improved jellyfin support

README.md

@@ -151,6 +151,37 @@ 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.
 
+After a successful run, the output JSON is pushed to the JRay Jellyfin
+plugin's Truth endpoint (`PUT /Plugins/JRay/Items/{itemId}/Truth`) so
+Jellyfin picks it up immediately, using `--api-key` (must be an
+**Administrator** key for the push to succeed). Pass `--no-push` to skip
+this and only write `--output` locally (e.g. for local debugging).
+
+### Worker mode
+
+Pass `--worker` instead of `--item-id`/`--title` to run this as an extraction
+worker: it polls the JRay plugin's `GET /Plugins/JRay/Tasks/Pending` endpoint
+for a random batch of items with no truth data yet, processes each one, and
+pushes the result back. The endpoint's sampling spreads work across the
+backlog without any server-side task tracking, so any number of workers can
+poll the same library concurrently.
+
+```bash
+python3 scripts/run_from_jellyfin.py \
+    --jellyfin-url http://jellyfin.local:8096 \
+    --api-key <ADMIN_API_KEY> \
+    --gallery whole_gallery.json \
+    --worker \
+    -- --fps 5
+```
+
+- `--poll-limit` — batch size requested from `Tasks/Pending` (default 10, max 100)
+- `--poll-interval` — seconds to sleep between polls when the backlog is empty (default 60)
+- `--once` — process a single batch and exit instead of looping forever
+
+A failure on one item (bad path, push rejected, etc.) is logged and the
+worker moves on to the next item rather than exiting.
+
 ## Output format
 
 **Minimal** (default) — Jellyfin-ready:

scripts/filter_gallery.py

@@ -64,8 +64,26 @@ def find_item_id(base_url: str, api_key: str, title: str, item_types: list[str])
 
 
 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")
+    # /Items/{id} (no user context) returns 400 on recent Jellyfin servers;
-    return {p["Id"] for p in data.get("People", []) if p.get("Type") == "Actor"}
+    # /Items?Ids=... works with an API key alone.
+    data = jf_get(base_url, api_key, "/Items", Ids=item_id, Fields="People", Recursive="true")
+    items = data.get("Items", [])
+    if not items:
+        raise ValueError(f"Item {item_id} not found")
+    item = items[0]
+    cast_ids = {p["Id"] for p in item.get("People", []) if p.get("Type") == "Actor"}
+
+    # Episodes generally only carry their own guest stars in "People" — the
+    # regular/recurring cast lives on the parent Series item, so pull that
+    # in too or the filtered gallery ends up missing the main cast.
+    series_id = item.get("SeriesId")
+    if series_id:
+        series_data = jf_get(base_url, api_key, "/Items", Ids=series_id, Fields="People", Recursive="true")
+        series_items = series_data.get("Items", [])
+        if series_items:
+            cast_ids |= {p["Id"] for p in series_items[0].get("People", []) if p.get("Type") == "Actor"}
+
+    return cast_ids
 
 
 def main():

scripts/make_gallery.py

@@ -24,7 +24,6 @@ Usage:
     # Additional options:
     #   --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
     #   --image-dir /tmp/gallery_imgs   where to cache downloaded images
 
@@ -46,6 +45,11 @@ from sae_embed_loader import load_embedder
 
 TMDB_BASE = "https://api.themoviedb.org/3"
 TMDB_IMG  = "https://image.tmdb.org/t/p/original"
+WIKIDATA_SPARQL = "https://query.wikidata.org/sparql"
+WIKIDATA_HEADERS = {
+    "Accept":     "application/sparql-results+json",
+    "User-Agent": "scene-actor-extraction/1.0 (https://github.com/; gallery builder)",
+}
 
 # ── TMDB helpers ──────────────────────────────────────────────────────────────
 
@@ -61,6 +65,29 @@ def tmdb_get(path: str, token: str, **params) -> dict:
     return r.json()
 
