faster calibration curve generation

jellyfin intergration

CMakeLists.txt

@@ -86,6 +86,16 @@ FetchContent_Declare(
 )
 FetchContent_MakeAvailable(nlohmann_json)
 
+# nanobind (Python bindings for the sae_embed module)
+find_package(Python 3.8 COMPONENTS Interpreter Development.Module REQUIRED)
+FetchContent_Declare(
+    nanobind
+    GIT_REPOSITORY https://github.com/wjakob/nanobind.git
+    GIT_TAG        v2.4.0
+    GIT_SHALLOW    TRUE
+)
+FetchContent_MakeAvailable(nanobind)
+
 # ── Model paths ───────────────────────────────────────────────────────────────
 set(SAE_MODELS_DIR "${CMAKE_SOURCE_DIR}/models"
     CACHE PATH "Directory containing ONNX model files")
@@ -120,6 +130,15 @@ target_link_libraries(embed_faces PRIVATE
 )
 target_compile_definitions(embed_faces PRIVATE SAE_MODELS_DIR="${SAE_MODELS_DIR}")
 
+# ── sae_embed — Python module: load SCRFD+ArcFace once, embed many images ───
+nanobind_add_module(sae_embed src/python_bindings.cpp)
+target_include_directories(sae_embed PRIVATE src)
+target_link_libraries(sae_embed PRIVATE
+    ${OpenCV_LIBS}
+    onnxruntime
+)
+target_compile_definitions(sae_embed PRIVATE SAE_MODELS_DIR="${SAE_MODELS_DIR}")
+
 # ── analyze — main analysis binary ───────────────────────────────────────────
 add_executable(scene_analyze src/main.cpp)
 target_link_libraries(scene_analyze PRIVATE sae_gallery)

README.md

@@ -24,6 +24,16 @@ cmake -B build -DCMAKE_BUILD_TYPE=Release
 cmake --build build -j$(nproc)
 ```
 
+This also builds `sae_embed`, a Python module (via nanobind) that loads the
+SCRFD detector and ArcFace embedder once and exposes a reusable `embed()`
+method. The gallery-builder scripts (`make_gallery.py`,
+`make_jellyfin_gallery.py`, `movienet_eval.py`) import it directly — there is
+no subprocess fallback, so if it's missing they exit with a build instruction:
+
+```bash
+cmake --build build --target sae_embed
+```
+
 Optional flags:
 
 | Flag | Default | Effect |
@@ -52,7 +62,7 @@ Models are placed in `external/`:
 | `scene_analyze_debug` | Same as above + per-frame annotated JPEGs (`SAE_DEBUG=1`) |
 | `scene_preview` | Live OpenCV display window while analysing |
 | `build_gallery` | Offline gallery builder from a directory of images |
-| `embed_faces` | Standalone embedder used by gallery scripts |
+| `sae_embed` | Python module (nanobind) used by gallery-builder scripts — loads SCRFD+ArcFace once |
 
 ### `scene_analyze`
 
@@ -74,13 +84,72 @@ Key options:
 | `--track-max-missing` | — | Frames a track survives without a detection |
 | `--track-min-frames` | 3 | Observations before a track's mean embedding is used for matching |
 
-### Gallery builder
+### Gallery builders
+
+**Per-movie (TMDB):**
 
 ```bash
 python3 scripts/make_gallery.py --tmdb-bearer <JWT> --movie-id <TMDB_ID> --output gallery.json
 ```
 
-Fetches cast images from TMDB and embeds them via `embed_faces`.
+Fetches cast images from TMDB and embeds them via `sae_embed`.
+
+**Whole-library (Jellyfin):**
+
+```bash
+python3 scripts/make_jellyfin_gallery.py \
+    --jellyfin-url http://jellyfin.local:8096 \
+    --api-key <API_KEY> \
+    --output gallery.json
+```
+
+Scans every Movie/Series in Jellyfin, collects the unique cast across the
+whole library, downloads each actor's headshot directly from Jellyfin (no
+TMDB key needed), and embeds them via `sae_embed` into one global
+gallery.json. Since `identity_matcher` scores faces against the entire
+gallery, `scene_analyze` can then recognise any actor from your library in
+any film — not just the cast listed for that one title. Pass `--merge` on
+later runs to only embed actors newly added to the library. Pass
+`--tmdb-key` to fall back to TMDB profile images for actors with no usable
+image cached in Jellyfin.
+
+Jellyfin/TMDB lookups and image downloads for different actors run
+concurrently (`--workers`, default 8). Embedding is GPU-bound, so it's
+gated separately via `--embed-concurrency` (default 1) — only that many
+embed calls run at once while other actors' downloads continue in the
+background.
+
+To restrict a single-title run to that title's credited cast (faster, fewer
+look-alike mismatches), filter the global gallery first:
+
+```bash
+python3 scripts/filter_gallery.py \
+    --gallery gallery.json \
+    --jellyfin-url http://jellyfin.local:8096 \
+    --api-key <API_KEY> \
+    --title "The Matrix" \
+    --output gallery_matrix.json
+```
+
+## Running directly from Jellyfin
+
+`scripts/run_from_jellyfin.py` resolves a title to its media file via the
+Jellyfin API, filters the gallery to that title's cast, and runs
+`scene_analyze` in one step. Requires this tool to run on a host that shares
+Jellyfin's media mount (it uses the item's on-disk `Path`, not a stream URL):
+
+```bash
+python3 scripts/run_from_jellyfin.py \
+    --jellyfin-url http://jellyfin.local:8096 \
+    --api-key <API_KEY> \
+    --title "The Matrix" \
+    --gallery gallery.json \
+    -- --fps 5 --verbosity 2
+```
+
+Anything after `--` is passed through to `scene_analyze` unchanged. Pass
+`--no-filter` to use the gallery as-is (skip per-title cast filtering), or
+`--item-id` instead of `--title` to skip the search.
 
 ## Output format
 

scripts/filter_gallery.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""filter_gallery.py — restrict a global gallery.json to one title's known cast.
+
+make_jellyfin_gallery.py builds a single gallery spanning the whole Jellyfin
+library, so identity_matcher can recognise any actor from any film. For a
+single-title run you may instead want to restrict matching to that title's
+credited cast only — fewer candidates means faster matching and fewer
+look-alike false positives.
+
+This script looks up the title's cast live from Jellyfin and writes a
+filtered gallery.json containing only those actors, matched via the
+"jellyfin_person_id" field recorded by make_jellyfin_gallery.py.
+
+Usage:
+    python scripts/filter_gallery.py \\
+        --gallery gallery.json \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --item-id <jellyfin item id> \\
+        --output gallery_movie.json
+
+    # Or search by title:
+    python scripts/filter_gallery.py \\
+        --gallery gallery.json \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --title "The Matrix" \\
+        --output gallery_movie.json
+"""
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+import requests
+
+
+def jf_get(base_url: str, api_key: str, path: str, **params) -> dict:
+    url = base_url.rstrip("/") + path
+    headers = {"X-Emby-Token": api_key, "Accept": "application/json"}
+    r = requests.get(url, params=params, headers=headers, timeout=30)
+    r.raise_for_status()
+    return r.json()
+
+
+def find_item_id(base_url: str, api_key: str, title: str, item_types: list[str]) -> str:
+    data = jf_get(
+        base_url, api_key, "/Items",
+        Recursive="true",
+        IncludeItemTypes=",".join(item_types),
+        SearchTerm=title,
+        Limit=10,
+    )
+    items = data.get("Items", [])
+    if not items:
+        raise ValueError(f"No item found matching title {title!r}")
+    if len(items) > 1:
+        print("Multiple matches found:", file=sys.stderr)
+        for it in items:
+            print(f"  {it['Id']}  {it.get('Type')}  {it.get('Name')} ({it.get('ProductionYear')})", file=sys.stderr)
+        print(f"Using first match: {items[0]['Name']}", file=sys.stderr)
+    return items[0]["Id"]
+
+
+def fetch_cast_person_ids(base_url: str, api_key: str, item_id: str) -> set[str]:
+    data = jf_get(base_url, api_key, f"/Items/{item_id}", Fields="People")
+    return {p["Id"] for p in data.get("People", []) if p.get("Type") == "Actor"}
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--gallery", required=True,
+                         help="Global gallery.json built by make_jellyfin_gallery.py")
+    parser.add_argument("--jellyfin-url", required=True)
+    parser.add_argument("--api-key", required=True)
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("--item-id", help="Jellyfin item id of the title")
+    group.add_argument("--title", help="Title to search for (uses first match)")
+    parser.add_argument("--item-types", default="Movie,Series",
+                         help="Item types to search when using --title (default: Movie,Series)")
+    parser.add_argument("--output", required=True, help="Output filtered gallery.json path")
+    args = parser.parse_args()
+
+    gallery = json.loads(Path(args.gallery).read_text())
+
+    item_id = args.item_id
+    if item_id is None:
+        item_id = find_item_id(args.jellyfin_url, args.api_key, args.title, args.item_types.split(","))
+        print(f"Resolved title to item id {item_id}", file=sys.stderr)
+
+    cast_ids = fetch_cast_person_ids(args.jellyfin_url, args.api_key, item_id)
+    print(f"Title has {len(cast_ids)} credited cast member(s)", file=sys.stderr)
+
+    # current make_jellyfin_gallery.py writes "jellyfin_id"; older galleries used
+    # "jellyfin_person_id" (see gallery_store.cpp's fallback for the same pair).
+    actors = [a for a in gallery.get("actors", [])
+              if (a.get("jellyfin_id") or a.get("jellyfin_person_id")) in cast_ids]
+    missing = len(cast_ids) - len(actors)
+    if missing > 0:
+        print(f"[warn] {missing} cast member(s) not present in gallery (not yet embedded)", file=sys.stderr)
+
+    Path(args.output).write_text(json.dumps({"actors": actors}, indent=2) + "\n")
+    print(f"Saved {len(actors)} actor(s) to {args.output}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

scripts/make_gallery.py

@@ -1,9 +1,9 @@
 #!/usr/bin/env python3
 """make_gallery.py — fetch actor images for a movie and build gallery.json.
 
-Fetches the cast from TMDB, downloads actor profile images, runs the C++
+Fetches the cast from TMDB, downloads actor profile images, embeds them via
-embed_faces binary (SCRFD + ArcFace, same models as scene_analyze) to produce
+the sae_embed module (SCRFD + ArcFace, same models as scene_analyze, loaded
-embeddings, then writes gallery.json.
+once), then writes gallery.json.
