenBLAS — required here, not optional. CI has no GPU, so SAE_GEMM_BACKEND
# =CPU is the only path it ever exercises, and without OpenBLAS the CPU GEMM
# falls back to a scalar loop that does not scale against a library-sized
# gallery (AR-027). The build only *warns* when it is missing so a developer
# without it still gets a working tree; the image must never be that case.
# Both the main build (CMakeLists.txt:162) and the test target
# (tests/CMakeLists.txt:42) discover it through pkg-config `openblas`.
libopenblas-dev \
# Python: the build itself needs the interpreter and headers
# (find_package(Python COMPONENTS Interpreter Development.Module) at
# CMakeLists.txt:254, for the nanobind modules). numpy/h5py/scipy are for
# the Python-side tooling — fixture generation, replay, validation scripts.
# From apt rather than pip: bookworm marks the environment externally
# managed (PEP 668), and apt's h5py is already linked against the same
# libhdf5 installed above. bookworm's python3 is 3.11, which has tomllib —
# the traceability gate needs it to read traceability.toml.
python3 \
python3-dev \
python3-numpy \
python3-h5py \
python3-scipy \
# Image codecs for the OpenCV build below. Without these OpenCV silently
# builds an imgcodecs that cannot read a JPEG, which fails at run time in a
# gallery build rather than at compile time here.
libjpeg62-turbo-dev \
libpng-dev \
libtiff-dev \
libwebp-dev \
libopenjp2-7-dev \
zlib1g-dev \
&& rm -rf /var/lib/apt/lists/*
# Fail the image build, not the CI run, if OpenBLAS or swresample are not
# discoverable the way CMakeLists.txt discovers them. An image that ships
# libopenblas but no openblas.pc would compile the scalar fallback in silence.
RUN set -eux; \
pkg-config --exists openblas; \
echo "openblas $(pkg-config --modversion openblas)"; \
pkg-config --exists libswresample; \
echo "swresample $(pkg-config --modversion libswresample)"
# ─── ONNX Runtime, CPU provider only ─────────────────────────────────────────
#
# The official prebuilt linux-x64 tarball is the CPU build: no CUDA, no
# TensorRT, no ROCm execution providers. That is the whole requirement here —
# excluding the GPU providers is not a size optimisation, it is the point.
#
# Verified against the 1.28.0 tarball: the shared object's highest versioned
# symbol requirement is GLIBC_2.27 / GLIBCXX_3.4.21, well under this base's
# 2.36, so ORT does not raise the floor set above.
#
# Installed to /usr/local/{lib,include/onnxruntime} because CMakeLists.txt
# includes <onnxruntime/onnxruntime_cxx_api.h> and needs the *parent* of that
# directory on the include path (CMakeLists.txt:108-113).
#
# CI never calls a model — the embedder measures ~930 ms/frame on this CPU
# provider — so ORT is present to satisfy the link, not to run inference.
RUN set -eux; \
curl -fsSL -o /tmp/ort.tgz \
"https://github.com/microsoft/onnxruntime/releases/download/v${ORT_VERSION}/onnxruntime-linux-x64-${ORT_VERSION}.tgz"; \
mkdir -p /tmp/ort; \
tar -xzf /tmp/ort.tgz -C /tmp/ort --strip-components=1; \
cp -a /tmp/ort/lib/libonnxruntime.so* /usr/local/lib/; \
mkdir -p /usr/local/include/onnxruntime; \
cp -a /tmp/ort/include/. /usr/local/include/onnxruntime/; \
ldconfig; \
rm -rf /tmp/ort /tmp/ort.tgz; \
test -f /usr/local/include/onnxruntime/onnxruntime_cxx_api.h
# ─── OpenCV 5, from source ───────────────────────────────────────────────────
#
# This is the reason the image is prebuilt at all. CMakeLists.txt:25 probes for
# OpenCV 5 first and falls back to 4; the branch targets 5, which no Debian
# release ships (bookworm has 4.6), and building it inside every CI run would
# dominate the run on an N100.
#
# BUILD_LIST is exactly the seven components find_package asks for
# (CMakeLists.txt:25-29) — OpenCV resolves their internal dependencies itself.
# Everything else is off: tests, samples, Java/Python bindings, and the apps.
#
# No GUI backend. highgui still builds (find_package REQUIREs the component) but
# with a stub — CI never calls imshow, and pulling GTK/Qt into a headless build
# image buys nothing. scene_preview is a developer tool, not a CI target.
#
# CUDA/cuDNN explicitly off: DP-007 excludes the GPU stack outright.
#
# The source tree and build tree are removed in the same layer, so the ~3 GB of
# intermediates cost nothing in the published image.
RUN set -eux; \
curl -fsSL -o /tmp/opencv.tar.gz \
"https://github.com/opencv/opencv/archive/refs/tags/${OPENCV_VERSION}.tar.gz"; \
mkdir -p /tmp/opencv-src; \
tar -xzf /tmp/opencv.tar.gz -C /tmp/opencv-src --strip-components=1; \
cmake -S /tmp/opencv-src -B /tmp/opencv-build -G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX=/usr/local \
-DBUILD_LIST=core,imgproc,imgcodecs,videoio,dnn,objdetect,highgui \
-DBUILD_SHARED_LIBS=ON \
-DBUILD_TESTS=OFF \
-DBUILD_PERF_TESTS=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_DOCS=OFF \
-DBUILD_opencv_apps=OFF \
-DBUILD_JAVA=OFF \
-DBUILD_opencv_python3=OFF \
-DWITH_FFMPEG=ON \
-DWITH_GTK=OFF \
-DWITH_QT=OFF \
-DWITH_OPENGL=OFF \
-DWITH_CUDA=OFF \
-DWITH_CUDNN=OFF \
-DOPENCV_GENERATE_PKGCONFIG=ON \
-DCMAKE_INSTALL_RPATH=/usr/local/lib; \
cmake --build /tmp/opencv-build --parallel; \
cmake --install /tmp/opencv-build; \
ldconfig; \
rm -rf /tmp/opencv-src /tmp/opencv-build /tmp/opencv.tar.gz
# ─── Vendored dependencies: Catch2, nlohmann/json, nanobind ──────────────────
#
# All three are FetchContent'ed by the build today, which makes every CI run
# depend on GitHub being reachable — a network outage would present as a code
# failure. Baking them in removes that dependency entirely.
#
# Catch2 is *installed*, so tests/CMakeLists.txt:6 `find_package(Catch2 3 QUIET)`
# succeeds and the FetchContent fallback is never reached. Its source is kept as
# well so the override below can cover the case where find_package somehow does
# not fire.
#
# nanobind must be cloned with submodules: its `ext/robin_map` is a git
# submodule, and a GitHub source tarball does not contain it. This is the one
# dependency where "download the tarball" produces a tree that configures and
# then fails to compile.
RUN set -eux; \
mkdir -p /opt/vendor; \
git clone --depth 1 --branch "${NLOHMANN_JSON_VERSION}" \
https://github.com/nlohmann/json.git /opt/vendor/nlohmann_json; \
git clone --depth 1 --branch "${NANOBIND_VERSION}" --recurse-submodules \
https://github.com/wjakob/nanobind.git /opt/vendor/nanobind; \
git clone --depth 1 --branch "${CATCH2_VERSION}" \
https://github.com/catchorg/Catch2.git /opt/vendor/Catch2; \
cmake -S /opt/vendor/Catch2 -B /tmp/catch2-build -G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX=/usr/local \
-DBUILD_TESTING=OFF; \
cmake --build /tmp/catch2-build --parallel; \
cmake --install /tmp/catch2-build; \
rm -rf /tmp/catch2-build; \
find /opt/vendor -maxdepth 2 -name .git -exec rm -rf {} +; \
ldconfig
# The initial-cache script the build is configured with. It lives in the image,
# not in the workflow, so the vendor paths have exactly one owner: move a
# directory here and no consumer needs editing.
#
# FETCHCONTENT_FULLY_DISCONNECTED=ON is the load-bearing line. With it, any
# FetchContent dependency that is *not* covered by an override above is a hard
# configure error instead of a silent download — so "this build does not touch
# GitHub" is enforced by the build system rather than asserted in a comment.
RUN set -eux; \
printf '%s\n' \
'# Baked into sae-builder-cpu. Use with: cmake -C /opt/vendor/vendored-deps.cmake ...' \
'# TRACES: DP-007' \
'set(FETCHCONTENT_SOURCE_DIR_NLOHMANN_JSON "/opt/vendor/nlohmann_json" CACHE PATH "vendored in the CI image")' \
'set(FETCHCONTENT_SOURCE_DIR_NANOBIND "/opt/vendor/nanobind" CACHE PATH "vendored in the CI image")' \
'set(FETCHCONTENT_SOURCE_DIR_CATCH2 "/opt/vendor/Catch2" CACHE PATH "vendored in the CI image")' \
'set(FETCHCONTENT_FULLY_DISCONNECTED ON CACHE BOOL "no CI build may fetch from the network")' \
> /opt/vendor/vendored-deps.cmake; \
cat /opt/vendor/vendored-deps.cmake
# ─── Self-check ──────────────────────────────────────────────────────────────
#
# Run the project's own dependency discovery — the same find_package and
# pkg_check_modules calls CMakeLists.txt makes — against this image, at image
# build time. An image that cannot satisfy them should fail here, loudly, once,
# rather than in every CI run that pulls it.
#
# Deliberately not a build of the project: the image must be buildable without
# the repository, and the repository's own configure step is what CI is for.
RUN set -eux; \
mkdir -p /tmp/selfcheck; \
printf '%s\n' \
'cmake_minimum_required(VERSION 3.21)' \
'project(sae_image_selfcheck LANGUAGES CXX)' \
'set(CMAKE_CXX_STANDARD 20)' \
'set(CMAKE_CXX_STANDARD_REQUIRED ON)' \
'find_package(OpenCV 5 REQUIRED COMPONENTS core imgproc imgcodecs videoio dnn objdetect highgui)' \
'message(STATUS "OpenCV ${OpenCV_VERSION}")' \
'find_package(HDF5 REQUIRED COMPONENTS CXX)' \
'message(STATUS "HDF5 ${HDF5_VERSION}")' \
'find_package(Catch2 3 REQUIRED)' \
'message(STATUS "Catch2 ${Catch2_VERSION}")' \
'find_package(Python 3.8 REQUIRED COMPONENTS Interpreter Development.Module)' \
'find_package(PkgConfig REQUIRED)' \
'pkg_check_modules(AVFORMAT REQUIRED libavformat)' \
'pkg_check_modules(AVCODEC REQUIRED libavcodec)' \
'pkg_check_modules(AVUTIL REQUIRED libavutil)' \
'pkg_check_modules(SWSCALE REQUIRED libswscale)' \
'pkg_check_modules(SWRESAMPLE REQUIRED libswresample)' \
'pkg_check_modules(OPENBLAS REQUIRED openblas)' \
'find_library(ORT_LIB onnxruntime REQUIRED HINTS /usr/lib /usr/local/lib)' \
'find_path(ORT_INCLUDE onnxruntime_cxx_api.h PATH_SUFFIXES onnxruntime' \
' HINTS /usr/include/onnxruntime /usr/local/include/onnxruntime /usr/local/include REQUIRED)' \
'message(STATUS "ORT ${ORT_LIB} / ${ORT_INCLUDE}")' \
> /tmp/selfcheck/CMakeLists.txt; \
cmake -S /tmp/selfcheck -B /tmp/selfcheck/build -G Ninja; \
rm -rf /tmp/selfcheck
# Python-side tooling the fixture and validation scripts import. Checked here so
# a missing wheel is an image failure rather than a mid-run traceback.
RUN python3 -c "import numpy, h5py, scipy; print('numpy', numpy.__version__, 'h5py', h5py.__version__, 'scipy', scipy.__version__)"
# ─── What is deliberately NOT here ───────────────────────────────────────────
#
# CUDA, TensorRT, ROCm, and the ORT GPU execution providers
# No GPU to use them. They belong to the sae-builder-cuda and
# sae-builder-rocm siblings (DP-008).
#
# The ONNX models
# Seven files, ~725 MB, in Git LFS. T1/T2 tests are model-free by design
# (tests/CMakeLists.txt:1-4), so the CI image needs none of them, and
# baking them in would inflate the image roughly tenfold to serve the T3
# smoke tests alone. Those pull the model they need via LFS in a separate
# job. The CI workflow checks out with LFS off for the same reason.
#
# The repository
# Nothing from the source tree is COPYed in. The image is a toolchain, and
# a toolchain that embeds the code it builds has to be rebuilt whenever the
# code changes — which is exactly the per-run cost this image exists to
# avoid.
WORKDIR /src
docs/SPEC.md
+558 -28
View File
@@ -40,12 +40,28 @@ Detect faces in sampled video frames.
presence (SR-002) a lower rate still answers the question, but it lengthens the
interval between samples and so weakens IoU-based association; sweep the two
together (VR-002).
- **Minimum face size is 66×66 px**, expressed in **original video resolution**,
- **Minimum face size is 40×40 px**, expressed in **original video resolution**,
not decoded-frame pixels. Stating it in original space decouples it from
`dense_scale`: otherwise a 0.5 downscale silently doubles the effective
threshold, and dense mode is exactly what scene detection uses.
66 is a working estimate of where ArcFace embeddings stop being reliable, not a
measured value — it should be replaced by the result of VR-005.
40 is **measured, not estimated** — it replaces an earlier 66 px guess. Two
studies bracket it, and the difference between them is the whole reason the
number is 40 rather than 32:
- **VR-005** degrades an already-aligned 112×112 crop and matches it against
a native-resolution gallery. Alignment is held perfect, so it isolates the
*embedder*: the knee sits at 24–32 px, and 32 px still returns 98.1% TPI.
- **VR-013** downscales the **whole frame before the detector**, so detection
and landmark regression degrade along with it. End to end, holding 90% of
the plateau needs roughly **50 px**, against VR-005's ~22 px.
The gap is detection and landmark error, which VR-005 excludes by construction
— so VR-005 is an **upper bound on quality**, not a threshold, and reading a
floor off it would admit faces in the falling region. **AR-002 therefore takes
VR-013's number.** 40 sits below the 50 px plateau deliberately: FPI is 0.0% at
every scale in both studies, so resolution loss costs recall and never
precision, and an over-tight floor discards presence that SR-002 requires.
- Emits bounding box, detector confidence, and 5-point landmarks.
- Bounding boxes must be reported in **original video pixel space**. When
`dense_scale < 1` downscales the decoded frame, coordinates are rescaled by
@@ -59,7 +75,9 @@ Detect faces in sampled video frames.
**Current:** SCRFD-500MF via `face_detector_node.hpp`, thresholds in `config.hpp`
(`detector_conf` 0.5, `detector_nms` 0.4), `min_face_px` 40, `max_faces` 10.
**Gap:** `min_face_px` → 66 and re-expressed in original resolution; `max_faces`
**Gap:** `min_face_px` re-expressed in original resolution — the value 40 is
already correct after VR-013, so what remains is the space it is measured in, not
the number; `max_faces`
removed, gated on backpressure (AR-004).
## AR-004 — Backpressure
@@ -80,8 +98,49 @@ by dropping work or growing without limit.
- Memory is the real limit: faces carry 112×112 crops plus 512-float embeddings.
Backpressure must engage on bytes in flight, not just item counts.
**Gap:** entire requirement. This is a prerequisite for removing `max_faces`, not
a follow-up to it.
### The fix is not in this repo
**Every node output in KPN uses the dropping `push()`** (`pool_node.hpp:404`,
`:710`; also `branch.hpp`, `fanout.hpp`, `interrupt_node.hpp`). A lossless
`push_blocking()` — "wait for the consumer to drain instead of dropping; the
producer just runs slower" — already exists on both `Channel`
(`channel.hpp:144`) and `OutputPort` (`variant_node.hpp:81`), **and nothing
calls it.**
So AR-004 is a change to the KPN repository, not to this one. It needs either a
per-channel lossless policy or a network-wide default, and this pipeline should
select lossless: a dropped frame here does not degrade a result, it silently
changes one.
**Measured, not inferred.** One 77 s clip at 5 fps should yield ~385 sampled
frames. On CPU it produced 49, ending at 51 s, with 285 frames dropped at
`camera_pos` and 51 at `face_aligner`. Rebuilt with CUDA the same clip ran in
29 s and reached EOF correctly — and still dropped **320** frames at
`camera_pos`, yielding 65. Faster hardware moves where the queue backs up; it
does not change what happens when it does.
Two consequences worth stating:
- **Raising channel capacity is a stopgap, not a fix.** It lowers the
probability of overflow without changing the behaviour on overflow, and the
failure it hides is silent corruption of the output.
- **Fixture generation is blocked on this** (VR-001), because what gets dropped
depends on timing. The same command run twice can produce different dumps, and
a golden fixture cannot be built on that.
**Current:** fixed in KPN — node data outputs use `push_blocking`, sentinels
remain out-of-band so EOF can always overtake a stalled data path. Verified on
the same clip: 385 of 385 sampled frames written, zero drops, and two
consecutive runs byte-identical where previously they were not.
It also ran *faster* (29 s → 17 s). A dropped frame has already cost its decode,
and the overflow exception cost more — so the lossy path was paying for work it
then discarded.
**Gap:** the remaining half — bounding by **bytes in flight** rather than item
count. Channel capacity is still a count of items, and a face carries a 112×112
crop plus a 512-float embedding, so a crowded frame occupies far more memory per
slot than a sparse one. That matters once `max_faces` is removed (AR-003).
## AR-005 — Face alignment and crop
@@ -92,9 +151,71 @@ Produce the exact input ArcFace expects.
nose, left mouth, right mouth).
- Alignment is the *only* geometric normalisation; no additional augmentation at
inference.
- **The transform is fitted by Umeyama's closed-form least squares over all five
points**, which is what InsightFace uses (skimage's `SimilarityTransform` *is*
`_umeyama`) and therefore what produced the crops ArcFace and LVFace were
trained on. The canonical warp is part of the input distribution, not an
implementation detail (AR-011).
- **Not a robust estimator.** A RANSAC fit buys a small residual by discarding
the landmarks that disagree with the model, and on a turned face those are the
foreshortened ones — the signal AR-030 reads. With five points and a two-point
minimal sample it also cannot separate a mis-detected landmark from honest
out-of-plane rotation, so the robustness is nominal while the cost to AR-030 is
total. It is RNG-driven besides, which made replay determinism a property of
thread scheduling.
**Current:** `align_face()` in `src/face_utils.hpp:9-22`, `cv::warpAffine` to
`{112, 112}`. **Gap:** none.
**Current:** `align_face()` in `src/face_utils.hpp`, Umeyama fit via
`umeyama_similarity()`, `cv::warpAffine` to `{112, 112}`. **Gap:** none.
> **Migration note — this was a defect, not a refinement.** Until this landed the
> fit was `cv::estimateAffinePartial2D(…, cv::RANSAC, 3.0)`. The expectation was
> that the two agree wherever RANSAC keeps all five points, leaving a small
> divergence on non-frontal faces. **Measured, that is wrong.** On 400 random
> gallery headshots, one model held fixed and only the estimator varied:
>
> | | median | p90 | max |
> |---|---|---|---|
> | Crop disagreement (source px, over the crop corners) | 16.97 | 75.91 | 223.31 |
> | `cos(umeyama, ransac)` for the resulting embedding | 0.791 | — | — |
>
> 83.5 % of crops embed to a cosine below 0.99 of their Umeyama counterpart —
> they are not the same face crop. The mechanism is that a 4-DoF similarity is
> exactly determined by **two** points, so every minimal RANSAC sample fits its
> own pair perfectly and is then scored on the other three. Real landmarks sit a
> median 2.74 canonical px from any similarity fit to the template (see AR-030
> below), so images with a landmark outside the 3 px band are the common case,
> not the exception; RANSAC then keeps two or three inliers and returns a wildly
> under-determined transform.
>
> **Every gallery baked before this change must be rebuilt** — GR-004's embedder
> stamp catches a model change, not an aligner change, so nothing else would say
> so.
>
> **How much this cost in accuracy is a separate question, and the answer appears
> to be: less than the crop numbers suggest.** Rebuilding the full gallery
> (2456 actors) moved the intra/inter separation the AR-023 calibration is fitted
> from only slightly:
>
> | | intra-actor | inter-actor | separation |
> |---|---|---|---|
> | RANSAC | 0.6234 | 0.0407 | 0.5827 |
> | Umeyama | 0.6340 | 0.0440 | 0.5900 |
>
> The reconciliation is that the old warp was *wrong but self-consistent*: it
> produced a differently-framed face rather than a scrambled one, gallery and
> probe went through the same estimator, and the embedder tolerates framing
> variation. So the figures in `model-bakeoff.md`, `best-model.md` and
> `pose-expansion.md` were all produced through the broken warp on both sides and
> should be re-run, but there is no measured basis for expecting them to move far.
>
> The sharper evidence of the old instability is duplicate detection: rebuilding
> with an unchanged `dedup_tol` dropped **1614** near-duplicate images, where the
> original build dropped on the order of a hundred. Near-identical source images
> used to embed to visibly different vectors — RANSAC fitting two-point subsets is
> unstable under small landmark perturbations, and being RNG-driven it was not
> reproducible either. That instability is what a tracker accumulating evidence
> across frames pays for, and it is the strongest reason the fix is worth having
> independently of any accuracy delta.
## AR-006 — Embedding
@@ -110,6 +231,121 @@ Generate a 512-d embedding per aligned crop.
**Current:** `embedder_node.hpp` + `face_embedder_engine.hpp`; default
LVFace-B_Glint360K. **Gap:** none.
## AR-028 … AR-030 — Embedding input quality
An embedder handed a face it cannot represent does not fail. It returns a
confident, plausible, wrong vector, and that vector then competes on equal terms
with every good one in the gallery — the same failure mode AR-011 names for
whole models, occurring here at the level of a single region. Quality assessment
is how that is caught **at inference**, rather than inferred afterwards from a
study of why a film scored badly.
Three axes, assessed on every face before its embedding is used as identity
evidence. They are kept separate and **not collapsed into one scalar**: they fail
for different reasons, have different remedies, and — as below — do not even earn
the same response.
- **Size** — already AR-002, floor at 40×40 px in original resolution, measured
end to end by VR-013. It is the precedent for the other two: the
threshold was *located*, not chosen.
- **Sharpness** — motion blur and soft focus destroy the high-frequency detail
the embedder keys on, and unlike size they leave the bounding box looking
perfectly healthy. Measured on the **112×112 aligned crop**, not the raw box:
the crop is already scale-normalised, so a measure taken there cannot silently
re-measure face size and double-count it against AR-002.
- **Visibility** — extreme pose or occlusion means the face presents fewer of the
features the embedding assumes are present. The measure is the **residual of
the AR-005 alignment fit**: the RMS landmark error, in canonical 112×112
pixels, left over after the best similarity transform onto the ArcFace
template. It costs nothing — the transform is computed for the warp regardless,
and the residual is what that fit could not explain.
Two properties earn it the job over an explicit yaw estimate:
- A similarity absorbs rotation, uniform scale and translation **exactly**,
so the residual is by construction the non-similarity part of the
deformation: out-of-plane rotation and foreshortening. In-plane roll
contributes nothing, so "a tilted head reads as a turned one" is excluded
structurally rather than by tuning. The destination frame is fixed, so face
size cannot leak in either — that is AR-002's axis, and double-counting it
would make a small frontal face look occluded.
- It responds to **occlusion** and to plainly broken landmark sets, which an
angle regressor by construction does not: a hand across the face is not a
rotation, but it does displace landmarks.
Indicative magnitudes from a synthetic foreshortening sweep (`k ≈ cos yaw`):
`k=1.0 → 0.00`, `0.9 → 1.18`, `0.75 → 3.11`, `0.5 → 6.72`, `0.3 → 9.85`
canonical px. Smooth and monotone with a usable range; the mapping onto real
faces is VR-012's to establish, and no threshold is set from these numbers.
**The synthetic ladder is noise-free and therefore optimistic about the low
end.** Measured on 400 real TMDB/Jellyfin headshots — the most frontal, most
cooperative population the pipeline ever sees — the residual runs p5 1.11,
median 2.74, p90 4.82, max 6.35 canonical px. So landmark noise alone occupies
roughly the first 3 px, and the synthetic sweep's "26° yaw ≈ 1.2 px" sits
*below* the noise floor on real data. VR-012 must set any threshold against
this measured distribution, and a discount curve has to treat the first few
pixels as uninformative rather than as mild pose.
Neither a dedicated landmark model (`models/2d106det.onnx` is present but
referenced nowhere — and it emits points, not pose) nor a direct pose CNN is
adopted unless VR-012 shows the residual insufficient. If one is needed the
candidate is **6DRepNet** (MIT, RepVGG-B1g2, 3.47° MAE on AFLW2000) rather than
Hopenet, which it dominates on accuracy, licence, recency and export
friendliness. Two caveats to record before that happens: both are trained on
**300W-LP**, which inherits research-only terms from 300W's constituent sets,
and both want their own loosely-framed ROI rather than the ArcFace crop — a
second warp and a second image in flight, which lands on AR-004's byte-based
backpressure gap. It would also have to run **per track** — over the bounded
view set AR-019's diversity buffer already keeps — not per face per frame,
which is the cost rule applied as written: fewer regions, never a degraded
input.
**Failing an axis discounts the observation; it does not delete the detection.**
Only size drops the face outright, and only because VR-005 measured a knee below
which the embedding carries no signal to discount. Blur and pose are different:
- A blurred or turned face is still evidence of **presence**, which is what
SR-002 actually asks about.
- The tracker admits a link on position *or* identity precisely so that a face
"whose embedding degraded (blur, profile turn)" stays linkable. Remove the
detection and the track fragments, costing the window extent AR-012/AR-013
exist to protect.
- AR-019 harvests non-frontal views *because* TMDB headshots are frontal.
Discarding turned faces starves the mechanism built to fix the pose problem of
its raw material, and AR-020 then has nothing to resolve at EOF.
The natural home for the discount is `EvidenceDiscounter` (AR-025), which already
weights how far one observation may move a track's belief. Note that its present
weight is pure *novelty*, so a profile view — maximally distant from everything
counted so far — currently scores near 1.0 and moves the belief hardest, when
against a frontal gallery it deserves the least trust. Novelty and reliability
are orthogonal and multiply; quality supplies the second term.
**Quality is carried, not consumed.** The vector travels with the face and is
written to the VR-001 dump alongside the embedding, so a threshold can be
re-litigated against recorded data instead of by re-running video, and so
VR-010's provenance records what the run actually admitted.
**No quality threshold is hand-set.** Each axis either has a measured knee
(VR-012, as VR-005 did for size) or it discounts rather than drops — a
hand-chosen cutoff on an uncalibrated measure is the same unfalsifiable magic
number AR-024 retired for similarity, and it would fail the same way: meaning
something different for every detector, every embedder and every film.
**Current:** visibility is measured and carried — `estimate_alignment()` in
`src/face_utils.hpp` returns the residual alongside the transform, and
`FaceAlignerFunc` writes it to `DetectedFace::alignment_residual`. Size is
`min_face_px` (40, decoded-frame space — AR-002 still open). Sharpness is
unmeasured. Nothing yet *consumes* any of it: no discount is applied, and
`align_face()` still drops the degenerate-fit case without counting it.
**Gap:** AR-029 entirely. For AR-030, the measure exists but the discount does
not — it must reach `EvidenceDiscounter` as the reliability term. For AR-028, the
residual does not yet reach the VR-001 dump, which is what VR-012 needs to run
from fixtures; that is the next step, since it unblocks the study that sets
every remaining behaviour.
## AR-007, AR-008 — Tracking
Link detections across frames into tracks representing one physical person.
@@ -154,6 +390,50 @@ Two distinct signals, deliberately kept separate:
- **`is_scene_boundary`** — opt-in (`--scene-detect`). TransNetV2 over a densely
decoded, downscaled stream flags a *true shot/scene boundary*.
> **`is_scene_boundary` currently has no producer.** `grep -rn is_scene_boundary
> src/` finds no assignment anywhere: `SceneDetectorFunc` is a *terminal sink*
> (`main.cpp:298-300`, `kpn::out<>`) that writes `scenes.json` and never
> annotates the `Frame` flowing to the face pipeline. The field is therefore
> always `false`, and the dump column (`embedding_dump_node.hpp:38`) is a
> constant 0. Compounding it, `main.cpp:280` returns from the
> `--dump-embeddings` branch *before* the `scene_detect` branch at `:296`, so no
> dump-producing path even instantiates the detector.
>
>
> **It cannot be fixed by making the node a pass-through.** TransNetV2 buffers
> `kWindow` = 100 dense frames before it can score any of them, runs inference
> every `scene_stride` (50) frames, and trusts only each window's centre. So a
> boundary at time *T* is not known until roughly 100 dense frames after *T* —
> about **3.3 s at 30 fps**. The face pipeline runs on a parallel branch and has
> long since passed *T* by then. An association hint that arrives after the
> association is worthless.
>
> Three ways out, none free:
>
> 1. **Two-pass.** Run scene detection to completion, then analyse faces with
> boundaries already known. Simple and correct; costs a second decode of the
> whole file, and dense decode is already the pipeline's dominant cost.
> 2. **Delay the face branch** by the detector's window latency. Keeps one pass;
> adds a buffering stage and couples the two branches' timing, which is the
> kind of coupling that produces heisenbugs under backpressure.
> 3. **Leave it unwired.** Accept that `is_cut` is the only association hint.
>
> **Option 3 costs less than it appears**, which is why this is a decision rather
> than a bug. Since the redesign made cuts and boundaries do the *same thing* —
> both say "spatial continuity is broken, associate on embedding" — TransNetV2
> adds nothing over the histogram except on transitions the histogram misses:
> slow dissolves and fades, where there is no frame-to-frame discontinuity to
> detect. That is a real but narrow gap.
>
> The value TransNetV2 retains is in **AR-019**, whose promotion gate requires a
> span with no cut *and* no boundary. There a late answer is still usable,
> because promotion happens when a track is confirmed rather than per frame.
> Wiring it there — offline, against the collected boundary list — is cheaper
> than any of the three options above and does not touch the hot path.
>
> **Recommendation: option 3 plus the AR-019 wiring**, and revisit if dissolve-
> heavy material shows association failures the histogram misses.
Both feed AR-007 as **association hints**: they tell the tracker that spatial
continuity is broken and that association should weight embedding over IoU.
Neither ends a presence window (AR-012).
@@ -386,12 +666,30 @@ An embedding is admitted only if its similarity to one already in the store fall
admitting it risks poisoning the store.
A starting band of roughly **0.90–0.95** is the working estimate, to be tuned
(VR-007). Note this is deliberately conservative compared to the current
`expand_novelty_sim` (0.55), which promotes embeddings *far* from the gallery —
(VR-007). Note this is deliberately conservative compared to the retired
`expand_novelty_sim` (0.55), which promoted embeddings *far* from the gallery —
much more aggressive, and much more exposed to admitting the wrong person.
Both bounds must be expressed as calibrated probabilities, not raw cosines (AR-024).
The lower bound is asked twice. `admit` compares a newcomer against its
*closest* existing member, which a gradually drifting track can chain past: every
step inside the band while the endpoints are strangers — the shape a track-ID
collision takes over a slow pan. So the same bound is re-applied across **every
pair** in the store before promotion. One bound, two enforcement points; not a
second constant.
Novelty is deliberately **not** a threshold. The store's eviction policy orders
its members by similarity to the actor's existing references and drops the
best-recognised one, so novelty-seeking is a ranking with nothing to tune, and
the band's upper bound already refuses the redundant views at the door.
**Current:** implemented in `gallery/track_gallery.hpp` — `admit` at the door,
`store_coherence` at promotion, both bounds from `Config::expand_band_lo/hi`.
Refusals are counted (`band_rejected`).
**Gap:** the bounds themselves are unswept working values (VR-007).
### AR-019 — Expansion of known actors
When a track is owned (AR-012), its store is promoted into a **per-film, in-memory
@@ -492,14 +790,14 @@ natural unit for anonymous presence, should that be adopted (AR-012, TBD).
open (VR-007).
**Current:** `src/gallery/track_gallery.hpp` implements a per-track diversity
buffer with eviction biased to gallery-far poses, promotion gated on
`expand_novelty_sim` / `expand_track_spread_max`, cleared on `is_cut`. Wired at
`identity_matcher_node.hpp:227`, cleared at `:126`.
buffer with eviction biased to gallery-far poses, admission and promotion both
gated on the AR-018 band in probability space, cleared on `is_cut`. Wired at
`identity_matcher_node.hpp:227`, cleared at `:126`; the calibration is handed
over at `:114`.
**Gap:** the band rule of AR-018 replacing the current novelty/spread gates; all
three quiet-signal conditions rather than only `is_cut`; probability space
throughout (AR-024); and the whole of AR-020 — the TBI queue, the deferred pass, and
deferring output until it completes.
**Gap:** all three quiet-signal conditions rather than only `is_cut`; and the
whole of AR-020 — the TBI queue, the deferred pass, and deferring output until it
completes.
## AR-022 — Unidentified-track capture
@@ -757,9 +1055,16 @@ prerequisite.
## DP-005 — Installation and provisioning
- Native install, **no Docker** — GPU passthrough is the most fragile part of a
containerised setup and exists only because of the container. Natively the GPU
works with the host drivers and media paths need no re-mounting.
- Native install, **no Docker at runtime** — GPU passthrough is the most fragile
part of a containerised setup and exists only because of the container.
Natively the GPU works with the host drivers and media paths need no
re-mounting. This constrains how the software *runs*, not how it is *built*:
DP-008 uses containers as build environments precisely because that side has
none of these problems.
- The installer may **fetch a prebuilt binary** (DP-008) instead of compiling.
Compiling stays supported, but should not be the only path — it is the slowest
and most fragile step of a first install. TRT engines are still built locally
either way (DP-008).
- An installer (`scripts/build_install.py`) consuming one `install.yaml`:
platform (nvidia/amd/cpu), embedder model, gallery scan cadence, install
prefix; runtime secrets written to a `.env`, editable without recompiling.
@@ -770,6 +1075,130 @@ prerequisite.
**Gap:** installer unbuilt.
## DP-007 — CI build image
CI runs on an Intel N100 with no discrete GPU, so the test build must configure
**CPU-only** and must not require CUDA, TensorRT or ROCm:
```
-DSAE_INFERENCE_BACKEND=ORT -DSAE_GEMM_BACKEND=CPU
```
A prebuilt container image supplies the toolchain, published to the **Gitea
container registry** and pinned by tag — matching the `jellytau-builder`
precedent. Building dependencies per CI run is untenable on an N100, and OpenCV 5
from source would dominate every run.
The same registry stores corpus dump fixtures as generic packages (see the
fixtures table in `requirements.md`). Rebuild the image when its dependency set
changes, not per run, and pin CI to a tag rather than `latest` so a rebuild
cannot silently change what a green build meant.
**Required in the image:**
| Dependency | Why |
|---|---|
| CMake, C++ toolchain, pkg-config | Build |
| **OpenCV 5** | `CMakeLists.txt:25` prefers 5, falls back to 4. The branch targets 5, so the image should carry it — it is not yet in most distro repos and building it per-run is prohibitive |
| HDF5 (C++) | Galleries are HDF5-native; also the dump format |
| FFmpeg dev libs — `libavformat`, `libavcodec`, `libavutil`, `libswscale`, **`libswresample`** | Decode. See the note below on swresample |
| Python 3 + numpy, h5py, scipy | Python-side tests, replay, traceability tooling |
| **OpenBLAS** | Backs the CPU similarity GEMM. Without it the fallback is a scalar loop, and the CPU path is exactly what this host runs — see below |
| Catch2, nlohmann/json | **Vendored into the image, not fetched.** Both are `FetchContent`-ed today (`CMakeLists.txt:220`, `tests/CMakeLists.txt:8`), which makes every CI run depend on GitHub reachability |
**OpenBLAS is not optional here, despite being optional in the build.** CI has no
GPU, so `SAE_GEMM_BACKEND=CPU` is the only path it exercises — and since AR-003
removed the per-frame face cap, a crowded frame scores many faces against a
library-scale gallery. The scalar fallback is correct but scales badly, which
would make the CPU path the bottleneck in the one place it cannot be avoided
(AR-027). The build warns when it is missing rather than failing, so a developer
without it still gets a working tree; the image must not be that case.
The test target links it too. Otherwise the suite compiles the scalar fallback
while the image ships CBLAS, and CI would verify a kernel that is not the one
running in production.
**Deliberately excluded:** CUDA, TensorRT, ROCm — no GPU to use them. Also the
ONNX Runtime *GPU* providers; only the CPU provider is relevant, and only for T3
smoke tests.
**Models are not baked into the image.** The seven ONNX files total ~725 MB and
live in Git LFS. T1/T2 tests are model-free by design
(`tests/CMakeLists.txt:1-4`), so the default image needs none. T3 smoke tests
require a model and should pull it via LFS in a separate job rather than
inflating the image tenfold for a minority of tests.
**`libswresample` is a real gap, not a formality.** The current
`pkg_check_modules` list (`CMakeLists.txt:200-203`) covers avformat, avcodec,
avutil and swscale but **not** swresample — which IR-004 needs to downmix to mono
and resample to 11025 Hz. It must be added alongside the audio-signature work.
**Gap:** entire requirement. The image does not exist, and no CI config is
present in this repo.
## DP-008 — Builder images and release binaries
Produce prebuilt binaries per backend so deployment does not require every user
to compile the project.
**This does not contradict DP-005.** That requirement rejects Docker as a
*runtime* — GPU passthrough is the most fragile part of a containerised setup and
exists only because of the container. Using Docker as a *build* environment is
the opposite case: hermetic, reproducible, and it lets one machine produce
binaries for backends it cannot itself run. Build in a container; run natively.
### Image matrix
The build has two independent axes (`CMakeLists.txt:48-49`), so the useful
combinations are:
| Image | `SAE_INFERENCE_BACKEND` | `SAE_GEMM_BACKEND` | Target |
|---|---|---|---|
| `sae-builder-cpu` | ORT | CPU | CI (DP-007), and the smoke-test fallback |
| `sae-builder-cuda` | TRT | CUDA | NVIDIA |
| `sae-builder-rocm` | ORT | ROCM | AMD |
All three carry the DP-007 dependency set (OpenCV 5, HDF5, FFmpeg incl.
swresample, vendored Catch2/nlohmann) and differ only in the accelerator stack.
The CPU image is the CI image — one artifact, two uses.
Published to the Gitea container registry, pinned by tag, rebuilt when the
dependency set changes rather than per run.
### What ships, and what cannot
**Ships:** the `scene_analyze` binary and its companions, per backend.
**Cannot ship: TensorRT engines.** `.engine` files are specific to the GPU
architecture and TRT version they were built on — `scripts/build_trt_engines.sh`
must still run on the target machine. A prebuilt binary shortens the install; it
does not remove the local engine-build step, and the installer must not imply
otherwise.
**Cannot ship: models.** ~725 MB in LFS, and orthogonal to the binary.
### The constraint that decides the base image
**A binary built in a container runs against the host's glibc.** Build on a
newer base than the oldest supported host and it fails at load with
`GLIBC_2.xx not found` — the classic and entirely avoidable trap when shipping
binaries out of containers.
So the base is chosen for the *oldest* glibc to be supported, not for
convenience or recency. Accelerator libraries have the same shape of problem:
the binary links against a driver-provided runtime, so each image must document
the CUDA/ROCm version range its output is compatible with, and the installer
must check it rather than discovering a mismatch at first inference.
### Jobs
A release job per backend, producing a tagged artifact in the registry. These are
**not** the CI gate — the gate runs the CPU image on every push (DP-007);
release builds run on tag. Their outputs are what DP-005's installer fetches
when the user does not want to compile.
**Gap:** entire requirement. No images, no release jobs.
## DP-006 — Gallery maintenance as a background concern
- Incremental gallery refresh runs on a timer (`gallery_scan_interval`, default
@@ -859,8 +1288,10 @@ rewritten.
**Media shorter than 120 s.** The window `runtime/2 ± 60 s` underflows, so no
signature is emitted and **no sync offset is applied**. Such items fall back to
the runtime/exact tiers, which is adequate: a 90-second extra or trailer is not
the content whose cut alignment matters. Both producers must apply the identical
the runtime tier, which is adequate: a 90-second extra or trailer is not the
content whose cut alignment matters. (There is no `exact` tier: the file-hash
tier was withdrawn on legal grounds — it fingerprinted an individual copy rather
than the cut the timings describe. See the server spec §3.) Both producers must apply the identical
rule, or they diverge on exactly the short items most likely to be
mis-identified.
@@ -882,7 +1313,47 @@ plugin. Consequences to carry through:
- Files never processed by this pipeline still get a signature from the plugin;
the two paths coexist deliberately.
**Gap:** entire requirement — no audio path exists in the pipeline today.
**Current:** `src/audio_signature.*` implements the construction, and
`tests/fixtures/audio/` holds the golden vector shared verbatim with the plugin
repo, which now matches it byte for byte from C# (jRay `JR-042`/`JR-043`).
`sae_audio` (nanobind, as `sae_embed` and `sae_kpn` are) exposes the same C++ to
Python so a study drives the shipped code rather than a numpy port.
**VR-014 measures what the golden vector cannot** — that the signature actually
aligns a differently trimmed release, on real film audio rather than a synthetic
tone. It does, with an order of magnitude to spare.
**The accuracy question is settled and is not close.** What the offset is *for*
is shifting scene windows, which are seconds long, so half a second of error is
invisible; the budget is 500 ms. Over 40 random offsets inside the ±600-frame cap
the recovered offset was the nearest frame every time — **worst error 46 ms**.
That figure is the quantisation floor rather than a measurement of quality: the
offset is expressed in whole 92.88 ms frames, so no correct answer can ever be
worse than half a frame. The `runtime/2` anchor behaves as specified through real
head-trimmed files (cutting `delta` from the head moves the window by
`delta/2`), and both an out-of-cap offset and unrelated content are declined
outright (0.10 and 0.07).
**Where it is soft is tier labelling, not alignment.** The *score* at the correct
offset falls with sub-frame misalignment — 0.94–0.99 when the true offset lands
within 0.1 of a frame boundary, 0.69–0.73 at half a frame — because the two
windows' frame grids no longer coincide. The offset stays right, but only 13 of
40 cleared the server's 0.85 `audio` threshold and the other 27 were demoted to
`loose`, a tier that means "possibly the same cut, degraded audio". The threshold
was calibrated on a re-encode at *zero* offset, where the score is 1.00.
The remedy is measured, not proposed (UT-108): counting a frame as agreeing if
its peak bin matches **within ±1 frame** returns all 40 to `audio` (worst 0.906)
while unrelated content and out-of-cap offsets stay at 0.12 and 0.16 — the gap
that makes the threshold mean anything is untouched. It costs 81 ms of offset
accuracy, of a 500 ms budget, because the flattened peak lets the argmax pick an
adjacent frame. ±2 frames buys nothing further. Adopting it is a
[server spec](../../JRay-public-server/SPEC.md) §3 change — the score is
normative and shared by three repos — so this repo measures it and leaves the
decision there.
**Gap:** the signature is computed but **not yet emitted** into the truth file —
that is the `IR-002` field and the coordinated `schema_version` bump.
## IR-006 — Jellyfin round-trip
@@ -945,8 +1416,57 @@ surfaced as a build report.
different models are meaningless but *look* plausible — this fails silently and
expensively otherwise.
**Gap:** named as step 4 of the `service-conversion.md` implementation plan;
unbuilt. This is the highest-value small fix in the document.
### The stamp
Two fields, written together: the model file's **basename** and the **SHA-256 of
its bytes** (plus `embed_dim` as a cheap extra guard). Stored as the `/embedder`
group in the gallery HDF5, and as an optional top-level `"embedder"` object in
the legacy JSON format.
The hash *decides*; the name is what a human *reads*. Neither alone is enough. A
name is a promise rather than a fact — models get re-exported, re-quantised and
overwritten in place under an unchanged filename, which is exactly the case where
the weights differ and nothing else does, so a name-only stamp is blind to the
failure it exists to catch. A hash alone is correct but unactionable: *"expected
3f2a…, got 9c1b…"* tells an operator nothing about what to do next. SHA-256 over
the file is derived from the artefact rather than asserted about it, needs no
registry kept up to date, and costs ~0.1 s for a 250 MB ONNX once per process.
### Verdicts
| Verdict | When | Default | Under strict mode |
|---|---|---|---|
| `match` | hashes agree | proceed | proceed |
| `weak_match` | names agree, one side unhashable | **warn** | **error** |
| `unstamped` | gallery predates GR-004 | **warn** | **error** |
| `unknown_embedder` | gallery stamped, embedder unidentifiable | **warn** | **error** |
| `mismatch` | proven different models | **error** | **error** |
**A mismatch is fatal in every mode, with no bypass**, and the message names both
sides — what the gallery was built with and what is loaded.
The three "cannot prove it" verdicts warn loudly instead, because they describe an
*unknown* state rather than a *known-bad* one, and because every gallery built
before this requirement is unstamped. Hard-failing all of them would make the
check something people route around rather than trust. Strict mode
(`--require-gallery-stamp`, or `SAE_REQUIRE_GALLERY_STAMP=1`, which propagates to
subprocesses) promotes them to errors — that is the mode measurement work runs in.
`scripts/stamp_gallery.py` re-binds an existing gallery without re-embedding, so
migration costs one command; that is what makes "warn" a temporary state rather
than a permanent one.
### Scope of the check
Embedding **dumps** carry the same stamp (`embedder_model` / `embedder_sha256`
root attributes, `scripts/optimizer/SCHEMA.md`): a replay has no live embedder, so
the dump *is* the embedder as far as the gallery is concerned. Derived galleries
(filter, cast-restrict) inherit their source's stamp; `--merge` and the JSON
gallery merge check *before* writing, since a merged file holding two embedding
spaces cannot be untangled afterwards by any later check.
**Gap:** none. Stamped in `gallery_builder.cpp` and the Python builders; verified
in `scene_analyze`, `scene_preview`, the `sae_kpn` matcher binding, `replay.py`,
`optimize.py`, `movienet_eval.py` and the merge paths.
## GR-006 … GR-009 — Provenance tiers and poisoning guard
@@ -1019,9 +1539,11 @@ Persist pipeline state at the point where the expensive work ends.
variable-length HDF5 types and reads straight into numpy.
- Stores per frame: `timestamp_sec`, `frame_idx`, `is_cut`, `is_scene_boundary`.
Per face: `embedding` [N,512], `bbox` [N,4], `landmarks` [N,10], `confidence`.
- Invariants: embeddings unit-norm; `face_offset` contiguous; bboxes already in
original resolution; frames with no faces still get a row so timestamps stay
dense; EOF sentinels not written.
- Invariants: embeddings unit-norm; `face_offset` contiguous; bboxes and
landmarks in **decoded-frame** pixels with `bbox_upscale` recorded alongside
(the dump is a faithful tap, so it does not transform what the tracker saw —
see VR-010); frames with no faces still get a row so timestamps stay dense;
EOF sentinels not written.
- Enabled by `--dump-embeddings out.h5`; teeing must not perturb the live result.
Schema owned by [`scripts/optimizer/SCHEMA.md`](../scripts/optimizer/SCHEMA.md).
@@ -1080,6 +1602,14 @@ Verify `landmarks`/`bbox`/`is_cut` suffice, and bump `schema_version` if not.
Quantify where ArcFace degrades, replacing the 66×66 estimate in A1 with a
measurement.
> **Result, and its limit.** Knee at 24–32 px; 32 px returns 98.1% TPI at 0.0
> FPI. But the probe is an already-aligned 112×112 crop, so alignment is held
> perfect and this measures the **embedder alone** — an upper bound, not a
> threshold. **VR-013** re-asks the question end to end, downscaling the whole
> frame before the detector, and lands near 50 px. AR-002's floor of 40 px comes
> from VR-013; this study is what shows how much of the gap is detection and
> landmark error rather than embedding.
**Method.**
1. Select ~100 gallery actors having more than one mugshot.
docs/benchmark.md
+186
View File
@@ -0,0 +1,186 @@
# Benchmark — SuperHero
The reference film for end-to-end accuracy. Replaces Road to Bali, which was
withdrawn for the reason in [Why not Road to Bali](#why-not-road-to-bali).
TRACES: AR-011, AR-012, AR-013 | VR-001, VR-005 | SR-002
---
## The film
SuperHero, from the [NIST TRECVID Deep Video Understanding development
set](https://www-nlpir.nist.gov/projects/trecvid/dvu/dvu.development.dataset/).
14 films are asserted Creative Commons and need no data agreement; only the 5
KinoLorber test films are gated.
| | |
|---|---|
| Runtime | 1025.5 s (17.1 min), 10 scenes |
| Resolution | 640×360 |
| Ground truth | Per-scene presence, from the scene knowledge graphs |
| Gallery | 5 characters, 14 references |
The DVU set is what makes this workable: it ships **character** face crops cut
from the film itself, so ground truth and gallery are both in character space
and scoring needs no actor→character mapping.
**Licence caveat.** NIST links licence evidence for only 4 of the 14 films, and
SuperHero is not one of them — its end credits carry no copyright or CC notice,
list a "Temporary Musical Score" and a SAG cast, and it has no traceable online
release. Fine for internal benchmarking; do not redistribute frames from it.
Valkaama is the one film with an independently documented licence (CC BY-SA 3.0)
if provenance ever has to be defended.
---
## Reproducing it
```sh
# 1. Annotations, character mugshots, scene segmentation.
# NIST names the same film three different ways, hence the overrides.
KG_DIR=superHero KG_FILE=superhero scripts/fetch_dvu.sh SuperHero ../dvu-hero
# 2. Scene clips (movie.shots), then fuse them into one stream.
# Fusing matters — see "Run it as one film" below.
# SuperHero-1.webm … SuperHero-10.webm from
# <dataset>/movie.shots/, then:
ffmpeg -f concat -safe 0 -i concat.txt -c copy SuperHero_full.webm
# 3. Gallery, with the face-size floor that keeps references in distribution.
./build/build_gallery --root ../dvu-hero/root \
--output ../dvu-hero/hero66.h5 --min-face-px 66
# 4. Run, on the GPU path (see "Check you are on the GPU").
./build/scene_analyze --movie hero/SuperHero_full.webm \
--gallery ../dvu-hero/hero66.h5 \
--detector-engine trt_cache/scrfd.scrfd_500m_bnkps.640.fp16.engine \
--arcface-engine trt_cache/arcface.LVFace-B_Glint360K.b4.fp16.engine \
--fps 5 --min-face-px 32 --expand-gallery \
--output pred.json
```
Nothing here is in git: the clips are ~130 MB and the annotations are
regenerable. Replay fixtures derived from the run ship through the artifact
registry instead:
```sh
scripts/artifacts/push_artifacts.sh replay-fixtures
scripts/artifacts/pull_artifacts.sh replay-fixtures [version]
```
The gallery travels in the same archive as the dumps deliberately — a dump only
replays meaningfully against the gallery it was produced with, and pairing one
with a different gallery silently changes every identity decision in it.
---
## Results
Measured on the fused film, gallery expansion on.
| Metric | Value |
|---|---|
| Precision | **1.00** |
| Recall | 0.65 |
| F1 | 0.79 |
| True positives | 13 |
| False positives | **0** |
| False negatives | 7 |
Six of ten scenes scored exactly right, including the three-character scenes 4
and 5.
**Zero false positives is the result worth keeping.** Every out-of-gallery
character — Beast, Mighty Celestial, Ms. Johnson, Doctor, two Masked Persons —
was declined rather than forced onto a nearest match. That is the calibrated
probability (AR-024) doing its job, and it is the right failure direction for an
X-Ray overlay: a miss is a gap, an invention is a lie.
**The misses have a shape.** Scenes 1, 2, 3 and 8 were missed, and 1–3 are the
three shortest scenes in the film (14 s, 38 s, 27 s). That is consistent with
per-track Bayesian accumulation (AR-025) needing enough sightings before belief
crosses threshold. Scene 8 is 65 s and does not fit that story — it is the one
to look at first when improving recall.
Running the same scenes as isolated clips did *not* do better, so cross-scene
gallery expansion is not currently compensating for short scenes.
### Run it as one film, not as clips
Per-scene clips defeat per-film gallery expansion (AR-019), which grows a
temporary gallery from track continuity across the whole film and re-assesses
unknown tracks at the end. Ten isolated clips give it nothing to work with, and
pay model and gallery load ten times over.
Fusing also makes presence windows cross real scene boundaries, which is how
SR-002's scene-scoped question is asked in production. Note the joins are
artificial cuts — consecutive scenes were never contiguous footage — so presence
bleeding across a boundary may be the join rather than a tracking fault.
---
## Throughput
| Path | Realtime factor | Sampled fps | 17-min film |
|---|---|---|---|
| `build/` (TensorRT) | **2.0×** | ~10 | ~8 min |
| `build-ort/` (ORT) | 0.54× | 2.7 | ~32 min |
Throughput varies strongly with face density; a sparse stretch measured 8×
realtime, so quote the whole-film average, not a window.
### Check you are on the GPU
ORT's CUDA execution provider fails to load on this machine and **silently falls
back to CPU**:
```
Failed to load library libonnxruntime_providers_cuda.so:
undefined symbol: cudnnGetConvolutionBackwardDataAlgorithm_v7
```
That symbol was removed in cuDNN 9; the packaged ORT is built against cuDNN 8.
ORT logs this once at startup and then runs happily on CPU, so a `build-ort`
timing is a CPU number wearing a GPU label — a 15× error with no symptom other
than a figure you have no baseline for. Grep the log for `Failed to load
library` before trusting any throughput measurement.
The TensorRT path (`build/`) needs prebuilt engines from
`scripts/build_trt_engines.sh` and reports what it loaded:
```
[TrtScrfd] loaded: … [TrtArcFace] loaded: … max_batch=4
[similarity] cuBLAS/CUDA engine: gallery resident on GPU
```
---
## Why not Road to Bali
Bali was chosen because DVU ships character mugshots for it. It was withdrawn on
**face scale**, measured on its own reference crops:
| | Bali | SuperHero |
|---|---|---|
| Median detected face | 27 px | **69 px** |
| Maximum detected face | 69 px | **241 px** |
| References ≥66 px | 2 of 69 | 14 of 27 |
The DVU images are scene crops, not mugshots, so the crop dimensions say nothing
about face scale — the face has to be detected and measured. Bali's median
reference was being upscaled roughly 4× to reach ArcFace's 112×112, and the
worst 7×, which violates AR-011: every model gets the input it was trained for.
A model run off-distribution returns confident, plausible, wrong output.
In a gallery that error is permanent. A bad frame costs one frame; a poisoned
reference corrupts every future match against that identity.
No threshold rescued it. At 66 px only 2 of 69 references survived — the largest
face in the entire set is 69 px — so there was no cut that both kept references
in distribution and left enough of them to calibrate. SuperHero's gallery builds
at a 66 px floor and calibrates on its own (`a=15.2867 b=-4.98633`, 100 % train
accuracy) rather than borrowing constants.
Any accuracy figure recorded against Bali predates this and should be treated as
measuring upscaling artifacts as much as the pipeline.
docs/plan.md
+79 -8
View File
@@ -243,7 +243,9 @@ Context crops opt-in behind `--dump-unidentified-crops`.
**Depends on:** nothing (AR-002, AR-011); AR-004 blocks AR-003.
- **AR-002** — `min_face_px` → 66, expressed in original resolution.
- **AR-002** — `min_face_px` stays **40** (VR-013 measured it end to end) but must
be expressed in original resolution rather than decoded-frame space. The value
is already right in `config.hpp`; the change is the coordinate space.
- **AR-011** — feed TransNetV2 at native rate; derive the dedup window from
source fps rather than the hardcoded `0.04 s`.
- **AR-004** — backpressure. `kMaxFaces` (`identity_matcher_node.hpp:133`)
@@ -260,13 +262,23 @@ Context crops opt-in behind `--dump-unidentified-crops`.
# Gallery
## GR-004 — Model binding
## GR-004 — Model binding — **DONE**
**Depends on:** nothing. **Startable immediately, highest value per line.**
**Depended on:** nothing. Landed before any measurement work, as intended.
Stamp embedder identity into the gallery at build; verify at load in
`scene_analyze`, `replay.py` and the optimizer. Mismatch is a hard error naming
both sides.
Stamp = model basename + SHA-256 of the ONNX, written as the `/embedder` group at
build time (`gallery_builder.cpp`, `sae_gallery.save_gallery_hdf5`) and verified
at load in `scene_analyze`, `scene_preview`, the `sae_kpn` matcher binding,
`replay.py`, `optimize.py` and `movienet_eval.py`. Mismatch is a hard error naming
both sides, with no bypass. Embedding dumps carry the same stamp, since a replay
has no live embedder to check against.
Unstamped legacy galleries **warn loudly and proceed** rather than failing:
unknown is not known-bad, and hard-failing every pre-existing gallery would turn
the check into something people disable. `--require-gallery-stamp` /
`SAE_REQUIRE_GALLERY_STAMP=1` promotes that to a hard error — measurement runs
should set it. `scripts/stamp_gallery.py` re-binds an existing gallery without
re-embedding, so the warning state is cheap to leave.
Cross-model similarities are meaningless but *look* plausible — this fails
silently and expensively, and it would corrupt every measurement taken during the
@@ -314,8 +326,67 @@ Windows carry belief and route; `extraction.*` gains `extinction_sec` and
## VR-005 — Minimum face size study
**Depends on:** nothing. Standalone Python, no C++ contact. Produces the measured
value replacing AR-002's 66 px estimate.
**Depends on:** nothing. Standalone Python, no C++ contact. **Done** — knee at
24–32 px. It measures the embedder with alignment held perfect, so it bounds the
answer from below rather than setting it; AR-002's floor comes from **VR-013**,
which sweeps input resolution end to end and lands at 40 px.
## VR-013 — Cross-source identification probe
**Depends on:** `sae_embed` exposing `detect()`, `align_face()`, `embed_crop()`
and the gallery calibration — it drives the shipped C++ rather than reimplementing
it, which is what VR-005 could not do.
Gallery from one recording, probes from another, sweeping the probe's **input
resolution before the detector**, so detection and landmark regression degrade
with the frame. `experiments/xsource/`.
**Findings.** Holding 90% of the plateau needs ~50 px end to end against VR-005's
~22 px; `min_face_px` 40 is right and 32 would admit faces in the falling region.
FPI is 0.0% at every scale — resolution loss goes entirely to TBI, never to a
wrong name. The ceiling is **cross-view, not resolution**: everyone matches
themselves within a recording (0.55–0.85) and collapses across two (0.14–0.45),
and only the subject with frontal *gallery* references identified reliably — so
the lever is gallery pose coverage (`docs/pose-expansion.md`), not a better
landmark model. Averaging SCRFD's NMS-discarded landmark estimates lifts
cross-clip TPI 41% → 49% for one forward pass.
**Open.** Four identities and one shoot, so the shape is the result and the
absolute rates are not. Both clips hold all four people, so there is no
out-of-gallery class and the 10×-weighted out-of-cast misID is untested — holding
one identity out of the gallery would fix that.
## VR-014 — Audio-signature offset recovery
**Depends on:** `sae_audio` exposing `compute_signature()` and
`signature_from_mono()` — it drives the shipped C++, as VR-013 does, so the
thing measured is the thing that ships.
`scripts/validation/test_audio_offset.py` over
`tests/fixtures/audio/superhero_offset_200s.flac`: 200 s of public-domain film audio
(the same SuperHero clips the replay fixtures use), long enough for a 120 s
window to slide past the ±600-frame search cap. The slide itself is numpy here
on purpose — matching belongs to the consumer, so writing it out keeps this a
test of the signature rather than of somebody's matcher.
**Findings.** Alignment is a solved problem here: the offset is the nearest frame
in every in-cap trial, worst error **46 ms against a 500 ms budget**, and 46 ms is
the quantisation floor — offsets are whole 92.88 ms frames, so no correct answer
can be worse. The `runtime/2` anchor's factor of two holds through real trimmed
files, and out-of-cap offsets and unrelated content are both declined.
**The score is where the slack is, and it costs a tier rather than accuracy.** It
tracks sub-frame misalignment — 0.94–0.99 near a frame boundary, 0.69–0.73 at
half a frame — so two thirds of correct alignments miss the server's 0.85 `audio`
threshold and land in `loose`. UT-108 measures the fix rather than proposing one:
±1 frame of slack in the score returns all 40 to `audio` (min 0.906) with false
matches unmoved at 0.12–0.16, costing 81 ms of the budget. See
[`SPEC.md`](SPEC.md) IR-004 — the score is normative in the server spec, so the
change is theirs to make.
**Open.** One source, one language, one era of recording. The shape (offset exact,
score set by sub-frame phase) should hold generally, but the absolute scores are
this fixture's.
## VR-001 — Dump audit
docs/requirements.md
+164 -48
View File
@@ -29,32 +29,35 @@ Status: `Done` · `In Progress` · `Planned` · `TBD` · `Withdrawn`
| ID | Requirement | Traces to | Priority | Status |
|---|---|---|---|---|
| AR-001 | Detect faces in sampled frames; emit bbox, confidence, 5-point landmarks in original pixel space | SR-002 | High | Done |
| AR-002 | Minimum face size 66×66 px, expressed in **original** resolution (decoupled from `dense_scale`) | SR-002 | High | Planned |
| AR-003 | No fixed per-frame face cap — crowd scenes must not lose background cast | SR-002 | Medium | Planned |
| AR-004 | Backpressure: unbounded faces/frame absorbed by slowing, never by dropping or throwing | SR-002 | High | Planned |
| AR-005 | Align to 112×112 via ArcFace 5-point similarity transform | SR-002 | High | Done |
| AR-002 | Minimum face size **40×40 px** (VR-013 measured end to end; VR-005's 32 px is an embedder-only upper bound), expressed in **original** resolution (decoupled from `dense_scale`) | SR-002 | High | Planned |
| AR-003 | No fixed per-frame face cap — crowd scenes must not lose background cast | SR-002 | Medium | **Done** — `max_faces` defaults to 0 (no cap); the matcher batches through its GEMM buffer instead of throwing |
| AR-004 | Backpressure: unbounded faces/frame absorbed by slowing, never by dropping or throwing | SR-002 | High | **Mostly** — node outputs *park* on a full channel: the value is held, the worker released, and a channel space-callback resumes the node. Replaces `push_blocking`, which parked a scheduler worker inside the push and, with one thread per node, stopped that node draining its own input. Verified: 385/385 frames, 0 drops. **Gap:** a rare hang survives, ~1 run in 20 at a 300 s timeout (was: every run). `FanoutNode` still drops on overflow (`fanout.hpp:129`) rather than parking, so the AR-010 scene join sheds frames exactly when the dense branch falls behind |
| AR-005 | Align to 112×112 via ArcFace 5-point similarity transform, fitted by **Umeyama least squares over all five points** (as InsightFace does) — never a robust fit, which would discard the landmarks AR-030 reads | SR-002 | High | **Done** — `umeyama_similarity()`. The RANSAC fit it replaces disagreed by a median 17 source px on 400 headshots, 83.5% of crops embedding below cos 0.99, and was unstable and RNG-driven: rebuilding caught 1614 near-duplicates against the original build's ~100. **All galleries rebuilt** (2456 actors, 10254 embeddings); measured separation gain is small (0.583 → 0.590), so recorded accuracy figures should be re-run but are not expected to move far |
| AR-006 | 512-d L2-normalised embeddings, batched | SR-002 | High | Done |
| AR-007 | Associate detections by IoU + embedding, with **frame-dependent** weighting | SR-002 | High | In Progress |
| AR-008 | One track pool keyed on `last_seen`; no separate revival path | SR-002 | High | Planned |
| AR-007 | Associate detections by IoU + embedding, with **frame-dependent** weighting | SR-002 | High | **Done** — `track_alpha` is the base for ordinary frames; drops to embedding-only on cut/boundary and for dormant tracks |
| AR-008 | One track pool keyed on `last_seen`; no separate revival path | SR-002 | High | **Done** — one pool keyed on `last_seen`; park/revive branch deleted |
| AR-009 | Camera-cut detection (histogram) as an association hint | SR-002 | High | Done |
| AR-010 | Scene-boundary detection (TransNetV2) as an association hint | SR-002 | Medium | In Progress |
| AR-010 | Scene-boundary detection (TransNetV2) as an association hint | SR-002 | Medium | **Done** — decode butterfly joined via `SceneBoundaries`; the sampled branch waits for the detector's watermark. Frames past its last scored window are counted as unverified, never assumed boundary-free |
| AR-011 | **Every model is fed the input it was trained for** — cost reduced by running less often, never by degrading one inference | SR-002 | High | Planned |
| AR-012 | Presence follows **track extent**, not per-frame recognition | **SR-002** | High | Planned |
| AR-013 | `last_seen` optional state machine; window ends at last sighting, never after | SR-002 | High | Planned |
| AR-014 | Belief swap A→B terminates the track and starts a new one | SR-002 | Medium | Planned |
| AR-015 | Two live tracks owned by one actor ⇒ treat as a detected cut, re-associate | SR-002 | Medium | Planned |
| AR-016 | All tracks closed at EOF — a film ends with faces on screen | SR-002 | High | Planned |
| AR-017 | Every presence claim carries its belief and identification route | SR-002 | High | Planned |
| AR-018 | Per-subject embedding store with banded admission (novel enough, safe enough) | SR-005 | Medium | Planned |
| AR-019 | Per-film gallery annex from owned tracks; acquires the non-frontal views TMDB lacks | SR-005 | Medium | In Progress |
| AR-012 | Presence follows **track extent**, not per-frame recognition | **SR-002** | High | **Done** — `src/track_registry.hpp`; window is `[first_seen, last_seen]` of an owned track |
| AR-013 | `last_seen` optional state machine; window ends at last sighting, never after | SR-002 | High | **Done** — `last_seen` optional is the whole state machine; interior gaps absorbed, trailing cool-down never claimed |
| AR-014 | Belief swap A→B terminates the track and starts a new one | SR-002 | Medium | **Done** — swap closes at `last_seen` and opens a successor at the swap frame; counted |
| AR-015 | Two live tracks owned by one actor ⇒ treat as a detected cut, re-associate | SR-002 | Medium | **Done** — reverse index detects it on the causing update; counted |
| AR-016 | All tracks closed at EOF — a film ends with faces on screen | SR-002 | High | **Done** — `flush()`, idempotent, closes at last sighting or final tick |
| AR-017 | Every presence claim carries its belief and identification route | SR-002 | High | **Done** — `DeadTrack` carries belief and observation count |
| AR-018 | Per-subject embedding store with banded admission (novel enough, safe enough) | SR-005 | Medium | **Done** — banded admission in probability space, bounds from `expand_band_lo/hi`; the lower bound re-asked pairwise at promotion, since `admit` compares only against the nearest member and a drifting track can chain past it. Retires `expand_novelty_sim` and `expand_track_spread_max` — novelty is now the eviction ordering, not a threshold. Rejections counted. Bounds unswept (VR-007) |
| AR-019 | Per-film gallery annex from owned tracks; acquires the non-frontal views TMDB lacks | SR-005 | Medium | **Done** — all three discontinuity signals clear the buffers; ownership comes from the registry, not a second local tally |
| AR-020 | Deferred re-identification of unknown tracks against the final expanded gallery | SR-005 | High | Planned |
| AR-021 | Cluster unknown tracks into one entity per person, under temporal cannot-link constraints | SR-005 | Medium | Planned |
| AR-022 | Capture still-unidentified tracks: embeddings, metadata, **context crops** for human review | §4 | Medium | Planned |
| AR-023 | Fit sigmoid calibration from intra/inter similarity distributions | SR-002 | High | Done |
| AR-024 | **Always the calibrated probability, never a raw cosine** — exceptions recorded | SR-002 | High | Planned |
| AR-025 | Per-track Bayesian accumulation in log-odds, with correlated-observation discounting | SR-002 | High | Planned |
| AR-024 | **Always the calibrated probability, never a raw cosine** — exceptions recorded | SR-002 | High | **Done** — association, accumulation and expansion all in probability space; `track_max_embed_dist`, `cut_revive_sim`, `expand_novelty_sim`, `expand_track_spread_max` retired |
| AR-025 | Per-track Bayesian accumulation in log-odds, with correlated-observation discounting | SR-002 | High | **Done** — log-odds accumulation with correlation discounting owned by the registry, `src/evidence_discount.hpp` |
| AR-026 | All similarity computed as GEMM, including annex and deferred pass | SR-001 | High | In Progress |
| AR-027 | Throughput acceptable for **arbitrary** gallery size | SR-001 | High | Planned |
| AR-028 | **Embedding input quality assessed and carried** — every face scored on size, sharpness and visibility before its embedding is used as identity evidence; the vector travels with the face and reaches the VR-001 dump | SR-002 | High | Planned |
| AR-029 | Sharpness measure on the **aligned crop** (scale-normalised, so it cannot re-measure size) | SR-002 | Medium | Planned |
| AR-030 | Visibility measure from the AR-001 5-point landmarks — extreme pose or occlusion **discounts the observation, never deletes the detection** | SR-002 | Medium | **In Progress** — measure is the AR-005 alignment residual (`estimate_alignment()`), carried on `DetectedFace`; roll/scale invariance and monotonicity under foreshortening asserted. Nothing consumes it as a discount yet |
## Deployment (DP)
@@ -66,18 +69,20 @@ Status: `Done` · `In Progress` · `Planned` · `TBD` · `Withdrawn`
| DP-004 | Opportunistic/idle mode: external trigger, hard stop, implicit re-queue | PR-004 | Medium | Planned |
| DP-005 | Native installer, no Docker; Fedora + Arch | PR-004 | Medium | Planned |
| DP-006 | Background incremental gallery refresh on a timer | PR-003 | Medium | Planned |
| DP-007 | CI builder image, CPU-only, pinned by tag in the Gitea container registry | PR-004 | High | Planned |
| DP-008 | Builder images + release jobs per backend (cpu / cuda / rocm); ship binaries, not engines | PR-004 | Medium | Planned |
## Integration (IR)
| ID | Requirement | Traces to | Priority | Status |
|---|---|---|---|---|
| IR-001 | Emit the JRay truth format as sibling `.jray.json` | SR-003 | High | Done |
| IR-002 | Windows carry belief + route; `extraction.*` carries `extinction_sec`, `gallery_scope` | SR-003 | High | Planned |
| IR-003 | Output written **after** the deferred pass, not at EOF | SR-003 | High | Planned |
| IR-004 | Compute the audio signature exactly per server spec §3 | SR-003 | Medium | Planned |
| IR-005 | Golden-vector fixture shared with the plugin repo to prove bit-exactness | SR-003 | High | Planned |
| IR-007 | Media < 120 s: emit no signature, apply no sync offset — identical rule in both producers | SR-003 | Low | Planned |
| IR-008 | Emit and honour the signature's own `v1:` version prefix | SR-003 | Low | Planned |
| IR-002 | Windows carry belief + route; `extraction.*` carries `extinction_sec`, `gallery_scope` | SR-003 | High | **Done** — `schema_version: 2`; windows are objects with `belief` + `route`; `extraction.*` carries `extinction_sec` and `gallery_scope`; `anneal_sec` removed |
| IR-003 | Output written **after** the deferred pass, not at EOF | SR-003 | High | **In Progress** — sink builds windows from registry claims and flushes at EOF; the deferred pass (AR-020) does not exist yet, so output is still final at EOF |
| IR-004 | Compute the audio signature exactly per server spec §3 | SR-003 | Medium | **Done** — `src/audio_signature.*`; not yet emitted into the truth file (IR-002) |
| IR-005 | Golden-vector fixture shared with the plugin repo to prove bit-exactness | SR-003 | High | **Done** — `tests/fixtures/audio/`; v1 parameters now normative in server spec §3 |
| IR-007 | Media < 120 s: emit no signature, apply no sync offset — identical rule in both producers | SR-003 | Low | **Done** |
| IR-008 | Emit and honour the signature's own `v1:` version prefix | SR-003 | Low | **Done** |
| IR-006 | Jellyfin round-trip: pull pending queue, push complete results only | SR-001 | High | Done |
## Gallery (GR)
@@ -86,8 +91,8 @@ Status: `Done` · `In Progress` · `Planned` · `TBD` · `Withdrawn`
|---|---|---|---|---|
| GR-001 | Build gallery from Jellyfin library cast, TMDB profile fallback | SR-001, SR-005 | High | Done |
| GR-002 | Incremental `--merge` refresh without re-embedding known actors | PR-003 | High | Done |
| GR-003 | Report coverage: zero-image actors, under-referenced actors, dedup, calibration PDFs | SR-001 | Medium | Planned |
| GR-004 | Stamp embedder identity into the gallery; **hard startup error** on mismatch | SR-001 | High | Planned |
| GR-003 | Report coverage: zero-image actors, under-referenced actors, dedup, calibration PDFs | SR-001 | Medium | **Done** — `gallery/gallery_report.hpp`, written next to the gallery by `build_gallery`. Zero-usable-image actors come from the build audit, which a stored gallery cannot reconstruct; also `distinct_references`, `duplicates_removed`, and the intra/inter distributions the calibration fits and would otherwise discard |
| GR-004 | Stamp embedder identity into the gallery; **hard startup error** on mismatch | SR-001 | High | **Done** — basename + SHA-256 + `embed_dim`; mismatch fatal with no bypass, unstamped warns unless `--require-gallery-stamp`; `scripts/stamp_gallery.py` migrates in place |
| GR-005 | Gallery data never leaves the instance | **SR-005** | High | Done |
| GR-006 | Provenance tiers: baked / harvested / confirmed, distinguishable per embedding | SR-005 | High | Planned |
| GR-007 | Persist harvested embeddings **flagged and reviewable**, never silently equal to baked | SR-005 | Medium | Planned |
@@ -99,14 +104,19 @@ Status: `Done` · `In Progress` · `Planned` · `TBD` · `Withdrawn`
| ID | Requirement | Traces to | Priority | Status |
|---|---|---|---|---|
| VR-001 | HDF5 post-inference dump at the embedded-frame boundary | PR-002 | High | Done |
| VR-002 | Replay drives the **real** KPN nodes, not a reimplementation | PR-002 | High | Done |
| VR-002 | Replay drives the **real** KPN nodes, not a reimplementation | PR-002 | High | **Done** — replay driven from committed fixtures in `tests/test_replay_fixtures.cpp`; determinism asserted |
| VR-003 | Scoring: micro-F1 against X-Ray, precision/recall logged at every evaluation | PR-002 | High | Done |
| VR-004 | Reproducible validation corpus with ground truth | PR-002 | High | Done |
| VR-005 | Minimum face size study — TPI/FPI vs probe size, gallery held at native res | PR-002 | Medium | Done |
| VR-005 | Minimum face size study — TPI/FPI vs probe size, gallery held at native res | PR-002 | Medium | **Done** — knee at 24–32 px; 32 px gives 98.1% TPI, 0.0 FPI at every size. Degrades an already-aligned 112×112 crop, so it isolates the embedder and is an **upper bound**; VR-013 measures the same question end to end and AR-002 takes its number, not this one |
| VR-006 | Re-tune `scene_threshold` once native-rate decode lands | PR-002 | Low | Planned |
| VR-007 | Expansion band, clustering threshold, and deferred-pass ablation | PR-002 | Medium | Planned |
| VR-008 | Gallery scaling benchmark — throughput vs gallery size | PR-002 | Medium | Planned |
| VR-009 | Verify accumulated posteriors are calibrated against held-out tracks | PR-002 | High | Planned |
| VR-010 | Dump provenance attributes — embedder model, detector settings, `dense_scale`, `scene_detect`, sample rate | PR-002 | **High** | Planned |
| VR-011 | Rewrite the replay harness for the post-AR-012 output contract | PR-002 | High | Planned |
| VR-012 | Quality-knee study — TPI/FPI vs sharpness and vs pose, as VR-005 did for size; also settles whether the 5-point pose proxy needs a dedicated landmark model | PR-002 | Medium | Planned |
| VR-014 | Audio-signature **offset recovery on real content** — a known trim recovered from film audio, not from the synthetic golden tone | PR-002 | Medium | **Done** — 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 floor rather than a result, since the offset is quantised to whole 92.88 ms frames. The `runtime/2` anchor confirmed through real head-trimmed files (a `delta` trim moves the window by `delta/2`). The one soft spot is **tier labelling, not accuracy**: the score falls with sub-frame misalignment (0.94–0.99 near a frame boundary, 0.69–0.73 at half a frame), so 27/40 correct alignments were demoted to `loose`. ±1 frame of slack in the *score* fixes it — measured, all 40 back to `audio` (min 0.906), false matches unmoved at 0.12–0.16, costing 81 ms of the budget |
| VR-013 | Cross-source identification probe — gallery from one recording, probes from another, swept over input resolution end to end | PR-002 | Medium | **In Progress** — holding 90% of the plateau needs ~50 px end to end against VR-005's ~22 px, the gap being detection and landmark error; **`min_face_px` 40, since 32 admits faces in the falling region** (AR-002). FPI 0.0% at every scale. Ceiling is cross-view, not resolution |
---
@@ -120,21 +130,62 @@ Four tiers, in decreasing order of preference:
| Tier | Runs in CI | What it covers |
|---|---|---|
| **T1 — CPU unit** | Yes | Pure logic: registry state machine, belief accumulation, clustering, band admission, calibration maths |
| **T2 — Replay** | Yes | Real pipeline nodes driven from an HDF5 fixture — no GPU, no video |
| **T1 — Functor unit** | Yes | A KPN node's `operator()` driven directly with hand-built inputs |
| **T2 — Replay** | Yes | The composed pipeline driven from an HDF5 fixture — no GPU, no video |
| **T3 — CPU inference** | Yes, slowly | ORT CPU provider over a handful of frames; smoke tests only |
| **T4 — GPU** | **No** | Throughput, TRT engines, large-gallery GEMM |
**T2 is the reason this is workable.** The HDF5 dump (VR-001) captures state
after decode → detect → align → embed and before tracking and matching, so
everything downstream — which is where nearly all of the new design lives — is
cheap CPU maths replayable from a fixture. Tracking, presence windows, belief
accumulation, expansion, deferred re-identification and clustering are all
verifiable on an N100 at full fidelity, not in miniature.
### T1 is the primary tier, and KPN is why
That was already true for the optimizer. It now doubles as the CI strategy, which
is a strong argument for keeping the dump schema honest (VR-001) and for the
replay driving the *real* nodes rather than a reimplementation (VR-002).
**Node functors are plain callable structs, constructed independently of the
network that wraps them** (`main.cpp:186-207` builds them as stack objects;
`ObjectNode` merely adapts them). So a node is testable by constructing it and
calling `operator()` — no channels, no threads, no network, no fixture.
This is already the established pattern, not a proposal:
`tests/test_face_tracker.cpp` "drives the node's `operator()` with hand-built
`EmbeddedSceneFrame`s and inspects the emitted `track_ids`", and does so
"pure, GPU-free, model-free".
The consequence is that most of the redesign is verifiable **without any
fixture at all**: construct exactly the awkward state — a belief swap, two live
tracks converging on one actor, a film ending mid-track, a gap one frame under
the timeout — rather than hunting for a clip that happens to exhibit it.
Four hazards this removes outright:
- **No fixture-provenance risk** for these tests — the inputs are synthetic and
explicit.
- **No "fixture must be replayed from frame 0"** concern — state is constructed
directly.
- **No cross-test state leakage** (e.g. a tracker's `next_id_` persisting) — each
test constructs a fresh functor.
- **No replay-harness nondeterminism** — no channels, so no EOF-tail heuristics
or silent drops.
It also means **a dead upstream producer does not block testing a downstream
consumer.** `is_scene_boundary` currently has no producer (see AR-010), which
would make a *replay* test of the frame-dependent `track_alpha` pass vacuously —
but a T1 test simply constructs a frame with `is_scene_boundary = true` and
asserts the weighting changes. The producer gap is a pipeline defect to fix, not
a verification blocker.
### T2 covers what T1 cannot
Replay remains necessary for **composition** — that the nodes wired together
behave as the sum of their parts — and for realistic data at scale, which
synthetic inputs cannot honestly imitate. It is the tier that would catch a
wiring error, a channel-capacity problem, or an ordering assumption that only
appears under concurrency.
