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.
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.
scene_gap_hist.py scans scene_analyze output JSONs and, for every actor,
computes the gap (next_scene_start - prev_scene_end) between consecutive
scenes, emitting a text histogram of the distribution. Used to inform the
anneal_sec default.
movienet_eval: replace the per-element dot() with numpy — actor references are
loaded once as an ndarray and scored with a single matmul, keeping a
whole-library gallery fast.
movienet_prep: count and report frames referenced by annotations but absent
from Image.zip instead of skipping them silently.
LVFace-B_Glint360K.onnx shares ArcFace's I/O contract (112x112 aligned crop ->
L2-normalised 512-d) and input scaling, so it drops in via --arcface-model.
Note the caveat that galleries and calib caches must be rebuilt with the same
embedder used for analysis.
Add two cameo hunters that flag actors recognised in a title but absent from
its cast:
- cameo_jellyfin.py — pure-Jellyfin cast-membership check (no id cross-walk)
- cameo_hunt.py — TMDB filmography check (actor's combined_credits)
run_from_jellyfin.py now stamps the analysed title's Jellyfin item GUID into
the output JSON as top-level 'jellyfin_item_id' (scene_analyze can't know it),
which cameo_jellyfin.py uses to look up the cast in Jellyfin's own id space.
Document that field in the result-sink output schema header.
Register a KPN event handler in both scene_analyze and scene_preview:
- Overflow events accumulate per-node dropped-frame counts, printed on exit.
- A Closed event from any node other than result_sink at EOF means a stage
died; trip an atomic so the main loop bails out instead of hanging on
'done' forever, and exit non-zero.
Bumps external/KPN to the commit that exposes set_event_handler / NodeEvent.
Consolidate copy-pasted logic across the gallery/run scripts into shared
modules:
- sae_env.py — zero-dependency .env loader (populates os.environ)
- sae_tmdb.py — TMDB API helpers (tmdb_get, person images, id lookups)
- sae_jellyfin.py— Jellyfin API helpers (jf_get, id/URL normalisation)
- sae_gallery.py — image download + gallery.json writing
make_gallery, make_jellyfin_gallery and filter_gallery now import these
instead of carrying their own near-identical copies.
These are regenerable per-run outputs that were polluting the worktree:
calibration caches (*.calib_cache.csv/png), cameo detection run outputs
(cameo_progress.txt, cameo_report.txt), and scene-gap analysis plots.
dtourolle