 
 Requirements:
     pip install requests Pillow
@@ -22,7 +22,7 @@ Usage:
         --output gallery.json
 
     # Additional options:
-    #   --embed-bin build/embed_faces   path to embed_faces binary
+    #   --build-dir build/             build dir containing sae_embed module
     #   --models-dir models/            directory with ONNX models
     #   --max-actors 20                 how many cast members to include
     #   --images-per-actor 3            profile images to download per actor
@@ -32,19 +32,18 @@ Get a free TMDB API key at: https://www.themoviedb.org/settings/api
 """
 
 import argparse
+import io
 import json
-import os
-import subprocess
 import sys
-import tempfile
 import time
 from pathlib import Path
 
-import io
-
 import requests
 from PIL import Image
 
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from sae_embed_loader import load_embedder
+
 TMDB_BASE = "https://api.themoviedb.org/3"
 TMDB_IMG  = "https://image.tmdb.org/t/p/original"
 
@@ -81,7 +80,7 @@ def fetch_cast(movie_id: int, key: str, max_actors: int) -> list[dict]:
 
         # Get IMDB ID for this person
         ext = tmdb_get(f"/person/{person_id}/external_ids", key)
-        imdb_id = ext.get("imdb_id") or f"tmdb_{person_id}"
+        imdb_id = ext.get("imdb_id") or ""
 
         # Get profile images (sorted by vote_average desc by TMDB)
         images_data = tmdb_get(f"/person/{person_id}/images", key)
@@ -96,6 +95,7 @@ def fetch_cast(movie_id: int, key: str, max_actors: int) -> list[dict]:
             "id":            person_id,
             "name":          member["name"],
             "imdb_id":       imdb_id,
+            "tmdb_id":       str(person_id),
             "profile_images": image_urls,
         })
         time.sleep(0.05)  # be polite to TMDB
@@ -124,42 +124,9 @@ def download_images(actor: dict, dest_dir: Path, n: int) -> list[Path]:
     return paths
 
 
-# ── Embedding via embed_faces binary ─────────────────────────────────────────
-
-def embed_images(image_paths: list[Path], embed_bin: str,
-                 detector: str, arcface: str) -> list[dict | None]:
-    """
-    Call the C++ embed_faces binary on a list of images.
-    Returns a list of result dicts (or None if no face / error) per image.
-    """
-    if not image_paths:
-        return []
-
-    cmd = [
-        embed_bin,
-        "--detector", detector,
-        "--arcface", arcface,
-    ] + [str(p) for p in image_paths]
-
-    try:
-        proc = subprocess.run(cmd, capture_output=True, text=True, check=True)
-    except subprocess.CalledProcessError as e:
-        print(f"[error] embed_faces failed:\n{e.stderr}", file=sys.stderr)
-        return [None] * len(image_paths)
-
-    try:
-        results = json.loads(proc.stdout)
-    except json.JSONDecodeError as e:
-        print(f"[error] embed_faces output is not valid JSON: {e}", file=sys.stderr)
-        return [None] * len(image_paths)
-
-    return results
-
-
 # ── Gallery assembly ─────────────────────────────────────────────────────────
 
-def build_gallery(movie_id: int, key: str, embed_bin: str,
+def build_gallery(movie_id: int, key: str, embedder,
-                  detector: str, arcface: str,
                   max_actors: int, images_per_actor: int,
                   image_root: Path) -> dict:
     """Fetch cast, download images, embed, return gallery dict."""
@@ -171,27 +138,27 @@ def build_gallery(movie_id: int, key: str, embed_bin: str,
 
     for actor in actors:
         safe_name = actor["name"].replace(" ", "_")
-        actor_dir = image_root / f"{actor['imdb_id']}_{safe_name}"
+        dir_id    = actor["imdb_id"] or f"tmdb_{actor['tmdb_id']}"
+        actor_dir = image_root / f"{dir_id}_{safe_name}"
 
-        print(f"\n{actor['name']} ({actor['imdb_id']})", file=sys.stderr)
+        print(f"\n{actor['name']} ({dir_id})", file=sys.stderr)
         image_paths = download_images(actor, actor_dir, images_per_actor)
         if not image_paths:
             print("  no images downloaded, skipping", file=sys.stderr)
             continue
 
         print(f"  embedding {len(image_paths)} image(s)…", file=sys.stderr)
-        results = embed_images(image_paths, embed_bin, detector, arcface)
 
         embeddings    = []
         source_images = []
-        for path, res in zip(image_paths, results):
+        for path in image_paths:
-            if res is None or res.get("embedding") is None:
+            res = embedder.embed(str(path))
-                reason = res.get("error", "unknown") if res else "binary error"
+            if not res.ok:
-                print(f"  [skip] {path.name}: {reason}", file=sys.stderr)
+                print(f"  [skip] {path.name}: {res.error}", file=sys.stderr)
                 continue
-            embeddings.append(res["embedding"])
+            embeddings.append(res.embedding)
             source_images.append(path.name)
-            print(f"  [ok]   {path.name}  conf={res.get('confidence', 0):.2f}",
+            print(f"  [ok]   {path.name}  conf={res.confidence:.2f}",
                   file=sys.stderr)
 
         if not embeddings:
@@ -200,6 +167,8 @@ def build_gallery(movie_id: int, key: str, embed_bin: str,
 
         gallery_actors.append({
             "imdb_id":      actor["imdb_id"],
+            "tmdb_id":      actor["tmdb_id"],
+            "jellyfin_id":  "",
             "name":         actor["name"],
             "source_images": source_images,
             "embeddings":   embeddings,
@@ -213,7 +182,7 @@ def build_gallery(movie_id: int, key: str, embed_bin: str,
 
 def main():
     parser = argparse.ArgumentParser(
-        description="Fetch TMDB cast images and build gallery.json via embed_faces")
+        description="Fetch TMDB cast images and build gallery.json via sae_embed")
     parser.add_argument("--tmdb-key",  required=True,
                         help="TMDB Bearer token (API Read Access Token from themoviedb.org/settings/api)")
     group = parser.add_mutually_exclusive_group(required=True)
@@ -222,8 +191,8 @@ def main():
     group.add_argument("--movie-id",  type=int,
                         help="TMDB movie ID (alternative to --imdb-id)")
     parser.add_argument("--output",          required=True,  help="Output gallery.json path")
-    parser.add_argument("--embed-bin",       default="build/embed_faces",
+    parser.add_argument("--build-dir",       default="build",
-                        help="Path to embed_faces binary (default: build/embed_faces)")
+                        help="Build directory containing the sae_embed module (default: build)")
     parser.add_argument("--models-dir",      default="models",
                         help="Directory containing ONNX models (default: models/)")
     parser.add_argument("--arcface",         default=None,
@@ -239,21 +208,10 @@ def main():
     args = parser.parse_args()
 
     # Resolve paths
-    embed_bin  = str(Path(args.embed_bin).resolve())
-    models_dir = Path(args.models_dir)
-    detector   = str(models_dir / "scrfd_500m_bnkps.onnx")
-    arcface    = args.arcface if args.arcface else str(models_dir / "arcface_w600k_r50.onnx")
     output     = Path(args.output)
     image_root = Path(args.image_dir) if args.image_dir else output.parent / "images"
 
-    # Validate
+    embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
-    if not Path(embed_bin).is_file():
-        sys.exit(f"embed_faces binary not found: {embed_bin}\n"
-                 f"Build it first: cmake --build build --target embed_faces")
-    for model, name in [(detector, "SCRFD"), (arcface, "ArcFace")]:
-        if not Path(model).is_file():
-            sys.exit(f"{name} model not found: {model}\n"
-                     f"Run: bash scripts/download_models.sh")
 
     # Resolve movie ID
     movie_id = args.movie_id
@@ -266,9 +224,7 @@ def main():
     gallery = build_gallery(
         movie_id      = movie_id,
         key           = args.tmdb_key,
-        embed_bin     = embed_bin,
+        embedder      = embedder,
-        detector      = detector,
-        arcface       = arcface,
         max_actors    = args.max_actors,
         images_per_actor = args.images_per_actor,
         image_root    = image_root,

scripts/make_jellyfin_gallery.py

@@ -0,0 +1,452 @@
+#!/usr/bin/env python3
+"""make_jellyfin_gallery.py — build a gallery.json spanning an entire Jellyfin library.
+
+Queries the Jellyfin API for every Movie/Series, collects the unique cast
+across the whole library, downloads each actor's headshot directly from
+Jellyfin (no TMDB key needed), embeds them with the sae_embed module (SCRFD +
+ArcFace, loaded once), and writes one global gallery.json.
+
+Because identity_matcher scores every detected face against the whole
+gallery, scene_analyze can then recognise any actor in your library in any
+film — not just the cast TMDB lists for that one title.
+
+For a single-title run, use scripts/filter_gallery.py afterwards to restrict
+matching to that title's credited cast (faster, fewer look-alike mismatches).
+
+Requirements:
+    pip install requests Pillow
+
+Usage:
+    python scripts/make_jellyfin_gallery.py \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --output gallery.json
+
+    # Re-run later to pick up newly added titles without re-embedding
+    # actors already in the gallery:
+    python scripts/make_jellyfin_gallery.py \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --output gallery.json --merge
+
+    # Fall back to TMDB profile images for actors with no usable Jellyfin image:
+    python scripts/make_jellyfin_gallery.py \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --tmdb-key YOUR_TMDB_KEY \\
+        --output gallery.json
+
+Get a Jellyfin API key from Dashboard → Advanced → API Keys.
+Get a free TMDB API key at: https://www.themoviedb.org/settings/api
+"""
+
+import argparse
+import concurrent.futures
+import io
+import json
+import sys
+from pathlib import Path
+
+import requests
+from PIL import Image
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from sae_embed_loader import load_embedder
+
+TMDB_BASE = "https://api.themoviedb.org/3"
+TMDB_IMG = "https://image.tmdb.org/t/p/original"
+
+
+# ── Jellyfin API helpers ────────────────────────────────────────────────────
+
+def jf_get(base_url: str, api_key: str, path: str, **params) -> dict:
+    url = base_url.rstrip("/") + path
+    headers = {"X-Emby-Token": api_key, "Accept": "application/json"}
+    r = requests.get(url, params=params, headers=headers, timeout=30)
+    r.raise_for_status()
+    return r.json()
+
+
+def fetch_library_items(base_url: str, api_key: str, item_types: list[str]):
+    """Yield every Movie/Series item dict (with its People field)."""