The HDF5 dump (VR-001) captures state after decode → detect → align → embed, so
replay needs no GPU and no video. That was built for the optimizer; it doubles as
CI, which is a strong argument for keeping the schema honest and for replay
driving the *real* nodes rather than a reimplementation (VR-002).
**Fixtures and studies are generated locally**, on the development machine where
the models, galleries and media already exist. CI consumes them; it never
produces them.
**Small committed fixtures are required.** A few HDF5 dumps covering the awkward
cases — a cut, a belief swap, two live tracks converging, a film ending
@@ -162,12 +213,54 @@ as such rather than counted as covered.
| GR-001 … GR-005 | T1 + T3 | Gallery assembly is I/O and bookkeeping; embedding is T3 smoke |
| GR-006 … GR-008 | T1 | Tiering and outlier detection operate on stored embeddings |
| VR-* | Out of CI | Studies are run deliberately and their results committed as documents |
| VR-014 | **T2** | The exception, and the reason the blanket row above is not the whole story: its fixture is committed and its signature is CPU-only DSP, so the study *is* a test a CI host can run — not a measurement someone has to remember to repeat |
**One consequence worth stating:** AR-027 (arbitrary gallery scale) is
structurally unverifiable on the CI host. It needs a GPU host and a synthetic
large gallery, so it is the requirement most likely to silently regress. Its
benchmark (VR-008) should run on a schedule rather than on demand.
### CI never calls a model
**Not "should not" — cannot.** The N100 has no GPU, and even the ONNX Runtime CPU
provider is impractical: a measured run of the embedder on this hardware sits at
~930 ms per frame, so a 77 s clip at 5 fps would take roughly six minutes of
inference alone. Every model invocation therefore happens **locally, ahead of
time**, and CI consumes the result as data.
This is what makes the T1/T2 split load-bearing rather than a preference: T1 and
T2 are the only tiers that can exist in CI at all.
### Fixture corpus — `hero/`
Five clips of **SuperHero (1952)**, ~77 s each, 480×360, 30 fps, 42 MB total.
Public domain, and that is the reason to use it rather than a convenience:
**derived fixtures — dumps, crops, golden outputs — can be committed without the
rights question that rules out sharing gallery data (SR-005).** A fixture cut
from a copyrighted title could not live in the repository at all.
Two properties to design around rather than discover:
- **480×360 means small faces.** At this resolution a face is often 40–80 px, so
the AR-002 minimum of 40 px (original resolution) sits at the very bottom of
that range: the filter is close to binding, and anything shot wider is lost.
Fixture generation must set `--min-face-px` explicitly and record it, or the
dumps will be sparse for reasons unrelated to what is being tested.
- **77 s is short.** At 1 fps that is 77 frames — too thin to exercise an
extinction window measured in tens of seconds. Generate at 5 fps (≈385 frames,
~1 MB) and record the rate in provenance, since the behaviour under test
changes with it.
> **AR-004 blocks reproducible fixture generation.** A trial run of one clip
> produced 49 frames of an expected ~385, ending at 51 s of 77 s, with the
> diagnostics reporting 285 frames dropped at `camera_pos` and 51 at
> `face_aligner`. Channels overflow and **drop** rather than blocking, and what
> gets dropped depends on timing — so the same command run twice can produce
> different dumps. Golden fixtures cannot be built on that. AR-004 is therefore
> a prerequisite for VR-001 fixtures, not merely a throughput concern for crowd
> scenes.
### Fixtures — precomputed inference, pulled by CI
The N100 cannot run inference at any useful rate, so **inference output is
@@ -176,16 +269,29 @@ what looks like GPU work into pure CPU replay.
| Fixture | Contents | Size | Storage |
|---|---|---|---|
| **Edge-case dumps** | ~6 short clips (30–60 s), one per awkward behaviour | ~1 MB each | **Committed in-repo** |
| **Corpus dumps** | Full-length titles from the validation corpus | ~30 MB each | Pinned artifact, fetched by checksum |
| **Edge-case dumps** | ~6 short clips (30–60 s), one per awkward behaviour | ~0.1–1 MB each | **Committed in-repo** |
| **Corpus dumps** | Full-length titles from the validation corpus | ~21–38 MB each | **Gitea package registry**, pinned by version + checksum |
| **Synthetic gallery** | Random unit-norm embeddings, fixed seed | small | Generated at test time |
| **Golden truth files** | Expected output for each edge-case dump | KB | Committed |
| **Audio golden vectors** | Short WAV + expected signature | KB | Committed, **shared with the plugin repo** |
| **Audio golden vectors** | FLAC + expected signature + parameter contract | ~600 KB | Committed, **shared with the plugin repo** |
Edge-case dumps are small enough to commit, and being in-repo means they version
with the code that reads them. Corpus dumps are pulled by pinned checksum from
the artifact store rather than committed, since they are large and change only
when the dump schema does.
with the code that reads them.
**Corpus dumps go to the Gitea package registry, not Git LFS.** Both are
available — the models already use LFS — but their fetch semantics differ in a
way that matters here. LFS objects are pulled on clone unless a developer
explicitly skips them, so ~38 MB per title behind LFS taxes everyone who clones,
forever, for data that only CI and the optimizer ever read. Registry artifacts
are fetched on demand by the job that needs them.
Rule of thumb: **LFS for what the build needs; the package registry for what a
particular job needs.** Models are the former; corpus dumps and the CI image
(DP-007) are the latter.
Pin by version and verify by checksum on fetch. A fixture that changes silently
under CI is worse than a missing one, because the failure presents as a code
regression.
**Generation must be reproducible and versioned.** A script, run on a GPU host,
regenerates every fixture from source clips; it is re-run when the VR-001 schema
@@ -204,10 +310,10 @@ because it will be trusted.
| ID | Tier | Test asserts | Edge cases to cover |
|---|---|---|---|
| AR-001 | T3 | Detector returns plausible boxes on a known frame | — smoke only |
| AR-002 | T2 | Faces below 66 px (original res) are dropped | Exactly at threshold; with `dense_scale` 0.5 — the interaction that motivated the requirement |
| AR-002 | T2 | Faces below 40 px (original res) are dropped | Exactly at threshold; with `dense_scale` 0.5 — the interaction that motivated the requirement |
| AR-003 | T2 | No cap applied; a 40-face frame yields 40 | Crowd frame |
| AR-004 | T1 | Saturated input blocks rather than drops or throws | Bounded queue at capacity; **byte-based** limit with large crops; SIGTERM mid-block |
| AR-005 | T1 | Known landmarks → expected 112×112 warp | Landmarks near frame edge; degenerate/collinear points |
| AR-005 | T1 | Known landmarks → expected 112×112 warp; the fit never mirrors | Landmarks near frame edge; degenerate/collinear points; a mirrored set — SVD returns a reflection unless the determinant guard rejects it |
| AR-006 | T3 | Embeddings are unit-norm | Batch smaller than, equal to, larger than `embed_batch_size` |
| AR-007 | T2 | Association picks the right track | Two faces crossing paths; one leaving frame as another enters |
| AR-008 | T2 | One pool; dormant tracks match on embedding, not IoU | Dormant track whose old bbox overlaps a *different* new face — must not match on position |
@@ -229,10 +335,20 @@ because it will be trusted.
| AR-025 | T1 | Log-odds accumulate; correlated frames discounted | 30 identical frames must **not** reach the certainty of 30 diverse ones |
| AR-026 | T1 + T4 | GEMM path produces same result as reference loop | Equivalence on small input in CI; throughput on GPU host |
| AR-027 | **T4** | Throughput at 10²…10⁵ actors | Scheduled, not on-demand |
| AR-028 | **T2** | No embedding reaches the matcher unscored; the vector survives into the dump | Face failing exactly one axis; all three healthy; a face whose landmarks are degenerate — scored, not silently vanished |
| AR-029 | T1 | Synthetic blur ladder → monotonically falling sharpness | Gaussian vs motion blur; **small sharp face vs large soft one** — size must not leak into this axis |
| AR-030 | T1 | Alignment residual rises monotonically with foreshortening | **In-plane roll, scale and translation must leave it at zero** — the property that makes it a pose measure rather than a pose-and-everything-else measure; face size must not shift it; degenerate landmarks report not-ok rather than a number |
| VR-012 | **T4** | Knee located per axis on held-out films | Report each candidate threshold's cost in **lost true presence**, not only its gain in precision — a gate that improves misID by discarding half the cast has not helped |
| VR-013 | **T4** | Identification holds across two recordings of the same people, and degrades to TBI rather than to a wrong name as input resolution falls | Gallery and probes must come from *different* recordings — a hold-one-out over one recording measures a much easier problem and will not surface the cross-view failure. Ground truth is hand-sorted; labels propagated by embedding similarity would keep only the faces the embedder already gets right |
| IR-001/002 | T1 | Serialised output matches golden file | Zero-length window; actor with many windows |
| IR-003 | T1 | Output written after deferred pass | Not at EOF |
| IR-004/005 | **T1** | Signature matches golden vector bit-for-bit | **Media < 120 s → no signature**; identical result in both repos |
| GR-004 | T1 | Mismatched embedder → hard startup error | Error names both sides |
| IR-004/005 | **T1** | Signature matches golden vector bit-for-bit | Identical result in both producer repos |
| VR-014 | **T2** | A known trim offset is recovered from **real film audio**, to the nearest frame | An offset past the ±600-frame cap and unrelated content must both be *declined*, never given a best-effort alignment. Fixture and signature are both CPU-only, so unlike the other VR rows this one is CI-*executable* — though the repo's only workflow today is the traceability gate, so nothing runs it there yet. The signature comes from the shipped C++ through `sae_audio`; a numpy port would be a third implementation nobody checks against the golden vector |
| IR-006 | T1 + manual | Queue pull and result push against a stubbed Jellyfin API | Partial result never pushed; push only after the deferred pass |
| IR-007 | **T1** | Media < 120 s emits no signature at all | Exactly 120 s; just under; zero-length audio. Must match the plugin's cutoff exactly — a caller-varying window length is what SR-004 forbids |
| IR-008 | T1 | `v1:` prefix emitted and honoured on read | Unknown prefix rejected, not guessed |
| GR-009 | T1 | Human-confirmed associations persist and are tier-tagged | Survives a gallery rebuild; distinguishable from baked and harvested |
| GR-004 | T1 | Mismatched embedder → hard startup error | Error names both sides; **unstamped warns, and errors under `SAE_REQUIRE_GALLERY_STAMP`**; same filename + different SHA-256 must still be a mismatch |
| GR-008 | T1 | Outlier flagged among an actor's references | Injected poisoned embedding detected |
| VR-009 | T1 | Posterior calibration holds | A 0.99 posterior is wrong ~1% of the time on held-out tracks |
docs/traceability.md
+829
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@@ -0,0 +1,829 @@
# Requirements traceability matrix
<!-- GENERATED FILE - do not edit by hand. -->
<!-- Regenerate: scripts/traceability/traceability-gate.sh -->
**Generated:** 2026-07-31T20:39:35+00:00
Denominators are read from [`requirements.md`](requirements.md) at run time, never hardcoded. Coverage counts a requirement only when it is tagged in source **and** has a verification tier this repo's CI host can execute (`T1, T2, T3, static`).
## Summary
| Metric | Value |
|---|---|
| Source files scanned | 112 |
| TRACES tags found | 148 |
| EXCEPTION tags found | 0 |
| Requirements defined | 69 |
| Requirements covered | 38 |
| **Coverage** | **55.1%** (38/69) |
| Coverage of CI-executable scope | 67.9% (38/56) |
| Tagged but unexecuted in CI | 8 |
| Orphan tags | 0 |
### By type
| Type | Covered | Tagged but unexecuted | Defined |
|---|---|---|---|
| AR | 22 | 1 | 30 |
| DP | 2 | 0 | 8 |
| IR | 8 | 0 | 8 |
| GR | 5 | 0 | 9 |
| VR | 1 | 7 | 14 |
- **UT** tags present (separate taxonomy, not counted in coverage): UT-001, UT-101, UT-102, UT-103, UT-104, UT-105, UT-106, UT-107, UT-108
- **IT** tags present (separate taxonomy, not counted in coverage): IT-001
- **PR** tags present (separate taxonomy, not counted in coverage): PR-002, PR-004
- **SR** tags present (separate taxonomy, not counted in coverage): SR-001, SR-002, SR-003, SR-005
## Not executable in CI
These requirements have no verification tier this repo's CI host can run, so a tag on them is evidence of *intent*, not of verification. They are never counted as covered.
| ID | Tiers | Tagged in source | Requirement |
|---|---|---|---|
| AR-027 | T4 | yes | Throughput acceptable for **arbitrary** gallery size |
| VR-001 | out-of-ci | yes | HDF5 post-inference dump at the embedded-frame boundary |
| VR-002 | out-of-ci | yes | Replay drives the **real** KPN nodes, not a reimplementation |
| VR-003 | out-of-ci | yes | Scoring: micro-F1 against X-Ray, precision/recall logged at every eva… |
| VR-004 | out-of-ci | yes | Reproducible validation corpus with ground truth |
| VR-005 | out-of-ci | yes | Minimum face size study — TPI/FPI vs probe size, gallery held at nati… |
| VR-006 | out-of-ci | no | Re-tune `scene_threshold` once native-rate decode lands |
| VR-007 | out-of-ci | no | Expansion band, clustering threshold, and deferred-pass ablation |
| VR-008 | out-of-ci | no | Gallery scaling benchmark — throughput vs gallery size |
| VR-010 | out-of-ci | yes | Dump provenance attributes — embedder model, detector settings, `dens… |
| VR-011 | out-of-ci | no | Rewrite the replay harness for the post-AR-012 output contract |
| VR-012 | T4, out-of-ci | no | Quality-knee study — TPI/FPI vs sharpness and vs pose, as VR-005 did … |
| VR-013 | T4, out-of-ci | yes | Cross-source identification probe — gallery from one recording, probe… |
**Tagged but unexecuted:** AR-027, VR-001, VR-002, VR-003, VR-004, VR-005, VR-010, VR-013 — a test exists and is tagged, but this CI host cannot run it. Report those runs separately.
## Orphan tags
A tag naming an ID `requirements.md` does not define. This is what renumbering produces, and what a typo produces.
_None._
## Requirements tracing up to nothing
A register row whose `Traces to` cell names no parent. Work serving no stated goal is how scope creeps in, and it is invisible unless something looks.
_None._
## Recorded exceptions
Deliberate, documented departures from an invariant (`EXCEPTION: XX-nnn <reason>`). Reported separately and never counted as coverage — an exception is a decision to be reviewed, not evidence a requirement is met.
_None._
## Register
| ID | Status | Tier | Traces to | Trace state | Tagged in | Requirement |
|---|---|---|---|---|---|---|
| AR-001 | Done | T3 | SR-002 | covered | `src/nodes/face_detector_node.hpp` | Detect faces in sampled frames; emit bbox, confidence, 5-point landma… |
| AR-002 | Planned | T2 | SR-002 | untagged | - | Minimum face size **40×40 px** (VR-013 measured end to end; VR-005's … |
| AR-003 | **Done** — `max_fac… | T1, T2, T4 | SR-002 | covered | `src/config.hpp`, `src/nodes/face_detector_node.hpp`, `src/nodes/identity_matcher_node.hpp` | No fixed per-frame face cap — crowd scenes must not lose background c… |
| AR-004 | **Done** — KPN node… | T1, T4 | SR-002 | covered | `src/main.cpp`, `src/nodes/identity_matcher_node.hpp`, `tests/test_replay_fixtures.cpp` | Backpressure: unbounded faces/frame absorbed by slowing, never by dro… |
| AR-005 | **Done** — `umeyama… | T1, T3 | SR-002 | covered | `src/face_utils.hpp`, `src/nodes/face_aligner_node.hpp`, `tests/test_face_utils.cpp` | Align to 112×112 via ArcFace 5-point similarity transform, fitted by … |
| AR-006 | Done | T3 | SR-002 | covered | `src/nodes/embedder_node.hpp` | 512-d L2-normalised embeddings, batched |
| AR-007 | **Done** — `track_a… | T2 | SR-002 | covered | `src/config.hpp`, `src/main.cpp`, `src/nodes/face_tracker_node.hpp`, `tests/test_face_tracker.cpp` | Associate detections by IoU + embedding, with **frame-dependent** wei… |
| AR-008 | **Done** — one pool… | T2 | SR-002 | covered | `src/config.hpp`, `src/main.cpp`, `src/nodes/face_tracker_node.hpp`, `tests/test_face_tracker.cpp` | One track pool keyed on `last_seen`; no separate revival path |
| AR-009 | Done | T2 | SR-002 | covered | `src/nodes/camera_position_change_detector_node.hpp` | Camera-cut detection (histogram) as an association hint |
| AR-010 | **Done** — decode b… | T2 | SR-002 | covered | `src/main.cpp`, `src/nodes/scene_boundary_annotator_node.hpp`, `src/nodes/scene_detector_node.hpp`, `src/scene_boundaries.hpp` | Scene-boundary detection (TransNetV2) as an association hint |
| AR-011 | Planned | T1, T2 | SR-002 | untagged | - | **Every model is fed the input it was trained for** — cost reduced by… |
| AR-012 | **Done** — `src/tra… | T2 | **SR-002** | covered | `src/main.cpp`, `src/nodes/identity_matcher_node.hpp`, `src/nodes/result_sink_node.hpp`, `src/track_registry.hpp`, `tests/test_replay_fixtures.cpp`, `tests/test_track_registry.cpp` | Presence follows **track extent**, not per-frame recognition |
| AR-013 | **Done** — `last_se… | T2 | SR-002 | covered | `src/track_registry.hpp`, `tests/test_replay_fixtures.cpp`, `tests/test_track_registry.cpp` | `last_seen` optional state machine; window ends at last sighting, nev… |
| AR-014 | **Done** — swap clo… | T2 | SR-002 | covered | `src/track_registry.hpp`, `tests/test_track_registry.cpp` | Belief swap A→B terminates the track and starts a new one |
| AR-015 | **Done** — reverse … | T2 | SR-002 | covered | `src/track_registry.hpp`, `tests/test_track_registry.cpp` | Two live tracks owned by one actor ⇒ treat as a detected cut, re-asso… |
| AR-016 | **Done** — `flush()… | T2 | SR-002 | covered | `src/main.cpp`, `src/nodes/result_sink_node.hpp`, `src/track_registry.hpp`, `tests/test_track_registry.cpp` | All tracks closed at EOF — a film ends with faces on screen |
| AR-017 | **Done** — `DeadTra… | T1, T2 | SR-002 | covered | `src/nodes/result_sink_node.hpp`, `src/track_registry.hpp`, `tests/test_track_registry.cpp` | Every presence claim carries its belief and identification route |
| AR-018 | **Done** — banded a… | T1, T2 | SR-005 | covered | `src/config.hpp`, `src/gallery/track_gallery.hpp`, `src/nodes/identity_matcher_node.hpp`, `tests/test_track_gallery.cpp` | Per-subject embedding store with banded admission (novel enough, safe… |
| AR-019 | **Done** — all thre… | T2 | SR-005 | covered | `src/gallery/track_gallery.hpp`, `src/nodes/identity_matcher_node.hpp`, `tests/test_track_gallery.cpp` | Per-film gallery annex from owned tracks; acquires the non-frontal vi… |
| AR-020 | Planned | T2 | SR-005 | untagged | - | Deferred re-identification of unknown tracks against the final expand… |
| AR-021 | Planned | T2 | SR-005 | untagged | - | Cluster unknown tracks into one entity per person, under temporal can… |
| AR-022 | Planned | T1, T2 | §4 | untagged | - | Capture still-unidentified tracks: embeddings, metadata, **context cr… |
| AR-023 | Done | T1 | SR-002 | covered | `src/gallery/gallery_calibration.hpp`, `src/nodes/identity_matcher_node.hpp`, `tests/test_calibration.cpp` | Fit sigmoid calibration from intra/inter similarity distributions |
| AR-024 | **Done** — associat… | T1, static | SR-002 | covered | `src/config.hpp`, `src/evidence_discount.hpp`, `src/gallery/gallery_calibration.hpp`, `src/gallery/track_gallery.hpp`, `src/main.cpp`, `src/nodes/face_tracker_node.hpp`, `src/nodes/identity_matcher_node.hpp`, `tests/test_track_gallery.cpp` | **Always the calibrated probability, never a raw cosine** — exception… |
| AR-025 | **Done** — log-odds… | T1 | SR-002 | covered | `src/evidence_discount.hpp`, `src/nodes/identity_matcher_node.hpp` | Per-track Bayesian accumulation in log-odds, with correlated-observat… |
| AR-026 | In Progress | T1, T4 | SR-001 | covered | `src/backends/gemm_backend.cpp`, `tests/test_similarity.cpp` | All similarity computed as GEMM, including annex and deferred pass |
| AR-027 | Planned | T4 | SR-001 | tagged, unexecuted | `src/backends/gemm_backend.cpp` | Throughput acceptable for **arbitrary** gallery size |
| AR-028 | Planned | T2 | SR-002 | untagged | - | **Embedding input quality assessed and carried** — every face scored … |
| AR-029 | Planned | T1 | SR-002 | untagged | - | Sharpness measure on the **aligned crop** (scale-normalised, so it ca… |
| AR-030 | **In Progress** — m… | T1 | SR-002 | covered | `src/face_utils.hpp`, `src/nodes/face_aligner_node.hpp`, `tests/test_face_utils.cpp` | Visibility measure from the AR-001 5-point landmarks — extreme pose o… |
| DP-001 | Done | T1, manual | PR-004 | covered | `src/main.cpp` | One analysis core; modes are front-ends and must not fork pipeline lo… |
| DP-002 | Done | T1, manual | PR-004 | covered | `src/main.cpp` | Batch CLI over one title |
| DP-003 | Planned | T1, manual | PR-004 | untagged | - | On-demand resident service with bounded, observable queue |
| DP-004 | Planned | T1, manual | PR-004 | untagged | - | Opportunistic/idle mode: external trigger, hard stop, implicit re-que… |
| DP-005 | Planned | T1, manual | PR-004 | untagged | - | Native installer, no Docker; Fedora + Arch |
| DP-006 | Planned | T1, manual | PR-003 | untagged | - | Background incremental gallery refresh on a timer |
| DP-007 | Planned | T1, manual | PR-004 | untagged | - | CI builder image, CPU-only, pinned by tag in the Gitea container regi… |
| DP-008 | Planned | T1, manual | PR-004 | untagged | - | Builder images + release jobs per backend (cpu / cuda / rocm); ship b… |
| IR-001 | Done | T1 | SR-003 | covered | `src/nodes/result_sink_node.hpp` | Emit the JRay truth format as sibling `.jray.json` |
| IR-002 | **Done** — `schema_… | T1 | SR-003 | covered | `src/config.hpp`, `src/main.cpp`, `src/nodes/result_sink_node.hpp` | Windows carry belief + route; `extraction.*` carries `extinction_sec`… |
| IR-003 | **In Progress** — s… | T1 | SR-003 | covered | `src/main.cpp` | Output written **after** the deferred pass, not at EOF |
| IR-004 | **Done** — `src/aud… | T1 | SR-003 | covered | `scripts/validation/test_audio_offset.py`, `src/audio_bindings.cpp`, `src/audio_signature.cpp`, `src/audio_signature.hpp`, `tests/test_audio_signature.cpp` | Compute the audio signature exactly per server spec §3 |
| IR-005 | **Done** — `tests/f… | T1 | SR-003 | covered | `src/audio_bindings.cpp`, `src/audio_signature.cpp`, `src/audio_signature.hpp`, `tests/test_audio_signature.cpp` | Golden-vector fixture shared with the plugin repo to prove bit-exactn… |
| IR-006 | Done | T1, manual | SR-001 | covered | `scripts/run_from_jellyfin.py` | Jellyfin round-trip: pull pending queue, push complete results only |
| IR-007 | **Done** | T1 | SR-003 | covered | `src/audio_signature.cpp`, `src/audio_signature.hpp`, `tests/test_audio_signature.cpp` | Media < 120 s: emit no signature, apply no sync offset — identical ru… |
| IR-008 | **Done** | T1 | SR-003 | covered | `src/audio_signature.cpp`, `src/audio_signature.hpp`, `tests/test_audio_signature.cpp` | Emit and honour the signature's own `v1:` version prefix |
| GR-001 | Done | T1, T3 | SR-001, SR-005 | covered | `scripts/make_jellyfin_gallery.py` | Build gallery from Jellyfin library cast, TMDB profile fallback |
| GR-002 | Done | T1, T3 | PR-003 | covered | `scripts/make_jellyfin_gallery.py` | Incremental `--merge` refresh without re-embedding known actors |
| GR-003 | **Done** — `gallery… | T1, T3 | SR-001 | covered | `src/build_gallery.cpp`, `src/gallery/gallery_calibration.hpp`, `src/gallery/gallery_report.hpp`, `tests/test_calibration.cpp` | Report coverage: zero-image actors, under-referenced actors, dedup, c… |
| GR-004 | **Done** — basename… | T1, T3 | SR-001 | covered | `scripts/filter_gallery.py`, `scripts/make_gallery.py`, `scripts/make_jellyfin_gallery.py`, `scripts/movienet_eval.py`, `scripts/optimizer/fetch_missing_actors.py`, `scripts/optimizer/optimize.py`, `scripts/optimizer/reembed_gallery.py`, `scripts/optimizer/replay.py`, `scripts/sae_embed_loader.py`, `scripts/sae_gallery.py`, `scripts/sae_stamp.py`, `scripts/stamp_gallery.py`, `src/config.hpp`, `src/gallery/embedder_stamp.cpp`, `src/gallery/embedder_stamp.hpp`, `src/gallery/gallery_builder.cpp`, `src/gallery/gallery_store.cpp`, `src/kpn_bindings.cpp`, `src/main.cpp`, `src/nodes/embedding_dump_node.hpp`, `src/scene_preview.cpp`, `src/types.hpp`, `tests/test_gallery_store.cpp` | Stamp embedder identity into the gallery; **hard startup error** on m… |
| GR-005 | Done | T1, T3 | **SR-005** | covered | `src/gallery/gallery_store.hpp` | Gallery data never leaves the instance |
| GR-006 | Planned | T1 | SR-005 | untagged | - | Provenance tiers: baked / harvested / confirmed, distinguishable per … |
| GR-007 | Planned | T1 | SR-005 | untagged | - | Persist harvested embeddings **flagged and reviewable**, never silent… |
| GR-008 | Planned | T1 | SR-005 | untagged | - | Flag distributional outliers among an actor's references (poisoning g… |
| GR-009 | TBD | T1 | §4 | untagged | - | Human-confirmed associations persist and improve future extractions |
| VR-001 | Done | out-of-ci | PR-002 | tagged, unexecuted | `src/nodes/embedding_dump_node.hpp`, `tests/test_replay_fixtures.cpp` | HDF5 post-inference dump at the embedded-frame boundary |
| VR-002 | **Done** — replay d… | out-of-ci | PR-002 | tagged, unexecuted | `scripts/optimizer/replay.py`, `tests/test_replay_fixtures.cpp` | Replay drives the **real** KPN nodes, not a reimplementation |
| VR-003 | Done | out-of-ci | PR-002 | tagged, unexecuted | `scripts/optimizer/second_score.py` | Scoring: micro-F1 against X-Ray, precision/recall logged at every eva… |
| VR-004 | Done | out-of-ci | PR-002 | tagged, unexecuted | `scripts/validation/ground_truth.py` | Reproducible validation corpus with ground truth |
| VR-005 | **Done** — knee at … | out-of-ci | PR-002 | tagged, unexecuted | `scripts/validation/min_face_size.py` | Minimum face size study — TPI/FPI vs probe size, gallery held at nati… |
| VR-006 | Planned | out-of-ci | PR-002 | untagged | - | Re-tune `scene_threshold` once native-rate decode lands |
| VR-007 | Planned | out-of-ci | PR-002 | untagged | - | Expansion band, clustering threshold, and deferred-pass ablation |
| VR-008 | Planned | out-of-ci | PR-002 | untagged | - | Gallery scaling benchmark — throughput vs gallery size |
| VR-009 | Planned | T1, out-of-ci | PR-002 | untagged | - | Verify accumulated posteriors are calibrated against held-out tracks |
| VR-010 | Planned | out-of-ci | PR-002 | tagged, unexecuted | `src/nodes/embedding_dump_node.hpp` | Dump provenance attributes — embedder model, detector settings, `dens… |
| VR-011 | Planned | out-of-ci | PR-002 | untagged | - | Rewrite the replay harness for the post-AR-012 output contract |
| VR-012 | Planned | T4, out-of-ci | PR-002 | untagged | - | Quality-knee study — TPI/FPI vs sharpness and vs pose, as VR-005 did … |
| VR-013 | **In Progress** — h… | T4, out-of-ci | PR-002 | tagged, unexecuted | `experiments/xsource/resolution_sweep.py`, `experiments/xsource/verify_labels.py` | Cross-source identification probe — gallery from one recording, probe… |
| VR-014 | **Done** — 40 rando… | T2, out-of-ci | PR-002 | covered | `scripts/validation/test_audio_offset.py` | Audio-signature **offset recovery on real content** — a known trim re… |
## Detailed mapping
### AR-001
**Locations:** 1
- [`src/nodes/face_detector_node.hpp:2`](../src/nodes/face_detector_node.hpp#L2) — `Unknown`
### AR-003
**Locations:** 3
- [`src/config.hpp:44`](../src/config.hpp#L44) — `Unknown`
- [`src/nodes/face_detector_node.hpp:47`](../src/nodes/face_detector_node.hpp#L47) — `private:`
- [`src/nodes/identity_matcher_node.hpp:166`](../src/nodes/identity_matcher_node.hpp#L166) — `std::vector<float> host_query(static_cast<size_t>(kMaxFaces) * 512);`
### AR-004
**Locations:** 4
- [`src/main.cpp:86`](../src/main.cpp#L86) — `static constexpr std::size_t kSceneJoinDepth = 256;`
- [`src/main.cpp:319`](../src/main.cpp#L319) — `Unknown`
- [`src/nodes/identity_matcher_node.hpp:166`](../src/nodes/identity_matcher_node.hpp#L166) — `std::vector<float> host_query(static_cast<size_t>(kMaxFaces) * 512);`
- [`tests/test_replay_fixtures.cpp:3`](../tests/test_replay_fixtures.cpp#L3) — `Unknown`
### AR-005
**Locations:** 3
- [`src/face_utils.hpp:2`](../src/face_utils.hpp#L2) — `Unknown`
- [`src/nodes/face_aligner_node.hpp:7`](../src/nodes/face_aligner_node.hpp#L7) — `struct FaceAlignerFunc`
- [`tests/test_face_utils.cpp:1`](../tests/test_face_utils.cpp#L1) — `Unknown`
### AR-006
**Locations:** 1
- [`src/nodes/embedder_node.hpp:21`](../src/nodes/embedder_node.hpp#L21) — `struct EmbedderFunc`
### AR-007
**Locations:** 4
- [`src/config.hpp:108`](../src/config.hpp#L108) — `Unknown`
- [`src/main.cpp:213`](../src/main.cpp#L213) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/nodes/face_tracker_node.hpp:2`](../src/nodes/face_tracker_node.hpp#L2) — `Unknown`
- [`tests/test_face_tracker.cpp:1`](../tests/test_face_tracker.cpp#L1) — `Unknown`
### AR-008
**Locations:** 4
- [`src/config.hpp:108`](../src/config.hpp#L108) — `Unknown`
- [`src/main.cpp:213`](../src/main.cpp#L213) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/nodes/face_tracker_node.hpp:2`](../src/nodes/face_tracker_node.hpp#L2) — `Unknown`
- [`tests/test_face_tracker.cpp:1`](../tests/test_face_tracker.cpp#L1) — `Unknown`
### AR-009
**Locations:** 1
- [`src/nodes/camera_position_change_detector_node.hpp:30`](../src/nodes/camera_position_change_detector_node.hpp#L30) — `struct CameraPositionChangeDetectorFunc`
### AR-010
**Locations:** 9
- [`src/main.cpp:86`](../src/main.cpp#L86) — `static constexpr std::size_t kSceneJoinDepth = 256;`
- [`src/main.cpp:329`](../src/main.cpp#L329) — `Unknown`
- [`src/main.cpp:399`](../src/main.cpp#L399) — `return run_net(std::move(net));`
- [`src/main.cpp:431`](../src/main.cpp#L431) — `Unknown`
- [`src/nodes/scene_boundary_annotator_node.hpp:2`](../src/nodes/scene_boundary_annotator_node.hpp#L2) — `Unknown`
- [`src/nodes/scene_detector_node.hpp:36`](../src/nodes/scene_detector_node.hpp#L36) — `static constexpr std::string_view label() { return "scene_detector"; }`
- [`src/nodes/scene_detector_node.hpp:110`](../src/nodes/scene_detector_node.hpp#L110) — `void flush_remaining()`
- [`src/nodes/scene_detector_node.hpp:144`](../src/nodes/scene_detector_node.hpp#L144) — `void write_output()`
- [`src/scene_boundaries.hpp:2`](../src/scene_boundaries.hpp#L2) — `Unknown`
### AR-012
**Locations:** 9
- [`src/main.cpp:213`](../src/main.cpp#L213) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/main.cpp:230`](../src/main.cpp#L230) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/nodes/identity_matcher_node.hpp:125`](../src/nodes/identity_matcher_node.hpp#L125) — `const GalleryCalibration& calibration() const { return cal_; }`
- [`src/nodes/identity_matcher_node.hpp:272`](../src/nodes/identity_matcher_node.hpp#L272) — `Unknown`
- [`src/nodes/result_sink_node.hpp:49`](../src/nodes/result_sink_node.hpp#L49) — `static constexpr std::string_view label() { return "result_sink"; }`
- [`src/nodes/result_sink_node.hpp:161`](../src/nodes/result_sink_node.hpp#L161) — `struct ActorMeta { std::string name, imdb_id, tmdb_id, jellyfin_id; };`
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_replay_fixtures.cpp:3`](../tests/test_replay_fixtures.cpp#L3) — `Unknown`
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### AR-013
**Locations:** 3
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_replay_fixtures.cpp:3`](../tests/test_replay_fixtures.cpp#L3) — `Unknown`
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### AR-014
**Locations:** 2
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### AR-015
**Locations:** 2
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### AR-016
**Locations:** 4
- [`src/main.cpp:230`](../src/main.cpp#L230) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/nodes/result_sink_node.hpp:63`](../src/nodes/result_sink_node.hpp#L63) — `void set_pre_write_hook(std::function<void(double)> fn) { pre_write_ = std::move(fn); }`
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### AR-017
**Locations:** 3
- [`src/nodes/result_sink_node.hpp:49`](../src/nodes/result_sink_node.hpp#L49) — `static constexpr std::string_view label() { return "result_sink"; }`
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### AR-018
**Locations:** 5
- [`src/config.hpp:152`](../src/config.hpp#L152) — `Unknown`
- [`src/gallery/track_gallery.hpp:164`](../src/gallery/track_gallery.hpp#L164) — `struct TrackState`
- [`src/gallery/track_gallery.hpp:274`](../src/gallery/track_gallery.hpp#L274) — `static int plurality_actor(const TrackState& ts)`
- [`src/nodes/identity_matcher_node.hpp:110`](../src/nodes/identity_matcher_node.hpp#L110) — `std::vector<float> host_gallery(static_cast<size_t>(n_gallery_) * 512);`
- [`tests/test_track_gallery.cpp:1`](../tests/test_track_gallery.cpp#L1) — `Unknown`
### AR-019
**Locations:** 4
- [`src/gallery/track_gallery.hpp:123`](../src/gallery/track_gallery.hpp#L123) — `void forget(int track_id) { tracks_.erase(track_id); }`
- [`src/nodes/identity_matcher_node.hpp:147`](../src/nodes/identity_matcher_node.hpp#L147) — `MatchedSceneFrame operator()(TrackedSceneFrame tf)`
- [`src/nodes/identity_matcher_node.hpp:285`](../src/nodes/identity_matcher_node.hpp#L285) — `Unknown`
- [`tests/test_track_gallery.cpp:1`](../tests/test_track_gallery.cpp#L1) — `Unknown`
### AR-023
**Locations:** 4
- [`src/gallery/gallery_calibration.hpp:2`](../src/gallery/gallery_calibration.hpp#L2) — `Unknown`
- [`src/gallery/gallery_calibration.hpp:53`](../src/gallery/gallery_calibration.hpp#L53) — `float boundary_at(float p = 0.5f, float log_prior_odds = 0.f) const`
- [`src/nodes/identity_matcher_node.hpp:117`](../src/nodes/identity_matcher_node.hpp#L117) — `const GalleryCalibration& calibration() const { return cal_; }`
- [`tests/test_calibration.cpp:1`](../tests/test_calibration.cpp#L1) — `Unknown`
### AR-024
**Locations:** 12
- [`src/config.hpp:108`](../src/config.hpp#L108) — `Unknown`
- [`src/config.hpp:152`](../src/config.hpp#L152) — `Unknown`
- [`src/evidence_discount.hpp:2`](../src/evidence_discount.hpp#L2) — `Unknown`
- [`src/gallery/gallery_calibration.hpp:53`](../src/gallery/gallery_calibration.hpp#L53) — `float boundary_at(float p = 0.5f, float log_prior_odds = 0.f) const`
- [`src/gallery/track_gallery.hpp:133`](../src/gallery/track_gallery.hpp#L133) — `void set_calibration(std::function<float(float)> c) { calibrate_ = std::move(c); }`
- [`src/gallery/track_gallery.hpp:164`](../src/gallery/track_gallery.hpp#L164) — `struct TrackState`
- [`src/gallery/track_gallery.hpp:274`](../src/gallery/track_gallery.hpp#L274) — `static int plurality_actor(const TrackState& ts)`
- [`src/main.cpp:213`](../src/main.cpp#L213) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/nodes/face_tracker_node.hpp:2`](../src/nodes/face_tracker_node.hpp#L2) — `Unknown`
- [`src/nodes/identity_matcher_node.hpp:110`](../src/nodes/identity_matcher_node.hpp#L110) — `std::vector<float> host_gallery(static_cast<size_t>(n_gallery_) * 512);`
- [`src/nodes/identity_matcher_node.hpp:117`](../src/nodes/identity_matcher_node.hpp#L117) — `const GalleryCalibration& calibration() const { return cal_; }`
- [`tests/test_track_gallery.cpp:1`](../tests/test_track_gallery.cpp#L1) — `Unknown`
### AR-025
**Locations:** 3
- [`src/evidence_discount.hpp:2`](../src/evidence_discount.hpp#L2) — `Unknown`
- [`src/nodes/identity_matcher_node.hpp:125`](../src/nodes/identity_matcher_node.hpp#L125) — `const GalleryCalibration& calibration() const { return cal_; }`
- [`src/nodes/identity_matcher_node.hpp:272`](../src/nodes/identity_matcher_node.hpp#L272) — `Unknown`
### AR-026
**Locations:** 2
- [`src/backends/gemm_backend.cpp:44`](../src/backends/gemm_backend.cpp#L44) — `constexpr int kDim = 512;`
- [`tests/test_similarity.cpp:1`](../tests/test_similarity.cpp#L1) — `Unknown`
### AR-027
**Locations:** 1
- [`src/backends/gemm_backend.cpp:44`](../src/backends/gemm_backend.cpp#L44) — `constexpr int kDim = 512;`
### AR-030
**Locations:** 3
- [`src/face_utils.hpp:2`](../src/face_utils.hpp#L2) — `Unknown`
- [`src/nodes/face_aligner_node.hpp:7`](../src/nodes/face_aligner_node.hpp#L7) — `struct FaceAlignerFunc`
- [`tests/test_face_utils.cpp:1`](../tests/test_face_utils.cpp#L1) — `Unknown`
### DP-001
**Locations:** 1
- [`src/main.cpp:3`](../src/main.cpp#L3) — `Unknown`
### DP-002
**Locations:** 1
- [`src/main.cpp:3`](../src/main.cpp#L3) — `Unknown`
### GR-001
**Locations:** 1
- [`scripts/make_jellyfin_gallery.py:4`](../scripts/make_jellyfin_gallery.py#L4) — `Unknown`
### GR-002
**Locations:** 1
- [`scripts/make_jellyfin_gallery.py:4`](../scripts/make_jellyfin_gallery.py#L4) — `Unknown`
### GR-003
**Locations:** 16
- [`src/build_gallery.cpp:83`](../src/build_gallery.cpp#L83) — `Unknown`
- [`src/gallery/gallery_calibration.hpp:80`](../src/gallery/gallery_calibration.hpp#L80) — `struct GalleryCalibrationStats`
- [`src/gallery/gallery_calibration.hpp:145`](../src/gallery/gallery_calibration.hpp#L145) — `std::vector<bool> actor_eligible(n_actors, false);`
- [`src/gallery/gallery_calibration.hpp:294`](../src/gallery/gallery_calibration.hpp#L294) — `Unknown`
- [`src/gallery/gallery_report.hpp:2`](../src/gallery/gallery_report.hpp#L2) — `Unknown`
- [`src/gallery/gallery_report.hpp:52`](../src/gallery/gallery_report.hpp#L52) — `struct GalleryBuildAudit`
- [`src/gallery/gallery_report.hpp:73`](../src/gallery/gallery_report.hpp#L73) — `struct GalleryReport`
- [`src/gallery/gallery_report.hpp:154`](../src/gallery/gallery_report.hpp#L154) — `inline GalleryReport build_gallery_report(const ActorGallery& gallery,`
- [`src/gallery/gallery_report.hpp:295`](../src/gallery/gallery_report.hpp#L295) — `inline nlohmann::json gallery_report_to_json(const GalleryReport& r)`
- [`src/gallery/gallery_report.hpp:361`](../src/gallery/gallery_report.hpp#L361) — `inline GalleryReport gallery_report_from_json(const nlohmann::json& j)`
- [`src/gallery/gallery_report.hpp:446`](../src/gallery/gallery_report.hpp#L446) — `inline void save_gallery_report(const std::string& path, const GalleryReport& r)`
- [`src/gallery/gallery_report.hpp:454`](../src/gallery/gallery_report.hpp#L454) — `inline GalleryReport load_gallery_report(const std::string& path)`
- [`src/gallery/gallery_report.hpp:464`](../src/gallery/gallery_report.hpp#L464) — `return gallery_report_from_json(j);`
- [`tests/test_calibration.cpp:192`](../tests/test_calibration.cpp#L192) — `Embedding unit_axis(int slot)`
- [`tests/test_calibration.cpp:222`](../tests/test_calibration.cpp#L222) — `Unknown`
- [`tests/test_calibration.cpp:248`](../tests/test_calibration.cpp#L248) — `Unknown`
### GR-004
**Locations:** 44
- [`src/config.hpp:54`](../src/config.hpp#L54) — `Unknown`
- [`src/gallery/embedder_stamp.cpp:1`](../src/gallery/embedder_stamp.cpp#L1) — `Unknown`
- [`src/gallery/embedder_stamp.hpp:2`](../src/gallery/embedder_stamp.hpp#L2) — `Unknown`
- [`src/gallery/gallery_builder.cpp:45`](../src/gallery/gallery_builder.cpp#L45) — `ActorGallery build_gallery(const BuildConfig& cfg)`
- [`src/gallery/gallery_store.cpp:82`](../src/gallery/gallery_store.cpp#L82) — `H5::StrType str(H5::PredType::C_S1, H5T_VARIABLE);`
- [`src/gallery/gallery_store.cpp:167`](../src/gallery/gallery_store.cpp#L167) — `H5::DataSpace scalar(H5S_SCALAR);`
- [`src/gallery/gallery_store.cpp:219`](../src/gallery/gallery_store.cpp#L219) — `Unknown`
- [`src/kpn_bindings.cpp:167`](../src/kpn_bindings.cpp#L167) — `Unknown`
- [`src/kpn_bindings.cpp:217`](../src/kpn_bindings.cpp#L217) — `Unknown`
- [`src/main.cpp:188`](../src/main.cpp#L188) — `Unknown`
- [`src/nodes/embedding_dump_node.hpp:127`](../src/nodes/embedding_dump_node.hpp#L127) — `static constexpr std::string_view label() { return "embedding_dump"; }`
- [`src/nodes/embedding_dump_node.hpp:238`](../src/nodes/embedding_dump_node.hpp#L238) — `H5::H5File file(path_, H5F_ACC_TRUNC);`
- [`src/scene_preview.cpp:133`](../src/scene_preview.cpp#L133) — `int main(int argc, char** argv)`
- [`src/types.hpp:148`](../src/types.hpp#L148) — `struct Actor`
- [`tests/test_gallery_store.cpp:182`](../tests/test_gallery_store.cpp#L182) — `TempFile tf("gallery_stamped.h5");`
- [`tests/test_gallery_store.cpp:201`](../tests/test_gallery_store.cpp#L201) — `TempFile tf("gallery_stamped.h5");`
- [`tests/test_gallery_store.cpp:219`](../tests/test_gallery_store.cpp#L219) — `TempFile tf("gallery_unstamped.h5");`
- [`tests/test_gallery_store.cpp:240`](../tests/test_gallery_store.cpp#L240) — `TempFile tf("gallery_json_stamp.json");`
- [`tests/test_gallery_store.cpp:252`](../tests/test_gallery_store.cpp#L252) — `Unknown`
- [`tests/test_gallery_store.cpp:268`](../tests/test_gallery_store.cpp#L268) — `Unknown`
- [`tests/test_gallery_store.cpp:300`](../tests/test_gallery_store.cpp#L300) — `Unknown`
- [`tests/test_gallery_store.cpp:309`](../tests/test_gallery_store.cpp#L309) — `Unknown`
- [`tests/test_gallery_store.cpp:333`](../tests/test_gallery_store.cpp#L333) — `Unknown`
- [`tests/test_gallery_store.cpp:348`](../tests/test_gallery_store.cpp#L348) — `Unknown`
- [`tests/test_gallery_store.cpp:367`](../tests/test_gallery_store.cpp#L367) — `Unknown`
- [`tests/test_gallery_store.cpp:384`](../tests/test_gallery_store.cpp#L384) — `TempFile tf("fake_model.onnx");`
- [`scripts/filter_gallery.py:80`](../scripts/filter_gallery.py#L80) — `if actor_jellyfin_id(a) in cast_ids]`
- [`scripts/make_gallery.py:181`](../scripts/make_gallery.py#L181) — `Unknown`
- [`scripts/make_jellyfin_gallery.py:448`](../scripts/make_jellyfin_gallery.py#L448) — `Unknown`
- [`scripts/make_jellyfin_gallery.py:456`](../scripts/make_jellyfin_gallery.py#L456) — `Unknown`
- [`scripts/movienet_eval.py:65`](../scripts/movienet_eval.py#L65) — `with open(args.gt) as f:`
- [`scripts/optimizer/fetch_missing_actors.py:62`](../scripts/optimizer/fetch_missing_actors.py#L62) — `def fetch(missing_path, out_path, token, build_dir, models_dir, arcface,`
- [`scripts/optimizer/fetch_missing_actors.py:109`](../scripts/optimizer/fetch_missing_actors.py#L109) — `def merge(base_path, add_path, out_path):`
- [`scripts/optimizer/fetch_missing_actors.py:124`](../scripts/optimizer/fetch_missing_actors.py#L124) — `def merge(base_path, add_path, out_path):`
- [`scripts/optimizer/optimize.py:186`](../scripts/optimizer/optimize.py#L186) — `Unknown`
- [`scripts/optimizer/optimize.py:202`](../scripts/optimizer/optimize.py#L202) — `if not Path(f["dump"]).exists():`
- [`scripts/optimizer/reembed_gallery.py:62`](../scripts/optimizer/reembed_gallery.py#L62) — `for i, a in enumerate(ref["actors"], 1):`
- [`scripts/optimizer/replay.py:113`](../scripts/optimizer/replay.py#L113) — `Unknown`
- [`scripts/optimizer/replay.py:253`](../scripts/optimizer/replay.py#L253) — `Unknown`
- [`scripts/sae_embed_loader.py:23`](../scripts/sae_embed_loader.py#L23) — `def resolve_arcface(models_dir: str, arcface: str \| None = None) -> str:`
- [`scripts/sae_gallery.py:171`](../scripts/sae_gallery.py#L171) — `if not _stamp_empty(embedder):`
- [`scripts/sae_gallery.py:200`](../scripts/sae_gallery.py#L200) — `for a in range(len(offset)):`
- [`scripts/sae_stamp.py:3`](../scripts/sae_stamp.py#L3) — `Unknown`
- [`scripts/stamp_gallery.py:4`](../scripts/stamp_gallery.py#L4) — `Unknown`
### GR-005
**Locations:** 1
- [`src/gallery/gallery_store.hpp:15`](../src/gallery/gallery_store.hpp#L15) — `Unknown`
### IR-001
**Locations:** 1
- [`src/nodes/result_sink_node.hpp:2`](../src/nodes/result_sink_node.hpp#L2) — `Unknown`
### IR-002
**Locations:** 5
- [`src/config.hpp:20`](../src/config.hpp#L20) — `struct Config`
- [`src/main.cpp:230`](../src/main.cpp#L230) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/nodes/result_sink_node.hpp:49`](../src/nodes/result_sink_node.hpp#L49) — `static constexpr std::string_view label() { return "result_sink"; }`
- [`src/nodes/result_sink_node.hpp:122`](../src/nodes/result_sink_node.hpp#L122) — `void write_output()`
- [`src/nodes/result_sink_node.hpp:161`](../src/nodes/result_sink_node.hpp#L161) — `struct ActorMeta { std::string name, imdb_id, tmdb_id, jellyfin_id; };`
### IR-003
**Locations:** 1
- [`src/main.cpp:230`](../src/main.cpp#L230) — `reg_cfg, EvidenceDiscounter(same_person));`
### IR-004
**Locations:** 18
- [`src/audio_bindings.cpp:3`](../src/audio_bindings.cpp#L3) — `Unknown`
- [`src/audio_signature.cpp:3`](../src/audio_signature.cpp#L3) — `Unknown`
- [`src/audio_signature.cpp:265`](../src/audio_signature.cpp#L265) — `std::vector<std::uint8_t> pack_frames(const std::vector<float>& mono)`
- [`src/audio_signature.cpp:320`](../src/audio_signature.cpp#L320) — `std::optional<std::string> signature_from_mono(const std::vector<float>& mono)`
- [`src/audio_signature.cpp:327`](../src/audio_signature.cpp#L327) — `std::optional<std::vector<float>> decode_centre_window(const std::string& path)`
- [`src/audio_signature.cpp:421`](../src/audio_signature.cpp#L421) — `std::optional<std::string> compute_signature(const std::string& path)`
- [`src/audio_signature.hpp:4`](../src/audio_signature.hpp#L4) — `Unknown`
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:129`](../tests/test_audio_signature.cpp#L129) — `Unknown`
- [`tests/test_audio_signature.cpp:151`](../tests/test_audio_signature.cpp#L151) — `Unknown`
- [`tests/test_audio_signature.cpp:167`](../tests/test_audio_signature.cpp#L167) — `Unknown`
- [`tests/test_audio_signature.cpp:236`](../tests/test_audio_signature.cpp#L236) — `Unknown`
- [`tests/test_audio_signature.cpp:263`](../tests/test_audio_signature.cpp#L263) — `TempWav w("centred300");`
- [`tests/test_audio_signature.cpp:301`](../tests/test_audio_signature.cpp#L301) — `Unknown`
- [`tests/test_audio_signature.cpp:314`](../tests/test_audio_signature.cpp#L314) — `kNumBands * std::log10(1000.0 / kBandLoHz) / std::log10(kBandHiHz / kBandLoHz)));`
- [`tests/test_audio_signature.cpp:328`](../tests/test_audio_signature.cpp#L328) — `std::vector<float> a(kWindowSamples / 50);`
- [`tests/test_audio_signature.cpp:344`](../tests/test_audio_signature.cpp#L344) — `return base64_encode(reinterpret_cast<const std::uint8_t*>(s.data()), s.size());`
- [`scripts/validation/test_audio_offset.py:5`](../scripts/validation/test_audio_offset.py#L5) — `The golden vector (IR-005) proves the *arithmetic* is identical in both`
### IR-005
**Locations:** 6
- [`src/audio_bindings.cpp:3`](../src/audio_bindings.cpp#L3) — `Unknown`
- [`src/audio_signature.cpp:421`](../src/audio_signature.cpp#L421) — `std::optional<std::string> compute_signature(const std::string& path)`
- [`src/audio_signature.hpp:4`](../src/audio_signature.hpp#L4) — `Unknown`
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:129`](../tests/test_audio_signature.cpp#L129) — `Unknown`
- [`tests/test_audio_signature.cpp:138`](../tests/test_audio_signature.cpp#L138) — `Unknown`
### IR-006
**Locations:** 1
- [`scripts/run_from_jellyfin.py:4`](../scripts/run_from_jellyfin.py#L4) — `Unknown`
### IR-007
**Locations:** 8
- [`src/audio_signature.cpp:3`](../src/audio_signature.cpp#L3) — `Unknown`
- [`src/audio_signature.cpp:327`](../src/audio_signature.cpp#L327) — `std::optional<std::vector<float>> decode_centre_window(const std::string& path)`
- [`src/audio_signature.cpp:421`](../src/audio_signature.cpp#L421) — `std::optional<std::string> compute_signature(const std::string& path)`
- [`src/audio_signature.hpp:4`](../src/audio_signature.hpp#L4) — `Unknown`
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:201`](../tests/test_audio_signature.cpp#L201) — `TempWav w("short30");`
- [`tests/test_audio_signature.cpp:219`](../tests/test_audio_signature.cpp#L219) — `TempWav w("exact120");`
- [`tests/test_audio_signature.cpp:229`](../tests/test_audio_signature.cpp#L229) — `TempWav w("exact120");`
### IR-008
**Locations:** 7
- [`src/audio_signature.cpp:3`](../src/audio_signature.cpp#L3) — `Unknown`
- [`src/audio_signature.cpp:320`](../src/audio_signature.cpp#L320) — `std::optional<std::string> signature_from_mono(const std::vector<float>& mono)`
- [`src/audio_signature.cpp:421`](../src/audio_signature.cpp#L421) — `std::optional<std::string> compute_signature(const std::string& path)`
- [`src/audio_signature.hpp:4`](../src/audio_signature.hpp#L4) — `Unknown`
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:129`](../tests/test_audio_signature.cpp#L129) — `Unknown`
- [`tests/test_audio_signature.cpp:167`](../tests/test_audio_signature.cpp#L167) — `Unknown`
### IT-001
**Locations:** 1
- [`tests/test_replay_fixtures.cpp:3`](../tests/test_replay_fixtures.cpp#L3) — `Unknown`
### PR-002
**Locations:** 10
- [`src/nodes/embedding_dump_node.hpp:2`](../src/nodes/embedding_dump_node.hpp#L2) — `Unknown`
- [`src/nodes/embedding_dump_node.hpp:18`](../src/nodes/embedding_dump_node.hpp#L18) — `Unknown`
- [`src/nodes/embedding_dump_node.hpp:133`](../src/nodes/embedding_dump_node.hpp#L133) — `static constexpr std::string_view label() { return "embedding_dump"; }`
- [`src/nodes/embedding_dump_node.hpp:159`](../src/nodes/embedding_dump_node.hpp#L159) — `void operator()(EmbeddedSceneFrame ef)`
- [`src/nodes/embedding_dump_node.hpp:242`](../src/nodes/embedding_dump_node.hpp#L242) — `H5::H5File file(path_, H5F_ACC_TRUNC);`
- [`scripts/optimizer/replay.py:5`](../scripts/optimizer/replay.py#L5) — `Reads an embedding dump (scripts/optimizer/SCHEMA.md), feeds each frame as an`
- [`scripts/optimizer/second_score.py:5`](../scripts/optimizer/second_score.py#L5) — `Unknown`
- [`scripts/validation/ground_truth.py:24`](../scripts/validation/ground_truth.py#L24) — `Unknown`
- [`experiments/xsource/resolution_sweep.py:4`](../experiments/xsource/resolution_sweep.py#L4) — `Unknown`
- [`experiments/xsource/verify_labels.py:4`](../experiments/xsource/verify_labels.py#L4) — `Unknown`
### PR-004
**Locations:** 1
- [`src/main.cpp:3`](../src/main.cpp#L3) — `Unknown`
### SR-001
**Locations:** 64
- [`src/backends/gemm_backend.cpp:44`](../src/backends/gemm_backend.cpp#L44) — `constexpr int kDim = 512;`
- [`src/build_gallery.cpp:83`](../src/build_gallery.cpp#L83) — `Unknown`
- [`src/config.hpp:54`](../src/config.hpp#L54) — `Unknown`
- [`src/gallery/embedder_stamp.cpp:1`](../src/gallery/embedder_stamp.cpp#L1) — `Unknown`
- [`src/gallery/embedder_stamp.hpp:2`](../src/gallery/embedder_stamp.hpp#L2) — `Unknown`
- [`src/gallery/gallery_builder.cpp:45`](../src/gallery/gallery_builder.cpp#L45) — `ActorGallery build_gallery(const BuildConfig& cfg)`
- [`src/gallery/gallery_calibration.hpp:80`](../src/gallery/gallery_calibration.hpp#L80) — `struct GalleryCalibrationStats`
- [`src/gallery/gallery_calibration.hpp:145`](../src/gallery/gallery_calibration.hpp#L145) — `std::vector<bool> actor_eligible(n_actors, false);`
- [`src/gallery/gallery_calibration.hpp:294`](../src/gallery/gallery_calibration.hpp#L294) — `Unknown`
- [`src/gallery/gallery_report.hpp:2`](../src/gallery/gallery_report.hpp#L2) — `Unknown`
- [`src/gallery/gallery_report.hpp:52`](../src/gallery/gallery_report.hpp#L52) — `struct GalleryBuildAudit`
- [`src/gallery/gallery_report.hpp:73`](../src/gallery/gallery_report.hpp#L73) — `struct GalleryReport`
- [`src/gallery/gallery_report.hpp:154`](../src/gallery/gallery_report.hpp#L154) — `inline GalleryReport build_gallery_report(const ActorGallery& gallery,`
- [`src/gallery/gallery_report.hpp:295`](../src/gallery/gallery_report.hpp#L295) — `inline nlohmann::json gallery_report_to_json(const GalleryReport& r)`
- [`src/gallery/gallery_report.hpp:361`](../src/gallery/gallery_report.hpp#L361) — `inline GalleryReport gallery_report_from_json(const nlohmann::json& j)`
- [`src/gallery/gallery_report.hpp:446`](../src/gallery/gallery_report.hpp#L446) — `inline void save_gallery_report(const std::string& path, const GalleryReport& r)`
- [`src/gallery/gallery_report.hpp:454`](../src/gallery/gallery_report.hpp#L454) — `inline GalleryReport load_gallery_report(const std::string& path)`
- [`src/gallery/gallery_report.hpp:464`](../src/gallery/gallery_report.hpp#L464) — `return gallery_report_from_json(j);`
- [`src/gallery/gallery_store.cpp:82`](../src/gallery/gallery_store.cpp#L82) — `H5::StrType str(H5::PredType::C_S1, H5T_VARIABLE);`
- [`src/gallery/gallery_store.cpp:167`](../src/gallery/gallery_store.cpp#L167) — `H5::DataSpace scalar(H5S_SCALAR);`
- [`src/gallery/gallery_store.cpp:219`](../src/gallery/gallery_store.cpp#L219) — `Unknown`
- [`src/kpn_bindings.cpp:167`](../src/kpn_bindings.cpp#L167) — `Unknown`
- [`src/kpn_bindings.cpp:217`](../src/kpn_bindings.cpp#L217) — `Unknown`
- [`src/main.cpp:188`](../src/main.cpp#L188) — `Unknown`
- [`src/nodes/embedding_dump_node.hpp:127`](../src/nodes/embedding_dump_node.hpp#L127) — `static constexpr std::string_view label() { return "embedding_dump"; }`
- [`src/nodes/embedding_dump_node.hpp:238`](../src/nodes/embedding_dump_node.hpp#L238) — `H5::H5File file(path_, H5F_ACC_TRUNC);`
- [`src/scene_preview.cpp:133`](../src/scene_preview.cpp#L133) — `int main(int argc, char** argv)`
- [`src/types.hpp:148`](../src/types.hpp#L148) — `struct Actor`
- [`tests/test_calibration.cpp:192`](../tests/test_calibration.cpp#L192) — `Embedding unit_axis(int slot)`
- [`tests/test_calibration.cpp:222`](../tests/test_calibration.cpp#L222) — `Unknown`
- [`tests/test_calibration.cpp:248`](../tests/test_calibration.cpp#L248) — `Unknown`
- [`tests/test_gallery_store.cpp:182`](../tests/test_gallery_store.cpp#L182) — `TempFile tf("gallery_stamped.h5");`
- [`tests/test_gallery_store.cpp:201`](../tests/test_gallery_store.cpp#L201) — `TempFile tf("gallery_stamped.h5");`
- [`tests/test_gallery_store.cpp:219`](../tests/test_gallery_store.cpp#L219) — `TempFile tf("gallery_unstamped.h5");`
- [`tests/test_gallery_store.cpp:240`](../tests/test_gallery_store.cpp#L240) — `TempFile tf("gallery_json_stamp.json");`
- [`tests/test_gallery_store.cpp:252`](../tests/test_gallery_store.cpp#L252) — `Unknown`
- [`tests/test_gallery_store.cpp:268`](../tests/test_gallery_store.cpp#L268) — `Unknown`
- [`tests/test_gallery_store.cpp:300`](../tests/test_gallery_store.cpp#L300) — `Unknown`
- [`tests/test_gallery_store.cpp:309`](../tests/test_gallery_store.cpp#L309) — `Unknown`
- [`tests/test_gallery_store.cpp:333`](../tests/test_gallery_store.cpp#L333) — `Unknown`
- [`tests/test_gallery_store.cpp:348`](../tests/test_gallery_store.cpp#L348) — `Unknown`
- [`tests/test_gallery_store.cpp:367`](../tests/test_gallery_store.cpp#L367) — `Unknown`
- [`tests/test_gallery_store.cpp:384`](../tests/test_gallery_store.cpp#L384) — `TempFile tf("fake_model.onnx");`
- [`tests/test_similarity.cpp:1`](../tests/test_similarity.cpp#L1) — `Unknown`
- [`scripts/filter_gallery.py:80`](../scripts/filter_gallery.py#L80) — `if actor_jellyfin_id(a) in cast_ids]`
- [`scripts/make_gallery.py:181`](../scripts/make_gallery.py#L181) — `Unknown`
- [`scripts/make_jellyfin_gallery.py:4`](../scripts/make_jellyfin_gallery.py#L4) — `Unknown`
- [`scripts/make_jellyfin_gallery.py:448`](../scripts/make_jellyfin_gallery.py#L448) — `Unknown`
- [`scripts/make_jellyfin_gallery.py:456`](../scripts/make_jellyfin_gallery.py#L456) — `Unknown`
- [`scripts/movienet_eval.py:65`](../scripts/movienet_eval.py#L65) — `with open(args.gt) as f:`
- [`scripts/optimizer/fetch_missing_actors.py:62`](../scripts/optimizer/fetch_missing_actors.py#L62) — `def fetch(missing_path, out_path, token, build_dir, models_dir, arcface,`
- [`scripts/optimizer/fetch_missing_actors.py:109`](../scripts/optimizer/fetch_missing_actors.py#L109) — `def merge(base_path, add_path, out_path):`
- [`scripts/optimizer/fetch_missing_actors.py:124`](../scripts/optimizer/fetch_missing_actors.py#L124) — `def merge(base_path, add_path, out_path):`
- [`scripts/optimizer/optimize.py:186`](../scripts/optimizer/optimize.py#L186) — `Unknown`
- [`scripts/optimizer/optimize.py:202`](../scripts/optimizer/optimize.py#L202) — `if not Path(f["dump"]).exists():`
- [`scripts/optimizer/reembed_gallery.py:62`](../scripts/optimizer/reembed_gallery.py#L62) — `for i, a in enumerate(ref["actors"], 1):`
- [`scripts/optimizer/replay.py:113`](../scripts/optimizer/replay.py#L113) — `Unknown`
- [`scripts/optimizer/replay.py:253`](../scripts/optimizer/replay.py#L253) — `Unknown`
- [`scripts/run_from_jellyfin.py:4`](../scripts/run_from_jellyfin.py#L4) — `Unknown`
- [`scripts/sae_embed_loader.py:23`](../scripts/sae_embed_loader.py#L23) — `def resolve_arcface(models_dir: str, arcface: str \| None = None) -> str:`
- [`scripts/sae_gallery.py:171`](../scripts/sae_gallery.py#L171) — `if not _stamp_empty(embedder):`
- [`scripts/sae_gallery.py:200`](../scripts/sae_gallery.py#L200) — `for a in range(len(offset)):`
- [`scripts/sae_stamp.py:3`](../scripts/sae_stamp.py#L3) — `Unknown`
- [`scripts/stamp_gallery.py:4`](../scripts/stamp_gallery.py#L4) — `Unknown`
### SR-002
**Locations:** 35
- [`src/config.hpp:44`](../src/config.hpp#L44) — `Unknown`
- [`src/config.hpp:108`](../src/config.hpp#L108) — `Unknown`
- [`src/evidence_discount.hpp:2`](../src/evidence_discount.hpp#L2) — `Unknown`
- [`src/face_utils.hpp:2`](../src/face_utils.hpp#L2) — `Unknown`
- [`src/gallery/gallery_calibration.hpp:2`](../src/gallery/gallery_calibration.hpp#L2) — `Unknown`
- [`src/gallery/gallery_calibration.hpp:53`](../src/gallery/gallery_calibration.hpp#L53) — `float boundary_at(float p = 0.5f, float log_prior_odds = 0.f) const`
- [`src/main.cpp:86`](../src/main.cpp#L86) — `static constexpr std::size_t kSceneJoinDepth = 256;`
- [`src/main.cpp:213`](../src/main.cpp#L213) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/main.cpp:230`](../src/main.cpp#L230) — `reg_cfg, EvidenceDiscounter(same_person));`
- [`src/main.cpp:319`](../src/main.cpp#L319) — `Unknown`
- [`src/main.cpp:329`](../src/main.cpp#L329) — `Unknown`
- [`src/main.cpp:399`](../src/main.cpp#L399) — `return run_net(std::move(net));`
- [`src/main.cpp:431`](../src/main.cpp#L431) — `Unknown`
- [`src/nodes/camera_position_change_detector_node.hpp:30`](../src/nodes/camera_position_change_detector_node.hpp#L30) — `struct CameraPositionChangeDetectorFunc`
- [`src/nodes/embedder_node.hpp:21`](../src/nodes/embedder_node.hpp#L21) — `struct EmbedderFunc`
- [`src/nodes/face_aligner_node.hpp:7`](../src/nodes/face_aligner_node.hpp#L7) — `struct FaceAlignerFunc`
- [`src/nodes/face_detector_node.hpp:2`](../src/nodes/face_detector_node.hpp#L2) — `Unknown`
- [`src/nodes/face_detector_node.hpp:47`](../src/nodes/face_detector_node.hpp#L47) — `private:`
- [`src/nodes/face_tracker_node.hpp:2`](../src/nodes/face_tracker_node.hpp#L2) — `Unknown`
- [`src/nodes/identity_matcher_node.hpp:117`](../src/nodes/identity_matcher_node.hpp#L117) — `const GalleryCalibration& calibration() const { return cal_; }`
- [`src/nodes/identity_matcher_node.hpp:125`](../src/nodes/identity_matcher_node.hpp#L125) — `const GalleryCalibration& calibration() const { return cal_; }`
- [`src/nodes/identity_matcher_node.hpp:166`](../src/nodes/identity_matcher_node.hpp#L166) — `std::vector<float> host_query(static_cast<size_t>(kMaxFaces) * 512);`
- [`src/nodes/identity_matcher_node.hpp:272`](../src/nodes/identity_matcher_node.hpp#L272) — `Unknown`
- [`src/nodes/result_sink_node.hpp:49`](../src/nodes/result_sink_node.hpp#L49) — `static constexpr std::string_view label() { return "result_sink"; }`
- [`src/nodes/result_sink_node.hpp:63`](../src/nodes/result_sink_node.hpp#L63) — `void set_pre_write_hook(std::function<void(double)> fn) { pre_write_ = std::move(fn); }`
- [`src/nodes/result_sink_node.hpp:161`](../src/nodes/result_sink_node.hpp#L161) — `struct ActorMeta { std::string name, imdb_id, tmdb_id, jellyfin_id; };`
- [`src/nodes/scene_boundary_annotator_node.hpp:2`](../src/nodes/scene_boundary_annotator_node.hpp#L2) — `Unknown`
- [`src/nodes/scene_detector_node.hpp:36`](../src/nodes/scene_detector_node.hpp#L36) — `static constexpr std::string_view label() { return "scene_detector"; }`
- [`src/nodes/scene_detector_node.hpp:110`](../src/nodes/scene_detector_node.hpp#L110) — `void flush_remaining()`
- [`src/nodes/scene_detector_node.hpp:144`](../src/nodes/scene_detector_node.hpp#L144) — `void write_output()`
- [`src/scene_boundaries.hpp:2`](../src/scene_boundaries.hpp#L2) — `Unknown`
- [`src/track_registry.hpp:2`](../src/track_registry.hpp#L2) — `Unknown`
- [`tests/test_calibration.cpp:1`](../tests/test_calibration.cpp#L1) — `Unknown`
- [`tests/test_face_tracker.cpp:1`](../tests/test_face_tracker.cpp#L1) — `Unknown`
- [`tests/test_face_utils.cpp:1`](../tests/test_face_utils.cpp#L1) — `Unknown`
### SR-003
**Locations:** 7
- [`src/audio_bindings.cpp:3`](../src/audio_bindings.cpp#L3) — `Unknown`
- [`src/audio_signature.cpp:3`](../src/audio_signature.cpp#L3) — `Unknown`
- [`src/audio_signature.hpp:4`](../src/audio_signature.hpp#L4) — `Unknown`
- [`src/config.hpp:20`](../src/config.hpp#L20) — `struct Config`
- [`src/nodes/result_sink_node.hpp:2`](../src/nodes/result_sink_node.hpp#L2) — `Unknown`
- [`src/nodes/result_sink_node.hpp:49`](../src/nodes/result_sink_node.hpp#L49) — `static constexpr std::string_view label() { return "result_sink"; }`
- [`src/nodes/result_sink_node.hpp:122`](../src/nodes/result_sink_node.hpp#L122) — `void write_output()`
### SR-005
**Locations:** 11
- [`src/config.hpp:152`](../src/config.hpp#L152) — `Unknown`
- [`src/gallery/gallery_store.hpp:15`](../src/gallery/gallery_store.hpp#L15) — `Unknown`
- [`src/gallery/track_gallery.hpp:123`](../src/gallery/track_gallery.hpp#L123) — `void forget(int track_id) { tracks_.erase(track_id); }`
- [`src/gallery/track_gallery.hpp:133`](../src/gallery/track_gallery.hpp#L133) — `void set_calibration(std::function<float(float)> c) { calibrate_ = std::move(c); }`
- [`src/gallery/track_gallery.hpp:164`](../src/gallery/track_gallery.hpp#L164) — `struct TrackState`
- [`src/gallery/track_gallery.hpp:274`](../src/gallery/track_gallery.hpp#L274) — `static int plurality_actor(const TrackState& ts)`
- [`src/nodes/identity_matcher_node.hpp:110`](../src/nodes/identity_matcher_node.hpp#L110) — `std::vector<float> host_gallery(static_cast<size_t>(n_gallery_) * 512);`
- [`src/nodes/identity_matcher_node.hpp:147`](../src/nodes/identity_matcher_node.hpp#L147) — `MatchedSceneFrame operator()(TrackedSceneFrame tf)`
- [`src/nodes/identity_matcher_node.hpp:285`](../src/nodes/identity_matcher_node.hpp#L285) — `Unknown`
- [`tests/test_track_gallery.cpp:1`](../tests/test_track_gallery.cpp#L1) — `Unknown`
- [`scripts/make_jellyfin_gallery.py:4`](../scripts/make_jellyfin_gallery.py#L4) — `Unknown`
### UT-001
**Locations:** 1
- [`tests/test_track_registry.cpp:3`](../tests/test_track_registry.cpp#L3) — `Unknown`
### UT-101
**Locations:** 5
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:129`](../tests/test_audio_signature.cpp#L129) — `Unknown`
- [`tests/test_audio_signature.cpp:138`](../tests/test_audio_signature.cpp#L138) — `Unknown`
- [`tests/test_audio_signature.cpp:151`](../tests/test_audio_signature.cpp#L151) — `Unknown`
- [`tests/test_audio_signature.cpp:167`](../tests/test_audio_signature.cpp#L167) — `Unknown`
### UT-102
**Locations:** 5
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:201`](../tests/test_audio_signature.cpp#L201) — `TempWav w("short30");`
- [`tests/test_audio_signature.cpp:219`](../tests/test_audio_signature.cpp#L219) — `TempWav w("exact120");`
- [`tests/test_audio_signature.cpp:229`](../tests/test_audio_signature.cpp#L229) — `TempWav w("exact120");`
- [`tests/test_audio_signature.cpp:236`](../tests/test_audio_signature.cpp#L236) — `Unknown`
### UT-103
**Locations:** 2
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:263`](../tests/test_audio_signature.cpp#L263) — `TempWav w("centred300");`
### UT-104
**Locations:** 5
- [`tests/test_audio_signature.cpp:3`](../tests/test_audio_signature.cpp#L3) — `Unknown`
- [`tests/test_audio_signature.cpp:301`](../tests/test_audio_signature.cpp#L301) — `Unknown`
- [`tests/test_audio_signature.cpp:314`](../tests/test_audio_signature.cpp#L314) — `kNumBands * std::log10(1000.0 / kBandLoHz) / std::log10(kBandHiHz / kBandLoHz)));`
- [`tests/test_audio_signature.cpp:328`](../tests/test_audio_signature.cpp#L328) — `std::vector<float> a(kWindowSamples / 50);`
- [`tests/test_audio_signature.cpp:344`](../tests/test_audio_signature.cpp#L344) — `return base64_encode(reinterpret_cast<const std::uint8_t*>(s.data()), s.size());`
### UT-105
**Locations:** 1
- [`scripts/validation/test_audio_offset.py:5`](../scripts/validation/test_audio_offset.py#L5) — `The golden vector (IR-005) proves the *arithmetic* is identical in both`
### UT-106
**Locations:** 1
- [`scripts/validation/test_audio_offset.py:5`](../scripts/validation/test_audio_offset.py#L5) — `The golden vector (IR-005) proves the *arithmetic* is identical in both`
### UT-107
**Locations:** 1
- [`scripts/validation/test_audio_offset.py:5`](../scripts/validation/test_audio_offset.py#L5) — `The golden vector (IR-005) proves the *arithmetic* is identical in both`
### UT-108
**Locations:** 1
- [`scripts/validation/test_audio_offset.py:5`](../scripts/validation/test_audio_offset.py#L5) — `The golden vector (IR-005) proves the *arithmetic* is identical in both`
### VR-001
**Locations:** 2
- [`src/nodes/embedding_dump_node.hpp:2`](../src/nodes/embedding_dump_node.hpp#L2) — `Unknown`
- [`tests/test_replay_fixtures.cpp:3`](../tests/test_replay_fixtures.cpp#L3) — `Unknown`
### VR-002
**Locations:** 2
- [`tests/test_replay_fixtures.cpp:3`](../tests/test_replay_fixtures.cpp#L3) — `Unknown`
- [`scripts/optimizer/replay.py:5`](../scripts/optimizer/replay.py#L5) — `Reads an embedding dump (scripts/optimizer/SCHEMA.md), feeds each frame as an`
### VR-003
**Locations:** 1
- [`scripts/optimizer/second_score.py:5`](../scripts/optimizer/second_score.py#L5) — `Unknown`
### VR-004
**Locations:** 1
- [`scripts/validation/ground_truth.py:24`](../scripts/validation/ground_truth.py#L24) — `Unknown`
### VR-005
**Locations:** 1
- [`scripts/validation/min_face_size.py:5`](../scripts/validation/min_face_size.py#L5) — `Unknown`
### VR-010
**Locations:** 5
- [`src/nodes/embedding_dump_node.hpp:2`](../src/nodes/embedding_dump_node.hpp#L2) — `Unknown`
- [`src/nodes/embedding_dump_node.hpp:18`](../src/nodes/embedding_dump_node.hpp#L18) — `Unknown`
- [`src/nodes/embedding_dump_node.hpp:133`](../src/nodes/embedding_dump_node.hpp#L133) — `static constexpr std::string_view label() { return "embedding_dump"; }`
- [`src/nodes/embedding_dump_node.hpp:159`](../src/nodes/embedding_dump_node.hpp#L159) — `void operator()(EmbeddedSceneFrame ef)`
- [`src/nodes/embedding_dump_node.hpp:242`](../src/nodes/embedding_dump_node.hpp#L242) — `H5::H5File file(path_, H5F_ACC_TRUNC);`
### VR-013
**Locations:** 2
- [`experiments/xsource/resolution_sweep.py:4`](../experiments/xsource/resolution_sweep.py#L4) — `Unknown`
- [`experiments/xsource/verify_labels.py:4`](../experiments/xsource/verify_labels.py#L4) — `Unknown`
### VR-014
**Locations:** 1
- [`scripts/validation/test_audio_offset.py:5`](../scripts/validation/test_audio_offset.py#L5) — `The golden vector (IR-005) proves the *arithmetic* is identical in both`
experiments/.gitignore
+11
View File
@@ -11,6 +11,17 @@ manifests/
trajectories/
results/
# Cross-source identification study: source clips and the hand-sorted face
# crops. The sorting is human ground truth and expensive to redo, so it goes to
# the artifact registry rather than being regenerated — push it once sorted.
xsource/clips/
xsource/labelling/
xsource/frames/
xsource/cache/
xsource/results_*.json
xsource/failure_analysis.json
xsource/*.jpg
# Raw run logs and scratch scripts (regenerated by every run).
_scratch/
experiments/xsource/README.md
+92
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@@ -0,0 +1,92 @@
# xsource — cross-source identification probe (VR-013)
Gallery from **one** recording, probes from **another**, swept over the probe's
input resolution. Complements VR-005, which asked the same question over gallery
mugshots: that one degrades an already-aligned 112×112 crop, holding alignment
perfect, so it isolates the embedder. This one downscales the **whole frame**
before the detector, so detection and landmark regression degrade with it.
Corpus: two Pexels clips of one shoot (4096×2160, 25 fps), four people, all four
present in both.
## Getting the data
Clips, frames and hand-sorted crops are gitignored; they live in the artifact
registry.
scripts/artifacts/pull_artifacts.sh xsource # clips + labelling, frames regenerated
scripts/artifacts/push_artifacts.sh xsource # after correcting labels
Pulling fetches the two clips and the hand-sorted crops, then regenerates the
frames with ffmpeg — ~320 MB of PNG that is deterministic from the clips, so it
is not worth shipping. Extraction settings are pinned in the pull script because
the manifests key on frame filenames *and* on detection order within each frame;
`verify_labels.py` runs at the end and will fail loudly if they drift.
Pull never overwrites an existing `labelling/`. That directory is human ground
truth — somebody looked at all 167 crops and put each one in a folder — and it
is the expensive part of this study, so push it once corrected.
Clips are Pexels-licensed: free to use, no attribution required, but not
CC or MIT. Fine as a frozen CI artifact on private infrastructure; do not
redistribute them as stock content.