 
+# ── Wikidata helpers ──────────────────────────────────────────────────────────
+
+def wikidata_image_urls(imdb_person_id: str) -> list[str]:
+    """Return Commons image URL(s) for a person via their IMDB ID (P345 -> P18)."""
+    if not imdb_person_id:
+        return []
+    query = (
+        "SELECT ?image WHERE { "
+        f'?person wdt:P345 "{imdb_person_id}" . '
+        "?person wdt:P18 ?image . "
+        "}"
+    )
+    try:
+        r = requests.get(WIKIDATA_SPARQL, params={"query": query, "format": "json"},
+                         headers=WIKIDATA_HEADERS, timeout=15)
+        r.raise_for_status()
+        bindings = r.json().get("results", {}).get("bindings", [])
+        return [b["image"]["value"] for b in bindings if "image" in b]
+    except Exception as e:
+        print(f"  [warn] wikidata lookup failed for {imdb_person_id}: {e}", file=sys.stderr)
+        return []
+
+
 def tmdb_id_from_imdb(imdb_id: str, key: str) -> int:
     data = tmdb_get(f"/find/{imdb_id}", key, external_source="imdb_id")
     results = data.get("movie_results", [])
@@ -69,10 +96,10 @@ def tmdb_id_from_imdb(imdb_id: str, key: str) -> int:
     return results[0]["id"]
 
 
-def fetch_cast(movie_id: int, key: str, max_actors: int) -> list[dict]:
+def fetch_cast(movie_id: int, key: str) -> list[dict]:
     """Return list of {id, name, imdb_id, profile_images: [...url...]}."""
     credits = tmdb_get(f"/movie/{movie_id}/credits", key)
-    cast = credits.get("cast", [])[:max_actors]
+    cast = credits.get("cast", [])
 
     actors = []
     for member in cast:
@@ -88,8 +115,13 @@ def fetch_cast(movie_id: int, key: str, max_actors: int) -> list[dict]:
         image_urls = [TMDB_IMG + p["file_path"] for p in profiles if p.get("file_path")]
 
         if not image_urls:
-            print(f"  [warn] no images for {member['name']}, skipping", file=sys.stderr)
+            image_urls = wikidata_image_urls(imdb_id)
-            continue
+            if image_urls:
+                print(f"  [info] no TMDB images for {member['name']}, "
+                      f"found {len(image_urls)} via Wikidata", file=sys.stderr)
+
+        if not image_urls:
+            print(f"  [warn] no images for {member['name']}", file=sys.stderr)
 
         actors.append({
             "id":            person_id,
@@ -127,14 +159,15 @@ def download_images(actor: dict, dest_dir: Path, n: int) -> list[Path]:
 # ── Gallery assembly ─────────────────────────────────────────────────────────
 
 def build_gallery(movie_id: int, key: str, embedder,
-                  max_actors: int, images_per_actor: int,
+                  images_per_actor: int,
-                  image_root: Path) -> dict:
+                  image_root: Path) -> tuple[dict, list[dict]]:
-    """Fetch cast, download images, embed, return gallery dict."""
+    """Fetch cast, download images, embed, return (gallery dict, actors needing more images)."""
     print(f"Fetching cast for TMDB movie {movie_id}…", file=sys.stderr)
-    actors = fetch_cast(movie_id, key, max_actors)
+    actors = fetch_cast(movie_id, key)
-    print(f"Found {len(actors)} actors with images", file=sys.stderr)
+    print(f"Found {len(actors)} cast member(s)", file=sys.stderr)
 
     gallery_actors = []
+    missing        = []
 
     for actor in actors:
         safe_name = actor["name"].replace(" ", "_")
@@ -142,9 +175,18 @@ def build_gallery(movie_id: int, key: str, embedder,
         actor_dir = image_root / f"{dir_id}_{safe_name}"
 
         print(f"\n{actor['name']} ({dir_id})", file=sys.stderr)
+
+        if not actor["profile_images"]:
+            print("  no images found, skipping", file=sys.stderr)
+            missing.append({"name": actor["name"], "imdb_id": actor["imdb_id"],
+                             "tmdb_id": actor["tmdb_id"], "reason": "no images found"})
+            continue
+
         image_paths = download_images(actor, actor_dir, images_per_actor)
         if not image_paths:
             print("  no images downloaded, skipping", file=sys.stderr)
+            missing.append({"name": actor["name"], "imdb_id": actor["imdb_id"],
+                             "tmdb_id": actor["tmdb_id"], "reason": "download failed"})
             continue
 
