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.

src/nodes/face_detector_node.hpp

@@ -26,10 +26,15 @@ struct FaceDetectorFunc {
 
         auto faces = detector_->detect(f.image);
 
-        // Drop faces below minimum pixel size (too small for reliable ArcFace alignment)
+        // Drop faces below minimum pixel size (too small for reliable ArcFace
+        // alignment). Note: when dense_scale downscaled the frame, both the
+        // detection coords and min_face_px are in downscaled space — so scale
+        // the threshold down to match, keeping the physical size cutoff constant.
+        const float min_px = (f.bbox_upscale != 1.f)
+                           ? min_face_px_ / f.bbox_upscale : min_face_px_;
         faces.erase(
             std::remove_if(faces.begin(), faces.end(), [&](const DetectedFace& d) {
-                return d.bbox.width < min_face_px_ || d.bbox.height < min_face_px_;
+                return d.bbox.width < min_px || d.bbox.height < min_px;
             }),
             faces.end());