+    start = 0
+    limit = 100
+    while True:
+        data = jf_get(
+            base_url, api_key, "/Items",
+            Recursive="true",
+            IncludeItemTypes=",".join(item_types),
+            Fields="People",
+            StartIndex=start,
+            Limit=limit,
+        )
+        items = data.get("Items", [])
+        for item in items:
+            yield item
+        start += limit
+        if start >= data.get("TotalRecordCount", 0) or not items:
+            break
+
+
+def collect_actors(base_url: str, api_key: str, item_types: list[str]) -> dict:
+    """Return {person_id: {name, primary_image_tag, appearances: [...]}}."""
+    actors: dict[str, dict] = {}
+    n_items = 0
+    for item in fetch_library_items(base_url, api_key, item_types):
+        n_items += 1
+        for person in item.get("People", []):
+            if person.get("Type") != "Actor":
+                continue
+            pid = person["Id"]
+            entry = actors.setdefault(pid, {
+                "name": person["Name"],
+                "primary_image_tag": person.get("PrimaryImageTag"),
+                "appearances": [],
+            })
+            if not entry["primary_image_tag"]:
+                entry["primary_image_tag"] = person.get("PrimaryImageTag")
+            entry["appearances"].append({
+                "item_id": item["Id"],
+                "title": item.get("Name", ""),
+                "type": item.get("Type", ""),
+            })
+        if n_items % 50 == 0:
+            print(f"  scanned {n_items} items, {len(actors)} unique actors so far…", file=sys.stderr)
+    print(f"Scanned {n_items} items, found {len(actors)} unique actors", file=sys.stderr)
+    return actors
+
+
+def fetch_imdb_id(base_url: str, api_key: str, person_id: str) -> str | None:
+    try:
+        data = jf_get(base_url, api_key, f"/Items/{person_id}", Fields="ProviderIds")
+    except requests.RequestException:
+        return None
+    return data.get("ProviderIds", {}).get("Imdb")
+
+
+# ── TMDB fallback (for actors with no usable Jellyfin image) ────────────────
+
+def tmdb_get(path: str, key: str, **params) -> dict:
+    url = TMDB_BASE + path
+    if key.startswith("eyJ"):
+        headers = {"Authorization": f"Bearer {key}", "Accept": "application/json"}
+        r = requests.get(url, params=params, headers=headers, timeout=10)
+    else:
+        params["api_key"] = key
+        r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=10)
+    r.raise_for_status()
+    return r.json()
+
+
+def tmdb_person_images(tmdb_person_id: str, tmdb_key: str) -> list[str]:
+    images_data = tmdb_get(f"/person/{tmdb_person_id}/images", tmdb_key)
+    return [TMDB_IMG + p["file_path"] for p in images_data.get("profiles", []) if p.get("file_path")]
+
+
+def tmdb_person_for_imdb(imdb_id: str, tmdb_key: str) -> tuple[str | None, list[str]]:
+    """Return (tmdb_person_id, profile_image_urls) for the TMDB person matching this IMDB person id."""
+    data = tmdb_get(f"/find/{imdb_id}", tmdb_key, external_source="imdb_id")
+    people = data.get("person_results", [])
+    if not people:
+        return None, []
+    tmdb_person_id = str(people[0]["id"])
+    return tmdb_person_id, tmdb_person_images(tmdb_person_id, tmdb_key)
+
+
+def tmdb_person_by_name(name: str, tmdb_key: str) -> tuple[str | None, list[str]]:
+    """Return (tmdb_person_id, profile_image_urls) for the best name match on TMDB.
+
+    Used when Jellyfin has no IMDB ProviderId for this person (the common case —
+    Jellyfin rarely populates ProviderIds on Person items), so /find/{imdb_id}
+    isn't an option. /search/person is sorted by popularity; take the top hit.
+    """
+    data = tmdb_get("/search/person", tmdb_key, query=name)
+    people = data.get("results", [])
+    if not people:
+        return None, []
+    tmdb_person_id = str(people[0]["id"])
+    return tmdb_person_id, tmdb_person_images(tmdb_person_id, tmdb_key)
+
+
+def download_urls(urls: list[str], dest_dir: Path, n: int, start_index: int = 0) -> list[Path]:
+    """Download up to n images from urls into dest_dir, numbered from start_index."""
+    dest_dir.mkdir(parents=True, exist_ok=True)
+    paths = []
+    for i, url in enumerate(urls[:n]):
+        out = dest_dir / f"{start_index + i:02d}.jpg"
+        if out.exists() and out.stat().st_size > 1024:
+            paths.append(out)
+            continue
+        try:
+            r = requests.get(url, timeout=15)
+            r.raise_for_status()
+            Image.open(io.BytesIO(r.content)).convert("RGB").save(out, "JPEG")
+            paths.append(out)
+        except Exception as e:
+            print(f"  [warn] TMDB image download failed: {url}: {e}", file=sys.stderr)
+    return paths
+
+
+# ── Image download ──────────────────────────────────────────────────────────
+
+def download_person_images(base_url: str, api_key: str, person_id: str,
+                            dest_dir: Path, n: int) -> list[Path]:
+    """Download up to n images for a Jellyfin person item into dest_dir."""
+    dest_dir.mkdir(parents=True, exist_ok=True)
+    headers = {"X-Emby-Token": api_key}
+    paths = []
+    for i in range(n):
+        out = dest_dir / f"{i:02d}.jpg"
+        if out.exists() and out.stat().st_size > 1024:
+            paths.append(out)
+            continue
+        url = base_url.rstrip("/") + f"/Items/{person_id}/Images/Primary/{i}"
+        try:
+            r = requests.get(url, headers=headers, params={"api_key": api_key}, timeout=15)
+            if r.status_code == 404:
+                break
+            r.raise_for_status()
+            ctype = r.headers.get("Content-Type", "")
+            if not ctype.startswith("image/"):
+                print(f"  [warn] unexpected response for {person_id} index {i}: "
+                      f"status={r.status_code} content-type={ctype!r} "
+                      f"len={len(r.content)} body={r.content[:200]!r}", file=sys.stderr)
+                break
+            Image.open(io.BytesIO(r.content)).convert("RGB").save(out, "JPEG")
+            paths.append(out)
+        except Exception as e:
+            print(f"  [warn] image download failed for {person_id} index {i}: {e}", file=sys.stderr)
+            break
+    return paths
+
+
+# ── Per-actor pipeline ───────────────────────────────────────────────────────
+
+def fetch_actor_images(base_url: str, api_key: str, pid: str, info: dict,
+                        images_per_actor: int, actor_dir: Path,
+                        fetch_imdb_ids: bool, tmdb_key: str | None
+                        ) -> tuple[list[Path], str | None, str | None]:
+    """Network-bound: download Jellyfin image(s), then fall back to TMDB if short."""
+    name = info["name"]
+    print(f"{name} ({pid}) — in {len(info['appearances'])} title(s)", file=sys.stderr)
+    image_paths = download_person_images(base_url, api_key, pid, actor_dir, images_per_actor)
+
+    imdb_id = None
+    if fetch_imdb_ids or tmdb_key:
+        imdb_id = fetch_imdb_id(base_url, api_key, pid)
+
+    # Resolve the TMDB person id whenever possible — independent of whether
+    # Jellyfin already gave us enough images, so tmdb_id is always populated
+    # when --tmdb-key is set. Jellyfin Person items rarely have an IMDB
+    # ProviderId, so fall back to a name search when imdb_id is unknown.
+    tmdb_id = None
+    tmdb_urls: list[str] = []
+    if tmdb_key:
+        try:
+            if imdb_id:
+                tmdb_id, tmdb_urls = tmdb_person_for_imdb(imdb_id, tmdb_key)
+            if tmdb_id is None:
+                tmdb_id, tmdb_urls = tmdb_person_by_name(name, tmdb_key)
+        except requests.RequestException as e:
+            print(f"  [warn] {name}: TMDB lookup failed: {e}", file=sys.stderr)
+
+    if len(image_paths) < images_per_actor and tmdb_urls:
+        needed = images_per_actor - len(image_paths)
+        print(f"  {name}: Jellyfin image missing/incomplete, falling back to TMDB "
+              f"({len(tmdb_urls)} image(s) available)…", file=sys.stderr)
+        image_paths += download_urls(tmdb_urls, actor_dir, needed, start_index=len(image_paths))
+
+    return image_paths, imdb_id, tmdb_id
+
+
+def embed_actor(pid: str, info: dict, image_paths: list[Path],
+                 imdb_id: str | None, tmdb_id: str | None, embedder, fetch_imdb_ids: bool,
+                 embed_executor: concurrent.futures.ThreadPoolExecutor) -> dict | None:
+    """GPU-bound: run sae_embed, always on embed_executor's single dedicated thread.
+
+    onnxruntime's CUDA EP / cudnn_frontend execution plans are not safe to run
+    from arbitrary threads — calling Run() from a different OS thread than the
+    one that last used the session corrupts the cudnn graph (CUDNN_FE failure
+    11 / CUDNN_BACKEND_API_FAILED). Routing every embed() call through one
+    persistent thread avoids that regardless of how many worker threads are
+    fetching images concurrently.