         print(f"  embedding {len(image_paths)} image(s)…", file=sys.stderr)
@@ -163,6 +205,8 @@ def build_gallery(movie_id: int, key: str, embedder,
 
         if not embeddings:
             print("  no valid embeddings, skipping actor", file=sys.stderr)
+            missing.append({"name": actor["name"], "imdb_id": actor["imdb_id"],
+                             "tmdb_id": actor["tmdb_id"], "reason": "no valid embeddings"})
             continue
 
         gallery_actors.append({
@@ -175,7 +219,7 @@ def build_gallery(movie_id: int, key: str, embedder,
         })
         print(f"  → {len(embeddings)} embedding(s) stored", file=sys.stderr)
 
-    return {"actors": gallery_actors}
+    return {"actors": gallery_actors}, missing
 
 
 # ── Entry point ───────────────────────────────────────────────────────────────
@@ -197,8 +241,6 @@ def main():
                         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("--max-actors",      type=int, default=20,
-                        help="Maximum number of cast members to include (default: 20)")
     parser.add_argument("--images-per-actor",type=int, default=3,
                         help="Profile images to download per actor (default: 3)")
     parser.add_argument("--image-dir",       default=None,
@@ -221,11 +263,10 @@ def main():
         print(f"TMDB movie ID: {movie_id}", file=sys.stderr)
 
     # Build gallery
-    gallery = build_gallery(
+    gallery, missing = build_gallery(
         movie_id      = movie_id,
         key           = args.tmdb_key,
         embedder      = embedder,
-        max_actors    = args.max_actors,
         images_per_actor = args.images_per_actor,
         image_root    = image_root,
     )
@@ -242,6 +283,11 @@ def main():
     output.write_text(json.dumps(gallery, indent=2) + "\n")
     print(f"Saved: {output}", file=sys.stderr)
 
+    if missing:
+        missing_path = output.with_name(output.stem + ".missing_images.json")
+        missing_path.write_text(json.dumps(missing, indent=2) + "\n")
+        print(f"{len(missing)} actor(s) need images — see {missing_path}", file=sys.stderr)
+
 
 if __name__ == "__main__":
     main()

scripts/make_jellyfin_gallery.py

@@ -261,7 +261,7 @@ def fetch_actor_images(base_url: str, api_key: str, pid: str, info: dict,
 
 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:
+                 embed_executor: concurrent.futures.ThreadPoolExecutor) -> tuple[dict | None, str | 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
@@ -274,7 +274,7 @@ def embed_actor(pid: str, info: dict, image_paths: list[Path],
     name = info["name"]
     if not image_paths:
         print(f"  {name}: no image available, skipping", file=sys.stderr)
-        return None
+        return None, "no image available"
 
     embeddings = []
     source_images = []
@@ -289,7 +289,7 @@ def embed_actor(pid: str, info: dict, image_paths: list[Path],
 
     if not embeddings:
         print(f"  {name}: no valid embeddings, skipping actor", file=sys.stderr)
-        return None
+        return None, "no valid embeddings"
 
     print(f"  {name}: → {len(embeddings)} embedding(s) stored", file=sys.stderr)
     return {
@@ -300,13 +300,13 @@ def embed_actor(pid: str, info: dict, image_paths: list[Path],
         "source_images": source_images,
         "embeddings": embeddings,
         "appearances": info["appearances"],
-    }
+    }, None
 