## Scripts
| script | does |
|---|---|
| `dump_faces.py` | detect every face, write a context crop per detection + a manifest |
| `redraw_boxes.py` | redraw those crops with the detection boxed, in place |
| `propose_labels.py` | propose labels for one clip from another clip's hand-sorted folders |
| `make_review_site.py` | local `review.html` — current label, crop, better match, correct and export |
| `apply_corrections.py` | apply the exported `corrections.json` |
| `verify_labels.py` | integrity gate: index consistency, duplicates, separation. Exits non-zero on failure |
| `resolution_sweep.py` | the VR-013 measurement |
| `failure_analysis.py` | what explains the misses — pose, size, blur, detector confidence |
| `landmark_voting.py` | average SCRFD's overlapping detections instead of discarding them |
| `pose_label.py` | mesh-estimated head pose, for hand correction (feeds VR-012) |
Everything drives the shipped C++ through `sae_embed`; nothing reimplements
detection, alignment, the embedder or the calibration. Scoring goes through the
production gallery sigmoid — never a raw cosine (AR-024).
LD_PRELOAD=/usr/lib/libcudnn_cnn.so.9 python3 resolution_sweep.py
The preload is needed while ORT's CUDA provider looks for
`cudnnGetConvolutionBackwardDataAlgorithm_v7`, which cuDNN 9 moved into
`libcudnn_cnn.so.9` behind a dispatch stub. Without it everything silently falls
back to CPU.
## What it found
**Resolution is not the binding constraint here.** TPI holds ~41–47% from 4096×2160
down to ~45 px faces, then falls: 23 px → 26%, 18 px → 12%, 14 px → 1.5%. Holding
90% of the plateau needs roughly 50 px end to end, against VR-005's ~22 px — the
gap is detection and landmark error, which VR-005 excludes by construction.
**FPI is 0.0% at every scale.** Resolution loss goes entirely to TBI: the pipeline
stops naming people rather than naming the wrong one.
**The ceiling is cross-view, not resolution.** Every person matches themselves
strongly *within* a recording (sim 0.55–0.85) and collapses *across* the two
(0.14–0.45, threshold 0.335). Only the person with frontal **gallery** references
identified reliably, whatever their probe pose — so the lever is gallery pose
coverage (`docs/pose-expansion.md`), not a better landmark model.
**Landmark voting helps.** SCRFD predicts each face from several anchors and NMS
discards all but one, throwing away a median of 3 landmark estimates per face.
Averaging them, weighted by confidence, lifts cross-clip TPI 41% → 49% for one
forward pass and no extra model. A MediaPipe mesh as landmark source went the
other way (41% → 16%): more stable within a recording, but a ring centroid is not
the annotated landmark ArcFace was trained on, and the embedder punishes the
off-distribution crop.
## Reading these numbers
Four identities, 70 probes, one shoot. The ~47% plateau is pose, not resolution —
half these faces are turned away and never clear threshold at any scale, so the
absolute rates say little and the *shape* is the result. Both clips contain all
four people, so there is no out-of-gallery class and the 10×-weighted out-of-cast
misID is **untested** here; holding one identity out of the gallery would fix
that. And the resolution curve is dominated by the single subject whose gallery
references are frontal.
experiments/xsource/apply_corrections.py
+44
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@@ -0,0 +1,44 @@
#!/usr/bin/env python3
"""Apply corrections.json exported from review.html.
python3 apply_corrections.py ~/Downloads/corrections.json [--dry-run]
Moves each crop to the folder you chose. "discard" goes to labelling/<clip>/discard/,
which the sweep ignores — nothing is deleted, so a misclick is recoverable.
Refuses to move a file it cannot find exactly once, rather than guessing: a
half-applied correction set would put a crop in two folders and quietly
duplicate a label.
"""
import sys, json, glob, os, shutil
if len(sys.argv) < 2:
sys.exit(__doc__)
path = sys.argv[1]
DRY = "--dry-run" in sys.argv
corr = json.load(open(path))
if not corr:
sys.exit("no corrections in that file")
moved = skipped = 0
for fname, c in corr.items():
clip, to = c["clip"], c["to"]
hits = glob.glob(f"labelling/{clip}/**/{fname}", recursive=True)
if len(hits) != 1:
print(f"[skip] {fname}: found {len(hits)} copies, expected 1")
skipped += 1
continue
src = hits[0]
dst_dir = f"labelling/{clip}/{to}"
dst = f"{dst_dir}/{fname}"
if os.path.abspath(src) == os.path.abspath(dst):
continue
print(f"{'would move' if DRY else 'move'} {c['from']} -> {to}: {fname}")
if not DRY:
os.makedirs(dst_dir, exist_ok=True)
shutil.move(src, dst)
moved += 1
print(f"\n{moved} moved, {skipped} skipped{' (dry run)' if DRY else ''}")
if not DRY and moved:
print("re-run verify_labels.py to confirm the set is still consistent")
experiments/xsource/dump_faces.py
+90
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@@ -0,0 +1,90 @@
#!/usr/bin/env python3
"""Dump face crops from both clips for hand-labelling.
Writes labelling/<clip>/unsorted/<name>.jpg — a context crop around each
detection, big enough to recognise a person by eye. Move them into
labelling/<clip>/person_A/, person_B/, ... and the sweep reads those folders as
ground truth.
Filenames carry a cNN_ cluster-hint prefix so visually similar faces sort next
to each other in a file manager. The hint is only an ordering convenience —
the folder you drop a file into is what counts, and the sweep never reads the
prefix.
Detection and alignment run through the shipped C++ (sae_embed). Every crop
keeps its clip, frame and native-resolution bbox in manifest.json, so probe
detections at reduced scale can be tied back to a labelled face geometrically,
by position, rather than by embedding similarity — which would be circular.
"""
import sys, glob, json, os, shutil
import numpy as np
import cv2
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
M = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/models/"
CLIPS = ["5157339", "5157344"]
MIN_PX = 60
CTX = 256 # context-crop side, for human recognisability
eng = sae_embed.FaceEmbedder(detector_model=M + "scrfd_500m_bnkps.onnx",
arcface_model=M + "arcface_w600k_r50.onnx",
conf=0.5, nms=0.4, max_side=0)
for clip in CLIPS:
out_dir = f"labelling/{clip}/unsorted"
if os.path.isdir(f"labelling/{clip}"):
print(f"[skip] labelling/{clip} exists — not overwriting your sorting",
file=sys.stderr)
continue
os.makedirs(out_dir, exist_ok=True)
entries = []
for p in sorted(glob.glob(f"pex/d{clip}_*.png")):
frame = p.rsplit("_", 1)[-1].split(".")[0]
img = cv2.imread(p)
for i, d in enumerate(eng.detect(img)):
x, y, w, h = d.bbox
if min(w, h) < MIN_PX:
continue
lm = np.array(d.landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
continue
emb = np.asarray(eng.embed_crop(crop), dtype=np.float32)
pad = int(0.5 * max(w, h))
x0, y0 = max(0, int(x) - pad), max(0, int(y) - pad)
x1, y1 = min(img.shape[1], int(x + w) + pad), min(img.shape[0], int(y + h) + pad)
ctx = cv2.resize(img[y0:y1, x0:x1], (CTX, CTX))
entries.append({"clip": clip, "frame": frame, "idx": i,
"bbox": [float(x), float(y), float(w), float(h)],
"px": float(min(w, h)), "conf": float(d.confidence),
"emb": emb, "ctx": ctx})
# cluster hint only — greedy, purely to group similar faces in the file list
E = np.stack([e["emb"] for e in entries])
hint = -np.ones(len(entries), int)
k = 0
for i in range(len(entries)):
if hint[i] >= 0:
continue
hint[i] = k
for j in range(i + 1, len(entries)):
if hint[j] < 0 and float(E[i] @ E[j]) > 0.5:
hint[j] = k
k += 1
manifest = []
for e, h in zip(entries, hint):
name = f"c{h:02d}_{e['clip']}_f{e['frame']}_i{e['idx']}_{int(e['px'])}px.jpg"
cv2.imwrite(f"{out_dir}/{name}", e["ctx"])
manifest.append({k: v for k, v in e.items() if k not in ("emb", "ctx")}
| {"file": name, "cluster_hint": int(h)})
json.dump(manifest, open(f"labelling/{clip}/manifest.json", "w"), indent=1)
print(f"[{clip}] {len(manifest)} crops in {out_dir}, {k} cluster hints, "
f"face px {min(m['px'] for m in manifest):.0f}–{max(m['px'] for m in manifest):.0f}",
file=sys.stderr)
experiments/xsource/failure_analysis.py
+174
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@@ -0,0 +1,174 @@
#!/usr/bin/env python3
"""What explains the misses? Head pose, face size, blur, detector confidence.
For every hand-labelled probe face, computes the calibrated probability against
its OWN gallery entry — so a low value is a false negative, not a mistake about
who it is — and pairs it with covariates that might explain the failure.
Head pose comes from solvePnP of the 5 landmarks against a canonical 3D face,
giving yaw/pitch/roll in degrees.
CAVEAT, and it matters: the pose estimate is derived from the same 5
landmarks the alignment uses. Where those landmarks are unreliable the pose
estimate is unreliable too, and both degrade for the same reason. So this
can show that failures concentrate at high yaw; it cannot cleanly separate
"the head was turned" from "the landmarks were wrong because the head was
turned". Those are the same physical cause, but not the same fix — the
first argues for gallery pose coverage, the second for a better landmark
source.
A sanity check is printed first: pose is estimated per person, and if it does
not recover what is visible in the review sheets (one subject frontal, another
in profile, another looking down) then the estimate is not worth reading.
Similarities go through the production gallery sigmoid, never compared raw.
"""
import sys, glob, json, os
import numpy as np
import cv2
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
GALLERY_CLIP, PROBE_CLIP = "5157344", "5157339"
PROB_THRESHOLD = 0.754
# Canonical 3D face, ordered as types.hpp:60 —
# [0] right-eye [1] left-eye [2] nose [3] right-mouth [4] left-mouth.
# The subject's right eye sits to the LEFT in image space, hence the negative X.
FACE_3D = np.array([
(-34.0, 35.0, -28.0),
( 34.0, 35.0, -28.0),
( 0.0, 0.0, 0.0),
(-26.0, -32.0, -25.0),
( 26.0, -32.0, -25.0),
], dtype=np.float64)
eng = sae_embed.FaceEmbedder(detector_model=M + "scrfd_500m_bnkps.onnx",
arcface_model=M + "LVFace-B_Glint360K.onnx",
conf=0.5, nms=0.4, max_side=0)
cal = sae_embed.gallery_calibration(ROOT + "gallery_lvface.h5")
def head_pose(lm, w, h):
"""yaw, pitch, roll in degrees. Focal length assumed = image width."""
cam = np.array([[w, 0, w / 2], [0, w, h / 2], [0, 0, 1]], dtype=np.float64)
ok, rvec, _ = cv2.solvePnP(FACE_3D, lm.astype(np.float64), cam, None,
flags=cv2.SOLVEPNP_EPNP)
if not ok:
return None
R, _ = cv2.Rodrigues(rvec)
sy = np.sqrt(R[0, 0] ** 2 + R[1, 0] ** 2)
if sy > 1e-6:
pitch = np.degrees(np.arctan2(-R[2, 0], sy))
yaw = np.degrees(np.arctan2(R[1, 0], R[0, 0]))
roll = np.degrees(np.arctan2(R[2, 1], R[2, 2]))
else:
pitch = np.degrees(np.arctan2(-R[2, 0], sy)); yaw = 0.0
roll = np.degrees(np.arctan2(-R[1, 2], R[1, 1]))
# solvePnP's yaw wraps near +/-180 for a face pointing at the camera;
# fold it to a "degrees away from frontal" magnitude.
yaw = ((yaw + 180) % 360) - 180
if abs(yaw) > 90:
yaw = np.sign(yaw) * (180 - abs(yaw))
return yaw, pitch, roll
def collect(clip):
lab = {os.path.basename(p): os.path.basename(os.path.dirname(p))
for p in glob.glob(f"labelling/{clip}/*/*.jpg")
if os.path.basename(os.path.dirname(p)) not in ("discard", "unsorted")}
man = {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
rows = []
for frame in sorted({man[f]["frame"] for f in lab}):
img = cv2.imread(f"frames/d{clip}_{frame}.png")
dets = eng.detect(img)
H, W = img.shape[:2]
for f, person in lab.items():
m = man[f]
if m["frame"] != frame or m["idx"] >= len(dets):
continue
d = dets[m["idx"]]
lm = np.array(d.landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
continue
pose = head_pose(lm, W, H)
x, y, w, h = d.bbox
g = cv2.cvtColor(np.asarray(crop), cv2.COLOR_BGR2GRAY)
rows.append({
"person": person, "px": float(min(w, h)), "conf": float(d.confidence),
"yaw": pose[0] if pose else np.nan, "pitch": pose[1] if pose else np.nan,
"roll": pose[2] if pose else np.nan,
"blur": float(cv2.Laplacian(g, cv2.CV_64F).var()),
"emb": np.asarray(eng.embed_crop(crop), dtype=np.float32)})
return rows
gal_rows = collect(GALLERY_CLIP)
prb_rows = collect(PROBE_CLIP)
gal = {}
for r in gal_rows:
gal.setdefault(r["person"], []).append(r["emb"])
gal = {p: np.stack(v) for p, v in gal.items()}
for r in prb_rows:
if r["person"] in gal:
s = float((gal[r["person"]] @ r["emb"]).max()) # best-of-N, own actor
r["p"] = cal.probability(s)
r["sim"] = s
else:
r["p"] = np.nan
rows = [r for r in prb_rows if not np.isnan(r.get("p", np.nan))]
print(f"[data] {len(rows)} labelled probe faces with a gallery entry\n", file=sys.stderr)
# ── sanity check: does the pose estimate recover what the sheets show? ───────
print("pose by person (does this match the review sheets?)")
print(f"{'person':>7}{'n':>5}{'|yaw| med':>11}{'pitch med':>11}{'P med':>8}{'hit rate':>10}")
for p in sorted({r['person'] for r in rows}):
sub = [r for r in rows if r["person"] == p]
print(f"{p:>7}{len(sub):>5}"
f"{np.median([abs(r['yaw']) for r in sub]):>11.1f}"
f"{np.median([r['pitch'] for r in sub]):>11.1f}"
f"{np.median([r['p'] for r in sub]):>8.3f}"
f"{100*np.mean([r['p'] > PROB_THRESHOLD for r in sub]):>9.0f}%")
# ── P binned by each covariate ───────────────────────────────────────────────
def binned(name, key, edges, fmt="{:.0f}"):
print(f"\nP(match) by {name}")
print(f"{'bin':>16}{'n':>5}{'P med':>9}{'hit rate':>10}{'sim med':>9}")
vals = np.array([r[key] for r in rows])
for lo, hi in zip(edges[:-1], edges[1:]):
sub = [r for r, v in zip(rows, vals) if lo <= v < hi]
if not sub:
continue
lbl = f"{fmt.format(lo)}–{fmt.format(hi)}"
print(f"{lbl:>16}{len(sub):>5}"
f"{np.median([r['p'] for r in sub]):>9.3f}"
f"{100*np.mean([r['p'] > PROB_THRESHOLD for r in sub]):>9.0f}%"
f"{np.median([r['sim'] for r in sub]):>9.3f}")
for r in rows:
r["absyaw"] = abs(r["yaw"])
r["abspitch"] = abs(r["pitch"])
binned("|yaw| (deg from frontal)", "absyaw", [0, 10, 20, 30, 45, 60, 91])
binned("|pitch| (deg)", "abspitch", [0, 10, 20, 30, 45, 91])
binned("face size (px)", "px", [0, 130, 150, 175, 200, 400])
binned("blur (laplacian var)", "blur", [0, 50, 150, 400, 1000, 1e9])
binned("detector confidence", "conf", [0.5, 0.6, 0.7, 0.8, 0.9, 1.01], "{:.2f}")
# ── how much does each covariate actually explain? ───────────────────────────
print("\nSpearman rank correlation with P(match):")
def spearman(a, b):
ra = np.argsort(np.argsort(a)); rb = np.argsort(np.argsort(b))
return float(np.corrcoef(ra, rb)[0, 1])
P = np.array([r["p"] for r in rows])
for key, label in [("absyaw", "|yaw|"), ("abspitch", "|pitch|"), ("px", "face px"),
("blur", "blur"), ("conf", "detector conf")]:
v = np.array([r[key] for r in rows])
print(f" {label:>14}: {spearman(v, P):+.3f}")
json.dump([{k: v for k, v in r.items() if k != "emb"} for r in rows],
open("failure_analysis.json", "w"), indent=1, default=float)
experiments/xsource/landmark_voting.py
+223
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#!/usr/bin/env python3
"""Landmark voting: average SCRFD's overlapping detections instead of discarding them.
SCRFD predicts a face from many anchors; NMS keeps the single highest-scoring
box and throws the rest away. Each discarded box carries its own 5-landmark
estimate of the SAME face, so the survivors are one sample from a distribution
we could be averaging over.
baseline conf 0.50, nms 0.40 — the shipped settings, one box per face
voted conf 0.30, nms 0.90 — duplicates survive, then grouped by IoU and
the 5 landmarks averaged, weighted by detection confidence
Why this is worth trying when the mesh failed: the mesh moved the landmarks off
the definition ArcFace was trained on (a lip-ring centroid is not an annotated
mouth corner), and the embedder punished it. A confidence-weighted mean of
SCRFD's OWN landmark predictions is the same kind of point, just with less
variance — it should stay on-distribution while being steadier.
Scored on cross-clip identification through the production sigmoid, which is
the thing that actually broke. Raw similarity shown only to locate the
threshold; it decides nothing.
LD_PRELOAD=/usr/lib/libcudnn_cnn.so.9 python3 landmark_voting.py
"""
import sys, glob, json, os
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed # before cv2 — see alignment_compare.py
import numpy as np
import cv2
import argparse
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
CLIPS = ["5157344", "5157339"]
PROB_THRESHOLD = 0.754
GROUP_IOU = 0.55 # detections overlapping this much are the same face
MATCH_IOU = 0.35 # tie a detection to the hand-labelled face
_ap = argparse.ArgumentParser()
_ap.add_argument("--detector", default="scrfd_500m_bnkps.onnx",
help="detector under models/. SCRFD sizes 500m / 2.5g / 10g come "
"from InsightFace's buffalo_sc / buffalo_m / buffalo_l packs")
_ap.add_argument("--vote-conf", type=float, default=None,
help="confidence floor for the voting pass. Omit to auto-tune "
"it to --target-votes")
_ap.add_argument("--target-votes", type=int, default=3,
help="votes per face to tune --vote-conf towards, so detectors "
"are compared at equal redundancy rather than equal settings")
_args = _ap.parse_args()
base_eng = sae_embed.FaceEmbedder(detector_model=M + _args.detector,
arcface_model=M + "LVFace-B_Glint360K.onnx",
conf=0.5, nms=0.4, max_side=0)
def _make_vote_engine(conf):
# Same models, looser suppression: keep the duplicates NMS would have removed.
return sae_embed.FaceEmbedder(detector_model=M + _args.detector,
arcface_model=M + "LVFace-B_Glint360K.onnx",
conf=conf, nms=0.9, max_side=0)
cal = sae_embed.gallery_calibration(ROOT + "gallery_lvface.h5")
def iou(a, b):
ax, ay, aw, ah = a; bx, by, bw, bh = b
x0, y0 = max(ax, bx), max(ay, by)
x1, y1 = min(ax + aw, bx + bw), min(ay + ah, by + bh)
if x1 <= x0 or y1 <= y0:
return 0.0
i = (x1 - x0) * (y1 - y0)
return i / (aw * ah + bw * bh - i)
def vote(dets):
"""Group overlapping detections, return (bbox, landmarks, conf, n_votes)."""
items = sorted(dets, key=lambda d: -d.confidence)
used, out = [False] * len(items), []
for i, d in enumerate(items):
if used[i]:
continue
grp = [d]
used[i] = True
for j in range(i + 1, len(items)):
if not used[j] and iou(list(d.bbox), list(items[j].bbox)) >= GROUP_IOU:
used[j] = True
grp.append(items[j])
w = np.array([g.confidence for g in grp], dtype=np.float32)
w = w / w.sum()
lms = np.stack([np.array(g.landmarks, dtype=np.float32).reshape(5, 2) for g in grp])
bxs = np.stack([np.array(list(g.bbox), dtype=np.float32) for g in grp])
out.append((( w[:, None] * bxs).sum(0), (w[:, None, None] * lms).sum(0),
float(grp[0].confidence), len(grp)))
return out
def tune_vote_conf(target, sample=6):
"""Pick the confidence floor giving ~target detections per face to average.
A larger SCRFD is more confident and suppresses harder, so at a fixed floor
it emits fewer overlapping anchors — median 2 against 500m's 3. Comparing
detectors at equal SETTINGS therefore also compares them at unequal
redundancy, and the voting arm is handicapped for the bigger models. Tuning
each to the same votes-per-face isolates landmark quality from how much
there was to average.
"""
frames = sorted(glob.glob(f"frames/d{CLIPS[0]}_*.png"))[:sample]
imgs = [cv2.imread(f) for f in frames]
best = (None, None, 1e9)
for conf in (0.30, 0.20, 0.12, 0.07, 0.04, 0.02, 0.01):
eng = _make_vote_engine(conf)
sizes = [n for img in imgs for _, _, _, n in vote(eng.detect(img))]
if not sizes:
continue
med = float(np.median(sizes))
if abs(med - target) < best[2]:
best = (conf, eng, abs(med - target))
print(f"[tune] conf={conf:.2f} -> median {med:.0f} votes/face", file=sys.stderr)
if med >= target:
break
if best[1] is None:
print(f"[tune] no confidence floor reached {target} votes/face; "
f"falling back to 0.30", file=sys.stderr)
return 0.30, _make_vote_engine(0.30)
print(f"[tune] chose conf={best[0]:.2f} for ~{target} votes/face", file=sys.stderr)
return best[0], best[1]
if _args.vote_conf is not None:
VOTE_CONF, vote_eng = _args.vote_conf, _make_vote_engine(_args.vote_conf)
else:
VOTE_CONF, vote_eng = tune_vote_conf(_args.target_votes)
cal = sae_embed.gallery_calibration(ROOT + "gallery_lvface.h5")
def collect(clip):
lab = {os.path.basename(p): os.path.basename(os.path.dirname(p))
for p in glob.glob(f"labelling/{clip}/*/*.jpg")
if os.path.basename(os.path.dirname(p)) not in ("discard", "unsorted")}
man = {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
rows, votes = [], []
for frame in sorted({man[f]["frame"] for f in lab}):
img = cv2.imread(f"frames/d{clip}_{frame}.png")
base = base_eng.detect(img)
voted = vote(vote_eng.detect(img))
for fname, person in lab.items():
m = man[fname]
if m["frame"] != frame or m["idx"] >= len(base):
continue
d = base[m["idx"]]
lm5 = np.array(d.landmarks, dtype=np.float32).reshape(5, 2)
c_b = sae_embed.align_face(img, lm5)
# the voted group covering the same face
best, best_v = None, 0.0
for bbox, lms, conf, n in voted:
v = iou(list(bbox), list(d.bbox))
if v > best_v:
best_v, best = v, (lms, n)
c_v = None
if best and best_v >= MATCH_IOU:
c_v = sae_embed.align_face(img, best[0].astype(np.float32))
votes.append(best[1])
rec = {"person": person}
rec["base"] = np.asarray(base_eng.embed_crop(c_b), np.float32) if c_b is not None else None
rec["voted"] = np.asarray(base_eng.embed_crop(c_v), np.float32) if c_v is not None else None
rows.append(rec)
return rows, votes
data, allv = {}, []
for c in CLIPS:
data[c], v = collect(c)
allv += v
print(f"[{c}] {len(data[c])} crops", file=sys.stderr)
print(f"[voting] group size: median {np.median(allv):.0f}, "
f"mean {np.mean(allv):.1f}, max {max(allv)} detections averaged per face",
file=sys.stderr)
GAL, PRB = "5157344", "5157339"
print(f"\ndetector={_args.detector} vote_conf={VOTE_CONF:.2f} "
f"gallery {GAL} -> probe {PRB}, P>{PROB_THRESHOLD}\n")
print(f"{'align':>8}{'person':>8}{'n_gal':>7}{'n_prb':>7}"
f"{'within-clip':>13}{'cross-clip':>12}{'hit rate':>10}")
summary = {}
for key in ("base", "voted"):
gal, prb = {}, {}
for r in data[GAL]:
if r[key] is not None:
gal.setdefault(r["person"], []).append(r[key])
for r in data[PRB]:
if r[key] is not None:
prb.setdefault(r["person"], []).append(r[key])
gal = {p: np.stack(v) for p, v in gal.items()}
prb = {p: np.stack(v) for p, v in prb.items()}
hits = tot = 0
for p in sorted(set(gal) & set(prb)):
pp = prb[p] @ prb[p].T
np.fill_diagonal(pp, -1)
within = float(np.median(pp.max(axis=1))) if len(pp) > 1 else float("nan")
cross = float(np.median((gal[p] @ prb[p].T).max(axis=0)))
h = 0
for e in prb[p]:
bp, bn = 0.0, None
for q in gal:
v = cal.probability(float((gal[q] @ e).max()))
if v > bp:
bp, bn = v, q
if bp > PROB_THRESHOLD and bn == p:
h += 1
hits += h; tot += len(prb[p])
print(f"{key:>8}{p:>8}{len(gal[p]):>7}{len(prb[p]):>7}"
f"{cal.probability(within):>6.3f}/{within:<6.3f}"
f"{cal.probability(cross):>6.3f}/{cross:<5.3f}{100*h/len(prb[p]):>9.0f}%")
summary[key] = (hits, tot)
print(f"{key:>8}{'ALL':>8}{'':>14}{'':>25}{100*hits/max(tot,1):>9.0f}%\n")
hb, tb = summary["base"]; hv, tv = summary["voted"]
print(f"voting vs baseline: {100*hv/max(tv,1) - 100*hb/max(tb,1):+.1f} points "
f"of cross-clip TPI ({hb}/{tb} -> {hv}/{tv})")
experiments/xsource/make_review_site.py
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#!/usr/bin/env python3
"""Build labelling/review.html — a local page for correcting the labels.
One row per crop, ordered most-suspicious first:
left the person it is currently filed under (medoid of that person's
hand-sorted crops, so the reference is one you trust)
centre the crop under review — context with the detection boxed, and
beneath it the 112x112 the embedder actually receives
right the person it matches better, if any, with both probabilities
Pick a destination per row, then Export to download corrections.json and apply
it with apply_corrections.py. Nothing is moved by this script.
Self-contained: images are inlined as data URIs and the page is opened from
disk, so no server runs and no face crop leaves the machine.
Ordering is by P(other) - P(self), both from the global gallery sigmoid, so
rows where the evidence disagrees with the label float to the top and the
agreement cases sink. It is a review order, not a verdict — you are the
arbiter, which is the whole point of labelling by hand.
"""
import sys, glob, json, os, base64
import numpy as np
import cv2
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
EMBEDDER = M + "LVFace-B_Glint360K.onnx"
GALLERY = ROOT + "gallery_lvface.h5"
REF_CLIP = "5157344" # the clip sorted by hand — reference faces come from here
CLIPS = ["5157344", "5157339"]
eng = sae_embed.FaceEmbedder(detector_model=M + "scrfd_500m_bnkps.onnx",
arcface_model=EMBEDDER, conf=0.5, nms=0.4, max_side=0)
cal = sae_embed.gallery_calibration(GALLERY)
def b64(img, size, q=72):
img = cv2.resize(img, (size, size))
ok, buf = cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, q])
return "data:image/jpeg;base64," + base64.b64encode(buf).decode() if ok else ""
rows = []
for clip in CLIPS:
man = {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
placed = {os.path.basename(p): (os.path.basename(os.path.dirname(p)), p)
for p in glob.glob(f"labelling/{clip}/*/*.jpg")}
by_frame = {}
for fname, (person, path) in placed.items():
if fname in man and person != "unsorted":
by_frame.setdefault(man[fname]["frame"], []).append((fname, person, path))
for frame, items in sorted(by_frame.items()):
img = cv2.imread(f"frames/d{clip}_{frame}.png")
if img is None:
continue
dets = eng.detect(img)
for fname, person, path in items:
i = man[fname]["idx"]
if i >= len(dets):
continue
lm = np.array(dets[i].landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
continue
rows.append({"clip": clip, "person": person, "file": fname, "path": path,
"px": man[fname]["px"], "aligned": np.asarray(crop),
"emb": np.asarray(eng.embed_crop(crop), dtype=np.float32)})
people = sorted({r["person"] for r in rows})
E = np.stack([r["emb"] for r in rows])
lab = np.array([people.index(r["person"]) for r in rows])
S = E @ E.T
np.fill_diagonal(S, -1.0)
# reference face per person: medoid of their REF_CLIP crops
ref_img = {}
for k, p in enumerate(people):
idx = [i for i in np.where(lab == k)[0] if rows[i]["clip"] == REF_CLIP]
if not idx:
idx = list(np.where(lab == k)[0])
if not idx:
continue
sub = S[np.ix_(idx, idx)].copy()
medoid = idx[int(np.argmax(sub.mean(axis=1)))]
ref_img[p] = b64(rows[medoid]["aligned"], 112)
items = []
for i, r in enumerate(rows):
k = lab[i]
same = [j for j in np.where(lab == k)[0] if j != i]
p_self = cal.probability(float(S[i, same].max())) if same else 0.0
best_other, p_other = None, 0.0
for k2, p2 in enumerate(people):
if k2 == k:
continue
other = np.where(lab == k2)[0]
if not len(other):
continue
pv = cal.probability(float(S[i, other].max()))
if pv > p_other:
p_other, best_other = pv, p2
ctx = cv2.imread(r["path"])
items.append({
"file": r["file"], "clip": r["clip"], "person": r["person"],
"px": int(r["px"]), "p_self": round(p_self, 3), "p_other": round(p_other, 3),
"other": best_other, "delta": round(p_other - p_self, 3),
"ctx": b64(ctx, 150) if ctx is not None else "",
"ali": b64(r["aligned"], 112),
})
items.sort(key=lambda x: -x["delta"])
payload = json.dumps({"people": people, "refs": ref_img, "items": items})
HTML = """<meta charset="utf-8"><title>JRay — label review</title>
<style>
:root{color-scheme:dark;--bg:#14161a;--fg:#e6e8ea;--mut:#8b929c;--line:#262b33;--warn:#e0654a;--ok:#4a9d6a}
body{margin:0;background:var(--bg);color:var(--fg);font:14px/1.5 system-ui,sans-serif}
header{position:sticky;top:0;background:#181b20;border-bottom:1px solid var(--line);
padding:12px 18px;display:flex;gap:18px;align-items:center;flex-wrap:wrap;z-index:5}
h1{font-size:15px;margin:0;font-weight:600}
.stat{color:var(--mut);font-size:13px}
button{background:#232830;color:var(--fg);border:1px solid var(--line);border-radius:6px;
padding:7px 13px;cursor:pointer;font:inherit}
button:hover{background:#2c323c}
button.go{background:#2f5d43;border-color:#3c7555}
.row{display:grid;grid-template-columns:150px 1fr 190px;gap:20px;align-items:center;
padding:14px 18px;border-bottom:1px solid var(--line)}
.row.flag{background:#1e1719}
.row.done{opacity:.4}
.cell{display:flex;gap:10px;align-items:center}
img{border-radius:5px;display:block;background:#000}
.lab{font-weight:600;font-size:15px}
.mut{color:var(--mut);font-size:12px}
.p{font-variant-numeric:tabular-nums}
.hi{color:var(--warn);font-weight:600}
.choices{display:flex;flex-wrap:wrap;gap:6px}
.choices button{padding:5px 10px;font-size:13px}
.choices button.sel{background:#2f5d43;border-color:#3c7555}
.legend{padding:10px 18px;color:var(--mut);font-size:12px;border-bottom:1px solid var(--line)}
</style>
<header>
<h1>Label review</h1>
<span class="stat" id="stat"></span>
<button id="exp" class="go">Export corrections.json</button>
<button id="onlyflag">Show only disagreements</button>
</header>
<div class="legend">Left: the person this crop is filed under. Centre: the crop (context with the
detection boxed, and the 112&times;112 the embedder actually sees). Right: the person it matches
better, if any. Ordered by P(other) &minus; P(self) &mdash; disagreements first.</div>
<div id="list"></div>
<script>
const D = __PAYLOAD__;
const choice = {};
const list = document.getElementById('list');
function render(){
list.innerHTML = '';
const flagOnly = document.body.dataset.flag === '1';
for (const it of D.items){
if (flagOnly && it.delta <= 0) continue;
const row = document.createElement('div');
row.className = 'row' + (it.delta > 0 ? ' flag' : '') + (choice[it.file] ? ' done' : '');
const left = document.createElement('div');
left.className = 'cell';
left.innerHTML = `<img src="${D.refs[it.person]||''}" width="72" height="72">
<div><div class="lab">${it.person}</div>
<div class="mut p">P(self) ${it.p_self.toFixed(3)}</div></div>`;
const mid = document.createElement('div');
mid.className = 'cell';
mid.innerHTML = `<img src="${it.ctx}" width="120" height="120">
<img src="${it.ali}" width="90" height="90">
<div><div class="mut">${it.clip} &middot; ${it.px}px</div>
<div class="mut">${it.file}</div></div>`;
const right = document.createElement('div');
const worse = it.delta > 0;
right.innerHTML = it.other
? `<div class="cell"><img src="${D.refs[it.other]||''}" width="56" height="56">
<div><div class="lab ${worse?'hi':''}">${it.other}</div>
<div class="mut p ${worse?'hi':''}">P ${it.p_other.toFixed(3)}</div></div></div>`
: '<div class="mut">—</div>';
const ch = document.createElement('div');
ch.className = 'choices';
for (const p of D.people.concat(['discard'])){
const b = document.createElement('button');
b.textContent = p === it.person ? p + ' (keep)' : p;
if (choice[it.file] === p || (!choice[it.file] && p === it.person)) b.classList.add('sel');
b.onclick = () => { choice[it.file] = p; render(); };
ch.appendChild(b);
}
right.appendChild(ch);
row.append(left, mid, right);
list.appendChild(row);
}
const changed = Object.entries(choice).filter(([f,p]) =>
p !== (D.items.find(i=>i.file===f)||{}).person).length;
document.getElementById('stat').textContent =
`${D.items.length} crops · ${D.items.filter(i=>i.delta>0).length} disagreements · ${changed} changes staged`;
}
document.getElementById('onlyflag').onclick = () => {
document.body.dataset.flag = document.body.dataset.flag === '1' ? '0' : '1';
render();
};
document.getElementById('exp').onclick = () => {
const out = {};
for (const it of D.items){
const p = choice[it.file] || it.person;
if (p !== it.person) out[it.file] = {from: it.person, to: p, clip: it.clip};
}
const blob = new Blob([JSON.stringify(out, null, 1)], {type:'application/json'});
const a = document.createElement('a');
a.href = URL.createObjectURL(blob); a.download = 'corrections.json'; a.click();
};
render();
</script>
"""
os.makedirs("labelling", exist_ok=True)
out = "labelling/review.html"
with open(out, "w") as f:
f.write(HTML.replace("__PAYLOAD__", payload))
size = os.path.getsize(out) / 1e6
flagged = sum(1 for i in items if i["delta"] > 0)
print(f"{out} {size:.1f} MB {len(items)} crops, {flagged} disagreements", file=sys.stderr)
print(f"open file://{os.path.abspath(out)}", file=sys.stderr)
experiments/xsource/pose_label.py
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#!/usr/bin/env python3
"""Estimate head pose per crop, and build a page to confirm or correct it.
Why not solvePnP on the 5 detector landmarks: those landmarks collapse on
turned faces, so the estimator breaks precisely on the crops whose pose we care
about. Run that way it reported the profile subject as the MOST frontal of the
four, which is how we know not to trust it.
Instead the estimate comes from the MediaPipe face mesh (468 points, run via
OpenCV DNN — the same model rPPG-kahn uses) and a symmetry measure that needs
no 3D model:
yaw_ratio = (dL - dR) / (dL + dR)
over left/right symmetric vertex pairs, where dL and dR are each side's
distance from the face midline. Frontal ~ 0, profile -> +/-1. It degrades
gracefully because it averages many pairs rather than trusting any one point,
and it is scale- and translation-free.
It is still an estimate. So this writes pose_review.html with the estimate
PRE-FILLED as a proposal, ordered by confidence, for you to correct — and the
correlation is only run against your corrected labels. If the estimate turns
out to disagree with you often, that is the finding, and the automatic number
gets dropped rather than reported.
Bins are coarse on purpose: frontal / three-quarter / profile / down-or-hidden.
Finer than that and the labelling is slower and less reliable, and the question
("does pose explain the misses") does not need degrees.
"""
import sys, glob, json, os, base64
# sae_embed MUST be imported before cv2: OpenCV's DNN module loads the system
# libonnxruntime, which then shadows the newer one this module links against and
# the import fails on a missing symbol version. Order matters, so do not tidy
# these into alphabetical order.
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
import numpy as np
import cv2
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
MESH = "/home/dtourolle/Development/rPPG-kahn/models/face_landmark.tflite"
CLIPS = ["5157344", "5157339"]
BINS = ["frontal", "three-quarter", "profile", "down-or-hidden"]
# Symmetric vertex pairs (subject-left, subject-right) on the MediaPipe mesh:
# outer eye corners, inner eye corners, cheeks, mouth corners, jaw.
PAIRS = [(33, 263), (133, 362), (130, 359), (243, 463),
(61, 291), (91, 321), (146, 375), (58, 288), (172, 397), (215, 435)]
MIDLINE = [10, 168, 1, 4, 5, 195, 197, 152] # forehead -> nose -> chin
net = cv2.dnn.readNetFromTFLite(MESH)
NAMES = net.getUnconnectedOutLayersNames()
LMI, PRI = NAMES.index("conv2d_21"), NAMES.index("conv2d_31")
eng = sae_embed.FaceEmbedder(detector_model=M + "scrfd_500m_bnkps.onnx",
arcface_model=M + "LVFace-B_Glint360K.onnx",
conf=0.5, nms=0.4, max_side=0)
def mesh_pose(img, bbox, expand=1.6):
"""(yaw_ratio, presence) or (nan, 0). yaw_ratio in [-1, 1], 0 = frontal."""
x, y, w, h = bbox
cx, cy, s = x + w / 2, y + h / 2, max(w, h) * expand
crop = cv2.getRectSubPix(img, (int(s), int(s)), (float(cx), float(cy)))
net.setInput(cv2.dnn.blobFromImage(crop, 1 / 255.0, (192, 192), (0, 0, 0), swapRB=True))
o = net.forward(NAMES)
pres = 1 / (1 + np.exp(-float(o[PRI].ravel()[0])))
lm = o[LMI].reshape(468, 3)[:, :2]
mid = lm[MIDLINE]
# least-squares midline direction, then signed distance of each pair member
c = mid.mean(axis=0)
u, _, _ = np.linalg.svd(mid - c)
d = (mid - c)
axis = np.linalg.svd(d.T @ d)[0][:, 0] # principal direction of the midline
normal = np.array([-axis[1], axis[0]])
ratios = []
for a, b in PAIRS:
dl = float(np.dot(lm[a] - c, normal))
dr = float(np.dot(lm[b] - c, normal))
if abs(dl) + abs(dr) < 1e-6:
continue
ratios.append((abs(dl) - abs(dr)) / (abs(dl) + abs(dr)))
return (float(np.median(ratios)) if ratios else np.nan), pres
def b64(img, size, q=72):
ok, buf = cv2.imencode(".jpg", cv2.resize(img, (size, size)),
[cv2.IMWRITE_JPEG_QUALITY, q])
return "data:image/jpeg;base64," + base64.b64encode(buf).decode() if ok else ""
items = []
for clip in CLIPS:
lab = {os.path.basename(p): (os.path.basename(os.path.dirname(p)), p)
for p in glob.glob(f"labelling/{clip}/*/*.jpg")
if os.path.basename(os.path.dirname(p)) not in ("discard", "unsorted")}
man = {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
for frame in sorted({man[f]["frame"] for f in lab}):
img = cv2.imread(f"frames/d{clip}_{frame}.png")
dets = eng.detect(img)
for fname, (person, path) in lab.items():
m = man[fname]
if m["frame"] != frame or m["idx"] >= len(dets):
continue
d = dets[m["idx"]]
lm5 = np.array(d.landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm5)
if crop is None:
continue
yaw, pres = mesh_pose(img, d.bbox)
a = abs(yaw) if not np.isnan(yaw) else 1.0
guess = ("frontal" if a < 0.15 else "three-quarter" if a < 0.45
else "profile")
if pres < 0.5:
guess = "down-or-hidden" # mesh could not fit at all
ctx = cv2.imread(path)
items.append({"file": fname, "clip": clip, "person": person,
"px": int(m["px"]), "yaw": None if np.isnan(yaw) else round(yaw, 3),
"pres": round(pres, 3), "guess": guess,
"ctx": b64(ctx, 140) if ctx is not None else "",
"ali": b64(np.asarray(crop), 112)})
# least-confident first: near a bin boundary, or the mesh could not fit
def uncertainty(it):
if it["pres"] < 0.5:
return 0.0
a = abs(it["yaw"]) if it["yaw"] is not None else 1.0
return min(abs(a - 0.15), abs(a - 0.45))
items.sort(key=uncertainty)
payload = json.dumps({"bins": BINS, "items": items})
HTML = """<meta charset="utf-8"><title>JRay — head pose labelling</title>
<style>
:root{color-scheme:dark}
body{margin:0;background:#14161a;color:#e6e8ea;font:14px/1.5 system-ui,sans-serif}
header{position:sticky;top:0;background:#181b20;border-bottom:1px solid #262b33;
padding:12px 18px;display:flex;gap:16px;align-items:center;flex-wrap:wrap;z-index:5}
h1{font-size:15px;margin:0}
button{background:#232830;color:#e6e8ea;border:1px solid #262b33;border-radius:6px;
padding:7px 12px;cursor:pointer;font:inherit}
button:hover{background:#2c323c}
button.go{background:#2f5d43;border-color:#3c7555}
.g{display:grid;grid-template-columns:repeat(auto-fill,minmax(300px,1fr));gap:10px;padding:14px}
.c{border:1px solid #262b33;border-radius:8px;padding:9px;display:flex;gap:9px;align-items:center}
.c.edited{border-color:#3c7555}
img{border-radius:5px;background:#000;display:block}
.m{color:#8b929c;font-size:11px}
.b{display:flex;flex-wrap:wrap;gap:4px;margin-top:5px}
.b button{padding:3px 7px;font-size:11px}
.b button.sel{background:#2f5d43;border-color:#3c7555}
</style>
<header><h1>Head pose</h1><span class="m" id="stat"></span>
<button class="go" id="exp">Export pose_labels.json</button></header>
<div class="g" id="g"></div>
<script>
const D=__PAYLOAD__; const pick={};
function render(){
const g=document.getElementById('g'); g.innerHTML='';
for(const it of D.items){
const cur=pick[it.file]||it.guess;
const c=document.createElement('div');
c.className='c'+(pick[it.file]&&pick[it.file]!==it.guess?' edited':'');
const b=D.bins.map(x=>`<button class="${x===cur?'sel':''}" data-f="${it.file}" data-b="${x}">${x}</button>`).join('');
c.innerHTML=`<img src="${it.ctx}" width="88" height="88"><img src="${it.ali}" width="66" height="66">
<div><div class="m">${it.person} · ${it.clip.slice(-3)} · ${it.px}px</div>
<div class="m">yaw ${it.yaw===null?'—':it.yaw} · presence ${it.pres}</div>
<div class="b">${b}</div></div>`;
g.appendChild(c);
}
g.onclick=e=>{const t=e.target; if(t.dataset&&t.dataset.b){pick[t.dataset.f]=t.dataset.b; render();}};
const ed=Object.entries(pick).filter(([f,v])=>v!==(D.items.find(i=>i.file===f)||{}).guess).length;
document.getElementById('stat').textContent=`${D.items.length} crops · ${ed} corrections`;
}
document.getElementById('exp').onclick=()=>{
const out={}; for(const it of D.items) out[it.file]={pose:pick[it.file]||it.guess,
guess:it.guess, yaw:it.yaw, pres:it.pres, person:it.person, clip:it.clip};
const a=document.createElement('a');
a.href=URL.createObjectURL(new Blob([JSON.stringify(out,null,1)],{type:'application/json'}));
a.download='pose_labels.json'; a.click();
};
render();
</script>
"""
out = "labelling/pose_review.html"
open(out, "w").write(HTML.replace("__PAYLOAD__", payload))
from collections import Counter
print(f"{out} {os.path.getsize(out)/1e6:.1f} MB {len(items)} crops", file=sys.stderr)
print(f"estimate: {dict(Counter(i['guess'] for i in items))}", file=sys.stderr)
print("\nestimated pose per person (does this match what you see?):", file=sys.stderr)
for p in sorted({i["person"] for i in items}):
for clip in CLIPS:
sub = [i for i in items if i["person"] == p and i["clip"] == clip]
if sub:
print(f" {p} {clip[-3:]}: {dict(Counter(i['guess'] for i in sub))}",
file=sys.stderr)
print(f"\nopen file://{os.path.abspath(out)}", file=sys.stderr)
experiments/xsource/propose_labels.py
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#!/usr/bin/env python3
"""Propose person labels for one clip using another clip's hand-sorted labels.
Reads the clip you have already sorted (REF_CLIP) as ground truth, then proposes
a person for every crop in the other clip (TARGET_CLIP) and writes them into
matching folders for you to correct.
python3 propose_labels.py # propose, write folders + sheets
python3 propose_labels.py --dry-run # report only, move nothing
Output:
labelling/<target>/unsorted/A|B|C|D/ proposed, same names as the ref clip
labelling/<target>/unsorted/ left in place when no person is
confident enough to name
labelling/review_<person>.jpg contact sheet spanning BOTH clips:
confirmed crops first, then
proposed ones with their P
Correcting it: open a review sheet. Every face on it should be one person. The
lower block is the proposal — move any intruder to the right folder, or back to
unsorted/. The folder a file sits in is the ground truth; nothing downstream
reads the proposed name or its probability.
The proposal is a labelling aid, never the label. Scoring the sweep against
embedding-derived labels would be circular: it keeps the faces the embedder
already gets right and drops the hard ones the sweep exists to find. Your
correction is what breaks that loop, which is why the proposal is deliberately
conservative and leaves anything doubtful unnamed.
Assignment is on the calibrated probability, per-actor best-of-N, exactly as
identity_matcher_node does — never a bare cosine (AR-024). The calibration is
fitted on your labelled reference crops, which is what calibrate_gallery is for.
"""
import sys, glob, json, os, shutil
import numpy as np
import cv2
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
# The embedder and the gallery whose calibration scores it MUST be the same
# model: a Platt fit is specific to one embedding space, so LVFace probabilities
# read through an ArcFace fit are meaningless.
EMBEDDER = M + "LVFace-B_Glint360K.onnx"
GALLERY = ROOT + "gallery_lvface.h5" # 291 actors, cached fit
REF_CLIP, TARGET_CLIP = "5157344", "5157339"
ASSIGN_P = 0.90 # propose a name only when this confident
SHEET_COLS = 8
THUMB = 150
DRY = "--dry-run" in sys.argv
eng = sae_embed.FaceEmbedder(detector_model=M + "scrfd_500m_bnkps.onnx",
arcface_model=EMBEDDER,
conf=0.5, nms=0.4, max_side=0)
def embed_manifest(clip):
"""Re-derive each dumped crop's embedding from its source frame, cached.
The dumped .jpg is a context thumbnail for human eyes; the embedding must
come from the aligned crop the pipeline would actually produce, so the
frame is re-detected and the manifest's idx picks the same face.
Detecting 24 4K frames per clip costs far more than the rest of this script
put together, and the result only changes when the manifest does — so it is
cached and keyed on the manifest's mtime. Delete cache/ to force a redo.
"""
man_path = f"labelling/{clip}/manifest.json"
cache_path = f"cache/emb_{clip}.npz"
os.makedirs("cache", exist_ok=True)
if os.path.exists(cache_path) and \
os.path.getmtime(cache_path) >= os.path.getmtime(man_path):
z = np.load(cache_path, allow_pickle=True)
print(f"[cache] {clip}: {len(z['meta'])} embeddings reused", file=sys.stderr)
return [{**m, "emb": e} for m, e in zip(z["meta"], z["emb"])]
man = json.load(open(man_path))
by_frame = {}
for m in man:
by_frame.setdefault(m["frame"], []).append(m)
out = []
for frame, ms in sorted(by_frame.items()):
img = cv2.imread(f"frames/d{clip}_{frame}.png")
if img is None:
sys.exit(f"missing frames/d{clip}_{frame}.png — extract with\n"
f" ffmpeg -i clips/{clip}.mp4 -vf fps=2 -frames:v 24 "
f"frames/d{clip}_%03d.png")
dets = eng.detect(img)
for m in ms:
if m["idx"] >= len(dets):
continue
d = dets[m["idx"]]
lm = np.array(d.landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
continue
out.append({**m, "emb": np.asarray(eng.embed_crop(crop), dtype=np.float32)})
np.savez(cache_path,
meta=np.array([{k: v for k, v in o.items() if k != "emb"} for o in out],
dtype=object),
emb=np.stack([o["emb"] for o in out]))
print(f"[cache] {clip}: {len(out)} embeddings written to {cache_path}",
file=sys.stderr)
return out
def sorted_dirs(clip):
"""Person folders you created, wherever you put them under labelling/<clip>."""
found = {}
for path in glob.glob(f"labelling/{clip}/**/", recursive=True):
name = os.path.basename(path.rstrip("/"))
if name in ("unsorted", "discard") or name.startswith("5157"):
continue
files = [os.path.basename(f) for f in glob.glob(path + "*.jpg")]
if files:
found[name] = files
return found
# ── reference side: your labels ──────────────────────────────────────────────
ref_rows = embed_manifest(REF_CLIP)
ref_dirs = sorted_dirs(REF_CLIP)
if not ref_dirs:
sys.exit(f"no person folders under labelling/{REF_CLIP} — sort that clip first")
file_to_person = {f: p for p, fs in ref_dirs.items() for f in fs}
ref = [(file_to_person[r["file"]], r["emb"]) for r in ref_rows
if r["file"] in file_to_person]
people = sorted({p for p, _ in ref})
print(f"[ref] {REF_CLIP}: {len(ref)} labelled crops over {len(people)} people "
f"{ {p: sum(1 for q, _ in ref if q == p) for p in people} }", file=sys.stderr)
R = np.stack([e for _, e in ref])
r_actor = [people.index(p) for p, _ in ref]
# The global gallery's sigmoid — NOT a fit over these four people. A Platt fit
# over a handful of identities saturates: it will hand back P=0.99 for faces it
# has no basis to separate, which is exactly how a wrong label acquires a
# convincing probability. The production fit spans the whole actor population,
# so a probability means the same thing here as it does in the matcher.
cal = sae_embed.gallery_calibration(GALLERY)
print(f"[calibration] global: {cal} assign boundary = sim "
f"{cal.boundary_at(ASSIGN_P):.4f}", file=sys.stderr)
# ── target side: propose ─────────────────────────────────────────────────────
tgt_rows = embed_manifest(TARGET_CLIP)
T = np.stack([t["emb"] for t in tgt_rows])
r_actor_arr = np.asarray(r_actor)
# per-actor best-of-N for every target crop at once: (n_people, n_target)
best_sim = np.stack([(R[r_actor_arr == people.index(p)] @ T.T).max(axis=0)
for p in people])
proposals = []
for j, t in enumerate(tgt_rows):
k = int(np.argmax(best_sim[:, j]))
prob = cal.probability(float(best_sim[k, j])) # calibrated, never a bare cosine
proposals.append({**t, "person": people[k] if prob >= ASSIGN_P else None,
"p": prob, "top1": people[k]})
# At the production threshold the global fit stays silent on most of these
# faces, which is the honest answer for profile and downward-gaze shots — but a
# labelling aid wants throughput, not caution. --all proposes the top-1 person
# for every crop and orders the review sheets by descending probability, so the
# proposals degrade visibly down the sheet and you can stop correcting where
# they stop being right. The probability is shown, never hidden.
if "--all" in sys.argv:
for x in proposals:
x["person"] = x["top1"]
named = [x for x in proposals if x["person"]]
print(f"[propose] {TARGET_CLIP}: {len(named)}/{len(proposals)} named at P>={ASSIGN_P}; "
f"{len(proposals) - len(named)} left unsorted", file=sys.stderr)
for p in people:
got = [x for x in named if x["person"] == p]
if got:
ps = [x["p"] for x in got]
print(f" {p}: {len(got):>3} crops P {min(ps):.3f}–{max(ps):.3f}", file=sys.stderr)
if DRY:
sys.exit(0)
# ── write proposed folders, mirroring the ref clip's layout ──────────────────
ref_parent = os.path.dirname(next(iter(glob.glob(f"labelling/{REF_CLIP}/**/{people[0]}/",
recursive=True))).rstrip("/"))
tgt_parent = ref_parent.replace(REF_CLIP, TARGET_CLIP)
for p in people:
d = f"{tgt_parent}/{p}"
if os.path.isdir(d): # never clobber corrections already made
print(f"[skip] {d} exists — leaving your sorting alone", file=sys.stderr)
continue
os.makedirs(d, exist_ok=True)
def find_crop(clip, fname):
"""Locate a crop wherever it currently sits under labelling/<clip>."""
hits = glob.glob(f"labelling/{clip}/**/{fname}", recursive=True)
return hits[0] if hits else None
moved = 0
for x in named:
src = find_crop(TARGET_CLIP, x["file"])
dst = f"{tgt_parent}/{x['person']}/{x['file']}"
if src and os.path.abspath(src) != os.path.abspath(dst):
shutil.move(src, dst)
moved += 1
print(f"[write] moved {moved} crops into proposed folders", file=sys.stderr)
# ── review sheets: confirmed block, then proposed block ─────────────────────
def load(clip, person, fname):
for cand in glob.glob(f"labelling/{clip}/**/{person}/{fname}", recursive=True):
return cv2.imread(cand)
return None
for person in people:
conf = [(REF_CLIP, f, None) for f in ref_dirs.get(person, [])]
prop = sorted([(TARGET_CLIP, x["file"], x["p"]) for x in named
if x["person"] == person],
key=lambda t: -t[2]) # most confident first
items = conf + prop
if not items:
continue
rows_n = (len(items) + SHEET_COLS - 1) // SHEET_COLS
sheet = np.full((rows_n * (THUMB + 26), SHEET_COLS * THUMB, 3), 30, np.uint8)
for n, (clip, fname, p) in enumerate(items):
img = load(clip, person, fname)
if img is None:
continue
rr, cc = divmod(n, SHEET_COLS)
y, x = rr * (THUMB + 26), cc * THUMB
sheet[y:y + THUMB, x:x + THUMB] = cv2.resize(img, (THUMB, THUMB))
if p is None:
tag, col = f"{clip[-3:]} CONFIRMED", (170, 170, 170)
else:
tag, col = f"{clip[-3:]} P={p:.2f}", (140, 255, 140)
cv2.putText(sheet, tag, (x + 3, y + THUMB + 17),
cv2.FONT_HERSHEY_SIMPLEX, 0.42, col, 1)
cv2.imwrite(f"labelling/review_{person}.jpg", sheet)
print(f" review_{person}.jpg: {len(conf)} confirmed + {len(prop)} proposed",
file=sys.stderr)
json.dump({x["file"]: {"person": x["person"], "p": x["p"]} for x in proposals},
open(f"labelling/proposed_{TARGET_CLIP}.json", "w"), indent=1)
experiments/xsource/redraw_boxes.py
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#!/usr/bin/env python3
"""Redraw every dumped crop with its detection box marked.
The original thumbnails padded by 0.5x the face on each side for
recognisability, which in a crowded frame pulls a neighbour into shot — often
more prominently than the subject. A label cannot be corrected from a picture
that does not say which face it refers to.
This rewrites each .jpg IN PLACE, wherever it currently sits, so any sorting
already done is preserved: only the pixels change, never the filename or the
folder. Re-run it after dump_faces.py, and re-check any sorting done before it.
"""
import glob, json, os, sys
import cv2
CLIPS = ["5157339", "5157344"]
OUT = 256
for clip in CLIPS:
man = {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
n = 0
for path in glob.glob(f"labelling/{clip}/**/*.jpg", recursive=True):
fname = os.path.basename(path)
m = man.get(fname)
if m is None:
continue
img = cv2.imread(f"frames/d{clip}_{m['frame']}.png")
if img is None:
sys.exit(f"missing frames/d{clip}_{m['frame']}.png")
x, y, w, h = (int(v) for v in m["bbox"])
pad = int(0.55 * max(w, h))
x0, y0 = max(0, x - pad), max(0, y - pad)
x1, y1 = min(img.shape[1], x + w + pad), min(img.shape[0], y + h + pad)
sub = img[y0:y1, x0:x1].copy()
# Box in the sub-image's coordinates, drawn before the resize so the
# line lands exactly on the face at any output size.
cv2.rectangle(sub, (x - x0, y - y0), (x - x0 + w, y - y0 + h), (0, 0, 255), 3)
# Dim everything outside the box so the subject is unmistakable even
# when a neighbour's face is larger or better lit.
mask = sub.copy()
mask[y - y0:y - y0 + h, x - x0:x - x0 + w] = 0
sub = cv2.addWeighted(sub, 1.0, mask, -0.35, 0)
scale = OUT / max(sub.shape[:2])
sub = cv2.resize(sub, (int(sub.shape[1] * scale), int(sub.shape[0] * scale)))
canvas = cv2.copyMakeBorder(
sub, 0, max(0, OUT - sub.shape[0]), 0, max(0, OUT - sub.shape[1]),
cv2.BORDER_CONSTANT, value=(20, 20, 20))[:OUT, :OUT]
cv2.putText(canvas, f"{int(m['px'])}px", (5, OUT - 8),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 255, 255), 1)
cv2.imwrite(path, canvas)
n += 1
print(f"[{clip}] redrew {n} crops in place", file=sys.stderr)
experiments/xsource/resolution_sweep.py
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#!/usr/bin/env python3
"""Impact of input resolution on cross-source identification.
TRACES: VR-013 | PR-002
Gallery is built from one clip at NATIVE resolution. Probes come from the other
clip with the WHOLE FRAME downscaled before it reaches the detector, so
detection and landmark regression degrade together with the pixels. That is the
measurement VR-005 structurally could not make: it degraded an already-aligned
112x112 crop, holding alignment perfect, so it isolated the embedder's
resolution sensitivity and excluded everything upstream of it.
python3 resolution_sweep.py [--gallery-clip 5157339] [--detector scrfd_500m_bnkps.onnx]
Ground truth
------------
Hand-sorted person folders. Probe detections at reduced scale are tied back to
a labelled face GEOMETRICALLY — the box is mapped to native coordinates and
matched by IoU. Never by embedding similarity, which would be circular: it
would keep the faces the embedder still gets right and silently drop the ones
this sweep exists to find.
A probe whose label is only in the probe clip is OUT OF GALLERY. Naming it is a
true out-of-cast misID, the error the per-scene scorer weights 10x, so it is
counted separately from naming the wrong gallery member.
Metric
------
The calibrated probability from the PRODUCTION gallery sigmoid, never a raw
cosine (AR-024). Per-actor best-of-N similarity -> probability -> accept above
prob_threshold. This is identification, so the matcher's prior applies;
config.hpp has match_prior 0.5, i.e. log_prior_odds = 0.
Everything runs through the shipped C++ via sae_embed.
"""
import sys, glob, json, os, argparse
import numpy as np
import cv2
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
PROB_THRESHOLD = 0.754 # config.hpp:67
LOG_PRIOR_ODDS = 0.0 # config.hpp:61 match_prior=0.5
IOU_MIN = 0.3 # geometric label carry-down
SCALES = [1.0, 0.8, 0.6, 0.5, 0.4, 0.3, 0.25, 0.2, 0.15, 0.12, 0.09, 0.06]
ap = argparse.ArgumentParser()
ap.add_argument("--gallery-clip", default="5157339")
ap.add_argument("--probe-clip", default="5157344")
ap.add_argument("--detector", default="scrfd_500m_bnkps.onnx")
ap.add_argument("--embedder", default="LVFace-B_Glint360K.onnx")
ap.add_argument("--gallery-calibration", default=ROOT + "gallery_lvface.h5")
ap.add_argument("--out", default="results_resolution_sweep.json")
args = ap.parse_args()
eng = sae_embed.FaceEmbedder(detector_model=M + args.detector,
arcface_model=M + args.embedder,
conf=0.5, nms=0.4, max_side=0)
cal = sae_embed.gallery_calibration(args.gallery_calibration)
print(f"[calibration] global: {cal}", file=sys.stderr)
def labelled(clip):
"""{filename: person} from the hand-sorted folders, ignoring discard."""
out = {}
for path in glob.glob(f"labelling/{clip}/*/*.jpg"):
person = os.path.basename(os.path.dirname(path))
if person in ("discard", "unsorted"):
continue
out[os.path.basename(path)] = person
return out
def manifest(clip):
return {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
def iou(a, b):
ax, ay, aw, ah = a; bx, by, bw, bh = b
x0, y0 = max(ax, bx), max(ay, by)
x1, y1 = min(ax + aw, bx + bw), min(ay + ah, by + bh)
if x1 <= x0 or y1 <= y0:
return 0.0
inter = (x1 - x0) * (y1 - y0)
return inter / (aw * ah + bw * bh - inter)
# ── gallery: native resolution, labelled faces only ──────────────────────────
g_lab, g_man = labelled(args.gallery_clip), manifest(args.gallery_clip)
gal = {}
for frame in sorted({g_man[f]["frame"] for f in g_lab}):
img = cv2.imread(f"frames/d{args.gallery_clip}_{frame}.png")
dets = eng.detect(img)
for fname, person in g_lab.items():
m = g_man[fname]
if m["frame"] != frame or m["idx"] >= len(dets):
continue
lm = np.array(dets[m["idx"]].landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
continue
gal.setdefault(person, []).append(np.asarray(eng.embed_crop(crop), dtype=np.float32))
gal = {p: np.stack(v) for p, v in gal.items() if v}
people = sorted(gal)
print(f"[gallery] {args.gallery_clip} @native: "
f"{ {p: len(v) for p, v in gal.items()} }", file=sys.stderr)
# ── probe ground truth at native resolution ──────────────────────────────────
p_lab, p_man = labelled(args.probe_clip), manifest(args.probe_clip)
truth = {} # frame -> [(bbox_native, person)]
for fname, person in p_lab.items():
m = p_man[fname]
truth.setdefault(m["frame"], []).append((m["bbox"], person))
n_out = sum(1 for p in set(p_lab.values()) if p not in people)
print(f"[probe] {args.probe_clip}: {len(p_lab)} labelled faces, "
f"{len(set(p_lab.values()))} people, {n_out} of them out-of-gallery",
file=sys.stderr)
# ── sweep ────────────────────────────────────────────────────────────────────
print(f"\n{'scale':>6}{'frame':>11}{'face px':>9}{'found':>7}{'matched':>9}"
f"{'TPI':>8}{'FPI-in':>8}{'FPI-out':>9}{'TBI':>8}")
results = []
for s in SCALES:
tpi = fpi_in = fpi_out = tbi = 0
n_found = n_matched = 0
pxs = []
for frame, gts in sorted(truth.items()):
img = cv2.imread(f"frames/d{args.probe_clip}_{frame}.png")
if s != 1.0:
img = cv2.resize(img, None, fx=s, fy=s, interpolation=cv2.INTER_AREA)
dets = eng.detect(img)
n_found += len(dets)
for d in dets:
x, y, w, h = d.bbox
native = (x / s, y / s, w / s, h / s) # geometric carry-down
best, best_iou = None, 0.0
for gt_box, person in gts:
v = iou(native, gt_box)
if v > best_iou:
best_iou, best = v, person
if best_iou < IOU_MIN:
continue # spurious / unlabelled
n_matched += 1
pxs.append(min(w, h))
lm = np.array(d.landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
tbi += 1 # degenerate alignment
continue
emb = np.asarray(eng.embed_crop(crop), dtype=np.float32)
best_p, best_name = 0.0, None
for p in people: # per-actor best-of-N
prob = cal.probability(float((gal[p] @ emb).max()), LOG_PRIOR_ODDS)
if prob > best_p:
best_p, best_name = prob, p
if best_p <= PROB_THRESHOLD:
tbi += 1
elif best not in people:
fpi_out += 1 # named someone absent from the gallery
elif best_name == best:
tpi += 1
else:
fpi_in += 1
n = max(1, n_matched)
med_px = float(np.median(pxs)) if pxs else 0.0
print(f"{s:>6.2f}{f'{int(4096*s)}x{int(2160*s)}':>11}{med_px:>9.0f}"
f"{n_found:>7}{n_matched:>9}"
f"{100*tpi/n:>7.1f}%{100*fpi_in/n:>7.1f}%{100*fpi_out/n:>8.1f}%{100*tbi/n:>7.1f}%")
results.append({"scale": s, "median_face_px": med_px, "detections": n_found,
"matched_to_truth": n_matched, "tpi_pct": 100*tpi/n,
"fpi_in_gallery_pct": 100*fpi_in/n, "fpi_out_of_gallery_pct": 100*fpi_out/n,
"tbi_pct": 100*tbi/n})
json.dump({"gallery_clip": args.gallery_clip, "probe_clip": args.probe_clip,
"detector": args.detector, "embedder": args.embedder,
"prob_threshold": PROB_THRESHOLD, "log_prior_odds": LOG_PRIOR_ODDS,
"calibration": {"a": cal.a, "b": cal.b},
"gallery_people": people, "results": results},
open(args.out, "w"), indent=2)
print(f"\nwrote {args.out}", file=sys.stderr)
experiments/xsource/verify_labels.py
+175
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@@ -0,0 +1,175 @@
#!/usr/bin/env python3
"""Integrity check on the labelled set, before it is used as ground truth.
TRACES: VR-013 | PR-002
VR-013's ground truth is hand-sorted rather than propagated by embedding
similarity, because propagation would keep only the faces the embedder already
gets right and silently drop the ones the sweep exists to find. This script is
what makes that claim checkable, so it is part of the requirement rather than a
helper of it.
Checks, loudest failure first:
1. INDEX INTEGRITY. Each crop's embedding is taken by re-detecting its source
frame and indexing with the manifest's `idx`. If detection order is not
reproducible, the thumbnail you sorted and the embedding that gets scored
are different faces — you would see a correct picture and score the wrong
person, with nothing to signal it. Every crop's re-detected bbox is compared
against the manifest's.
2. NO CROP IN TWO FOLDERS, and every manifest entry accounted for — so a
move that half-completed cannot silently duplicate or drop a label.
3. ALIGNMENT. The 112x112 warp is what the embedder actually sees; the
thumbnail is only context for your eyes. verify_<person>.jpg pairs them:
context-with-box on top, the real aligned crop beneath. A profile face whose
alignment has collapsed is obvious there and nowhere else.
4. SEPARATION. Per person, the calibrated P of their own crops against the
other people's, using the global gallery sigmoid. A label set where someone
matches another person better than themselves is mislabelled.
Nothing here changes a label. It reports.
"""
import sys, glob, json, os
import numpy as np
import cv2
sys.path.insert(0, "/home/dtourolle/Development/Jray-project/scene-actor-extraction/build-ort")
import sae_embed
ROOT = "/home/dtourolle/Development/Jray-project/scene-actor-extraction/"
M = ROOT + "models/"
EMBEDDER = M + "LVFace-B_Glint360K.onnx"
GALLERY = ROOT + "gallery_lvface.h5"
CLIPS = ["5157344", "5157339"]
THUMB = 130
COLS = 10
eng = sae_embed.FaceEmbedder(detector_model=M + "scrfd_500m_bnkps.onnx",
arcface_model=EMBEDDER, conf=0.5, nms=0.4, max_side=0)
fail = 0
rows = []
for clip in CLIPS:
man = {m["file"]: m for m in json.load(open(f"labelling/{clip}/manifest.json"))}
# where each crop currently sits -> its label
placed = {}
for path in glob.glob(f"labelling/{clip}/*/*.jpg"):
person = os.path.basename(os.path.dirname(path))
if person in ("discard", "unsorted"):
continue # not people; scoring them would invent an extra identity
fname = os.path.basename(path)
if fname in placed:
print(f"[FAIL] {fname} appears in both {placed[fname][0]} and {person}")
fail += 1
placed[fname] = (person, path)
missing = set(man) - set(placed)
extra = set(placed) - set(man)
if missing:
print(f"[warn] {clip}: {len(missing)} manifest crops not in any folder")
if extra:
print(f"[FAIL] {clip}: {len(extra)} files with no manifest entry: "
f"{sorted(extra)[:3]}")
fail += 1
# index integrity + alignment, frame by frame
by_frame = {}
for fname, (person, path) in placed.items():
if fname in man:
by_frame.setdefault(man[fname]["frame"], []).append((fname, person, path))
bad_idx = 0
for frame, items in sorted(by_frame.items()):
img = cv2.imread(f"frames/d{clip}_{frame}.png")
if img is None:
print(f"[FAIL] missing frames/d{clip}_{frame}.png")
fail += 1
continue
dets = eng.detect(img)
for fname, person, path in items:
m = man[fname]
i = m["idx"]
if i >= len(dets):
print(f"[FAIL] {fname}: idx {i} >= {len(dets)} detections now")
bad_idx += 1
continue
got = [float(v) for v in dets[i].bbox]
want = m["bbox"]
if max(abs(a - b) for a, b in zip(got, want)) > 1.0:
print(f"[FAIL] {fname}: manifest bbox {[round(v) for v in want]} "
f"!= re-detected {[round(v) for v in got]}")
bad_idx += 1
continue
lm = np.array(dets[i].landmarks, dtype=np.float32).reshape(5, 2)
crop = sae_embed.align_face(img, lm)
if crop is None:
print(f"[warn] {fname}: alignment degenerate, no crop reaches the embedder")
continue
rows.append({"clip": clip, "person": person, "file": fname, "path": path,
"px": m["px"], "aligned": np.asarray(crop),
"emb": np.asarray(eng.embed_crop(crop), dtype=np.float32)})
fail += bad_idx
print(f"[{clip}] {len(placed)} placed, {len(by_frame)} frames, "
f"index mismatches: {bad_idx}")
if not rows:
sys.exit("nothing to verify")
# ── separation, through the global gallery sigmoid ───────────────────────────
cal = sae_embed.gallery_calibration(GALLERY)
E = np.stack([r["emb"] for r in rows])
people = sorted({r["person"] for r in rows})
lab = np.array([people.index(r["person"]) for r in rows])
S = E @ E.T
np.fill_diagonal(S, -1.0)
print(f"\n{'person':>8}{'crops':>7}{'344':>6}{'339':>6}"
f"{'P(self)':>10}{'P(other)':>10}{'worst':>8}")
for k, p in enumerate(people):
mine = np.where(lab == k)[0]
if len(mine) < 2:
continue
self_sim = S[np.ix_(mine, mine)].max(axis=1)
other_sim = S[np.ix_(mine, np.where(lab != k)[0])].max(axis=1)
p_self = np.array([cal.probability(float(s)) for s in self_sim])
p_other = np.array([cal.probability(float(s)) for s in other_sim])
n344 = sum(1 for i in mine if rows[i]["clip"] == "5157344")
n339 = len(mine) - n344
# a crop that matches someone else better than anyone of its own label
worst = int((other_sim > self_sim).sum())
print(f"{p:>8}{len(mine):>7}{n344:>6}{n339:>6}"
f"{np.median(p_self):>10.3f}{np.median(p_other):>10.3f}{worst:>8}")
if worst:
for i in mine[other_sim > self_sim]:
print(f" suspect: {rows[i]['file']} "
f"P(self)={cal.probability(float(self_sim[list(mine).index(i)])):.3f} "
f"< P(other)={cal.probability(float(other_sim[list(mine).index(i)])):.3f}")
# ── verify sheets: context+box over the actual aligned crop ──────────────────
for p in people:
items = [r for r in rows if r["person"] == p]
items.sort(key=lambda r: (r["clip"], r["file"]))
n = len(items)
sheet_rows = (n + COLS - 1) // COLS
H = THUMB * 2 + 22
sheet = np.full((sheet_rows * H, COLS * THUMB, 3), 25, np.uint8)
for j, r in enumerate(items):
rr, cc = divmod(j, COLS)
y, x = rr * H, cc * THUMB
ctx = cv2.imread(r["path"])
if ctx is not None:
sheet[y:y + THUMB, x:x + THUMB] = cv2.resize(ctx, (THUMB, THUMB))
sheet[y + THUMB:y + 2 * THUMB, x:x + THUMB] = cv2.resize(r["aligned"], (THUMB, THUMB))
cv2.putText(sheet, f"{r['clip'][-3:]} {int(r['px'])}px",
(x + 3, y + 2 * THUMB + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.38, (150, 220, 150), 1)
cv2.imwrite(f"labelling/verify_{p}.jpg", sheet)
print(f" verify_{p}.jpg: {n} crops (top row context, bottom row what the embedder sees)")
print(f"\n{'PASS' if fail == 0 else f'{fail} FAILURES'}")
sys.exit(1 if fail else 0)
external/KPN
Vendored
+1 -1
Submodule external/KPN updated: 4b6e498ba7...454f72c167
mkdocs.yml
+1
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@@ -35,6 +35,7 @@ extra_css:
nav:
- Home: index.md
- How We Score Against X-Ray: methodology.md
- Benchmark — SuperHero: benchmark.md
- Findings:
- Best Model: best-model.md
- Gallery Scope (Full vs. Limited): gallery-scope.md
models/2d106det.onnx
LFS
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models/scrfd_10g_bnkps.onnx
LFS
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models/scrfd_2.5g_bnkps.onnx
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scripts/artifacts/pull_artifacts.sh
+89 -1
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@@ -10,7 +10,9 @@
# scripts/artifacts/pull_artifacts.sh galleries [version]
# scripts/artifacts/pull_artifacts.sh montage-frames <film-slug> [version]
# scripts/artifacts/pull_artifacts.sh experiment-data [version]
# scripts/artifacts/pull_artifacts.sh replay-fixtures [version]
# scripts/artifacts/pull_artifacts.sh report-highlights <name> [version]
# scripts/artifacts/pull_artifacts.sh xsource [version]
# version defaults to "latest" (newest uploaded version, by created_at).
set -euo pipefail
@@ -41,6 +43,22 @@ print(matches[-1]['version'])
"
}
pull_replay_fixtures() {
local version="$1"
local dest="${REPO_ROOT}/tests/fixtures/dumps"
mkdir -p "$dest"
echo "=== replay-fixtures (version ${version}) ==="
local tmp; tmp="$(mktemp -d)"
if curl -sf "${DL_BASE}/generic/replay-fixtures/${version}/replay-fixtures.zip" \
-o "${tmp}/f.zip"; then
unzip -qo "${tmp}/f.zip" -d "$dest"
echo " restored: $(ls "$dest" | wc -l) files into tests/fixtures/dumps/"
else
echo " [warn] replay-fixtures.zip not found at version ${version}" >&2
fi
rm -rf "$tmp"
}
pull_galleries() {
local version="$1"
local dest="${REPO_ROOT}/experiments/galleries"
@@ -83,11 +101,71 @@ pull_report_highlight() {
curl -sf "${DL_BASE}/generic/report-highlights/${version}/${name}" -o "${dest}/${name}"
}
pull_xsource() {
local version="$1"
local dest="${REPO_ROOT}/experiments/xsource"
echo "=== xsource (version ${version}) ==="
mkdir -p "${dest}/clips" "${dest}/frames"
for clip in 5157339 5157344; do
if [ -f "${dest}/clips/${clip}.mp4" ]; then
echo " ${clip}.mp4 already present, skipping"
else
echo " fetching ${clip}.mp4..."
curl -sf "${DL_BASE}/generic/xsource/${version}/${clip}.mp4" \
-o "${dest}/clips/${clip}.mp4" \
|| { echo " [warn] ${clip}.mp4 not found at version ${version}" >&2; continue; }
fi
done
if [ -d "${dest}/labelling" ]; then
echo " labelling/ already present — NOT overwriting (it is hand-sorted"
echo " ground truth; move it aside first if you really want the remote copy)"
else
echo " fetching labelling.zip..."
local tmp; tmp="$(mktemp)"
curl -sf "${DL_BASE}/generic/xsource/${version}/labelling.zip" -o "$tmp"
unzip -qo "$tmp" -d "$dest"
rm "$tmp"
fi
# Frames are regenerated rather than shipped: they are ~320 MB of PNG that
# ffmpeg reproduces exactly from the clips. The manifests key on these
# filenames and on detection order within each frame, so the extraction
# settings must match the ones dump_faces.py ran against — hence fps and
# frame count are pinned here rather than left to the caller.
if ! command -v ffmpeg >/dev/null; then
echo " [warn] ffmpeg not found — frames not regenerated; the study" >&2
echo " scripts will fail until you extract them" >&2
return
fi
for clip in 5157339 5157344; do
[ -f "${dest}/clips/${clip}.mp4" ] || continue
if [ -f "${dest}/frames/d${clip}_001.png" ]; then
echo " frames for ${clip} already present, skipping"
continue
fi
echo " extracting frames for ${clip}..."
ffmpeg -v error -i "${dest}/clips/${clip}.mp4" -vf fps=2 -frames:v 24 \
"${dest}/frames/d${clip}_%03d.png"
done
echo " verifying the labelled set..."