+    """
+    name = info["name"]
+    if not image_paths:
+        print(f"  {name}: no image available, skipping", file=sys.stderr)
+        return None
+
+    embeddings = []
+    source_images = []
+    print(f"  {name}: embedding {len(image_paths)} image(s)…", file=sys.stderr)
+    for path in image_paths:
+        res = embed_executor.submit(embedder.embed, str(path)).result()
+        if not res.ok:
+            print(f"  [skip] {name}/{path.name}: {res.error}", file=sys.stderr)
+            continue
+        embeddings.append(res.embedding)
+        source_images.append(path.name)
+
+    if not embeddings:
+        print(f"  {name}: no valid embeddings, skipping actor", file=sys.stderr)
+        return None
+
+    print(f"  {name}: → {len(embeddings)} embedding(s) stored", file=sys.stderr)
+    return {
+        "imdb_id": imdb_id if (fetch_imdb_ids and imdb_id) else "",
+        "tmdb_id": tmdb_id or "",
+        "jellyfin_id": pid,
+        "name": name,
+        "source_images": source_images,
+        "embeddings": embeddings,
+        "appearances": info["appearances"],
+    }
+
+
+def process_actor(pid: str, info: dict, base_url: str, api_key: str,
+                   embedder, images_per_actor: int, image_root: Path,
+                   fetch_imdb_ids: bool, tmdb_key: str | None,
+                   embed_executor: concurrent.futures.ThreadPoolExecutor) -> dict | None:
+    safe_name = info["name"].replace(" ", "_")
+    actor_dir = image_root / f"{pid}_{safe_name}"
+    image_paths, imdb_id, tmdb_id = fetch_actor_images(
+        base_url, api_key, pid, info, images_per_actor, actor_dir, fetch_imdb_ids, tmdb_key)
+    return embed_actor(pid, info, image_paths, imdb_id, tmdb_id, embedder, fetch_imdb_ids, embed_executor)
+
+
+# ── Gallery assembly ─────────────────────────────────────────────────────────
+
+def build_gallery(base_url: str, api_key: str, embedder, item_types: list[str],
+                   images_per_actor: int, image_root: Path,
+                   fetch_imdb_ids: bool, existing_actors: dict,
+                   tmdb_key: str | None = None, workers: int = 8) -> dict:
+    actors = collect_actors(base_url, api_key, item_types)
+
+    gallery_actors = []
+    todo = []
+    for pid, info in actors.items():
+        if pid in existing_actors:
+            gallery_actors.append(existing_actors[pid])
+        else:
+            todo.append((pid, info))
+
+    if len(gallery_actors):
+        print(f"Skipping {len(gallery_actors)} actor(s) already present in existing gallery", file=sys.stderr)
+    print(f"Processing {len(todo)} new actor(s) with {workers} worker(s)…", file=sys.stderr)
+
+    n_done = 0
+    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as executor, \
+         concurrent.futures.ThreadPoolExecutor(max_workers=1, thread_name_prefix="embed") as embed_executor:
+        futures = {
+            executor.submit(process_actor, pid, info, base_url, api_key, embedder,
+                             images_per_actor, image_root,
+                             fetch_imdb_ids, tmdb_key, embed_executor): info["name"]
+            for pid, info in todo
+        }
+        for future in concurrent.futures.as_completed(futures):
+            n_done += 1
+            name = futures[future]
+            try:
+                actor = future.result()
+            except Exception as e:
+                print(f"  [error] {name}: {e}", file=sys.stderr)
+                continue
+            if actor:
+                gallery_actors.append(actor)
+            if n_done % 25 == 0:
+                print(f"  progress: {n_done}/{len(todo)} actors processed", file=sys.stderr)
+
+    return {"actors": gallery_actors}
+
+
+# ── Entry point ───────────────────────────────────────────────────────────────
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Build a gallery.json spanning an entire Jellyfin library",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--jellyfin-url", required=True,
+                        help="Jellyfin base server URL only, e.g. http://jellyfin.local:8096 "
+                             "(no /Items or other API path)")
+    parser.add_argument("--api-key", required=True,
+                        help="Jellyfin API key (Dashboard → Advanced → API Keys)")
+    parser.add_argument("--output", required=True, help="Output gallery.json path")
+    parser.add_argument("--item-types", default="Movie,Series",
+                        help="Comma-separated Jellyfin item types to scan (default: Movie,Series)")
+    parser.add_argument("--build-dir", default="build",
+                        help="Build directory containing the sae_embed module (default: build)")
+    parser.add_argument("--models-dir", default="models",
+                        help="Directory containing ONNX models (default: models/)")
+    parser.add_argument("--arcface", default=None,
+                        help="Path to ArcFace ONNX model (overrides --models-dir selection)")
+    parser.add_argument("--images-per-actor", type=int, default=1,
+                        help="Images to download per actor (default: 1 — Jellyfin usually caches one)")
+    parser.add_argument("--image-dir", default=None,
+                        help="Where to store downloaded images (default: <output_dir>/images)")
+    parser.add_argument("--fetch-imdb-ids", action="store_true",
+                        help="Resolve each actor's real IMDB id via Jellyfin ProviderIds "
+                             "(one extra API call per new actor; otherwise imdb_id is left empty)")
+    parser.add_argument("--tmdb-key", default=None,
+                        help="TMDB API key/bearer token. If set, actors with no usable "
+                             "Jellyfin image fall back to TMDB profile images (looked up "
+                             "via the actor's IMDB id, requires one extra Jellyfin call per actor)")
+    parser.add_argument("--merge", action="store_true",
+                        help="If --output already exists, keep its actors and only embed "
+                             "actors not already present (matched by jellyfin_id)")
+    parser.add_argument("--workers", type=int, default=8,
+                        help="Concurrent worker threads for Jellyfin/TMDB lookups and "
+                             "image downloads (default: 8). Embedding itself always runs "
+                             "on a single dedicated thread regardless of this value.")
+    args = parser.parse_args()
+
+    # Defend against a base URL that accidentally includes an API path,
+    # e.g. "https://host/Items?" — strip any query string and trailing
+    # /Items so we don't build double-nested, query-mangled URLs.
+    jellyfin_url = args.jellyfin_url.split("?", 1)[0].rstrip("/")
+    if jellyfin_url.endswith("/Items"):
+        jellyfin_url = jellyfin_url[: -len("/Items")]
+
+    output = Path(args.output)
+    image_root = Path(args.image_dir) if args.image_dir else output.parent / "images"
+    item_types = [t.strip() for t in args.item_types.split(",") if t.strip()]
+
+    embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
+
+    existing_actors = {}
+    if args.merge and output.is_file():
+        existing = json.loads(output.read_text())
+        for actor in existing.get("actors", []):
+            # older galleries used "jellyfin_person_id"
+            pid = actor.get("jellyfin_id") or actor.get("jellyfin_person_id")
+            if pid:
+                existing_actors[pid] = actor
+        print(f"Loaded {len(existing_actors)} actor(s) from existing gallery for merge", file=sys.stderr)
+
+    gallery = build_gallery(
+        base_url=jellyfin_url,
+        api_key=args.api_key,
+        embedder=embedder,
+        item_types=item_types,
+        images_per_actor=args.images_per_actor,
+        image_root=image_root,
+        fetch_imdb_ids=args.fetch_imdb_ids,
+        existing_actors=existing_actors,
+        tmdb_key=args.tmdb_key,
+        workers=args.workers,
+    )
+
+    n_actors = len(gallery["actors"])
+    n_embeddings = sum(len(a["embeddings"]) for a in gallery["actors"])
+    print(f"\nGallery: {n_actors} actors, {n_embeddings} total embeddings", file=sys.stderr)
+
+    if n_actors == 0:
+        sys.exit("No actors could be processed — check Jellyfin URL/API key and models.")
+
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(json.dumps(gallery, indent=2) + "\n")
+    print(f"Saved: {output}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

scripts/movienet_eval.py

@@ -8,8 +8,7 @@ Usage:
     --arcface models/arcface_w600k_r50.onnx \
     --gt eval/gt.json \
     --output eval/predictions_r50.json \
-    [--yunet models/face_detection_yunet_2023mar.onnx] \
+    [--build-dir build]
-    [--embed-bin build/embed_faces]
 
 Input  (--gt): list of {"crop": <path>, "imdb_id": <str>, "actor_name": <str>}
 Output: list of {"crop", "gt", "pred", "similarity", "detection_failed", "all_scores"}
@@ -17,11 +16,12 @@ Output: list of {"crop", "gt", "pred", "similarity", "detection_failed", "all_sc
 
 import argparse
 import json
-import math
-import subprocess
 import sys
 from pathlib import Path
 
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from sae_embed_loader import load_embedder
+
 
 def load_gallery(path: str) -> dict[str, dict]:
     """Return {imdb_id: {"name": str, "embeddings": [[float]]}}."""
@@ -35,22 +35,6 @@ def dot(a: list[float], b: list[float]) -> float:
     return sum(x * y for x, y in zip(a, b))
 
 
-def embed_images(paths: list[Path], embed_bin: str, yunet: str, arcface: str) -> list[dict | None]:
-    if not paths:
-        return []
-    cmd = [embed_bin, "--yunet", yunet, "--arcface", arcface] + [str(p) for p in paths]
-    try:
-        proc = subprocess.run(cmd, capture_output=True, text=True, check=True)
-    except subprocess.CalledProcessError as e:
-        print(f"[error] embed_faces failed:\n{e.stderr}", file=sys.stderr)
-        return [None] * len(paths)
-    try:
-        return json.loads(proc.stdout)
-    except json.JSONDecodeError as e:
-        print(f"[error] embed_faces JSON parse error: {e}", file=sys.stderr)
-        return [None] * len(paths)
-
-
 def match(embedding: list[float], gallery: dict[str, dict]) -> tuple[str, float, dict[str, float]]:
     """Return (best_imdb_id, best_similarity, {imdb_id: similarity})."""
     scores: dict[str, float] = {}
@@ -67,10 +51,14 @@ def main():
     p.add_argument("--arcface",   required=True)
     p.add_argument("--gt",        required=True)
     p.add_argument("--output",    required=True)
-    p.add_argument("--yunet",     default="models/face_detection_yunet_2023mar.onnx")
+    p.add_argument("--build-dir", default="build",
-    p.add_argument("--embed-bin", default="build/embed_faces")
+                   help="Build directory containing the sae_embed module (default: build)")
+    p.add_argument("--models-dir", default="models",
+                   help="Directory containing ONNX models (default: models/)")
     args = p.parse_args()
 
+    embedder = load_embedder(args.build_dir, args.models_dir, args.arcface)
+
     gallery = load_gallery(args.gallery)
     print(f"[eval] gallery: {len(gallery)} actors", file=sys.stderr)
 
@@ -78,29 +66,19 @@ def main():
         gt_entries = json.load(f)
     print(f"[eval] probe crops: {len(gt_entries)}", file=sys.stderr)
 
-    # Batch all crops in one embed_faces call to amortise startup cost
     crop_paths = [Path(e["crop"]) for e in gt_entries]
     missing = [p for p in crop_paths if not p.exists()]
     if missing:
         print(f"[warn] {len(missing)} crop(s) not found on disk, skipping", file=sys.stderr)
 
-    results_raw = embed_images(
+    embed_results = [embedder.embed(str(p)) if p.exists() else None for p in crop_paths]
-        [p for p in crop_paths if p.exists()],
-        args.embed_bin, args.yunet, args.arcface
-    )
-
-    # Re-index results back to original list (missing files get None)
-    raw_iter = iter(results_raw)
-    embed_results: list[dict | None] = []
-    for p in crop_paths:
-        embed_results.append(next(raw_iter) if p.exists() else None)
 
     predictions = []
     n_det_fail = 0
     n_correct = 0
 
     for entry, result in zip(gt_entries, embed_results):
-        detection_failed = result is None or result.get("embedding") is None
+        detection_failed = result is None or not result.ok
         if detection_failed:
             n_det_fail += 1
             predictions.append({
@@ -113,7 +91,7 @@ def main():
             })
             continue
 
-        pred_id, sim, all_scores = match(result["embedding"], gallery)
+        pred_id, sim, all_scores = match(result.embedding, gallery)
         correct = pred_id == entry["imdb_id"]
         if correct:
             n_correct += 1

scripts/run_from_jellyfin.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+"""run_from_jellyfin.py — resolve a Jellyfin title to its media file and run scene_analyze.