 
 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:
+                   embed_executor: concurrent.futures.ThreadPoolExecutor) -> tuple[dict | None, str | None]:
     safe_name = info["name"].replace(" ", "_")
     actor_dir = image_root / f"{pid}_{safe_name}"
     image_paths, imdb_id, tmdb_id = fetch_actor_images(
@@ -319,44 +319,54 @@ def process_actor(pid: str, info: dict, base_url: str, api_key: str,
 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:
+                   tmdb_key: str | None = None, workers: int = 8) -> tuple[dict, list[dict]]:
     actors = collect_actors(base_url, api_key, item_types)
 
     gallery_actors = []
     todo = []
+    n_retry = 0
     for pid, info in actors.items():
-        if pid in existing_actors:
+        existing = existing_actors.get(pid)
-            gallery_actors.append(existing_actors[pid])
+        if existing is not None and existing.get("embeddings"):
+            gallery_actors.append(existing)
         else:
+            if existing is not None:
+                n_retry += 1
             todo.append((pid, info))
 
     if len(gallery_actors):
         print(f"Skipping {len(gallery_actors)} actor(s) already present in existing gallery", file=sys.stderr)
+    if n_retry:
+        print(f"Retrying {n_retry} actor(s) with no embeddings in existing gallery", file=sys.stderr)
     print(f"Processing {len(todo)} new actor(s) with {workers} worker(s)…", file=sys.stderr)
 
+    missing = []
     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"]
+                             fetch_imdb_ids, tmdb_key, embed_executor): (pid, info["name"])
             for pid, info in todo
         }
         for future in concurrent.futures.as_completed(futures):
             n_done += 1
-            name = futures[future]
+            pid, name = futures[future]
             try:
-                actor = future.result()
+                actor, reason = future.result()
             except Exception as e:
                 print(f"  [error] {name}: {e}", file=sys.stderr)
+                missing.append({"jellyfin_id": pid, "name": name, "reason": str(e)})
                 continue
             if actor:
                 gallery_actors.append(actor)
+            else:
+                missing.append({"jellyfin_id": pid, "name": name, "reason": reason})
             if n_done % 25 == 0:
                 print(f"  progress: {n_done}/{len(todo)} actors processed", file=sys.stderr)
 
-    return {"actors": gallery_actors}
+    return {"actors": gallery_actors}, missing
 
 
 # ── Entry point ───────────────────────────────────────────────────────────────
@@ -423,7 +433,7 @@ def main():
                 existing_actors[pid] = actor
         print(f"Loaded {len(existing_actors)} actor(s) from existing gallery for merge", file=sys.stderr)
 
-    gallery = build_gallery(
+    gallery, missing = build_gallery(
         base_url=jellyfin_url,
         api_key=args.api_key,
         embedder=embedder,
@@ -447,6 +457,11 @@ def main():
     output.write_text(json.dumps(gallery, indent=2) + "\n")
     print(f"Saved: {output}", file=sys.stderr)
 
+    if missing:
+        missing_path = output.with_name(output.stem + ".missing_images.json")
+        missing_path.write_text(json.dumps(missing, indent=2) + "\n")
+        print(f"{len(missing)} actor(s) need images — see {missing_path}", file=sys.stderr)
+
 
 if __name__ == "__main__":
     main()

scripts/run_from_jellyfin.py

@@ -15,6 +15,16 @@ Usage:
         -- --fps 5 --verbosity 2
 
 Anything after "--" is passed through unchanged to scene_analyze.
+
+Worker mode (--worker) polls the JRay plugin's Tasks/Pending endpoint for a
+random batch of items with no truth data yet, processing each in turn:
+
+    python scripts/run_from_jellyfin.py \\
+        --jellyfin-url http://jellyfin.local:8096 \\
+        --api-key YOUR_API_KEY \\
+        --gallery whole_gallery.json \\
+        --worker \\
+        -- --fps 5
 """
 
 import argparse
@@ -22,56 +32,70 @@ import json
 import subprocess
 import sys
 import tempfile
+import time
 from pathlib import Path
 
+import requests
+
 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")
+    data = jf_get(base_url, api_key, "/Items", Ids=item_id, Fields="Path", Recursive="true")
-    path = data.get("Path")
+    items = data.get("Items", [])
+    if not items:
+        raise ValueError(f"Item {item_id} not found")
+    path = items[0].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
+    return items[0].get("Name", item_id), path
 
