1477c538850a9a108dd0df37f87334510ea41daa
5
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 dtourolle
|
4dcef8d6c5 |
fix(AR-004): the TransNetV2 window stores the model's input, not the frame
The rolling window held frames as decoded — `images_.push_back(f.image)` — and
left the downscale to the backend. TransNetV2's input is 48x27, so the buffer
held roughly 590 MB at 1080p to feed a model that needs about 380 KB. The
config note for `dense_scale` says as much outright: "TransNetV2 downsamples to
48x27 regardless".
This is not a channel capacity, so no amount of tuning channel depths would
ever have found it. It is a `std::deque<cv::Mat>` member, and it is the single
largest allocation in the scene branch.
It is also redundant work. Windows overlap by `kWindow - stride`, so a frame
appears in several of them and was re-downscaled once per window it appeared
in; now it is downscaled once, on arrival.
**The risk here is the invariant, not the memory.** Every model gets the input
it was trained for — a model run off-distribution returns confident, plausible,
wrong output, and for a boundary detector that means fabricated cuts, which are
indistinguishable from real ones in the output. So this reproduces the
backends' preprocessing exactly rather than doing its own: both
ort_backend.cpp and trt_backend.cpp guard mis-sized input with
`convertTo(CV_8UC3)` and then
`cv::resize(..., {kFrameW, kFrameH}, 0, 0, cv::INTER_AREA)`, in that order, and
`to_model_input` performs the same two operations. The backend guard then sees
a correctly-sized frame and does nothing, so the tensor the model receives is
unchanged. The interface has always specified this as the caller's job — "Each
frame must already be kFrameW x kFrameH, BGR, CV_8UC3" — so the node now meets
a contract it was already given.
The tests assert equivalence, not size. They perform the backend's own two
operations independently and compare byte for byte, on a gradient rather than a
flat fill, since INTER_AREA averages and a constant image would compare equal
under almost any resize. Order is pinned too: converting a 4-channel frame
after downscaling averages alpha into the colour channels and gives different
pixels.
Verified in both directions. With INTER_LINEAR substituted for INTER_AREA —
the most plausible way to get this subtly wrong — three assertions fail. With
the backend's own operations, byte-identical at 1920x1080, 640x360 and 720x480.
149/149.
Still unmeasured on real content, as with the previous commit: the equivalence
argument says the model sees the same tensor, but a run comparing scenes.json
before and after on a real clip is what would settle it, and I could not launch
one here.
TRACES: AR-004, AR-010 | SR-002
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|
dtourolle
|
88c42573a5 |
fix(pipeline): finish three changes that had only been half applied
Each of these was recorded as done and was done in one place out of two. AR-011 -- the TransNetV2 dedup window. The derived window (dedup_window_sec, median observed interval halved) reached scenes.json and nothing else. SceneBoundaries, the path that actually feeds is_scene_boundary to the tracker, kept the literal 0.04 s under a comment claiming it "matches the dedup scenes.json applies, so the two views agree". They did not agree. 0.04 is one frame at 25 fps and wider than a frame at 30, so two cuts on consecutive frames merged into one and the loss was invisible: the pipeline simply saw fewer boundaries. The detector now supplies the window it derived. AR-019 -- ownership. The register says ownership "comes from the registry, not a second local tally". Both existed: promotion fired on a local accepted-frame count and fell back to a local per-actor plurality when the registry had not yet claimed the track. That fallback was reachable in the live pipeline, not just in tests -- three accepted frames arrive well before a posterior crosses the ownership threshold -- so in practice the plurality usually decided, and it could not see the AR-025 correlation discounting it was meant to defer to. The tally is gone; promotion now requires the registry's verdict, with the accepted -frame count demoted to an explicit evidence floor. AR-017 -- the route. DeadTrack carried belief but no route, and the sink wrote the literal string "live", so a field the schema publishes could not distinguish anything. AR-017's own verification asks for "deferred and pooled routes distinguishable". Route is now an enum on the claim. Only `live` occurs today; `deferred` exists so AR-020's pass has somewhere to write instead of a serialisation change to make. Also: TrackGallery::forget had no callers, under a comment asserting the matcher called it "on a cut or track disappearance". The cut half was true by another route; the disappearance half was not, so a track that died quietly kept its diversity buffer until the next cut cleared everything. Replaced with prune_dead against the registry's own liveness, the same shape as the tracker's prune_boxes -- a second opinion about which tracks exist is a second thing that can be wrong. Removes dead logistic/logit helpers and fixes five TRACES tags that used a comma where a pipe separates requirement types, which the gate had been reporting as diagnostics. TRACES: AR-011, AR-017, AR-019 | IR-002 | SR-002, SR-005 |
||
 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 |
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dtourolleandClaude Opus 5
|
13bdc27566 |
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 |
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dtourolle
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26139ffe8a |
feat(engine): add Python replay bindings, gallery pose-expansion, scene detection, embedding dumps
New C++ sources: - kpn_bindings.cpp (sae_kpn): assembles the real face_tracker/identity_matcher/ scene_tracker nodes inside a Python-driven KPN network via nanobind, for offline threshold-sweep replay against dumped embeddings (scripts/optimizer/). - track_gallery.hpp: per-film gallery expansion — promotes a confidently- identified track's novel-pose reference views into an in-memory annex so later frames/tracks of that actor at similar poses are recognised, without touching the baked gallery. - dump_embeddings.cpp: standalone exe that runs detect→embed only (no gallery, no matching) and dumps per-frame face embeddings + metadata to HDF5, so a parameter sweep can replay the expensive half once and vary tracking/matching config freely downstream. - scene_detector.hpp / scene_detector_node.hpp: TransNetV2-based shot-boundary detection, opt-in alongside the always-on histogram cut detector. - camera_position_change_detector_node.hpp, embedding_dump_node.hpp: supporting nodes for the above. |