+
+Looks up a Movie/Episode in Jellyfin, reads its on-disk Path (Jellyfin and this
+tool must share the same media mount), filters the gallery down to that
+title's credited cast (via filter_gallery's logic, fewer look-alike
+mismatches), and runs scene_analyze against the resolved file.
+
+Usage:
+    python scripts/run_from_jellyfin.py \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --title "The Matrix" \\
+        --gallery whole_gallery.json \\
+        -- --fps 5 --verbosity 2
+
+Anything after "--" is passed through unchanged to scene_analyze.
+"""
+
+import argparse
+import json
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from filter_gallery import jf_get, find_item_id, fetch_cast_person_ids
+
+
+def fetch_item_path(base_url: str, api_key: str, item_id: str) -> tuple[str, str]:
+    """Return (Name, Path) for a Jellyfin item."""
+    data = jf_get(base_url, api_key, f"/Items/{item_id}", Fields="Path")
+    path = data.get("Path")
+    if not path:
+        raise ValueError(f"Item {item_id} has no Path (not a single media file?)")
+    return data.get("Name", item_id), path
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description=__doc__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument("--jellyfin-url", required=True)
+    parser.add_argument("--api-key", required=True)
+    group = parser.add_mutually_exclusive_group(required=True)
+    group.add_argument("--item-id", help="Jellyfin item id of the title")
+    group.add_argument("--title", help="Title to search for (uses first match)")
+    parser.add_argument("--item-types", default="Movie,Episode",
+                         help="Item types to search when using --title (default: Movie,Episode)")
+    parser.add_argument("--gallery", required=True,
+                         help="Global gallery.json built by make_jellyfin_gallery.py")
+    parser.add_argument("--no-filter", action="store_true",
+                         help="Skip per-title cast filtering and pass --gallery through as-is")
+    parser.add_argument("--output", default=None,
+                         help="scene_analyze output JSON (default: <title>.json)")
+    parser.add_argument("--bin", default="build/scene_analyze",
+                         help="Path to scene_analyze binary (default: build/scene_analyze)")
+    parser.add_argument("--dry-run", action="store_true",
+                         help="Resolve and print the scene_analyze command without running it")
+    args, extra = parser.parse_known_args()
+    if extra and extra[0] == "--":
+        extra = extra[1:]
+
+    item_id = args.item_id
+    if item_id is None:
+        item_id = find_item_id(args.jellyfin_url, args.api_key, args.title, args.item_types.split(","))
+        print(f"Resolved title to item id {item_id}", file=sys.stderr)
+
+    name, movie_path = fetch_item_path(args.jellyfin_url, args.api_key, item_id)
+    print(f"Resolved {name!r} -> {movie_path}", file=sys.stderr)
+    if not Path(movie_path).is_file():
+        sys.exit(f"Resolved path does not exist on this filesystem: {movie_path}\n"
+                 f"(this tool must share Jellyfin's media mount)")
+
+    output = args.output or f"{name}.json"
+
+    gallery_path = args.gallery
+    filtered_file = None
+    if not args.no_filter:
+        cast_ids = fetch_cast_person_ids(args.jellyfin_url, args.api_key, item_id)
+        gallery = json.loads(Path(args.gallery).read_text())
+        actors = [a for a in gallery.get("actors", [])
+                  if (a.get("jellyfin_id") or a.get("jellyfin_person_id")) in cast_ids]
+        print(f"Filtered gallery to {len(actors)}/{len(gallery.get('actors', []))} "
+              f"actor(s) credited in {name!r}", file=sys.stderr)
+        filtered_file = tempfile.NamedTemporaryFile(
+            mode="w", suffix=".json", prefix="sae_gallery_", delete=False)
+        json.dump({"actors": actors}, filtered_file)
+        filtered_file.close()
+        gallery_path = filtered_file.name
+
+    cmd = [args.bin, "--movie", movie_path, "--gallery", gallery_path,
+           "--output", output, *extra]
+    print("Running:", " ".join(cmd), file=sys.stderr)
+
+    if args.dry_run:
+        return
+
+    try:
+        subprocess.run(cmd, check=True)
+    finally:
+        if filtered_file is not None:
+            Path(filtered_file.name).unlink(missing_ok=True)
+
+
+if __name__ == "__main__":
+    main()

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)

src/face_embedder_engine.hpp

@@ -0,0 +1,104 @@
+#pragma once
+// FaceEmbedderEngine — load SCRFD + ArcFace once, embed many images.
+//
+// Extracted from embed_faces.cpp so the same detect→align→embed pipeline can
+// be driven from a long-lived process (the sae_embed Python module) instead
+// of a fresh CLI invocation per image, which would reload both ONNX sessions
+// every time.
+
+#include "arcface_embedder.hpp"
+#include "face_utils.hpp"
+#include "ort_provider.hpp"
+#include "scrfd_decoder.hpp"
+#include "types.hpp"
+
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc.hpp>
+
+#include <algorithm>
+#include <array>
+#include <iostream>
+#include <memory>
+#include <string>
+
+struct FaceEmbedResult {
+    bool                       ok{false};
+    std::string                error;
+    Embedding                  embedding{};
+    float                      confidence{0.f};
+    float                      bbox[4]{};   // x, y, w, h
+    std::array<cv::Point2f, 5> landmarks{};
+};
+
+class FaceEmbedderEngine {
+public:
+    FaceEmbedderEngine(const std::string& detector_model,
+                        const std::string& arcface_model,
+                        float conf = 0.5f, float nms = 0.4f, int max_side = 500)
+        : max_side_(max_side)
+    {
+        const OrtProvider provider = detect_ort_provider();
+        std::cerr << "[FaceEmbedderEngine] inference provider: "
+                  << provider_name(provider) << "\n";
+        detector_ = std::make_unique<SCRFDDecoder>(detector_model, conf, nms, provider);
+        embedder_ = std::make_unique<ArcFaceEmbedder>(arcface_model, provider);
+    }
+
+    FaceEmbedResult embed_path(const std::string& path) const {
+        cv::Mat img = cv::imread(path);
+        if (img.empty()) {
+            FaceEmbedResult res;
+            res.error = "cannot read image";
+            return res;
+        }
+        return embed_mat(img);
+    }
+
+    FaceEmbedResult embed_mat(cv::Mat img) const {
+        FaceEmbedResult res;
+
+        if (max_side_ > 0) {
+            const int big = std::max(img.cols, img.rows);
+            if (big > max_side_) {
+                const double s = static_cast<double>(max_side_) / big;
+                cv::resize(img, img, {}, s, s, cv::INTER_AREA);
+            }
+        }
+
+        std::vector<DetectedFace> faces = detector_->detect(img);
+        if (faces.empty()) {
+            res.error = "no face detected";
+            return res;
+        }
+        if (faces.size() > 1)
+            std::cerr << "[warn] " << faces.size()
+                      << " faces detected, using highest-confidence one\n";
+
+        const auto& best = *std::max_element(
+            faces.begin(), faces.end(),
+            [](const DetectedFace& a, const DetectedFace& b) {
+                return a.confidence < b.confidence;
+            });
+
+        cv::Mat crop = align_face(img, best.landmarks);
+        if (crop.empty()) {
+            res.error = "alignment failed";
+            return res;
+        }
+
+        res.ok         = true;
+        res.embedding  = embedder_->embed_one(crop);
+        res.confidence = best.confidence;
+        res.bbox[0]    = best.bbox.x;
+        res.bbox[1]    = best.bbox.y;
+        res.bbox[2]    = best.bbox.width;
+        res.bbox[3]    = best.bbox.height;
+        res.landmarks  = best.landmarks;
+        return res;
+    }
+
+private:
+    std::unique_ptr<SCRFDDecoder>    detector_;
+    std::unique_ptr<ArcFaceEmbedder> embedder_;
+    int                               max_side_;
+};

src/gallery/gallery_calibration.hpp

@@ -1,10 +1,24 @@
 #pragma once
 #include "types.hpp"
 
+#include <algorithm>
+#include <chrono>
 #include <cmath>
+#include <cstdint>
+#include <cstdlib>
+#include <cstring>
+#include <fstream>
 #include <iostream>
+#include <stdexcept>
+#include <string>
+#include <string_view>
 #include <vector>
 
+#include <nlohmann/json.hpp>
+#include <opencv2/core/ocl.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <opencv2/imgproc.hpp>
+
 // ── GalleryCalibration ────────────────────────────────────────────────────────
 // Platt-style sigmoid calibration: P(match) = σ(a · similarity + b)
 // where similarity = cosine similarity ∈ [-1, 1] (dot product of L2-normalised
@@ -43,24 +57,147 @@ inline GalleryCalibration calibrate_gallery(
     const std::vector<Embedding>& flat_emb,
     const std::vector<int>&       flat_actor)
 {
-    const int n = static_cast<int>(flat_emb.size());
+    constexpr int   kMinEmbeddingsForPositive = 5;
+    constexpr float kDedupSimThreshold        = 1.f - 1e-7f; // sim above this = duplicate
+    constexpr int   kHistBins                 = 200;
 
-    std::vector<float> X;  // cosine similarities
+    // ── Per-actor dedup, then eligibility filter ────────────────────────────
-    std::vector<float> Y;  // labels: 1 = same actor, 0 = different
+    // Drop near-identical duplicate embeddings within each actor (e.g. the
+    // same source image embedded twice). Actors left with fewer than
+    // kMinEmbeddingsForPositive distinct embeddings can't supply meaningful
+    // same-actor (positive) pairs, but still contribute negative
+    // (cross-actor) pairs for the p(unknown) side of the fit.
