Commit Graph
6 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
2026-08-04 21:17:57 +02:00
dtourolleandClaude Opus 5 f99f1c5ccc 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 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
dtourolle 41a277bc19 feat(engine): HDF5-native galleries with embedded calibration; TensorRT backends; scene detection
Gallery format switches from JSON to HDF5 exclusively (JSON read-only kept for
back-compat): save_gallery always writes HDF5, and the fitted Platt-sigmoid
calibration (a, b, valid, hash) is now embedded directly in the gallery file
instead of a sidecar .calib_cache.json — identity_matcher reads it from the
loaded gallery and writes back only when the embeddings actually changed
(hash mismatch), skipping the O(n^2) refit otherwise.

Also includes: TensorRT inference backend support (ort_backend.cpp,
trt_backend.cpp), gemm_backend improvements, TransNetV2-based scene-boundary
detection wired through frame_source/face_tracker/main, and CMake build
target updates for the new sources.

Bumps the KPN submodule to feature/persistent-pipeline-reuse (push_blocking
backpressure, node_ptr/node_stats introspection, ObjectVariantNodeWrapper for
stateful functors) — needed by the optimizer's sae_kpn Python bindings.
2026-07-19 19:04:03 +02:00
dtourolle 0ee131a692 Add AMD support via ort alternative to trt 2026-06-28 11:50:05 +02:00
dtourolle d753062c6c Initial commit: scene-actor-extraction pipeline
Source (KPN++ pipeline nodes, ArcFace embedders, SCRFD/YuNet detectors,
gallery builder), build scripts, and eval artifacts.

- external/KPN as a git submodule (gitea.tourolle.paris/dtourolle/KPN)
- ONNX models tracked via Git LFS (models/*.onnx)
- generated outputs, TensorRT engines, reference repos, and media ignored
2026-06-12 15:29:01 +02:00