Sharpness is not a sufficient statistic for identity loss. Six cells at
effectively identical measured sharpness span 15.3% to 91.0% TPI,
ordered entirely by source size, because a scalar keyed on
high-frequency energy cannot separate attenuated high frequencies from
destroyed spatial sampling. AR-028's "kept separate, not collapsed into
one scalar" now rests on a measurement rather than an argument -- and
the reasoning it used to rest on, that the aligned crop is
scale-normalised so a measure there cannot re-measure size, was wrong
and is corrected in place.
Variance of Laplacian -- the most widely used blur metric there is -- is
anti-predictive at fixed degradation on all three blur families. The
decile it calls sharpest is 2.6x less identifiable than the decile it
calls blurriest, monotone across ten bins, because within a cell its
residual variance is native contrast rather than detail, and hard
shadows and JPEG ringing raise it while making a face harder to match.
Gating on it would preferentially discard the more identifiable faces.
Blur breaks confidence, not identity: rank-1 holds at 80.2% where TPI is
15.3%, and FPI never left 0.1% in any of the 108 cells. Degradation
produces abstention, never a wrong name. That is also why sharpness
fails as a compute gate -- even a visually destroyed face stays 46.9%
identifiable, so a gate discards recoverable evidence at three times the
cost of the free size filter. Discount, do not gate; the rule AR-028
already stated now has evidence for why it is right rather than merely
cautious.
Records the shape a discount must have (flat, then a cliff between sigma
2 and 3), that its cost scales with proximity to the decision boundary
rather than with blur, and that the pose half of VR-012 has not been
run.
TRACES: VR-012, AR-028, AR-029, AR-030 | SR-002
Extends the VR-005 protocol -- hold out one mugshot per actor, degrade
only the probe, match against a gallery held at native resolution,
decide through the Platt calibration -- from one axis to two, over 1670
actors rather than 100.
Joint rather than separable, because the interaction is the question: a
16 px face upscaled to 112 has already lost its high frequencies, so
further blur costs it almost nothing, while the same blur at full
resolution is expensive. Sweeping the axes independently would measure
each with the other implicitly at its best and miss that entirely.
Three blur families, compared at matched per-axis PSF spread rather than
at equal raw parameter. Optical defocus is a uniform disc whose transfer
function is a jinc with exact zeros, not a Gaussian that merely rolls
off, and it is also how a face ends up large and useless -- the case a
size filter cannot catch. Sweeping Gaussian alone, as the first version
did, understates real lens blur by a factor of five in error rate.
Every candidate measure is scored on every degraded crop and the
candidates are ranked by how well each predicts the pipeline's actual
decision, not by how smooth its synthetic ladder looks. Both a pooled
and a within-cell AUC are reported: they answer different questions and
the candidates rank differently under each.
Runs through sae_embed throughout. Stages gains optional engine paths so
the same study can drive a TRT build, which is what makes the full grid
five minutes rather than four and a half hours.
TRACES: VR-012, AR-028, AR-029 | SR-002
detect_ort_provider() only tested for CUDAExecutionProvider inside
#ifdef SAE_ORT_WITH_TRT_EP, so any build that did not also opt into the
TensorRT execution provider could never select CUDA and fell straight
through to the CPU. The two are independent: the TRT EP needs the
headers and profile plumbing and is rightly an opt-in, CUDA is a plain
ORT provider and is not.
The failure is silent rather than loud, which is why it survived --
inference runs on the CPU and every answer is still correct, just far
slower. Measured on the VR-012 study: 0.32 s/crop against 0.0021 s/crop
once a GPU backend is actually used, with the card sitting at 212 MiB
and 0% utilisation throughout.
Only the TensorrtExecutionProvider line stays inside the guard.
assess_sharpness() scores a 112x112 crop on variance-of-Laplacian, a
contrast-normalised variant, Tenengrad, a spectral high-frequency ratio
and dir_min_tenengrad, over a fixed 64x64 window on the face interior.
The window excludes the corners because studio headshots are routinely
shot at a wide aperture, and background bokeh measured over the whole
crop would drag the score down on the sharpest images in the set.
Five rather than one because AR-029's threshold has to be located, not
chosen: VR-012 ranks them by how well each predicts real identity loss.
The T1 ladders drove two corrections during development. The spectral
ratio applied its Hann window before removing the mean, so the DC term
smeared into the low-frequency bins and the "ratio" tracked absolute
brightness (a 20/255 brightening moved it 23%). And no measure taken
from the literature survived directional blur: normalising by total
energy divides out the loss being measured, so both ratio measures are
U-shaped in motion-blur length and score a 21 px smear about as sharp as
a 3 px one. dir_min_tenengrad exists to fix that -- a low-frequency
contrast denominator that blur leaves alone, and the worse of the two
Sobel axes rather than their sum.
The tests pin the disqualifying behaviours as well as the desirable
ones, so a change that makes var_laplacian contrast-free is a deliberate
act rather than an accident. They also record that every candidate falls
under downscale-upscale as well as under blur: the aligned crop is
scale-normalised geometrically, not informationally.
Exposed through sae_embed alongside the AR-030 alignment residual, so a
study scores through shipped code rather than a numpy copy -- the same
argument that already applies to the calibration.
TRACES: AR-028, AR-029 | SR-002
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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dtourolle