+    int n_actors = 0;
+    for (int a : flat_actor) n_actors = std::max(n_actors, a + 1);
 
-    for (int i = 0; i < n; ++i) {
+    std::vector<std::vector<Embedding>> by_actor(n_actors);
-        for (int j = i + 1; j < n; ++j) {
+    for (int i = 0; i < static_cast<int>(flat_emb.size()); ++i)
-            float dot = 0.f;
+        by_actor[flat_actor[i]].push_back(flat_emb[i]);
-            for (int k = 0; k < 512; ++k)
+
-                dot += flat_emb[i][k] * flat_emb[j][k];
+    std::vector<Embedding> flat_emb_dedup;
-            X.push_back(dot);
+    std::vector<int>       flat_actor_dedup;
-            Y.push_back(flat_actor[i] == flat_actor[j] ? 1.f : 0.f);
+    std::vector<bool>      actor_eligible(n_actors, false);
+    int                    n_eligible = 0;
+
+    for (int ai = 0; ai < n_actors; ++ai) {
+        std::vector<Embedding> kept;
+        for (const auto& e : by_actor[ai]) {
+            bool dup = false;
+            for (const auto& k : kept) {
+                if (cosine_similarity(e, k) > kDedupSimThreshold) { dup = true; break; }
+            }
+            if (!dup) kept.push_back(e);
+        }
+        if (static_cast<int>(kept.size()) >= kMinEmbeddingsForPositive) {
+            actor_eligible[ai] = true;
+            ++n_eligible;
+        }
+        for (auto& e : kept) {
+            flat_emb_dedup.push_back(e);
+            flat_actor_dedup.push_back(ai);
         }
     }
 
-    int n_pos = 0;
+    const int n = static_cast<int>(flat_emb_dedup.size());
-    for (float y : Y) if (y > 0.5f) ++n_pos;
+    std::cerr << "[calibration] dedup: " << flat_emb.size() << " -> " << n
-    int n_neg = static_cast<int>(Y.size()) - n_pos;
+              << " embeddings  (" << n_eligible << "/" << n_actors
+              << " actors have >= " << kMinEmbeddingsForPositive
+              << " distinct embeddings, eligible for positive pairs)\n";
+
+    // Pairwise cosine-similarity matrix S = E * E^T, computed via cv::gemm
+    // (BLAS/SIMD on CPU) instead of a naive O(n^2 * 512) scalar loop.
+    //
+    // SAE_CALIB_GEMM controls the backend:
+    //   "cpu"   - cv::Mat gemm only (default if OpenCL unavailable)
+    //   "gpu"   - cv::UMat gemm via OpenCL, falls back to CPU if unavailable
+    //   "bench" - run both and report timings + max abs diff (default)
+    cv::Mat E(n, 512, CV_32F);
+    for (int i = 0; i < n; ++i)
+        std::memcpy(E.ptr<float>(i), flat_emb_dedup[i].data(), 512 * sizeof(float));
+
+    std::string mode = [] {
+        const char* env = std::getenv("SAE_CALIB_GEMM");
+        return env ? std::string(env) : std::string("bench");
+    }();
+
+    bool have_ocl = cv::ocl::haveOpenCL();
+    std::cerr << "[calibration] OpenCL available: " << (have_ocl ? "yes" : "no");
+    if (have_ocl) {
+        auto dev = cv::ocl::Device::getDefault();
+        std::cerr << "  device=\"" << dev.name() << "\""
+                  << "  type=" << (dev.type() == cv::ocl::Device::TYPE_GPU ? "GPU"
+                                  : dev.type() == cv::ocl::Device::TYPE_CPU ? "CPU"
+                                  : "OTHER");
+    }
+    std::cerr << "  (SAE_CALIB_GEMM=" << mode << ")\n";
+
+    std::cerr << "[calibration] similarity matrix: " << n << "x512 ("
+              << (n * 512LL * sizeof(float)) / (1024 * 1024) << " MB input, "
+              << (n * (long long)n * sizeof(float)) / (1024 * 1024)
+              << " MB output)\n" << std::flush;
+
+    cv::Mat S;
+    bool have_cpu = false, have_gpu = false;
+    cv::Mat S_cpu, S_gpu_host;
+
+    if (mode == "cpu" || mode == "bench") {
+        std::cerr << "[calibration] running cv::gemm on CPU...\n" << std::flush;
+        auto t0 = std::chrono::steady_clock::now();
+        cv::gemm(E, E, 1.0, cv::noArray(), 0.0, S_cpu, cv::GEMM_2_T);
+        auto t1 = std::chrono::steady_clock::now();
+        double secs = std::chrono::duration<double>(t1 - t0).count();
+        std::cerr << "[calibration] cv::gemm CPU: " << secs << "s for "
+                  << n << "x" << n << " similarity matrix\n";
+        have_cpu = true;
+    }
+
+    if ((mode == "gpu" || mode == "bench") && have_ocl) {
+        std::cerr << "[calibration] running cv::gemm on OpenCL device...\n" << std::flush;
+        cv::UMat E_gpu, S_gpu;
+        E.copyTo(E_gpu);
+        auto t0 = std::chrono::steady_clock::now();
+        cv::gemm(E_gpu, E_gpu, 1.0, cv::noArray(), 0.0, S_gpu, cv::GEMM_2_T);
+        S_gpu_host = S_gpu.getMat(cv::ACCESS_READ).clone();
+        auto t1 = std::chrono::steady_clock::now();
+        double secs = std::chrono::duration<double>(t1 - t0).count();
+        std::cerr << "[calibration] cv::gemm OpenCL: " << secs << "s for "
+                  << n << "x" << n << " similarity matrix";
+        if (have_cpu) std::cerr << "  max|diff|=" << cv::norm(S_cpu, S_gpu_host, cv::NORM_INF);
+        std::cerr << "\n";
+        have_gpu = true;
+    } else if (mode == "gpu") {
+        std::cerr << "[calibration] SAE_CALIB_GEMM=gpu requested but OpenCL is unavailable\n";
+    }
+
+    if (mode == "gpu" && have_gpu)      S = S_gpu_host;
+    else if (have_cpu)                  S = S_cpu;
+    else if (have_gpu)                  S = S_gpu_host;
+    else                                 throw std::runtime_error("calibrate_gallery: no gemm backend produced a result");
+
+    // ── Histogram of pairwise similarities ──────────────────────────────────
+    // Instead of storing one (similarity, label) sample per pair (~n^2/2
+    // entries — too many for the gradient descent below), bucket pairs into
+    // kHistBins bins over sim ∈ [-1, 1] and fit the sigmoid against the
+    // per-bin (positive_count, negative_count).
+    std::vector<double> pos_hist(kHistBins, 0.0), neg_hist(kHistBins, 0.0);
+    constexpr float bin_width = 2.f / kHistBins;
+
+    for (int i = 0; i < n; ++i) {
+        const float* row = S.ptr<float>(i);
+        for (int j = i + 1; j < n; ++j) {
+            int bin = static_cast<int>((row[j] + 1.f) / bin_width);
+            bin = std::clamp(bin, 0, kHistBins - 1);
+            if (flat_actor_dedup[i] == flat_actor_dedup[j]) {
+                if (actor_eligible[flat_actor_dedup[i]]) pos_hist[bin] += 1.0;
+                // same actor but ineligible (< kMinEmbeddingsForPositive) — skip
+            } else {
+                neg_hist[bin] += 1.0;
+            }
+        }
+    }
+
+    double n_pos = 0.0, n_neg = 0.0;
+    for (int b = 0; b < kHistBins; ++b) { n_pos += pos_hist[b]; n_neg += neg_hist[b]; }
 
     if (n_pos < 2 || n_neg < 1) {
         std::cerr << "[calibration] insufficient pairs (+" << n_pos
@@ -69,50 +206,192 @@ inline GalleryCalibration calibrate_gallery(
     }
 
     // Class weights to handle pos/neg imbalance
-    float total = static_cast<float>(Y.size());
+    double total = n_pos + n_neg;
-    float w_pos = total / (2.f * n_pos);
+    double w_pos = total / (2.0 * n_pos);
-    float w_neg = total / (2.f * n_neg);
+    double w_neg = total / (2.0 * n_neg);
 
-    // Gradient descent logistic regression (2 parameters: a, b)
+    std::vector<float> bin_center(kHistBins);
-    float a = 10.f, b = -5.f;
+    for (int b = 0; b < kHistBins; ++b) bin_center[b] = -1.f + (b + 0.5f) * bin_width;
+
+    // Gradient descent logistic regression (2 parameters: a, bias)
+    float a = 10.f, bias = -5.f;
     constexpr float lr      = 0.05f;
     constexpr int   max_iter = 20000;
     constexpr float tol     = 1e-7f;
 
     for (int iter = 0; iter < max_iter; ++iter) {
-        float da = 0.f, db = 0.f;
+        double da = 0.0, db = 0.0;
-        for (int i = 0; i < static_cast<int>(X.size()); ++i) {
+        for (int b = 0; b < kHistBins; ++b) {
-            float z   = a * X[i] + b;
+            float x   = bin_center[b];
+            float z   = a * x + bias;
             float sig = (z >= 0.f) ? 1.f / (1.f + std::exp(-z))
                                    : std::exp(z) / (1.f + std::exp(z));
-            float err = sig - Y[i];
+            if (pos_hist[b] > 0.0) {
-            float w   = (Y[i] > 0.5f) ? w_pos : w_neg;
+                double err = (sig - 1.f) * w_pos * pos_hist[b];
-            da += w * err * X[i];
+                da += err * x;
-            db += w * err;
+                db += err;
+            }
+            if (neg_hist[b] > 0.0) {
+                double err = sig * w_neg * neg_hist[b];
+                da += err * x;
+                db += err;
+            }
         }
         da /= total;
         db /= total;
-        a -= lr * da;
+        a    -= lr * static_cast<float>(da);
-        b -= lr * db;
+        bias -= lr * static_cast<float>(db);
         if (da * da + db * db < tol * tol) break;
     }
 
     // Training accuracy at P=0.5 decision boundary
-    int correct = 0;
+    double correct = 0.0;
-    for (int i = 0; i < static_cast<int>(X.size()); ++i) {
+    for (int b = 0; b < kHistBins; ++b) {
-        float z   = a * X[i] + b;
+        float z   = a * bin_center[b] + bias;
         float sig = (z >= 0.f) ? 1.f / (1.f + std::exp(-z))
                                : std::exp(z) / (1.f + std::exp(z));
-        if ((sig > 0.5f) == (Y[i] > 0.5f)) ++correct;
+        correct += (sig > 0.5f) ? pos_hist[b] : neg_hist[b];
     }
-    float acc = 100.f * correct / static_cast<float>(Y.size());
+    double acc = 100.0 * correct / total;
 
-    GalleryCalibration cal{a, b, true};
+    GalleryCalibration cal{a, bias, true};
     std::cerr << "[calibration] sigmoid fitted:"
-              << "  a=" << a << "  b=" << b
+              << "  a=" << a << "  b=" << bias
               << "  boundary(P=0.5)=sim" << cal.boundary_at(0.5f)
-              << "  pairs=" << Y.size()
+              << "  pairs=" << static_cast<long long>(total)
-              << " (+" << n_pos << "/-" << n_neg << ")"
+              << " (+" << static_cast<long long>(n_pos) << "/-" << static_cast<long long>(n_neg) << ")"
+              << "  bins=" << kHistBins
               << "  train_acc=" << acc << "%\n";
     return cal;
 }
+
+// Dumps the fitted P(match | similarity) sigmoid as both a CSV table
+// (similarity, p_match) and a PNG plot, to <base_path>.csv / <base_path>.png.