if (cd "$dest" && python3 verify_labels.py >/dev/null 2>&1); then
echo " verify_labels.py passed"
else
echo " [warn] verify_labels.py failed — run it directly to see why." >&2
echo " A frame/manifest mismatch means the extraction settings" >&2
echo " differ from the ones the crops were dumped against." >&2
fi
}
if [ $# -eq 0 ]; then
echo "usage: $0 galleries [version]" >&2
echo " $0 montage-frames <film-slug> [version]" >&2
echo " $0 experiment-data [version]" >&2
echo " $0 report-highlights <name> [version]" >&2
echo " $0 xsource [version]" >&2
exit 1
fi
@@ -115,8 +193,18 @@ case "$TARGET" in
[ "$VERSION" = "latest" ] && VERSION="$(resolve_latest_version report-highlights)"
pull_report_highlight "$VERSION" "$NAME"
;;
xsource)
VERSION="${2:-latest}"
[ "$VERSION" = "latest" ] && VERSION="$(resolve_latest_version xsource)"
pull_xsource "$VERSION"
;;
replay-fixtures)
VERSION="${2:-latest}"
[ "$VERSION" = "latest" ] && VERSION="$(resolve_latest_version replay-fixtures)"
pull_replay_fixtures "$VERSION"
;;
*)
echo "unknown target: $TARGET (expected galleries, montage-frames, experiment-data, or report-highlights)" >&2
echo "unknown target: $TARGET (expected galleries, montage-frames, experiment-data, report-highlights, xsource, or replay-fixtures)" >&2
exit 1
;;
esac
scripts/artifacts/push_artifacts.sh
+57 -2
View File
@@ -12,10 +12,13 @@
# scripts/artifacts/push_artifacts.sh montage-frames
# scripts/artifacts/push_artifacts.sh experiment-data
# scripts/artifacts/push_artifacts.sh report-highlights
# scripts/artifacts/push_artifacts.sh xsource
# scripts/artifacts/push_artifacts.sh replay-fixtures
# scripts/artifacts/push_artifacts.sh galleries montage-frames experiment-data report-highlights
#
# Package layout (owner=dtourolle, repo=scene-actor-extraction):
# generic/galleries/<version>/gallery_<model>.h5 (one file per model)
# generic/replay-fixtures/<version>/replay-fixtures.zip (T2 dumps + their gallery)
# generic/montage-frames/<version>/<film-slug>.zip (zipped per-film frames)
# generic/experiment-data/<version>/experiment-data.zip (manifests/trajectories/results)
# generic/report-highlights/<version>/<name>.jpg (individual, hand-picked
@@ -49,6 +52,29 @@ upload() {
-o /dev/null -w " HTTP %{http_code}\n"
}
push_replay_fixtures() {
echo "=== replay-fixtures (version ${VERSION}) ==="
# T2 replay fixtures: per-frame detections, landmarks and embeddings dumped
# from a real run, so the tracker and identity stages can be replayed on CPU
# with no GPU, no models and no film. Too large for git (superhero.h5 alone
# is ~9 MB) and regenerating them needs the film plus a GPU, which CI has
# neither of — so they ship as artifacts and CI pulls them.
#
# The gallery travels with them: a dump replays against the gallery it was
# produced with, and pairing a dump with a different gallery silently
# changes every identity decision in it.
local dir="${REPO_ROOT}/tests/fixtures/dumps"
if [ ! -d "$dir" ]; then
echo " no tests/fixtures/dumps dir, skipping" >&2
return
fi
local tmp
tmp="$(mktemp -d)"
( cd "$dir" && zip -qr "$tmp/replay-fixtures.zip" . )
upload "replay-fixtures" "replay-fixtures.zip" "$tmp/replay-fixtures.zip"
rm -rf "$tmp"
}
push_galleries() {
echo "=== galleries (version ${VERSION}) ==="
local dir="${REPO_ROOT}/experiments/galleries"
@@ -109,8 +135,35 @@ push_report_highlights() {
upload "report-highlights" "germar_beats_xray.jpg" "$src"
}
push_xsource() {
echo "=== xsource (version ${VERSION}) ==="
local root="${REPO_ROOT}/experiments/xsource"
if [ ! -d "$root/labelling" ]; then
echo " no experiments/xsource/labelling found, skipping" >&2
return
fi
# Source recordings. Already compressed, so uploaded as-is rather than zipped.
shopt -s nullglob
for f in "$root"/clips/*.mp4; do
upload "xsource" "$(basename "$f")" "$f"
done
shopt -u nullglob
# The hand-sorted crops and their manifests. This is human ground truth and
# the expensive part of the study — a person looked at every crop and put it
# in a folder. Frames are deliberately NOT pushed: they are deterministic
# from the clips, and pulling regenerates them.
local tmp; tmp="$(mktemp -d)"
trap 'rm -rf "$tmp"' RETURN
local zipfile="${tmp}/labelling.zip"
(cd "$root" && zip -qr "$zipfile" labelling -x 'labelling/*.html' -x 'labelling/review_*.jpg' \
-x 'labelling/verify_*.jpg')
upload "xsource" "labelling.zip" "$zipfile"
}
if [ $# -eq 0 ]; then
echo "usage: $0 <galleries|montage-frames|experiment-data|report-highlights> [...]" >&2
echo "usage: $0 <galleries|montage-frames|experiment-data|report-highlights|xsource> [...]" >&2
exit 1
fi
@@ -120,7 +173,9 @@ for target in "$@"; do
montage-frames) push_montage_frames ;;
experiment-data) push_experiment_data ;;
report-highlights) push_report_highlights ;;
*) echo "unknown target: $target (expected galleries, montage-frames, experiment-data, or report-highlights)" >&2; exit 1 ;;
xsource) push_xsource ;;
replay-fixtures) push_replay_fixtures ;;
*) echo "unknown target: $target (expected galleries, montage-frames, experiment-data, report-highlights, xsource, or replay-fixtures)" >&2; exit 1 ;;
esac
done
scripts/ci/build_builder_image.sh
+118
View File
@@ -0,0 +1,118 @@
#!/bin/bash
# build_builder_image.sh — build and publish the DP-007 CI builder image to the
# Gitea container registry.
#
# TRACES: DP-007 | PR-004
#
# Usage:
# scripts/ci/build_builder_image.sh # build only, tag v1
# scripts/ci/build_builder_image.sh --push # build and push
# scripts/ci/build_builder_image.sh --tag v2 --push # bump the pinned tag
# scripts/ci/build_builder_image.sh --no-cache # force a clean rebuild
#
# The tag is the contract with CI. .gitea/workflows/unit-tests.yml names an
# explicit tag in its `container:` block and never `latest`, so that rebuilding
# the image cannot silently change what a previous green build meant. Bumping
# the dependency set means bumping the tag AND editing the workflow — the two
# edits landing in the same commit is the point, not an inconvenience.
#
# Registry auth: this script does not log in. Do it once, out of band:
# docker login gitea.tourolle.paris
# The CI host is already authenticated this way (its cached credentials in
# ~/.docker/config.json are what the kpnpp-builder push relies on), so a
# workflow that calls this script needs no secret plumbing.
set -euo pipefail
REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
REGISTRY="gitea.tourolle.paris"
OWNER="dtourolle"
IMAGE="sae-builder-cpu"
DOCKERFILE="Dockerfile.builder-cpu"
# The tag CI pins to today. Keep this in step with the `container.image` line in
# .gitea/workflows/unit-tests.yml; the workflow asserts at run time that the
# image it landed in reports this same version, so a drift shows up as a failed
# job rather than as a build against the wrong toolchain.
TAG="v1"
PUSH=0
EXTRA_ARGS=()
while [ $# -gt 0 ]; do
case "$1" in
--push) PUSH=1 ;;
--tag) TAG="${2:?--tag needs a value}"; shift ;;
--no-cache) EXTRA_ARGS+=(--no-cache) ;;
-h|--help) sed -n '2,30p' "${BASH_SOURCE[0]}"; exit 0 ;;
*) echo "error: unknown argument '$1'" >&2; exit 2 ;;
esac
shift
done
if [ "$TAG" = "latest" ]; then
echo "error: refusing to build the tag 'latest'." >&2
echo "DP-007 requires CI to pin an immutable tag. A moving 'latest' means a" >&2
echo "rebuild retroactively changes what every earlier green build proved." >&2
exit 2
fi
REF="${REGISTRY}/${OWNER}/${IMAGE}:${TAG}"
# A second tag carrying the commit that produced the image. The workflow pins
# the human-readable tag; this one is the audit trail — given any image you can
# recover the Dockerfile that built it.
SHA="$(git -C "$REPO_ROOT" rev-parse --short HEAD)"
REF_SHA="${REGISTRY}/${OWNER}/${IMAGE}:${TAG}-${SHA}"
# The Dockerfile COPYs nothing from the repository on purpose (see its closing
# comment), so the build context is an empty directory rather than the repo
# root. Sending ~1 GB of models, fixtures and experiment data to the daemon for
# a build that reads none of it is pure latency.
CONTEXT="$(mktemp -d)"
trap 'rm -rf "$CONTEXT"' EXIT
echo "=== building ${REF}"
echo " dockerfile: ${REPO_ROOT}/${DOCKERFILE}"
echo " context: (empty — the image embeds no repository content)"
echo
echo " Expect this to take a while: OpenCV 5 is compiled from source because"
echo " no Debian release ships it. That cost is paid once per image, which is"
echo " the entire reason DP-007 asks for a prebuilt image instead of"
echo " installing dependencies inside each CI run."
echo
docker build \
"${EXTRA_ARGS[@]}" \
--build-arg "IMAGE_TAG=${TAG}" \
-f "${REPO_ROOT}/${DOCKERFILE}" \
-t "${REF}" \
-t "${REF_SHA}" \
"${CONTEXT}"
echo
echo "=== built"
docker image inspect "${REF}" --format ' {{.RepoTags}} {{.Size}} bytes'
docker run --rm "${REF}" sh -c 'echo " SAE_BUILDER=$SAE_BUILDER version=$SAE_BUILDER_VERSION ort=$SAE_ORT_VERSION opencv=$SAE_OPENCV_VERSION"'
if [ "$PUSH" -eq 0 ]; then
echo
echo "Not pushed. Re-run with --push, or push by hand:"
echo " docker push ${REF}"
echo " docker push ${REF_SHA}"
exit 0
fi
echo
echo "=== pushing"
# No `latest` tag is pushed, by design. Publishing one invites a workflow to use
# it, and DP-007 exists to prevent exactly that.
docker push "${REF}"
docker push "${REF_SHA}"
echo
echo "=== published ${REF}"
echo "If this was a dependency-set change, bump the tag in"
echo " .gitea/workflows/unit-tests.yml (container.image)"
echo " scripts/ci/build_builder_image.sh (TAG, above)"
echo "in the same commit, so no run can build against an image the repository"
echo "does not describe."
scripts/fetch_dvu.sh
+98
View File
@@ -0,0 +1,98 @@
#!/bin/bash
# fetch_dvu.sh — pull one film's character mugshots and presence annotations from
# the NIST TRECVID Deep Video Understanding development set.
#
# The DVU dev set is the reason Road to Bali is our benchmark film: it ships
# 5-7 face crops per *character*, cut from the film itself, alongside
# scene-scoped presence annotations. That matches SR-002 directly — presence is
# per scene, not per frame — and it keeps ground truth in character space, so
# scoring needs no actor->character mapping.
#
# This exists as a script, rather than as ad hoc commands, because the first
# copy of this data lived in a temp directory and was lost to a /tmp wipe,
# taking the working gallery with it.
#
# 14 films are asserted Creative Commons and need no data agreement (only the
# 5 KinoLorber test films are gated).
#
# Usage:
# scripts/fetch_dvu.sh [film] [dest]
# film default Road_To_Bali
# dest default ./dvu
set -euo pipefail
BASE="https://www-nlpir.nist.gov/projects/trecvid/dvu/dvu.development.dataset"
FILM="${1:-Road_To_Bali}"
DEST="${2:-dvu}"
mkdir -p "$DEST/images" "$DEST/scenes"
echo "[dvu] $FILM -> $DEST"
# Scene segmentation: start/end as HH:MM:SS. Note valkaama.csv line 38 carries a
# shift-key typo (01:!4:00) — parse defensively if you extend this to that film.
echo "[dvu] scene segmentation"
curl -fsSL "$BASE/scene.segmentation.reference/${FILM}.csv" \
-o "$DEST/${FILM}.csv" || echo " (missing: ${FILM}.csv)"
# Entity types: which entities are Person vs Location/Concept. Only Person rows
# become gallery identities — the images/ directory also holds Location and
# Concept crops (bedroom, boat, ...), which must not enter a face gallery.
#
# Directory and file naming are inconsistent with the film slug used elsewhere:
# the folder is Road_to_Bali (lowercase "to") while the entity file is
# RoadToBali.entity.types.txt. Both are derived here rather than assumed.
# NIST is inconsistent across all three axes, and not by a rule worth deriving:
# Road to Bali is Road_To_Bali.csv / Road_to_Bali/ / RoadToBali.entity.types.txt,
# while SuperHero is SuperHero.csv / superHero/ / superhero.entity.types.txt.
# Defaults cover the Bali shape; override per film rather than guessing.
# KG_DIR=superHero KG_FILE=superhero scripts/fetch_dvu.sh SuperHero dvu-hero
KG_DIR="${KG_DIR:-${FILM//_To_/_to_}}"
KG_FILE="${KG_FILE:-$(echo "$FILM" | sed -E 's/_([a-z])/\U\1/g; s/_//g')}"
echo "[dvu] entity types ($KG_DIR/$KG_FILE)"
curl -fsSL "$BASE/movie_knowledge_graph/${KG_DIR}/${KG_FILE}.entity.types.txt" \
-o "$DEST/${FILM}.entity.types.txt" || echo " (missing: entity types)"
# Character face crops. Names are discovered from the directory listing rather
# than probed as <Character>_N, since the crop count varies per character and
# the listing is authoritative.
echo "[dvu] character mugshots"
PERSONS="$DEST/persons.txt"
if [ -f "$DEST/${FILM}.entity.types.txt" ]; then
grep -iE "person" "$DEST/${FILM}.entity.types.txt" \
| sed -E 's/[[:space:]]*[:,].*$//' | tr -d '\r' \
| awk '{print tolower($1)}' | sort -u > "$PERSONS"
fi
curl -fsSL "$BASE/movie_knowledge_graph/${KG_DIR}/images/" 2>/dev/null \
| grep -oE 'href="[^"?/][^"]*\.png"' | sed -E 's/href="//; s/"//' | sort -u \
> "$DEST/all_images.txt"
while read -r img; do
[ -z "$img" ] && continue
# Strip the trailing _N to recover the entity name.
who="$(echo "$img" | sed -E 's/_[0-9]+\.png$//' | awk '{print tolower($0)}')"
if [ -s "$PERSONS" ] && ! grep -qx "$who" "$PERSONS"; then
continue # Location/Concept crop, not a face
fi
curl -fsSL "$BASE/movie_knowledge_graph/${KG_DIR}/images/${img}" \
-o "$DEST/images/${img}" 2>/dev/null || rm -f "$DEST/images/${img}"
done < "$DEST/all_images.txt"
# Per-scene knowledge graphs. A Person->Location edge means that person was
# present for the whole scene. Some of these contain a stray ", ," that breaks
# strict JSON parsers.
echo "[dvu] scene graphs"
for n in $(seq 1 60); do
curl -fsSL "$BASE/scenes_knowledge_graphs/${FILM//_/ }-${n}.json" \
-o "$DEST/scenes/${FILM}-${n}.json" 2>/dev/null \
|| curl -fsSL "$BASE/scenes_knowledge_graphs/${FILM}-${n}.json" \
-o "$DEST/scenes/${FILM}-${n}.json" 2>/dev/null \
|| rm -f "$DEST/scenes/${FILM}-${n}.json"
done
echo "[dvu] done:"
echo " mugshots: $(ls "$DEST/images" 2>/dev/null | wc -l)"
echo " scenes: $(ls "$DEST/scenes" 2>/dev/null | wc -l)"
echo " csv: $([ -f "$DEST/${FILM}.csv" ] && echo yes || echo no)"
scripts/filter_gallery.py
+6 -1
View File
@@ -77,7 +77,12 @@ def main():
if missing > 0:
print(f"[warn] {missing} cast member(s) not present in gallery (not yet embedded)", file=sys.stderr)
save_gallery_hdf5({"actors": actors}, Path(args.output))
# TRACES: GR-004 | SR-001
# a filtered gallery holds the SAME vectors as its
# source, so it inherits the source's binding. Dropping the stamp here would
# silently launder a stamped gallery into an unstamped one.
save_gallery_hdf5({"actors": actors}, Path(args.output),
gallery.get("embedder"))
print(f"Saved {len(actors)} actor(s) to {args.output}", file=sys.stderr)
scripts/make_fixtures.sh
Executable
+67
View File
@@ -0,0 +1,67 @@
#!/usr/bin/env bash
# make_fixtures.sh — regenerate the committed replay fixtures.
#
# TRACES: VR-001 | PR-002
#
# CI never calls a model (see docs/requirements.md, "CI never calls a model"):
# the embedder is impractical on the N100 CI host, so inference happens HERE, on
# a machine with a GPU, and CI consumes the HDF5 dumps as data. Everything
# downstream of embedding — tracking, presence windows, belief accumulation,
# expansion — is cheap CPU maths and replays from these files.
#
# Reproducibility is a requirement, not a nicety. A fixture whose provenance is
# unknown is worse than no fixture, because it will be trusted. Every parameter
# that affects the output is pinned below rather than left to a default, and the
# dumps carry the embedder identity and SHA-256 (GR-004) so a replay cannot be
# silently scored against the wrong gallery.
#
# These are byte-reproducible only because node outputs block rather than drop
# on a full channel (AR-004). Before that fix the same command produced
# different dumps run to run, since what got dropped depended on timing.
#
# Source: hero/ — SuperHero, from the TRECVID DVU development set. Chosen over
# SuperHero on face scale: Bali reference crops had a median detected face of
# 27 px against a 69 px maximum, so every reference was upscaled far past what
# the embedder was trained for. SuperHero is 69 px median, 241 px max. That matters: derived
# fixtures can be committed, where anything cut from a copyrighted title could
# not live in the repository at all.
set -euo pipefail
REPO="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
CLIPS="${CLIPS:-$REPO/../hero}"
GALLERY="${GALLERY:-$REPO/gallery_lvface.h5}"
BIN="${BIN:-$REPO/build/scene_analyze}"
OUT="$REPO/tests/fixtures/dumps"
# Pinned. Changing either invalidates every committed fixture.
# fps 5 — 1 fps over a 77 s clip is 77 frames, too thin to exercise an
# extinction window measured in tens of seconds.
# min-face — 32 px. This is a *fixture* setting, deliberately below AR-002's
# production floor of 40 px (VR-013, measured end to end): the
# corpus is 480x360, where faces run 40-80 px, so pinning at 40
# would thin the dumps for reasons unrelated to what they test.
# 32 px is where VR-005 still shows 98.1% TPI, so the faces kept
# are identifiable; it is not the threshold the pipeline ships.
FPS=5
MIN_FACE_PX=32
[[ -x "$BIN" ]] || { echo "no scene_analyze at $BIN (set BIN=)" >&2; exit 1; }
[[ -f "$GALLERY" ]] || { echo "no gallery at $GALLERY (set GALLERY=)" >&2; exit 1; }
[[ -d "$CLIPS" ]] || { echo "no clips at $CLIPS (set CLIPS=)" >&2; exit 1; }
mkdir -p "$OUT"
for clip in "$CLIPS"/SuperHero-*.webm; do
n="$(basename "$clip" .webm)"; n="${n##*-}"
echo "── superhero_$n"
"$BIN" --movie "$clip" --gallery "$GALLERY" \
--fps "$FPS" --min-face-px "$MIN_FACE_PX" \
--dump-embeddings "$OUT/superhero_$n.h5" \
--output /dev/null 2>&1 | grep -E "wrote|dropped" || true
done
echo
echo "Regenerated in $OUT — verify the diff is empty if nothing upstream changed."
echo "A non-empty diff means detection, alignment or embedding moved. That is"
echo "either a regression or a deliberate change, and either way the golden"
echo "outputs derived from these fixtures need reviewing."
scripts/make_gallery.py
+9 -3
View File
@@ -36,8 +36,9 @@ import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent))
from sae_embed_loader import load_embedder
from sae_gallery import download_images, save_gallery, wikidata_image_urls
from sae_embed_loader import load_embedder, resolve_arcface
from sae_gallery import (download_images, embedder_stamp, save_gallery,
wikidata_image_urls)
from sae_tmdb import TMDB_IMG, tmdb_get, tmdb_id_from_imdb
@@ -177,7 +178,12 @@ def main():
output = Path(args.output)
image_root = Path(args.image_dir) if args.image_dir else output.parent / "images"
# TRACES: GR-004 | SR-001
# stamp with the model actually loaded, resolved
# through the same helper load_embedder uses so the two cannot diverge.
arcface_path = resolve_arcface(args.models_dir, args.arcface)
embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
stamp = embedder_stamp(arcface_path)
# Resolve movie ID
movie_id = args.movie_id
@@ -203,7 +209,7 @@ def main():
if n_actors == 0:
sys.exit("No actors could be processed — check models and images.")
save_gallery(gallery, missing, output)
save_gallery(gallery, missing, output, embedder=stamp)
if __name__ == "__main__":
scripts/make_jellyfin_gallery.py
+16 -3
View File
@@ -1,6 +1,8 @@
#!/usr/bin/env python3
"""make_jellyfin_gallery.py — build a gallery.h5 spanning an entire Jellyfin library.
TRACES: GR-001, GR-002 | SR-001, SR-005
Queries the Jellyfin API for every Movie/Series, collects the unique cast
across the whole library, downloads each actor's headshot directly from
Jellyfin (no TMDB key needed), embeds them with the sae_embed module (SCRFD +
@@ -51,8 +53,9 @@ import requests
sys.path.insert(0, str(Path(__file__).resolve().parent))
import sae_env # noqa: F401 — loads .env into os.environ on import
from sae_embed_loader import load_embedder
from sae_gallery import (download_image, download_images, load_gallery_hdf5,
from sae_embed_loader import load_embedder, resolve_arcface
from sae_gallery import (download_image, download_images, embedder_stamp,
enforce_embedder_stamp, load_gallery_hdf5,
save_gallery, wikidata_image_urls)
from sae_jellyfin import actor_jellyfin_id, jf_get, normalize_jellyfin_url
from sae_tmdb import (
@@ -442,11 +445,21 @@ def main():
image_root = Path(args.image_dir) if args.image_dir else output.parent / "images"
item_types = [t.strip() for t in args.item_types.split(",") if t.strip()]
# TRACES: GR-004 | SR-001
arcface_path = resolve_arcface(args.models_dir, args.arcface)
embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
stamp = embedder_stamp(arcface_path)
existing_actors = {}
if args.merge and output.is_file():
existing = load_gallery_hdf5(output)
# TRACES: GR-004 | SR-001
# --merge keeps the existing actors' vectors and
# embeds the new ones with THIS model. If they disagree, the result is one
# gallery holding two incompatible embedding spaces, which is worse than a
# mismatched gallery: no later check can separate them again.
enforce_embedder_stamp(existing.get("embedder"), stamp, str(output),
arcface_path)
for actor in existing.get("actors", []):
pid = actor_jellyfin_id(actor)
if pid:
@@ -475,7 +488,7 @@ def main():
if n_actors == 0:
sys.exit("No actors could be processed — check Jellyfin URL/API key and models.")
save_gallery(gallery, missing, output)
save_gallery(gallery, missing, output, embedder=stamp)
if __name__ == "__main__":
scripts/movienet_eval.py
+8 -2
View File
@@ -22,8 +22,8 @@ from pathlib import Path
import numpy as np
sys.path.insert(0, str(Path(__file__).resolve().parent))
from sae_embed_loader import load_embedder
from sae_gallery import load_gallery_hdf5
from sae_embed_loader import load_embedder, resolve_arcface
from sae_gallery import load_gallery_hdf5, verify_gallery_stamp
def load_gallery(path: str) -> dict[str, dict]:
@@ -62,6 +62,12 @@ def main():
args = p.parse_args()
embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
# TRACES: GR-004 | SR-001
# match() below is a bare dot product against the
# gallery's vectors; if the gallery came from another model those numbers are
# noise wearing a similarity's clothes.
verify_gallery_stamp(args.gallery,
resolve_arcface(args.models_dir, args.arcface))
gallery = load_gallery(args.gallery)
print(f"[eval] gallery: {len(gallery)} actors", file=sys.stderr)
scripts/optimizer/SCHEMA.md
+102 -6
View File
@@ -19,10 +19,33 @@ variable-length HDF5 types and reads straight into numpy.
/ (root)
attrs:
schema_version : int = 1
movie : str (source video path)
sample_fps : float
embed_dim : int = 512
# ── what produced the vectors (GR-004) ──────────────────────────────────
embedder_model : str basename of the embedding model
embedder_sha256: str SHA-256 of that model file
# ── what produced the faces (VR-010) ────────────────────────────────────
detector_model : str basename of the detector .onnx
detector_conf : float score floor a detection had to clear to be dumped
detector_nms : float NMS IoU threshold
min_face_px : float minimum box side, ORIGINAL-resolution px (AR-002)
max_faces : int per-frame cap; 0 = uncapped, the default (AR-003)
# ── what produced the frames (VR-010) ───────────────────────────────────
movie : str source video path
sample_fps : float frames analysed per second of movie
start_sec : float seek point
end_sec : float stop point; -1 = end of file
cut_threshold : float histogram correlation below which is_cut fires
dense_scale : float decoded-frame downscale in dense mode; 1 = off
bbox_upscale : float multiply faces/bbox and faces/landmarks by this to
reach original video pixels; 1 when dense_scale is 1
scene_detect : uint8 0/1 — was TransNetV2 running at all (see below)
# ── downstream setting recorded for comparability (VR-010) ──────────────
track_assoc_min_prob : float the run's tracker admission probability
frames/ group — one row per sampled frame
timestamp_sec : float64 [F]
frame_idx : int64 [F]
@@ -33,18 +56,91 @@ variable-length HDF5 types and reads straight into numpy.
faces/ group — one row per detected face, concatenated
embedding : float32 [N, 512] L2-normalised ArcFace embedding
bbox : float32 [N, 4] x, y, w, h in original video pixels
landmarks : float32 [N, 10] 5 (x,y) pairs, SCRFD/ArcFace order
bbox : float32 [N, 4] x, y, w, h in DECODED-frame pixels
landmarks : float32 [N, 10] 5 (x,y) pairs, SCRFD/ArcFace order,
same space as bbox
confidence : float32 [N] detector confidence
```
`F` = number of sampled frames, `N` = total faces (= sum of face_count).
Frame *i*'s faces are `faces/*[ face_offset[i] : face_offset[i]+face_count[i] ]`.
## Provenance (VR-010)
The attributes above are not documentation; they are the only thing that makes a
dump interpretable. Two dumps of the same film at `detector_conf` 0.5 and 0.7, or
at `dense_scale` 1.0 and 0.5, or with scene detection on and off, are different
measurements of different things — and they are byte-shaped identically. Without
provenance a consumer that mixes them gets a plausible number from an incoherent
input, and nothing anywhere reports a problem.
**`scene_detect` is the one that cannot be inferred.** `is_scene_boundary` is
all-zero both when TransNetV2 found no boundaries in the clip and when it was
never enabled, and those mean opposite things: the first says *this footage has
no shot changes*, the second says *nobody looked*. A consumer that reads the
array alone must guess. The flag is what removes the guess. (`dump_embeddings`
has no `--scene-detect`, so every dump it writes records `false` — which is
exactly the fact the committed fixtures needed to state.)
**`bbox_upscale` is recorded, not applied.** See the coordinate-space note below.
Reading is by name with a default or an existence check on **both** sides —
`replay.py` (`f.attrs.get(...)`) and `read_dump_provenance()` in
`src/nodes/embedding_dump_node.hpp` (`attrExists`). So the attributes are
additive and `schema_version` stays 1: a pre-VR-010 dump still loads, and a
post-VR-010 dump still reads on old code.
A missing attribute means **unknown**, never a default value. Substituting
`detector_conf = 0.5` for a dump that does not say so manufactures the provenance
the requirement exists to prevent — per `docs/requirements.md`, *"a fixture whose
provenance is unknown is worse than no fixture, because it will be trusted."*
The committed `tests/fixtures/dumps/*.h5` predate VR-010 and carry none of these
attributes; re-dump to bind them, as with GR-004.
## Model binding (GR-004)
`embedder_model` / `embedder_sha256` record which embedder produced every vector
in `faces/embedding`. A replay has no live embedder, so **the dump is the embedder
as far as the gallery is concerned**: `replay.py` checks these two attributes
against the gallery's own `/embedder` stamp and refuses to run on a mismatch,
naming both sides. Cross-model cosines are meaningless but look plausible.
The attributes are additive, not a format break — `schema_version` stays 1. Dumps
written before GR-004 simply lack them, which reports as *unverifiable* (a loud
warning, or a hard error under `SAE_REQUIRE_GALLERY_STAMP=1`) rather than as a
pass. Re-dump to bind an old dump; there is no in-place migration, because unlike
a gallery nobody can assert after the fact which model produced a vector.
## Coordinate space — `bbox`, `landmarks`, `bbox_upscale`
`bbox` and `landmarks` are in **decoded-frame pixels**: exactly the numbers SCRFD
produced, untransformed. To reach original video pixels, multiply by
`bbox_upscale`. With `dense_scale == 1` (the default, and every committed
fixture) `bbox_upscale == 1` and the two spaces coincide.
> Earlier revisions of this document claimed the upscale was applied at dump time.
> It never was. `embedding_dump_node.hpp` writes `f.bbox` raw; the upscale lives
> in `identity_matcher_node.hpp`, which is *downstream* of the dump tap. The
> claim was harmless only because `dense_scale` was 1 in practice.
The fix is to record the factor rather than to apply it, because the dump's whole
contract is to be a **faithful tap** at the `EmbeddedSceneFrame` channel — VR-002
requires replay to drive the real nodes, and a replay is only equivalent to the
live run if the tracker is fed the geometry the live tracker saw. Rescaling at
the tap would break that: the replayed tracker would associate on boxes the live
one never received. Two further reasons:
- The matcher's upscale is applied to `bbox` **only**, not to `landmarks`.
Pre-multiplying at the tap would leave the two arrays in different coordinate
spaces inside one file — a worse trap than the one being fixed.
- Pre-multiplying is lossy in the sense that matters: a dump that had been
upscaled would be indistinguishable from one taken at `dense_scale == 1`, so
you would have to record `bbox_upscale` anyway to know which you were holding.
## Invariants
- `embedding` rows are unit-norm (cosine == dot product against the gallery).
- `face_offset[0] == 0`; `face_offset[i+1] == face_offset[i] + face_count[i]`.
- `bbox` is already mapped to original resolution (bbox_upscale applied at dump time),
matching what the identity matcher would emit.
- `bbox` and `landmarks` share one coordinate space; `bbox_upscale` maps both to
original resolution (see above).
- A frame with no faces has `face_count == 0` (still gets a row, so timestamps stay dense).
- EOF sentinel frames are NOT written.
scripts/optimizer/fetch_missing_actors.py
+15 -2
View File
@@ -35,7 +35,9 @@ REPO = Path(__file__).resolve().parent.parent.parent
sys.path.insert(0, str(REPO / "scripts"))
import sae_env # noqa: E402 loads .env
from sae_tmdb import tmdb_get, tmdb_person_for_imdb, TMDB_IMG # noqa: E402
from sae_gallery import download_images, wikidata_image_urls # noqa: E402
from sae_embed_loader import resolve_arcface # noqa: E402
from sae_gallery import (download_images, embedder_stamp, # noqa: E402
enforce_embedder_stamp, wikidata_image_urls)
from sae_embed_loader import load_embedder # noqa: E402
@@ -57,6 +59,7 @@ def fetch(missing_path, out_path, token, build_dir, models_dir, arcface,
src = "TMDB + Wikidata fallback" if use_wikidata else "TMDB"
print(f"[fetch] {len(missing)} missing actors to resolve via {src}", file=sys.stderr)
embedder = load_embedder(build_dir, models_dir, arcface)
stamp = embedder_stamp(resolve_arcface(models_dir, arcface)) # TRACES: GR-004 | SR-001
img_root = Path(tempfile.mkdtemp(prefix="missing_gallery_"))
actors = []
@@ -103,7 +106,10 @@ def fetch(missing_path, out_path, token, build_dir, models_dir, arcface,
f"(wiki={n_via_wikidata}) no_tmdb={n_no_tmdb} no_img={n_no_img} "
f"no_face={n_no_face}", file=sys.stderr)
Path(out_path).write_text(json.dumps({"actors": actors}, indent=2))
# TRACES: GR-004 | SR-001
# the legacy JSON gallery carries the same stamp as
# the HDF5 one; src/gallery/gallery_store.cpp reads it from either.
Path(out_path).write_text(json.dumps({"embedder": stamp, "actors": actors}, indent=2))
n_emb = sum(len(a["embeddings"]) for a in actors)
print(f"\n[fetch] recovered {n_resolved}/{len(missing)} actors "
f"({n_via_wikidata} via Wikidata), {n_emb} embeddings → {out_path}",
@@ -115,6 +121,13 @@ def fetch(missing_path, out_path, token, build_dir, models_dir, arcface,
def merge(base_path, add_path, out_path):
base = json.loads(Path(base_path).read_text())
add = json.loads(Path(add_path).read_text())
# TRACES: GR-004 | SR-001
# merging two galleries from different models makes
# ONE file containing two incompatible embedding spaces. Nothing downstream can
# ever untangle that, so this is the one place the check must run before, not
# after, the write.
enforce_embedder_stamp(base.get("embedder"), add.get("embedder"),
str(base_path), str(add_path))
have = {a.get("imdb_id") for a in base["actors"] if a.get("imdb_id")}
added = [a for a in add["actors"] if a.get("imdb_id") not in have]
base["actors"].extend(added)
scripts/optimizer/optimize.py
+24
View File
@@ -34,6 +34,7 @@ from scipy.optimize import differential_evolution
REPO = Path(__file__).resolve().parent.parent.parent
sys.path.insert(0, str(REPO / "scripts" / "optimizer"))
sys.path.insert(0, str(REPO / "scripts" / "validation"))
sys.path.insert(0, str(REPO / "scripts"))
import json as _json
import os
@@ -56,6 +57,8 @@ DE_WORKERS = int(os.environ.get("DE_WORKERS", "1"))
from second_score import score_seconds # noqa: E402 uniform per-second TPI/FPI scoring
from sample_eval import load_gallery_keys # noqa: E402
from replay import dump_embedder_stamp # noqa: E402
from sae_stamp import EmbedderMismatch, verify_gallery_stamp # noqa: E402
_GAL_KEYS: dict = {} # gallery path → key set (fair-recall FN mask), loaded once
_REPLAY_TIMEOUT = 45 # seconds per film; a wedged replay is killed, not left to hang
@@ -180,7 +183,15 @@ def main():
p.add_argument("--seed", type=int, default=0)
p.add_argument("--trajectory", help="write every evaluation here (JSON lines)")
p.add_argument("--out", help="write best config + metrics")
# TRACES: GR-004 | SR-001
p.add_argument("--require-gallery-stamp", action="store_true",
help="unprovable gallery/dump model binding is a hard error, "
"not a warning (also via SAE_REQUIRE_GALLERY_STAMP=1)")
args = p.parse_args()
if args.require_gallery_stamp:
# Set the env var rather than threading a flag through cfg: replays run as
# subprocesses and inherit it, so strictness cannot be lost in the handoff.
os.environ["SAE_REQUIRE_GALLERY_STAMP"] = "1"
films = json.loads(Path(args.manifest).read_text())
for f in films:
@@ -188,6 +199,19 @@ def main():
if not Path(f["dump"]).exists():
sys.exit(f"[opt] missing dump for {f['name']}: {f['dump']}")
# TRACES: GR-004 | SR-001
# every (dump, gallery) pair is checked ONCE here,
# before the first evaluation. A DE sweep is thousands of replays; discovering
# a cross-model pair at the end (or never) means every number it produced was
# noise. Each replay subprocess re-checks its own pair anyway.
for f in films:
try:
verify_gallery_stamp(f["gallery"], stamp=dump_embedder_stamp(f["dump"]),
embedder_desc=f"embedding dump {Path(f['dump']).name}",
require_stamp=args.require_gallery_stamp)
except EmbedderMismatch as e:
sys.exit(f"[opt] {f['name']}: {e}")
names, bounds = [], []
int_knobs = {"track_max_frames_missing", "cut_inactive_max_frames"}
for spec in args.params:
scripts/optimizer/reembed_gallery.py
+10 -3
View File
@@ -27,8 +27,9 @@ from pathlib import Path
REPO = Path(__file__).resolve().parent.parent.parent
sys.path.insert(0, str(REPO / "scripts"))
from sae_embed_loader import load_embedder # noqa: E402
from sae_gallery import load_gallery_hdf5, save_gallery_hdf5 # noqa: E402
from sae_embed_loader import load_embedder, resolve_arcface # noqa: E402
from sae_gallery import (embedder_stamp, load_gallery_hdf5, # noqa: E402
save_gallery_hdf5)
def find_dir(images_root: Path, jellyfin_id: str, name: str) -> Path | None:
@@ -58,6 +59,12 @@ def main():
ref = load_gallery_hdf5(Path(args.ref))
images_root = Path(args.images)
embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
# TRACES: GR-004 | SR-001
# this script exists to produce a gallery in a
# DIFFERENT model's space from the reference. The output must therefore never
# inherit the reference's stamp; it carries the stamp of --arcface, which is
# the whole point of the bake-off being safe to run.
stamp = embedder_stamp(resolve_arcface(args.models_dir, args.arcface))
out_actors = []
n_ok = n_nodir = n_noemb = 0
@@ -84,7 +91,7 @@ def main():
print(f" [{i}/{total}] ok={n_ok} no_dir={n_nodir} no_emb={n_noemb}",
file=sys.stderr)
save_gallery_hdf5({"actors": out_actors}, Path(args.out))
save_gallery_hdf5({"actors": out_actors}, Path(args.out), stamp)
n_emb = sum(len(a["embeddings"]) for a in out_actors)
print(f"[reembed] {Path(args.arcface).stem}: {n_ok}/{total} actors, {n_emb} embeddings "
f"→ {args.out}", file=sys.stderr)
scripts/optimizer/replay.py
+37 -1
View File
@@ -2,6 +2,8 @@
"""
replay.py — replay a dumped embedding HDF5 through the real KPN downstream nodes.
TRACES: VR-002 | PR-002
Reads an embedding dump (scripts/optimizer/SCHEMA.md), feeds each frame as an
EmbeddedSceneFrame into a Python-assembled KPN network wiring the *real* C++
face_tracker → identity_matcher → scene_tracker, and returns the same presence-window
@@ -25,6 +27,22 @@ import h5py
import numpy as np
REPO = Path(__file__).resolve().parent.parent.parent
sys.path.insert(0, str(REPO / "scripts"))
from sae_stamp import verify_gallery_stamp # noqa: E402
def dump_embedder_stamp(dump_path: str) -> dict:
"""The GR-004 embedder stamp recorded in an embedding dump.
A replay has no live embedder — the dump IS the embedder as far as the gallery
is concerned, so the dump's stamp is what the gallery must be checked against.
Dumps written before GR-004 have no attributes and yield an empty stamp, which
the check reports as unverifiable rather than silently accepting."""
with h5py.File(dump_path, "r") as f:
name = f.attrs.get("embedder_model", "")
sha = f.attrs.get("embedder_sha256", "")
dec = lambda v: v.decode() if isinstance(v, bytes) else ("" if v is None else str(v))
return {"model_name": dec(name), "model_sha256": dec(sha), "embed_dim": 512}
def load_frames(dump_path: str, min_conf: float = 0.0):
@@ -92,6 +110,16 @@ def replay(dump_path: str, gallery: str, cfg: dict, build_dir: str, stop: bool =
sys.path.insert(0, build_dir)
import sae_kpn
# TRACES: GR-004 | SR-001
# checked here, before any network is built, so a
# cross-model replay dies with one readable error instead of producing a
# plausible-looking score. add_identity_matcher re-checks it C++-side below;
# that is the backstop for any other caller of the binding.
stamp = dump_embedder_stamp(dump_path)
verify_gallery_stamp(gallery, stamp=stamp,
embedder_desc=f"embedding dump {Path(dump_path).name}",
require_stamp=bool(cfg.get("require_gallery_stamp", False)))
frames, movie, fps = load_frames(dump_path, min_conf=float(cfg.get("detector_conf", 0.0)))
net = sae_kpn.Network()
@@ -122,7 +150,8 @@ def replay(dump_path: str, gallery: str, cfg: dict, build_dir: str, stop: bool =
cap = len(frames) * 2 + 64
sae_kpn.add_node_python(net, "replay", source, [], ["EmbeddedSceneFrame"], cap)
sae_kpn.add_face_tracker(net, "tracker", cfg, cap)
sae_kpn.add_identity_matcher(net, "matcher", gallery, cfg, cap)
sae_kpn.add_identity_matcher(net, "matcher", gallery, cfg, cap,
stamp["model_name"], stamp["model_sha256"])
sae_kpn.add_scene_tracker(net, "scene", cfg, cap)
net.connect("replay", 0, "tracker", 0)
net.connect("tracker", 0, "matcher", 0)
@@ -221,11 +250,18 @@ def main():
# per-film gallery expansion: promotes pose-varied views of confidently-identified
# actors into an in-memory annex, recovering ~+4 recall at no precision cost.
p.add_argument("--expand-gallery", action="store_true")
# TRACES: GR-004 | SR-001
# promote an unprovable gallery/dump binding from a
# loud warning to a hard error. Measurement sweeps should set this (or
# SAE_REQUIRE_GALLERY_STAMP=1) so no number comes from an unbound pair.
p.add_argument("--require-gallery-stamp", action="store_true")
args = p.parse_args()
cfg = {k: getattr(args, k) for k in CFG_KEYS if getattr(args, k) is not None}
if args.expand_gallery:
cfg["expand_gallery"] = True
if args.require_gallery_stamp:
cfg["require_gallery_stamp"] = True
# stop=True: PyNode::stop() sets stop_flag_ before joining, so the source
# thread's run_loop actually exits. stop=False skips that, leaving stop_flag_
# false forever — the PyNode destructor's jthread.join() then blocks forever
scripts/optimizer/second_score.py
+2
View File
@@ -2,6 +2,8 @@
"""
second_score.py — uniform per-second agreement with X-Ray.
TRACES: VR-003 | PR-002
Unlike scene_score.py (which unions our detections over a whole X-Ray scene), this
samples EVERY SECOND of the film and asks: at second t, do we name the same actors
X-Ray says are on screen?
scripts/run_from_jellyfin.py
+2
View File
@@ -1,6 +1,8 @@
#!/usr/bin/env python3
"""run_from_jellyfin.py — resolve a Jellyfin title to its media file and run scene_analyze.
TRACES: IR-006 | SR-001
Looks up a Movie/Episode in Jellyfin, reads its on-disk Path (Jellyfin and this
tool must share the same media mount), filters the gallery down to that
title's credited cast (via filter_gallery's logic, fewer look-alike
scripts/sae_embed_loader.py
+30 -4
View File
@@ -3,11 +3,29 @@
sae_embed.FaceEmbedder loads both ONNX sessions once and exposes an
embed(path) -> FaceResult method, avoiding the per-process model reload cost
of spawning the embed_faces CLI binary for every image.
resolve_arcface() exposes the same default-resolution logic load_embedder uses,
so a caller can stamp the gallery it is about to write with the model that
actually produced its embeddings (GR-004) — the resolved path, not the CLI
argument, which is often None.
"""
import sys
import os
from pathlib import Path
DEFAULT_ARCFACE = "arcface_w600k_r50.onnx"
def resolve_arcface(models_dir: str, arcface: str | None = None) -> str:
"""The ArcFace/LVFace ONNX path load_embedder would use for these arguments.
TRACES: GR-004 | SR-001
Single source of truth for "which model is this", so the stamp written into
a gallery can never drift from the model loaded."""
return arcface if arcface else str(Path(models_dir) / DEFAULT_ARCFACE)
def load_embedder(build_dir: str, models_dir: str, arcface: str | None = None,
conf: float = 0.5, nms: float = 0.4, max_side: int = 500):
@@ -28,19 +46,27 @@ def load_embedder(build_dir: str, models_dir: str, arcface: str | None = None,
models_path = Path(models_dir)
detector_path = str(models_path / "scrfd_500m_bnkps.onnx")
arcface_path = arcface if arcface else str(models_path / "arcface_w600k_r50.onnx")
arcface_path = resolve_arcface(models_dir, arcface)
for model, name in [(detector_path, "SCRFD"), (arcface_path, "ArcFace")]:
if not Path(model).is_file():
sys.exit(f"{name} model not found: {model}\nRun: bash scripts/download_models.sh")
# A TRT-backend build cannot load .onnx; it needs pre-built engines from
# scripts/build_trt_engines.sh. Pass them when present (ignored by ORT).
# scripts/build_trt_engines.sh.
#
# These are passed only on request. The old comment here claimed they were
# "ignored by ORT" — they are not. The ORT backend treats an engine path as
# an instruction and raises, which is the right behaviour (silently ignoring
# a requested engine would be worse), but it meant that merely HAVING a
# populated trt_cache/ broke every ORT gallery build in the repo, with an
# error naming a flag the caller never set.
use_engines = os.environ.get("SAE_USE_TRT_ENGINES", "") not in ("", "0", "false")
trt = Path(models_path).parent / "trt_cache"
det_engine = trt / "scrfd.scrfd_500m_bnkps.640.fp16.engine"
arc_engine = trt / f"arcface.{Path(arcface_path).stem}.b4.fp16.engine"
return sae_embed.FaceEmbedder(
detector_path, arcface_path, conf, nms, max_side,
str(det_engine) if det_engine.is_file() else "",
str(arc_engine) if arc_engine.is_file() else "",
str(det_engine) if (use_engines and det_engine.is_file()) else "",
str(arc_engine) if (use_engines and arc_engine.is_file()) else "",
)
scripts/sae_gallery.py
+62 -10
View File
@@ -6,9 +6,13 @@ make_jellyfin_gallery.download_urls + download_person_images) and the duplicated
Galleries are written directly as HDF5 — never JSON. Same layout the C++ side
reads/writes (src/gallery/gallery_store.cpp): flat [N,512] embeddings + per-actor
offset/count, parallel imdb_id/tmdb_id/jellyfin_id/name string arrays, and a
per-embedding-row source_images array. calibration is left absent (calib_hash=0);
the C++ identity_matcher fits and writes it back into the file on first use.
offset/count, parallel imdb_id/tmdb_id/jellyfin_id/name string arrays, a
per-embedding-row source_images array, and an /embedder group carrying the
GR-004 model binding. calibration is left absent (calib_hash=0); the C++
identity_matcher fits and writes it back into the file on first use.
The GR-004 embedder stamp written into that /embedder group lives in sae_stamp
and is re-exported below, so existing callers keep importing it from here.
"""
import io
@@ -101,11 +105,36 @@ def download_images(urls: list[str], dest_dir: Path, n: int,
return paths
def save_gallery_hdf5(gallery: dict, output: Path) -> None:
# ── GR-004: gallery ↔ embedder binding ───────────────────────────────────────
# Implemented in sae_stamp (kept dependency-light so the optimizer's replay
# subprocesses can import it without pulling requests/Pillow); re-exported here
# because the gallery writers and every existing caller reach for it via this
# module. See src/gallery/embedder_stamp.hpp for the C++ twin and the rationale.
from sae_stamp import ( # noqa: F401
EmbedderMismatch,
check_embedder_stamp,
describe_stamp,
embedder_stamp,
enforce_embedder_stamp,
read_gallery_stamp,
require_gallery_stamp_from_env,
sha256_file,
verify_gallery_stamp,
_as_str,
_stamp_empty,
)
def save_gallery_hdf5(gallery: dict, output: Path, embedder: dict | None = None) -> None:
"""Write a gallery dict ({"actors": [...]}) directly as HDF5 — same schema
src/gallery/gallery_store.cpp reads/writes. No calibration group; the
C++ identity_matcher computes and writes it back into this file on first
use against an unseen set of embeddings."""
use against an unseen set of embeddings.
`embedder` is the GR-004 stamp (see embedder_stamp()); it may also be carried
on the gallery dict under "embedder", which is how a filtered/derived gallery
keeps its binding without the caller having to re-hash anything."""
embedder = embedder if embedder is not None else gallery.get("embedder")
actors = gallery["actors"]
embs, offsets, counts = [], [], []
imdb, tmdb, jf, name, src_images = [], [], [], [], []
@@ -139,8 +168,18 @@ def save_gallery_hdf5(gallery: dict, output: Path) -> None:
f.create_dataset("jellyfin_id", data=np.asarray(jf, dtype=object), dtype=str_t)
f.create_dataset("name", data=np.asarray(name, dtype=object), dtype=str_t)
f.create_dataset("source_images", data=np.asarray(src_images, dtype=object), dtype=str_t)
print(f"Saved: {output} ({len(actors)} actors, {emb_arr.shape[0]} embeddings)",
file=sys.stderr)
# TRACES: GR-004 | SR-001
# omitted entirely when unknown, so "unstamped"
# round-trips as unstamped rather than as a stamp naming no model.
if not _stamp_empty(embedder):
g = f.create_group("embedder")
g.attrs["model_name"] = embedder.get("model_name", "")
g.attrs["model_sha256"] = embedder.get("model_sha256", "")
g.attrs["embed_dim"] = np.int32(embedder.get("embed_dim", 512))
stamp_note = (f", embedder {embedder['model_name']}" if not _stamp_empty(embedder)
else ", NO EMBEDDER STAMP (GR-004)")
print(f"Saved: {output} ({len(actors)} actors, {emb_arr.shape[0]} embeddings"
f"{stamp_note})", file=sys.stderr)
def load_gallery_hdf5(path: Path) -> dict:
@@ -158,6 +197,15 @@ def load_gallery_hdf5(path: Path) -> dict:
if "source_images" in f:
src_images = [s.decode() if isinstance(s, bytes) else s
for s in f["source_images"][:]]
# TRACES: GR-004 | SR-001
# carried through so a derived gallery (filter,
# merge, cast-restrict) keeps the binding of the gallery it came from.
stamp = None
if "embedder" in f:
a = f["embedder"].attrs
stamp = {"model_name": _as_str(a.get("model_name", "")),
"model_sha256": _as_str(a.get("model_sha256", "")),
"embed_dim": int(a.get("embed_dim", 512))}
actors = []
for a in range(len(offset)):
@@ -167,15 +215,19 @@ def load_gallery_hdf5(path: Path) -> dict:
if src_images is not None:
actor["source_images"] = [src_images[s + i] for i in range(n)]
actors.append(actor)
return {"actors": actors}
out = {"actors": actors}
if stamp is not None:
out["embedder"] = stamp
return out
def save_gallery(gallery: dict, missing: list[dict], output: Path) -> None:
def save_gallery(gallery: dict, missing: list[dict], output: Path,
embedder: dict | None = None) -> None:
"""Write the gallery as HDF5 (forcing a .h5 extension) and, if any actors
lack images, a .missing_images.json sidecar."""
if output.suffix not in (".h5", ".hdf5"):
output = output.with_suffix(".h5")
save_gallery_hdf5(gallery, output)
save_gallery_hdf5(gallery, output, embedder)
if missing:
missing_path = output.with_name(output.stem + ".missing_images.json")
scripts/sae_stamp.py
+217
View File
@@ -0,0 +1,217 @@
"""Gallery ↔ embedder model binding (GR-004).
TRACES: GR-004 | SR-001
Python twin of src/gallery/embedder_stamp.{hpp,cpp}; the two implement the same
comparison rules and must stay in agreement. Kept as its own module — rather than
folded into sae_gallery — because scripts/optimizer/replay.py imports it once per
replay subprocess, thousands of times in a DE sweep, and must not pay for
sae_gallery's requests/Pillow imports to ask "were these made by the same model?".
Dependencies here are hashlib, json and h5py, all of which a replay already loads.
A gallery is only valid for the embedder that built it: cosine similarities across
models are meaningless but look plausible, so the mistake is silent and every
measurement taken afterwards is suspect. Identity = model filename + SHA-256 of
the model file. The hash decides (a model re-exported in place keeps its name but
not its bytes); the name is what makes the error readable. See
src/gallery/embedder_stamp.hpp for the full rationale.
"""
import hashlib
import json
import os
import sys
from pathlib import Path
import h5py
def _as_str(v) -> str:
return v.decode() if isinstance(v, bytes) else ("" if v is None else str(v))
_STAMP_CACHE: dict = {}
class EmbedderMismatch(RuntimeError):
"""Gallery was built with a different embedder than the one about to be used."""
def sha256_file(path) -> str:
"""Lowercase hex SHA-256 of a file's bytes; "" if it cannot be read."""
path = Path(path)
try:
st = path.stat()
except OSError:
return ""
key = (str(path), st.st_mtime_ns, st.st_size)
if key in _STAMP_CACHE:
return _STAMP_CACHE[key]
h = hashlib.sha256()
try:
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(1 << 20), b""):
h.update(chunk)
except OSError:
return ""
_STAMP_CACHE[key] = h.hexdigest()
return _STAMP_CACHE[key]
def embedder_stamp(model_path, embed_dim: int = 512) -> dict:
"""Identify an embedder model file → {"model_name", "model_sha256", "embed_dim"}.
A model file that is absent (e.g. a TRT deployment running from a prebuilt
.engine) yields a name-only stamp: still comparable, just not provable."""
if not model_path:
return {"model_name": "", "model_sha256": "", "embed_dim": embed_dim}
sha = sha256_file(model_path)
if not sha:
print(f"[gallery] cannot hash embedder model {model_path} — model binding "
f"falls back to filename only (GR-004)", file=sys.stderr)
return {"model_name": Path(model_path).name, "model_sha256": sha,
"embed_dim": embed_dim}
def _stamp_empty(s) -> bool:
return not s or (not s.get("model_name") and not s.get("model_sha256"))
def describe_stamp(s) -> str:
if _stamp_empty(s):
return "UNKNOWN"
name = s.get("model_name") or "<unnamed model>"
sha = s.get("model_sha256") or ""
return f"{name} (sha256 {sha[:12]}…)" if sha else f"{name} (sha256 unavailable)"
def require_gallery_stamp_from_env() -> bool:
"""SAE_REQUIRE_GALLERY_STAMP=1 → an unprovable binding is fatal, not a warning."""
return os.environ.get("SAE_REQUIRE_GALLERY_STAMP", "0") not in ("", "0")
def check_embedder_stamp(built_with: dict | None, loading_with: dict | None,
gallery_desc: str = "gallery",
embedder_desc: str = "embedder") -> tuple[str, str]:
"""Pure comparison. Returns (verdict, message); verdict is one of
match / weak_match / unstamped / unknown_embedder / mismatch.
Same rules as compare_embedder_stamps() in src/gallery/embedder_stamp.cpp."""
if _stamp_empty(built_with):
return "unstamped", (
f"gallery '{gallery_desc}' carries no embedder stamp (GR-004).\n"
f" gallery was built with : UNKNOWN — this file predates model binding\n"
f" embedder now loaded : {describe_stamp(loading_with)} [{embedder_desc}]\n"
f" If these are not the same model every similarity from this run is\n"
f" meaningless but will look plausible. Rebuild or re-stamp the gallery\n"
f" (scripts/stamp_gallery.py), or run with SAE_REQUIRE_GALLERY_STAMP=1 to\n"
f" make this a hard error.")
if _stamp_empty(loading_with):
return "unknown_embedder", (
f"cannot identify the embedder being used against gallery "
f"'{gallery_desc}' (GR-004).\n"
f" gallery was built with : {describe_stamp(built_with)}\n"
f" embedder now loaded : UNKNOWN [{embedder_desc}]\n"
f" The binding cannot be checked, so it is not being checked.")
mismatch_tail = (
" Cosine similarities between embeddings from different models are\n"
" meaningless but look plausible. Rebuild the gallery with the loaded\n"
" model, or point the embedder at the model the gallery was built with.")
if int(built_with.get("embed_dim", 512)) != int(loading_with.get("embed_dim", 512)):
return "mismatch", (
f"gallery/embedder MODEL MISMATCH — refusing to run (GR-004).\n"
f" gallery was built with : {describe_stamp(built_with)}, "
f"dim={built_with.get('embed_dim')} [{gallery_desc}]\n"
f" embedder now loaded : {describe_stamp(loading_with)}, "
f"dim={loading_with.get('embed_dim')} [{embedder_desc}]\n"
f" Embedding dimensions differ; these are not the same space.")
a, b = built_with.get("model_sha256", ""), loading_with.get("model_sha256", "")
if a and b:
if a == b:
note = ""
if built_with.get("model_name") != loading_with.get("model_name"):
note = (f" (gallery recorded it as '{built_with.get('model_name')}', "
f"loaded from '{loading_with.get('model_name')}' — "
f"same bytes, renamed file)")
return "match", f"embedder binding verified: {describe_stamp(built_with)}{note}"
return "mismatch", (
f"gallery/embedder MODEL MISMATCH — refusing to run (GR-004).\n"
f" gallery was built with : {built_with.get('model_name')} sha256={a}\n"
f" [{gallery_desc}]\n"
f" embedder now loaded : {loading_with.get('model_name')} sha256={b}\n"
f" [{embedder_desc}]\n" + mismatch_tail)
if built_with.get("model_name") and \
built_with.get("model_name") == loading_with.get("model_name"):
return "weak_match", (
f"embedder binding UNPROVEN for gallery '{gallery_desc}' (GR-004).\n"
f" gallery was built with : {describe_stamp(built_with)}\n"
f" embedder now loaded : {describe_stamp(loading_with)} [{embedder_desc}]\n"
f" Filenames agree but at least one SHA-256 is unavailable, so an\n"
f" in-place re-export under the same name would not be detected.")
return "mismatch", (
f"gallery/embedder MODEL MISMATCH — refusing to run (GR-004).\n"
f" gallery was built with : {describe_stamp(built_with)} [{gallery_desc}]\n"
f" embedder now loaded : {describe_stamp(loading_with)} [{embedder_desc}]\n"
+ mismatch_tail)
def enforce_embedder_stamp(built_with, loading_with, gallery_desc, embedder_desc,
require_stamp: bool = False) -> str:
"""Apply check_embedder_stamp: raise EmbedderMismatch when fatal, else warn.
A mismatch is fatal unconditionally — there is no bypass, because a mismatch is
a known-wrong state, not an unknown one. The three "cannot prove it" verdicts
warn loudly and become fatal under require_stamp / SAE_REQUIRE_GALLERY_STAMP."""
strict = require_stamp or require_gallery_stamp_from_env()
verdict, msg = check_embedder_stamp(built_with, loading_with,
gallery_desc, embedder_desc)
if verdict == "mismatch":
raise EmbedderMismatch(msg)
if strict and verdict != "match":
raise EmbedderMismatch(
msg + "\n (fatal because SAE_REQUIRE_GALLERY_STAMP is set)")
if verdict == "match":
print(f"[gallery] {msg}", file=sys.stderr)
else:
print(f"\n[gallery] ***** WARNING (GR-004) *****\n{msg}\n"
f"[gallery] ****************************\n", file=sys.stderr)
return verdict
def read_gallery_stamp(path) -> dict | None:
"""The embedder stamp recorded in a gallery file, or None if unstamped.
Handles both the HDF5 /embedder group and the legacy JSON "embedder" object."""
path = Path(path)
if path.suffix in (".h5", ".hdf5"):
with h5py.File(path, "r") as f:
if "embedder" not in f:
return None
a = f["embedder"].attrs
return {"model_name": _as_str(a.get("model_name", "")),
"model_sha256": _as_str(a.get("model_sha256", "")),
"embed_dim": int(a.get("embed_dim", 512))}
data = json.loads(path.read_text())
return data.get("embedder") or None
def verify_gallery_stamp(gallery_path, model_path=None, *, stamp=None,
embedder_desc: str | None = None,
require_stamp: bool = False) -> str:
"""Load a gallery's stamp and check it against a model file (or an explicit
stamp, e.g. one read off an embedding dump). Raises EmbedderMismatch."""
loading = stamp if stamp is not None else embedder_stamp(model_path)
return enforce_embedder_stamp(read_gallery_stamp(gallery_path), loading,
str(gallery_path),
embedder_desc or str(model_path or "unknown"),
require_stamp)
def _as_str(v) -> str:
return v.decode() if isinstance(v, bytes) else ("" if v is None else str(v))
scripts/stamp_gallery.py
+86
View File
@@ -0,0 +1,86 @@
#!/usr/bin/env python3
"""stamp_gallery.py — bind an existing gallery to the embedder that built it.
TRACES: GR-004 | SR-001
Galleries built before model binding carry no embedder stamp. They still load,
but every consumer warns that it cannot tell whether the gallery and the embedder
belong together — and under SAE_REQUIRE_GALLERY_STAMP=1 they refuse to run.
This is the migration path, and the reason the unstamped case is a warning rather
than a hard failure: re-binding an existing gallery costs one command and no
re-embedding, so nobody has to choose between a bricked setup and a check they
route around.
python scripts/stamp_gallery.py --gallery gallery.h5 \\
--arcface models/LVFace-B_Glint360K.onnx
The stamp is an ASSERTION: you are stating which model produced these vectors.
Nothing can verify it from the vectors themselves, which is exactly why the stamp
has to be written at build time going forward. Stamping the wrong model is worse
than leaving it unstamped, because it converts a loud warning into a false
all-clear — so --show it first if you are not certain.
python scripts/stamp_gallery.py --gallery gallery.h5 --show
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
import h5py
sys.path.insert(0, str(Path(__file__).resolve().parent))
from sae_gallery import (describe_stamp, embedder_stamp, # noqa: E402
read_gallery_stamp)
def main() -> int:
p = argparse.ArgumentParser(description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter)
p.add_argument("--gallery", required=True, help="gallery .h5 to stamp in place")
p.add_argument("--arcface", help="the ONNX that built it (hashed into the stamp)")
p.add_argument("--show", action="store_true", help="print the current stamp and exit")
p.add_argument("--force", action="store_true",
help="overwrite an existing stamp (refused otherwise)")
args = p.parse_args()
path = Path(args.gallery)
if path.suffix not in (".h5", ".hdf5"):
return err(f"{path}: only HDF5 galleries can be stamped in place")
current = read_gallery_stamp(path)
print(f"{path}: current stamp = {describe_stamp(current)}", file=sys.stderr)
if args.show:
return 0
if not args.arcface:
return err("--arcface is required (or use --show)")
if current and not args.force:
return err("gallery is already stamped — pass --force to overwrite, but be "
"sure: a wrong stamp turns a warning into a false all-clear")
stamp = embedder_stamp(args.arcface)
if not stamp["model_sha256"]:
return err(f"cannot hash {args.arcface} — refusing to write a name-only "
"stamp, which would claim more certainty than it has")
with h5py.File(path, "r+") as f:
if "embedder" in f:
del f["embedder"]
g = f.create_group("embedder")
g.attrs["model_name"] = stamp["model_name"]
g.attrs["model_sha256"] = stamp["model_sha256"]
g.attrs["embed_dim"] = stamp["embed_dim"]
print(f"{path}: stamped with {describe_stamp(stamp)}", file=sys.stderr)
return 0
def err(msg: str) -> int:
print(f"[stamp_gallery] {msg}", file=sys.stderr)
return 1
if __name__ == "__main__":
raise SystemExit(main())
scripts/validation/ground_truth.py
+5
View File
@@ -17,6 +17,11 @@ X-Ray/MovieNet key on IMDb nm-ids — see [[per-scene-presence-eval-design]].
Two sources implemented:
* XRayGroundTruth — Zenodo scene-level Amazon X-Ray CSVs (cast-in-scene).
* MovieNetGroundTruth — MovieNet-PS per-shot face annotations (on-screen faces).
Both are published corpora addressed by title, so a scoring run is reproducible
from the identifiers alone — no annotation of ours travels with the code.
TRACES: VR-004 | PR-002
"""
from __future__ import annotations
scripts/validation/test_audio_offset.py
+370
View File
@@ -0,0 +1,370 @@
#!/usr/bin/env python3
"""
VR-014 — the v1 audio signature recovers a known trim offset on real audio.
TRACES: UT-105, UT-106, UT-107, UT-108 | VR-014 | IR-004
python scripts/validation/test_audio_offset.py [build_dir]
The golden vector (IR-005) proves the *arithmetic* is identical in both
producers. It cannot prove the thing the signature exists for: that when the
same cut arrives trimmed differently, sliding one signature against the other
finds the true alignment and only the true alignment. Its fixture is a synthetic
tone sweep, which is pathologically easy to align; film dialogue and score are
not, and that is what this measures.
The signature is computed by the **shipped C++**, through the `sae_audio`
nanobind module — never a numpy port. A third implementation of a fingerprint
whose whole value rests on three implementations agreeing byte for byte would be
the one nobody checks against the golden vector.
The slide *is* written here in numpy, deliberately: matching is the consumer's
algorithm (server SPEC.md section 3), owned by the server and the jRay plugin,
not by this repo. Writing it out is what makes this a test of the signature
rather than a test of somebody's matcher.
Two independent offset mechanisms are checked, because they can fail
separately:
* a **window offset** (UT-105) — two 120 s excerpts taken from different
points, which is the alignment search itself; and
* a **head trim** (UT-106) — a real file with delta seconds removed from the
front, which additionally exercises the runtime/2 anchor: the window follows
the midpoint, so cutting delta from the head moves it by delta/2, not delta.
That factor of two is the easiest thing in the whole feature to get wrong
and nothing else checks it.
"""
import base64
import random
import shutil
import subprocess
import sys
import tempfile
from pathlib import Path
import numpy as np
REPO = Path(__file__).resolve().parent.parent.parent
BUILD = Path(sys.argv[1]) if len(sys.argv) > 1 else REPO / "build"
sys.path.insert(0, str(BUILD))
import sae_audio # noqa: E402
FIXTURE = REPO / "tests" / "fixtures" / "audio" / "superhero_offset_200s.flac"
TONE = REPO / "tests" / "fixtures" / "audio" / "jray_audio_v1_tone.flac"
# Server SPEC.md section 3, "Matching and offset recovery". The cap is the
# spec's, not a convenience: +/-600 frames is ~56 s, which covers realistic trim
# differences, and an offset outside it must be declined rather than guessed at.
SEARCH_CAP_FRAMES = 600
AUDIO_TIER = 0.85
LOOSE_TIER = 0.60
TRIALS = 40
SEED = 20250731
HOP_SEC = sae_audio.hop_size / sae_audio.sample_rate
# What the offset is actually *for*: shifting scene windows, which are seconds
# long. Half a second of error is invisible against them, and that budget is
# what makes the numbers below readable — an offset is quantised to whole
# frames, so no correct answer can be worse than half a frame (46 ms) and the
# feature has an order of magnitude in hand before anything is at stake.
OFFSET_BUDGET_SEC = 0.5
def peak_bins(signature):
"""The per-frame peak band index, which is what the slide compares.
The `v1:` prefix is checked against the constant the C++ exports rather
than a literal, so a producer bump cannot be silently parsed as v1 here
(IR-008).
"""
prefix = sae_audio.version_prefix
if not signature.startswith(prefix):
raise AssertionError(f"signature is not {prefix!r}: {signature[:8]!r}")
packed = np.frombuffer(base64.b64decode(signature[len(prefix):]), dtype=np.uint8)
if np.any(packed & 0x80):
raise AssertionError("reserved bit set — not a structurally valid signature")
return packed >> 2
def best_match(reference, query, cap=SEARCH_CAP_FRAMES, slack=0):
"""Slide `query` against `reference`; return (score, offset_frames).
`offset` is how many frames later the query's window begins, so
``query[i]`` lines up with ``reference[i + offset]``. Score is the fraction
of overlapping frames whose peak bin agrees, exactly as the spec defines it.
`slack` widens what counts as agreement to a frame within +/-slack, which is
not the spec's rule — it is the candidate remedy UT-108 measures. It changes
the *score* only; the offset it reports is still a whole-frame alignment.
"""
best_score, best_offset = -1.0, 0
for offset in range(-cap, cap + 1):
if offset >= 0:
a, b = reference[offset:], query[: len(query) - offset]
else:
a, b = reference[: len(reference) + offset], query[-offset:]
n = min(len(a), len(b))
if n < 100: # too little overlap to mean anything
continue
a, b = a[:n], b[:n]
if slack == 0:
agree = a == b
else:
agree = np.zeros(n, dtype=bool)
for shift in range(-slack, slack + 1):
shifted = np.roll(a, shift)
# 255 is not a band index, so the wrapped end can never agree.
if shift > 0:
shifted[:shift] = 255
elif shift < 0:
shifted[shift:] = 255
agree |= shifted == b
score = float(np.mean(agree))
if score > best_score:
best_score, best_offset = score, offset
return best_score, best_offset
def decode_mono(path):
"""The whole fixture as float32 mono at 11025 Hz — the signature's own rate."""
raw = subprocess.run(
["ffmpeg", "-nostdin", "-v", "error", "-i", str(path),
"-ac", "1", "-ar", str(sae_audio.sample_rate), "-f", "f32le", "-"],
capture_output=True, check=True).stdout
return np.frombuffer(raw, dtype="<f4")
def trim_head(source, seconds, out):
"""`source` with `seconds` removed from the front — a differently trimmed release."""
subprocess.run(
["ffmpeg", "-nostdin", "-v", "error", "-y", "-ss", f"{seconds:.3f}", "-i", str(source),
"-ac", "1", "-ar", str(sae_audio.sample_rate), "-sample_fmt", "s16",
"-c:a", "flac", str(out)], check=True)
return out
def tier(score):
if score >= AUDIO_TIER:
return "audio"
return "loose" if score >= LOOSE_TIER else "none"
# ── The random trial set, signed once and reused ─────────────────────────────
def random_trials(pcm):
"""(reference bins, [(expected_frames, query bins)]) for TRIALS excerpts.
Signing 40 windows is the expensive part of this file, so UT-105 and UT-108
share one set — they ask different questions of the same measurements.
"""
window = sae_audio.window_samples
reference = peak_bins(sae_audio.signature_from_mono(pcm[:window]))
rng = random.Random(SEED)
queries = []
for _ in range(TRIALS):
# Within the search cap: past it, no offset is recoverable by
# construction, which UT-107 checks separately.
start = rng.randrange(0, SEARCH_CAP_FRAMES * sae_audio.hop_size)
signature = sae_audio.signature_from_mono(pcm[start:start + window])
assert signature is not None, "a full window must always sign"
queries.append((start / sae_audio.hop_size, peak_bins(signature)))
return reference, queries
# ── UT-105 — window offsets from random excerpt starts ───────────────────────
def test_random_window_offsets(reference, queries):
"""Every in-cap offset is recovered to the nearest frame, on real audio."""
rows = []
for want, query in queries:
score, offset = best_match(reference, query)
rows.append((want, offset, score, abs(want - round(want))))
expected = np.array([r[0] for r in rows])
offset = np.array([r[1] for r in rows])
score = np.array([r[2] for r in rows])
subframe = np.array([r[3] for r in rows])
error = np.abs(offset - expected)
# The offset is quantised to whole frames, so the best any correct answer
# can do is half a frame — 46 ms. What matters is the budget that half-frame
# is measured against, and it is an order of magnitude away from it.
assert error.max() <= 1.0, f"offset missed by {error.max():.2f} frames"
assert error.max() * HOP_SEC <= OFFSET_BUDGET_SEC, (
f"offset error {error.max() * HOP_SEC:.3f}s exceeds the {OFFSET_BUDGET_SEC}s budget")
# Never mistaken for different content. This is the floor that matters: the
# audio genuinely is the same cut, so a "no match" would be a false negative
# on the case the feature exists for.
assert score.min() >= LOOSE_TIER, f"same content scored {score.min():.3f}"
# An offset that lands near a frame boundary has no excuse: it should reach
# the top tier, and does.
aligned = subframe <= 0.1
assert aligned.any(), "seed no longer produces a near-aligned trial"
assert score[aligned].min() >= AUDIO_TIER, (
f"near-frame-aligned offset scored only {score[aligned].min():.3f}")
print(f"UT-105 {TRIALS} random window offsets, all within the +/-600 frame cap")
print(f" offset error : max {error.max():.2f} frames"
f" = {error.max() * HOP_SEC * 1000:.0f} ms, against a"
f" {OFFSET_BUDGET_SEC * 1000:.0f} ms budget")
print(f" score : min {score.min():.3f} median {np.median(score):.3f}"
f" max {score.max():.3f}")
print(" score by sub-frame misalignment — the offset is exact in every row:")
for lo, hi in ((0.0, 0.1), (0.1, 0.2), (0.2, 0.3), (0.3, 0.4), (0.4, 0.5)):
m = (subframe >= lo) & (subframe < hi)
if m.any():
print(f" {lo:.1f}-{hi:.1f} frame n={m.sum():2d}"
f" score {score[m].min():.3f}-{score[m].max():.3f}"
f" tier {tier(np.median(score[m]))}")
counts = {t: int(sum(1 for s in score if tier(s) == t)) for t in ("audio", "loose", "none")}
print(f" tiers : {counts}")
return counts
# ── UT-106 — head trims through real files, including the runtime/2 anchor ───
def test_head_trims():
"""A release with delta seconds of head removed aligns at delta/2 frames."""
reference = peak_bins(sae_audio.compute_signature(str(FIXTURE)))
results = []
with tempfile.TemporaryDirectory() as tmp:
for delta in (7.0, 23.5, 41.25, 60.0):
trimmed = trim_head(FIXTURE, delta, Path(tmp) / f"trim_{delta}.flac")
signature = sae_audio.compute_signature(str(trimmed))
assert signature is not None, f"trim of {delta}s should still sign"
score, offset = best_match(reference, peak_bins(signature))
# The window follows the midpoint, so removing delta from the head
# moves it by delta/2 — not by delta.
expected = (delta / 2.0) / HOP_SEC
assert abs(offset - expected) <= 1.0, (
f"head trim {delta}s: expected ~{expected:.1f} frames, got {offset}")
assert score >= LOOSE_TIER, f"head trim {delta}s scored {score:.3f}"
results.append((delta, expected, offset, score))
print("UT-106 head trims through the real decode path (compute_signature on a file)")
for delta, expected, offset, score in results:
print(f" -{delta:6.2f}s head expected {expected:7.2f} fr"
f" recovered {offset:5d} score {score:.3f} ({tier(score)})")
# ── UT-107 — what must NOT match ─────────────────────────────────────────────
def test_declines(pcm, reference):
"""Out-of-cap offsets and unrelated content are declined, not guessed at."""
window = sae_audio.window_samples
beyond = int(75.0 * sae_audio.sample_rate) # ~807 frames, past the cap
assert beyond + window <= len(pcm), "fixture too short for the out-of-cap case"
far = peak_bins(sae_audio.signature_from_mono(pcm[beyond:beyond + window]))
score_beyond, offset_beyond = best_match(reference, far)
assert score_beyond < LOOSE_TIER, (
f"an offset past the cap scored {score_beyond:.3f} at {offset_beyond} — "
"the search invented an alignment rather than declining")
tone = peak_bins(sae_audio.compute_signature(str(TONE)))
score_tone, offset_tone = best_match(reference, tone)
assert score_tone < LOOSE_TIER, f"unrelated content scored {score_tone:.3f}"
print("UT-107 declines rather than guesses")
print(f" offset past the +/-600 frame cap : best {score_beyond:.3f}"
f" at {offset_beyond} ({tier(score_beyond)})")
print(f" unrelated content (tone fixture) : best {score_tone:.3f}"
f" at {offset_tone} ({tier(score_tone)})")
return far, tone
# ── UT-108 — the sub-frame demotion, and what one frame of slack costs ───────
def test_scoring_slack(reference, queries, far, tone):
"""Measured: +/-1 frame of slack in the *score* restores the `audio` tier.
UT-105 leaves a real question open. Every offset is right, but two thirds of
them score below the server's 0.85 `audio` threshold purely because the two
windows' frame grids do not coincide — so a correctly aligned release is
demoted to `loose`, which is the tier meaning "possibly the same cut,
degraded audio". The obvious remedy is to stop demanding that frames line up
exactly, and the question is what that costs in discrimination.
Nothing is asserted about the spec's own rule here; this measures a
candidate change to it, which is the server's to make (SPEC.md section 3).
"""
print("UT-108 cost of relaxing the score's frame alignment")
print(f" {'slack':>5} {'audio':>6} {'loose':>6} {'none':>5}"
f" {'min true':>9} {'worst err':>10} {'unrelated':>10} {'out-of-cap':>11}")
measured = {}
for slack in (0, 1, 2):
score, error = [], []
for want, query in queries:
s, offset = best_match(reference, query, slack=slack)
score.append(s)
error.append(abs(offset - want))
score, error = np.array(score), np.array(error)
false_tone, _ = best_match(reference, tone, slack=slack)
false_far, _ = best_match(reference, far, slack=slack)
counts = {t: int(sum(1 for s in score if tier(s) == t)) for t in ("audio", "loose", "none")}
measured[slack] = (score, error, max(false_tone, false_far))
print(f" {slack:>5} {counts['audio']:>6} {counts['loose']:>6} {counts['none']:>5}"
f" {score.min():>9.3f} {error.max() * HOP_SEC * 1000:>7.0f} ms"
f" {false_tone:>10.3f} {false_far:>11.3f}")
score, error, worst_false = measured[1]
# One frame of slack lifts every correct alignment to the top tier...
assert score.min() >= AUDIO_TIER, (
f"one frame of slack still leaves a true match at {score.min():.3f}")
# ...without narrowing the gap that makes the threshold mean anything...
assert worst_false < LOOSE_TIER, (
f"slack lifted a false match to {worst_false:.3f}")
# ...and the offset it costs is still far inside the budget: the score's
# peak flattens slightly, so the argmax can pick an adjacent frame.
assert error.max() * HOP_SEC <= OFFSET_BUDGET_SEC, (
f"slack cost {error.max() * HOP_SEC:.3f}s of offset accuracy")
print(f" +/-1 frame: every true match reaches `audio` (min {score.min():.3f}),"
f" worst false stays at {worst_false:.3f},")
print(f" and the offset costs {error.max() * HOP_SEC * 1000:.0f} ms of a"
f" {OFFSET_BUDGET_SEC * 1000:.0f} ms budget. +/-2 buys nothing more.")
def main():
if not FIXTURE.exists():
print(f"missing fixture {FIXTURE} — regenerate with make_offset_fixture.sh", file=sys.stderr)
return 2
if shutil.which("ffmpeg") is None:
print("this validation needs the ffmpeg CLI to trim the fixture", file=sys.stderr)
return 2
print(f"VR-014 audio-signature offset recovery on {FIXTURE.name}")
print(f" {sae_audio.expected_frames} frames per signature,"
f" {HOP_SEC * 1000:.2f} ms per frame, cap +/-{SEARCH_CAP_FRAMES} frames")
pcm = decode_mono(FIXTURE)
reference, queries = random_trials(pcm)
counts = test_random_window_offsets(reference, queries)
test_head_trims()
far, tone = test_declines(pcm, reference)
test_scoring_slack(reference, queries, far, tone)
print()
print(f"PASS — every in-cap offset recovered to the nearest frame, worst"
f" {1000 * HOP_SEC / 2:.0f} ms against a {OFFSET_BUDGET_SEC * 1000:.0f} ms budget.")
if counts["audio"] < TRIALS:
# Stated rather than asserted against the spec's rule: the offset is
# right in every case, so this is the 0.85 threshold meeting a sub-frame
# shift, not a defect in the signature. The threshold was calibrated on
# a re-encode at zero offset, where the score is 1.00. UT-108 measures
# the remedy; adopting it is the server spec's call, not this repo's.
print(f"NOTE — under the spec's exact-frame score only {counts['audio']}/{TRIALS}"
f" reach `audio`; {counts['loose']} are demoted to `loose` by sub-frame"
" shift alone. See UT-108.")
return 0
if __name__ == "__main__":
sys.exit(main())
scripts/vendor/jray-project
Vendored Submodule
+1
Submodule scripts/vendor/jray-project added at 17106f3370
src/audio_bindings.cpp
+125
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@@ -0,0 +1,125 @@
// sae_audio — Python module wrapping the v1 audio signature (audio_signature.*).
//
/// TRACES: IR-004, IR-005 | SR-003
//
// Exists so a study or a test can drive the **shipped** signature code from
// Python instead of porting the DSP to numpy. A numpy port would be a third
// implementation of a fingerprint that only works if every implementation
// agrees byte for byte, and it would be the one nobody checks against the
// golden vector — so the offset-recovery validation (VR-014) calls this.
//
// Bound with nanobind, as `sae_embed` and `sae_kpn` are. Not pybind11: a second
// binding framework in one build is a second set of ABI and lifetime rules to
// get right, for a module that needs nothing nanobind lacks.
//
// The module deliberately stops at the producer's edge. Matching — sliding one
// signature against another and scoring the overlap — is the *consumer's*
// algorithm (server SPEC §3, and the jRay plugin implements it), so it is not
// bound here and a caller writing a slide in numpy is not re-implementing
// anything this repo owns.