 
-def main():
+def fetch_pending_tasks(base_url: str, api_key: str, limit: int = 10) -> list[dict]:
-    parser = argparse.ArgumentParser(
+    """GET /Plugins/JRay/Tasks/Pending — a random batch of items with no truth data yet.
-        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
+    Returns a list of {"item_id", "path", "name"}; an empty list means
-    if item_id is None:
+    there's nothing left to do (for now).
-        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)
+    return jf_get(base_url, api_key, "/Plugins/JRay/Tasks/Pending", limit=limit)
 
+
+def push_truth(base_url: str, api_key: str, item_id: str, output_path: str) -> None:
+    """PUT the scene_analyze output JSON to the JRay plugin's Truth endpoint.
+
+    Requires an Administrator API key (reuses --api-key).
+    """
+    url = base_url.rstrip("/") + f"/Plugins/JRay/Items/{item_id}/Truth"
+    headers = {"X-Emby-Token": api_key, "Content-Type": "application/json"}
+
+    # --verbosity 1 (standard) adds a "frames" array with per-frame bbox/
+    # similarity data for local debugging — the Truth schema only needs
+    # schema_version/movie/sample_fps/anneal_sec/actors, and "frames" can
+    # be tens of MB for a full episode, well past typical proxy body limits.
+    payload = json.loads(Path(output_path).read_text())
+    if payload.pop("frames", None) is not None:
+        print(f"Stripped per-frame data before push ({output_path} keeps it locally)", file=sys.stderr)
+    body = json.dumps(payload)
+
+    r = requests.put(url, headers=headers, data=body, timeout=30)
+    if r.status_code == 204:
+        print(f"Pushed {output_path} -> {url}", file=sys.stderr)
+        return
+    if r.status_code in (401, 403):
+        raise RuntimeError(f"Push failed ({r.status_code}): API key needs Administrator rights for {url}")
+    if r.status_code == 400:
+        raise RuntimeError(f"Push failed (400 Bad Request): {r.text}")
+    r.raise_for_status()
+
+
+def process_item(args, item_id: str, extra: list[str]) -> None:
+    """Resolve, analyze, and (unless --no-push) push truth for one Jellyfin item."""
     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"
+        raise RuntimeError(f"Resolved path does not exist on this filesystem: {movie_path} "
                             f"(this tool must share Jellyfin's media mount)")
 
     output = args.output or f"{name}.json"
@@ -91,8 +115,11 @@ def main():
         filtered_file.close()
         gallery_path = filtered_file.name
 
+    # Default to minimal output (just actor/scene windows, no per-frame
+    # data) — that's all the Truth push needs. Placed before *extra so a
+    # debug run can override with `-- --verbosity 1` (last flag wins).
     cmd = [args.bin, "--movie", movie_path, "--gallery", gallery_path,
-           "--output", output, *extra]
+           "--output", output, "--verbosity", "0", *extra]
     print("Running:", " ".join(cmd), file=sys.stderr)
 
     if args.dry_run:
@@ -104,6 +131,104 @@ def main():
         if filtered_file is not None:
             Path(filtered_file.name).unlink(missing_ok=True)
 
