bedc859d3c528e9853d41f546d8772c042fdbfc7
4
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
  dtourolleandClaude Opus 5
|
f33403fff8 |
feat(scene): feed TransNetV2 at native rate, derive the dedup window from it
Closes both violations SPEC.md named under "Every model gets the input it was trained for". They are one bug, not two. The dense stream defaulted to 12 fps, so a 100-frame TransNetV2 window spanned ~8.3 s against the ~4 s it was trained on: half-speed motion over twice its temporal context. Boundary timestamps stayed correct throughout, which is exactly why the degradation was invisible and why the compressed separation it produced (~0.50 baseline against ~0.7+ peaks) was read as a property of the ONNX export rather than of the input. Dedup then merged boundaries closer than a literal 0.04 s — one frame at 25 fps, and wider than a frame at 30, so two cuts on consecutive frames became one. Nothing in scenes.json showed it; the file simply had fewer boundaries. Native rate is where that constant did the most damage, which is why fixing the decode rate without fixing the dedup would have made things worse. dedup_window_sec() now takes the median interval the detector was actually fed and halves it. Half a frame rather than a whole one: the only thing being merged is one frame scored by two overlapping windows, and two distinct frames are a full interval apart. Cost is real — dense decode is the pipeline's cost driver. It is accepted; dense_scale and scene_stride remain the reductions that do not run the model off-distribution. scene_threshold 0.60 was fitted against the 12 fps input and is now stale, so VR-006 goes from Low to Medium: it is no longer a refinement, it is a constant that no longer describes the input. AR-002 rides along because it was already implemented, just untagged and unverified — the register said Planned while the code was correct. The size filter becomes FaceDetectorFunc::drop_undersized(), tested at the threshold and at dense_scale 0.5, and checked end to end against the superhero dump, whose smallest face is exactly its recorded 32 px minimum, so the fixture check cannot pass vacuously. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> TRACES: AR-002, AR-011 | SR-002 | UT-002, UT-003, IT-001 |
||
|
dtourolle
|
d98dc2855a |
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 |
||
|
dtourolleandClaude Opus 5
|
b4318f8d9e |
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 |
||
|
dtourolleandClaude Opus 5
|
61e487fbee |
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 |