faster calibration curve generation

jellyfin intergration

scripts/sae_embed_loader.py

@@ -0,0 +1,36 @@
+"""Shared loader for the sae_embed nanobind module (SCRFD + ArcFace).
+
+sae_embed.FaceEmbedder loads both ONNX sessions once and exposes an
+embed(path) -> FaceResult method, avoiding the per-process model reload cost
+of spawning the embed_faces CLI binary for every image.
+"""
+
+import sys
+from pathlib import Path
+
+
+def load_embedder(build_dir: str, models_dir: str, arcface: str | None = None,
+                   conf: float = 0.5, nms: float = 0.4, max_side: int = 500):
+    """Import sae_embed from build_dir and construct a FaceEmbedder.
+
+    Exits with a clear error if the module or models are missing — there is
+    no subprocess fallback.
+    """
+    build_path = Path(build_dir).resolve()
+    sys.path.insert(0, str(build_path))
+    try:
+        import sae_embed
+    except ImportError as e:
+        sys.exit(
+            f"sae_embed module not found in {build_path}: {e}\n"
+            f"Build it first: cmake --build {build_dir} --target sae_embed"
+        )
+
+    models_path = Path(models_dir)
+    detector_path = str(models_path / "scrfd_500m_bnkps.onnx")
+    arcface_path = arcface if arcface else str(models_path / "arcface_w600k_r50.onnx")
+    for model, name in [(detector_path, "SCRFD"), (arcface_path, "ArcFace")]:
+        if not Path(model).is_file():
+            sys.exit(f"{name} model not found: {model}\nRun: bash scripts/download_models.sh")
+
+    return sae_embed.FaceEmbedder(detector_path, arcface_path, conf, nms, max_side)