+    if not args.no_push:
+        push_truth(args.jellyfin_url, args.api_key, item_id, output)
+
+
+def run_worker(args, extra: list[str]) -> None:
+    """Poll /Plugins/JRay/Tasks/Pending and process items until the backlog is empty.
+
+    The endpoint returns a random sample, so repeated polling naturally
+    spreads work across the backlog without server-side task tracking. An
+    empty array means there's nothing left to do (for now).
+    """
+    while True:
+        try:
+            tasks = fetch_pending_tasks(args.jellyfin_url, args.api_key, args.poll_limit)
+        except requests.HTTPError as e:
+            print(f"Failed to fetch pending tasks: {e}", file=sys.stderr)
+            tasks = []
+
+        if not tasks:
+            print("No pending tasks.", file=sys.stderr)
+            if args.once:
+                return
+            time.sleep(args.poll_interval)
+            continue
+
+        for task in tasks:
+            item_id = task["item_id"]
+            label = task.get("name") or task.get("path") or item_id
+            print(f"=== {label} ({item_id}) ===", file=sys.stderr)
+            try:
+                process_item(args, item_id, extra)
+            except Exception as e:
+                print(f"Failed to process {label!r} ({item_id}): {e}", file=sys.stderr)
+
+        if args.once:
+            return
+
+
+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()
+    group.add_argument("--item-id", help="Jellyfin item id of the title")
+    group.add_argument("--title", help="Title to search for (uses first match)")
+    group.add_argument("--worker", action="store_true",
+                        help="Poll /Plugins/JRay/Tasks/Pending for work instead of "
+                             "processing a single --item-id/--title")
+    parser.add_argument("--poll-limit", type=int, default=10,
+                         help="Tasks/Pending batch size for --worker (default: 10, max 100)")
+    parser.add_argument("--poll-interval", type=float, default=60,
+                         help="Seconds to wait between polls when --worker finds nothing "
+                              "(default: 60)")
+    parser.add_argument("--once", action="store_true",
+                         help="With --worker, process one batch and exit instead of "
+                              "looping forever")
+    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; "
+                              "ignored with --worker, which always uses <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")
+    parser.add_argument("--no-push", action="store_true",
+                         help="Don't push results to the JRay plugin after the run "
+                              "(local-only/debug; --output is still written)")
+    args, extra = parser.parse_known_args()
+    if extra and extra[0] == "--":
+        extra = extra[1:]
+
+    if args.worker:
+        if args.output:
+            sys.exit("--output is incompatible with --worker (each item needs its own file)")
+        run_worker(args, extra)
+        return
+
+    if not args.item_id and not args.title:
+        sys.exit("one of --item-id, --title, or --worker is required")
+
+    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)
+
+    try:
+        process_item(args, item_id, extra)
+    except RuntimeError as e:
+        sys.exit(str(e))
+
 
 if __name__ == "__main__":
     main()

src/embed_faces.cpp

@@ -129,6 +129,13 @@ static FaceResult process(const std::string& path,
 
     std::vector<DetectedFace> faces = detect(img);
 
+    if (faces.empty()) {
+        cv::Mat enhanced = enhance_for_retry(img);
+        faces = detect(enhanced);
+        if (!faces.empty())
+            img = enhanced;
+    }
+
     if (faces.empty()) {
         res.error = "no face detected";
         return res;

src/face_embedder_engine.hpp

@@ -66,6 +66,12 @@ public:
         }
 
         std::vector<DetectedFace> faces = detector_->detect(img);
+        if (faces.empty()) {
+            cv::Mat enhanced = enhance_for_retry(img);
+            faces = detector_->detect(enhanced);
+            if (!faces.empty())
+                img = enhanced;
+        }
         if (faces.empty()) {
             res.error = "no face detected";
             return res;

src/face_utils.hpp

@@ -24,6 +24,28 @@ inline cv::Mat align_face(const cv::Mat& img,
     return crop;
 }
 
+// ── enhance_for_retry ────────────────────────────────────────────────────────
+// Used when initial face detection finds nothing. Pads the image by 50%
+// (border-replicated, so the detector doesn't see a hard edge) and applies
+// CLAHE to boost local contrast, giving the detector a second try.
+inline cv::Mat enhance_for_retry(const cv::Mat& img) {
+    cv::Mat padded;
+    const int pad_x = img.cols / 4;
+    const int pad_y = img.rows / 4;
+    cv::copyMakeBorder(img, padded, pad_y, pad_y, pad_x, pad_x, cv::BORDER_REPLICATE);
+
+    cv::Mat lab;
+    cv::cvtColor(padded, lab, cv::COLOR_BGR2Lab);
+    std::vector<cv::Mat> channels;
+    cv::split(lab, channels);
+    cv::createCLAHE(2.0, cv::Size(8, 8))->apply(channels[0], channels[0]);
+    cv::merge(channels, lab);
+
+    cv::Mat out;
+    cv::cvtColor(lab, out, cv::COLOR_Lab2BGR);
+    return out;
+}
+
 // ── l2_normalise ──────────────────────────────────────────────────────────────
 inline Embedding l2_normalise(const float* row) {
     float norm = 0.f;