#include "audio_signature.hpp"
#include <nanobind/nanobind.h>
#include <nanobind/ndarray.h>
#include <nanobind/stl/optional.h>
#include <nanobind/stl/pair.h>
#include <nanobind/stl/string.h>
#include <nanobind/stl/vector.h>
#include <cstdint>
#include <optional>
#include <string>
#include <utility>
#include <vector>
namespace nb = nanobind;
using namespace nb::literals;
using namespace sae::audio;
namespace {
using MonoArray = nb::ndarray<const float, nb::ndim<1>, nb::c_contig, nb::device::cpu>;
// Hand the vector's buffer to Python without copying 1.3 M samples, and let a
// capsule own it: the array outlives this call, so the storage has to as well.
nb::object own_as_ndarray(std::vector<float>&& samples) {
auto* held = new std::vector<float>(std::move(samples));
nb::capsule owner(held, [](void* p) noexcept {
delete static_cast<std::vector<float>*>(p);
});
const std::size_t n = held->size();
return nb::cast(nb::ndarray<nb::numpy, float, nb::ndim<1>>(held->data(), {n}, owner));
}
std::vector<float> to_vector(const MonoArray& a) {
return std::vector<float>(a.data(), a.data() + a.shape(0));
}
} // namespace
NB_MODULE(sae_audio, m) {
m.doc() =
"JRay v1 audio signature (JRay-public-server SPEC.md section 3), as the "
"extraction pipeline computes it. The constants below are the contract: "
"changing any of them is a v1 -> v2 change.";
m.attr("sample_rate") = kSampleRate;
m.attr("frame_size") = kFrameSize;
m.attr("hop_size") = kHopSize;
m.attr("num_bands") = kNumBands;
m.attr("band_lo_hz") = kBandLoHz;
m.attr("band_hi_hz") = kBandHiHz;
m.attr("window_sec") = kWindowSec;
m.attr("window_samples") = kWindowSamples;
m.attr("expected_frames") = kExpectedFrames;
m.attr("version_prefix") = std::string(kVersionPrefix);
m.def(
"compute_signature",
[](const std::string& path) { return compute_signature(path); },
"path"_a,
"Signature of the 120 s window centred on the media's midpoint, or None "
"for media shorter than the window (IR-007), media with no audio "
"stream, and any decode failure — degradation, never an exception.");
m.def(
"decode_centre_window",
[](const std::string& path) -> nb::object {
std::optional<std::vector<float>> mono = decode_centre_window(path);
if (!mono) {
return nb::none();
}
return own_as_ndarray(std::move(*mono));
},
"path"_a,
"The decoded centre window as float32 mono at 11025 Hz, or None. Exposed "
"so a caller can slice or perturb real audio and re-sign it without "
"going back through a container.");
m.def(
"signature_from_mono",
[](const MonoArray& mono) { return signature_from_mono(to_vector(mono)); },
"mono"_a,
"Signature of mono float32 samples already at 11025 Hz, in [-1, 1). None "
"when fewer than one whole frame is given.");
m.def(
"pack_frames",
[](const MonoArray& mono) {
std::vector<std::uint8_t> packed = pack_frames(to_vector(mono));
return nb::bytes(reinterpret_cast<const char*>(packed.data()), packed.size());
},
"mono"_a,
"One packed byte per whole STFT frame: (band << 2) | energy_class. This "
"is the payload the signature base64-encodes.");
m.def(
"band_fft_bins",
[] {
const auto& table = band_fft_bins();
return std::vector<std::pair<int, int>>(table.begin(), table.end());
},
"The half-open FFT bin range owned by each of the 32 log-spaced bands.");
}
src/audio_signature.cpp
+428
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@@ -0,0 +1,428 @@
// ── JRay audio signature, v1 — implementation ────────────────────────────────
//
/// TRACES: IR-004, IR-007, IR-008 | SR-003
//
// The contract this implements is documented in full in audio_signature.hpp;
// read that before changing anything here. Every constant is load-bearing: the
// JRay Jellyfin plugin computes the same bytes in C#, and a signature that
// differs in any parameter simply does not match.
//
// Audio decode is a *second stream from an existing dependency* — the pipeline
// already links libavformat/libavcodec/libavutil for video (ffmpeg_decoder.hpp);
// this adds libswresample for the downmix+resample, no new project dependency.
// The FFT is written out here rather than pulled from a library for the same
// reason the plugin vendors one: it is a fixed, fully specified transform, and
// a dependency whose version could change the numerics is a liability when the
// output has to be bit-identical across two languages.
#include "audio_signature.hpp"
extern "C" {
#include <libavcodec/avcodec.h>
#include <libavformat/avformat.h>
#include <libavutil/avutil.h>
#include <libavutil/channel_layout.h>
#include <libavutil/opt.h>
#include <libavutil/samplefmt.h>
#include <libswresample/swresample.h>
}
#include <algorithm>
#include <cmath>
#include <cstring>
#include <vector>
namespace sae::audio {
namespace {
constexpr double kPi = 3.14159265358979323846;
// ── Band table ───────────────────────────────────────────────────────────────
// edge[b] = 300 * 10^(b/32); band b owns FFT bins [k_lo[b], k_lo[b+1]).
// ceil() of the edge in bins, so membership is decided once by integers rather
// than by a float comparison per bin per frame. The bands tile [112, 1115)
// contiguously with no gap and no overlap, which is what lets the frame energy
// below be accumulated from the per-band sums.
std::array<std::pair<int, int>, kNumBands> build_band_table() {
const double hz_per_bin = static_cast<double>(kSampleRate) / kFrameSize;
std::array<int, kNumBands + 1> k{};
for (int b = 0; b <= kNumBands; ++b) {
const double edge = kBandLoHz * std::pow(kBandHiHz / kBandLoHz,
static_cast<double>(b) / kNumBands);
k[b] = static_cast<int>(std::ceil(edge / hz_per_bin));
}
std::array<std::pair<int, int>, kNumBands> tbl{};
for (int b = 0; b < kNumBands; ++b) tbl[b] = {k[b], k[b + 1]};
return tbl;
}
// Hann, periodic: w[n] = 0.5 * (1 - cos(2*pi*n/N)). Not the symmetric (N-1)
// variant — the two differ, and the difference is observable.
const std::vector<double>& hann_window() {
static const std::vector<double> w = [] {
std::vector<double> v(kFrameSize);
for (int n = 0; n < kFrameSize; ++n)
v[n] = 0.5 * (1.0 - std::cos(2.0 * kPi * n / kFrameSize));
return v;
}();
return w;
}
// ── Radix-2 decimation-in-time complex FFT, in place, no normalisation ──────
// Twiddles are precomputed per stage from cos/sin of -2*pi*j/len so the angle
// is an exactly reproducible double in any language and only the libm rounding
// of cos/sin (≤1 ulp) can differ — orders of magnitude below the decision
// margins in the golden fixture.
struct FftTables {
std::vector<int> rev; // bit-reversal permutation
std::vector<std::vector<double>> wr, wi; // per stage
};
const FftTables& fft_tables() {
static const FftTables t = [] {
FftTables f;
f.rev.resize(kFrameSize);
int bits = 0;
while ((1 << bits) < kFrameSize) ++bits;
for (int i = 0; i < kFrameSize; ++i) {
int r = 0;
for (int b = 0; b < bits; ++b)
if (i & (1 << b)) r |= 1 << (bits - 1 - b);
f.rev[i] = r;
}
for (int len = 2; len <= kFrameSize; len <<= 1) {
const int half = len / 2;
std::vector<double> cr(half), ci(half);
for (int j = 0; j < half; ++j) {
const double ang = -2.0 * kPi * j / len;
cr[j] = std::cos(ang);
ci[j] = std::sin(ang);
}
f.wr.push_back(std::move(cr));
f.wi.push_back(std::move(ci));
}
return f;
}();
return t;
}
void fft_4096(std::vector<double>& re, std::vector<double>& im) {
const FftTables& t = fft_tables();
for (int i = 0; i < kFrameSize; ++i) {
const int j = t.rev[i];
if (i < j) { std::swap(re[i], re[j]); std::swap(im[i], im[j]); }
}
int stage = 0;
for (int len = 2; len <= kFrameSize; len <<= 1, ++stage) {
const int half = len / 2;
const std::vector<double>& wr = t.wr[stage];
const std::vector<double>& wi = t.wi[stage];
for (int base = 0; base < kFrameSize; base += len) {
for (int j = 0; j < half; ++j) {
const int a = base + j;
const int b = a + half;
const double tr = re[b] * wr[j] - im[b] * wi[j];
const double ti = re[b] * wi[j] + im[b] * wr[j];
re[b] = re[a] - tr; im[b] = im[a] - ti;
re[a] = re[a] + tr; im[a] = im[a] + ti;
}
}
}
}
int energy_class(double r) {
if (r < kEnergyClassEdges[0]) return 0;
if (r < kEnergyClassEdges[1]) return 1;
if (r < kEnergyClassEdges[2]) return 2;
return 3;
}
// ── FFmpeg RAII ─────────────────────────────────────────────────────────────
struct DecodeCtx {
AVFormatContext* fmt = nullptr;
AVCodecContext* dec = nullptr;
SwrContext* swr = nullptr;
AVFrame* frm = nullptr;
AVPacket* pkt = nullptr;
~DecodeCtx() {
if (swr) swr_free(&swr);
if (frm) av_frame_free(&frm);
if (pkt) av_packet_free(&pkt);
if (dec) avcodec_free_context(&dec);
if (fmt) avformat_close_input(&fmt);
}
};
bool open_resampler(DecodeCtx& c, const AVFrame* f) {
#if LIBAVUTIL_VERSION_INT >= AV_VERSION_INT(57, 24, 100)
AVChannelLayout out_layout;
av_channel_layout_default(&out_layout, 1); // mono
AVChannelLayout in_layout;
if (av_channel_layout_copy(&in_layout, &f->ch_layout) < 0) return false;
if (in_layout.nb_channels <= 0) {
av_channel_layout_uninit(&in_layout);
av_channel_layout_default(&in_layout, 1);
}
const int rc = swr_alloc_set_opts2(
&c.swr,
&out_layout, AV_SAMPLE_FMT_FLT, kSampleRate,
&in_layout, static_cast<AVSampleFormat>(f->format),
f->sample_rate ? f->sample_rate : kSampleRate,
0, nullptr);
av_channel_layout_uninit(&in_layout);
av_channel_layout_uninit(&out_layout);
if (rc < 0 || !c.swr) return false;
#else
const int64_t in_layout = f->channel_layout
? static_cast<int64_t>(f->channel_layout)
: av_get_default_channel_layout(f->channels ? f->channels : 1);
c.swr = swr_alloc_set_opts(
nullptr,
AV_CH_LAYOUT_MONO, AV_SAMPLE_FMT_FLT, kSampleRate,
in_layout, static_cast<AVSampleFormat>(f->format),
f->sample_rate ? f->sample_rate : kSampleRate,
0, nullptr);
if (!c.swr) return false;
#endif
return swr_init(c.swr) >= 0;
}
// Push one decoded frame (or a flush) through the resampler, dropping the
// leading `to_skip` output samples, and append to `out`.
void drain(SwrContext* swr, const AVFrame* f, int in_rate,
std::size_t& to_skip, std::vector<float>& out) {
const int64_t delay = swr_get_delay(swr, in_rate ? in_rate : kSampleRate);
const int in_n = f ? f->nb_samples : 0;
const int max_out = static_cast<int>(av_rescale_rnd(
delay + in_n, kSampleRate, in_rate ? in_rate : kSampleRate, AV_ROUND_UP)) + 32;
if (max_out <= 0) return;
std::vector<float> buf(static_cast<std::size_t>(max_out));
uint8_t* dst = reinterpret_cast<uint8_t*>(buf.data());
const int n = swr_convert(swr, &dst, max_out,
f ? const_cast<const uint8_t**>(f->extended_data) : nullptr,
in_n);
if (n <= 0) return;
std::size_t produced = static_cast<std::size_t>(n);
std::size_t off = 0;
if (to_skip) {
const std::size_t drop = std::min(to_skip, produced);
to_skip -= drop;
off = drop;
produced -= drop;
}
if (produced)
out.insert(out.end(), buf.begin() + off, buf.begin() + off + produced);
}
} // namespace
// ── Public surface ──────────────────────────────────────────────────────────
const std::array<std::pair<int, int>, kNumBands>& band_fft_bins() {
static const std::array<std::pair<int, int>, kNumBands> tbl = build_band_table();
return tbl;
}
std::string base64_encode(const std::uint8_t* data, std::size_t n) {
static constexpr char kAlphabet[] =
"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
std::string out;
out.reserve(((n + 2) / 3) * 4);
std::size_t i = 0;
for (; i + 3 <= n; i += 3) {
const std::uint32_t v = (std::uint32_t(data[i]) << 16) |
(std::uint32_t(data[i + 1]) << 8) |
std::uint32_t(data[i + 2]);
out += kAlphabet[(v >> 18) & 0x3F];
out += kAlphabet[(v >> 12) & 0x3F];
out += kAlphabet[(v >> 6) & 0x3F];
out += kAlphabet[v & 0x3F];
}
if (i < n) {
const bool two = (n - i) == 2;
const std::uint32_t v = (std::uint32_t(data[i]) << 16) |
(two ? (std::uint32_t(data[i + 1]) << 8) : 0u);
out += kAlphabet[(v >> 18) & 0x3F];
out += kAlphabet[(v >> 12) & 0x3F];
out += two ? kAlphabet[(v >> 6) & 0x3F] : '=';
out += '=';
}
return out;
}
std::uint64_t fnv1a64(const void* data, std::size_t n) {
const auto* p = static_cast<const std::uint8_t*>(data);
std::uint64_t h = 0xcbf29ce484222325ULL;
for (std::size_t i = 0; i < n; ++i) {
h ^= p[i];
h *= 0x100000001b3ULL;
}
return h;
}
/// TRACES: IR-004
std::vector<std::uint8_t> pack_frames(const std::vector<float>& mono) {
if (mono.size() < static_cast<std::size_t>(kFrameSize)) return {};
const std::size_t nframes = 1 + (mono.size() - kFrameSize) / kHopSize;
const auto& bands = band_fft_bins();
const auto& win = hann_window();
const int k_lo = bands.front().first;
const int k_hi = bands.back().second; // exclusive
const double bin_count = static_cast<double>(k_hi - k_lo);
std::vector<double> re(kFrameSize), im(kFrameSize);
std::vector<std::uint8_t> peak(nframes);
std::vector<double> energy(nframes);
for (std::size_t f = 0; f < nframes; ++f) {
const float* src = mono.data() + f * kHopSize;
for (int n = 0; n < kFrameSize; ++n) {
re[n] = static_cast<double>(src[n]) * win[n];
im[n] = 0.0;
}
fft_4096(re, im);
// Per-band mean magnitude; the bands tile the 300–3000 Hz range with no
// gaps, so the frame's band-limited energy is the sum of the band sums.
double best = -1.0, total = 0.0;
int best_b = 0;
for (int b = 0; b < kNumBands; ++b) {
double sum = 0.0;
for (int k = bands[b].first; k < bands[b].second; ++k)
sum += std::sqrt(re[k] * re[k] + im[k] * im[k]);
total += sum;
const double mean = sum / (bands[b].second - bands[b].first);
if (mean > best) { best = mean; best_b = b; } // ties → lowest index
}
peak[f] = static_cast<std::uint8_t>(best_b);
energy[f] = total / bin_count;
}
// Reference is the upper median of the frame energies: an actually observed
// value (no averaging of the two middle samples), so it is bit-reproducible,
// gain-invariant and barely moves when the window is trimmed.
std::vector<double> sorted = energy;
std::sort(sorted.begin(), sorted.end());
const double ref = sorted[sorted.size() / 2];
std::vector<std::uint8_t> out(nframes);
for (std::size_t f = 0; f < nframes; ++f) {
const double r = std::log10((energy[f] + kEnergyEps) / (ref + kEnergyEps));
out[f] = static_cast<std::uint8_t>(((peak[f] & 0x1F) << 2) |
(energy_class(r) & 0x03));
}
return out;
}
/// TRACES: IR-004, IR-008
std::optional<std::string> signature_from_mono(const std::vector<float>& mono) {
const std::vector<std::uint8_t> packed = pack_frames(mono);
if (packed.empty()) return std::nullopt;
return std::string(kVersionPrefix) + base64_encode(packed.data(), packed.size());
}
/// TRACES: IR-004, IR-007
std::optional<std::vector<float>> decode_centre_window(const std::string& path) {
av_log_set_level(AV_LOG_ERROR);
DecodeCtx c;
if (avformat_open_input(&c.fmt, path.c_str(), nullptr, nullptr) < 0)
return std::nullopt;
if (avformat_find_stream_info(c.fmt, nullptr) < 0) return std::nullopt;
if (c.fmt->duration == AV_NOPTS_VALUE) return std::nullopt;
const double duration = static_cast<double>(c.fmt->duration) / AV_TIME_BASE;
// IR-007 — the window underflows, so there is no signature and no sync
// offset downstream. The plugin applies the identical rule.
if (duration < kWindowSec) return std::nullopt;
const int idx = av_find_best_stream(c.fmt, AVMEDIA_TYPE_AUDIO, -1, -1, nullptr, 0);
if (idx < 0) return std::nullopt; // no audio → no signature
AVStream* st = c.fmt->streams[idx];
const AVCodec* codec = avcodec_find_decoder(st->codecpar->codec_id);
if (!codec) return std::nullopt;
c.dec = avcodec_alloc_context3(codec);
if (!c.dec) return std::nullopt;
if (avcodec_parameters_to_context(c.dec, st->codecpar) < 0) return std::nullopt;
c.dec->thread_count = 0;
if (avcodec_open2(c.dec, codec, nullptr) < 0) return std::nullopt;
const double start_sec = duration / 2.0 - kWindowSec / 2.0;
// Seek to a packet at or before the window start; the exact start is then
// reached by discarding the leading output samples, which is what
// `ffmpeg -ss <t> -i <file>` does and therefore what the plugin sees.
if (start_sec > 0.0) {
const int64_t tgt = av_rescale_q(
static_cast<int64_t>(start_sec * AV_TIME_BASE), AV_TIME_BASE_Q, st->time_base);
if (av_seek_frame(c.fmt, idx, tgt, AVSEEK_FLAG_BACKWARD) >= 0)
avcodec_flush_buffers(c.dec);
}
c.frm = av_frame_alloc();
c.pkt = av_packet_alloc();
if (!c.frm || !c.pkt) return std::nullopt;
std::vector<float> mono;
mono.reserve(kWindowSamples + kSampleRate);
std::size_t to_skip = 0;
bool have_swr = false;
int in_rate = kSampleRate;
bool eof = false;
while (mono.size() < kWindowSamples && !eof) {
const int rr = av_read_frame(c.fmt, c.pkt);
if (rr < 0) {
eof = true;
avcodec_send_packet(c.dec, nullptr); // flush the decoder
} else if (c.pkt->stream_index != idx) {
av_packet_unref(c.pkt);
continue;
} else {
avcodec_send_packet(c.dec, c.pkt);
av_packet_unref(c.pkt);
}
while (avcodec_receive_frame(c.dec, c.frm) == 0) {
if (!have_swr) {
if (!open_resampler(c, c.frm)) return std::nullopt;
have_swr = true;
in_rate = c.frm->sample_rate ? c.frm->sample_rate : kSampleRate;
int64_t pts = c.frm->best_effort_timestamp;
if (pts == AV_NOPTS_VALUE) pts = c.frm->pts;
const double t0 = (pts == AV_NOPTS_VALUE)
? start_sec : av_q2d(st->time_base) * static_cast<double>(pts);
const double lead = start_sec - t0;
to_skip = lead > 0.0
? static_cast<std::size_t>(std::llround(lead * kSampleRate)) : 0;
}
drain(c.swr, c.frm, in_rate, to_skip, mono);
av_frame_unref(c.frm);
if (mono.size() >= kWindowSamples) break;
}
}
if (have_swr && mono.size() < kWindowSamples)
drain(c.swr, nullptr, in_rate, to_skip, mono); // flush the resampler
if (mono.empty()) return std::nullopt;
// Truncate to exactly 120.000 s so the frame count is 1288 for every input
// and does not wobble with seek granularity or the resampler tail.
if (mono.size() > kWindowSamples) mono.resize(kWindowSamples);
return mono;
}
/// TRACES: IR-004, IR-005, IR-007, IR-008
std::optional<std::string> compute_signature(const std::string& path) {
const std::optional<std::vector<float>> mono = decode_centre_window(path);
if (!mono) return std::nullopt;
return signature_from_mono(*mono);
}
} // namespace sae::audio
src/audio_signature.hpp
+158
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#pragma once
// ── JRay audio signature, v1 ─────────────────────────────────────────────────
//
/// TRACES: IR-004, IR-005, IR-007, IR-008 | SR-003
//
// A content-derived spectral-peak signature taken from the *centre* of the
// media, so a truth file is self-identifying: a consumer can tell whether a
// local file is the same cut as the one a manifest describes, and recover the
// frame offset when it is the same cut trimmed differently.
//
// The construction is owned by `JRay-public-server/SPEC.md` §3 and is
// reproduced by the JRay Jellyfin plugin in C#. **The two implementations must
// agree byte for byte** — a signature that differs in any parameter simply does
// not match, which defeats the entire point. Every deviation is therefore a
// breaking change and must go through the `v1:` prefix (see kVersionPrefix).
//
// Server spec §3, restated:
//
// 1. Decode a 120 s window centred on the midpoint (runtime/2 ± 60 s).
// 2. Downmix to mono, resample to 11025 Hz.
// 3. STFT: 4096-sample frame, 1024-sample hop, Hann window (~1290 frames).
// 4. Per frame, log-magnitude spectrum over 300–3000 Hz.
// 5. 32 logarithmically spaced bins; peak bin index + coarse 2-bit energy
// class.
// 6. Pack one byte per frame; base64-encode.
// 7. Prefix `v1:`.
//
// ── Details the server spec leaves open, pinned here for v1 ──────────────────
//
// The prose above is not sufficient to reproduce a byte stream, so the choices
// below are the contract. They are mirrored in
// `tests/fixtures/audio/jray_audio_v1_golden.json`, which is the artefact
// shared with the plugin repo (IR-005).
//
// Arithmetic All DSP in IEEE-754 **double**. float32 is not sufficient:
// the golden fixture has frames whose two strongest bands are
// within 1.3% of each other, which double resolves identically
// everywhere and float32 does not.
// Sample scale FFmpeg's native s16→flt conversion, x * (1/32768), then
// widened to double. Values in [-1, 1).
// Framing Only whole frames: n_frames = 1 + (n_samples - 4096) / 1024,
// integer division, 0 when n_samples < 4096. A 120.000 s
// window is 1 323 000 samples → **1288 frames**.
// ("~1290" in the spec; the server accepts a tolerance.)
// Window Hann, **periodic**: w[n] = 0.5 * (1 - cos(2*pi*n/4096)).
// Not the symmetric (N-1) variant.
// Transform Plain radix-2 decimation-in-time complex FFT over 4096 real
// samples (imag = 0), no normalisation. Magnitude is
// sqrt(re² + im²). Twiddles from cos/sin of
// -2*pi*k/len computed in double.
// Band edges edge[b] = 300 * (3000/300)^(b/32), b = 0..32. Band b spans
// FFT bins [k_lo[b], k_lo[b+1]) with
// k_lo[b] = ceil(edge[b] * 4096 / 11025) — i.e. bins 112..1114
// inclusive, 8 bins in the narrowest band. Precomputed as an
// integer table so no float comparison decides membership.
// Band value **Mean** of the linear magnitudes in the band. Mean, not
// sum, so a wide high band is not favoured over a narrow low
// one; magnitude, not power, because it is an energy proxy and
// more codec-robust than a single bin's peak.
// Peak bin argmax over the 32 band values; ties resolve to the **lowest
// index**. The log of step 4 is a monotone squash and so
// cannot change an argmax — it is applied only where it is
// observable, in the energy class below.
// Energy class The spec says "coarse 2-bit energy class" and no more. v1
// defines it as the frame's band-limited energy relative to
// the window, which is invariant to gain (loudness
// normalisation must not change a signature) and robust to
// trimming (the median barely moves):
// E_f = mean magnitude over *all* FFT bins 112..1114
// Eref = median over frames of E_f, taken as the upper
// median sorted[n/2] — no averaging of the two middle
// values, so the reference is always an actual
// observed value and is bit-reproducible
// r = log10((E_f + 1e-12) / (Eref + 1e-12))
// class = 0 if r < -0.6, 1 if r < -0.2, 2 if r < 0.2, else 3
// The thresholds deliberately straddle r = 0 rather than sit
// on it, so the median frame itself is not on a boundary.
// Byte layout bit 7 = 0 (reserved), bits 6..2 = 5-bit band index,
// bits 1..0 = 2-bit energy class:
// byte = (band << 2) | class → always 0..127
// This is the structural constraint the server validates on
// upload (§3 "Validation and abuse").
// Base64 Standard alphabet A–Za–z0–9+/ with '=' padding.
//
// ── Short media (IR-007) ─────────────────────────────────────────────────────
//
// `runtime/2 ± 60 s` underflows below 120 s, so **no signature is emitted** and
// no sync offset is applied downstream. Both producers apply the identical
// rule; diverging here would break exactly the short items most likely to be
// misidentified. `compute_signature` returns `std::nullopt`.
//
// The same nullopt is returned for a file with no audio stream, an unopenable
// file, or an unknown duration. UR-9 is an enhancement and must never be able
// to break a fetch — degradation, not failure.
#include <array>
#include <cstddef>
#include <cstdint>
#include <optional>
#include <string>
#include <utility>
#include <vector>
namespace sae::audio {
// ── Contract constants — changing any of these is a `v1:` bump ───────────────
inline constexpr int kSampleRate = 11025;
inline constexpr int kFrameSize = 4096;
inline constexpr int kHopSize = 1024;
inline constexpr int kNumBands = 32;
inline constexpr double kBandLoHz = 300.0;
inline constexpr double kBandHiHz = 3000.0;
inline constexpr double kWindowSec = 120.0;
inline constexpr double kEnergyEps = 1e-12;
// Class thresholds on log10(E_frame / E_median); see the header comment.
inline constexpr double kEnergyClassEdges[3] = {-0.6, -0.2, 0.2};
// 120.000 s at 11025 Hz. The decoded window is truncated to exactly this so the
// frame count does not wobble with seek granularity or resampler tail.
inline constexpr std::size_t kWindowSamples =
static_cast<std::size_t>(kWindowSec * kSampleRate); // 1 323 000
inline constexpr std::size_t kExpectedFrames =
1 + (kWindowSamples - kFrameSize) / kHopSize; // 1288
static_assert(kWindowSamples == 1323000, "120 s at 11025 Hz");
static_assert(kExpectedFrames == 1288, "server spec's ~1290 frames");
/// The version prefix is the signature's own, separate from `schema_version`:
/// a future change to the DSP chain must be *detectable* rather than silently
/// producing non-matching signatures (IR-008).
inline constexpr const char* kVersionPrefix = "v1:";
/// FFT bin range [first, last) for each of the 32 log-spaced bands.
/// Computed once from the constants above; exposed so the golden fixture can
/// assert the table itself, not merely the signature it produces.
const std::array<std::pair<int, int>, kNumBands>& band_fft_bins();
/// Decode the centre window of `path` as mono float PCM at 11025 Hz.
/// nullopt when the media is shorter than 120 s (IR-007), has no audio stream,
/// or cannot be opened. Never throws.
std::optional<std::vector<float>> decode_centre_window(const std::string& path);
/// One packed byte per whole STFT frame. Empty when `mono` is shorter than one
/// frame. This is the payload that gets base64-encoded.
std::vector<std::uint8_t> pack_frames(const std::vector<float>& mono);
/// `v1:` + base64(pack_frames(mono)). nullopt when no whole frame fits.
std::optional<std::string> signature_from_mono(const std::vector<float>& mono);
/// Decode + sign. The one call the pipeline makes. nullopt per IR-007 and on
/// any decode failure — degradation, not failure.
std::optional<std::string> compute_signature(const std::string& path);
// ── Small utilities, exposed for the golden-fixture test ────────────────────
std::string base64_encode(const std::uint8_t* data, std::size_t n);
/// FNV-1a 64. Used only to pin the *decoded PCM* in the golden fixture, so a
/// codec-level difference is distinguishable from a DSP-level one.
std::uint64_t fnv1a64(const void* data, std::size_t n);
} // namespace sae::audio
src/backends/gemm_backend.cpp
+36 -4
View File
@@ -34,9 +34,23 @@ constexpr int kDim = 512;
#if defined(SAE_GEMM_CPU)
#if defined(SAE_GEMM_CBLAS)
#include <cblas.h>
#endif
// ── CPU reference engine ──────────────────────────────────────────────────────
// Portable, dependency-free path used for CI and as the correctness oracle for
// the GPU backends. The gallery is L2-normalised (as are the queries), so each
// Used for CI and as the correctness oracle for the GPU backends.
//
// TRACES: AR-026, AR-027 | SR-001
// Backed by CBLAS (OpenBLAS) when available, falling back to a scalar loop when
// not. The fallback is portable but scales badly: scoring one face against a
// 5000-embedding gallery is 2.6 MFLOP, and a crowded frame multiplies that by
// the face count. Since AR-003 removed the per-frame face cap and CI has no GPU,
// the CPU path is now the one that has to hold up under a library-scale gallery
// (AR-027) rather than merely be correct.
//
// The fallback is kept rather than made mandatory so the build has no hard new
// dependency, and so the two can be diffed when a similarity looks wrong. The gallery is L2-normalised (as are the queries), so each
// similarity is a plain dot product. S is stored column-major to match the GPU
// backends: the gallery similarities for face fi start at result + fi*n_gallery.
class SimilarityEngine final : public ISimilarityEngine {
@@ -47,7 +61,13 @@ public:
gallery_row_major + static_cast<size_t>(n_gallery) * kDim)
{
host_sims_.resize(static_cast<size_t>(max_faces_) * n_gallery_);
std::cerr << "[similarity] CPU reference engine: gallery resident in host RAM ("
std::cerr << "[similarity] CPU engine ("
#if defined(SAE_GEMM_CBLAS)
<< "CBLAS"
#else
<< "scalar fallback — no CBLAS; expect poor scaling on a large gallery"
#endif
<< "): gallery resident in host RAM ("
<< (gallery_.size() * sizeof(float)) / (1024 * 1024) << " MiB)\n";
}
@@ -58,7 +78,18 @@ public:
if (n_faces > max_faces_)
throw std::runtime_error("SimilarityEngine: n_faces exceeds max_faces");
// S(g, f) col-major = dot(gallery[g], query[f]).
// S(g, f) col-major = dot(gallery[g], query[f]). Viewed as row-major
// [n_faces x n_gallery] that is exactly query * gallery^T, so it is one
// GEMM rather than a loop nest.
#if defined(SAE_GEMM_CBLAS)
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans,
/*M=*/n_faces, /*N=*/n_gallery_, /*K=*/kDim,
/*alpha=*/1.0f,
query_row_major, /*lda=*/kDim,
gallery_.data(), /*ldb=*/kDim,
/*beta=*/0.0f,
host_sims_.data(), /*ldc=*/n_gallery_);
#else
for (int f = 0; f < n_faces; ++f) {
const float* q = query_row_major + static_cast<size_t>(f) * kDim;
float* out = host_sims_.data() + static_cast<size_t>(f) * n_gallery_;
@@ -69,6 +100,7 @@ public:
out[g] = acc;
}
}
#endif
return host_sims_.data();
}
src/build_gallery.cpp
+31 -1
View File
@@ -18,6 +18,8 @@
// --nms <f> NMS IoU threshold (default: 0.4)
#include "gallery/gallery_builder.hpp"
#include "gallery/gallery_report.hpp"
#include "gallery/gallery_calibration.hpp"
#include "gallery/gallery_store.hpp"
#include "config.hpp"
@@ -32,6 +34,7 @@ int main(int argc, char** argv) {
std::string arcface_model = kDefaultArcfaceModel;
float conf = 0.5f, nms_thr = 0.4f;
int max_side = 500;
float min_face_px = 0.f;
for (int i = 1; i < argc; ++i) {
auto arg = [&](const char* f) { return std::strcmp(argv[i], f) == 0; };
@@ -48,6 +51,7 @@ int main(int argc, char** argv) {
else if (arg("--conf")) conf = std::stof(next());
else if (arg("--nms")) nms_thr = std::stof(next());
else if (arg("--max-side")) max_side = std::stoi(next());
else if (arg("--min-face-px")) min_face_px = std::stof(next());
else { std::cerr << "[warn] unknown flag: " << argv[i] << "\n"; }
} catch (const std::exception& e) {
std::cerr << "Error: " << e.what() << "\n";
@@ -57,7 +61,8 @@ int main(int argc, char** argv) {
if (root_path.empty() || output_path.empty()) {
std::cerr << "Usage: build_gallery --root <dir> --output <gallery.json> "
"[--detector <path>] [--arcface <path>] [--max-side <N>]\n";
"[--detector <path>] [--arcface <path>] [--max-side <N>]\n"
" [--min-face-px <px>]\n";
return 1;
}
@@ -68,6 +73,7 @@ int main(int argc, char** argv) {
cfg.detector_conf = conf;
cfg.detector_nms = nms_thr;
cfg.max_side = max_side;
cfg.min_face_px = min_face_px;
try {
ActorGallery gallery = build_gallery(cfg);
@@ -77,6 +83,30 @@ int main(int argc, char** argv) {
}
save_gallery(output_path, gallery);
std::cerr << "Gallery saved to: " << output_path << "\n";
/// TRACES: GR-003 | SR-001
// Fit the calibration here and persist what it learned. The matcher
// fits the same sigmoid at analysis time, but that is the wrong place
// to audit a gallery from: by then the answer is per-run and nobody is
// looking. Build time is when the gallery's quality is decided, and a
// gallery can be quietly bad — heavily overlapping intra/inter
// distributions, actors with no usable image — while looking fine.
std::vector<Embedding> flat;
std::vector<int> flat_actor;
for (int ai = 0; ai < static_cast<int>(gallery.actors.size()); ++ai)
for (const auto& e : gallery.actors[ai].embeddings) {
flat.push_back(e);
flat_actor.push_back(ai);
}
GalleryCalibrationStats stats;
GalleryCalibration cal = calibrate_gallery(flat, flat_actor, &stats);
const GalleryReport report =
build_gallery_report(gallery, cal, stats, nullptr, output_path);
const std::string report_path = gallery_report_path(output_path);
save_gallery_report(report_path, report);
std::cerr << "Gallery report saved to: " << report_path << "\n";
} catch (const std::exception& e) {
std::cerr << "Fatal: " << e.what() << "\n";
return 1;
src/config.hpp
+51 -21
View File
@@ -16,7 +16,13 @@ enum class Verbosity {
struct Config {
// ── Input ─────────────────────────────────────────────────────────────────
std::string movie_path;
std::string gallery_path; // gallery.json produced by build_gallery
std::string gallery_path;
// TRACES: IR-002 | SR-003
// "global" (matched against the whole library) or "limited" (this title's
// credited cast only). The strongest single quality signal when two
// manifests compete for the same cut: identical gallery_size can mean very
// different recall depending on which was used.
std::string gallery_scope{"global"}; // gallery.json produced by build_gallery
// ── Output ───────────────────────────────────────────────────────────────
std::string output_path; // annotations.json
@@ -35,11 +41,24 @@ struct Config {
// ── Detection (SCRFD-500MF via cv::dnn::Net) ──────────────────────────────
std::string detector_model;
std::string detector_engine; // optional path to pre-built TRT engine; bypasses ORT
int max_faces{10}; // pipeline cap: keep only the N largest faces
// TRACES: AR-003 | SR-002
// 0 = no cap, the default. A fixed cap discards the SMALLEST faces first,
// which are exactly the background cast X-Ray still credits with scene
// membership. Per-frame cost is contained by backpressure (AR-004) rather
// than by throwing work away. Set >0 only to bound a pathological source.
int max_faces{0};
float min_face_px{40.f}; // discard detections narrower or shorter than this
float detector_conf{0.5f};
float detector_nms{0.4f};
/// TRACES: GR-004 | SR-001
// Gallery ↔ embedder binding. A gallery built with a different model than the
// one loaded here is a hard error, always. This flag additionally promotes
// "cannot prove they match" (unstamped legacy gallery, or a name-only match
// because the ONNX could not be hashed) from a loud warning to a hard error.
// Also settable via SAE_REQUIRE_GALLERY_STAMP=1. Measurement runs want it on.
bool require_gallery_stamp{false}; // --require-gallery-stamp
// ── Recognition (ArcFace ONNX) ────────────────────────────────────────────
std::string arcface_model;
std::string arcface_engine; // optional path to a pre-built TRT engine; bypasses ORT
@@ -86,21 +105,25 @@ struct Config {
float dense_scale{1.0f}; // dense-mode frame downscale (1 = off)
// ── Face tracking (frame-to-frame) ───────────────────────────────────────
float track_alpha{0.4f}; // cost weight: 0=embedding only, 1=spatial only
/// TRACES: AR-007, AR-008, AR-024 | SR-002
// track_alpha is the *base* weight, used on ordinary frames. It is
// frame-dependent (AR-007): on is_cut / is_scene_boundary, and for any track
// that is no longer on screen, it drops to 0 (embedding only), because
// position carries no information across a viewpoint change or a gap.
float track_alpha{0.4f}; // base cost weight: 0=embedding only, 1=spatial only
float track_min_iou{0.1f}; // IoU below which spatial link alone is rejected
float track_max_embed_dist{0.7f}; // cosine dist above which embedding link alone is rejected
int track_max_frames_missing{5}; // expire track after N consecutive missed frames
// ── Cross-cut track re-association ────────────────────────────────────────
// A camera-angle change (Frame::is_cut) breaks spatial (IoU) continuity but
// not identity: the same people are usually still on screen from a new angle.
// Instead of destroying tracks on a cut, the tracker parks them in an
// inactive pool. A post-cut detection whose raw cosine similarity to a parked
// track's last-frame embedding is ≥ cut_revive_sim revives that track_id
// (identity continuity survives the cut); otherwise it starts a fresh track.
// Parked tracks that go unrevived for cut_inactive_max_frames are dropped.
float cut_revive_sim{0.50f}; // min raw cosine sim (last-frame emb) to revive across a cut
int cut_inactive_max_frames{5}; // drop a parked track after N frames without revival
// Minimum P(same person) for an association to be admissible on appearance
// alone. This replaces track_max_embed_dist (a raw cosine distance, AR-024).
// 0.5 is not a tuned constant: it is the decision boundary. Below it the pair
// is more likely two people than one, and no amount of IoU makes that a link
// worth asserting on identity grounds.
float track_assoc_min_prob{0.5f};
// How long a track that has gone off screen stays available for association
// before the registry reaps it and emits its presence claim (AR-013).
// Replaces track_max_frames_missing: a frame count silently changed meaning
// with sample_fps, and the same number had to be guessed twice (once for an
// ordinary miss, once for a cut). Seconds mean one thing at any sample rate.
double track_extinction_sec{5.0};
// ── Scene tracking ────────────────────────────────────────────────────────
// extinction_sec re-tuned by DE against X-Ray per-second presence, 4-film rep4
@@ -126,11 +149,18 @@ struct Config {
// opposite of the earlier assumption that it only helps restricted galleries.
bool expand_gallery{true}; // master switch
int expand_buffer_size{20}; // per-track diversity buffer capacity
float expand_novelty_sim{0.55f}; // promote only embeddings whose best sim to the
// actor's refs is below this (gallery-far / novel)
float expand_track_spread_max{0.60f}; // reject promotion if the retained buffer's
// internal spread (1 - min pairwise sim) exceeds
// this — guards track-ID collisions / two people
// TRACES: AR-018, AR-024 | SR-005
// Banded admission for the per-subject store, in PROBABILITY space. An
// embedding joins only if P(same person) against something already stored
// lands inside [lo, hi]: above hi it is redundant, below lo it is evidence
// the track is not one person. The same lo is re-applied to the whole store
// at promotion time — see track_gallery.hpp. This is the only threshold the
// expansion path has: it replaces the raw-cosine expand_novelty_sim (0.55)
// and expand_track_spread_max (0.60), which are retired (AR-024).
// Working values pending VR-007; sweep both bounds, they fail in opposite
// directions.
float expand_band_lo{0.90f};
float expand_band_hi{0.95f};
int expand_min_anchor_frames{3}; // require ≥N accepted frames naming the actor before
// the track is confirmed and its buffer promoted
std::string expand_debug_dir; // if set, dump promoted mugshots + embeddings here
src/embed_faces.cpp
+154 -17
View File
@@ -25,11 +25,25 @@
// expected to contain exactly one subject). A warning is printed to stderr
// when more than one face is found.
//
// --all-faces emits every detection instead, which is what a caller analysing
// a frame rather than a gallery portrait needs:
// [ { "image": "frame.png",
// "faces": [ {"bbox": [...], "landmarks": [[x,y] x5],
// "confidence": 0.89, "embedding": [...]} , ... ] } ]
//
// --calibration <gallery> additionally emits the gallery's fitted Platt
// sigmoid, so a non-Python client can turn a similarity into P(match) with the
// same parameters the C++ matcher uses. Output becomes
// {"calibration": {...}, "images": [...]}. Clients must score through it:
// AR-024 requires the calibrated probability, never a bare cosine — a raw
// threshold means something different for every model, gallery and face size.
//
// This binary is intentionally a thin wrapper around the same ONNX models
// used by scene_analyze, so embeddings are guaranteed compatible.
#include "config.hpp"
#include "face_utils.hpp"
#include "gallery/gallery_store.hpp"
#include "inference/face_detector.hpp"
#include "inference/face_embedder.hpp"
@@ -100,6 +114,77 @@ static void save_debug(const std::string& dir,
cv::imwrite(dir + "/" + stem + "_aligned.jpg", aligned);
}
// ── Process one image, keeping every detection ────────────────────────────────
// The --all-faces path. Same detect → align → embed chain as process() below,
// but without the highest-confidence reduction: a frame legitimately contains
// several people, and dropping all but one is a gallery-portrait assumption.
// Faces that fail alignment are reported with a null embedding rather than
// silently dropped, so a caller can count what the detector found against what
// survived the ArcFace warp.
struct MultiFaceResult {
std::string image_path;
std::string error; // set only when the image itself failed
std::vector<FaceResult> faces;
};
static MultiFaceResult process_all(
const std::string& path,
const std::function<std::vector<DetectedFace>(const cv::Mat&)>& detect,
const std::function<Embedding(const cv::Mat&)>& embed_one,
int max_side,
const std::string& debug_dir = "") {
MultiFaceResult out;
out.image_path = path;
cv::Mat img = cv::imread(path);
if (img.empty()) {
out.error = "cannot read image";
return out;
}
if (max_side > 0) {
const int big = std::max(img.cols, img.rows);
if (big > max_side) {
const double s = static_cast<double>(max_side) / big;
cv::resize(img, img, {}, s, s, cv::INTER_AREA);
}
}
std::vector<DetectedFace> faces = detect(img);
if (faces.empty()) {
cv::Mat enhanced = enhance_for_retry(img);
faces = detect(enhanced);
if (!faces.empty())
img = enhanced;
}
if (faces.empty()) {
out.error = "no face detected";
return out;
}
for (const auto& face : faces) {
FaceResult r;
r.image_path = path;
r.confidence = face.confidence;
r.bbox[0] = face.bbox.x; r.bbox[1] = face.bbox.y;
r.bbox[2] = face.bbox.width; r.bbox[3] = face.bbox.height;
r.landmarks = face.landmarks;
cv::Mat crop = align_face(img, face.landmarks);
if (crop.empty()) {
r.error = "alignment failed";
} else {
r.ok = true;
r.embedding = embed_one(crop);
if (!debug_dir.empty())
save_debug(debug_dir, path, img, face, crop);
}
out.faces.push_back(std::move(r));
}
return out;
}
// ── Process one image ─────────────────────────────────────────────────────────
static FaceResult process(const std::string& path,
@@ -177,6 +262,8 @@ int main(int argc, char** argv) {
std::string arcface_model = kDefaultArcfaceModel;
std::string arcface_engine;
std::string debug_dir;
std::string calibration_gallery;
bool all_faces = false;
float conf = 0.5f, nms = 0.4f;
int max_side = 500;
std::vector<std::string> images;
@@ -190,13 +277,16 @@ int main(int argc, char** argv) {
else if (std::strcmp(argv[i], "--nms") == 0 && i+1 < argc) { nms = std::stof(argv[++i]); }
else if (std::strcmp(argv[i], "--save-debug") == 0 && i+1 < argc) { debug_dir = argv[++i]; }
else if (std::strcmp(argv[i], "--max-side") == 0 && i+1 < argc) { max_side = std::stoi(argv[++i]); }
else if (std::strcmp(argv[i], "--calibration")== 0 && i+1 < argc) { calibration_gallery = argv[++i]; }
else if (std::strcmp(argv[i], "--all-faces") == 0) { all_faces = true; }
else if (argv[i][0] != '-') { images.push_back(argv[i]); }
else { std::cerr << "[warn] unknown flag: " << argv[i] << "\n"; }
}
if (images.empty()) {
std::cerr << "Usage: embed_faces [--detector <path>] [--arcface <path>] "
"[--save-debug <dir>] [--max-side <N>] image1.jpg ...\n";
"[--save-debug <dir>] [--max-side <N>] [--all-faces] "
"[--calibration <gallery>] image1.jpg ...\n";
return 1;
}
@@ -217,31 +307,78 @@ int main(int argc, char** argv) {
std::function<Embedding(const cv::Mat&)> embed_one =
[&](const cv::Mat& c) { return embedder->embed_one(c); };
// One face's fields, shared by both output shapes.
auto face_json = [](const FaceResult& r) {
json f;
f["confidence"] = r.confidence;
f["bbox"] = {r.bbox[0], r.bbox[1], r.bbox[2], r.bbox[3]};
json lms = json::array();
for (const auto& pt : r.landmarks) lms.push_back({pt.x, pt.y});
f["landmarks"] = std::move(lms);
if (r.ok) f["embedding"] = std::vector<float>(r.embedding.begin(),
r.embedding.end());
else { f["embedding"] = nullptr; f["error"] = r.error; }
return f;
};
// Process images and build JSON output
json output = json::array();
json images_out = json::array();
for (const auto& path : images) {
std::cerr << "[embed_faces] " << path << "\n";
FaceResult res = process(path, detect, embed_one, max_side, debug_dir);
json entry;
entry["image"] = res.image_path;
if (res.ok) {
entry["embedding"] = std::vector<float>(res.embedding.begin(),
res.embedding.end());
entry["confidence"] = res.confidence;
entry["bbox"] = {res.bbox[0], res.bbox[1], res.bbox[2], res.bbox[3]};
json lms = json::array();
for (const auto& pt : res.landmarks) lms.push_back({pt.x, pt.y});
entry["landmarks"] = std::move(lms);
entry["image"] = path;
if (all_faces) {
MultiFaceResult res = process_all(path, detect, embed_one, max_side, debug_dir);
if (!res.error.empty()) {
entry["faces"] = json::array();
entry["error"] = res.error;
std::cerr << " [skip] " << res.error << "\n";
} else {
json faces = json::array();
for (const auto& f : res.faces) faces.push_back(face_json(f));
entry["faces"] = std::move(faces);
}
} else {
entry["embedding"] = nullptr;
entry["error"] = res.error;
std::cerr << " [skip] " << res.error << "\n";
FaceResult res = process(path, detect, embed_one, max_side, debug_dir);
if (res.ok) {
entry.merge_patch(face_json(res));
} else {
entry["embedding"] = nullptr;
entry["error"] = res.error;
std::cerr << " [skip] " << res.error << "\n";
}
}
output.push_back(std::move(entry));
images_out.push_back(std::move(entry));
}
std::cout << output.dump() << "\n";
// Without --calibration the output stays a bare array, unchanged, so
// existing callers (build_gallery, fetch_missing_actors) are unaffected.
if (calibration_gallery.empty()) {
std::cout << images_out.dump() << "\n";
return 0;
}
ActorGallery gallery = load_gallery(calibration_gallery);
if (!gallery.calib_valid)
std::cerr << "[warn] " << calibration_gallery
<< " carries no valid calibration; a client cannot convert a "
"similarity to a probability from it (AR-024)\n";
json out;
out["calibration"] = {
{"a", gallery.calib_a},
{"b", gallery.calib_b},
{"valid", gallery.calib_valid},
{"form", "P(match) = 1/(1+exp(-(a*similarity + b + log_prior_odds)))"},
{"note", "Score through this. AR-024: a bare cosine threshold means "
"something different for every model, gallery and face size. "
"log_prior_odds = log(p0/(1-p0)) for a base-rate prior p0; use "
"0 for association (are these two faces one person)."},
};
out["images"] = std::move(images_out);
std::cout << out.dump() << "\n";
return 0;
}
src/evidence_discount.hpp
+131
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@@ -0,0 +1,131 @@
#pragma once
/// TRACES: AR-024, AR-025 | SR-002
///
/// EvidenceDiscounter — how much a single observation is allowed to move a
/// track's belief.
///
/// **The independence problem.** Per-frame identity evidence is accumulated as
/// log-odds along a track (AR-025), which is only valid for *independent*
/// observations. Consecutive frames of one track are nothing of the kind: near
/// identical pose, lighting and expression. Treating them as independent drives
/// the posterior to certainty on what is effectively one measurement — thirty
/// frames of the same face at the same angle is not thirty pieces of evidence.
///
/// The mitigation is to weight each observation by how much it *adds*: a view
/// the track has already contributed is discounted toward zero, a genuinely new
/// pose counts in full. This reuses the same judgement the diversity buffer
/// makes for gallery expansion (AR-019) — which embeddings on a track are
/// mutually distinct — rather than inventing a second notion of novelty.
///
/// Owned by TrackRegistry rather than left to callers. A caller that forgot to
/// discount, or applied it twice, would silently produce confident wrong
/// answers, and the registry is the one place where all evidence converges.
///
/// **Similarity enters as a calibrated probability, never a raw cosine**
/// (AR-024): "is this the same view" is a decision, and a bare cosine threshold
/// means something different for every model and every face size.
#include "types.hpp"
#include <algorithm>
#include <cmath>
#include <functional>
#include <vector>
class EvidenceDiscounter {
public:
/// cosine similarity → P(same view). Supplied by the caller so the
/// calibration fitted for the active embedder is used (AR-023/AR-024).
using Calibrate = std::function<float(float)>;
struct Config {
int max_views{8}; ///< distinct views remembered per track
float admit_below{0.6f}; ///< P(same view) under this ⇒ a new view
/// Ceiling on the correlation between two observations of one track.
///
/// This is what bounds the accumulation. `n_eff = n / (1 + (n-1)·rho)`
/// tends to `1/rho` as `n` grows, so `rho_max` sets how much a single
/// repeated view can ever be worth: 0.5 caps it at two observations,
/// no matter how long the shot runs.
///
/// 0.5 caps a repeated view at two independent observations' worth,
/// which is what lets a track the matcher accepts on frame after frame
/// actually become owned. Higher values starve ownership; the sweep
/// (VR-007) decides where it belongs.
///
/// It is capped below 1 deliberately. P(same view) near 1 says the two
/// crops look alike; it does not say the second carries no information.
/// A fresh frame is a fresh detection, a fresh alignment and a fresh
/// noise realisation, so a little independent evidence survives even a
/// perfectly held pose. Setting this to 1 recovers the original bug —
/// belief frozen after the first frame.
float rho_max{0.5f};
};
// Two constructors rather than a defaulted argument: `Config{}` as a default
// argument would reference Config's own member initializers before the
// enclosing class is complete, which is ill-formed.
explicit EvidenceDiscounter(Calibrate cal)
: cal_(std::move(cal)), cfg_() {}
EvidenceDiscounter(Calibrate cal, Config cfg)
: cal_(std::move(cal)), cfg_(cfg) {}
/// The marginal evidence one observation adds, in units of independent
/// observations.
///
/// Each frame is a Bayesian update, so confidence must keep growing — but
/// correlated observations must grow it less, and must not grow it without
/// bound. The standard treatment is **effective sample size**:
///
/// n_eff(n) = n / (1 + (n-1)·rho)
///
/// and this returns `n_eff(n) - n_eff(n-1)`, the gain from *this* frame.
/// The shape is right at both ends: with rho = 0 every frame counts fully
/// and the belief accumulates linearly, while as rho rises the series
/// converges on `1/rho` and a held pose stops adding no matter how long it
/// is held.
///
/// The two failure modes it sits between are both real and both were hit:
/// a weight of 0 for repeats froze the belief after one frame, so a track
/// recognised on 318 frames was owned on none; a constant floor grew it
/// linearly forever, so a long shot could out-argue genuinely varied
/// evidence purely by lasting longer.
///
/// `rho` is estimated from P(same view) against the closest stored view,
/// capped by `rho_max`. The first observation has nothing to be redundant
/// with and counts in full.
/// `n_seen` is the count of observations already folded into THIS track.
/// It is a parameter rather than discounter state because one discounter
/// serves every track: holding the count internally would pool unrelated
/// tracks into one effective sample, so a busy film would silently discount
/// each track by how many others happened to be on screen.
float weight(std::vector<Embedding>& views, int n_seen, const Embedding& e) const {
if (views.empty()) {
views.push_back(e);
return 1.0f;
}
float p_same = 0.0f;
for (const auto& v : views)
p_same = std::max(p_same, cal_(cosine_similarity(v, e)));
const float rho = std::min(cfg_.rho_max, std::max(0.0f, p_same));
const float n_prev = static_cast<float>(std::max(1, n_seen));
const float n_now = n_prev + 1.0f;
auto n_eff = [rho](float n) { return n / (1.0f + (n - 1.0f) * rho); };
const float w = std::max(0.0f, n_eff(n_now) - n_eff(n_prev));
if (p_same < cfg_.admit_below &&
static_cast<int>(views.size()) < cfg_.max_views) {
views.push_back(e);
}
return w;
}
private:
Calibrate cal_;
Config cfg_;
};
src/face_embedder_engine.hpp
+19
View File
@@ -112,6 +112,25 @@ public:
return res;
}
// ── Stage accessors ──────────────────────────────────────────────────────
// embed_mat() above is the whole detect→align→embed chain, which is the
// right entry point for embedding a gallery image. Studies that need to
// intervene between the stages — swapping the landmark source, degrading a
// crop before it reaches the embedder — drive these instead, so they still
// exercise the shipped detector, alignment and embedder rather than a
// re-implementation of them.
std::vector<DetectedFace> detect(const cv::Mat& img) { return detector_->detect(img); }
Embedding embed_crop(const cv::Mat& crop) { return embedder_->embed_one(crop); }
// Batched form. A study embedding thousands of crops one at a time pays the
// per-call overhead thousands of times over; the backend already batches.
std::vector<Embedding> embed_crops(const std::vector<cv::Mat>& crops) {
return embedder_->embed(crops);
}
int max_batch() const { return embedder_->max_batch(); }
private:
std::unique_ptr<IFaceDetector> detector_;
std::unique_ptr<IFaceEmbedder> embedder_;
src/face_utils.hpp
+130 -11
View File
@@ -1,25 +1,144 @@
#pragma once
/// TRACES: AR-005, AR-030 | SR-002
#include "types.hpp"
#include <opencv2/calib3d.hpp>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <cmath>
// ── align_face ────────────────────────────────────────────────────────────────
// Produces a 112×112 BGR crop using the ArcFace 5-point similarity transform.
// Returns an empty Mat if the affine fit fails (degenerate detection).
inline cv::Mat align_face(const cv::Mat& img,
const std::array<cv::Point2f, 5>& landmarks) {
std::vector<cv::Point2f> src(landmarks.begin(), landmarks.end());
std::vector<cv::Point2f> dst(5);
// ── umeyama_similarity ────────────────────────────────────────────────────────
// Closed-form least-squares similarity transform (rotation + uniform scale +
// translation, 4 DoF) mapping `src` onto `dst`, by Umeyama's solution.
//
// This is the estimator InsightFace aligns with — skimage's SimilarityTransform
// is `_umeyama(..., estimate_scale=True)` — and therefore the one that produced
// the crops ArcFace and LVFace were *trained* on. The canonical warp is part of
// the input distribution, not a free implementation choice (AR-011).
//
// Deliberately **not** `cv::estimateAffinePartial2D(..., cv::RANSAC)`:
//
// - A robust estimator earns a small residual by discarding the points that
// disagree with the model. On a turned face those are precisely the
// foreshortened landmarks — the pose signal AR-030 exists to measure. RANSAC
// would suppress exactly the quantity we want to read.
// - With five points and a two-point minimal sample there is almost no
// redundancy, so it cannot distinguish a mis-detected landmark from honest
// out-of-plane rotation. The robustness is nominal.
// - It is RNG-driven (`cv::theRNG()` is thread-local); this is exact, so
// replay determinism stops depending on thread scheduling.
//
// Returns an empty Mat when the source points are degenerate (all coincident).
inline cv::Mat umeyama_similarity(const std::array<cv::Point2f, 5>& src,
const std::array<cv::Point2f, 5>& dst) {
constexpr int N = 5;
double mu_sx = 0, mu_sy = 0, mu_dx = 0, mu_dy = 0;
for (int i = 0; i < N; ++i) {
mu_sx += src[i].x; mu_sy += src[i].y;
mu_dx += dst[i].x; mu_dy += dst[i].y;
}
mu_sx /= N; mu_sy /= N; mu_dx /= N; mu_dy /= N;
// var_src and the cross-covariance Σ = (1/N) Σ (d - μ_d)(s - μ_s)ᵀ
double var_s = 0;
cv::Matx22d sigma = cv::Matx22d::zeros();
for (int i = 0; i < N; ++i) {
const double sx = src[i].x - mu_sx, sy = src[i].y - mu_sy;
const double dx = dst[i].x - mu_dx, dy = dst[i].y - mu_dy;
var_s += sx * sx + sy * sy;
sigma(0, 0) += dx * sx; sigma(0, 1) += dx * sy;
sigma(1, 0) += dy * sx; sigma(1, 1) += dy * sy;
}
var_s /= N;
sigma *= 1.0 / N;
if (var_s < 1e-12) return {}; // every source point coincides — no scale
cv::Mat w, u, vt;
cv::SVD::compute(cv::Mat(sigma), w, u, vt, cv::SVD::FULL_UV);
const cv::Matx22d U (u.at<double>(0,0), u.at<double>(0,1),
u.at<double>(1,0), u.at<double>(1,1));
const cv::Matx22d Vt(vt.at<double>(0,0), vt.at<double>(0,1),
vt.at<double>(1,0), vt.at<double>(1,1));
// A similarity may rotate but never mirror: if the fit came out
// orientation-reversing, flip the least-significant singular direction.
cv::Matx22d S = cv::Matx22d::eye();
if (cv::determinant(U) * cv::determinant(Vt) < 0) S(1, 1) = -1;
const cv::Matx22d R = U * S * Vt;
const double c = (w.at<double>(0) * S(0,0) + w.at<double>(1) * S(1,1)) / var_s;
cv::Mat M(2, 3, CV_64F);
M.at<double>(0,0) = c * R(0,0); M.at<double>(0,1) = c * R(0,1);
M.at<double>(1,0) = c * R(1,0); M.at<double>(1,1) = c * R(1,1);
M.at<double>(0,2) = mu_dx - c * (R(0,0) * mu_sx + R(0,1) * mu_sy);
M.at<double>(1,2) = mu_dy - c * (R(1,0) * mu_sx + R(1,1) * mu_sy);
return M;
}
// ── Alignment ─────────────────────────────────────────────────────────────────
// The 5-point fit, plus what it could not explain.
//
// `residual` is the RMS landmark error in **canonical 112×112 pixels** after the
// best similarity fit. Two properties make it the AR-030 visibility measure:
//
// - The similarity transform absorbs rotation, uniform scale and translation
// exactly, so the residual is by construction the part of the deformation a
// similarity *cannot* explain — out-of-plane rotation and foreshortening,
// plus landmark noise. In-plane roll contributes nothing. The "roll must not
// read as yaw" failure is excluded structurally rather than by tuning.
// - The destination frame is fixed, so a 40 px face and a 400 px face are both
// measured in the same canonical space. The measure cannot silently
// re-express face size (already AR-002's job) the way a raw-pixel one would.
//
// It also responds to occlusion and to plainly broken landmark sets, which a
// yaw-angle estimator by construction does not.
struct Alignment {
cv::Mat M; ///< 2×3 CV_64F: source pixels → canonical 112×112
float residual{0.f}; ///< RMS canonical-pixel error; 0 ⇒ a perfect fit
bool ok{false}; ///< false ⇒ degenerate landmarks, no transform
};
/// Fit the canonical ArcFace template to `landmarks` and report the misfit.
inline Alignment estimate_alignment(const std::array<cv::Point2f, 5>& landmarks) {
std::array<cv::Point2f, 5> dst;
for (int i = 0; i < 5; ++i) dst[i] = {kArcFaceRef[i][0], kArcFaceRef[i][1]};
cv::Mat M = cv::estimateAffinePartial2D(src, dst, cv::noArray(), cv::RANSAC, 3.0);
if (M.empty()) return {};
Alignment a;
a.M = umeyama_similarity(landmarks, dst);
if (a.M.empty()) return a;
double sq = 0;
for (int i = 0; i < 5; ++i) {
const double x = a.M.at<double>(0,0) * landmarks[i].x
+ a.M.at<double>(0,1) * landmarks[i].y + a.M.at<double>(0,2);
const double y = a.M.at<double>(1,0) * landmarks[i].x
+ a.M.at<double>(1,1) * landmarks[i].y + a.M.at<double>(1,2);
const double ex = x - dst[i].x, ey = y - dst[i].y;
sq += ex * ex + ey * ey;
}
a.residual = static_cast<float>(std::sqrt(sq / 5.0));
a.ok = true;
return a;
}
// ── align_face ────────────────────────────────────────────────────────────────
// Produces a 112×112 BGR crop using the ArcFace 5-point similarity transform.
// Returns an empty Mat if the fit fails (degenerate detection). When
// `residual_out` is non-null it receives the AR-030 misfit for the same fit —
// free, since the transform has already been computed.
inline cv::Mat align_face(const cv::Mat& img,
const std::array<cv::Point2f, 5>& landmarks,
float* residual_out = nullptr) {
const Alignment a = estimate_alignment(landmarks);
if (!a.ok) return {};
if (residual_out) *residual_out = a.residual;
cv::Mat crop;
cv::warpAffine(img, crop, M, {112, 112},
cv::warpAffine(img, crop, a.M, {112, 112},
cv::INTER_LINEAR, cv::BORDER_CONSTANT, {0, 0, 0});
return crop;
}
src/gallery/embedder_stamp.cpp
+326
View File
@@ -0,0 +1,326 @@
/// TRACES: GR-004 | SR-001
#include "embedder_stamp.hpp"
#include "types.hpp"
#include <algorithm>
#include <cstdlib>
#include <cstring>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <map>
#include <mutex>
#include <sstream>
#include <stdexcept>
#include <vector>
namespace fs = std::filesystem;
// ── SHA-256 (FIPS 180-4) ──────────────────────────────────────────────────────
// Self-contained rather than pulled from OpenSSL: the gallery library already
// links OpenCV, HDF5, FFmpeg and a GPU backend, and the unit tests deliberately
// link none of those crypto stacks. ~80 lines of table-driven code is cheaper
// than another find_package that CI has to satisfy on an Intel N100.
namespace {
struct Sha256 {
uint32_t h[8] = {0x6a09e667u, 0xbb67ae85u, 0x3c6ef372u, 0xa54ff53au,
0x510e527fu, 0x9b05688cu, 0x1f83d9abu, 0x5be0cd19u};
uint64_t len = 0;
uint8_t buf[64]{};
size_t buf_n = 0;
static uint32_t ror(uint32_t x, int n) { return (x >> n) | (x << (32 - n)); }
void block(const uint8_t* p) {
static const uint32_t k[64] = {
0x428a2f98u,0x71374491u,0xb5c0fbcfu,0xe9b5dba5u,0x3956c25bu,0x59f111f1u,
0x923f82a4u,0xab1c5ed5u,0xd807aa98u,0x12835b01u,0x243185beu,0x550c7dc3u,
0x72be5d74u,0x80deb1feu,0x9bdc06a7u,0xc19bf174u,0xe49b69c1u,0xefbe4786u,
0x0fc19dc6u,0x240ca1ccu,0x2de92c6fu,0x4a7484aau,0x5cb0a9dcu,0x76f988dau,
0x983e5152u,0xa831c66du,0xb00327c8u,0xbf597fc7u,0xc6e00bf3u,0xd5a79147u,
0x06ca6351u,0x14292967u,0x27b70a85u,0x2e1b2138u,0x4d2c6dfcu,0x53380d13u,
0x650a7354u,0x766a0abbu,0x81c2c92eu,0x92722c85u,0xa2bfe8a1u,0xa81a664bu,
0xc24b8b70u,0xc76c51a3u,0xd192e819u,0xd6990624u,0xf40e3585u,0x106aa070u,
0x19a4c116u,0x1e376c08u,0x2748774cu,0x34b0bcb5u,0x391c0cb3u,0x4ed8aa4au,
0x5b9cca4fu,0x682e6ff3u,0x748f82eeu,0x78a5636fu,0x84c87814u,0x8cc70208u,
0x90befffau,0xa4506cebu,0xbef9a3f7u,0xc67178f2u};
uint32_t w[64];
for (int i = 0; i < 16; ++i)
w[i] = (uint32_t(p[i * 4]) << 24) | (uint32_t(p[i * 4 + 1]) << 16) |
(uint32_t(p[i * 4 + 2]) << 8) | uint32_t(p[i * 4 + 3]);
for (int i = 16; i < 64; ++i) {
uint32_t s0 = ror(w[i - 15], 7) ^ ror(w[i - 15], 18) ^ (w[i - 15] >> 3);
uint32_t s1 = ror(w[i - 2], 17) ^ ror(w[i - 2], 19) ^ (w[i - 2] >> 10);
w[i] = w[i - 16] + s0 + w[i - 7] + s1;
}
uint32_t a = h[0], b = h[1], c = h[2], d = h[3];
uint32_t e = h[4], f = h[5], g = h[6], hh = h[7];
for (int i = 0; i < 64; ++i) {
uint32_t S1 = ror(e, 6) ^ ror(e, 11) ^ ror(e, 25);
uint32_t ch = (e & f) ^ (~e & g);
uint32_t t1 = hh + S1 + ch + k[i] + w[i];
uint32_t S0 = ror(a, 2) ^ ror(a, 13) ^ ror(a, 22);
uint32_t mj = (a & b) ^ (a & c) ^ (b & c);
uint32_t t2 = S0 + mj;
hh = g; g = f; f = e; e = d + t1;
d = c; c = b; b = a; a = t1 + t2;
}
h[0] += a; h[1] += b; h[2] += c; h[3] += d;
h[4] += e; h[5] += f; h[6] += g; h[7] += hh;
}
void update(const uint8_t* p, size_t n) {
len += n;
while (n) {
size_t take = std::min(n, size_t(64) - buf_n);
std::memcpy(buf + buf_n, p, take);
buf_n += take; p += take; n -= take;
if (buf_n == 64) { block(buf); buf_n = 0; }
}
}
std::string hex() {
uint64_t bits = len * 8;
uint8_t pad = 0x80;
update(&pad, 1);
uint8_t zero = 0;
while (buf_n != 56) update(&zero, 1);
uint8_t tail[8];
for (int i = 0; i < 8; ++i) tail[i] = uint8_t(bits >> (56 - i * 8));
// update() would re-count these into len, but len is already frozen in bits.
std::memcpy(buf + buf_n, tail, 8);
block(buf);
buf_n = 0;
static const char* d = "0123456789abcdef";
std::string out;
out.reserve(64);
for (int i = 0; i < 8; ++i)
for (int s = 28; s >= 0; s -= 4)
out += d[(h[i] >> s) & 0xF];
return out;
}
};
// (path, mtime, size) → digest. Hashing a 250 MB ONNX is cheap but not free, and
// the optimizer constructs many networks in one process against the same model.
std::mutex g_hash_mu;
std::map<std::string, std::string> g_hash_cache;
std::string short_hash(const std::string& hex) {
return hex.size() > 12 ? hex.substr(0, 12) + "…" : hex;
}
} // namespace
std::string sha256_hex(const std::string& bytes) {
Sha256 s;
s.update(reinterpret_cast<const uint8_t*>(bytes.data()), bytes.size());
return s.hex();
}
std::string sha256_file_hex(const std::string& path) {
if (path.empty()) return "";
std::error_code ec;
auto size = fs::file_size(path, ec);
if (ec) return "";
auto mtime = fs::last_write_time(path, ec);
if (ec) return "";
std::ostringstream key;
key << path << '|' << size << '|'
<< mtime.time_since_epoch().count();
{
std::lock_guard<std::mutex> lk(g_hash_mu);
auto it = g_hash_cache.find(key.str());
if (it != g_hash_cache.end()) return it->second;
}
std::ifstream f(path, std::ios::binary);
if (!f) return "";
Sha256 s;
std::vector<char> chunk(1 << 20);
while (f) {
f.read(chunk.data(), static_cast<std::streamsize>(chunk.size()));
std::streamsize got = f.gcount();
if (got > 0) s.update(reinterpret_cast<const uint8_t*>(chunk.data()),
static_cast<size_t>(got));
}
std::string hex = s.hex();
std::lock_guard<std::mutex> lk(g_hash_mu);
g_hash_cache[key.str()] = hex;
return hex;
}
// ── EmbedderStamp ─────────────────────────────────────────────────────────────
std::string EmbedderStamp::describe() const {
std::string name = model_name.empty() ? "<unnamed model>" : model_name;
if (model_sha256.empty())
return name + " (sha256 unavailable)";
return name + " (sha256 " + short_hash(model_sha256) + ")";
}
EmbedderStamp make_embedder_stamp(const std::string& model_path) {
EmbedderStamp s;
if (model_path.empty()) return s;
s.model_name = fs::path(model_path).filename().string();
s.model_sha256 = sha256_file_hex(model_path);
if (s.model_sha256.empty())
std::cerr << "[gallery] cannot hash embedder model " << model_path
<< " — model binding falls back to filename only (GR-004)\n";
return s;
}
bool require_gallery_stamp_from_env() {
const char* v = std::getenv("SAE_REQUIRE_GALLERY_STAMP");
return v && *v && std::strcmp(v, "0") != 0;
}
// ── Comparison ────────────────────────────────────────────────────────────────
StampCheck compare_embedder_stamps(const EmbedderStamp& built_with,
const EmbedderStamp& loading_with,
const std::string& gallery_desc,
const std::string& embedder_desc) {
StampCheck out;
std::ostringstream m;
// The gallery predates GR-004 (or was written by a tool that does not stamp).
if (built_with.empty()) {
out.verdict = StampVerdict::unstamped;
m << "gallery '" << gallery_desc << "' carries no embedder stamp (GR-004).\n"
<< " gallery was built with : UNKNOWN — this file predates model binding\n"
<< " embedder now loaded : " << loading_with.describe()
<< " [" << embedder_desc << "]\n"
<< " If these are not the same model every similarity from this run is\n"
<< " meaningless but will look plausible. Rebuild or re-stamp the gallery\n"
<< " (scripts/stamp_gallery.py), or run with SAE_REQUIRE_GALLERY_STAMP=1 to\n"
<< " make this a hard error.";
out.message = m.str();
return out;
}
// Gallery is stamped but we cannot say what is about to embed.
if (loading_with.empty()) {
out.verdict = StampVerdict::unknown_embedder;
m << "cannot identify the embedder being used against gallery '"
<< gallery_desc << "' (GR-004).\n"
<< " gallery was built with : " << built_with.describe() << "\n"
<< " embedder now loaded : UNKNOWN [" << embedder_desc << "]\n"
<< " The binding cannot be checked, so it is not being checked.";
out.message = m.str();
return out;
}
const bool have_both_hashes =
!built_with.model_sha256.empty() && !loading_with.model_sha256.empty();
// Embedding width disagreeing is a mismatch on its own terms — different
// spaces entirely, and it will not even be caught by a cosine that "looks fine".
if (built_with.embed_dim != loading_with.embed_dim) {
out.verdict = StampVerdict::mismatch;
m << "gallery/embedder MODEL MISMATCH — refusing to run (GR-004).\n"
<< " gallery was built with : " << built_with.describe()
<< ", dim=" << built_with.embed_dim << " [" << gallery_desc << "]\n"
<< " embedder now loaded : " << loading_with.describe()
<< ", dim=" << loading_with.embed_dim << " [" << embedder_desc << "]\n"
<< " Embedding dimensions differ; these are not the same space.";
out.message = m.str();
return out;
}
if (have_both_hashes) {
if (built_with.model_sha256 == loading_with.model_sha256) {
out.verdict = StampVerdict::match;
m << "embedder binding verified: " << built_with.describe();
if (built_with.model_name != loading_with.model_name)
m << " (gallery recorded it as '" << built_with.model_name
<< "', loaded from '" << loading_with.model_name
<< "' — same bytes, renamed file)";
out.message = m.str();
return out;
}
out.verdict = StampVerdict::mismatch;
m << "gallery/embedder MODEL MISMATCH — refusing to run (GR-004).\n"
<< " gallery was built with : " << built_with.model_name
<< " sha256=" << built_with.model_sha256 << "\n"
<< " [" << gallery_desc << "]\n"
<< " embedder now loaded : " << loading_with.model_name
<< " sha256=" << loading_with.model_sha256 << "\n"
<< " [" << embedder_desc << "]\n"
<< " Cosine similarities between embeddings from different models are\n"
<< " meaningless but look plausible. Rebuild the gallery with the loaded\n"
<< " model, or point the embedder at the model the gallery was built with.";
out.message = m.str();
return out;
}
// One side has no hash (e.g. a TRT deployment with the .onnx absent). Names
// are all we have; agreeing on them is evidence, not proof.
if (!built_with.model_name.empty() &&
built_with.model_name == loading_with.model_name) {
out.verdict = StampVerdict::weak_match;
m << "embedder binding UNPROVEN for gallery '" << gallery_desc << "' (GR-004).\n"
<< " gallery was built with : " << built_with.describe() << "\n"
<< " embedder now loaded : " << loading_with.describe()
<< " [" << embedder_desc << "]\n"
<< " Filenames agree but at least one SHA-256 is unavailable, so an\n"
<< " in-place re-export under the same name would not be detected.";
out.message = m.str();
return out;
}
out.verdict = StampVerdict::mismatch;
m << "gallery/embedder MODEL MISMATCH — refusing to run (GR-004).\n"
<< " gallery was built with : " << built_with.describe()
<< " [" << gallery_desc << "]\n"
<< " embedder now loaded : " << loading_with.describe()
<< " [" << embedder_desc << "]\n"
<< " Cosine similarities between embeddings from different models are\n"
<< " meaningless but look plausible. Rebuild the gallery with the loaded\n"
<< " model, or point the embedder at the model the gallery was built with.";
out.message = m.str();
return out;
}
void enforce_embedder_stamp(const EmbedderStamp& built_with,
const EmbedderStamp& loading_with,
const std::string& gallery_desc,
const std::string& embedder_desc,
bool require_stamp) {
const bool strict = require_stamp || require_gallery_stamp_from_env();
StampCheck chk = compare_embedder_stamps(built_with, loading_with,
gallery_desc, embedder_desc);
if (chk.fatal(strict)) {
if (chk.verdict != StampVerdict::mismatch)
throw std::runtime_error(chk.message +
"\n (fatal because SAE_REQUIRE_GALLERY_STAMP / --require-gallery-stamp is set)");
throw std::runtime_error(chk.message);
}
if (chk.verdict == StampVerdict::match) {
std::cerr << "[gallery] " << chk.message << "\n";
} else {
std::cerr << "\n[gallery] ***** WARNING (GR-004) *****\n"
<< chk.message << "\n"
<< "[gallery] ****************************\n\n";
}
}
void verify_gallery_embedder(const ActorGallery& gallery,
const std::string& gallery_path,
const std::string& arcface_model_path,
bool require_stamp) {
enforce_embedder_stamp(gallery.embedder,
make_embedder_stamp(arcface_model_path),
gallery_path,
arcface_model_path.empty() ? "no --arcface given"
: arcface_model_path,
require_stamp);
}
src/gallery/embedder_stamp.hpp
+115
View File
@@ -0,0 +1,115 @@
#pragma once
/// TRACES: GR-004 | SR-001
//
// Gallery ↔ embedder binding.
//
// A gallery is only valid for the embedder that built it. Cosine similarities
// between embeddings from two different models are meaningless but *look*
// plausible — nothing crashes, nothing is obviously wrong, and every number
// measured downstream is quietly garbage. So the embedder's identity is stamped
// into the gallery at build time and checked by every consumer at load time.
//
// ── What identifies an embedder ───────────────────────────────────────────────
// Two fields, carried together:
//
// model_name basename of the model file, e.g. "LVFace-B_Glint360K.onnx"
// model_sha256 hex SHA-256 of that file's bytes
//
// The hash is what *decides*; the name is what a human *reads*. Neither alone is
// enough:
//
// • A name alone is a promise, not a fact. Models get re-exported, re-quantised
// and overwritten in place under an unchanged filename — which is precisely
// the case where the weights differ and nothing else does. A name-only stamp
// is blind to exactly the failure it exists to catch.
// • A hash alone is correct but unreadable: "expected 3f2a… got 9c1b…" tells an
// operator nothing about what to do next.
//
// SHA-256 over the file bytes is derived from the artefact rather than asserted
// about it, is stable across machines and filesystems, and needs no registry to
// be kept up to date. Cost is ~0.1 s for a 250 MB ONNX, paid once per process
// (results are memoised on path+mtime+size), which is noise next to model load.
//
// ── Degraded and legacy cases ─────────────────────────────────────────────────
// A TRT-backend deployment may run from a prebuilt .engine with the source .onnx
// absent, so the hash cannot be computed. Then the name is compared alone and the
// result is reported as a *weak* match — believed, not proven.
//
// Galleries built before GR-004 carry no stamp at all. They warn loudly rather
// than fail, because the state is unknown rather than known-bad, and because
// hard-failing every pre-existing gallery would make the check something people
// route around rather than trust. Set require_stamp (or SAE_REQUIRE_GALLERY_STAMP=1)
// to promote "unknown" to a hard error — that is the mode measurement work runs in.
//
// A *mismatch* is always fatal, in every mode, with no bypass.
#include <cstdint>
#include <string>
struct EmbedderStamp {
std::string model_name; // basename of the model file
std::string model_sha256; // lowercase hex SHA-256 of the file's bytes ("" = unavailable)
int32_t embed_dim{512};
bool empty() const { return model_name.empty() && model_sha256.empty(); }
// "LVFace-B_Glint360K.onnx (sha256 3f2a1c4d…)" — for error messages.
std::string describe() const;
};
// Identify the model at `model_path`. Missing/unreadable file → name filled from
// the path, hash left empty (the weak-match path). Empty path → empty stamp.
EmbedderStamp make_embedder_stamp(const std::string& model_path);
enum class StampVerdict {
match, // hashes agree — binding proven
weak_match, // names agree, no hash on one side — believed, unproven
unstamped, // gallery predates GR-004 / was written without a stamp
unknown_embedder, // gallery is stamped but the loaded embedder can't be identified
mismatch, // proven different models — always fatal
};
struct StampCheck {
StampVerdict verdict{StampVerdict::match};
std::string message; // human-readable, names BOTH sides
// A mismatch is fatal unconditionally. The three "cannot prove it" verdicts
// are fatal only in strict mode.
bool fatal(bool require_stamp) const {
return verdict == StampVerdict::mismatch ||
(require_stamp && verdict != StampVerdict::match);
}
};
// Pure comparison — no file I/O, no model loading. This is the unit under test.
// `gallery_desc`/`embedder_desc` are only used to make the message locatable
// (a gallery path, a dump path, "the embedder being loaded", …).
StampCheck compare_embedder_stamps(const EmbedderStamp& built_with,
const EmbedderStamp& loading_with,
const std::string& gallery_desc = "gallery",
const std::string& embedder_desc = "embedder");
// Apply the comparison: throw std::runtime_error on a fatal verdict, otherwise
// log to stderr. `require_stamp` is OR-ed with SAE_REQUIRE_GALLERY_STAMP.
void enforce_embedder_stamp(const EmbedderStamp& built_with,
const EmbedderStamp& loading_with,
const std::string& gallery_desc,
const std::string& embedder_desc,
bool require_stamp);
// Convenience for the common consumer shape: "I loaded this gallery and I am
// about to embed with this model file." Hashes the model, then enforces.
struct ActorGallery;
void verify_gallery_embedder(const ActorGallery& gallery,
const std::string& gallery_path,
const std::string& arcface_model_path,
bool require_stamp);
// SAE_REQUIRE_GALLERY_STAMP=1 → treat an unprovable binding as fatal.
bool require_gallery_stamp_from_env();
// Lowercase hex SHA-256. Exposed so a test can pin the digest against the
// published vectors, which is what guarantees the C++ and Python (hashlib)
// stamps of the same file agree.
std::string sha256_hex(const std::string& bytes);
std::string sha256_file_hex(const std::string& path); // "" if unreadable
src/gallery/gallery_builder.cpp
+28
View File
@@ -1,5 +1,6 @@
#include "gallery_builder.hpp"
#include "config.hpp"
#include "embedder_stamp.hpp"
#include "face_utils.hpp"
#include "inference/face_detector.hpp"
#include "inference/face_embedder.hpp"
@@ -41,6 +42,12 @@ ActorGallery build_gallery(const BuildConfig& cfg) {
ActorGallery gallery;
/// TRACES: GR-004 | SR-001
// Stamp before the first embedding exists, so there is no window in which a
// gallery holds vectors without recording what produced them.
gallery.embedder = make_embedder_stamp(cfg.arcface_model);
std::cerr << "[build_gallery] embedder: " << gallery.embedder.describe() << "\n";
for (const auto& actor_dir : fs::directory_iterator(cfg.gallery_root)) {
if (!actor_dir.is_directory()) continue;
@@ -89,6 +96,27 @@ ActorGallery build_gallery(const BuildConfig& cfg) {
return a.confidence < b.confidence;
});
// Reject faces too small to embed honestly.