+inline void save_calibration_curve(const GalleryCalibration& cal, const std::string& base_path) {
+    if (!cal.valid) return;
+
+    constexpr int kSamples = 200;
+
+    std::ofstream csv(base_path + ".csv");
+    if (csv.is_open()) {
+        csv << "similarity,p_match\n";
+        for (int i = 0; i <= kSamples; ++i) {
+            float sim = -1.f + i * (2.f / kSamples);
+            csv << sim << "," << cal.probability(sim) << "\n";
+        }
+    }
+
+    constexpr int W = 800, H = 600, M = 50;
+    cv::Mat img(H, W, CV_8UC3, cv::Scalar(255, 255, 255));
+    cv::line(img, {M, H - M}, {W - M, H - M}, {0, 0, 0}, 1);  // x-axis: similarity [-1,1]
+    cv::line(img, {M, M}, {M, H - M}, {0, 0, 0}, 1);          // y-axis: P(match) [0,1]
+
+    auto to_point = [&](float sim, float p) {
+        int x = M + static_cast<int>((sim + 1.f) * 0.5f * (W - 2 * M));
+        int y = (H - M) - static_cast<int>(p * (H - 2 * M));
+        return cv::Point(x, y);
+    };
+
+    // P=0.5 reference line and decision boundary
+    float boundary = cal.boundary_at(0.5f);
+    cv::line(img, to_point(-1.f, 0.5f), to_point(1.f, 0.5f), {200, 200, 200}, 1);
+    if (boundary >= -1.f && boundary <= 1.f)
+        cv::line(img, to_point(boundary, 0.f), to_point(boundary, 1.f), {200, 200, 200}, 1);
+
+    cv::Point prev = to_point(-1.f, cal.probability(-1.f));
+    for (int i = 1; i <= kSamples; ++i) {
+        float sim = -1.f + i * (2.f / kSamples);
+        cv::Point pt = to_point(sim, cal.probability(sim));
+        cv::line(img, prev, pt, {255, 0, 0}, 2);
+        prev = pt;
+    }
+
+    cv::imwrite(base_path + ".png", img);
+    std::cerr << "[calibration] saved curve to " << base_path << ".csv / " << base_path << ".png\n";
+}
+
+// FNV-1a 64-bit hash over the gallery's reference embeddings and actor
+// assignments, used to detect whether a cached calibration is still valid.
+inline uint64_t hash_gallery_embeddings(
+    const std::vector<Embedding>& flat_emb,
+    const std::vector<int>&       flat_actor)
+{
+    uint64_t h = 1469598103934665603ULL;
+    constexpr uint64_t prime = 1099511628211ULL;
+    auto mix = [&](const void* data, size_t n) {
+        const auto* p = static_cast<const unsigned char*>(data);
+        for (size_t i = 0; i < n; ++i) {
+            h ^= p[i];
+            h *= prime;
+        }
+    };
+
+    uint64_t n = flat_emb.size();
+    mix(&n, sizeof(n));
+    for (const auto& emb : flat_emb)
+        mix(emb.data(), emb.size() * sizeof(float));
+    for (int a : flat_actor)
+        mix(&a, sizeof(a));
+    return h;
+}
+
+// Calibrates the gallery, caching the fitted (a, b, valid) result on disk
+// keyed by a hash of the reference embeddings. The O(n^2) pairwise fit only
+// re-runs when the gallery's embeddings/actor assignments actually change.
+inline GalleryCalibration calibrate_gallery_cached(
+    const std::vector<Embedding>& flat_emb,
+    const std::vector<int>&       flat_actor,
+    const std::string&            cache_path)
+{
+    uint64_t hash = hash_gallery_embeddings(flat_emb, flat_actor);
+
+    std::string base_path = cache_path;
+    constexpr std::string_view kJsonExt = ".json";
+    if (base_path.size() >= kJsonExt.size() &&
+        base_path.compare(base_path.size() - kJsonExt.size(), kJsonExt.size(), kJsonExt) == 0)
+        base_path.resize(base_path.size() - kJsonExt.size());
+
+    std::ifstream in(cache_path);
+    if (in.is_open()) {
+        try {
+            nlohmann::json j;
+            in >> j;
+            if (j.at("hash").get<uint64_t>() == hash) {
+                GalleryCalibration cal;
+                cal.a     = j.at("a").get<float>();
+                cal.b     = j.at("b").get<float>();
+                cal.valid = j.at("valid").get<bool>();
+                std::cerr << "[calibration] using cached calibration from "
+                          << cache_path << " (a=" << cal.a << " b=" << cal.b
+                          << " valid=" << cal.valid << ")\n";
+                save_calibration_curve(cal, base_path);
+                return cal;
+            }
+            std::cerr << "[calibration] cache at " << cache_path
+                      << " is stale, recomputing\n";
+        } catch (const std::exception&) {
+            std::cerr << "[calibration] cache at " << cache_path
+                      << " is unreadable, recomputing\n";
+        }
+    }
+
+    auto t0 = std::chrono::steady_clock::now();
+    GalleryCalibration cal = calibrate_gallery(flat_emb, flat_actor);
+    auto t1 = std::chrono::steady_clock::now();
+    double secs = std::chrono::duration<double>(t1 - t0).count();
+    std::cerr << "[calibration] fit took " << secs << "s for "
+              << flat_emb.size() << " embeddings\n";
+
+    nlohmann::json j;
+    j["hash"]      = hash;
+    j["a"]         = cal.a;
+    j["b"]         = cal.b;
+    j["valid"]     = cal.valid;
+    j["fit_secs"]  = secs;
+    std::ofstream out(cache_path);
+    if (out.is_open()) out << j.dump(2) << "\n";
+
+    save_calibration_curve(cal, base_path);
+
+    return cal;
+}

src/gallery/gallery_store.cpp

@@ -1,7 +1,9 @@
 #include "gallery_store.hpp"
 
 #include <nlohmann/json.hpp>
+#include <chrono>
 #include <fstream>
+#include <iostream>
 #include <stdexcept>
 
 using json = nlohmann::json;
@@ -11,14 +13,24 @@ ActorGallery load_gallery(const std::string& path) {
     if (!f.is_open())
         throw std::runtime_error("load_gallery: cannot open " + path);
 
+    std::cerr << "[gallery] loading " << path << "..." << std::flush;
+    auto t0 = std::chrono::steady_clock::now();
+
     json j;
     f >> j;
 
+    auto t1 = std::chrono::steady_clock::now();
+    std::cerr << " parsed JSON in "
+              << std::chrono::duration<double>(t1 - t0).count() << "s\n";
+
     ActorGallery gallery;
     for (const auto& ja : j.at("actors")) {
         ActorGallery::Actor actor;
-        actor.imdb_id = ja.at("imdb_id").get<std::string>();
+        actor.imdb_id     = ja.value("imdb_id", "");
-        actor.name    = ja.at("name").get<std::string>();
+        actor.tmdb_id     = ja.value("tmdb_id", "");
+        // older make_jellyfin_gallery.py galleries used "jellyfin_person_id"
+        actor.jellyfin_id = ja.value("jellyfin_id", ja.value("jellyfin_person_id", ""));
+        actor.name        = ja.at("name").get<std::string>();
 
         if (ja.contains("source_images"))
             actor.source_images = ja.at("source_images").get<std::vector<std::string>>();
@@ -30,6 +42,14 @@ ActorGallery load_gallery(const std::string& path) {
 
         gallery.actors.push_back(std::move(actor));
     }
+
+    size_t n_emb = 0;
+    for (const auto& actor : gallery.actors) n_emb += actor.embeddings.size();
+    auto t2 = std::chrono::steady_clock::now();
+    std::cerr << "[gallery] built " << gallery.actors.size() << " actors / "
+              << n_emb << " embeddings in "
+              << std::chrono::duration<double>(t2 - t1).count() << "s\n";
+
     return gallery;
 }
 
@@ -40,6 +60,8 @@ void save_gallery(const std::string& path, const ActorGallery& gallery) {
     for (const auto& actor : gallery.actors) {
         json ja;
         ja["imdb_id"]      = actor.imdb_id;
+        ja["tmdb_id"]      = actor.tmdb_id;
+        ja["jellyfin_id"]  = actor.jellyfin_id;
         ja["name"]         = actor.name;
         ja["source_images"] = actor.source_images;
 

src/gallery/gallery_store.hpp

@@ -8,7 +8,9 @@
 // {
 //   "actors": [
 //     {
-//       "imdb_id":      "nm0000093",
+//       "imdb_id":      "nm0000093",         // optional, "" if unknown
+//       "tmdb_id":      "287",                // optional, "" if unknown
+//       "jellyfin_id":  "abc123-guid",        // optional, "" unless from make_jellyfin_gallery.py
 //       "name":         "Brad Pitt",
 //       "source_images": ["img1.jpg", "img2.jpg"],
 //       "embeddings":   [[0.012, -0.034, ...], ...]   // one 512-float array per image

src/nodes/identity_matcher_node.hpp

@@ -37,6 +37,7 @@ struct IdentityMatcherFunc {
         , ratio_(cfg.match_ratio)
         , ratio_ceil_(cfg.match_ratio_ceil)
     {
+        std::cerr << "[identity_matcher] flattening gallery embeddings...\n";
         for (int ai = 0; ai < static_cast<int>(gallery_.actors.size()); ++ai) {
             for (const auto& emb : gallery_.actors[ai].embeddings) {
                 flat_emb_.push_back(emb);
@@ -44,7 +45,10 @@ struct IdentityMatcherFunc {
             }
         }
 
-        cal_ = calibrate_gallery(flat_emb_, flat_actor_);
+        std::cerr << "[identity_matcher] starting calibration ("
+                  << flat_emb_.size() << " embeddings)...\n";
+        cal_ = calibrate_gallery_cached(flat_emb_, flat_actor_,
+                                         cfg.gallery_path + ".calib_cache.json");
 
         if (cal_.valid) {
             std::cerr << "[identity_matcher] calibrated Bayesian matching"
@@ -126,9 +130,11 @@ struct IdentityMatcherFunc {
             ia.track_id = tf.track_ids[fi];
 
             if (accept) {
-                ia.actor_idx  = best_actor;
+                ia.actor_idx   = best_actor;
-                ia.name       = gallery_.actors[best_actor].name;
+                ia.name        = gallery_.actors[best_actor].name;
-                ia.imdb_id    = gallery_.actors[best_actor].imdb_id;
+                ia.imdb_id     = gallery_.actors[best_actor].imdb_id;
+                ia.tmdb_id     = gallery_.actors[best_actor].tmdb_id;
+                ia.jellyfin_id = gallery_.actors[best_actor].jellyfin_id;
                 ia.similarity = cal_.valid
                                 ? cal_.probability(best_s, log_prior_odds_)
                                 : best_s;

src/nodes/result_sink_node.hpp

@@ -18,7 +18,16 @@ using json = nlohmann::json;
 // KPN sink node: accumulates SceneAnnotations and writes the final JSON on EOF.
