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
dtourolle
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