//
// The reference images are crops cut from the film, not mugshots, so
// the detected face can be a small fraction of the image. Upscaling a
// 30 px face to ArcFace's 112x112 feeds the model an input it was
// never trained for, and it answers with a confident, plausible,
// wrong embedding.
//
// At inference that costs one frame. Here it is permanent: a poisoned
// reference sits in the gallery and corrupts every future match
// against that character, which is exactly the kind of error that is
// invisible without a study that should not have been needed.
if (cfg.min_face_px > 0.f) {
const float side = std::min(best.bbox.width, best.bbox.height);
if (side < cfg.min_face_px) {
std::cerr << " [skip] face " << side << "px < " << cfg.min_face_px
<< "px: " << img_file.path().filename() << "\n";
continue;
}
}
cv::Mat crop = align_face(img, best.landmarks);
if (crop.empty()) {
std::cerr << " [skip] alignment failed: " << img_file.path().filename() << "\n";
src/gallery/gallery_builder.hpp
+11
View File
@@ -1,4 +1,5 @@
#pragma once
#include "gallery/gallery_report.hpp"
#include "types.hpp"
#include <string>
@@ -28,6 +29,16 @@ struct BuildConfig {
float detector_conf{0.5f};
float detector_nms{0.4f};
int max_side{500}; // downscale source images to this max dimension
/// Minimum detected-face side, in pixels of the (possibly downscaled)
/// source image. 0 disables the check.
///
/// References below this are dropped rather than upscaled: a face smaller
/// than the embedder's input is off-distribution, and a bad reference
/// poisons every match against that identity for the life of the gallery.
/// Mirrors the inference-side --min-face-px so the gallery is built from
/// the same face scales it will be matched against.
float min_face_px{0.f};
// before detection — TMDB portraits are ~2k px,
// SCRFD trains on smaller faces and detection
// confidence drops on huge inputs. 0 = disabled.
src/gallery/gallery_calibration.hpp
+105 -1
View File
@@ -1,4 +1,5 @@
#pragma once
/// TRACES: AR-023 | SR-002
#include "types.hpp"
#include <algorithm>
@@ -8,6 +9,7 @@
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <functional>
#include <iostream>
#include <stdexcept>
#include <string>
@@ -48,14 +50,75 @@ struct GalleryCalibration {
}
};
/// TRACES: AR-023, AR-024 | SR-002
///
/// cosine → P(same person). The one probability space the pipeline reasons in.
///
/// Handed to every stage that has to decide whether two embeddings are the same
/// person — track association (AR-007), evidence discounting (AR-025), identity
/// matching — so a threshold of 0.5 means the same thing in all of them. A stage
/// that thresholded a raw cosine instead would be using a number that means
/// something different for every model, gallery and face size (AR-024).
///
/// **No prior term.** `log_prior_odds` adjusts for the gallery's base rate, which
/// is a question about *which of N actors*; association asks whether two faces
/// are one person, where the balanced fit is the right answer. Passing the
/// matcher's prior here would silently bias tracking by the size of the cast.
inline std::function<float(float)> same_person_probability(const GalleryCalibration& cal) {
if (!cal.valid) {
// Loud, because the failure mode is invisible: an untuned sigmoid still
// returns plausible probabilities, and every threshold downstream of it
// is then a guess wearing a calibrated number's clothes.
std::cerr << "[calibration] WARNING: no fitted calibration — association and "
"evidence weighting fall back to the untuned default sigmoid "
"(a=" << cal.a << ", b=" << cal.b << "). Probabilities are "
"not meaningful for this embedder.\n";
}
return [cal](float similarity) { return cal.probability(similarity); };
}
/// TRACES: GR-003 | SR-001
///
/// Everything the fit learns about the gallery on its way to two numbers.
///
/// The fit computes per-actor dedup counts, which actors can supply positive
/// pairs at all, and the two similarity distributions the sigmoid is derived
/// from — and then returns only (a, b, valid). GR-003 exists because that is the
/// evidence for whether the calibration, and so every threshold expressed in its
/// probability space (AR-024), rests on anything. Filling this struct costs
/// nothing: the values already exist at the point they are copied out.
///
/// Per-actor vectors are indexed by the actor index used in `flat_actor`.
struct GalleryCalibrationStats {
int n_actors = 0;
int min_embeddings_for_positive = 0;
float dedup_sim_threshold = 0.f;
std::vector<int> distinct_per_actor; // after near-duplicate removal
std::vector<int> duplicates_removed_per_actor;
std::vector<char> eligible; // 1 = supplies positive pairs
int hist_bins = 0; // over sim ∈ [-1, 1]
std::vector<double> intra_hist;
std::vector<double> inter_hist;
double n_intra_pairs = 0.0;
double n_inter_pairs = 0.0;
double train_accuracy_pct = 0.0;
};
// Fit a logistic sigmoid to gallery pair similarities.
// Positive pairs: same actor, different reference images.
// Negative pairs: different actors (all cross-actor embedding pairs).
// Class weights balance the (typically skewed) pos/neg ratio.
// Requires ≥2 positive pairs and ≥1 negative pair.
//
// `stats` is optional (GR-003): pass one to receive the dedup, eligibility and
// distribution detail the fit would otherwise discard.
inline GalleryCalibration calibrate_gallery(
const std::vector<Embedding>& flat_emb,
const std::vector<int>& flat_actor)
const std::vector<int>& flat_actor,
GalleryCalibrationStats* stats = nullptr)
{
constexpr int kMinEmbeddingsForPositive = 5;
constexpr float kDedupSimThreshold = 1.f - 1e-7f; // sim above this = duplicate
@@ -79,6 +142,21 @@ inline GalleryCalibration calibrate_gallery(
std::vector<bool> actor_eligible(n_actors, false);
int n_eligible = 0;
/// TRACES: GR-003 | SR-001
// Record what the filter did, per actor, while the counts still exist.
if (stats) {
*stats = GalleryCalibrationStats{};
stats->n_actors = n_actors;
stats->min_embeddings_for_positive = kMinEmbeddingsForPositive;
stats->dedup_sim_threshold = kDedupSimThreshold;
stats->distinct_per_actor.assign(n_actors, 0);
stats->duplicates_removed_per_actor.assign(n_actors, 0);
stats->eligible.assign(n_actors, 0);
stats->hist_bins = kHistBins;
stats->intra_hist.assign(kHistBins, 0.0);
stats->inter_hist.assign(kHistBins, 0.0);
}
for (int ai = 0; ai < n_actors; ++ai) {
std::vector<Embedding> kept;
for (const auto& e : by_actor[ai]) {
@@ -92,6 +170,12 @@ inline GalleryCalibration calibrate_gallery(
actor_eligible[ai] = true;
++n_eligible;
}
if (stats) {
stats->distinct_per_actor[ai] = static_cast<int>(kept.size());
stats->duplicates_removed_per_actor[ai] =
static_cast<int>(by_actor[ai].size() - kept.size());
stats->eligible[ai] = actor_eligible[ai] ? 1 : 0;
}
for (auto& e : kept) {
flat_emb_dedup.push_back(e);
flat_actor_dedup.push_back(ai);
@@ -99,6 +183,14 @@ inline GalleryCalibration calibrate_gallery(
}
const int n = static_cast<int>(flat_emb_dedup.size());
// Nothing to fit and nothing to multiply. Returning here keeps the report
// buildable for a degenerate gallery instead of handing cv::gemm an empty
// matrix; the per-actor stats above are already filled and still useful.
if (n == 0) {
std::cerr << "[calibration] no embeddings — calibration skipped\n";
return {};
}
std::cerr << "[calibration] dedup: " << flat_emb.size() << " -> " << n
<< " embeddings (" << n_eligible << "/" << n_actors
<< " actors have >= " << kMinEmbeddingsForPositive
@@ -199,6 +291,17 @@ inline GalleryCalibration calibrate_gallery(
double n_pos = 0.0, n_neg = 0.0;
for (int b = 0; b < kHistBins; ++b) { n_pos += pos_hist[b]; n_neg += neg_hist[b]; }
/// TRACES: GR-003 | SR-001
// The two distributions the sigmoid is about to be fitted from. Emitted
// whether or not the fit succeeds — a failed fit is exactly the case where
// someone needs to see why.
if (stats) {
stats->intra_hist = pos_hist;
stats->inter_hist = neg_hist;
stats->n_intra_pairs = n_pos;
stats->n_inter_pairs = n_neg;
}
if (n_pos < 2 || n_neg < 1) {
std::cerr << "[calibration] insufficient pairs (+" << n_pos
<< "/-" << n_neg << ") — calibration skipped\n";
@@ -253,6 +356,7 @@ inline GalleryCalibration calibrate_gallery(
correct += (sig > 0.5f) ? pos_hist[b] : neg_hist[b];
}
double acc = 100.0 * correct / total;
if (stats) stats->train_accuracy_pct = acc;
GalleryCalibration cal{a, bias, true};
std::cerr << "[calibration] sigmoid fitted:"
src/gallery/gallery_report.hpp
+495
View File
@@ -0,0 +1,495 @@
#pragma once
/// TRACES: GR-003 | SR-001
///
/// The gallery build report — what the gallery *is*, written next to it.
///
/// A gallery is a silent artefact: it loads, it scores, it never complains. The
/// two ways it fails are both invisible from the outside.
///
/// 1. **An actor with zero usable images can never be recognised.** They are
/// dropped at build time (`gallery_builder.cpp` skips a directory whose
/// images all fail detection or alignment), so afterwards nothing in the
/// file records that they were ever meant to be there. Every scene they
/// appear in is a guaranteed miss, and recall is capped at a number nobody
/// computed. This is the single most useful line in the report.
/// 2. **A gallery can be quietly bad and look fine.** The Platt sigmoid
/// (AR-023) is fitted from two distributions — intra-class (same actor,
/// different reference) and inter-class (different actors) similarity — and
/// *every* threshold in the pipeline is expressed in the probability space
/// that fit defines (AR-024): identity acceptance, track association,
/// expansion admission, cluster merging. If those two distributions overlap
/// heavily the fit is weak, and every downstream decision silently inherits
/// that weakness while still reporting confident-looking probabilities. The
/// fit already computes the distributions and throws them away; emitting
/// them is what makes the quality of the whole probability space auditable
/// instead of assumed.
///
/// The report is therefore a build artefact, not a debug aid: it is the only
/// place the recall ceiling and the calibration's conditioning are written down.
///
/// **On the histograms being in cosine space.** They bin raw similarity, and
/// that is not an AR-024 violation: no decision is taken here. These two
/// distributions are the *input* the calibration is fitted from — they cannot be
/// expressed in the probability space the calibration defines, because that
/// space is their output. GR-003 asks for exactly this ("the intra/inter
/// distributions behind it"), for the same reason GR-008 characterises an
/// actor's reference spread in the metric space: shape is a property of the
/// metric, decisions are a property of the probability.
#include "gallery/gallery_calibration.hpp"
#include "gallery/embedder_stamp.hpp"
#include "types.hpp"
#include <nlohmann/json.hpp>
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <fstream>
#include <stdexcept>
#include <string>
#include <vector>
/// TRACES: GR-003 | SR-001
///
/// Per-actor image accounting from the build pass, including the actors that
/// produced nothing and were therefore dropped from the gallery.
///
/// Filled by `build_gallery()`. It has to be collected there and cannot be
/// recovered later: by the time a gallery exists, an actor with no usable image
/// is indistinguishable from an actor who was never requested.
struct GalleryBuildAudit {
struct ActorImages {
std::string imdb_id;
std::string name;
int images_seen = 0; // candidate image files in the actor's directory
int images_used = 0; // ...that yielded an embedding
int unreadable = 0; // cv::imread failed
int no_face = 0; // detector found nothing
int align_failed = 0; // 5-point warp failed
};
std::vector<ActorImages> actors; // every directory seen, in build order
};
/// TRACES: GR-003 | SR-001
struct GalleryReport {
// One row per actor the build considered. Actors with references == 0 are
// the zero-usable-image case: present in the source tree, absent from the
// gallery, unrecognisable for the life of the file.
struct Actor {
std::string imdb_id;
std::string name;
int images_seen = -1; // -1 = unknown (report built without a build audit)
int references = 0; // embeddings stored in the gallery
int distinct_references = 0; // ...after near-duplicate removal
int duplicates_removed = 0;
bool eligible_for_positive_pairs = false;
};
// The two distributions the sigmoid is fitted from, as the fit itself saw
// them: counts per similarity bin over [sim_min, sim_max].
struct Distributions {
int bins = 0;
float sim_min = -1.f;
float sim_max = 1.f;
std::vector<double> intra; // same actor, different reference image
std::vector<double> inter; // different actors
double intra_pairs = 0.0;
double inter_pairs = 0.0;
double intra_mean = 0.0;
double inter_mean = 0.0;
// Normalised histogram intersection, Σ_b min(p_intra[b], p_inter[b]).
// 0 = perfectly separated, 1 = indistinguishable. This is the number
// that says whether the calibration — and so every threshold expressed
// in its probability space — rests on anything.
double overlap = 0.0;
};
// GR-003 / AR-023 open question, reported but NOT applied. The spec asks for
// a gallery-derived prior of intra/(intra+inter); the shipped default is
// 0.5. Persisting the distributions makes the real value computable, so the
// decision can be taken on evidence rather than left implicit. Behaviour is
// unchanged: `applied` is always false here.
struct Prior {
double derived = 0.0; // intra_pairs / (intra_pairs + inter_pairs)
double derived_log_odds = 0.0; // log(p/(1-p)), the term AR-023 would add
float configured_default = 0.5f;
bool applied = false;
std::string note;
};
std::string schema{"sae.gallery_report/1"};
std::string gallery_path;
EmbedderStamp embedder;
// ── Summary ──────────────────────────────────────────────────────────────
int actors_total = 0; // considered (gallery + zero-usable)
int actors_in_gallery = 0;
int actors_zero_usable = 0;
int actors_below_positive_threshold = 0;
int64_t embeddings_total = 0;
int64_t distinct_embeddings_total = 0;
int64_t duplicates_removed_total = 0;
double mean_embeddings_per_actor = 0.0; // over actors in the gallery
int min_embeddings_for_positive_pairs = 0;
float dedup_similarity_threshold = 0.f;
// ── Calibration ──────────────────────────────────────────────────────────
float calib_a = 10.f;
float calib_b = -5.f;
bool calib_valid = false;
uint64_t calib_hash = 0;
double calib_train_accuracy_pct = 0.0;
float calib_boundary_p50 = 0.f; // similarity at which P(match) = 0.5
Distributions distributions;
Prior prior;
std::vector<Actor> actors;
// Names duplicated out of `actors` so the two failure modes are greppable
// without a JSON query. These are the lines a human reads first.
std::vector<std::string> zero_usable;
std::vector<std::string> below_positive_threshold;
};
/// TRACES: GR-003 | SR-001
///
/// Assembles the report from the three things that know a piece of the answer:
/// the gallery itself (who is in it, with how many references), the calibration
/// stats (dedup, eligibility, the two distributions), and the build audit (who
/// was considered and produced nothing). The audit is optional — a report built
/// from a stored gallery simply cannot know about the actors that never made it.
///
/// `stats` is indexed by actor index, so the flat arrays handed to
/// `calibrate_gallery()` must have used the gallery's own actor ordering.
inline GalleryReport build_gallery_report(const ActorGallery& gallery,
const GalleryCalibration& cal,
const GalleryCalibrationStats& stats,
const GalleryBuildAudit* audit = nullptr,
const std::string& gallery_path = "",
float configured_prior = 0.5f)
{
GalleryReport r;
r.gallery_path = gallery_path;
r.embedder = gallery.embedder;
r.calib_a = cal.a;
r.calib_b = cal.b;
r.calib_valid = cal.valid;
r.calib_hash = gallery.calib_hash;
r.calib_train_accuracy_pct = stats.train_accuracy_pct;
r.calib_boundary_p50 = cal.boundary_at(0.5f);
r.min_embeddings_for_positive_pairs = stats.min_embeddings_for_positive;
r.dedup_similarity_threshold = stats.dedup_sim_threshold;
auto audit_for = [&](const ActorGallery::Actor& a) -> const GalleryBuildAudit::ActorImages* {
if (!audit) return nullptr;
for (const auto& e : audit->actors) {
if (!a.imdb_id.empty() && e.imdb_id == a.imdb_id) return &e;
if (a.imdb_id.empty() && e.name == a.name) return &e;
}
return nullptr;
};
for (size_t i = 0; i < gallery.actors.size(); ++i) {
const auto& ga = gallery.actors[i];
GalleryReport::Actor row;
row.imdb_id = ga.imdb_id;
row.name = ga.name;
row.references = static_cast<int>(ga.embeddings.size());
if (const auto* au = audit_for(ga)) row.images_seen = au->images_seen;
if (i < stats.distinct_per_actor.size()) {
row.distinct_references = stats.distinct_per_actor[i];
row.duplicates_removed = stats.duplicates_removed_per_actor[i];
row.eligible_for_positive_pairs = stats.eligible[i] != 0;
} else {
// No calibration stats for this actor (the fit never saw them).
// Report the raw count rather than a fabricated distinct count.
row.distinct_references = row.references;
}
r.embeddings_total += row.references;
r.distinct_embeddings_total += row.distinct_references;
r.duplicates_removed_total += row.duplicates_removed;
if (!row.eligible_for_positive_pairs) {
++r.actors_below_positive_threshold;
r.below_positive_threshold.push_back(row.name);
}
r.actors.push_back(std::move(row));
}
r.actors_in_gallery = static_cast<int>(gallery.actors.size());
// Actors the build considered and could not use at all. They are not in the
// gallery, so this is the only record that they exist.
if (audit) {
for (const auto& e : audit->actors) {
if (e.images_used > 0) continue;
GalleryReport::Actor row;
row.imdb_id = e.imdb_id;
row.name = e.name;
row.images_seen = e.images_seen;
row.references = 0;
r.zero_usable.push_back(e.name);
r.actors.push_back(std::move(row));
}
}
r.actors_zero_usable = static_cast<int>(r.zero_usable.size());
r.actors_total = r.actors_in_gallery + r.actors_zero_usable;
r.mean_embeddings_per_actor =
r.actors_in_gallery > 0
? static_cast<double>(r.embeddings_total) / r.actors_in_gallery
: 0.0;
// ── The two distributions, straight out of the fit ───────────────────────
auto& d = r.distributions;
d.bins = stats.hist_bins;
d.sim_min = -1.f;
d.sim_max = 1.f;
d.intra = stats.intra_hist;
d.inter = stats.inter_hist;
d.intra_pairs = stats.n_intra_pairs;
d.inter_pairs = stats.n_inter_pairs;
if (d.bins > 0) {
const double bin_w = (d.sim_max - d.sim_min) / d.bins;
double si = 0.0, se = 0.0;
for (int b = 0; b < d.bins; ++b) {
const double centre = d.sim_min + (b + 0.5) * bin_w;
si += d.intra[b] * centre;
se += d.inter[b] * centre;
}
if (d.intra_pairs > 0.0) d.intra_mean = si / d.intra_pairs;
if (d.inter_pairs > 0.0) d.inter_mean = se / d.inter_pairs;
if (d.intra_pairs > 0.0 && d.inter_pairs > 0.0) {
double ov = 0.0;
for (int b = 0; b < d.bins; ++b)
ov += std::min(d.intra[b] / d.intra_pairs, d.inter[b] / d.inter_pairs);
d.overlap = ov;
}
}
// ── The prior AR-023 leaves open — computed, reported, not applied ────────
r.prior.configured_default = configured_prior;
r.prior.applied = false;
const double pair_total = d.intra_pairs + d.inter_pairs;
if (pair_total > 0.0) {
r.prior.derived = d.intra_pairs / pair_total;
const double p = std::clamp(r.prior.derived, 1e-12, 1.0 - 1e-12);
r.prior.derived_log_odds = std::log(p / (1.0 - p));
}
r.prior.note =
"AR-023 specifies a gallery-derived prior of intra/(intra+inter); the shipped "
"match_prior default is 0.5 (calibrated sigmoid used directly). The derived value "
"is the base rate of same-actor pairs among ALL enumerated gallery pairs, so it "
"falls as the cast grows (roughly (k-1)/((k-1)+(A-1)k) for A actors with k "
"references each) — it is a property of gallery size as much as of the embedder. "
"Reported here as evidence; NOT applied. Behaviour is unchanged until the choice "
"is recorded in the spec.";
return r;
}
// ── JSON ─────────────────────────────────────────────────────────────────────
/// TRACES: GR-003 | SR-001
inline nlohmann::json gallery_report_to_json(const GalleryReport& r) {
nlohmann::json j;
j["schema"] = r.schema;
j["gallery_path"] = r.gallery_path;
j["embedder"] = {{"model_name", r.embedder.model_name},
{"model_sha256", r.embedder.model_sha256},
{"embed_dim", r.embedder.embed_dim}};
j["summary"] = {
{"actors_total", r.actors_total},
{"actors_in_gallery", r.actors_in_gallery},
{"actors_zero_usable", r.actors_zero_usable},
{"actors_below_positive_threshold", r.actors_below_positive_threshold},
{"embeddings_total", r.embeddings_total},
{"distinct_embeddings_total", r.distinct_embeddings_total},
{"duplicates_removed_total", r.duplicates_removed_total},
{"mean_embeddings_per_actor", r.mean_embeddings_per_actor},
{"min_embeddings_for_positive_pairs", r.min_embeddings_for_positive_pairs},
{"dedup_similarity_threshold", r.dedup_similarity_threshold}};
j["calibration"] = {
{"a", r.calib_a},
{"b", r.calib_b},
{"valid", r.calib_valid},
{"hash", r.calib_hash},
{"train_accuracy_pct", r.calib_train_accuracy_pct},
{"boundary_p50", r.calib_boundary_p50}};
const auto& d = r.distributions;
j["distributions"] = {
{"bins", d.bins},
{"sim_min", d.sim_min},
{"sim_max", d.sim_max},
{"intra", d.intra},
{"inter", d.inter},
{"intra_pairs", d.intra_pairs},
{"inter_pairs", d.inter_pairs},
{"intra_mean", d.intra_mean},
{"inter_mean", d.inter_mean},
{"overlap", d.overlap}};
j["prior"] = {
{"derived", r.prior.derived},
{"derived_log_odds", r.prior.derived_log_odds},
{"configured_default", r.prior.configured_default},
{"applied", r.prior.applied},
{"note", r.prior.note}};
j["zero_usable"] = r.zero_usable;
j["below_positive_threshold"] = r.below_positive_threshold;
j["actors"] = nlohmann::json::array();
for (const auto& a : r.actors) {
j["actors"].push_back({
{"imdb_id", a.imdb_id},
{"name", a.name},
{"images_seen", a.images_seen},
{"references", a.references},
{"distinct_references", a.distinct_references},
{"duplicates_removed", a.duplicates_removed},
{"eligible_for_positive_pairs", a.eligible_for_positive_pairs}});
}
return j;
}
/// TRACES: GR-003 | SR-001
inline GalleryReport gallery_report_from_json(const nlohmann::json& j) {
GalleryReport r;
r.schema = j.value("schema", std::string{});
r.gallery_path = j.value("gallery_path", std::string{});
if (j.contains("embedder")) {
const auto& je = j.at("embedder");
r.embedder.model_name = je.value("model_name", "");
r.embedder.model_sha256 = je.value("model_sha256", "");
r.embedder.embed_dim = je.value("embed_dim", 512);
}
if (j.contains("summary")) {
const auto& s = j.at("summary");
r.actors_total = s.value("actors_total", 0);
r.actors_in_gallery = s.value("actors_in_gallery", 0);
r.actors_zero_usable = s.value("actors_zero_usable", 0);
r.actors_below_positive_threshold = s.value("actors_below_positive_threshold", 0);
r.embeddings_total = s.value("embeddings_total", int64_t{0});
r.distinct_embeddings_total = s.value("distinct_embeddings_total", int64_t{0});
r.duplicates_removed_total = s.value("duplicates_removed_total", int64_t{0});
r.mean_embeddings_per_actor = s.value("mean_embeddings_per_actor", 0.0);
r.min_embeddings_for_positive_pairs = s.value("min_embeddings_for_positive_pairs", 0);
r.dedup_similarity_threshold = s.value("dedup_similarity_threshold", 0.f);
}
if (j.contains("calibration")) {
const auto& c = j.at("calibration");
r.calib_a = c.value("a", 10.f);
r.calib_b = c.value("b", -5.f);
r.calib_valid = c.value("valid", false);
r.calib_hash = c.value("hash", uint64_t{0});
r.calib_train_accuracy_pct = c.value("train_accuracy_pct", 0.0);
r.calib_boundary_p50 = c.value("boundary_p50", 0.f);
}
if (j.contains("distributions")) {
const auto& d = j.at("distributions");
r.distributions.bins = d.value("bins", 0);
r.distributions.sim_min = d.value("sim_min", -1.f);
r.distributions.sim_max = d.value("sim_max", 1.f);
r.distributions.intra = d.value("intra", std::vector<double>{});
r.distributions.inter = d.value("inter", std::vector<double>{});
r.distributions.intra_pairs = d.value("intra_pairs", 0.0);
r.distributions.inter_pairs = d.value("inter_pairs", 0.0);
r.distributions.intra_mean = d.value("intra_mean", 0.0);
r.distributions.inter_mean = d.value("inter_mean", 0.0);
r.distributions.overlap = d.value("overlap", 0.0);
}
if (j.contains("prior")) {
const auto& p = j.at("prior");
r.prior.derived = p.value("derived", 0.0);
r.prior.derived_log_odds = p.value("derived_log_odds", 0.0);
r.prior.configured_default = p.value("configured_default", 0.5f);
r.prior.applied = p.value("applied", false);
r.prior.note = p.value("note", "");
}
r.zero_usable = j.value("zero_usable", std::vector<std::string>{});
r.below_positive_threshold = j.value("below_positive_threshold", std::vector<std::string>{});
if (j.contains("actors")) {
for (const auto& ja : j.at("actors")) {
GalleryReport::Actor a;
a.imdb_id = ja.value("imdb_id", "");
a.name = ja.value("name", "");
a.images_seen = ja.value("images_seen", -1);
a.references = ja.value("references", 0);
a.distinct_references = ja.value("distinct_references", 0);
a.duplicates_removed = ja.value("duplicates_removed", 0);
a.eligible_for_positive_pairs = ja.value("eligible_for_positive_pairs", false);
r.actors.push_back(std::move(a));
}
}
return r;
}
// "<dir>/cast.h5" → "<dir>/cast.report.json". A known gallery extension is
// replaced rather than appended to, so the report sits beside the gallery under
// the same stem.
inline std::string gallery_report_path(const std::string& gallery_path) {
auto slash = gallery_path.find_last_of("/\\");
auto dot = gallery_path.find_last_of('.');
std::string stem =
(dot != std::string::npos && (slash == std::string::npos || dot > slash))
? gallery_path.substr(0, dot)
: gallery_path;
return stem + ".report.json";
}
/// TRACES: GR-003 | SR-001
inline void save_gallery_report(const std::string& path, const GalleryReport& r) {
std::ofstream out(path);
if (!out.is_open())
throw std::runtime_error("save_gallery_report: cannot write " + path);
out << gallery_report_to_json(r).dump(2) << "\n";
}
/// TRACES: GR-003 | SR-001
inline GalleryReport load_gallery_report(const std::string& path) {
std::ifstream in(path);
if (!in.is_open())
throw std::runtime_error("load_gallery_report: cannot open " + path);
nlohmann::json j;
in >> j;
return gallery_report_from_json(j);
}
/// TRACES: GR-003 | SR-001
///
/// The report's headline, on stderr, at build time. The file is the audit trail;
/// this is what stops a bad gallery from being shipped without anyone noticing.
inline void log_gallery_report(const GalleryReport& r) {
std::cerr << "[gallery-report] " << r.actors_in_gallery << " actors / "
<< r.embeddings_total << " embeddings"
<< " (mean " << r.mean_embeddings_per_actor << " per actor)\n";
if (r.actors_zero_usable > 0) {
std::cerr << "[gallery-report] WARNING: " << r.actors_zero_usable
<< " actor(s) have NO usable image — they can never be recognised:\n";
for (const auto& n : r.zero_usable) std::cerr << " - " << n << "\n";
}
if (r.actors_below_positive_threshold > 0) {
std::cerr << "[gallery-report] " << r.actors_below_positive_threshold
<< " actor(s) below " << r.min_embeddings_for_positive_pairs
<< " distinct references — they contribute no positive pairs and "
"weaken the calibration\n";
}
if (r.duplicates_removed_total > 0)
std::cerr << "[gallery-report] " << r.duplicates_removed_total
<< " near-duplicate reference(s) removed\n";
std::cerr << "[gallery-report] calibration valid=" << r.calib_valid
<< " a=" << r.calib_a << " b=" << r.calib_b
<< " intra/inter overlap=" << r.distributions.overlap
<< " (intra mean=" << r.distributions.intra_mean
<< ", inter mean=" << r.distributions.inter_mean << ")\n";
std::cerr << "[gallery-report] gallery-derived prior would be "
<< r.prior.derived << " (log-odds " << r.prior.derived_log_odds
<< "); shipped default " << r.prior.configured_default
<< " is in force — reported, not applied\n";
}
src/gallery/gallery_store.cpp
+40
View File
@@ -79,6 +79,21 @@ static ActorGallery load_gallery_hdf5(const std::string& path) {
gallery.actors.push_back(std::move(actor));
}
/// TRACES: GR-004 | SR-001
// Absent /embedder group == a gallery written before model binding existed.
// It stays readable; verify_gallery_embedder() decides what that means.
if (file.nameExists("embedder")) {
H5::Group eg = file.openGroup("embedder");
H5::StrType str(H5::PredType::C_S1, H5T_VARIABLE);
if (eg.attrExists("model_name"))
eg.openAttribute("model_name").read(str, gallery.embedder.model_name);
if (eg.attrExists("model_sha256"))
eg.openAttribute("model_sha256").read(str, gallery.embedder.model_sha256);
if (eg.attrExists("embed_dim"))
eg.openAttribute("embed_dim").read(H5::PredType::NATIVE_INT32,
&gallery.embedder.embed_dim);
}
if (file.nameExists("calibration")) {
H5::Group cal = file.openGroup("calibration");
cal.openAttribute("a").read(H5::PredType::NATIVE_FLOAT, &gallery.calib_a);
@@ -149,6 +164,20 @@ static void save_gallery_hdf5(const std::string& path, const ActorGallery& galle
write_str_dataset(file, "name", name);
write_str_dataset(file, "source_images", src_images);
/// TRACES: GR-004 | SR-001
// Bind the file to the embedder that produced its vectors. Written only when
// known — an empty stamp must round-trip as "unstamped", not as a stamp
// claiming an unnamed model.
if (!gallery.embedder.empty()) {
H5::Group eg = file.createGroup("embedder");
H5::DataSpace scalar(H5S_SCALAR);
H5::StrType str(H5::PredType::C_S1, H5T_VARIABLE);
eg.createAttribute("model_name", str, scalar).write(str, gallery.embedder.model_name);
eg.createAttribute("model_sha256", str, scalar).write(str, gallery.embedder.model_sha256);
eg.createAttribute("embed_dim", H5::PredType::NATIVE_INT32, scalar)
.write(H5::PredType::NATIVE_INT32, &gallery.embedder.embed_dim);
}
if (gallery.calib_hash != 0) {
H5::Group cal = file.createGroup("calibration");
H5::DataSpace scalar(H5S_SCALAR);
@@ -186,6 +215,17 @@ ActorGallery load_gallery(const std::string& path) {
<< std::chrono::duration<double>(t1 - t0).count() << "s\n";
ActorGallery gallery;
/// TRACES: GR-004 | SR-001
// Optional top-level "embedder" object, matching the HDF5 /embedder group.
// Written by the JSON-era helper scripts; absent in anything older.
if (j.contains("embedder") && j.at("embedder").is_object()) {
const auto& je = j.at("embedder");
gallery.embedder.model_name = je.value("model_name", "");
gallery.embedder.model_sha256 = je.value("model_sha256", "");
gallery.embedder.embed_dim = je.value("embed_dim", 512);
}
for (const auto& ja : j.at("actors")) {
ActorGallery::Actor actor;
actor.imdb_id = ja.value("imdb_id", "");
src/gallery/gallery_store.hpp
+14
View File
@@ -6,12 +6,25 @@
// legacy gallery.json files are still readable for backward compatibility but
// save_gallery always writes HDF5 regardless of the requested extension.
//
// GR-005 is preserved here by absence: this is the only path that serialises a
// gallery, and it reads and writes the local filesystem only. There is no
// upload, no client, and no encoder that could put an embedding on a wire — the
// public server refuses to carry one (SR-004/UR-012), and the prohibition holds
// on this side by there being nothing that would try.
//
/// TRACES: GR-005 | SR-005
//
// HDF5 layout:
// /embeddings float32 [N, 512] all actors' refs concatenated, row-major
// /offset int64 [A] first row of actor a in /embeddings
// /count int32 [A] number of refs for actor a
// /imdb_id /tmdb_id /jellyfin_id /name : variable-length string [A]
// /source_images : variable-length string [N], parallel to /embeddings rows
// /embedder/model_name : scalar var-len string attr — embedder file basename
// /embedder/model_sha256 : scalar var-len string attr — SHA-256 of that file
// /embedder/embed_dim : scalar int32 attr
// The GR-004 model binding. Absent group == unstamped
// (pre-GR-004 file); see gallery/embedder_stamp.hpp.
// /calibration/a, /b : scalar float32 attrs — Platt-sigmoid P(match|sim) fit
// /calibration/valid : scalar int8 attr (0/1)
// /calibration/hash : scalar uint64 attr — hash of the embeddings the fit
@@ -19,6 +32,7 @@
//
// Legacy JSON format (read-only):
// {
// "embedder": {"model_name": "...", "model_sha256": "...", "embed_dim": 512},
// "actors": [
// {
// "imdb_id": "nm0000093", // optional, "" if unknown
src/gallery/track_gallery.hpp
+128 -47
View File
@@ -2,6 +2,8 @@
#include "types.hpp"
#include "config.hpp"
#include <functional>
#include <cmath>
#include <cstdio>
#include <iostream>
@@ -34,13 +36,15 @@
// 2. Promotion (on confirmation). A track is "owned" by actor A once ≥N frames
// have been accepted (by the matcher's calibrated posterior) as A. On
// confirmation the retained buffer — the hard, gallery-far poses — is
// promoted into A's per-film annex, after two safety gates:
// • novelty: only embeddings whose best sim to A's refs is below
// expand_novelty_sim are added (skip poses already covered);
// • spread: if the retained buffer's internal spread (1 − min pairwise
// cosine sim) exceeds expand_track_spread_max the whole track is
// rejected — such spread signals a track-ID collision merging two
// people, whose embeddings must never enter A's annex.
// promoted into A's per-film annex, subject to one safety gate: the band's
// lower bound, re-applied across the whole store (see `store_coherence`).
//
// There is exactly one threshold here, the AR-018 band, and it is a calibrated
// probability. Novelty is no longer a threshold at all — the eviction policy
// above *orders* by gallery similarity rather than cutting at a constant, and
// the band's upper bound refuses the redundant views at the door. The raw
// cosines this replaces, expand_novelty_sim and expand_track_spread_max, are
// retired under AR-024.
//
// The annex is CPU-side and in-memory: it is small (tens of embeddings) so the
// matcher scans it with a scalar loop, and it is discarded when the process
@@ -57,16 +61,15 @@ struct TrackGallery {
explicit TrackGallery(const Config& cfg)
: enabled_(cfg.expand_gallery)
, buffer_size_(std::max(1, cfg.expand_buffer_size))
, novelty_sim_(cfg.expand_novelty_sim)
, spread_max_(cfg.expand_track_spread_max)
, band_lo_(cfg.expand_band_lo)
, band_hi_(cfg.expand_band_hi)
, min_anchor_frames_(std::max(1, cfg.expand_min_anchor_frames))
, debug_dir_(cfg.expand_debug_dir)
{
if (!enabled_) return;
std::cerr << "[track_gallery] per-film expansion ON"
<< " buffer=" << buffer_size_
<< " novelty_sim<" << novelty_sim_
<< " spread_max=" << spread_max_
<< " band=[" << band_lo_ << ", " << band_hi_ << "]"
<< " min_anchor_frames=" << min_anchor_frames_;
if (!debug_dir_.empty()) {
std::filesystem::create_directories(debug_dir_);
@@ -86,7 +89,9 @@ struct TrackGallery {
// track_id : face_tracker track (−1 = untracked, ignored)
// emb : this frame's raw embedding
// best_actor : actor with the highest gallery similarity for this face
// best_gal_sim : that similarity (best sim to best_actor's baked+annex refs)
// best_gal_sim : that similarity (best sim to best_actor's baked+annex
// refs) — a raw cosine, the last one in this class: it is
// calibrated on entry and only the probability is stored
// accepted : true if the matcher accepted this face as best_actor
// crop : aligned crop, retained only when debug dumping is on
void observe(int track_id, const Embedding& emb,
@@ -117,13 +122,36 @@ struct TrackGallery {
// matcher when it observes a cut or track disappearance.
void forget(int track_id) { tracks_.erase(track_id); }
/// TRACES: AR-019 | SR-005
/// The registry's verdict on who this track is. Authoritative: it comes from
/// the Bayesian accumulation (AR-025), where the local tally counted raw
/// accepted frames and so weighted thirty near-identical looks the same as
/// thirty distinct ones.
void set_owner(int track_id, int actor_idx) {
if (track_id < 0 || actor_idx < 0) return;
tracks_[track_id].registry_owner = actor_idx;
}
/// TRACES: AR-024 | SR-005
/// Supply the calibration belonging to the active embedder. Without it the
/// band falls back to treating cosine as probability, which is wrong but
/// bounded — and the default is loud in the header rather than silent.
void set_calibration(std::function<float(float)> c) { calibrate_ = std::move(c); }
/// Embeddings the band refused. A store that admits nothing is as wrong as
/// one that admits everything, and neither is visible without this.
std::size_t band_rejected() const { return rejected_; }
// Drop every track buffer (scene cut / EOF). Mirrors face_tracker's clear.
void clear_tracks() { tracks_.clear(); }
private:
struct BufEntry {
Embedding emb;
float gal_sim{0.f}; // best sim to owning actor's refs when observed
/// P(same person) against the owning actor's refs when observed —
/// calibrated at the door (AR-024), so the eviction ordering below is a
/// comparison of probabilities and the struct holds no bare cosine.
float gal_p{0.f};
cv::Mat crop; // populated only when debug_dir_ set
};
@@ -132,14 +160,46 @@ private:
std::map<int, int> actor_votes; // actor_idx → accepted-frame count
int accepted_frames{0};
bool promoted{false};
int registry_owner{-1}; ///< AR-019: authoritative
};
/// TRACES: AR-018, AR-024 | SR-005
/// Banded admission: an embedding joins the store only if its similarity to
/// something already there falls **inside a band**.
///
/// above the upper bound → redundant. It is another look at a pose the
/// store already covers, and adding it teaches the annex nothing while
/// costing a slot that a novel view could have used.
/// below the lower bound → suspect. Within one track every face is the
/// same person by construction, so an embedding unlike everything else
/// on the track is evidence the construction failed — a track-ID
/// collision or a bad detection. Admitting it is how an actor's annex
/// gets poisoned with someone else's face.
///
/// Both bounds are calibrated probabilities, never raw cosines (AR-024): a
/// bare similarity threshold means something different for every model and
/// every face size, and this gate has to hold across both.
///
/// The first embedding is always admitted — there is nothing for it to be
/// redundant with, and nothing to contradict it.
bool admit(const TrackState& ts, const Embedding& emb) const {
if (ts.buf.empty()) return true;
float p_max = 0.f;
for (const auto& b : ts.buf)
p_max = std::max(p_max, calibrate_(cosine_similarity(b.emb, emb)));
return p_max >= band_lo_ && p_max <= band_hi_;
}
void insert_into_buffer(TrackState& ts, const Embedding& emb,
float gal_sim, const cv::Mat& crop)
{
if (!admit(ts, emb)) { ++rejected_; return; }
BufEntry e;
e.emb = emb;
e.gal_sim = gal_sim;
e.emb = emb;
e.gal_p = calibrate_(gal_sim);
if (!debug_dir_.empty() && !crop.empty()) e.crop = crop.clone();
if (static_cast<int>(ts.buf.size()) < buffer_size_) {
@@ -148,13 +208,17 @@ private:
}
// Buffer full: evict the member the gallery recognises best (highest
// gal_sim) — least informative — but only if the newcomer is at least as
// gal_p) — least informative — but only if the newcomer is at least as
// novel. Keeping the most gallery-far views is the whole point.
int worst_i = -1;
float worst_sim = e.gal_sim; // newcomer's sim is the bar to beat
//
// This is an *ordering*, not a threshold: there is no constant to tune,
// and novelty-seeking lives here rather than in a cutoff. It ranks
// probabilities, so it says the same thing across models (AR-024).
int worst_i = -1;
float worst_p = e.gal_p; // newcomer's probability is the bar to beat
for (int i = 0; i < static_cast<int>(ts.buf.size()); ++i) {
if (ts.buf[i].gal_sim > worst_sim) {
worst_sim = ts.buf[i].gal_sim;
if (ts.buf[i].gal_p > worst_p) {
worst_p = ts.buf[i].gal_p;
worst_i = i;
}
}
@@ -166,28 +230,21 @@ private:
void promote(int track_id, TrackState& ts) {
ts.promoted = true; // idempotent: never promote a track twice
int actor = plurality_actor(ts);
int actor = owning_actor(ts);
if (actor < 0) return;
// ── Safety gate: internal spread ─────────────────────────────────────
// A legitimate single-person track varies in pose but stays reasonably
// self-similar. Large spread signals two people merged under one track
// ID — reject the whole track rather than poison the actor's annex.
float spread = buffer_spread(ts.buf);
if (spread > spread_max_) {
// ── Safety gate: the band's lower bound, across the whole store ──────
float worst = store_coherence(ts.buf);
if (worst < band_lo_) {
std::cerr << "[track_gallery] track " << track_id
<< " → actor " << actor
<< " REJECTED (spread " << spread
<< " > " << spread_max_ << ", likely ID collision)\n";
<< " REJECTED (worst pairwise P=" << worst
<< " < " << band_lo_ << ", likely ID collision)\n";
return;
}
int added = 0;
for (const auto& be : ts.buf) {
// ── Safety gate: novelty ─────────────────────────────────────────
// Skip poses the gallery already covers; only gallery-far views are
// worth the annex slot (and the extra per-frame scan cost).
if (be.gal_sim >= novelty_sim_) continue;
annex_.push_back({be.emb, actor});
if (!debug_dir_.empty() && !be.crop.empty())
dump_mugshot(track_id, actor, added, be);
@@ -196,10 +253,16 @@ private:
std::cerr << "[track_gallery] track " << track_id
<< " confirmed actor " << actor
<< " (" << ts.accepted_frames << " accepted frames, spread "
<< spread << ") — promoted " << added << "/"
<< ts.buf.size() << " views; annex now "
<< annex_.size() << "\n";
<< " (" << ts.accepted_frames << " accepted frames, worst "
<< "pairwise P=" << worst << ") — promoted " << added
<< " views; annex now " << annex_.size() << "\n";
}
/// Prefer the registry's verdict; fall back to the local tally only when no
/// registry is attached (unit tests, replay harness).
static int owning_actor(const TrackState& ts) {
if (ts.registry_owner >= 0) return ts.registry_owner;
return plurality_actor(ts);
}
static int plurality_actor(const TrackState& ts) {
@@ -210,31 +273,49 @@ private:
return best;
}
// Spread = 1 − min pairwise cosine similarity over the buffer (0 when <2).
static float buffer_spread(const std::vector<BufEntry>& buf) {
float min_sim = std::numeric_limits<float>::max();
/// TRACES: AR-018, AR-024 | SR-005
/// The store's weakest pairwise P(same person) — the band's lower bound
/// asked of every pair, not just of the best match at the door.
///
/// `admit` compares a newcomer against its *closest* existing member, so a
/// track that drifts gradually can chain A→B→C with every step inside the
/// band while A and C are strangers. That is precisely the shape a track-ID
/// collision takes when two people are merged over a slow pan, so the bound
/// is re-asked here across all pairs before anything reaches an actor's
/// annex. Same bound, same probability space — not a second constant.
///
/// A store of one has no pair to disagree; it is coherent by construction,
/// hence 1.
float store_coherence(const std::vector<BufEntry>& buf) const {
float worst = std::numeric_limits<float>::max();
for (size_t i = 0; i < buf.size(); ++i)
for (size_t j = i + 1; j < buf.size(); ++j)
min_sim = std::min(min_sim, cosine_similarity(buf[i].emb, buf[j].emb));
if (min_sim == std::numeric_limits<float>::max()) return 0.f;
return 1.f - min_sim;
worst = std::min(worst,
calibrate_(cosine_similarity(buf[i].emb, buf[j].emb)));
if (worst == std::numeric_limits<float>::max()) return 1.f;
return worst;
}
void dump_mugshot(int track_id, int actor, int idx, const BufEntry& be) {
#ifdef SAE_DEBUG
char name[64];
std::snprintf(name, sizeof(name), "trk%d_actor%d_%d_sim%.3f.jpg",
track_id, actor, idx, be.gal_sim);
std::snprintf(name, sizeof(name), "trk%d_actor%d_%d_p%.3f.jpg",
track_id, actor, idx, be.gal_p);
cv::imwrite((std::filesystem::path(debug_dir_) / name).string(), be.crop);
#else
(void)track_id; (void)actor; (void)idx; (void)be;
#endif
}
/// cosine → P(same person). The one probability space the pipeline reasons
/// in; see gallery_calibration.hpp's same_person_probability.
std::function<float(float)> calibrate_{[](float c) { return std::max(0.f, c); }};
std::size_t rejected_{0}; ///< admissions refused by the band
bool enabled_;
int buffer_size_;
float novelty_sim_;
float spread_max_;
float band_lo_; ///< AR-018, from cfg.expand_band_lo
float band_hi_; ///< AR-018, from cfg.expand_band_hi
int min_anchor_frames_;
std::string debug_dir_;
src/kpn_bindings.cpp
+28 -2
View File
@@ -15,6 +15,7 @@
#include "types.hpp"
#include "config.hpp"
#include "gallery/embedder_stamp.hpp"
#include "gallery/gallery_store.hpp"
#include "nodes/face_tracker_node.hpp"
#include "nodes/identity_matcher_node.hpp"
@@ -163,6 +164,9 @@ static Config config_from_dict(nb::dict d) {
getd("anneal_sec", cfg.anneal_sec);
// gallery expansion (usually off for sweeps; expose so it can be toggled)
if (d.contains("expand_gallery")) cfg.expand_gallery = nb::cast<bool>(d["expand_gallery"]);
/// TRACES: GR-004 | SR-001
if (d.contains("require_gallery_stamp"))
cfg.require_gallery_stamp = nb::cast<bool>(d["require_gallery_stamp"]);
return cfg;
}
@@ -210,8 +214,17 @@ NB_MODULE(sae_kpn, m) {
net.add(std::move(name), std::move(node));
}, "net"_a, "name"_a, "config"_a, "capacity"_a = 16);
/// TRACES: GR-004 | SR-001
// embedder_model / embedder_sha256 identify whatever produced the embeddings
// that will be fed in. In a replay those come from the dump's own stamp (see
// scripts/optimizer/SCHEMA.md), because there is no live embedder in the
// network — the dump *is* the embedder as far as this gallery is concerned.
// Passing neither leaves the binding unverifiable, which warns loudly and is
// fatal under SAE_REQUIRE_GALLERY_STAMP.
m.def("add_identity_matcher", [](Net& net, std::string name, std::string gallery_path,
nb::dict cfg_dict, std::size_t cap) {
nb::dict cfg_dict, std::size_t cap,
std::string embedder_model,
std::string embedder_sha256) {
Config cfg = config_from_dict(cfg_dict);
cfg.gallery_path = gallery_path; // needed to persist refreshed calibration back
// Cache loaded galleries by path so a threshold sweep (many networks, same
@@ -222,11 +235,24 @@ NB_MODULE(sae_kpn, m) {
if (it == cache.end())
it = cache.emplace(gallery_path,
std::make_shared<ActorGallery>(load_gallery(gallery_path))).first;
// Checked on every construction, not only on the cache miss: the same
// process may replay several dumps against one cached gallery.
EmbedderStamp feeding;
feeding.model_name = std::move(embedder_model);
feeding.model_sha256 = std::move(embedder_sha256);
enforce_embedder_stamp(it->second->embedder, feeding, gallery_path,
feeding.model_name.empty()
? "embeddings fed into this network"
: feeding.model_name,
cfg.require_gallery_stamp);
auto node = std::make_shared<kpn::ObjectVariantNodeWrapper<
IdentityMatcherFunc, SaeVariant, kpn::in<"tracked">, kpn::out<"matched">>>(
cap, *it->second, cfg);
net.add(std::move(name), std::move(node));
}, "net"_a, "name"_a, "gallery"_a, "config"_a, "capacity"_a = 16);
}, "net"_a, "name"_a, "gallery"_a, "config"_a, "capacity"_a = 16,
"embedder_model"_a = "", "embedder_sha256"_a = "");
m.def("add_scene_tracker", [](Net& net, std::string name, nb::dict cfg_dict, std::size_t cap) {
Config cfg = config_from_dict(cfg_dict);
src/main.cpp
+148 -13
View File
@@ -1,5 +1,9 @@
// scene_analyze — identify actors in a movie using a KPN pipeline
//
// TRACES: DP-001, DP-002 | PR-004
// One analysis core; the CLI is a front-end over it and must not fork pipeline
// logic. Other deployment modes (DP-003, DP-004) wrap this same core.
//
// KPN topology (release build):
//
// [frame_source] ──Frame──► [face_detector] ──SceneFrame──► [face_aligner]
@@ -35,8 +39,8 @@
// --max-faces <N> max faces kept per frame (default: 10)
// --expand-gallery enable per-film gallery expansion from track continuity
// --expand-buffer <N> per-track diversity buffer size (default: 20)
// --expand-novelty-sim <f> promote only views with best sim < f (default: 0.55)
// --expand-spread-max <f> reject track if buffer spread > f (default: 0.60)
// --expand-band-lo <p> store admission floor, P(same person) (default: 0.90)
// --expand-band-hi <p> store admission ceiling, P(same person) (default: 0.95)
// --expand-min-anchor <N> accepted frames before a track confirms (default: 3)
// --expand-debug-dir <p> dump promoted mugshots + embeddings here (SAE_DEBUG)
// (SAE_DEBUG only)
@@ -45,6 +49,7 @@
#include "config.hpp"
#include "types.hpp"
#include "gallery/embedder_stamp.hpp"
#include "gallery/gallery_store.hpp"
#include "nodes/frame_source_node.hpp"
#include "nodes/camera_position_change_detector_node.hpp"
@@ -55,6 +60,8 @@
#include "nodes/identity_matcher_node.hpp"
#include "nodes/scene_tracker_node.hpp"
#include "nodes/scene_detector_node.hpp"
#include "scene_boundaries.hpp"
#include "nodes/scene_boundary_annotator_node.hpp"
#include "nodes/result_sink_node.hpp"
#include "nodes/embedding_dump_node.hpp"
#ifdef SAE_DEBUG
@@ -76,6 +83,18 @@
// ── CLI parsing ───────────────────────────────────────────────────────────────
/// TRACES: AR-010, AR-004 | SR-002
/// How deeply the sampled branch is buffered behind the dense one. TransNetV2
/// needs kWindow (100) dense frames before it can score any of them, so the face
/// branch must lag by at least that much or it asks about frames nobody has
/// looked at yet. Backpressure turns depth into lag: the fanout blocks on the
/// slower branch rather than dropping, so the detector simply runs ahead.
static constexpr std::size_t kSceneJoinDepth = 256;
/// Set when the scene branch is built, so shutdown can report whether the join
/// actually worked.
static std::shared_ptr<SceneBoundaries> scene_stats;
static Config parse_args(int argc, char** argv) {
Config cfg;
cfg.detector_model = kDefaultDetectorModel;
@@ -114,6 +133,7 @@ static Config parse_args(int argc, char** argv) {
else if (arg("--detector")) cfg.detector_model = next();
else if (arg("--detector-engine")) cfg.detector_engine = next();
else if (arg("--arcface")) cfg.arcface_model = next();
else if (arg("--require-gallery-stamp")) cfg.require_gallery_stamp = true;
else if (arg("--arcface-engine")) cfg.arcface_engine = next();
else if (arg("--conf")) cfg.detector_conf = std::stof(next());
else if (arg("--max-faces")) cfg.max_faces = std::stoi(next());
@@ -122,15 +142,13 @@ static Config parse_args(int argc, char** argv) {
else if (arg("--ratio-ceil")) cfg.match_ratio_ceil = std::stof(next());
else if (arg("--track-alpha")) cfg.track_alpha = std::stof(next());
else if (arg("--track-min-iou")) cfg.track_min_iou = std::stof(next());
else if (arg("--track-max-embed")) cfg.track_max_embed_dist = std::stof(next());
else if (arg("--track-max-missing")) cfg.track_max_frames_missing = std::stoi(next());
else if (arg("--cut-revive-sim")) cfg.cut_revive_sim = std::stof(next());
else if (arg("--cut-inactive-max")) cfg.cut_inactive_max_frames = std::stoi(next());
else if (arg("--track-min-prob")) cfg.track_assoc_min_prob = std::stof(next());
else if (arg("--track-extinction")) cfg.track_extinction_sec = std::stod(next());
else if (arg("--anneal")) cfg.anneal_sec = std::stod(next());
else if (arg("--expand-gallery")) cfg.expand_gallery = true;
else if (arg("--expand-buffer")) cfg.expand_buffer_size = std::stoi(next());
else if (arg("--expand-novelty-sim")) cfg.expand_novelty_sim = std::stof(next());
else if (arg("--expand-spread-max")) cfg.expand_track_spread_max = std::stof(next());
else if (arg("--expand-band-lo")) cfg.expand_band_lo = std::stof(next());
else if (arg("--expand-band-hi")) cfg.expand_band_hi = std::stof(next());
else if (arg("--expand-min-anchor")) cfg.expand_min_anchor_frames = std::stoi(next());
else if (arg("--expand-debug-dir")) cfg.expand_debug_dir = next();
else if (arg("--trt-cache")) cfg.trt.cache_dir = next();
@@ -167,6 +185,11 @@ int main(int argc, char** argv) {
ActorGallery gallery;
try {
gallery = load_gallery(cfg.gallery_path);
/// TRACES: GR-004 | SR-001
// Hard startup error before a single frame is decoded: a gallery built
// with another embedder yields plausible-looking, meaningless matches.
verify_gallery_embedder(gallery, cfg.gallery_path, cfg.arcface_model,
cfg.require_gallery_stamp);
} catch (const std::exception& e) {
std::cerr << "Gallery error: " << e.what() << "\n";
return 1;
@@ -183,10 +206,36 @@ int main(int argc, char** argv) {
FaceDetectorFunc detector_fn{cfg};
FaceAlignerFunc aligner_fn;
EmbedderFunc embedder_fn{cfg};
FaceTrackerFunc ftracker_fn{cfg};
// Constructed before the tracker: it fits (or loads) the calibration, and
// the tracker must decide in that same probability space (AR-024).
IdentityMatcherFunc matcher_fn {gallery, cfg};
/// TRACES: AR-007, AR-008, AR-012, AR-024 | SR-002
// The registry is created here and shared, not owned by a node: track state
// is not a stage in the stream, it is state several stages read and write,
// and its final answer is only known when a track dies.
auto same_person = same_person_probability(matcher_fn.calibration());
TrackRegistry::Config reg_cfg;
reg_cfg.extinction_sec = cfg.track_extinction_sec;
auto registry = std::make_shared<TrackRegistry>(
reg_cfg, EvidenceDiscounter(same_person));
matcher_fn.set_registry(registry);
FaceTrackerFunc ftracker_fn{cfg, registry, same_person};
SceneTrackerFunc tracker_fn {cfg};
ResultSinkFunc sink_fn {cfg, done};
/// TRACES: AR-012, AR-016 | IR-002, IR-003 | SR-002
// A reaped track goes straight to the aggregator, so the registry holds only
// live tracks and its size is bounded by concurrent on-screen faces rather
// than growing with the film.
registry->on_track_dead([&sink_fn](const DeadTrack& d) { sink_fn.add_claim(d); });
// AR-016: a film ends with faces on screen and those tracks have not timed
// out. Without this flush the closing scene's cast is silently never
// emitted — a loss that reads as a recognition miss, not a bookkeeping bug.
sink_fn.set_pre_write_hook([registry](double last_ts) { registry->flush(last_ts); });
#ifdef SAE_DEBUG
DebugRendererFunc debug_fn {cfg};
#endif
@@ -232,6 +281,26 @@ int main(int argc, char** argv) {
}
});
// Report *why* a node died. A Closed event alone says only that one
// stopped; the exception it carried is what identifies the fault, and
// without this listener it is discarded at the node boundary. Returning
// false keeps the existing semantics — the node still stops and the
// Closed handler above still aborts the run — but the run now names the
// cause instead of leaving it to be reconstructed from a debugger.
net.set_error_handler(
[&](std::string_view node_name, std::exception_ptr eptr) {
std::string what = "unknown exception";
try {
if (eptr) std::rethrow_exception(eptr);
} catch (const std::exception& e) {
what = e.what();
} catch (...) {
}
std::lock_guard<std::mutex> lk(event_mtx);
std::cerr << "[main] node '" << node_name << "' threw: " << what << "\n";
return false;
});
std::cerr << "[main] starting pipeline…\n";
net.start();
@@ -247,15 +316,45 @@ int main(int argc, char** argv) {
net.stop();
net.print_diagnostics();
/// TRACES: AR-004 | SR-002
// A dropped frame does not degrade a result, it silently changes one —
// the output is a claim about footage that was never analysed, and
// nothing in the file says so. Since AR-004 made data pushes block, a
// drop can no longer happen on the data path, so any drop here means
// either that fix regressed (it lives in the KPN submodule, one line,
// easy to lose in an update) or a channel was disabled mid-run.
//
// Reporting it in a footer and exiting 0 made both invisible: the run
// "succeeded" and the truth file looked complete. Fail instead.
/// TRACES: AR-010 | SR-002
if (scene_stats) {
std::cerr << "[scene_annotate] boundaries=" << scene_stats->count()
<< " scored_through=" << scene_stats->scored_through() << "s";
// The tail is expected: frames after the detector's last full
// window are never covered, and no amount of buffering changes
// that. They are counted rather than silently treated as
// boundary-free, which is the distinction that matters.
if (scene_stats->outran() > 0)
std::cerr << " unscored=" << scene_stats->outran()
<< " frame(s) past the detector's last window — treated as"
" boundary-free, which is unverified rather than known";
std::cerr << "\n";
}
bool dropped = false;
{
std::lock_guard<std::mutex> lk(event_mtx);
if (!overflow_counts.empty()) {
std::cerr << "[main] dropped frames (channel overflow):\n";
dropped = true;
std::cerr << "[main] ERROR: frames were dropped (channel overflow):\n";
for (const auto& [name, count] : overflow_counts)
std::cerr << " " << name << ": " << count << "\n";
std::cerr << "[main] The output would describe footage that was never "
"analysed. Refusing to report success.\n";
}
}
return node_crashed.load(std::memory_order_acquire) ? 1 : 0;
if (node_crashed.load(std::memory_order_acquire)) return 1;
return dropped ? 2 : 0;
};
// ── Build static network and run ──────────────────────────────────────────
@@ -296,6 +395,21 @@ int main(int argc, char** argv) {
if (cfg.scene_detect) {
scene_done.store(false, std::memory_order_release); // now a real terminal branch
SceneDetectorFunc scene_fn{cfg, scene_done};
/// TRACES: AR-010 | SR-002
// The join of the decode butterfly. source fans out to the dense
// TransNetV2 branch and the sampled face branch; boundaries found on the
// first have to reach the second, and cannot ride the frames because the
// branches run in parallel.
//
// TransNetV2 buffers kWindow frames before it can score any of them, so
// the face branch must lag by at least that much or it will ask about
// frames nobody has looked at yet. Channel depth is what creates the lag:
// with backpressure (AR-004) the fanout blocks on the slower branch, so
// a deep face-branch channel lets the detector run ahead by its window
// rather than dropping anything.
auto boundaries = std::make_shared<SceneBoundaries>();
scene_fn.set_boundaries(boundaries);
kpn::ObjectNode<SceneDetectorFunc, kpn::in<"dense">, kpn::out<>, "scene_detector", 0>
scene_node(scene_fn, 128);
@@ -312,13 +426,34 @@ int main(int argc, char** argv) {
return true;
}
return false;
}, 32);
}, kSceneJoinDepth);
/// TRACES: AR-010 | SR-002
// Stamp is_scene_boundary from the detector's published verdict. tol is
// half a sample interval: the two branches sample at different rates, so
// a boundary found on a dense frame rarely lands exactly on a sampled
// one, and half an interval attributes it to the nearest sampled frame
// and no further.
//
// outran() counts frames that arrived before the detector had scored
// them. Nonzero means the join depth is too shallow for the window, and
// those frames were annotated from an incomplete verdict — which would
// otherwise look exactly like "no boundary here".
SceneBoundaryAnnotatorFunc annotate_fn{boundaries, 0.5 / cfg.sample_fps};
kpn::ObjectNode<SceneBoundaryAnnotatorFunc, kpn::in<"frame">, kpn::out<"frame">,
"scene_annotate", 0> annotate(annotate_fn, kSceneJoinDepth);
// Reported at shutdown: without this the join is unverifiable, and an
// annotator that never fired looks identical to footage with no
// boundaries.
scene_stats = boundaries;
auto net = kpn::make_network(
kpn::edge(source.output<"raw">(), campos.input<"raw">()),
kpn::edge(source.output<"raw">(), scene_node.input<"dense">()),
kpn::edge(campos.output<"frame">(), decimate.input<0>()),
kpn::edge(decimate.output<0>(), detector.input<"frame">()),
kpn::edge(decimate.output<0>(), annotate.input<"frame">()),
kpn::edge(annotate.output<"frame">(), detector.input<"frame">()),
kpn::edge(detector.output<"scene">(), aligner.input<"scene">()),
kpn::edge(aligner.output<"aligned">(), embedder.input<"aligned">()),
kpn::edge(embedder.output<"embedded">(), ftracker.input<"embedded">()),
src/nodes/camera_position_change_detector_node.hpp
+2 -1
View File
@@ -26,7 +26,8 @@
// The node is a pure pass-through: it forwards the Frame unchanged except for
// is_cut, so it slots between frame_source and face_detector without altering the
// downstream contract. eof frames are forwarded immediately without processing.
//
/// TRACES: AR-009 | SR-002
struct CameraPositionChangeDetectorFunc {
static constexpr std::string_view label() { return "camera_position_change_detector"; }
src/nodes/embedder_node.hpp
+2 -1
View File
@@ -17,7 +17,8 @@
// All crops in one frame are batched into a single forward pass (capped at
// embed_batch_size). The backend serialises itself; we only call it from the
// single embedder thread.
//
/// TRACES: AR-006 | SR-002
struct EmbedderFunc {
static constexpr std::string_view label() { return "embedder"; }
src/nodes/embedding_dump_node.hpp
+183 -12
View File
@@ -1,14 +1,113 @@
#pragma once
/// TRACES: VR-001, VR-010 | PR-002
#include "types.hpp"
#include "config.hpp"
#include "gallery/embedder_stamp.hpp"
#include <H5Cpp.h>
#include <atomic>
#include <cstdint>
#include <iostream>
#include <optional>
#include <string>
#include <type_traits>
#include <vector>
// ── DumpProvenance ────────────────────────────────────────────────────────────
/// TRACES: VR-010 | PR-002
// Everything that determined a dump's *content*, read back tolerantly.
//
// Two dumps of the same film with different detector thresholds, a different
// `dense_scale`, or scene detection on versus off are different measurements of
// different things — but they are byte-shaped identically, so a consumer that
// mixes them gets a plausible number from an incoherent input. GR-004 closed the
// worst case (a cross-model replay, where every cosine is meaningless); this
// closes the rest.
//
// Every field is optional because dumps written before VR-010 lack the
// attributes. A missing field reads as *unknown*, never as a default — a
// silently-defaulted `detector_conf` is exactly the fabricated provenance the
// requirement exists to prevent ("a fixture whose provenance is unknown is worse
// than no fixture, because it will be trusted").
struct DumpProvenance {
// Model identity
std::optional<std::string> embedder_model; // GR-004
std::optional<std::string> embedder_sha256; // GR-004
std::optional<std::string> detector_model;
// Sampling
std::optional<std::string> movie;
std::optional<float> sample_fps;
std::optional<double> start_sec;
std::optional<double> end_sec; // -1 = to end of file
// Detection — what the run admitted into the dump
std::optional<float> detector_conf;
std::optional<float> detector_nms;
std::optional<float> min_face_px;
std::optional<int> max_faces; // 0 = uncapped (AR-003)
// Frame geometry
std::optional<float> dense_scale;
std::optional<float> bbox_upscale; // faces/bbox × this = original-resolution px
std::optional<float> cut_threshold;
// Scene detection. The reason this flag exists: `is_scene_boundary` is
// all-zero both when TransNetV2 found no boundaries and when it never ran,
// and no amount of staring at the array distinguishes them.
std::optional<bool> scene_detect;
// Downstream knob that shaped nothing in the dump but everything a replay is
// compared against — recorded so a sweep can be told apart from the baseline.
std::optional<float> track_assoc_min_prob;
};
// Read whatever provenance a dump carries. Never throws on a missing attribute;
// an old dump simply yields a DumpProvenance full of empty optionals.
inline DumpProvenance read_dump_provenance(const H5::H5File& f) {
DumpProvenance p;
auto str = [&](const char* n, std::optional<std::string>& out) {
if (!f.attrExists(n)) return;
// Written as a variable-length string, so the read must name the same
// type explicitly — the default would truncate to a fixed length.
H5::StrType vlen(H5::PredType::C_S1, H5T_VARIABLE);
std::string v;
f.openAttribute(n).read(vlen, v);
out = v;
};
auto num = [&](const char* n, const H5::PredType& dt, auto& out) {
if (!f.attrExists(n)) return;
typename std::decay_t<decltype(out)>::value_type v{};
f.openAttribute(n).read(dt, &v);
out = v;
};
str("embedder_model", p.embedder_model);
str("embedder_sha256", p.embedder_sha256);
str("detector_model", p.detector_model);
str("movie", p.movie);
num("sample_fps", H5::PredType::NATIVE_FLOAT, p.sample_fps);
num("start_sec", H5::PredType::NATIVE_DOUBLE, p.start_sec);
num("end_sec", H5::PredType::NATIVE_DOUBLE, p.end_sec);
num("detector_conf", H5::PredType::NATIVE_FLOAT, p.detector_conf);
num("detector_nms", H5::PredType::NATIVE_FLOAT, p.detector_nms);
num("min_face_px", H5::PredType::NATIVE_FLOAT, p.min_face_px);
num("max_faces", H5::PredType::NATIVE_INT, p.max_faces);
num("dense_scale", H5::PredType::NATIVE_FLOAT, p.dense_scale);
num("bbox_upscale", H5::PredType::NATIVE_FLOAT, p.bbox_upscale);
num("cut_threshold", H5::PredType::NATIVE_FLOAT, p.cut_threshold);
num("track_assoc_min_prob", H5::PredType::NATIVE_FLOAT, p.track_assoc_min_prob);
if (f.attrExists("scene_detect")) {
uint8_t v = 0;
f.openAttribute("scene_detect").read(H5::PredType::NATIVE_UINT8, &v);
p.scene_detect = (v != 0);
}
return p;
}
// ── EmbeddingDumpFunc ─────────────────────────────────────────────────────────
// KPN sink that taps the EmbeddedSceneFrame channel and writes the per-frame face
// metadata + embeddings to one HDF5 file (schema: scripts/optimizer/SCHEMA.md).
@@ -25,12 +124,44 @@ struct EmbeddingDumpFunc {
: path_(cfg.dump_embeddings_path), movie_(cfg.movie_path),
sample_fps_(cfg.sample_fps), done_(done)
{
std::cerr << "[embedding_dump] writing " << path_ << "\n";
/// TRACES: GR-004 | SR-001
// A dump is a bag of embeddings with no model attached, replayed against a
// gallery hours or weeks later — the same silent cross-model hazard as the
// gallery itself, so it carries the same stamp.
stamp_ = make_embedder_stamp(cfg.arcface_model);
/// TRACES: VR-010 | PR-002
// The rest of what determined this file's content. Captured from the live
// Config at construction, so it describes the run that is being written
// rather than whatever config happens to be lying around at read time.
prov_.detector_model = basename_of(cfg.detector_model);
prov_.detector_conf = cfg.detector_conf;
prov_.detector_nms = cfg.detector_nms;
prov_.min_face_px = cfg.min_face_px;
prov_.max_faces = cfg.max_faces;
prov_.cut_threshold = cfg.cut_threshold;
prov_.dense_scale = cfg.dense_scale;
prov_.start_sec = cfg.start_sec;
prov_.end_sec = cfg.end_sec;
prov_.scene_detect = cfg.scene_detect;
prov_.track_assoc_min_prob = cfg.track_assoc_min_prob;
std::cerr << "[embedding_dump] writing " << path_
<< " embedder: " << stamp_.describe()
<< " detector: " << *prov_.detector_model
<< " @conf " << cfg.detector_conf
<< " scene_detect=" << (cfg.scene_detect ? "on" : "off") << "\n";
}
void operator()(EmbeddedSceneFrame ef) {
if (ef.source.eof) { flush(); return; }
/// TRACES: VR-010 | PR-002
// Taken from the frames themselves, not recomputed from dense_scale — the
// factor the source actually stamped on them is the one that maps
// faces/bbox back to original resolution, whatever rule produced it.
if (!prov_.bbox_upscale) prov_.bbox_upscale = ef.source.bbox_upscale;
const int32_t n = static_cast<int32_t>(ef.faces.size());
ts_.push_back(ef.source.timestamp_sec);
fidx_.push_back(ef.source.frame_idx);
@@ -63,9 +194,18 @@ struct EmbeddingDumpFunc {
}
private:
// Root attributes are additive: schema_version stays 1 across VR-010, because
// every reader takes attributes by name with a default (replay.py) or an
// existence check (read_dump_provenance), so an old dump loses nothing and a
// new dump breaks nothing. A bump is for a change to the *datasets*.
static constexpr int kSchemaVersion = 1;
static constexpr int kEmbedDim = 512;
static std::string basename_of(const std::string& path) {
const auto slash = path.find_last_of("/\\");
return slash == std::string::npos ? path : path.substr(slash + 1);
}
template<typename T>
void write_vec(H5::Group& g, const char* name, const std::vector<T>& v,
const H5::PredType& dtype, hsize_t cols = 0) {
@@ -77,20 +217,49 @@ private:
if (!v.empty()) ds.write(v.data(), dtype);
}
static void attr_str(H5::H5File& f, const char* name, const std::string& v) {
H5::StrType str(H5::PredType::C_S1, H5T_VARIABLE);
f.createAttribute(name, str, H5::DataSpace(H5S_SCALAR)).write(str, v);
}
template<typename T>
static void attr_num(H5::H5File& f, const char* name, const H5::PredType& dt, T v) {
f.createAttribute(name, dt, H5::DataSpace(H5S_SCALAR)).write(dt, &v);
}
void write_hdf5() {
H5::H5File file(path_, H5F_ACC_TRUNC);
// root attrs
auto scalar = H5::DataSpace(H5S_SCALAR);
auto ver = file.createAttribute("schema_version", H5::PredType::NATIVE_INT, scalar);
int sv = kSchemaVersion; ver.write(H5::PredType::NATIVE_INT, &sv);
auto ed = file.createAttribute("embed_dim", H5::PredType::NATIVE_INT, scalar);
int dim = kEmbedDim; ed.write(H5::PredType::NATIVE_INT, &dim);
auto fps = file.createAttribute("sample_fps", H5::PredType::NATIVE_FLOAT, scalar);
fps.write(H5::PredType::NATIVE_FLOAT, &sample_fps_);
H5::StrType str(H5::PredType::C_S1, H5T_VARIABLE);
auto mv = file.createAttribute("movie", str, scalar);
mv.write(str, movie_);
attr_num(file, "schema_version", H5::PredType::NATIVE_INT, kSchemaVersion);
attr_num(file, "embed_dim", H5::PredType::NATIVE_INT, kEmbedDim);
attr_num(file, "sample_fps", H5::PredType::NATIVE_FLOAT, sample_fps_);
attr_str(file, "movie", movie_);
/// TRACES: GR-004 | SR-001
attr_str(file, "embedder_model", stamp_.model_name);
attr_str(file, "embedder_sha256", stamp_.model_sha256);
/// TRACES: VR-010 | PR-002
attr_str(file, "detector_model", prov_.detector_model.value_or(""));
attr_num(file, "detector_conf", H5::PredType::NATIVE_FLOAT, *prov_.detector_conf);
attr_num(file, "detector_nms", H5::PredType::NATIVE_FLOAT, *prov_.detector_nms);
attr_num(file, "min_face_px", H5::PredType::NATIVE_FLOAT, *prov_.min_face_px);
attr_num(file, "max_faces", H5::PredType::NATIVE_INT, *prov_.max_faces);
attr_num(file, "cut_threshold", H5::PredType::NATIVE_FLOAT, *prov_.cut_threshold);
attr_num(file, "dense_scale", H5::PredType::NATIVE_FLOAT, *prov_.dense_scale);
// Recorded, NOT applied — faces/bbox stays in the detector's own frame
// space so a replay feeds the tracker exactly what the live run fed it.
attr_num(file, "bbox_upscale", H5::PredType::NATIVE_FLOAT,
prov_.bbox_upscale.value_or(1.f));
attr_num(file, "start_sec", H5::PredType::NATIVE_DOUBLE, *prov_.start_sec);
attr_num(file, "end_sec", H5::PredType::NATIVE_DOUBLE, *prov_.end_sec);
attr_num(file, "track_assoc_min_prob", H5::PredType::NATIVE_FLOAT,
*prov_.track_assoc_min_prob);
// 0/1, matching the uint8 booleans in frames/. Tells "TransNetV2 found no
// boundaries" apart from "TransNetV2 never ran", which is/was the same
// all-zero is_scene_boundary array either way.
attr_num(file, "scene_detect", H5::PredType::NATIVE_UINT8,
static_cast<uint8_t>(*prov_.scene_detect ? 1 : 0));
H5::Group frames = file.createGroup("frames");
write_vec(frames, "timestamp_sec", ts_, H5::PredType::NATIVE_DOUBLE);
@@ -110,7 +279,9 @@ private:
<< conf_.size() << " faces → " << path_ << "\n";
}
std::string path_, movie_;
std::string path_, movie_;
EmbedderStamp stamp_;
DumpProvenance prov_;
float sample_fps_;
std::atomic<bool>& done_;
std::atomic<bool> written_{false};
src/nodes/face_aligner_node.hpp
+12 -4
View File
@@ -4,12 +4,16 @@
#include <iostream>
// ── FaceAlignerFunc ───────────────────────────────────────────────────────────
/// TRACES: AR-005, AR-030 | SR-002
///
// KPN node: applies a 5-point similarity transform to each detected face,
// producing a 112×112 BGR crop suitable for ArcFace inference.
//
// Alignment uses cv::estimateAffinePartial2D (RANSAC) to fit the detected
// landmarks to ArcFace canonical positions. Degenerate detections (where the
// affine fit fails) are silently dropped from the output vectors.
// Alignment is an Umeyama least-squares fit over all five landmarks (AR-005),
// not a robust one: a RANSAC fit discards the very landmarks AR-030 reads.
// Degenerate detections (where the fit fails) are dropped from the output
// vectors. The fit's residual is the AR-030 visibility measure and comes free,
// since the warp needs the transform anyway.
struct FaceAlignerFunc {
static constexpr std::string_view label() { return "face_aligner"; }
@@ -24,11 +28,15 @@ struct FaceAlignerFunc {
crops.reserve(sf.faces.size());
for (auto& face : sf.faces) {
cv::Mat crop = align_face(sf.source.image, face.landmarks);
// The AR-030 misfit comes from the transform the warp already needs,
// so visibility costs no extra fit.
float residual = -1.f;
cv::Mat crop = align_face(sf.source.image, face.landmarks, &residual);
if (crop.empty()) {
std::cerr << "[face_aligner] degenerate detection skipped\n";
continue;
}
face.alignment_residual = residual;
good_faces.push_back(face);
crops.push_back(std::move(crop));
}
src/nodes/face_detector_node.hpp
+6 -1
View File
@@ -1,4 +1,5 @@
#pragma once
/// TRACES: AR-001 | SR-002
#include "config.hpp"
#include "inference/face_detector.hpp"
@@ -43,7 +44,11 @@ struct FaceDetectorFunc {
[](const DetectedFace& a, const DetectedFace& b) {
return a.bbox.area() > b.bbox.area();
});
if (static_cast<int>(faces.size()) > max_faces_)
// TRACES: AR-003 | SR-002
// Largest-first ordering is kept regardless: it is load-bearing for
// deterministic association, since the Hungarian solver tie-breaks on
// index order (see the replay determinism test).
if (max_faces_ > 0 && static_cast<int>(faces.size()) > max_faces_)
faces.resize(max_faces_);
return {std::move(f), std::move(faces)};
src/nodes/face_tracker_node.hpp
+169 -162
View File
@@ -1,102 +1,124 @@
#pragma once
/// TRACES: AR-007, AR-008, AR-024 | SR-002
///
/// FaceTrackerFunc — KPN node that links face detections into tracks.
///
/// **The registry is the tracker's state.** The node owns no track map of its
/// own: it drives `TrackRegistry` through a `FrameScope` and reads the same
/// `Track` objects everything else reads. Two parallel copies could disagree,
/// and every divergence would surface as a wrong presence window rather than as
/// a crash — silently, and only in the output.
///
/// **One candidate pool** (AR-008). `last_seen` alone distinguishes a track that
/// is on screen from one that is dormant, and it only affects whether IoU means
/// anything. There is no parked pool and no revival branch: re-associating a
/// track whose face was lost — across a cut or not — is ordinary inter-frame
/// association, and it falls out of the embedding comparison already being done.
///
/// Assignment cost (track i, detection j):
///
/// p = P(same person | cosine(track mean, detection)) ← calibrated
/// alpha = base weight, or 0 when position carries no information
/// cost = alpha·(1 − IoU) + (1 − alpha)·(1 − p)
///
/// gated to INF unless the pair is admissible on position *or* on identity.
///
/// **alpha is frame- and track-dependent** (AR-007). It falls to 0 —
/// embedding only — when either:
/// - the frame is flagged `is_cut` / `is_scene_boundary`: the viewpoint
/// changed, so the same person is at a new position; or
/// - the track is dormant (`last_seen` set): time has passed since its box was
/// last observed, so that box is stale regardless of cuts.
/// Both are the same statement — spatial continuity is broken — arrived at from
/// two directions, which is why they collapse into one rule rather than two
/// branches.
///
/// **Everything is thresholded in probability space** (AR-024). The cosine goes
/// through the calibration before it is compared to anything; the raw-cosine
/// constants `track_max_embed_dist` and `cut_revive_sim` are retired.
#include "types.hpp"
#include "config.hpp"
#include "track_registry.hpp"
#include <algorithm>
#include <cmath>
#include <functional>
#include <iostream>
#include <limits>
#include <map>
#include <memory>
#include <stdexcept>
#include <string_view>
#include <vector>
// ── FaceTrackerFunc ───────────────────────────────────────────────────────────
// KPN node: links face detections across consecutive frames using the Hungarian
// algorithm on a combined spatial (IoU) + embedding (cosine distance) cost.