 //
 // Verbosity::minimal  — merges per-frame presence into contiguous time windows.
-//   Output: { "movie": "...", "actors": [{ "name", "imdb_id", "scenes": [[t0,t1], ...] }] }
+//   Output: {
+//     "schema_version": 1, "movie": "...", "sample_fps": ..., "anneal_sec": ...,
+//     "actors": [{ "name", "imdb_id", "tmdb_id", "jellyfin_id", "scenes": [[t0,t1], ...] }]
+//   }
+//   This is the spec consumed by the Jellyfin plugin: each actor carries every
+//   identity key the gallery knows (empty string if not resolved). The plugin
+//   should prefer "jellyfin_id" (direct Person item GUID) when non-empty, and
+//   otherwise resolve "imdb_id"/"tmdb_id" against the item's People ProviderIds.
+//   To find who's on screen at timestamp t, scan each actor's "scenes" for a
+//   window where start <= t <= end.
 //
 // Verbosity::standard — per-frame detail including bboxes, similarity, unknowns.
 //   Output: { "frames": [{ "t", "identified": [...], "unknowns": [...] }] }
@@ -55,6 +64,10 @@ struct ResultSinkFunc {
     }
 
 private:
+    // Bump when the minimal/standard output JSON structure changes in a way
+    // the Jellyfin plugin needs to detect.
+    static constexpr int kSchemaVersion = 1;
+
     static int count_known(const std::vector<IdentifiedActor>& v) {
         int n = 0;
         for (const auto& a : v) if (a.actor_idx >= 0) ++n;
@@ -73,6 +86,7 @@ private:
         if (cfg_.verbosity == Verbosity::xray) {
             root = build_xray();
         } else {
+            root["schema_version"] = kSchemaVersion;
             root["movie"]      = cfg_.movie_path;
             root["sample_fps"] = cfg_.sample_fps;
             root["anneal_sec"] = cfg_.anneal_sec;
@@ -91,20 +105,20 @@ private:
     }
 
     struct ActorWindow {
-        std::string name, imdb_id;
+        std::string name, imdb_id, tmdb_id, jellyfin_id;
         std::vector<std::pair<double, double>> scenes;  // [start_sec, end_sec]
     };
 
     // Core logic: merge per-frame detections into annealed [start, end] windows.
     std::vector<ActorWindow> build_actor_windows() {
-        struct Info { std::string name, imdb_id; };
+        struct Info { std::string name, imdb_id, tmdb_id, jellyfin_id; };
         std::map<int, Info>                actor_info;
         std::map<int, std::vector<double>> timestamps;
 
         for (const auto& frame : frames_) {
             for (const auto& ia : frame.visible_actors) {
                 if (ia.actor_idx < 0) continue;
-                actor_info[ia.actor_idx] = {ia.name, ia.imdb_id};
+                actor_info[ia.actor_idx] = {ia.name, ia.imdb_id, ia.tmdb_id, ia.jellyfin_id};
                 timestamps[ia.actor_idx].push_back(frame.timestamp_sec);
             }
         }
@@ -112,8 +126,10 @@ private:
         std::vector<ActorWindow> result;
         for (auto& [idx, ts_vec] : timestamps) {
             ActorWindow aw;
-            aw.name    = actor_info[idx].name;
+            aw.name        = actor_info[idx].name;
-            aw.imdb_id = actor_info[idx].imdb_id;
+            aw.imdb_id     = actor_info[idx].imdb_id;
+            aw.tmdb_id     = actor_info[idx].tmdb_id;
+            aw.jellyfin_id = actor_info[idx].jellyfin_id;
 
             double win_start = ts_vec[0], win_end = ts_vec[0];
             for (size_t i = 1; i < ts_vec.size(); ++i) {
@@ -136,9 +152,11 @@ private:
             for (const auto& [s, e] : aw.scenes)
                 windows.push_back({s, e});
             json ja;
-            ja["name"]    = aw.name;
+            ja["name"]        = aw.name;
-            ja["imdb_id"] = aw.imdb_id;
+            ja["imdb_id"]     = aw.imdb_id;
-            ja["scenes"]  = std::move(windows);
+            ja["tmdb_id"]     = aw.tmdb_id;
+            ja["jellyfin_id"] = aw.jellyfin_id;
+            ja["scenes"]      = std::move(windows);
             actors.push_back(std::move(ja));
         }
         return actors;
@@ -178,8 +196,10 @@ private:
 
                 if (ia.actor_idx >= 0) {
                     json ja;
-                    ja["name"]       = ia.name;
+                    ja["name"]        = ia.name;
-                    ja["imdb_id"]    = ia.imdb_id;
+                    ja["imdb_id"]     = ia.imdb_id;
+                    ja["tmdb_id"]     = ia.tmdb_id;
+                    ja["jellyfin_id"] = ia.jellyfin_id;
                     ja["similarity"] = ia.similarity;
                     ja["track_id"]   = ia.track_id;
                     ja["bbox"]       = jbox;

src/nodes/scene_tracker_node.hpp

@@ -41,6 +41,8 @@ struct SceneTrackerFunc {
             slot.last_crop   = ia.crop;
             slot.name        = ia.name;
             slot.imdb_id     = ia.imdb_id;
+            slot.tmdb_id     = ia.tmdb_id;
+            slot.jellyfin_id = ia.jellyfin_id;
             // Keep the best (highest) similarity seen in this window
             if (ia.similarity > slot.best_similarity)
                 slot.best_similarity = ia.similarity;
@@ -60,9 +62,11 @@ struct SceneTrackerFunc {
 
         for (const auto& [actor_idx, slot] : active_) {
             IdentifiedActor ia;
-            ia.actor_idx  = actor_idx;
+            ia.actor_idx   = actor_idx;
-            ia.name       = slot.name;
+            ia.name        = slot.name;
-            ia.imdb_id    = slot.imdb_id;
+            ia.imdb_id     = slot.imdb_id;
+            ia.tmdb_id     = slot.tmdb_id;
+            ia.jellyfin_id = slot.jellyfin_id;
             ia.similarity = slot.best_similarity;
             ia.bbox       = slot.last_bbox;
             ia.crop       = slot.last_crop;
@@ -85,6 +89,8 @@ private:
         cv::Mat     last_crop;
         std::string name;
         std::string imdb_id;
+        std::string tmdb_id;
+        std::string jellyfin_id;
     };
 
     double               extinction_sec_;

src/python_bindings.cpp

@@ -0,0 +1,40 @@
+// sae_embed — Python module wrapping FaceEmbedderEngine (SCRFD + ArcFace).
+//
+// Loads both ONNX sessions once per FaceEmbedder instance, then embeds many
+// images via repeated embed() calls — avoiding the per-process model-load
+// cost of the embed_faces CLI when embedding a large gallery.
+
+#include "face_embedder_engine.hpp"
+
+#include <nanobind/nanobind.h>
+#include <nanobind/stl/optional.h>
+#include <nanobind/stl/string.h>
+#include <nanobind/stl/vector.h>
+
+namespace nb = nanobind;
+using namespace nb::literals;
+
+NB_MODULE(sae_embed, m) {
+    m.doc() = "SCRFD + ArcFace face embedding, models loaded once per FaceEmbedder";
+
+    nb::class_<FaceEmbedResult>(m, "FaceResult")
+        .def_ro("ok", &FaceEmbedResult::ok)
+        .def_ro("error", &FaceEmbedResult::error)
+        .def_ro("confidence", &FaceEmbedResult::confidence)
+        .def_prop_ro("embedding", [](const FaceEmbedResult& r) -> std::optional<std::vector<float>> {
+            if (!r.ok) return std::nullopt;
+            return std::vector<float>(r.embedding.begin(), r.embedding.end());
+        })
+        .def_prop_ro("bbox", [](const FaceEmbedResult& r) {
+            return std::vector<float>{r.bbox[0], r.bbox[1], r.bbox[2], r.bbox[3]};
+        });
+
+    nb::class_<FaceEmbedderEngine>(m, "FaceEmbedder")
+        .def(nb::init<std::string, std::string, float, float, int>(),
+             "detector_model"_a, "arcface_model"_a,
+             "conf"_a = 0.5f, "nms"_a = 0.4f, "max_side"_a = 500)
+        .def("embed", &FaceEmbedderEngine::embed_path, "path"_a,
+             nb::call_guard<nb::gil_scoped_release>(),
+             "Detect the highest-confidence face in the image, align it, and "
+             "return a FaceResult with its 512-d ArcFace embedding.");
+}

src/types.hpp

@@ -92,6 +92,8 @@ struct IdentifiedActor {
     int         track_id{-1};    // face track ID from FaceTrackerFunc
     std::string name;
     std::string imdb_id;
+    std::string tmdb_id;
+    std::string jellyfin_id;     // Jellyfin Person item GUID, if gallery was built from Jellyfin
     float       similarity{0.f}; // calibrated P(match) or cosine similarity; 0 for unknowns
     cv::Rect2f  bbox;
     cv::Mat     crop;            // 112×112 aligned crop (stored as shared_ptr by KPN)
@@ -116,6 +118,8 @@ struct SceneAnnotation {
 struct ActorGallery {
     struct Actor {
         std::string              imdb_id;
+        std::string              tmdb_id;
+        std::string              jellyfin_id;  // Jellyfin Person item GUID, if known
         std::string              name;
         std::vector<Embedding>   embeddings;  // one per reference image
         std::vector<std::string> source_images;