//
// Each track accumulates a running directional mean of its ArcFace embeddings
// (averaged then re-normalised to the unit sphere), used as the embedding side
// of the assignment cost below for more stable track continuity.
//
// Assignment cost (track i, detection j):
// cost = alpha * (1 - IoU) + (1-alpha) * min(cosine_dist/2, 1)
// Gated to INF when IoU < min_iou AND cosine_dist > max_embed_dist.
//
// Unmatched tracks have their frames_missing counter incremented; they are
// expired once frames_missing > max_frames_missing.
//
// Cross-cut re-association. A camera-angle change (Frame::is_cut, set by
// camera_position_change_detector) destroys spatial (IoU) continuity — the same
// person reappears at a new position — but not identity. On a cut the tracker
// does NOT discard its tracks; it parks them in an inactive pool keyed by their
// last-frame raw embedding. A post-cut detection whose raw cosine similarity to
// a parked track's last-frame embedding is ≥ cut_revive_sim revives that track:
// the original track_id, mean embedding and n_frames are restored (only the bbox
// jumps to the new detection), so identity continuity survives the cut. Parked
// tracks left unrevived for cut_inactive_max_frames are finally dropped.
struct FaceTrackerFunc {
static constexpr std::string_view label() { return "face_tracker"; }
struct TrackState {
cv::Rect2f bbox;
Embedding mean_emb{};
Embedding last_emb{}; // raw embedding of the most recent matched frame
int n_frames{0};
int frames_missing{0};
};
/// cosine similarity → P(same person). Supplied by the caller so the fit
/// belonging to the active embedder is used (AR-023/AR-024) — the same
/// pattern, and normally the same function object, as
/// `EvidenceDiscounter::Calibrate`.
using Calibrate = std::function<float(float)>;
explicit FaceTrackerFunc(const Config& cfg)
: alpha_(cfg.track_alpha)
/// The registry is a constructor argument, not an option: a tracker without
/// one would have to keep its own tracks, which is the defect this replaces.
FaceTrackerFunc(const Config& cfg,
std::shared_ptr<TrackRegistry> registry,
Calibrate calibrate)
: registry_(std::move(registry))
, calibrate_(std::move(calibrate))
, alpha_base_(cfg.track_alpha)
, min_iou_(cfg.track_min_iou)
, max_embed_dist_(cfg.track_max_embed_dist)
, max_missing_(cfg.track_max_frames_missing)
, revive_sim_(cfg.cut_revive_sim)
, inactive_max_(cfg.cut_inactive_max_frames)
, min_assoc_prob_(cfg.track_assoc_min_prob)
{
std::cerr << "[face_tracker] alpha=" << alpha_
if (!registry_)
throw std::invalid_argument("face_tracker: registry must not be null");
if (!calibrate_)
throw std::invalid_argument("face_tracker: a calibration is required — "
"association is decided in probability space");
std::cerr << "[face_tracker] alpha_base=" << alpha_base_
<< " min_iou=" << min_iou_
<< " max_embed_dist=" << max_embed_dist_
<< " max_missing=" << max_missing_
<< " cut_revive_sim=" << revive_sim_
<< " cut_inactive_max=" << inactive_max_ << "\n";
<< " min_assoc_prob=" << min_assoc_prob_ << "\n";
}
TrackedSceneFrame operator()(EmbeddedSceneFrame ef) {
if (ef.source.eof) {
tracks_.clear();
inactive_.clear();
// Deliberately does *not* flush the registry. The identity matcher
// runs downstream and its votes for the final frames are still in
// flight; reaping here would drop them (they would land on ids that
// no longer exist and show up as dropped_votes). AR-016's flush
// belongs at the pipeline's termination point, after the last vote.
boxes_.clear();
TrackedSceneFrame out;
out.source = std::move(ef.source);
return out;
}
const int n_det = static_cast<int>(ef.embeddings.size());
const double t = ef.source.timestamp_sec;
const int n_det = static_cast<int>(ef.embeddings.size());
// Camera-angle change: park active tracks instead of destroying them so
// they can be revived by identity (raw last-frame embedding cosine) once
// the same people reappear from the new angle.
if (ef.source.is_cut && !tracks_.empty()) {
std::cerr << "[face_tracker] cut — parking " << tracks_.size()
<< " track(s) into inactive pool\n";
for (auto& [tid, ts] : tracks_) {
ts.frames_missing = 0; // repurpose as time-since-parked counter
inactive_[tid] = std::move(ts);
}
tracks_.clear();
}
// Unconditional: the clock must advance on frames with no detections
// too, or a track only dies when some unrelated face happens to appear
// and a film that ends mid-track never closes it (AR-013).
auto scope = registry_->begin_frame(t);
// Age the inactive pool every frame and drop tracks parked too long.
for (auto it = inactive_.begin(); it != inactive_.end(); ) {
it->second.frames_missing++;
it = (it->second.frames_missing > inactive_max_)
? inactive_.erase(it) : std::next(it);
}
// One pool (AR-008) — on-screen and dormant tracks compete together.
std::vector<Track*> cands = scope.candidates();
const int n_trk = static_cast<int>(cands.size());
// Snapshot active track IDs so the map can be modified safely below
std::vector<int> tids;
tids.reserve(tracks_.size());
for (auto& [tid, _] : tracks_) tids.push_back(tid);
const int n_trk = static_cast<int>(tids.size());
prune_boxes(cands);
std::vector<Spatial*> sp(n_trk);
for (int ti = 0; ti < n_trk; ++ti)
sp[ti] = &boxes_.try_emplace(cands[ti]->id, Spatial{{}, t, false})
.first->second;
// AR-007 — the frame half of the frame-dependent weighting. Both flags
// say the same thing to the tracker: whatever was at that position is
// not there any more.
const bool viewpoint_change =
ef.source.is_cut || ef.source.is_scene_boundary;
// ── Cost matrix [n_trk × n_det] ──────────────────────────────────────
constexpr float INF_COST = 1e6f;
@@ -104,109 +126,108 @@ struct FaceTrackerFunc {
std::vector<float>(n_det, INF_COST));
for (int ti = 0; ti < n_trk; ++ti) {
const TrackState& ts = tracks_[tids[ti]];
// Spatial continuity holds only for a track that was on screen, whose
// box we have actually observed, on a frame that did not change the
// viewpoint. Otherwise the box is stale and IoU is noise.
const bool spatial_meaningful =
sp[ti]->observed && cands[ti]->on_screen() && !viewpoint_change;
const float alpha = spatial_meaningful ? alpha_base_ : 0.f;
for (int di = 0; di < n_det; ++di) {
float iou_v = iou(ts.bbox, ef.faces[di].bbox);
float emb_d = (ts.n_frames > 0)
? 1.f - cosine_similarity(ts.mean_emb, ef.embeddings[di])
: 1.f;
if (iou_v < min_iou_ && emb_d > max_embed_dist_) continue;
float s = 1.f - iou_v;
float e = std::min(emb_d * 0.5f, 1.f);
cost[ti][di] = alpha_ * s + (1.f - alpha_) * e;
// AR-024 — the cosine is converted before it is used for
// anything, including the gate below.
const float p = calibrate_(
cosine_similarity(cands[ti]->mean, ef.embeddings[di]));
const float iou_v = spatial_meaningful
? iou(sp[ti]->bbox, ef.faces[di].bbox) : 0.f;
// Either signal on its own can admit a link: a face that moved a
// little but whose embedding degraded (blur, profile turn) is
// still linkable on position, and a face that jumped across the
// frame is still linkable on identity. Neither ⇒ no link.
const bool spatial_ok = spatial_meaningful && iou_v >= min_iou_;
const bool identity_ok = p >= min_assoc_prob_;
if (!spatial_ok && !identity_ok) continue;
cost[ti][di] = alpha * (1.f - iou_v) + (1.f - alpha) * (1.f - p);
}
}
// ── Hungarian assignment ──────────────────────────────────────────────
// ── Hungarian assignment ─────────────────────────────────────────────
std::vector<int> assign(n_trk, -1);
if (n_trk > 0 && n_det > 0)
assign = hungarian(cost, n_trk, n_det);
// ── Build output frame ────────────────────────────────────────────────
// ── Build output frame ───────────────────────────────────────────────
TrackedSceneFrame out;
out.source = ef.source;
out.faces = ef.faces;
out.crops = ef.crops;
out.embeddings = ef.embeddings;
out.source = ef.source;
out.faces = ef.faces;
out.crops = ef.crops;
out.embeddings = ef.embeddings;
out.track_ids.assign(n_det, -1);
std::vector<bool> det_matched(n_det, false);
// Update matched tracks
for (int ti = 0; ti < n_trk; ++ti) {
int di = assign[ti];
bool valid = (di >= 0 && di < n_det && cost[ti][di] < INF_COST * 0.5f);
TrackState& ts = tracks_[tids[ti]];
const int di = assign[ti];
const int id = cands[ti]->id;
const bool valid =
(di >= 0 && di < n_det && cost[ti][di] < INF_COST * 0.5f);
if (!valid) {
ts.frames_missing++;
// Only a track that *was* on screen can become lost, and it
// becomes lost as of its last sighting, never as of now — the
// gap after the final sighting is never claimed (AR-013). A
// track already dormant is left alone so its extinction clock
// keeps running from the right instant.
if (cands[ti]->on_screen()) scope.mark_lost(id, sp[ti]->last_ts);
continue;
}
update_mean(ts.mean_emb, ts.n_frames, ef.embeddings[di]);
ts.last_emb = ef.embeddings[di];
ts.bbox = ef.faces[di].bbox;
ts.n_frames++;
ts.frames_missing = 0;
det_matched[di] = true;
out.track_ids[di] = tids[ti];
scope.mark_seen(id, t, ef.embeddings[di]);
sp[ti]->bbox = ef.faces[di].bbox;
sp[ti]->last_ts = t;
sp[ti]->observed = true;
det_matched[di] = true;
out.track_ids[di] = id;
}
// Handle unmatched detections: first try to revive a parked track by
// identity (raw last-frame embedding cosine), else start a fresh track.
for (int di = 0; di < n_det; ++di) {
if (det_matched[di]) continue;
int tid = revive_from_inactive(ef.embeddings[di]);
if (tid >= 0) {
// Restore the parked track: keep its identity statistics
// (mean_emb, n_frames), jump the bbox to the new detection.
TrackState ts = std::move(inactive_[tid]);
inactive_.erase(tid);
update_mean(ts.mean_emb, ts.n_frames, ef.embeddings[di]);
ts.last_emb = ef.embeddings[di];
ts.bbox = ef.faces[di].bbox;
ts.n_frames++;
ts.frames_missing = 0;
tracks_[tid] = std::move(ts);
out.track_ids[di] = tid;
std::cerr << "[face_tracker] revived track " << tid
<< " across cut\n";
continue;
}
tid = next_id_++;
TrackState ts;
ts.bbox = ef.faces[di].bbox;
ts.mean_emb = ef.embeddings[di];
ts.last_emb = ef.embeddings[di];
ts.n_frames = 1;
tracks_[tid] = ts;
out.track_ids[di] = tid;
}
// Expire stale tracks
for (auto it = tracks_.begin(); it != tracks_.end(); ) {
it = (it->second.frames_missing > max_missing_)
? tracks_.erase(it) : std::next(it);
const int id = scope.create(t, ef.embeddings[di]);
boxes_[id] = Spatial{ef.faces[di].bbox, t, true};
out.track_ids[di] = id;
}
// No reaping here: begin_frame's tick owns the extinction sweep, so
// there is exactly one place a track can die.
return out;
}
private:
// Pick the parked track whose last-frame embedding is most similar to emb,
// returning its id if that raw cosine similarity clears revive_sim_, else -1.
// The caller removes the returned track from the pool, so a later detection in
// the same frame cannot claim it again.
int revive_from_inactive(const Embedding& emb) const {
int best_tid = -1;
float best_sim = revive_sim_; // threshold is the bar to beat (inclusive)
for (const auto& [tid, ts] : inactive_) {
float sim = cosine_similarity(ts.last_emb, emb);
if (sim >= best_sim) { best_sim = sim; best_tid = tid; }
// subsequent ties keep the later id; harmless, all clear the threshold
// ── Spatial annotation ───────────────────────────────────────────────────
// The one piece of per-track state the registry does not hold, because it is
// not about presence: where the face was, and when it was last seen there.
// Keyed by registry track id and pruned against `candidates()` every frame,
// so it cannot outlive or contradict the registry — it annotates the pool
// rather than duplicating it.
struct Spatial {
cv::Rect2f bbox{};
double last_ts{0.0}; ///< timestamp of the last frame this track matched
bool observed{false}; ///< false until a detection has been assigned
};
// Drop boxes for ids the registry no longer has. `candidates()` is the
// authority on what exists; anything else is a leak (and, for a reused id,
// would be a stale box attached to a different person).
void prune_boxes(const std::vector<Track*>& cands) {
if (boxes_.size() == cands.size()) return; // common case: nothing died
std::map<int, Spatial> kept;
for (const Track* t : cands) {
auto it = boxes_.find(t->id);
if (it != boxes_.end()) kept.emplace(t->id, it->second);
}
return best_tid;
boxes_.swap(kept);
}
// IoU of two axis-aligned bounding boxes
@@ -220,17 +241,6 @@ private:
return inter / (a.width * a.height + b.width * b.height - inter);
}
// Online directional mean: average then re-normalise to unit sphere
static void update_mean(Embedding& mean, int n_prev, const Embedding& emb) {
float norm_sq = 0.f;
for (int k = 0; k < 512; ++k) {
mean[k] = (mean[k] * n_prev + emb[k]) / (n_prev + 1);
norm_sq += mean[k] * mean[k];
}
float inv = 1.f / std::sqrt(norm_sq);
for (int k = 0; k < 512; ++k) mean[k] *= inv;
}
// O(n³) potential-based Hungarian algorithm (Jonker-Volgenant / Kuhn-Munkres).
// Returns assign[row] = col (0-indexed), or -1 when row is matched to a
// padded virtual column (i.e., unmatched). Rectangular matrices are padded
@@ -292,13 +302,10 @@ private:
return ans;
}
std::map<int, TrackState> tracks_;
std::map<int, TrackState> inactive_; // parked across a cut, keyed by track id
int next_id_{0};
float alpha_;
std::shared_ptr<TrackRegistry> registry_;
Calibrate calibrate_;
std::map<int, Spatial> boxes_; ///< track id → where it was, when
float alpha_base_;
float min_iou_;
float max_embed_dist_;
int max_missing_;
float revive_sim_;
int inactive_max_;
float min_assoc_prob_;
};
src/nodes/identity_matcher_node.hpp
+81 -11
View File
@@ -5,6 +5,7 @@
#include "gallery/gallery_store.hpp"
#include "gallery/gallery_calibration.hpp"
#include "gallery/track_gallery.hpp"
#include "track_registry.hpp"
#include <cstdint>
#include <cstring>
@@ -105,8 +106,28 @@ struct IdentityMatcherFunc {
flat_emb_[i].data(), 512 * sizeof(float));
sim_engine_ = make_similarity_engine(host_gallery.data(), n_gallery_, kMaxFaces);
/// TRACES: AR-018, AR-024 | SR-005
// The expansion store thresholds in the same probability space as
// association and evidence weighting, so a "0.9" means one thing
// pipeline-wide rather than three.
track_gallery_.set_calibration(same_person_probability(cal_));
}
/// TRACES: AR-023, AR-024 | SR-002
/// The fitted sigmoid. Exposed because the matcher is where it gets fitted
/// (and cached back to the gallery), but it is not the matcher's private
/// property: track association and evidence weighting must threshold in the
/// *same* probability space, or a "0.5" in one stage and a "0.5" in another
/// mean different things. See `same_person_probability`.
const GalleryCalibration& calibration() const { return cal_; }
/// TRACES: AR-012, AR-025 | SR-002
/// Where per-frame identity evidence reaches the registry. Optional: with no
/// registry attached the matcher behaves exactly as before, which keeps the
/// replay harness and the unit tests working unchanged.
void set_registry(std::shared_ptr<TrackRegistry> r) { registry_ = std::move(r); }
// Runtime setter — lets a persistent pipeline be reused across a threshold sweep
// without rebuilding the (expensive, gallery-resident) matcher. The gallery,
// calibration and GPU sim-engine stay put; only the accept threshold changes.
@@ -123,27 +144,51 @@ struct IdentityMatcherFunc {
// mix embeddings from two viewpoints under one buffer, so we still drop
// every diversity buffer here — a revived track simply re-accumulates its
// buffer from post-cut frames. Stale cross-cut embeddings are never promoted.
if (tf.source.is_cut) track_gallery_.clear_tracks();
/// TRACES: AR-019 | SR-005
// Promotion may only borrow same-identity evidence from a span where
// identity is certain, so ALL THREE discontinuity signals clear the
// buffers, not just the histogram cut:
// is_cut — camera-angle change
// is_scene_boundary — different scene (AR-010; previously never set,
// so this half of the gate was dead)
// The third, an identity contradiction (AR-015), is enforced by the
// registry: a track whose belief swapped is closed outright, so it can
// no longer promote anything.
if (tf.source.is_cut || tf.source.is_scene_boundary)
track_gallery_.clear_tracks();
const int n_faces = static_cast<int>(tf.embeddings.size());
std::vector<IdentifiedActor> actors;
actors.reserve(n_faces);
if (n_faces == 0) return {std::move(tf.source), {}};
if (n_faces > kMaxFaces)
throw std::runtime_error("identity_matcher: n_faces exceeds kMaxFaces");
std::vector<float> host_query(static_cast<size_t>(n_faces) * 512);
for (int fi = 0; fi < n_faces; ++fi) {
std::memcpy(host_query.data() + static_cast<size_t>(fi) * 512,
tf.embeddings[fi].data(), 512 * sizeof(float));
/// TRACES: AR-003, AR-004 | SR-002
// kMaxFaces sizes the similarity engine's preallocated buffer, so it
// bounds MEMORY, not how many faces a frame may contain. It used to
// throw above the bound, which made it a hard cap on crowd scenes by
// accident; now the frame is scored in batches of that size.
//
// Faces per frame are unbounded (AR-003) because X-Ray credits scene
// membership to background cast too, and a fixed cap discards exactly
// those — the smallest faces are dropped first. Cost is contained by
// backpressure (AR-004), which slows the producer, rather than by
// silently throwing work away.
std::vector<float> host_query(static_cast<size_t>(kMaxFaces) * 512);
for (int base = 0; base < n_faces; base += kMaxFaces) {
const int chunk = std::min(kMaxFaces, n_faces - base);
for (int k = 0; k < chunk; ++k) {
std::memcpy(host_query.data() + static_cast<size_t>(k) * 512,
tf.embeddings[base + k].data(), 512 * sizeof(float));
}
// S (N_gallery × n_faces) col-major: face fi's gallery sims at sims + fi*n_gallery.
const float* host_sims = sim_engine_->compute(host_query.data(), n_faces);
// S (N_gallery × chunk) col-major: face k's gallery sims at sims + k*n_gallery.
const float* host_sims = sim_engine_->compute(host_query.data(), chunk);
for (int fi = 0; fi < n_faces; ++fi) {
const float* sims = host_sims + static_cast<size_t>(fi) * n_gallery_;
for (int ci = 0; ci < chunk; ++ci) {
const int fi = base + ci;
const float* sims = host_sims + static_cast<size_t>(ci) * n_gallery_;
std::vector<float> best_sim(gallery_.actors.size(),
-std::numeric_limits<float>::max());
@@ -224,11 +269,35 @@ struct IdentityMatcherFunc {
// face (annex already folded in above); the track's diversity buffer
// keeps the gallery-far views and promotes them once the track is
// confirmed. No-op unless --expand-gallery is set.
// TRACES: AR-012, AR-025 | SR-002
// Every scored face is evidence, not only the accepted ones: a run of
// near-misses for one actor is itself informative, and discarding it
// would make ownership depend on a per-frame threshold the redesign
// exists to stop relying on. The registry discounts for correlation
// and decides ownership from the accumulated posterior (AR-025).
if (registry_ && best_actor >= 0 && tf.track_ids[fi] >= 0) {
const float p = cal_.valid
? cal_.probability(best_s, log_prior_odds_)
: std::max(0.f, best_s);
registry_->observe(tf.track_ids[fi], best_actor, p, tf.embeddings[fi]);
}
// TRACES: AR-019 | SR-005
// Ownership is the registry's, computed once. TrackGallery used to
// tally its own plurality vote over accepted frames, which meant two
// different answers to "who is this track" could coexist — and the
// expansion one ignored the Bayesian accumulation entirely.
if (registry_ && tf.track_ids[fi] >= 0) {
if (auto owner = registry_->owner(tf.track_ids[fi]))
track_gallery_.set_owner(tf.track_ids[fi], *owner);
}
track_gallery_.observe(tf.track_ids[fi], tf.embeddings[fi],
best_actor, best_s, accept, tf.crops[fi]);
actors.push_back(std::move(ia));
}
} // chunk loop
return {std::move(tf.source), std::move(actors)};
}
@@ -247,4 +316,5 @@ private:
std::unique_ptr<ISimilarityEngine> sim_engine_;
TrackGallery track_gallery_;
std::shared_ptr<TrackRegistry> registry_;
};
src/nodes/result_sink_node.hpp
+86 -34
View File
@@ -1,6 +1,8 @@
#pragma once
/// TRACES: IR-001 | SR-003
#include "types.hpp"
#include "config.hpp"
#include "track_registry.hpp"
#include <nlohmann/json.hpp>
#include <algorithm>
@@ -9,6 +11,8 @@
#include <fstream>
#include <iostream>
#include <map>
#include <mutex>
#include <functional>
#include <string>
#include <vector>
@@ -42,10 +46,26 @@ using json = nlohmann::json;
struct ResultSinkFunc {
static constexpr std::string_view label() { return "result_sink"; }
/// TRACES: AR-012, AR-017 | IR-002 | SR-002, SR-003
/// A finished presence claim from the registry. Called from inside the
/// registry's reap while it holds its own lock, so this must stay a cheap
/// push and must never re-enter the registry.
void add_claim(const DeadTrack& d) {
if (d.actor_idx < 0) return; // never owned: nothing to claim
std::lock_guard<std::mutex> g(claims_mu_);
claims_.push_back(d);
}
ResultSinkFunc(const Config& cfg, std::atomic<bool>& done)
: cfg_(cfg), done_(done)
{}
/// TRACES: AR-016 | SR-002
/// Runs immediately before the output is written, with the last timestamp
/// seen. Used to flush tracks still live at EOF, which have not timed out
/// and would otherwise never be emitted.
void set_pre_write_hook(std::function<void(double)> fn) { pre_write_ = std::move(fn); }
void operator()(SceneAnnotation sa) {
if (sa.eof) {
flush();
@@ -58,12 +78,20 @@ struct ResultSinkFunc {
<< " unknowns=" << count_unknown(sa.visible_actors)
<< std::flush;
for (const auto& ia : sa.visible_actors) {
if (ia.actor_idx < 0) continue;
auto& m = actor_meta_[ia.actor_idx];
if (m.name.empty())
m = {ia.name, ia.imdb_id, ia.tmdb_id, ia.jellyfin_id};
}
last_ts_ = sa.timestamp_sec;
frames_.push_back(std::move(sa));
}
// Write accumulated results and signal done. Safe to call more than once.
void flush() {
if (written_.exchange(true)) return;
if (pre_write_) pre_write_(last_ts_);
write_output();
done_.store(true, std::memory_order_release);
}
@@ -71,7 +99,7 @@ struct ResultSinkFunc {
private:
// Bump when the minimal/standard output JSON structure changes in a way
// the Jellyfin plugin needs to detect.
static constexpr int kSchemaVersion = 1;
static constexpr int kSchemaVersion = 2; // SR-003 coordinated bump
static int count_known(const std::vector<IdentifiedActor>& v) {
int n = 0;
@@ -91,10 +119,19 @@ private:
if (cfg_.verbosity == Verbosity::xray) {
root = build_xray();
} else {
/// TRACES: IR-002 | SR-003
/// schema_version 2, per jRay/SPEC.md JR-002. anneal_sec is REMOVED
/// rather than zeroed: a field naming a mechanism the pipeline no
/// longer has is actively misleading, and would outlive everyone who
/// remembers why it reads 0. extinction_sec succeeds it as the
/// parameter that actually shapes window extent.
root["schema_version"] = kSchemaVersion;
root["movie"] = cfg_.movie_path;
root["sample_fps"] = cfg_.sample_fps;
root["anneal_sec"] = cfg_.anneal_sec;
root["movie"] = cfg_.movie_path;
root["extraction"] = {
{"sample_fps", cfg_.sample_fps},
{"extinction_sec", cfg_.track_extinction_sec},
{"gallery_scope", cfg_.gallery_scope},
};
root["actors"] = build_epochs();
if (cfg_.verbosity == Verbosity::standard)
root["frames"] = build_standard();
@@ -109,42 +146,45 @@ private:
std::cerr << "[result_sink] done.\n";
}
struct Window {
double start{0.0};
double end{0.0};
float belief{0.f}; ///< the posterior that justified the claim (AR-017)
};
struct ActorWindow {
std::string name, imdb_id, tmdb_id, jellyfin_id;
std::vector<std::pair<double, double>> scenes; // [start_sec, end_sec]
std::vector<Window> scenes;
};
struct ActorMeta { std::string name, imdb_id, tmdb_id, jellyfin_id; };
// Core logic: merge per-frame detections into annealed [start, end] windows.
/// TRACES: AR-012 | IR-002 | SR-002
/// A claim already IS a window — `[first_seen, last_seen]` of a track the
/// actor owned. There is no annealing pass: `anneal_sec` existed to bridge
/// gaps between isolated accepted frames, and a track that survives its own
/// gaps leaves it nothing to do (see the AR-012 withdrawal note).
std::vector<ActorWindow> build_actor_windows() {
struct Info { std::string name, imdb_id, tmdb_id, jellyfin_id; };
std::map<int, Info> actor_info;
std::map<int, std::vector<double>> timestamps;
std::lock_guard<std::mutex> g(claims_mu_);
for (const auto& frame : frames_) {
for (const auto& ia : frame.visible_actors) {
if (ia.actor_idx < 0) continue;
actor_info[ia.actor_idx] = {ia.name, ia.imdb_id, ia.tmdb_id, ia.jellyfin_id};
timestamps[ia.actor_idx].push_back(frame.timestamp_sec);
std::map<int, ActorWindow> by_actor;
for (const auto& c : claims_) {
auto& aw = by_actor[c.actor_idx];
if (aw.name.empty()) {
auto it = actor_meta_.find(c.actor_idx);
if (it != actor_meta_.end()) {
aw.name = it->second.name;
aw.imdb_id = it->second.imdb_id;
aw.tmdb_id = it->second.tmdb_id;
aw.jellyfin_id = it->second.jellyfin_id;
}
}
aw.scenes.push_back({c.first_seen, c.last_seen, c.belief});
}
std::vector<ActorWindow> result;
for (auto& [idx, ts_vec] : timestamps) {
ActorWindow aw;
aw.name = actor_info[idx].name;
aw.imdb_id = actor_info[idx].imdb_id;
aw.tmdb_id = actor_info[idx].tmdb_id;
aw.jellyfin_id = actor_info[idx].jellyfin_id;
double win_start = ts_vec[0], win_end = ts_vec[0];
for (size_t i = 1; i < ts_vec.size(); ++i) {
if (ts_vec[i] - win_end > cfg_.anneal_sec) {
aw.scenes.push_back({win_start, win_end});
win_start = ts_vec[i];
}
win_end = ts_vec[i];
}
aw.scenes.push_back({win_start, win_end});
for (auto& [idx, aw] : by_actor) {
std::sort(aw.scenes.begin(), aw.scenes.end(),
[](const Window& a, const Window& b) { return a.start < b.start; });
result.push_back(std::move(aw));
}
return result;
@@ -153,9 +193,16 @@ private:
json build_epochs() {
json actors = json::array();
for (const auto& aw : build_actor_windows()) {
// Objects, not float pairs: a window carries the belief that
// justified it and the route by which it was identified (AR-017),
// so a consumer can caveat or filter rather than treating every
// window as equally certain.
json windows = json::array();
for (const auto& [s, e] : aw.scenes)
windows.push_back({s, e});
for (const auto& w : aw.scenes)
windows.push_back({{"start", w.start},
{"end", w.end},
{"belief", w.belief},
{"route", "live"}});
json ja;
ja["name"] = aw.name;
ja["imdb_id"] = aw.imdb_id;
@@ -173,9 +220,9 @@ private:
json build_xray() {
std::map<int, std::vector<std::string>> xray;
for (const auto& aw : build_actor_windows()) {
for (const auto& [start, end] : aw.scenes) {
int t0 = static_cast<int>(std::floor(start));
int t1 = static_cast<int>(std::ceil(end));
for (const auto& w : aw.scenes) {
int t0 = static_cast<int>(std::floor(w.start));
int t1 = static_cast<int>(std::ceil(w.end));
for (int t = t0; t <= t1; ++t)
xray[t].push_back(aw.name);
}
@@ -226,4 +273,9 @@ private:
std::atomic<bool>& done_;
std::atomic<bool> written_{false};
std::vector<SceneAnnotation> frames_;
std::function<void(double)> pre_write_;
double last_ts_{0.0};
std::mutex claims_mu_;
std::vector<DeadTrack> claims_;
std::map<int, ActorMeta> actor_meta_; ///< actor_idx → identity keys
};
src/nodes/scene_boundary_annotator_node.hpp
+67
View File
@@ -0,0 +1,67 @@
#pragma once
/// TRACES: AR-010 | SR-002
///
/// SceneBoundaryAnnotatorFunc — the join of the decode butterfly.
///
/// `source` fans out to two branches: dense frames to TransNetV2, sampled frames
/// to face detection. A boundary found on the first has to reach the second, and
/// cannot ride along in the frame because the branches run in parallel.
///
/// This node sits on the sampled branch and stamps `Frame::is_scene_boundary`
/// from the detector's published verdict.
///
/// **It only works because the sampled branch lags.** TransNetV2 buffers
/// `kWindow` frames before it can score any of them, so this node must not reach
/// a frame before the detector has an opinion about it. Channel depth creates
/// that lag: with backpressure (AR-004) the fanout blocks on the slower branch,
/// so a deep channel here lets the detector run ahead by its window instead of
/// anything being dropped.
///
/// When the lag is insufficient the node **counts it** rather than guessing.
/// Annotating an unscored frame as boundary-free is indistinguishable from a
/// genuine "no boundary here", and that is the failure that makes a downstream
/// test pass while verifying nothing.
#include "scene_boundaries.hpp"
#include "types.hpp"
#include <memory>
#include <string_view>
#include <utility>
struct SceneBoundaryAnnotatorFunc {
static constexpr std::string_view label() { return "scene_annotate"; }
/// `tol` is half a sample interval. The branches sample at different rates,
/// so a boundary found on a dense frame rarely lands exactly on a sampled
/// one; half an interval attributes it to the nearest sampled frame and no
/// further.
SceneBoundaryAnnotatorFunc(std::shared_ptr<SceneBoundaries> b, double tol)
: bounds_(std::move(b)), tol_(tol) {}
Frame operator()(Frame f) {
if (f.eof || !bounds_) return f;
// Wait for the detector's verdict to cover this frame. Channel depth
// alone cannot provide the lag: it holds frames back only when the
// consumer is slower, and this branch is orders of magnitude faster per
// frame than TransNetV2. Blocking here is what makes the join real.
//
// Safe under backpressure because the branches are independent: this
// node stalling does not stop the detector consuming dense frames, and
// the fanout keeps feeding it.
if (!bounds_->wait_until_scored(f.timestamp_sec)) {
// The detector finished without covering this frame — the tail after
// its last full window. Unknown, not negative; counted so it cannot
// pass for "no boundary here".
bounds_->note_outran();
return f;
}
f.is_scene_boundary = bounds_->is_boundary(f.timestamp_sec, tol_);
return f;
}
private:
std::shared_ptr<SceneBoundaries> bounds_;
double tol_{0.0};
};
src/nodes/scene_detector_node.hpp
+35 -2
View File
@@ -1,6 +1,9 @@
#pragma once
#include "types.hpp"
#include "config.hpp"
#include "scene_boundaries.hpp"
#include <memory>
#include "inference/scene_detector.hpp"
#include <nlohmann/json.hpp>
@@ -30,6 +33,11 @@
struct SceneDetectorFunc {
static constexpr std::string_view label() { return "scene_detector"; }
/// TRACES: AR-010 | SR-002
/// Publish each window's verdict as it is scored, so the face branch — held
/// back by channel depth — can consult it for frames it has not reached yet.
void set_boundaries(std::shared_ptr<SceneBoundaries> b) { shared_ = std::move(b); }
SceneDetectorFunc(const Config& cfg, std::atomic<bool>& done)
: detector_(make_scene_detector(cfg))
, threshold_(cfg.scene_threshold)
@@ -51,6 +59,9 @@ struct SceneDetectorFunc {
void operator()(Frame f) {
if (f.eof) {
flush_remaining();
// Release anyone waiting on the join: the tail frames after the last
// full window will never be covered, so waiting for them would hang.
if (shared_) shared_->finish();
write_output();
done_.store(true, std::memory_order_release);
return;
@@ -82,6 +93,7 @@ private:
// otherwise skip the leading guard already covered by the previous window.
const int lo = (window_base_ == 0) ? 0 : guard_;
const int hi = ISceneDetector::kWindow - guard_;
std::vector<double> fresh;
for (int i = lo; i < hi; ++i) {
if (probs[i] <= threshold_) continue;
// Local maximum → the boundary frame (avoid a run of high scores
@@ -89,9 +101,19 @@ private:
const bool peak =
(i == 0 || probs[i] >= probs[i-1]) &&
(i == kLast_() || probs[i] >= probs[i+1]);
if (peak)
if (peak) {
boundaries_.push_back({times_[i], probs[i]});
fresh.push_back(times_[i]);
}
}
/// TRACES: AR-010 | SR-002
// Publish with a watermark: everything up to times_[hi-1] now has a
// final verdict. The face branch consults this for frames it has not
// reached yet, and the watermark is what lets it tell "no boundary
// here" from "not scored yet".
if (shared_ && hi > lo)
shared_->publish(fresh, times_[hi - 1]);
}
// At EOF the tail (< kWindow frames) never formed a full window. Pad it out
@@ -107,14 +129,24 @@ private:
std::vector<float> probs = detector_->detect_window(win);
const int lo = (window_base_ == 0) ? 0 : guard_;
std::vector<double> fresh;
for (int i = lo; i < n; ++i) { // only real (non-padded) frames
if (probs[i] <= threshold_) continue;
const bool peak =
(i == 0 || probs[i] >= probs[i-1]) &&
(i == n - 1 || probs[i] >= probs[i+1]);
if (peak)
if (peak) {
boundaries_.push_back({times_[i], probs[i]});
fresh.push_back(times_[i]);
}
}
/// TRACES: AR-010 | SR-002
// Publish the tail too. Without this the final frames — everything after
// the last full window — reach the join with no verdict and are treated
// as boundary-free without evidence, which is precisely the ambiguity
// the watermark exists to prevent.
if (shared_ && n > 0) shared_->publish(fresh, times_[n - 1]);
}
void write_output() {
@@ -176,4 +208,5 @@ private:
int64_t window_base_{0}; // frame index of images_.front()
std::vector<Boundary> boundaries_;
bool written_{false};
std::shared_ptr<SceneBoundaries> shared_; ///< AR-010 join point
};
src/python_bindings.cpp
+205 -1
View File
@@ -3,17 +3,77 @@
// Loads both ONNX sessions once per FaceEmbedder instance, then embeds many
// images via repeated embed() calls — avoiding the per-process model-load
// cost of the embed_faces CLI when embedding a large gallery.
//
// Beyond whole-image embed(), the individual pipeline stages are exposed —
// detect(), align_face(), embed_crop() — plus the gallery calibration. A study
// that needs to step between stages (a different landmark source, a degraded
// crop) drives the shipped C++ from Python rather than re-implementing
// detection, alignment, the ArcFace warp or the Platt fit in numpy. Those
// re-implementations drift from what ships, and the calibration is the one
// that must not: AR-024 requires every similarity to pass through
// GalleryCalibration::probability, never a bare cosine.
#include "face_embedder_engine.hpp"
#include "gallery/gallery_calibration.hpp"
#include "gallery/gallery_store.hpp"
#include <nanobind/nanobind.h>
#include <nanobind/ndarray.h>
#include <nanobind/stl/optional.h>
#include <nanobind/stl/string.h>
#include <nanobind/stl/vector.h>
#include <array>
#include <cstring>
#include <stdexcept>
namespace nb = nanobind;
using namespace nb::literals;
namespace {
using ImageArray = nb::ndarray<const uint8_t, nb::ndim<3>, nb::c_contig, nb::device::cpu>;
// numpy HxWx3 uint8 (BGR, as cv::imread yields) → cv::Mat sharing that buffer.
// The Mat is a view: it must not outlive the caller's array, so every use here
// copies or consumes it before returning.
cv::Mat as_mat(const ImageArray& a) {
if (a.shape(2) != 3)
throw std::invalid_argument("expected an HxWx3 uint8 BGR image");
return cv::Mat(static_cast<int>(a.shape(0)), static_cast<int>(a.shape(1)),
CV_8UC3, const_cast<uint8_t*>(a.data()));
}
// cv::Mat → freshly-allocated numpy array (owns its buffer).
nb::ndarray<nb::numpy, uint8_t> mat_to_numpy(const cv::Mat& m) {
cv::Mat c = m.isContinuous() ? m : m.clone();
auto* buf = new uint8_t[c.total() * c.elemSize()];
std::memcpy(buf, c.data, c.total() * c.elemSize());
nb::capsule owner(buf, [](void* p) noexcept { delete[] static_cast<uint8_t*>(p); });
size_t shape[3] = {static_cast<size_t>(c.rows), static_cast<size_t>(c.cols),
static_cast<size_t>(c.channels())};
return nb::ndarray<nb::numpy, uint8_t>(buf, 3, shape, owner);
}
nb::ndarray<nb::numpy, float> vec_to_numpy(std::vector<float>&& v) {
auto* buf = new float[v.size()];
std::memcpy(buf, v.data(), v.size() * sizeof(float));
nb::capsule owner(buf, [](void* p) noexcept { delete[] static_cast<float*>(p); });
size_t shape[1] = {v.size()};
return nb::ndarray<nb::numpy, float>(buf, 1, shape, owner);
}
// numpy (5,2) float32 → the landmark array align_face expects. Order is
// types.hpp:60 — [0] right-eye [1] left-eye [2] nose [3] right-mouth [4] left-mouth.
std::array<cv::Point2f, 5> as_landmarks(
const nb::ndarray<const float, nb::shape<5, 2>, nb::c_contig, nb::device::cpu>& a) {
std::array<cv::Point2f, 5> lm;
for (int i = 0; i < 5; ++i) lm[i] = {a(i, 0), a(i, 1)};
return lm;
}
} // namespace
NB_MODULE(sae_embed, m) {
m.doc() = "SCRFD + ArcFace face embedding, models loaded once per FaceEmbedder";
@@ -27,6 +87,23 @@ NB_MODULE(sae_embed, m) {
})
.def_prop_ro("bbox", [](const FaceEmbedResult& r) {
return std::vector<float>{r.bbox[0], r.bbox[1], r.bbox[2], r.bbox[3]};
})
.def_prop_ro("landmarks", [](const FaceEmbedResult& r) {
std::vector<float> v;
for (const auto& p : r.landmarks) { v.push_back(p.x); v.push_back(p.y); }
return v;
});
nb::class_<DetectedFace>(m, "Detection")
.def_ro("confidence", &DetectedFace::confidence)
.def_prop_ro("bbox", [](const DetectedFace& d) {
return std::vector<float>{d.bbox.x, d.bbox.y, d.bbox.width, d.bbox.height};
})
.def_prop_ro("landmarks", [](const DetectedFace& d) {
// (5,2): [0] right-eye [1] left-eye [2] nose [3] right-mouth [4] left-mouth
std::vector<float> v;
for (const auto& p : d.landmarks) { v.push_back(p.x); v.push_back(p.y); }
return v;
});
nb::class_<FaceEmbedderEngine>(m, "FaceEmbedder")
@@ -38,5 +115,132 @@ NB_MODULE(sae_embed, m) {
.def("embed", &FaceEmbedderEngine::embed_path, "path"_a,
nb::call_guard<nb::gil_scoped_release>(),
"Detect the highest-confidence face in the image, align it, and "
"return a FaceResult with its 512-d ArcFace embedding.");
"return a FaceResult with its 512-d ArcFace embedding.")
.def("embed_mat", [](FaceEmbedderEngine& e, ImageArray img) {
return e.embed_mat(as_mat(img).clone());
}, "image"_a,
"As embed(), on an in-memory HxWx3 uint8 BGR array.")
.def("detect", [](FaceEmbedderEngine& e, ImageArray img) {
return e.detect(as_mat(img));
}, "image"_a,
"Run the configured detector. Returns every Detection, unfiltered — "
"min_face_px is applied downstream in face_detector_node.")
.def("embed_crop", [](FaceEmbedderEngine& e, ImageArray crop) {
cv::Mat c = as_mat(crop);
if (c.rows != 112 || c.cols != 112)
throw std::invalid_argument("embed_crop expects a 112x112 aligned crop");
Embedding emb = e.embed_crop(c);
return vec_to_numpy(std::vector<float>(emb.begin(), emb.end()));
}, "crop"_a,
"Embed a caller-supplied 112x112 aligned BGR crop. The stage-level "
"entry point for studies that degrade or re-align a crop themselves.")
.def("embed_crops", [](FaceEmbedderEngine& e,
nb::ndarray<const uint8_t, nb::ndim<4>, nb::c_contig,
nb::device::cpu> crops) {
if (crops.shape(1) != 112 || crops.shape(2) != 112 || crops.shape(3) != 3)
throw std::invalid_argument("embed_crops expects (N,112,112,3) uint8 BGR");
const size_t n = crops.shape(0);
std::vector<cv::Mat> mats;
mats.reserve(n);
for (size_t i = 0; i < n; ++i)
mats.emplace_back(112, 112, CV_8UC3,
const_cast<uint8_t*>(crops.data()) + i * 112 * 112 * 3);
std::vector<Embedding> out = e.embed_crops(mats);
auto* buf = new float[n * 512];
for (size_t i = 0; i < n; ++i)
std::memcpy(buf + i * 512, out[i].data(), 512 * sizeof(float));
nb::capsule owner(buf, [](void* p) noexcept { delete[] static_cast<float*>(p); });
size_t shape[2] = {n, 512};
return nb::ndarray<nb::numpy, float>(buf, 2, shape, owner);
}, "crops"_a,
"Batched embed_crop: (N,112,112,3) uint8 BGR in, (N,512) float32 out. "
"The backend batches internally, so this avoids paying per-call "
"overhead once per crop across a large study.")
.def_prop_ro("max_batch", [](FaceEmbedderEngine& e) { return e.max_batch(); });
m.def("align_face", [](ImageArray img,
nb::ndarray<const float, nb::shape<5, 2>, nb::c_contig,
nb::device::cpu> landmarks)
-> std::optional<nb::ndarray<nb::numpy, uint8_t>> {
cv::Mat crop = ::align_face(as_mat(img), as_landmarks(landmarks));
if (crop.empty()) return std::nullopt; // degenerate fit
return mat_to_numpy(crop);
}, "image"_a, "landmarks"_a,
"The ArcFace 5-point similarity transform (face_utils.hpp, AR-005). "
"Returns a 112x112 BGR crop, or None if the affine fit is degenerate. "
"Landmark order is types.hpp:60 — right-eye, left-eye, nose, "
"right-mouth, left-mouth.");
m.def("enhance_for_retry", [](ImageArray img) {
return mat_to_numpy(::enhance_for_retry(as_mat(img)));
}, "image"_a,
"Border-replicate pad by 50% and CLAHE, for a detector second try.");
// ── Calibration ──────────────────────────────────────────────────────────
// AR-024: the pipeline reasons in one probability space. Exposed so Python
// scores through the same sigmoid the C++ matcher uses, rather than a numpy
// copy of it that can silently disagree.
nb::class_<GalleryCalibration>(m, "GalleryCalibration")
.def_ro("a", &GalleryCalibration::a)
.def_ro("b", &GalleryCalibration::b)
.def_ro("valid", &GalleryCalibration::valid)
.def("probability", &GalleryCalibration::probability,
"similarity"_a, "log_prior_odds"_a = 0.f,
"P(match | sim) = sigma(a*sim + b + log_prior_odds). Pass "
"log_prior_odds = log(p0/(1-p0)) for a base-rate prior p0; leave it "
"at 0 for association (is this one person), which is what the "
"balanced fit answers — see gallery_calibration.hpp:63.")
.def("boundary_at", &GalleryCalibration::boundary_at,
"p"_a = 0.5f, "log_prior_odds"_a = 0.f,
"The similarity at which P(match) == p. Diagnostic only — decisions "
"threshold the probability, not this.")
.def("__repr__", [](const GalleryCalibration& c) {
return "<GalleryCalibration a=" + std::to_string(c.a) +
" b=" + std::to_string(c.b) +
(c.valid ? " valid>" : " INVALID>");
});
m.def("gallery_calibration", [](const std::string& gallery_path) {
ActorGallery g = load_gallery(gallery_path);
if (g.calib_valid) {
std::cerr << "[calibration] " << gallery_path << ": cached fit"
<< " over " << g.actors.size() << " actors\n";
return GalleryCalibration{g.calib_a, g.calib_b, true};
}
// Legacy JSON galleries carry no stored fit; compute it over the
// whole gallery, which is the point — the calibration must come
// from the production actor population, not a handful of people.
std::cerr << "[calibration] " << gallery_path
<< ": no cached fit, computing over " << g.actors.size()
<< " actors\n";
std::vector<Embedding> flat;
std::vector<int> actor;
for (size_t a = 0; a < g.actors.size(); ++a)
for (const auto& e : g.actors[a].embeddings) {
flat.push_back(e);
actor.push_back(static_cast<int>(a));
}
return ::calibrate_gallery(flat, actor);
}, "gallery_path"_a,
"The production gallery's calibration — the global fit over every "
"actor in it. Use this to score, not a fit over a handful of people: "
"a sigmoid fitted on a few identities saturates, so its probabilities "
"mean nothing. Reads the cached fit stored in an HDF5 gallery, or "
"computes it over the whole gallery for a legacy JSON one.");
m.def("calibrate_gallery", [](nb::ndarray<const float, nb::shape<-1, 512>, nb::c_contig,
nb::device::cpu> emb,
std::vector<int> actor) {
const size_t n = emb.shape(0);
if (actor.size() != n)
throw std::invalid_argument("embeddings and actor ids differ in length");
std::vector<Embedding> flat(n);
for (size_t i = 0; i < n; ++i)
std::memcpy(flat[i].data(), &emb(i, 0), 512 * sizeof(float));
return ::calibrate_gallery(flat, actor);
}, "embeddings"_a, "actor_ids"_a,
"Fit the Platt sigmoid from intra/inter-class pairs — the same fit the "
"gallery build performs (gallery_calibration.hpp:85). embeddings is "
"(N,512) L2-normalised float32; actor_ids is a length-N list of "
"0-based actor indices.");
}
src/scene_boundaries.hpp
+126
View File
@@ -0,0 +1,126 @@
#pragma once
/// TRACES: AR-010 | SR-002
///
/// SceneBoundaries — the join point of the decode butterfly.
///
/// The topology forks after decode: one branch runs TransNetV2 over dense
/// frames, the other runs face detection over the sampled cadence. Boundaries
/// found on the first branch have to reach the second, and they cannot be
/// carried in the frames themselves because the branches are parallel.
///
/// **Why this needs a watermark.** TransNetV2 buffers `kWindow` frames before it
/// can score any of them, so at any instant the detector has an opinion about
/// everything up to some time T and nothing after it. Without recording T, a
/// consumer asking "is there a boundary at t?" cannot distinguish *no* from
/// *not yet* — and those demand opposite behaviour. Silently treating unscored
/// frames as boundary-free is exactly the class of failure that makes a
/// verification pass vacuously.
///
/// The consumer is held back by channel depth (see main.cpp) so that by the time
/// it pulls a frame, the detector has already scored past it. `scored_through()`
/// is what lets that assumption be *checked* rather than assumed.
#include <algorithm>
#include <condition_variable>
#include <mutex>
#include <vector>
class SceneBoundaries {
public:
/// Peaks closer than this are one boundary. Matches the dedup scenes.json
/// applies, so the two views agree.
static constexpr double kMergeSec = 0.04;
/// Called by the scene detector as each window is scored. `through` is the
/// timestamp up to which its verdict is now final.
void publish(const std::vector<double>& ts, double through) {
{
std::lock_guard<std::mutex> g(mu_);
// Dedup on insert, matching what scenes.json does at write time. A run
// of adjacent high-scoring frames is one boundary, not several, and
// leaving them raw made this view report 357 where the file said 13 —
// the same event counted many times. Harmless for is_boundary(), which
// absorbs them in its tolerance, but a count nobody can reconcile with
// the output file is a bad diagnostic.
bounds_.insert(bounds_.end(), ts.begin(), ts.end());
std::sort(bounds_.begin(), bounds_.end());
bounds_.erase(std::unique(bounds_.begin(), bounds_.end(),
[](double a, double b) { return b - a < kMergeSec; }),
bounds_.end());
scored_through_ = std::max(scored_through_, through);
}
cv_.notify_all();
}
/// True if a boundary falls within `tol` of `t`.
///
/// `tol` exists because the two branches sample at different rates: a
/// boundary found on a dense frame rarely lands exactly on a sampled one.
/// Half a sample interval is the natural width — it attributes the boundary
/// to the nearest sampled frame and no further.
bool is_boundary(double t, double tol) const {
std::lock_guard<std::mutex> g(mu_);
auto it = std::lower_bound(bounds_.begin(), bounds_.end(), t - tol);
return it != bounds_.end() && *it <= t + tol;
}
/// The timestamp through which the detector's verdict is final. A consumer
/// past this point is asking about frames nobody has looked at yet.
double scored_through() const {
std::lock_guard<std::mutex> g(mu_);
return scored_through_;
}
/// Block until the detector's verdict covers `t`, or it finishes.
///
/// Channel depth alone does NOT create the required lag: it only holds
/// frames back when the consumer is slower, and the face branch is roughly
/// four orders of magnitude faster per frame than TransNetV2. So the join
/// has to wait explicitly.
///
/// Returns false if the detector finished without ever covering `t`, which
/// happens for the tail frames after its last full window. The caller must
/// distinguish that from a genuine "no boundary" rather than assuming.
bool wait_until_scored(double t) const {
std::unique_lock<std::mutex> lk(mu_);
cv_.wait(lk, [&] { return finished_ || scored_through_ >= t; });
return scored_through_ >= t;
}
/// Called when the detector will publish nothing further. Without this the
/// join would deadlock on the tail: those frames are never covered by a full
/// window, so waiting for them would wait forever.
void finish() {
{
std::lock_guard<std::mutex> g(mu_);
finished_ = true;
}
cv_.notify_all();
}
std::size_t count() const {
std::lock_guard<std::mutex> g(mu_);
return bounds_.size();
}
/// Consumers that outran the detector. Nonzero means the face branch is not
/// buffered deeply enough for the detector's window, so some frames were
/// annotated from an incomplete verdict — a real misconfiguration, and one
/// that would otherwise be invisible.
void note_outran() const {
std::lock_guard<std::mutex> g(mu_);
++outran_;
}
std::size_t outran() const {
std::lock_guard<std::mutex> g(mu_);
return outran_;
}
private:
mutable std::mutex mu_;
mutable std::condition_variable cv_;
bool finished_{false};
std::vector<double> bounds_;
double scored_through_{-1.0};
mutable std::size_t outran_{0};
};
src/scene_preview.cpp
+10 -3
View File
@@ -21,6 +21,7 @@
#include "config.hpp"
#include "types.hpp"
#include "gallery/embedder_stamp.hpp"
#include "gallery/gallery_store.hpp"
#include "nodes/frame_source_node.hpp"
#include "nodes/camera_position_change_detector_node.hpp"
@@ -76,6 +77,7 @@ static Config parse_args(int argc, char** argv) {
else if (arg("--detector-engine")) cfg.detector_engine = next();
else if (arg("--arcface")) cfg.arcface_model = next();
else if (arg("--arcface-engine")) cfg.arcface_engine = next();
else if (arg("--require-gallery-stamp")) cfg.require_gallery_stamp = true;
else if (arg("--conf")) cfg.detector_conf = std::stof(next());
else if (arg("--max-faces")) cfg.max_faces = std::stoi(next());
else if (arg("--min-face-px")) cfg.min_face_px = std::stof(next());
@@ -94,8 +96,8 @@ static Config parse_args(int argc, char** argv) {
// Per-film gallery expansion — preview supports it (same cfg fields).
else if (arg("--expand-gallery")) cfg.expand_gallery = true;
else if (arg("--expand-buffer")) cfg.expand_buffer_size = std::stoi(next());
else if (arg("--expand-novelty-sim")) cfg.expand_novelty_sim = std::stof(next());
else if (arg("--expand-spread-max")) cfg.expand_track_spread_max = std::stof(next());
else if (arg("--expand-band-lo")) cfg.expand_band_lo = std::stof(next());
else if (arg("--expand-band-hi")) cfg.expand_band_hi = std::stof(next());
else if (arg("--expand-min-anchor")) cfg.expand_min_anchor_frames = std::stoi(next());
// Scene detection is scene_analyze-only (needs the dense TransNetV2 branch).
// Accept the flags so a shared command line runs, but note they're inert
@@ -126,7 +128,12 @@ int main(int argc, char** argv) {
}
ActorGallery gallery;
try { gallery = load_gallery(cfg.gallery_path); }
try {
gallery = load_gallery(cfg.gallery_path);
/// TRACES: GR-004 | SR-001
verify_gallery_embedder(gallery, cfg.gallery_path, cfg.arcface_model,
cfg.require_gallery_stamp);
}
catch (const std::exception& e) {
std::cerr << "Gallery error: " << e.what() << "\n";
return 1;
src/track_registry.hpp
+355
View File
@@ -0,0 +1,355 @@
#pragma once
/// TRACES: AR-012, AR-013, AR-014, AR-015, AR-016, AR-017 | SR-002
///
/// TrackRegistry — the single owner of track state and of presence.
///
/// Presence follows **track extent**, not per-frame recognition (AR-012): a
/// window is `[first_seen, last_seen]` of a track an actor owns, so it starts
/// when the actor appeared rather than when the recogniser first succeeded.
///
/// `last_seen` carries the entire liveness state (AR-013):
///
/// unset → on screen now
/// set → went off screen at that timestamp, still revivable
/// reaped → emitted to the aggregator and erased
///
/// There is no missing-frame counter and no expired flag; the optional *is* the
/// state machine, and it subsumes what was previously a two-pool split in the
/// tracker (active vs. parked-across-a-cut).
///
/// **Interior gaps are claimed, the trailing cool-down is not.** A face lost at
/// t1 and re-associated at t2 within the timeout never closed its track, so the
/// actor is present across [t1, t2] — correct, since someone briefly occluded or
/// off-camera has not left the scene. But a track that dies ends its window at
/// `last_seen`, never at the moment of death. That asymmetry is what removes the
/// over-claim the retired `extinction_sec` keep-alive produced.
///
/// The registry is created in `main` and shared by `shared_ptr`; it is *not* a
/// KPN node. Ownership is not a stage in the stream — it is state several stages
/// read and write, whose final answer is only known when a track dies.
#include "types.hpp"
#include "evidence_discount.hpp"
#include <cmath>
#include <cstdint>
#include <functional>
#include <map>
#include <mutex>
#include <optional>
#include <string>
#include <vector>
// ── DeadTrack ────────────────────────────────────────────────────────────────
// A finished presence claim, emitted exactly once when a track is reaped or
// flushed. Immutable by construction: it carries everything needed to justify
// itself (AR-017), with no back-reference into registry state.
struct DeadTrack {
int track_id{-1};
double first_seen{0.0};
double last_seen{0.0}; ///< always the last sighting, never the death time
int actor_idx{-1}; ///< -1 when the track was never owned
float belief{0.0f}; ///< accumulated posterior for actor_idx
int observations{0}; ///< evidence updates that landed on this track
float effective_obs{0.f}; ///< sum of discounted weights — the evidence that counted
};
// ── Track ────────────────────────────────────────────────────────────────────
struct Track {
int id{-1};
double first_seen{0.0};
std::optional<double> last_seen; ///< unset ⇒ on screen
std::optional<int> actor; ///< set once a posterior crosses
/// actor_idx → accumulated log(1 − P). Lazy-OR (noisy-OR) accumulation:
/// each frame is new evidence that this track is that actor, and the
/// combined belief is the probability that *at least one* sighting was
/// right. Stored as log(1−P) because that makes the update additive and
/// keeps precision where it matters — as P approaches 1, (1−P) is the
/// quantity with the significant digits.
std::map<int, float> belief;
Embedding mean{}; ///< running directional mean
std::vector<Embedding> views; ///< distinct looks, for AR-025 discounting
float discounted_weight{0.f}; ///< sum of applied weights
int n_obs{0};
bool on_screen() const { return !last_seen.has_value(); }
};
// ── TrackRegistry ────────────────────────────────────────────────────────────
class TrackRegistry {
public:
using DeadTrackFn = std::function<void(const DeadTrack&)>;
struct Config {
double extinction_sec{5.0}; ///< how long a lost track stays revivable
float ownership_logodds{2.0f}; ///< belief needed to own a track (~0.88 posterior)
};
/// The discounter is a constructor argument rather than an option: there is
/// no correct way to accumulate per-frame evidence without it.
TrackRegistry(Config cfg, EvidenceDiscounter discounter)
: cfg_(cfg), discounter_(std::move(discounter)) {}
void on_track_dead(DeadTrackFn fn) { on_dead_ = std::move(fn); }
// ── Frame scope ──────────────────────────────────────────────────────────
// The tracker mutates registry state across a whole association pass, so
// that pass must be atomic as a unit — per-call locking would let another
// thread observe a half-updated frame. FrameScope holds the lock for its
// lifetime and exposes the mutating operations without re-locking.
class FrameScope {
public:
FrameScope(TrackRegistry& reg, double now)
: reg_(reg), lock_(reg.mu_) { reg_.tick_locked(now); }
/// All live tracks — **one pool**. `last_seen` tells the caller whether
/// IoU is meaningful; a dormant track is matched on embedding alone.
/// There is no separate revival path (AR-008).
std::vector<Track*> candidates() {
std::vector<Track*> out;
out.reserve(reg_.tracks_.size());
for (auto& [id, t] : reg_.tracks_) out.push_back(&t);
return out;
}
int create(double t, const Embedding& e) { return reg_.create_locked(t, e); }
void mark_seen(int id, double t, const Embedding& e){ reg_.mark_seen_locked(id, t, e); }
void mark_lost(int id, double last_on_screen) { reg_.mark_lost_locked(id, last_on_screen); }
private:
TrackRegistry& reg_;
std::unique_lock<std::mutex> lock_;
};
FrameScope begin_frame(double now) { return FrameScope(*this, now); }
/// Advance the clock and reap. Called every sampled frame **whether or not
/// it had detections** — without it a track only dies when some other face
/// happens to appear, and a film ending mid-track never closes.
void tick(double now) { std::lock_guard g(mu_); tick_locked(now); }
// ── Evidence ─────────────────────────────────────────────────────────────
/// Fold one observation into a track's belief (AR-025).
///
/// `posterior` is a **calibrated probability**, never a raw cosine
/// (AR-024) — the registry converts it to log-odds itself, so the
/// accumulation cannot be fed an uncalibrated number by a careless caller.
///
/// Correlation discounting is applied **here**, not by the caller.
/// Consecutive frames of one track are near-identical, and accumulating
/// them as independent evidence drives the posterior to certainty on what
/// is effectively a single measurement. Leaving that to callers would mean
/// a forgotten or doubly-applied discount produces confident wrong answers
/// silently; the registry is the one place all evidence converges, so it is
/// the one place the correction belongs.
///
/// A vote for a track that has already been reaped is dropped and counted:
/// a nonzero `dropped_votes()` means the timeout is shorter than the
/// matcher's lag, which is a real misconfiguration and must not be silent.
void observe(int track_id, int actor_idx, float posterior, const Embedding& e) {
std::lock_guard g(mu_);
auto it = tracks_.find(track_id);
if (it == tracks_.end()) { ++dropped_votes_; return; }
Track& t = it->second;
const float w = discounter_.weight(t.views, t.n_obs, e);
// Weighted lazy-OR: P_new = 1 − (1 − P_old)·(1 − p)^w, which in log
// space is a plain sum. w is the discounted evidence (AR-025), so a
// repeated view still advances the belief but by a fraction of what a
// genuinely new look would.
const float p = std::min(1.f - 1e-6f, std::max(1e-6f, posterior));
t.belief[actor_idx] += w * std::log(1.f - p);
t.discounted_weight += w;
++t.n_obs;
const int best = argmax_belief(t);
const float best_p = 1.f - std::exp(t.belief[best]);
if (best_p < own_threshold()) return;
if (!t.actor.has_value()) {
claim_locked(t, best);
return;
}
if (*t.actor != best) {
// AR-014 — belief swapped A→B. Not a correction: a track_id almost
// certainly carried across a viewpoint change onto a different
// person. Two non-twins both clearing the threshold on one face is
// not realistic; a track spanning two people is. Continuing would
// emit one window blending both, so close here and start afresh.
split_locked(t, best);
}
}
/// Snapshot read: tally and verdict under one lock. Reading them separately
/// would let a track be both unowned and owned within a single promotion
/// decision, since the matcher may be voting concurrently.
std::optional<int> owner(int track_id) const {
std::lock_guard g(mu_);
auto it = tracks_.find(track_id);
return it == tracks_.end() ? std::nullopt : it->second.actor;
}
// ── Termination ──────────────────────────────────────────────────────────
/// Emit every still-live track and empty the registry (AR-016). A film ends
/// with faces on screen and those tracks have not timed out, so without this
/// the closing scene's cast is silently never emitted — a loss that presents
/// as a recognition miss rather than a bookkeeping bug.
///
/// Idempotent: calling it twice emits nothing the second time.
void flush(double final_ts) {
std::lock_guard g(mu_);
for (auto& [id, t] : tracks_) emit_locked(t, t.last_seen.value_or(final_ts));
tracks_.clear();
}
// ── Diagnostics ──────────────────────────────────────────────────────────
// These measure how often tracking is silently wrong, which nothing in the
// pipeline currently reveals.
int dropped_votes() const { std::lock_guard g(mu_); return dropped_votes_; }
int belief_swaps() const { std::lock_guard g(mu_); return belief_swaps_; }
int actor_conflicts() const { std::lock_guard g(mu_); return actor_conflicts_; }
std::size_t live() const { std::lock_guard g(mu_); return tracks_.size(); }
private:
// ── Locked internals ─────────────────────────────────────────────────────
void tick_locked(double now) {
for (auto it = tracks_.begin(); it != tracks_.end(); ) {
const auto& ls = it->second.last_seen;
if (ls && (now - *ls) > cfg_.extinction_sec) {
emit_locked(it->second, *ls);
it = tracks_.erase(it);
} else {
++it;
}
}
}
int create_locked(double t, const Embedding& e) {
const int id = next_id_++;
Track tr;
tr.id = id;
tr.first_seen = t;
tr.mean = e;
tr.n_obs = 0;
tracks_.emplace(id, std::move(tr));
return id;
}
void mark_seen_locked(int id, double t, const Embedding& e) {
auto it = tracks_.find(id);
if (it == tracks_.end()) return;
Track& tr = it->second;
tr.last_seen.reset(); // back on screen; the gap is absorbed
update_mean(tr, e);
(void)t;
}
void mark_lost_locked(int id, double last_on_screen) {
auto it = tracks_.find(id);
if (it == tracks_.end()) return;
it->second.last_seen = last_on_screen;
}
void claim_locked(Track& t, int actor) {
// AR-015 — if another live track already owns this actor, at least one
// is wrong: a person cannot be in two places at once. The cause is the
// same as a belief swap — a missed camera or scene change. Detected on
// the update that causes it via the reverse index, not by scanning.
auto seen = owner_index_.find(actor);
if (seen != owner_index_.end() && seen->second != t.id
&& tracks_.count(seen->second)) {
++actor_conflicts_;
}
t.actor = actor;
owner_index_[actor] = t.id;
}
void split_locked(Track& t, int new_actor) {
++belief_swaps_;
const double boundary = t.last_seen.value_or(t.first_seen);
emit_locked(t, boundary);
// The successor inherits the embedding and the belief that caused the
// swap, and starts at the swap frame — so the two windows abut without
// overlapping and neither blends the two people.
Track next;
next.id = next_id_++;
next.first_seen = boundary;
next.mean = t.mean;
next.belief[new_actor] = t.belief[new_actor];
next.n_obs = 1;
const int old_id = t.id;
Track stash = std::move(next);
tracks_.erase(old_id);
const int nid = stash.id;
tracks_.emplace(nid, std::move(stash));
claim_locked(tracks_.at(nid), new_actor);
}
void emit_locked(Track& t, double end_ts) {
if (!on_dead_) return;
DeadTrack d;
d.track_id = t.id;
d.first_seen = t.first_seen;
d.last_seen = end_ts;
d.observations = t.n_obs;
d.effective_obs = t.discounted_weight;
if (t.actor) {
d.actor_idx = *t.actor;
d.belief = 1.f - std::exp(t.belief[*t.actor]);
auto oi = owner_index_.find(*t.actor);
if (oi != owner_index_.end() && oi->second == t.id) owner_index_.erase(oi);
}
on_dead_(d);
}
/// Most-believed actor. belief holds log(1 − P), so the strongest claim is
/// the *most negative* entry, not the largest.
static int argmax_belief(const Track& t) {
int best = -1;
float lo = 1e30f;
for (const auto& [a, v] : t.belief) if (v < lo) { lo = v; best = a; }
return best;
}
/// Ownership expressed as a probability. Config still carries log-odds so
/// the knob keeps its meaning across this change.
float own_threshold() const {
return 1.f / (1.f + std::exp(-cfg_.ownership_logodds));
}
static void update_mean(Track& t, const Embedding& e) {
// Directional mean: accumulate then re-normalise to the unit sphere, so
// cosine against it stays a plain dot product.
double norm = 0.0;
for (int i = 0; i < 512; ++i) {
t.mean[i] = t.mean[i] * static_cast<float>(t.n_obs ? t.n_obs : 1) + e[i];
norm += static_cast<double>(t.mean[i]) * t.mean[i];
}
norm = norm > 0 ? std::sqrt(norm) : 1.0;
for (int i = 0; i < 512; ++i) t.mean[i] = static_cast<float>(t.mean[i] / norm);
}
static float logistic(float z) {
return z >= 0 ? 1.f / (1.f + std::exp(-z))
: std::exp(z) / (1.f + std::exp(z));
}
static float logit(float p) {
const float eps = 1e-6f;
p = std::min(1.f - eps, std::max(eps, p));
return std::log(p / (1.f - p));
}
Config cfg_;
EvidenceDiscounter discounter_;
mutable std::mutex mu_;
std::map<int, Track> tracks_;
std::map<int, int> owner_index_; ///< actor_idx → live track_id (AR-015)
DeadTrackFn on_dead_;
int next_id_{0};
int dropped_votes_{0};
int belief_swaps_{0};
int actor_conflicts_{0};
};
src/types.hpp
+14
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@@ -6,6 +6,8 @@
#include <opencv2/core.hpp>
#include "gallery/embedder_stamp.hpp"
// ── Embedding ─────────────────────────────────────────────────────────────────
// 512-dim L2-normalised ArcFace embedding
using Embedding = std::array<float, 512>;
@@ -61,6 +63,13 @@ struct DetectedFace {
cv::Rect2f bbox;
std::array<cv::Point2f, 5> landmarks;
float confidence{0.f};
// AR-030 visibility: RMS landmark misfit, in canonical 112×112 pixels, left
// over after the best similarity fit to the ArcFace template. Rises with
// out-of-plane pose and with occlusion; blind to in-plane roll and to face
// size, both of which the fit absorbs. Set by the aligner, which is where
// the transform is computed; -1 until then.
float alignment_residual{-1.f};
};
// ── Pipeline messages ─────────────────────────────────────────────────────────
@@ -136,6 +145,11 @@ struct ActorGallery {
};
std::vector<Actor> actors;
/// TRACES: GR-004 | SR-001
// Which embedder produced every embedding above. Empty == the file predates
// model binding; see gallery/embedder_stamp.hpp for what is checked and why.
EmbedderStamp embedder;
// Cached Platt-sigmoid calibration (see gallery/gallery_calibration.hpp),
// stored alongside the gallery in HDF5 so it never needs recomputing
// unless the reference embeddings actually change. calib_valid=false and
tests/CMakeLists.txt
+25 -1
View File
@@ -21,21 +21,45 @@ add_executable(sae_tests
test_face_utils.cpp
test_track_gallery.cpp
test_face_tracker.cpp
test_track_registry.cpp
test_replay_fixtures.cpp
test_audio_signature.cpp
${CMAKE_SOURCE_DIR}/src/backends/gemm_backend.cpp
${CMAKE_SOURCE_DIR}/src/gallery/gallery_store.cpp
${CMAKE_SOURCE_DIR}/src/audio_signature.cpp
${CMAKE_SOURCE_DIR}/src/gallery/embedder_stamp.cpp
)
target_include_directories(sae_tests PRIVATE ${CMAKE_SOURCE_DIR}/src)
# SAE_GEMM_CPU: build the CPU reference GEMM regardless of the main backend.
# SAE_MODELS_DIR: config.hpp (pulled in by track_gallery.hpp) bakes model paths.
# SAE_TEST_FIXTURES_DIR: the audio golden vector is read from the source tree,
# not copied, so the file the plugin repo shares is the file under test.
# AR-026/AR-027: exercise the same kernel CI actually runs. Without this the
# suite compiles the scalar fallback while the CPU builder image links OpenBLAS,
# so the tested path and the shipped path would differ.
find_package(PkgConfig QUIET)
if(PkgConfig_FOUND)
pkg_check_modules(OPENBLAS_T QUIET openblas)
endif()
if(OPENBLAS_T_FOUND)
target_include_directories(sae_tests PRIVATE ${OPENBLAS_T_INCLUDE_DIRS})
target_link_libraries(sae_tests PRIVATE ${OPENBLAS_T_LINK_LIBRARIES})
endif()
target_compile_definitions(sae_tests PRIVATE
$<$<BOOL:${OPENBLAS_T_FOUND}>:SAE_GEMM_CBLAS>
SAE_GEMM_CPU
SAE_MODELS_DIR="${SAE_MODELS_DIR}")
SAE_MODELS_DIR="${SAE_MODELS_DIR}"
SAE_TEST_FIXTURES_DIR="${CMAKE_CURRENT_SOURCE_DIR}/fixtures")
# gallery_store.cpp + gallery_calibration.hpp use nlohmann/json and HDF5
# (galleries are HDF5-native, see src/gallery/gallery_store.cpp); face_utils.hpp
# and the calibration GEMM pull in OpenCV (calib3d/imgproc/core) via types.hpp.
# ffmpeg_libs: audio_signature.cpp decodes the golden fixture (avformat/avcodec/
# avutil/swresample). Still GPU-free — the audio path is pure CPU.
target_link_libraries(sae_tests PRIVATE
Catch2::Catch2WithMain
nlohmann_json::nlohmann_json
ffmpeg_libs
${OpenCV_LIBS}
${HDF5_CXX_LIBRARIES})
target_include_directories(sae_tests PRIVATE ${HDF5_INCLUDE_DIRS})
tests/fixtures/audio/jray_audio_v1_golden.json
Vendored
+194
View File
@@ -0,0 +1,194 @@
{
"_": "Golden vector for the JRay v1 audio signature (JRay-public-server SPEC.md \u00a73). Shared verbatim between scene-actor-extraction (C++) and the jRay Jellyfin plugin (C#) so the two implementations can be proven bit-identical. IR-004, IR-005, IR-007, IR-008.",
"version": "v1",
"signature": "v1: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",
"frame_count": 1288,
"media": {
"file": "jray_audio_v1_tone.flac",
"generator": "make_fixture.py",
"container": "FLAC (lossless \u2014 decodes to exactly the PCM make_fixture.py emits)",
"duration_sec": 120.0,
"sample_rate": 11025,
"channels": 1,
"sample_format": "s16",
"sha256": "912ecd426cd426dccb37753e0249694227619c701cb9f533502b37da0fbe8096",
"bytes": 585142
},
"decoded_window": {
"_": "Checksums of the 120 s centre window after downmix to mono and resample to 11025 Hz, i.e. exactly the stream `ffmpeg -ss <mid-60> -t 120 -i <file> -vn -ac 1 -ar 11025 -f f32le -` produces. Check these first: a mismatch here is a decode problem, not a DSP one.",
"samples": 1323000,
"f32le_fnv1a64": "0x1ef7899cd4d12662",
"s16le_fnv1a64": "0xf824fa56f125c0dc"
},
"params": {
"window_sec": 120.0,
"window_centre": "runtime/2, i.e. samples from runtime/2 - 60 s; truncated to exactly 1323000 samples",
"min_duration_sec": 120.0,
"min_duration_rule": "IR-007 \u2014 below this emit NO signature and apply no sync offset",
"sample_rate": 11025,
"channels": 1,
"arithmetic": "IEEE-754 double throughout; float32 is not sufficient",
"sample_scale": "s16 * (1/32768), FFmpeg's native s16->flt",
"frame_size": 4096,
"hop_size": 1024,
"frame_count_rule": "1 + (n_samples - 4096) / 1024, integer division; whole frames only",
"window_fn": "Hann, PERIODIC: w[n] = 0.5 * (1 - cos(2*pi*n/4096))",
"transform": "radix-2 DIT complex FFT over the 4096 real samples (imag=0), no normalisation",
"magnitude": "sqrt(re^2 + im^2), linear",
"band_lo_hz": 300.0,
"band_hi_hz": 3000.0,
"num_bands": 32,
"band_edges": "edge[b] = 300 * (3000/300)^(b/32), b = 0..32",
"band_bins": "band b owns FFT bins [k_lo[b], k_lo[b+1]) with k_lo[b] = ceil(edge[b] * 4096 / 11025); see band_fft_bins",
"band_value": "MEAN of the linear magnitudes in the band (not sum, not max)",
"peak_bin": "argmax over the 32 band values; ties resolve to the LOWEST index",
"energy_metric": "E = mean magnitude over all FFT bins 112..1114, i.e. the whole 300-3000 Hz band",
"energy_reference": "upper median of E over all frames: sorted[n/2], no averaging of the two middle values",
"energy_ratio": "r = log10((E + 1e-12) / (E_ref + 1e-12))",
"energy_class_edges": [
-0.6,
-0.2,
0.2
],
"energy_class": "0 if r < -0.6, 1 if r < -0.2, 2 if r < 0.2, else 3",
"byte_layout": "bit7 = 0 (reserved), bits6..2 = 5-bit band index, bits1..0 = 2-bit energy class; byte = (band << 2) | class",
"base64": "standard alphabet A-Za-z0-9+/ with '=' padding",
"prefix": "v1:"
},
"band_fft_bins": [
[
112,
120
],
[
120,
129
],
[
129,
139
],
[
139,
149
],
[
149,
160
],
[
160,
172
],
[
172,
185
],
[
185,
199
],
[
199,
213
],
[
213,
229
],
[
229,
246
],
[
246,
265
],
[
265,
285
],
[
285,
306
],
[
306,
328
],
[
328,
353
],
[
353,
379
],
[
379,
408
],
[
408,
438
],
[
438,
471
],
[
471,
506
],
[
506,
543
],
[
543,
584
],
[
584,
627
],
[
627,
674
],
[
674,
724
],
[
724,
778
],
[
778,
836
],
[
836,
899
],
[
899,
966
],
[
966,
1038
],
[
1038,
1115
]
],
"notes": [
"The server spec fixes the window, rate, STFT geometry, band and the 5+2 bit packing. Everything under params beyond that (Hann periodicity, band aggregation, the energy-class definition, tie-breaking, base64 alphabet) is pinned HERE for v1 \u2014 the spec does not constrain it, and two implementations that guess differently produce non-matching signatures.",
"Decision margins on this fixture: the two strongest bands are within 1.3% on the closest frame, and the closest frame to an energy-class edge is 3.6e-3 away in log10. Both are many orders of magnitude above double-precision FFT differences, so any two correct double- precision implementations agree; a float32 implementation is not guaranteed to.",
"Coverage: all 32 bands and all 4 energy classes appear in the golden signature.",
"Robustness observed on this fixture: identical peak-bin sequence after a stereo/44100 Hz round trip and after AAC 128 kbit/s re-encoding."
]
}
tests/fixtures/audio/jray_audio_v1_tone.flac
Vendored
BIN
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tests/fixtures/audio/make_fixture.py
Vendored
+114
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@@ -0,0 +1,114 @@
#!/usr/bin/env python3
"""Regenerate the JRay audio-signature golden fixture.
python3 make_fixture.py # writes jray_audio_v1_tone.flac here
This is the *source of truth* for the fixture media: `jray_audio_v1_tone.flac`
is a lossless FLAC encoding of exactly the PCM this script emits, so any repo
that wants to check its own audio-signature implementation against the golden
vector in `jray_audio_v1_golden.json` can regenerate the input from scratch and
confirm it is byte-identical (the golden file records `pcm_fnv1a64`, a hash of
the decoded 16-bit samples).
Deliberately dependency-free (no numpy) and written in plain arithmetic so it
ports to any language in ~20 lines.
Signal — 120.000 s, mono, 11025 Hz, 16-bit signed PCM:
* split into segments of 32768 samples (~2.97 s), 40.4 segments in total;
* segment `s` carries one sine at the geometric centre of log-band
`(s * 7) mod 32` of the 300-3000 Hz band, so all 32 bands are exercised;
* its amplitude walks a golden-ratio low-discrepancy sequence over
[10^-1.55, 10^-0.02] so frame energies spread continuously across ~1.5
decades and all four energy classes are exercised, without a dense cluster
of frames sitting on a class boundary;
* phase is carried across segment boundaries (no clicks);
* a constant, far quieter 777 Hz tone sits underneath so no frame is
degenerate;
* samples are quantised with floor(x * 32767 + 0.5).
Why FLAC and not WAV: 120 s of 11025 Hz 16-bit PCM is 2.6 MB and does not
compress in git. FLAC is lossless — FFmpeg decodes it to exactly the PCM
written here — and is ~3.5x smaller. `--wav` writes the uncompressed original
if you want to diff it.
"""
import math
import struct
import subprocess
import sys
import os
SAMPLE_RATE = 11025
DURATION_SEC = 120.0
SEGMENT = 32768 # samples per tone segment
BAND_STRIDE = 7 # coprime with 32 -> visits every band
BAND_LO_HZ = 300.0
BAND_HI_HZ = 3000.0
NUM_BANDS = 32
AMP_LOG_MIN = -1.55 # 10^-1.55 ~= 0.028
AMP_LOG_SPAN = 1.53 # up to 10^-0.02 ~= 0.955
PHI_FRAC = 0.6180339887498949
BG_HZ = 777.0
BG_AMP = 0.004
OUT_FLAC = "jray_audio_v1_tone.flac"
OUT_WAV = "jray_audio_v1_tone.wav"
def generate():
"""Return the 120 s signal as a list of int16 sample values."""
n = int(round(SAMPLE_RATE * DURATION_SEC))
out = [0] * n
phase = 0.0
two_pi = 2.0 * math.pi
for start in range(0, n, SEGMENT):
s = start // SEGMENT
end = min(n, start + SEGMENT)
band = (s * BAND_STRIDE) % NUM_BANDS
# geometric centre of log-band `band`
freq = BAND_LO_HZ * (BAND_HI_HZ / BAND_LO_HZ) ** ((band + 0.5) / NUM_BANDS)
amp = 10.0 ** (AMP_LOG_MIN + AMP_LOG_SPAN * ((s * PHI_FRAC) % 1.0))
step = two_pi * freq / SAMPLE_RATE
for k in range(end - start):
i = start + k
x = amp * math.sin(phase + step * k)
x += BG_AMP * math.sin(two_pi * BG_HZ * i / SAMPLE_RATE)
if x > 1.0:
x = 1.0
elif x < -1.0:
x = -1.0
out[i] = int(math.floor(x * 32767.0 + 0.5))
phase = (phase + step * (end - start)) % two_pi
return out
def write_wav(path, samples):
data = struct.pack("<%dh" % len(samples), *samples)
hdr = b"RIFF" + struct.pack("<I", 36 + len(data)) + b"WAVE"
hdr += b"fmt " + struct.pack("<IHHIIHH", 16, 1, 1, SAMPLE_RATE,
SAMPLE_RATE * 2, 2, 16)
hdr += b"data" + struct.pack("<I", len(data))
with open(path, "wb") as fh:
fh.write(hdr + data)
def main():
here = os.path.dirname(os.path.abspath(__file__))
samples = generate()
wav = os.path.join(here, OUT_WAV)
write_wav(wav, samples)
if "--wav" in sys.argv:
print("wrote", wav)
return
flac = os.path.join(here, OUT_FLAC)
# -compression_level 12 is deterministic for a given libFLAC/ffmpeg build;
# only the container bytes vary, never the decoded PCM.
subprocess.run(["ffmpeg", "-nostdin", "-v", "error", "-y", "-i", wav,
"-c:a", "flac", "-compression_level", "12", flac],
check=True)
os.remove(wav)
print("wrote", flac)
if __name__ == "__main__":
main()
tests/fixtures/audio/make_offset_fixture.sh
Vendored Executable
+60
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@@ -0,0 +1,60 @@
#!/bin/sh
#
# Regenerate superhero_offset_200s.flac — the real-audio fixture behind VR-014, the
# audio-signature offset-recovery validation.
#
# sh make_offset_fixture.sh /path/to/clips
#
# Why real audio and not a second synthetic tone: jray_audio_v1_tone.flac pins
# the *arithmetic* (IR-005) and is deliberately built so every band and every
# energy class appears. It cannot answer the question VR-014 asks — whether the
# peak-bin sequence of ordinary film audio is distinctive enough that sliding
# one signature against another finds the true alignment and only the true
# alignment. Tones are pathologically easy for that; dialogue and score are not.
#
# Source: scene clips from SuperHero (TRECVID DVU development set), the corpus
# this repo already uses for the replay fixtures — tests/fixtures/dumps/superhero.h5
# are dumps of these same clips. Each is under the 120 s window on its own
# (29-77 s), so they are concatenated in scene order to make a source long
# enough that a 120 s window can slide inside it.
#
# 200 s is chosen, not arbitrary: the window is 120 s and the match search is
# capped at +/-600 frames (~55.7 s), so a source of 120 + 56 s is the shortest
# one that can place two windows at the edge of the cap. The 200 s here leaves
# room to go past it as well, which is what lets the test check that an
# out-of-range offset is declined rather than guessed.
#
# Encoded mono at 11025 Hz, 16-bit, which is exactly what the signature decodes
# to anyway. That keeps a 200 s fixture at ~2.4 MB instead of ~20 MB, and makes
# every trim below sample-exact — the test measures offset recovery, not the
# resampler, which tests/test_audio_signature.cpp already covers (UT-103).
#
# FLAC because it is lossless: the decoded PCM is the same on every machine, so
# a signature computed from this file is reproducible. A lossy fixture would
# make the measurement depend on the decoder version.
#
# sha256 of the committed file:
# 4a952e46a090a9acd9eae56996250ec03e08e0d04ee139ac0a42f1690a536c83
# A regenerated file that hashes differently means the source clips or the
# encoder changed, and VR-014's recorded numbers should be re-measured — the
# offsets will still be exact, but the scores are this audio's.
set -eu
CLIPS="${1:-../../../../hero}"
OUT="$(dirname "$0")/superhero_offset_200s.flac"
LIST="$(mktemp)"
trap 'rm -f "$LIST"' EXIT
for scene in 13 27 28 31 46; do
clip="$CLIPS/SuperHero-$scene.webm"
[ -f "$clip" ] || { echo "missing clip: $clip" >&2; exit 1; }
echo "file '$(cd "$(dirname "$clip")" && pwd)/$(basename "$clip")'" >> "$LIST"
done
ffmpeg -nostdin -v error -y -f concat -safe 0 -i "$LIST" \
-vn -t 200 -ac 1 -ar 11025 -sample_fmt s16 \
-c:a flac -compression_level 12 "$OUT"
echo "wrote $OUT"
sha256sum "$OUT" 2>/dev/null || shasum -a 256 "$OUT"
tests/fixtures/dumps/.gitignore
+17
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@@ -0,0 +1,17 @@
# Replay fixtures are distributed as artifacts, not through git.
#
# They are large (superhero.h5 is ~9 MB) and regenerating one needs the film,
# the models and a GPU — none of which CI has. So they live in the Gitea
# generic package registry and are fetched on demand:
#
# scripts/artifacts/pull_artifacts.sh replay-fixtures [version]
# scripts/artifacts/push_artifacts.sh replay-fixtures
#
# The gallery ships alongside the dumps deliberately: a dump only replays
# meaningfully against the gallery it was produced with.
#
# bali_*.h5 predate this and remain tracked; do not add more to git.
superhero.h5
hero66.h5
gt.json
scene_bounds.json
tests/test_audio_signature.cpp
+359
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@@ -0,0 +1,359 @@
// Unit tests for the JRay v1 audio signature (src/audio_signature.*).
//
/// TRACES: UT-101, UT-102, UT-103, UT-104 | IR-004, IR-005, IR-007, IR-008
//
// The headline test is the golden vector: a deterministic tone fixture checked
// into tests/fixtures/audio/ together with the signature it must produce. That
// fixture is the artefact shared with the jRay plugin repo, and it is what
// makes "both producers agree bit-for-bit" a checked claim rather than an
// assertion (IR-005).
//
// GPU-free, model-free, no network. Pure CPU DSP plus an FFmpeg decode of a
// 585 KB file — which is precisely why this is the right cross-repo check: it
// runs anywhere, including the N100 CI host.
#include <catch2/catch_test_macros.hpp>
#include <catch2/matchers/catch_matchers_floating_point.hpp>
#include "audio_signature.hpp"
#include <nlohmann/json.hpp>
#include <cmath>
#include <cstdint>
#include <filesystem>
#include <fstream>
#include <stdexcept>
#include <string>
#include <vector>
using namespace sae::audio;
namespace fs = std::filesystem;
namespace {
const std::string kFixtureDir = SAE_TEST_FIXTURES_DIR "/audio";
const std::string kGoldenPath = kFixtureDir + "/jray_audio_v1_golden.json";
const std::string kMediaPath = kFixtureDir + "/jray_audio_v1_tone.flac";
const nlohmann::json& golden() {
static const nlohmann::json j = [] {
std::ifstream in(kGoldenPath);
if (!in.good())
throw std::runtime_error("golden fixture not found: " + kGoldenPath);
nlohmann::json parsed;
in >> parsed;
return parsed;
}();
return j;
}
std::uint64_t hex64(const std::string& s) {
return std::stoull(s, nullptr, 16);
}
// The fixture is 120 s of audio: decoding and signing it is the expensive part
// of this file, so both results are computed once and shared. Every test below
// still asserts against the on-disk golden values, not against each other.
const std::optional<std::vector<float>>& fixture_window() {
static const std::optional<std::vector<float>> w = decode_centre_window(kMediaPath);
return w;
}
const std::optional<std::string>& fixture_signature() {
static const std::optional<std::string> s = compute_signature(kMediaPath);
return s;
}
// ── Minimal WAV writer, so the short-media and resample cases need no fixture ─
// 16-bit PCM, interleaved.
struct TempWav {
fs::path path;
explicit TempWav(const std::string& name)
: path(fs::temp_directory_path() / ("sae_audio_test_" + name + ".wav")) {}
~TempWav() { std::error_code ec; fs::remove(path, ec); }
void write(const std::vector<std::int16_t>& samples, int rate, int channels) const {
const std::uint32_t bytes = static_cast<std::uint32_t>(samples.size() * 2);
const std::uint32_t byte_rate = static_cast<std::uint32_t>(rate * channels * 2);
std::ofstream out(path, std::ios::binary);
auto u32 = [&](std::uint32_t v) { out.write(reinterpret_cast<const char*>(&v), 4); };
auto u16 = [&](std::uint16_t v) { out.write(reinterpret_cast<const char*>(&v), 2); };
out.write("RIFF", 4); u32(36 + bytes); out.write("WAVE", 4);
out.write("fmt ", 4); u32(16); u16(1); u16(static_cast<std::uint16_t>(channels));
u32(static_cast<std::uint32_t>(rate)); u32(byte_rate);
u16(static_cast<std::uint16_t>(channels * 2)); u16(16);
out.write("data", 4); u32(bytes);
out.write(reinterpret_cast<const char*>(samples.data()), bytes);
}
};
// A plain 1 kHz tone, mono, at the signature's own rate.
std::vector<std::int16_t> tone(double seconds, int rate = kSampleRate) {
const std::size_t n = static_cast<std::size_t>(std::llround(seconds * rate));
std::vector<std::int16_t> s(n);
for (std::size_t i = 0; i < n; ++i)
s[i] = static_cast<std::int16_t>(std::llround(
20000.0 * std::sin(2.0 * 3.14159265358979323846 * 1000.0 * double(i) / rate)));
return s;
}
std::vector<std::uint8_t> base64_decode(const std::string& in) {
auto val = [](char c) -> int {
if (c >= 'A' && c <= 'Z') return c - 'A';
if (c >= 'a' && c <= 'z') return c - 'a' + 26;
if (c >= '0' && c <= '9') return c - '0' + 52;
if (c == '+') return 62;
if (c == '/') return 63;
return -1;
};
std::vector<std::uint8_t> out;
std::uint32_t acc = 0;
int bits = 0;
for (char c : in) {
const int v = val(c);
if (v < 0) continue; // '=' padding
acc = (acc << 6) | static_cast<std::uint32_t>(v);
bits += 6;
if (bits >= 8) {
bits -= 8;
out.push_back(static_cast<std::uint8_t>((acc >> bits) & 0xFF));
}
}
return out;
}
} // namespace
// ── UT-101 — the golden vector ──────────────────────────────────────────────
/// TRACES: UT-101 | IR-004, IR-005, IR-008
TEST_CASE("signature of the golden fixture matches the recorded value exactly",
"[audio_signature][golden]") {
REQUIRE(fs::exists(kMediaPath));
const std::optional<std::string>& sig = fixture_signature();
REQUIRE(sig.has_value());
CHECK(*sig == golden()["signature"].get<std::string>());
}
/// TRACES: UT-101 | IR-005
TEST_CASE("decoded centre window matches the recorded PCM checksum",
"[audio_signature][golden]") {
// Checked separately from the signature so a codec-level difference is
// distinguishable from a DSP-level one: if this passes and the signature
// test fails, the DSP diverged; if this fails, the decode did.
const std::optional<std::vector<float>>& mono = fixture_window();
REQUIRE(mono.has_value());
CHECK(mono->size() == golden()["decoded_window"]["samples"].get<std::size_t>());
CHECK(fnv1a64(mono->data(), mono->size() * sizeof(float)) ==
hex64(golden()["decoded_window"]["f32le_fnv1a64"].get<std::string>()));
}
/// TRACES: UT-101 | IR-004
TEST_CASE("log-spaced band table matches the recorded one", "[audio_signature][golden]") {
// The band->FFT-bin table is the part of the construction most likely to
// drift between two implementations, so it is pinned independently of the
// signature it produces.
const auto& tbl = band_fft_bins();
const auto& want = golden()["band_fft_bins"];
REQUIRE(want.size() == tbl.size());
for (std::size_t b = 0; b < tbl.size(); ++b) {
CHECK(tbl[b].first == want[b][0].get<int>());
CHECK(tbl[b].second == want[b][1].get<int>());
CHECK(tbl[b].second > tbl[b].first); // no empty band
if (b) CHECK(tbl[b].first == tbl[b - 1].second); // contiguous, no overlap
}
}
/// TRACES: UT-101 | IR-004, IR-008
TEST_CASE("signature is well-formed: v1 prefix, 1288 frames, structural bytes",
"[audio_signature][golden]") {
const std::optional<std::string>& sig = fixture_signature();
REQUIRE(sig.has_value());
// IR-008 — the signature carries its own version, separate from
// schema_version, so a future DSP change is detectable rather than silently
// producing non-matching signatures.
REQUIRE(sig->rfind(kVersionPrefix, 0) == 0);
const std::vector<std::uint8_t> bytes = base64_decode(sig->substr(3));
CHECK(bytes.size() == kExpectedFrames);
CHECK(bytes.size() == golden()["frame_count"].get<std::size_t>());
// The server validates this structure on upload (server SPEC §3): each byte
// is a 5-bit band index plus a 2-bit energy class, so bit 7 is always clear
// and arbitrary bytes are invalid. That is what keeps the field from being
// a payload channel.
bool bands_seen[kNumBands] = {};
bool classes_seen[4] = {};
for (std::uint8_t b : bytes) {
REQUIRE((b & 0x80) == 0);
bands_seen[(b >> 2) & 0x1F] = true;
classes_seen[b & 0x03] = true;
}
// The fixture is built to exercise the whole output alphabet — if it ever
// stops doing so, the golden vector has become a weaker check than it looks.
for (bool seen : bands_seen) CHECK(seen);
for (bool seen : classes_seen) CHECK(seen);
}
// ── UT-102 — IR-007, media shorter than the window ──────────────────────────
/// TRACES: UT-102 | IR-007
TEST_CASE("media shorter than 120 s emits no signature", "[audio_signature][short]") {
// The window runtime/2 ± 60 s underflows, so there is no signature and no
// sync offset downstream. Both producers must apply the identical rule or
// they diverge on exactly the short items most likely to be misidentified.
SECTION("30 s") {
TempWav w("short30");
w.write(tone(30.0), kSampleRate, 1);
CHECK_FALSE(compute_signature(w.path.string()).has_value());
CHECK_FALSE(decode_centre_window(w.path.string()).has_value());
}
SECTION("just under the boundary") {
TempWav w("short11999");
w.write(tone(119.99), kSampleRate, 1);
CHECK_FALSE(compute_signature(w.path.string()).has_value());
}
}
/// TRACES: UT-102 | IR-007
TEST_CASE("media of exactly 120 s emits a full-length signature",
"[audio_signature][short]") {
TempWav w("exact120");
w.write(tone(120.0), kSampleRate, 1);
const std::optional<std::string> sig = compute_signature(w.path.string());
REQUIRE(sig.has_value());
CHECK(base64_decode(sig->substr(3)).size() == kExpectedFrames);
}
/// TRACES: UT-102 | IR-007
TEST_CASE("unreadable media degrades to no signature rather than failing",
"[audio_signature][short]") {
// UR-9 is an enhancement and must never be able to break a fetch.
CHECK_FALSE(compute_signature("/nonexistent/definitely-not-here.mkv").has_value());
}
/// TRACES: UT-102 | IR-004
TEST_CASE("the window is taken from the centre, not the head",
"[audio_signature][centre]") {
// Sampling from the centre is the whole reason the construction avoids the
// head and tail (logos, cold opens, credits), so it needs its own check:
// wrap the fixture's own 120 s in 90 s of silence either side and the
// signature of the 300 s file must be the golden value, byte for byte.
// Nothing else pins the seek offset — a head-anchored window would pass
// every other test in this file.
const std::optional<std::vector<float>>& mono = fixture_window();
REQUIRE(mono.has_value());
const std::size_t pad = 90 * kSampleRate;
std::vector<std::int16_t> padded(pad * 2 + mono->size(), 0);
for (std::size_t i = 0; i < mono->size(); ++i)
padded[pad + i] = static_cast<std::int16_t>(std::llround(double((*mono)[i]) * 32768.0));
TempWav w("centred300");
w.write(padded, kSampleRate, 1);
const std::optional<std::string> sig = compute_signature(w.path.string());
REQUIRE(sig.has_value());
CHECK(*sig == golden()["signature"].get<std::string>());
}
// ── UT-103 — downmix and resample ───────────────────────────────────────────
/// TRACES: UT-103 | IR-004
TEST_CASE("stereo, non-native sample rate yields the same peak-bin sequence",
"[audio_signature][resample]") {
// The golden fixture is already mono at 11025 Hz so the golden vector does
// not depend on the resampler's version. This case exercises the path that
// real media takes — downmix plus resample — by rebuilding the fixture's own
// audio as 22050 Hz stereo and checking the peak bins survive it.
const std::optional<std::vector<float>>& mono = fixture_window();
REQUIRE(mono.has_value());
std::vector<std::int16_t> stereo;
stereo.reserve(mono->size() * 4);
for (float f : *mono) {
const auto s = static_cast<std::int16_t>(std::llround(double(f) * 32768.0));
stereo.push_back(s); stereo.push_back(s); // sample 1, L/R
stereo.push_back(s); stereo.push_back(s); // sample 2 (zero-order hold)
}
TempWav w("stereo22050");
w.write(stereo, 2 * kSampleRate, 2);
const std::optional<std::string> sig = compute_signature(w.path.string());
REQUIRE(sig.has_value());
const std::vector<std::uint8_t> got = base64_decode(sig->substr(3));
const std::vector<std::uint8_t> want =
base64_decode(golden()["signature"].get<std::string>().substr(3));
REQUIRE(got.size() == want.size());
std::size_t agree = 0;
for (std::size_t i = 0; i < got.size(); ++i)
agree += ((got[i] >> 2) == (want[i] >> 2)) ? 1 : 0;
// The server treats ≥ 0.85 as the `audio` match tier; this path scores 1.0
// in practice, and the margin is left for libswresample version drift.
CHECK(double(agree) / double(got.size()) >= 0.85);
}
// ── UT-104 — the pure DSP surface ───────────────────────────────────────────
/// TRACES: UT-104 | IR-004
TEST_CASE("pack_frames uses whole frames only", "[audio_signature][dsp]") {
CHECK(pack_frames(std::vector<float>(kFrameSize - 1, 0.f)).empty());
CHECK(pack_frames(std::vector<float>(kFrameSize, 0.f)).size() == 1);
CHECK(pack_frames(std::vector<float>(kFrameSize + kHopSize - 1, 0.f)).size() == 1);
CHECK(pack_frames(std::vector<float>(kFrameSize + kHopSize, 0.f)).size() == 2);
// The full 120 s window is 1288 frames — asserted as a constant rather than
// by running the DSP over 1.3 M zeros, which is the same claim for free.
CHECK(kWindowSamples == 1323000u);
CHECK(kExpectedFrames == 1288u);
CHECK_FALSE(signature_from_mono(std::vector<float>(kFrameSize - 1, 0.f)).has_value());
}
/// TRACES: UT-104 | IR-004
TEST_CASE("a pure tone lands in the band that contains it", "[audio_signature][dsp]") {
// 1000 Hz sits in log-band floor(32 * log10(1000/300)) = 16.
const int expect = static_cast<int>(std::floor(
kNumBands * std::log10(1000.0 / kBandLoHz) / std::log10(kBandHiHz / kBandLoHz)));
std::vector<float> mono(kWindowSamples / 100);
for (std::size_t i = 0; i < mono.size(); ++i)
mono[i] = static_cast<float>(0.5 * std::sin(
2.0 * 3.14159265358979323846 * 1000.0 * double(i) / kSampleRate));
const std::vector<std::uint8_t> packed = pack_frames(mono);
REQUIRE_FALSE(packed.empty());
for (std::uint8_t b : packed) CHECK(((b >> 2) & 0x1F) == expect);
}
/// TRACES: UT-104 | IR-004
TEST_CASE("signature is invariant to overall gain", "[audio_signature][dsp]") {
// Loudness normalisation between two releases of the same cut must not
// change the signature — that is why the energy class is relative.
std::vector<float> a(kWindowSamples / 50);
for (std::size_t i = 0; i < a.size(); ++i) {
const double t = double(i) / kSampleRate;
a[i] = static_cast<float>(0.4 * std::sin(2.0 * 3.14159265358979323846 * 640.0 * t) +
0.2 * std::sin(2.0 * 3.14159265358979323846 * 1900.0 * t) *
std::sin(2.0 * 3.14159265358979323846 * 0.7 * t));
}
std::vector<float> b(a.size());
for (std::size_t i = 0; i < a.size(); ++i) b[i] = a[i] * 0.25f;
CHECK(pack_frames(a) == pack_frames(b));
}
/// TRACES: UT-104 | IR-004
TEST_CASE("base64 encoder matches the standard alphabet and padding",
"[audio_signature][dsp]") {
auto enc = [](const std::string& s) {
return base64_encode(reinterpret_cast<const std::uint8_t*>(s.data()), s.size());
};
CHECK(enc("") == "");
CHECK(enc("f") == "Zg==");
CHECK(enc("fo") == "Zm8=");
CHECK(enc("foo") == "Zm9v");
CHECK(enc("foob") == "Zm9vYg==");
CHECK(enc("fooba") == "Zm9vYmE=");
CHECK(enc("foobar") == "Zm9vYmFy");
const std::uint8_t all[] = {0xFB, 0xFF, 0xBF}; // exercises '+' and '/'
CHECK(base64_encode(all, 3) == "+/+/");
}
tests/test_calibration.cpp
+102
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@@ -1,6 +1,12 @@
// TRACES: AR-023 | SR-002
//
// Unit tests for gallery calibration: the sigmoid math, the pairwise fit on
// separable data, and the in-memory hash-keyed cache (hit / stale / cold).
// All pure, GPU-free, model-free.
//
// The three `[report]` cases at the bottom carry their own GR-003 tags: they
// verify the build report, which is fitted from the same distributions but is a
// separate requirement.
#include <catch2/catch_test_macros.hpp>
#include <catch2/matchers/catch_matchers_floating_point.hpp>
@@ -168,3 +174,99 @@ TEST_CASE("calibrate_gallery_cached treats hash=0 as always-recompute", "[calibr
CHECK(recomputed);
CHECK(cal.valid);
}
// ── GR-003 — the build report ────────────────────────────────────────────────
#include "gallery/gallery_report.hpp"
namespace {
// A unit vector on one axis. Distinct axes are orthogonal, which is unrealistic
// as a same-actor cluster but irrelevant here: these tests count actors, they do
// not assess fit quality.
Embedding unit_axis(int slot) {
Embedding e{};
e[slot % 512] = 1.0f;
return e;
}
} // namespace
// TRACES: GR-003 | SR-001
TEST_CASE("report surfaces actors that can never be recognised", "[report][GR-003]") {
// An actor with no usable image is a silent recall ceiling: the pipeline
// will never name them, and nothing in the gallery says why. This is the
// single most useful number in the report.
ActorGallery g;
for (int a = 0; a < 3; ++a) {
ActorGallery::Actor act;
act.name = "actor" + std::to_string(a);
if (a != 1) // actor1 gets nothing
for (int i = 0; i < 6; ++i) act.embeddings.push_back(unit_axis(a * 10 + i));
g.actors.push_back(std::move(act));
}
std::vector<Embedding> flat;
std::vector<int> flat_actor;
for (int a = 0; a < static_cast<int>(g.actors.size()); ++a)
for (const auto& e : g.actors[a].embeddings) { flat.push_back(e); flat_actor.push_back(a); }
GalleryCalibrationStats stats;
GalleryCalibration cal = calibrate_gallery(flat, flat_actor, &stats);
GalleryReport r = build_gallery_report(g, cal, stats);
// An actor present in the gallery with no embeddings is counted as
// in-gallery but contributes nothing; the zero-usable list is populated
// from the build audit, which a stored gallery cannot supply.
CHECK(r.actors_in_gallery == 3);
CHECK(r.actors[1].references == 0);
}
// TRACES: GR-003 | SR-001
TEST_CASE("report surfaces actors too thin to calibrate on", "[report][GR-003]") {
// Below the positive-pair threshold an actor contributes nothing to the
// intra-class side of the fit. They are not broken, so nothing complains —
// they just quietly weaken every threshold downstream.
ActorGallery g;
for (int a = 0; a < 2; ++a) {
ActorGallery::Actor act;
act.name = "actor" + std::to_string(a);
const int n = (a == 0) ? 6 : 2; // actor1 is under-referenced
for (int i = 0; i < n; ++i) act.embeddings.push_back(unit_axis(a * 10 + i));
g.actors.push_back(std::move(act));
}
std::vector<Embedding> flat;
std::vector<int> flat_actor;
for (int a = 0; a < static_cast<int>(g.actors.size()); ++a)
for (const auto& e : g.actors[a].embeddings) { flat.push_back(e); flat_actor.push_back(a); }
GalleryCalibrationStats stats;
GalleryCalibration cal = calibrate_gallery(flat, flat_actor, &stats);
GalleryReport r = build_gallery_report(g, cal, stats);
CHECK(r.actors_below_positive_threshold >= 1);
}
// TRACES: GR-003 | SR-001
TEST_CASE("report round-trips", "[report][GR-003]") {
ActorGallery g;
ActorGallery::Actor act;
act.name = "solo";
for (int i = 0; i < 6; ++i) act.embeddings.push_back(unit_axis(i));
g.actors.push_back(std::move(act));
std::vector<Embedding> flat;
std::vector<int> flat_actor;
for (const auto& e : g.actors[0].embeddings) { flat.push_back(e); flat_actor.push_back(0); }
GalleryCalibrationStats stats;
GalleryCalibration cal = calibrate_gallery(flat, flat_actor, &stats);
GalleryReport r = build_gallery_report(g, cal, stats);
const std::string path = "/tmp/gr003_roundtrip.report.json";
save_gallery_report(path, r);
GalleryReport back = load_gallery_report(path);
CHECK(back.actors_in_gallery == r.actors_in_gallery);
CHECK(back.actors_below_positive_threshold == r.actors_below_positive_threshold);
CHECK(back.calib_a == r.calib_a);
std::remove(path.c_str());
}
tests/test_face_tracker.cpp
+126 -58
View File
@@ -1,20 +1,28 @@
// TRACES: AR-007, AR-008 | SR-002
//
// Unit tests for FaceTrackerFunc (nodes/face_tracker_node.hpp): frame-to-frame
// track linking and, crucially, cross-cut re-association. Pure, GPU-free,
// model-free — drives the node's operator() with hand-built EmbeddedSceneFrames
// and inspects the emitted track_ids.
//
// The behaviour under test: on a camera-angle change (Frame::is_cut) the tracker
// parks its tracks instead of destroying them, and revives a parked track_id
// when a post-cut detection's raw last-frame-embedding cosine similarity clears
// cut_revive_sim. IoU is deliberately driven to 0 across the cut (boxes moved) so
// only the embedding path can re-link — exactly the scenario a cut creates.
// The behaviour under test: there is one track pool keyed on `last_seen`
// (AR-008), so a face lost across a camera-angle change (Frame::is_cut) is an
// ordinary association candidate rather than a parked track needing a revival
// path — the raw-cosine `cut_revive_sim` that guarded that path is retired
// (AR-024). On a cut the association weight drops to embedding-only (AR-007),
// and IoU is deliberately driven to 0 across the cut (boxes moved) so only the
// embedding path can re-link — exactly the scenario a cut creates.
#include <catch2/catch_test_macros.hpp>
#include "config.hpp"
#include "nodes/face_tracker_node.hpp"
#include "types.hpp"
#include "track_registry.hpp"
#include "evidence_discount.hpp"
#include <algorithm>
#include <cmath>
#include <memory>
namespace {
@@ -55,85 +63,145 @@ EmbeddedSceneFrame frame(double t, float x, float y, const Embedding& emb,
return ef;
}
Config tracker_cfg() {
Config cfg;
cfg.cut_revive_sim = 0.50f;
cfg.cut_inactive_max_frames = 5;
return cfg;
}
// Build a tracker over a fresh registry. The registry IS the tracker's state
// now (AR-008), so a test constructs both together and can inspect either.
struct Rig {
std::shared_ptr<TrackRegistry> reg;
FaceTrackerFunc ft;
explicit Rig(double extinction = 30.0, float assoc_min_prob = 0.5f)
: reg(std::make_shared<TrackRegistry>(
[extinction] {
TrackRegistry::Config c;
c.extinction_sec = extinction;
return c;
}(),
EvidenceDiscounter([](float cos) { return std::max(0.f, cos); })))
, ft([&] {
Config c;
c.track_assoc_min_prob = assoc_min_prob;
return c;
}(),
reg,
// Trivial calibration: cosine passed through as P(same). Real runs use
// the fit belonging to the active embedder (AR-023/AR-024).
[](float cos) { return std::max(0.f, cos); })
{}
int track_of(EmbeddedSceneFrame f) { return ft(std::move(f)).track_ids[0]; }
};
} // namespace
TEST_CASE("track id is stable across ordinary frames", "[face_tracker]") {
FaceTrackerFunc ft(tracker_cfg());
// ── AR-008 — one pool, ordinary association ──────────────────────────────────
TEST_CASE("track id is stable across ordinary frames", "[face_tracker][AR-008]") {
Rig r;
Embedding e = axis(0);
int id0 = ft(frame(0.0, 10, 10, e)).track_ids[0];
int id1 = ft(frame(1.0, 11, 10, e)).track_ids[0]; // overlaps → same track
int id0 = r.track_of(frame(0.0, 10, 10, e));
int id1 = r.track_of(frame(1.0, 11, 10, e)); // overlaps → same track
CHECK(id0 >= 0);
CHECK(id1 == id0);
}
TEST_CASE("cut revives the same track id for a matching identity", "[face_tracker]") {
FaceTrackerFunc ft(tracker_cfg());
TEST_CASE("a face lost across a cut and re-associated is the SAME track",
"[face_tracker][AR-008]") {
// Previously this was a distinct "revival" path guarded by a raw-cosine
// constant. There is no such path now: a dormant track is an ordinary
// association candidate, and continuity falls out of the embedding match.
Rig r;
// Pre-cut: establish a track for a person whose embedding is near-identical
// across the cut (sim well above cut_revive_sim), but whose box jumps so IoU
// is 0 — the ordinary spatial path cannot re-link it.
Embedding pre = at_sim(0, 1, 0.99f);
int id_pre = ft(frame(0.0, 10, 10, pre)).track_ids[0];
int id_pre = r.track_of(frame(0.0, 10, 10, pre));
REQUIRE(id_pre >= 0);
Embedding post = at_sim(0, 1, 0.98f); // cos(diff) ≈ 0.9997 > 0.50
auto out = ft(frame(1.0, 300, 300, post, /*is_cut=*/true));
CHECK(out.track_ids[0] == id_pre); // revived, not a fresh id
// Box jumps so IoU is zero — only the embedding can link it.
Embedding post = at_sim(0, 1, 0.98f);
CHECK(r.track_of(frame(1.0, 300, 300, post, /*is_cut=*/true)) == id_pre);
}
TEST_CASE("cut starts a fresh track when identity does not match", "[face_tracker]") {
FaceTrackerFunc ft(tracker_cfg());
int id_pre = ft(frame(0.0, 10, 10, axis(0))).track_ids[0];
TEST_CASE("a cut starts a fresh track when identity does not match",
"[face_tracker][AR-008]") {
Rig r;
int id_pre = r.track_of(frame(0.0, 10, 10, axis(0)));
REQUIRE(id_pre >= 0);
// Post-cut face is orthogonal (sim 0 < cut_revive_sim) and spatially disjoint
// → no revival, brand-new id.
auto out = ft(frame(1.0, 300, 300, axis(5), /*is_cut=*/true));
CHECK(out.track_ids[0] != id_pre);
CHECK(out.track_ids[0] >= 0);
// Orthogonal embedding and disjoint box: nothing links them.
int id_post = r.track_of(frame(1.0, 300, 300, axis(5), /*is_cut=*/true));
CHECK(id_post != id_pre);
CHECK(id_post >= 0);
}
TEST_CASE("parked track expires after cut_inactive_max_frames", "[face_tracker]") {
Config cfg = tracker_cfg();
cfg.cut_inactive_max_frames = 2;
FaceTrackerFunc ft(cfg);
// ── AR-007 — a cut makes association ignore position ─────────────────────────
TEST_CASE("on a cut, identity follows the embedding rather than the box",
"[face_tracker][AR-007]") {
// Two people swap screen positions across a cut while keeping their faces.
// If IoU still carried weight the ids would follow the boxes and swap; with
// alpha driven to embedding-only on a cut, they must follow the faces.
Rig r;
Embedding a = at_sim(0, 1, 0.99f);
Embedding b = at_sim(2, 3, 0.99f);
EmbeddedSceneFrame f0;
f0.source.timestamp_sec = 0.0;
f0.faces = {face_at(10, 10), face_at(300, 300)};
f0.crops = {cv::Mat(), cv::Mat()};
f0.embeddings = {a, b};
auto out0 = r.ft(std::move(f0));
const int id_a = out0.track_ids[0];
const int id_b = out0.track_ids[1];
REQUIRE(id_a >= 0);
REQUIRE(id_b >= 0);
REQUIRE(id_a != id_b);
// Same two people, positions exchanged, on a cut frame.
EmbeddedSceneFrame f1;
f1.source.timestamp_sec = 1.0;
f1.source.is_cut = true;
f1.faces = {face_at(300, 300), face_at(10, 10)};
f1.crops = {cv::Mat(), cv::Mat()};
f1.embeddings = {a, b};
auto out1 = r.ft(std::move(f1));
CHECK(out1.track_ids[0] == id_a); // A kept its id despite moving to B's box
CHECK(out1.track_ids[1] == id_b);
}
// ── AR-013 — extinction replaces the parked-pool frame counter ───────────────
TEST_CASE("a track past the extinction window is gone, not revived",
"[face_tracker][AR-013]") {
// The old design aged a parked pool in frames, which silently changed
// meaning with sample_fps. Extinction is in seconds and lives in the
// registry, so the tracker no longer counts anything.
Rig r(/*extinction=*/2.0);
Embedding person = at_sim(0, 1, 0.99f);
int id_pre = ft(frame(0.0, 10, 10, person)).track_ids[0];
int id_pre = r.track_of(frame(0.0, 10, 10, person));
REQUIRE(id_pre >= 0);
// Cut with an unrelated face parks id_pre; then let the pool age past its
// limit with more unrelated, spatially-disjoint faces (each ages the pool by
// one). By the time the person returns, id_pre must be gone.
ft(frame(1.0, 300, 300, axis(7), /*is_cut=*/true)); // park (age 1)
ft(frame(2.0, 300, 300, axis(7))); // age 2
ft(frame(3.0, 300, 300, axis(7))); // age 3 → id_pre dropped
// Unrelated faces elsewhere while the clock runs well past extinction.
r.track_of(frame(1.0, 300, 300, axis(7), /*is_cut=*/true));
r.track_of(frame(10.0, 300, 300, axis(7)));
auto out = ft(frame(4.0, 10, 10, person)); // same identity returns
CHECK(out.track_ids[0] != id_pre); // too late — fresh id
CHECK(r.track_of(frame(11.0, 10, 10, person)) != id_pre);
}
TEST_CASE("eof clears active and parked tracks", "[face_tracker]") {
FaceTrackerFunc ft(tracker_cfg());
Embedding person = at_sim(0, 1, 0.99f);
int id_pre = ft(frame(0.0, 10, 10, person)).track_ids[0];
ft(frame(1.0, 300, 300, axis(7), /*is_cut=*/true)); // park id_pre
TEST_CASE("a track within the extinction window is still a candidate",
"[face_tracker][AR-013]") {
Rig r(/*extinction=*/30.0);
Embedding person = at_sim(0, 1, 0.99f);
int id_pre = r.track_of(frame(0.0, 10, 10, person));
r.track_of(frame(1.0, 300, 300, axis(7), /*is_cut=*/true));
// Back inside the window: the same person continues the same track, so the
// gap is absorbed into one window rather than splitting it.
CHECK(r.track_of(frame(3.0, 10, 10, person)) == id_pre);
}
TEST_CASE("eof is forwarded", "[face_tracker]") {
Rig r;
EmbeddedSceneFrame eof;
eof.source.eof = true;
auto out = ft(std::move(eof));
CHECK(out.source.eof);
// After eof the pools are empty: the returning identity must get a fresh id,
// not the parked one.
auto out2 = ft(frame(2.0, 10, 10, person));
CHECK(out2.track_ids[0] != id_pre);
CHECK(r.ft(std::move(eof)).source.eof);
}
tests/test_face_utils.cpp
+107 -2
View File
@@ -1,6 +1,8 @@
// TRACES: AR-005, AR-030 | SR-002
//
// Unit tests for the geometric/numeric helpers in types.hpp and face_utils.hpp:
// cosine_similarity and the ArcFace 5-point alignment transform. GPU-free,
// model-free.
// cosine_similarity, the ArcFace 5-point alignment transform, and the alignment
// residual that AR-030 reads as its visibility measure. GPU-free, model-free.
#include <catch2/catch_test_macros.hpp>
#include <catch2/matchers/catch_matchers_floating_point.hpp>
@@ -74,3 +76,106 @@ TEST_CASE("align_face returns empty on degenerate (collinear) landmarks", "[face
cv::Mat crop = align_face(img, lm);
CHECK(crop.empty());
}
// ── AR-030: the alignment residual as a visibility measure ────────────────────
// These assert the *properties* the measure is relied on for, not a magic value.
// Each would fail under a RANSAC fit, which buys a small residual by discarding
// the very landmarks that carry the signal.
namespace {
std::array<cv::Point2f, 5> canonical() {
std::array<cv::Point2f, 5> lm;
for (int i = 0; i < 5; ++i) lm[i] = {kArcFaceRef[i][0], kArcFaceRef[i][1]};
return lm;
}
// Rotate by `deg` in-plane, scale uniformly, translate — i.e. exactly the 4 DoF
// the similarity transform models.
std::array<cv::Point2f, 5> similarity(const std::array<cv::Point2f, 5>& in,
float deg, float s, float tx, float ty) {
const float r = deg * 3.14159265358979f / 180.f;
const float c = std::cos(r), sn = std::sin(r);
std::array<cv::Point2f, 5> out;
for (int i = 0; i < 5; ++i)
out[i] = {s * (c * in[i].x - sn * in[i].y) + tx,
s * (sn * in[i].x + c * in[i].y) + ty};
return out;
}
// Squash x about the centroid by `k`: the anisotropic deformation an out-of-plane
// yaw produces, and the one a similarity provably cannot absorb.
std::array<cv::Point2f, 5> foreshorten(const std::array<cv::Point2f, 5>& in, float k) {
float cx = 0.f;
for (const auto& p : in) cx += p.x;
cx /= 5.f;
std::array<cv::Point2f, 5> out = in;
for (auto& p : out) p.x = cx + (p.x - cx) * k;
return out;
}
} // namespace
TEST_CASE("residual is zero for a face in canonical pose", "[face_utils][AR-030]") {
const Alignment a = estimate_alignment(canonical());
REQUIRE(a.ok);
CHECK_THAT(a.residual, WithinAbs(0.0f, 1e-3f));
}
TEST_CASE("residual ignores in-plane roll, scale and translation", "[face_utils][AR-030]") {
// The structural claim behind AR-030: the fit absorbs all four similarity
// DoF exactly, so what remains is only the deformation a similarity cannot
// explain. A rolled head must not read as a turned one.
for (float deg : {-40.f, -12.f, 0.f, 17.f, 65.f}) {
const Alignment a = estimate_alignment(similarity(canonical(), deg, 3.5f, 220.f, -40.f));
REQUIRE(a.ok);
CHECK_THAT(a.residual, WithinAbs(0.0f, 1e-3f));
}
}
TEST_CASE("residual rises monotonically with foreshortening", "[face_utils][AR-030]") {
float prev = -1.f;
for (float k : {1.0f, 0.9f, 0.75f, 0.5f, 0.3f}) {
const Alignment a = estimate_alignment(foreshorten(canonical(), k));
REQUIRE(a.ok);
CHECK(a.residual > prev);
prev = a.residual;
}
}
TEST_CASE("residual is independent of face size", "[face_utils][AR-030]") {
// The measure must not silently re-express face size — that is AR-002's job,
// and double-counting it would make a small frontal face look occluded.
// Same deformation, two very different face sizes, one answer.
const auto small = similarity(foreshorten(canonical(), 0.7f), 20.f, 1.0f, 0.f, 0.f);
const auto large = similarity(foreshorten(canonical(), 0.7f), 20.f, 12.0f, 500.f, 300.f);
const Alignment a = estimate_alignment(small);
const Alignment b = estimate_alignment(large);
REQUIRE(a.ok);
REQUIRE(b.ok);
CHECK_THAT(b.residual, WithinAbs(a.residual, 1e-2f));
}
TEST_CASE("the fit never mirrors the face", "[face_utils][AR-030]") {
// SVD will happily return an orientation-reversing solution; a similarity
// transform may rotate but never reflect. Without the determinant guard a
// mirrored landmark set fits "perfectly" as a reflection.
const auto mirrored = foreshorten(canonical(), -1.f);
const Alignment a = estimate_alignment(mirrored);
REQUIRE(a.ok);
const double det = a.M.at<double>(0,0) * a.M.at<double>(1,1)
- a.M.at<double>(0,1) * a.M.at<double>(1,0);
CHECK(det > 0.0);
CHECK(a.residual > 1.0f); // and the mirroring shows up as misfit
}
TEST_CASE("degenerate landmarks report not-ok rather than a residual", "[face_utils][AR-030]") {
std::array<cv::Point2f, 5> lm;
for (auto& p : lm) p = {50.f, 50.f};
const Alignment a = estimate_alignment(lm);
CHECK_FALSE(a.ok);
CHECK(a.M.empty());
}
tests/test_gallery_store.cpp
+251 -1
View File
@@ -1,13 +1,17 @@
// Unit tests for gallery (de)serialisation: HDF5 round-trip fidelity (the only
// format save_gallery writes), legacy JSON read back-compat (optional field
// defaults, the legacy "jellyfin_person_id" fallback). GPU-free, model-free.
// defaults, the legacy "jellyfin_person_id" fallback), and the GR-004 embedder
// stamp. GPU-free, model-free — the stamp tests exercise the comparison logic
// with synthetic stamps and never load an ONNX, so they run on CI's Intel N100.
#include <catch2/catch_test_macros.hpp>
#include "gallery/embedder_stamp.hpp"
#include "gallery/gallery_store.hpp"
#include "types.hpp"
#include <cstdio>
#include <fstream>
#include <stdexcept>
#include <string>
#include <nlohmann/json.hpp>
@@ -27,6 +31,22 @@ Embedding make_embedding(float base) {
return e;
}
// A stamp built by hand — no ONNX is read, so these tests never need a model.
EmbedderStamp stamp(const std::string& name, const std::string& sha, int32_t dim = 512) {
EmbedderStamp s;
s.model_name = name;
s.model_sha256 = sha;
s.embed_dim = dim;
return s;
}
const std::string kShaA(64, 'a');
const std::string kShaB(64, 'b');
bool mentions(const std::string& haystack, const std::string& needle) {
return haystack.find(needle) != std::string::npos;
}
} // namespace
TEST_CASE("gallery save/load round-trips actors and embeddings", "[gallery]") {
@@ -153,3 +173,233 @@ TEST_CASE("load_gallery reads the legacy jellyfin_person_id key", "[gallery]") {
TEST_CASE("load_gallery throws on a missing file", "[gallery]") {
CHECK_THROWS(load_gallery("/nonexistent/path/gallery.json"));
}
// ── GR-004: gallery ↔ embedder binding ───────────────────────────────────────
// Verification plan row GR-004/T1: "Mismatched embedder → hard startup error;
// error names both sides." The comparison is a pure function over two stamps, so
// none of this needs a GPU, an ONNX, or even a file.
/// TRACES: GR-004 | SR-001
TEST_CASE("gallery save/load round-trips the embedder stamp", "[gallery][GR-004]") {
ActorGallery g;
ActorGallery::Actor a;
a.name = "Stamped Actor";
a.embeddings = {make_embedding(0.3f)};
g.actors.push_back(a);
g.embedder = stamp("LVFace-B_Glint360K.onnx", kShaA);
TempFile tf("gallery_stamped.h5");
save_gallery(tf.path, g);
ActorGallery loaded = load_gallery(tf.path);
CHECK(loaded.embedder.model_name == "LVFace-B_Glint360K.onnx");
CHECK(loaded.embedder.model_sha256 == kShaA);
CHECK(loaded.embedder.embed_dim == 512);
CHECK_FALSE(loaded.embedder.empty());
}
/// TRACES: GR-004 | SR-001
TEST_CASE("a gallery written without a stamp loads as unstamped", "[gallery][GR-004]") {
// The back-compat case: pre-GR-004 files have no /embedder group at all. The
// absence must survive the round trip as an absence — a stamp naming no model
// would read as "checked and fine" to every consumer.
ActorGallery g;
ActorGallery::Actor a;
a.name = "Legacy Actor";
a.embeddings = {make_embedding(0.f)};
g.actors.push_back(a);
TempFile tf("gallery_unstamped.h5");
save_gallery(tf.path, g);
ActorGallery loaded = load_gallery(tf.path);
CHECK(loaded.embedder.empty());
}
/// TRACES: GR-004 | SR-001
TEST_CASE("legacy JSON galleries carry an optional embedder stamp", "[gallery][GR-004]") {
nlohmann::json j;
j["embedder"] = {{"model_name", "arcface_w600k_r50.onnx"},
{"model_sha256", kShaB},
{"embed_dim", 512}};
j["actors"] = nlohmann::json::array();
nlohmann::json ja;
ja["name"] = "JSON Actor";
ja["embeddings"] = nlohmann::json::array();
ja["embeddings"].push_back(std::vector<float>(512, 0.1f));
j["actors"].push_back(ja);
TempFile tf("gallery_json_stamp.json");
{ std::ofstream out(tf.path); out << j.dump(); }
ActorGallery g = load_gallery(tf.path);
CHECK(g.embedder.model_name == "arcface_w600k_r50.onnx");
CHECK(g.embedder.model_sha256 == kShaB);
}
/// TRACES: GR-004 | SR-001
TEST_CASE("matching embedder stamps pass", "[gallery][GR-004]") {
auto chk = compare_embedder_stamps(stamp("model.onnx", kShaA),
stamp("model.onnx", kShaA));
CHECK(chk.verdict == StampVerdict::match);
CHECK_FALSE(chk.fatal(false));
CHECK_FALSE(chk.fatal(true)); // a proven match is never fatal, even in strict mode
CHECK_NOTHROW(enforce_embedder_stamp(stamp("model.onnx", kShaA),
stamp("model.onnx", kShaA),
"g.h5", "model.onnx", true));
}
/// TRACES: GR-004 | SR-001
TEST_CASE("the hash decides, not the filename", "[gallery][GR-004]") {
// Same bytes under a different filename is the SAME model — a renamed or
// relocated file must not be treated as a different one.
auto same = compare_embedder_stamps(stamp("lvface.onnx", kShaA),
stamp("LVFace-B_Glint360K.onnx", kShaA));
CHECK(same.verdict == StampVerdict::match);
// Different bytes under the SAME filename is a DIFFERENT model — this is the
// in-place re-export a name-only stamp would miss entirely, and the reason the
// stamp carries a hash at all.
auto differ = compare_embedder_stamps(stamp("model.onnx", kShaA),
stamp("model.onnx", kShaB));
CHECK(differ.verdict == StampVerdict::mismatch);
}
/// TRACES: GR-004 | SR-001
TEST_CASE("mismatched embedder is fatal and names both sides", "[gallery][GR-004]") {
const auto built = stamp("LVFace-B_Glint360K.onnx", kShaA);
const auto loaded = stamp("arcface_w600k_r50.onnx", kShaB);
auto chk = compare_embedder_stamps(built, loaded, "cast.h5", "models/r50.onnx");
REQUIRE(chk.verdict == StampVerdict::mismatch);
CHECK(chk.fatal(false)); // no bypass: a mismatch is fatal in every mode
CHECK(chk.fatal(true));
// Both sides must be identifiable from the message alone.
CHECK(mentions(chk.message, "LVFace-B_Glint360K.onnx"));
CHECK(mentions(chk.message, "arcface_w600k_r50.onnx"));
CHECK(mentions(chk.message, kShaA));
CHECK(mentions(chk.message, kShaB));
CHECK(mentions(chk.message, "cast.h5"));
CHECK(mentions(chk.message, "models/r50.onnx"));
// ...and it must reach the caller as an error, not a log line.
CHECK_THROWS_AS(enforce_embedder_stamp(built, loaded, "cast.h5",
"models/r50.onnx", false),
std::runtime_error);
try {
enforce_embedder_stamp(built, loaded, "cast.h5", "models/r50.onnx", false);
FAIL("mismatch must throw");
} catch (const std::runtime_error& e) {
const std::string what = e.what();
CHECK(mentions(what, "LVFace-B_Glint360K.onnx"));
CHECK(mentions(what, "arcface_w600k_r50.onnx"));
}
}
/// TRACES: GR-004 | SR-001
TEST_CASE("differing embedding width is a mismatch", "[gallery][GR-004]") {
auto chk = compare_embedder_stamps(stamp("a.onnx", kShaA, 512),
stamp("a.onnx", kShaA, 256));
CHECK(chk.verdict == StampVerdict::mismatch);
CHECK(mentions(chk.message, "512"));
CHECK(mentions(chk.message, "256"));
}
/// TRACES: GR-004 | SR-001
TEST_CASE("an unstamped gallery warns by default and fails under strict",
"[gallery][GR-004]") {
// Decision recorded in src/gallery/embedder_stamp.hpp: unstamped is UNKNOWN,
// not known-bad, and every pre-GR-004 gallery is unstamped. Hard-failing them
// all would make the check something people disable rather than trust; so it
// warns loudly, names the risk, and is promotable to fatal for measurement runs.
EmbedderStamp none;
auto chk = compare_embedder_stamps(none, stamp("model.onnx", kShaA), "old.h5");
REQUIRE(chk.verdict == StampVerdict::unstamped);
CHECK_FALSE(chk.fatal(false));
CHECK(chk.fatal(true));
CHECK(mentions(chk.message, "old.h5"));
CHECK(mentions(chk.message, "model.onnx")); // the loaded side is still named
CHECK(mentions(chk.message, "UNKNOWN")); // ...and the gallery side is honest
CHECK_NOTHROW(enforce_embedder_stamp(none, stamp("model.onnx", kShaA),
"old.h5", "model.onnx", false));
CHECK_THROWS_AS(enforce_embedder_stamp(none, stamp("model.onnx", kShaA),
"old.h5", "model.onnx", true),
std::runtime_error);
}
/// TRACES: GR-004 | SR-001
TEST_CASE("an unidentifiable embedder against a stamped gallery is not silent",
"[gallery][GR-004]") {
// e.g. a replay whose dump predates GR-004: we know what built the gallery but
// not what produced the vectors being fed in. Unverifiable, so it must not
// report success.
auto chk = compare_embedder_stamps(stamp("model.onnx", kShaA), EmbedderStamp{},
"g.h5", "old dump.h5");
CHECK(chk.verdict == StampVerdict::unknown_embedder);
CHECK_FALSE(chk.fatal(false));
CHECK(chk.fatal(true));
CHECK(mentions(chk.message, "model.onnx"));
CHECK(mentions(chk.message, "old dump.h5"));
}
/// TRACES: GR-004 | SR-001
TEST_CASE("name-only agreement is a weak match, not a clean pass", "[gallery][GR-004]") {
// A TRT deployment can run from a prebuilt .engine with the .onnx absent, so
// no hash is computable. Names agreeing is evidence, not proof.
auto weak = compare_embedder_stamps(stamp("model.onnx", kShaA),
stamp("model.onnx", ""));
CHECK(weak.verdict == StampVerdict::weak_match);
CHECK_FALSE(weak.fatal(false));
CHECK(weak.fatal(true));
// Names disagreeing with no hash available is still a mismatch — the weaker
// evidence is enough to convict, just not to acquit.
auto bad = compare_embedder_stamps(stamp("lvface.onnx", ""),
stamp("arcface.onnx", ""));
CHECK(bad.verdict == StampVerdict::mismatch);
CHECK(mentions(bad.message, "lvface.onnx"));
CHECK(mentions(bad.message, "arcface.onnx"));
}
/// TRACES: GR-004 | SR-001
TEST_CASE("sha256 matches the published vectors", "[gallery][GR-004]") {
// Pins the in-tree FIPS 180-4 implementation against the standard vectors.
// This is what guarantees the C++ stamp and the Python (hashlib) stamp in
// scripts/sae_gallery.py agree on the same model file — without it the two
// halves of GR-004 could silently diverge and every check would be a mismatch.
CHECK(sha256_hex("") ==
"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855");
CHECK(sha256_hex("abc") ==
"ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad");
CHECK(sha256_hex("abcdbcdecdefdefgefghfghighijhijkijkljklmklmnlmnomnopnopq") ==
"248d6a61d20638b8e5c026930c3e6039a33ce45964ff2167f6ecedd419db06c1");
// Multi-block input, exercising the length-padding path past 64 bytes.
CHECK(sha256_hex(std::string(1000, 'a')) ==
"41edece42d63e8d9bf515a9ba6932e1c20cbc9f5a5d134645adb5db1b9737ea3");
}
/// TRACES: GR-004 | SR-001
TEST_CASE("make_embedder_stamp hashes a real file and degrades gracefully",
"[gallery][GR-004]") {
// Stands in for an ONNX: the stamp does not care what the bytes mean.
TempFile tf("fake_model.onnx");
{ std::ofstream out(tf.path, std::ios::binary); out << "abc"; }
EmbedderStamp s = make_embedder_stamp(tf.path);
CHECK(s.model_sha256 ==
"ba7816bf8f01cfea414140de5dae2223b00361a396177a9cb410ff61f20015ad");
CHECK_FALSE(s.model_name.empty());
CHECK(s.model_name.find('/') == std::string::npos); // basename, not full path
// A model path that does not exist still yields a comparable name-only stamp
// rather than an empty one, which is what keeps engine-only deployments usable.
EmbedderStamp missing = make_embedder_stamp("/nonexistent/models/foo.onnx");
CHECK(missing.model_name == "foo.onnx");
CHECK(missing.model_sha256.empty());
CHECK_FALSE(missing.empty());
CHECK(make_embedder_stamp("").empty());
}
tests/test_replay_fixtures.cpp
+210
View File
@@ -0,0 +1,210 @@
// Replay tests — the real tracker and registry driven from committed fixtures.
//
// TRACES: AR-004, AR-012, AR-013 | VR-001, VR-002 | IT-001
//
// Tier T2: composition, not units. The registry tests construct awkward states
// directly; these check that the pieces behave when wired together and fed real
// footage — 480x360 public-domain clips at 5 fps, with the cuts, gaps and
// crowded frames that actual film produces and synthetic input does not.
//
// No GPU and no model: the fixtures are HDF5 dumps taken after embedding, so
// everything here is CPU maths. That is what lets this run on the CI host at
// all (see docs/requirements.md, "CI never calls a model").
//
// Driving the node functors directly rather than through a KPN network is
// deliberate: functors are plain objects, so there are no threads, no channels
// and no scheduling — the same input gives the same output every time, which is
// exactly what a fixture-based test needs.
#include <catch2/catch_test_macros.hpp>
#include "config.hpp"
#include "evidence_discount.hpp"
#include "nodes/face_tracker_node.hpp"
#include "track_registry.hpp"
#include "types.hpp"
#include <H5Cpp.h>
#include <algorithm>
#include <cmath>
#include <memory>
#include <string>
#include <vector>
namespace {
// ── Fixture reader ───────────────────────────────────────────────────────────
// The flat/ragged layout of scripts/optimizer/SCHEMA.md: per-face arrays
// concatenated, with a per-frame index table pointing into them.
struct Dump {
std::vector<double> ts;
std::vector<uint8_t> is_cut;
std::vector<int64_t> face_offset;
std::vector<int32_t> face_count;
std::vector<Embedding> emb;
std::vector<float> bbox; // 4 per face
std::string embedder;
std::size_t frames() const { return ts.size(); }
std::size_t faces() const { return emb.size(); }
};
template <typename T>
std::vector<T> read1d(H5::Group& g, const char* name, const H5::DataType& dt) {
H5::DataSet ds = g.openDataSet(name);
hsize_t n = 0;
ds.getSpace().getSimpleExtentDims(&n, nullptr);
std::vector<T> out(n);
if (n) ds.read(out.data(), dt);
return out;
}
Dump load(const std::string& path) {
H5::H5File f(path, H5F_ACC_RDONLY);
H5::Group frames = f.openGroup("frames");
H5::Group faces = f.openGroup("faces");
Dump d;
d.ts = read1d<double>(frames, "timestamp_sec", H5::PredType::NATIVE_DOUBLE);
d.is_cut = read1d<uint8_t>(frames, "is_cut", H5::PredType::NATIVE_UINT8);
d.face_offset = read1d<int64_t>(frames, "face_offset", H5::PredType::NATIVE_INT64);
d.face_count = read1d<int32_t>(frames, "face_count", H5::PredType::NATIVE_INT32);
H5::DataSet e = faces.openDataSet("embedding");
hsize_t dims[2]{0, 0};
e.getSpace().getSimpleExtentDims(dims, nullptr);
std::vector<float> flat(dims[0] * dims[1]);
if (!flat.empty()) e.read(flat.data(), H5::PredType::NATIVE_FLOAT);
d.emb.resize(dims[0]);
for (hsize_t i = 0; i < dims[0]; ++i)
std::copy_n(flat.begin() + i * dims[1], 512, d.emb[i].begin());
// bbox is 2-D [N,4]; reading it with the 1-D helper would size the buffer
// from the first extent only and then read four times that many floats.
{
H5::DataSet bs = faces.openDataSet("bbox");
hsize_t bd[2]{0, 0};
bs.getSpace().getSimpleExtentDims(bd, nullptr);
d.bbox.resize(bd[0] * bd[1]);
if (!d.bbox.empty()) bs.read(d.bbox.data(), H5::PredType::NATIVE_FLOAT);
}
// GR-004: the dump records which embedder produced it, so a replay cannot
// be silently scored against a gallery from a different model.
if (f.attrExists("embedder_model")) {
// Written as a variable-length string (embedding_dump_node.hpp:99), so
// the read must name the same type explicitly.
H5::StrType vlen(H5::PredType::C_S1, H5T_VARIABLE);
f.openAttribute("embedder_model").read(vlen, d.embedder);
}
return d;
}
std::string fixture(const char* name) {
return std::string(SAE_TEST_FIXTURES_DIR) + "/dumps/" + name;
}
// ── Harness ──────────────────────────────────────────────────────────────────
struct Replay {
std::vector<DeadTrack> claims;
std::vector<int> track_ids; // per face, in fixture order
std::size_t faces_seen{0};
};
Replay run(const Dump& d, double extinction = 10.0) {
Replay r;
TrackRegistry::Config rc;
rc.extinction_sec = extinction;
auto cal = [](float cos) { return std::max(0.f, cos); };
auto reg = std::make_shared<TrackRegistry>(rc, EvidenceDiscounter(cal));
reg->on_track_dead([&r](const DeadTrack& t) { r.claims.push_back(t); });
Config cfg;
cfg.track_assoc_min_prob = 0.5f;
FaceTrackerFunc ft(cfg, reg, cal);
for (std::size_t i = 0; i < d.frames(); ++i) {
EmbeddedSceneFrame ef;
ef.source.timestamp_sec = d.ts[i];
ef.source.is_cut = d.is_cut[i] != 0;
const int64_t off = d.face_offset[i];
const int32_t n = d.face_count[i];
for (int32_t k = 0; k < n; ++k) {
DetectedFace face;
const float* b = &d.bbox[(off + k) * 4];
face.bbox = cv::Rect2f(b[0], b[1], b[2], b[3]);
face.confidence = 1.0f;
ef.faces.push_back(face);
ef.crops.push_back(cv::Mat());
ef.embeddings.push_back(d.emb[off + k]);
}
r.faces_seen += static_cast<std::size_t>(n);
auto out = ft(std::move(ef));
for (int id : out.track_ids) r.track_ids.push_back(id);
}
reg->flush(d.ts.empty() ? 0.0 : d.ts.back());
return r;
}
} // namespace
// ── AR-004 / VR-001 — the fixtures are intact and self-describing ────────────
TEST_CASE("superhero fixture is complete", "[replay][VR-001]") {
Dump d = load(fixture("superhero.h5"));
CHECK(d.frames() == 5128);
CHECK(d.faces() == 4307);
CHECK(d.embedder == "LVFace-B_Glint360K.onnx");
int64_t running = 0;
for (std::size_t i = 0; i < d.frames(); ++i) {
REQUIRE(d.face_offset[i] == running);
running += d.face_count[i];
}
CHECK(static_cast<std::size_t>(running) == d.faces());
}
TEST_CASE("replaying the superhero fixture twice gives identical tracks",
"[replay][VR-002]") {
Dump d = load(fixture("superhero.h5"));
Replay a = run(d);
Replay b = run(d);
REQUIRE(a.track_ids.size() == b.track_ids.size());
CHECK(a.track_ids == b.track_ids);
REQUIRE(a.claims.size() == b.claims.size());
}
TEST_CASE("every face is assigned a track and every track closes",
"[replay][AR-012]") {
Dump d = load(fixture("superhero.h5"));
Replay r = run(d);
CHECK(r.track_ids.size() == r.faces_seen);
for (int id : r.track_ids) CHECK(id >= 0); // nothing silently unassigned
// flush() must leave nothing behind: a track still open at EOF would be a
// window that never reaches the output.
CHECK(r.claims.size() > 0);
}
TEST_CASE("windows are well-formed and inside the film", "[replay][AR-013]") {
for (const char* f : {"superhero.h5", "superhero.h5", "superhero.h5",
"superhero.h5", "superhero.h5"}) {
INFO(f);
Dump d = load(fixture(f));
Replay r = run(d);
const double t0 = d.ts.front(), t1 = d.ts.back();
for (const auto& c : r.claims) {
// A window ends at the last sighting, never after it — so it can
// never extend past the footage that produced it.
CHECK(c.first_seen <= c.last_seen);
CHECK(c.first_seen >= t0);
CHECK(c.last_seen <= t1);
}
}
}
tests/test_similarity.cpp
+6
View File
@@ -1,5 +1,11 @@
// TRACES: AR-026 | SR-001
//
// Unit tests for the CPU reference similarity engine (backends/gemm_backend.cpp,
// SAE_GEMM_CPU) and the l2_normalise helper. All pure, GPU-free, model-free.
//
// This is the CI half of AR-026: equivalence between the GEMM path and
// hand-computed dot products on small input. Throughput at scale (AR-027) is T4
// and cannot run here.
#include <catch2/catch_test_macros.hpp>
#include <catch2/matchers/catch_matchers_floating_point.hpp>
tests/test_track_gallery.cpp
+245 -60
View File
@@ -1,7 +1,16 @@
// TRACES: AR-018, AR-019, AR-024 | SR-005
//
// Unit tests for TrackGallery (gallery/track_gallery.hpp): per-film gallery
// expansion driven by track continuity. Pure, GPU-free, model-free — exercises
// the diversity-buffer eviction policy, the novelty/spread safety gates,
// plurality ownership, and idempotent promotion via the public interface.
// the AR-018 banded admission at both bounds, the promotion-time coherence
// gate, the diversity-buffer eviction policy, plurality ownership, and
// idempotent promotion, all through the public interface.
//
// The band is defined in PROBABILITY space (AR-024), so every case below states
// its own cosine → probability map instead of inheriting the header's fallback.
// A test that never names the mapping is not testing the band, it is testing a
// coincidence: with the fallback the two spaces happen to coincide, and a gate
// that silently reverted to raw cosine would still pass.
#include <catch2/catch_test_macros.hpp>
#include <catch2/matchers/catch_matchers_floating_point.hpp>
@@ -14,30 +23,60 @@
namespace {
// Unit-norm embedding pointing along one axis (cosine sim to another one-hot is
// 0, to itself 1) — lets tests dial gallery similarity precisely.
constexpr float kPi = 3.14159265358979323846f;
// The band as the tests drive it. Kept in one place so a change to the shipped
// defaults does not silently invalidate the arithmetic in each case.
constexpr float kBandLo = 0.90f;
constexpr float kBandHi = 0.95f;
// Identity map: probability == cosine, so a case can place an embedding at an
// exact probability. cosine_similarity is a bare dot product over unit vectors
// (types.hpp), so the placements below are bit-exact, not approximate.
float identity_cal(float c) { return c; }
// Unit-norm embedding pointing along one axis. Cosine sim to another one-hot is
// 0, to itself 1.
Embedding one_hot(int slot) {
Embedding e{};
e[slot] = 1.0f;
return e;
}
// Unit-norm embedding in the plane of axes i,j at angle t from i. Cosine sim to
// one_hot(i) is cos(t) — used to place a view at a chosen gallery similarity.
// Unit-norm embedding in the plane of axes i,j at cosine `cos_t` from axis i.
// Cosine sim to one_hot(i) is exactly cos_t.
Embedding at_sim(int i, int j, float cos_t) {
Embedding e{};
float s = std::sqrt(std::max(0.f, 1.f - cos_t * cos_t));
e[i] = cos_t;
e[j] = s;
e[j] = std::sqrt(std::max(0.f, 1.f - cos_t * cos_t));
return e;
}
// A spoke: shares axis 0 with every other spoke, and is otherwise unique. Any
// two DISTINCT spokes have cosine similarity exactly cos_t², so one constant
// places a whole mutually-in-band store. A spoke against itself is 1.0 — above
// the band's ceiling, i.e. redundant, which is the intended reading.
Embedding spoke(int k, float cos_t) { return at_sim(0, k, cos_t); }
// cos_t chosen so pairwise similarity between distinct spokes is 0.9197 —
// comfortably inside [0.90, 0.95], clear of both bounds.
constexpr float kSpokeCos = 0.959f;
// Two embeddings `deg` apart in the plane of axes 0,1. Cosine is cos(deg), so a
// chain of these can step through the band while its endpoints fall outside it.
Embedding on_circle(float deg) {
Embedding e{};
e[0] = std::cos(deg * kPi / 180.f);
e[1] = std::sin(deg * kPi / 180.f);
return e;
}
Config expand_cfg() {
Config cfg;
cfg.expand_gallery = true;
cfg.expand_buffer_size = 3;
cfg.expand_novelty_sim = 0.55f;
cfg.expand_track_spread_max = 0.60f;
cfg.expand_gallery = true;
cfg.expand_buffer_size = 3;
cfg.expand_band_lo = kBandLo;
cfg.expand_band_hi = kBandHi;
cfg.expand_min_anchor_frames = 3;
return cfg;
}
@@ -59,82 +98,228 @@ TEST_CASE("disabled: no annex growth when expand_gallery is off", "[track_galler
CHECK(tg.annex().empty());
}
TEST_CASE("confirmed track promotes gallery-far views", "[track_gallery]") {
// ── AR-018: the band ─────────────────────────────────────────────────────────
TEST_CASE("band bounds come from config, not a hardcoded default", "[track_gallery][AR-018]") {
// The bounds were declared in Config and read nowhere, so the gate ran at
// whatever the header happened to initialise. Drive them somewhere the
// defaults are not and require the gate to follow.
Config cfg = expand_cfg();
cfg.expand_band_lo = 0.40f;
cfg.expand_band_hi = 0.60f;
TrackGallery tg(cfg);
tg.set_calibration(identity_cal);
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop);
// P = 0.50: inside the configured band, far below the shipped default lo.
tg.observe(1, at_sim(0, 1, 0.50f), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 0);
// P = 0.92: inside the shipped default band, above the configured ceiling.
tg.observe(1, at_sim(0, 2, 0.92f), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 1);
}
TEST_CASE("band admits at each bound exactly", "[track_gallery][AR-018]") {
// The verification plan asks for the bounds themselves, not a point safely
// inside them: an off-by-one in the comparison is invisible anywhere else.
// Both bounds are inclusive.
SECTION("lower bound exactly") {
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop);
tg.observe(1, at_sim(0, 1, kBandLo), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 0);
}
SECTION("upper bound exactly") {
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop);
tg.observe(1, at_sim(0, 1, kBandHi), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 0);
}
}
TEST_CASE("store never admits below the lower bound", "[track_gallery][AR-018]") {
// The lower bound is the poisoning guard: an embedding unlike everything
// already on the track is evidence the track is not one person.
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop);
tg.observe(1, at_sim(0, 1, kBandLo - 0.01f), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 1);
tg.observe(1, one_hot(400), 0, 0.30f, true, kNoCrop); // orthogonal: P = 0
CHECK(tg.band_rejected() == 2);
}
TEST_CASE("store never admits above the upper bound", "[track_gallery][AR-018]") {
// The upper bound is the redundancy guard: another look at a pose the store
// already covers teaches the annex nothing and costs a slot.
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop);
tg.observe(1, at_sim(0, 1, kBandHi + 0.01f), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 1);
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop); // identical: P = 1
CHECK(tg.band_rejected() == 2);
}
TEST_CASE("band thresholds probability, not cosine", "[track_gallery][AR-018][AR-024]") {
// The invariant's actual claim, and the one a raw-cosine gate passes by
// accident under an identity calibration. With a calibration that shifts by
// +0.10, two embeddings get the OPPOSITE verdict from the one their bare
// cosines would earn — so admission here can only come from the calibrated
// value having been used.
TrackGallery tg(expand_cfg());
tg.set_calibration([](float c) { return c + 0.10f; });
tg.observe(1, one_hot(0), 0, 0.30f, true, kNoCrop);
// cosine 0.84 (below lo, would be refused raw) → P = 0.94, inside the band.
tg.observe(1, at_sim(0, 1, 0.84f), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 0);
// cosine 0.92 (inside the band, would be admitted raw) → P = 1.02, above it.
tg.observe(1, at_sim(0, 2, 0.92f), 0, 0.30f, true, kNoCrop);
CHECK(tg.band_rejected() == 1);
}
TEST_CASE("a two-person track never poisons the annex", "[track_gallery][AR-018]") {
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
// Two orthogonal identities under one track ID — a track-ID collision.
// The band refuses the outsider at the door, so the store never becomes
// two-person in the first place.
tg.observe(3, spoke(1, kSpokeCos), 0, 0.30f, true, kNoCrop);
tg.observe(3, spoke(2, kSpokeCos), 0, 0.30f, true, kNoCrop);
tg.observe(3, one_hot(400), 0, 0.30f, true, kNoCrop); // orthogonal outlier
CHECK(tg.band_rejected() == 1); // refused at the door
REQUIRE_FALSE(tg.annex().empty()); // the legitimate views still promote
for (const auto& e : tg.annex())
CHECK(cosine_similarity(e.emb, one_hot(400)) < 0.5f);
}
TEST_CASE("a track that drifts through the band is refused at promotion",
"[track_gallery][AR-018]") {
// `admit` compares a newcomer against its CLOSEST existing member, so a
// gradual drift chains past it: each step is in-band while the endpoints are
// strangers. This is the shape a collision takes over a slow pan, and the
// reason the lower bound is re-asked across every pair before promotion.
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
tg.observe(5, on_circle(0.f), 0, 0.30f, true, kNoCrop);
tg.observe(5, on_circle(25.f), 0, 0.30f, true, kNoCrop); // P=0.906 vs 0° → in band
tg.observe(5, on_circle(50.f), 0, 0.30f, true, kNoCrop); // P=0.906 vs 25° → in band
CHECK(tg.band_rejected() == 0); // every step passed the door...
// ...but 0° and 50° are P=0.643 apart, below the floor: the whole track goes.
CHECK(tg.annex().empty());
}
// ── AR-019: ownership and promotion ──────────────────────────────────────────
TEST_CASE("confirmed track promotes its store", "[track_gallery][AR-019]") {
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
REQUIRE(tg.enabled());
// A track owned by actor 0. Every frame is accepted as actor 0, but each
// view is gallery-far (sim 0.30 < novelty 0.55) yet mutually self-similar
// enough to pass the spread gate.
for (int f = 0; f < 3; ++f)
tg.observe(7, at_sim(0, 1, 0.30f + 0.001f * f), 0, 0.30f + 0.001f * f, true, kNoCrop);
// A track owned by actor 0: every frame accepted, every view mutually
// in-band (P = 0.9197 between distinct spokes) and gallery-far (0.30).
for (int k = 1; k <= 3; ++k)
tg.observe(7, spoke(k, kSpokeCos), 0, 0.30f, true, kNoCrop);
// 3 accepted frames == min_anchor_frames → confirmed and promoted.
CHECK_FALSE(tg.annex().empty());
CHECK(tg.annex().size() == 3);
for (const auto& ae : tg.annex()) CHECK(ae.actor_idx == 0);
}
TEST_CASE("novelty gate skips views the gallery already covers", "[track_gallery]") {
TEST_CASE("registry ownership overrides the local tally", "[track_gallery][AR-019]") {
TrackGallery tg(expand_cfg());
// All views are recognised well (sim 0.90 ≥ novelty 0.55): nothing worth
// promoting even though the track is confirmed.
for (int f = 0; f < 3; ++f)
tg.observe(2, one_hot(0), 0, 0.90f, true, kNoCrop);
tg.set_calibration(identity_cal);
// Local accepted-frame plurality says actor 5; the registry's accumulated
// posterior says actor 9. The registry is authoritative.
tg.set_owner(11, 9);
for (int k = 1; k <= 3; ++k)
tg.observe(11, spoke(k, kSpokeCos), 5, 0.30f, true, kNoCrop);
REQUIRE_FALSE(tg.annex().empty());
for (const auto& ae : tg.annex()) CHECK(ae.actor_idx == 9);
}
TEST_CASE("unconfirmed track (too few accepts) does not promote", "[track_gallery][AR-019]") {
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
// Only 2 accepted frames < min_anchor_frames 3; the third fills the buffer
// but doesn't count toward ownership.
tg.observe(4, spoke(1, kSpokeCos), 0, 0.30f, true, kNoCrop);
tg.observe(4, spoke(2, kSpokeCos), 0, 0.30f, true, kNoCrop);
tg.observe(4, spoke(3, kSpokeCos), 0, 0.30f, false, kNoCrop);
CHECK(tg.annex().empty());
}
TEST_CASE("spread gate rejects a two-person track", "[track_gallery]") {
TEST_CASE("plurality actor wins a mixed-vote track", "[track_gallery][AR-019]") {
TrackGallery tg(expand_cfg());
// Two orthogonal identities under one track ID: pairwise sim 0 → spread 1.0
// > spread_max 0.60. Whole track rejected, annex stays empty even though
// frames are accepted and gallery-far.
tg.observe(3, at_sim(0, 1, 0.30f), 0, 0.30f, true, kNoCrop);
tg.observe(3, at_sim(0, 1, 0.30f), 0, 0.30f, true, kNoCrop);
tg.observe(3, one_hot(400), 0, 0.30f, true, kNoCrop); // orthogonal outlier
CHECK(tg.annex().empty());
}
TEST_CASE("unconfirmed track (too few accepts) does not promote", "[track_gallery]") {
TrackGallery tg(expand_cfg());
// Only 2 accepted frames < min_anchor_frames 3; extra non-accepted frames
// fill the buffer but don't count toward ownership.
tg.observe(4, at_sim(0, 1, 0.30f), 0, 0.30f, true, kNoCrop);
tg.observe(4, at_sim(0, 1, 0.31f), 0, 0.31f, true, kNoCrop);
tg.observe(4, at_sim(0, 1, 0.32f), 0, 0.32f, false, kNoCrop);
CHECK(tg.annex().empty());
}
TEST_CASE("plurality actor wins a mixed-vote track", "[track_gallery]") {
Config cfg = expand_cfg();
cfg.expand_min_anchor_frames = 3;
TrackGallery tg(cfg);
// Actor 5 accepted twice, actor 6 once → plurality is 5. All views novel.
tg.observe(8, at_sim(0, 1, 0.30f), 5, 0.30f, true, kNoCrop);
tg.observe(8, at_sim(0, 1, 0.31f), 5, 0.31f, true, kNoCrop);
tg.observe(8, at_sim(0, 1, 0.32f), 6, 0.32f, true, kNoCrop);
tg.set_calibration(identity_cal);
// No registry attached (unit-test path): actor 5 accepted twice, actor 6
// once → plurality is 5.
tg.observe(8, spoke(1, kSpokeCos), 5, 0.30f, true, kNoCrop);
tg.observe(8, spoke(2, kSpokeCos), 5, 0.30f, true, kNoCrop);
tg.observe(8, spoke(3, kSpokeCos), 6, 0.30f, true, kNoCrop);
REQUIRE_FALSE(tg.annex().empty());
for (const auto& ae : tg.annex()) CHECK(ae.actor_idx == 5);
}
TEST_CASE("promotion is idempotent across a long track", "[track_gallery]") {
TEST_CASE("promotion is idempotent across a long track", "[track_gallery][AR-019]") {
TrackGallery tg(expand_cfg());
for (int f = 0; f < 3; ++f)
tg.observe(9, at_sim(0, 1, 0.30f + 0.001f * f), 0, 0.30f + 0.001f * f, true, kNoCrop);
tg.set_calibration(identity_cal);
for (int k = 1; k <= 3; ++k)
tg.observe(9, spoke(k, kSpokeCos), 0, 0.30f, true, kNoCrop);
size_t after_confirm = tg.annex().size();
REQUIRE(after_confirm > 0);
// Keep feeding the confirmed track: annex must not grow again.
for (int f = 0; f < 10; ++f)
tg.observe(9, at_sim(0, 1, 0.30f), 0, 0.30f, true, kNoCrop);
tg.observe(9, spoke(4 + f, kSpokeCos), 0, 0.30f, true, kNoCrop);
CHECK(tg.annex().size() == after_confirm);
}
TEST_CASE("clear_tracks drops buffers before confirmation", "[track_gallery]") {
TEST_CASE("clear_tracks drops buffers before confirmation", "[track_gallery][AR-019]") {
TrackGallery tg(expand_cfg());
tg.set_calibration(identity_cal);
// Two accepts, then a cut clears buffers; the third accept starts fresh and
// can't reach the anchor threshold on its own.
tg.observe(1, at_sim(0, 1, 0.30f), 0, 0.30f, true, kNoCrop);
tg.observe(1, at_sim(0, 1, 0.31f), 0, 0.31f, true, kNoCrop);
tg.observe(1, spoke(1, kSpokeCos), 0, 0.30f, true, kNoCrop);
tg.observe(1, spoke(2, kSpokeCos), 0, 0.30f, true, kNoCrop);
tg.clear_tracks();
tg.observe(1, at_sim(0, 1, 0.32f), 0, 0.32f, true, kNoCrop);
tg.observe(1, spoke(3, kSpokeCos), 0, 0.30f, true, kNoCrop);
CHECK(tg.annex().empty());
}
TEST_CASE("eviction keeps the gallery-far views", "[track_gallery][AR-018]") {
// Novelty is no longer a threshold — it is this ordering. With the buffer
// full, a more gallery-far newcomer must displace the best-recognised
// member, and a less novel one must be dropped rather than displace a
// better sample.
Config cfg = expand_cfg();
cfg.expand_buffer_size = 2;
cfg.expand_min_anchor_frames = 4;
TrackGallery tg(cfg);
tg.set_calibration(identity_cal);
tg.observe(6, spoke(1, kSpokeCos), 0, 0.80f, true, kNoCrop); // well recognised
tg.observe(6, spoke(2, kSpokeCos), 0, 0.40f, true, kNoCrop);
tg.observe(6, spoke(3, kSpokeCos), 0, 0.20f, true, kNoCrop); // novel: evicts the 0.80
tg.observe(6, spoke(4, kSpokeCos), 0, 0.90f, true, kNoCrop); // least novel: dropped
REQUIRE(tg.annex().size() == 2);
// Survivors are the two most gallery-far views: spokes 2 and 3.
for (const auto& ae : tg.annex()) {
const bool is_2 = cosine_similarity(ae.emb, spoke(2, kSpokeCos)) > 0.99f;
const bool is_3 = cosine_similarity(ae.emb, spoke(3, kSpokeCos)) > 0.99f;
CHECK((is_2 || is_3));
}
}

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