chore: remove dead embedding paths and suppress insightface FutureWarning in image_processing

- immich_api.py: remove FaceData.embedding field and numpy import — Immich face
  embeddings were never consumed after being fetched; bbox/confidence is all that's used
- embeddings.py: remove immich_embedding param from get_embedding() — never passed by
  any caller; simplify docstring accordingly
- image_processing.py: wrap insightface_app.get() in warnings filter to suppress the
  scikit-image FutureWarning about estimate being deprecated (already suppressed in
  embeddings.py for the diversity path, was leaking from the crop-alignment path)
- README.md: remove two stale "object mode" / "object classification" fragments

README.md

@@ -9,7 +9,7 @@
 
 **Docs:** [Setup](https://github.com/sudolulo/winnow/wiki/Setup) · [Troubleshooting](https://github.com/sudolulo/winnow/wiki/Troubleshooting) · [FAQ](https://github.com/sudolulo/winnow/wiki/FAQ)
 
-`winnow` pulls photos from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition and object classification models.
+`winnow` pulls photos from your [Immich](https://immich.app) library, selects the most diverse and highest-quality subset using AI embeddings, and delivers them as training data for [Frigate](https://frigate.video)'s face recognition.
 
 The best Frigate training data is images you curate manually — photos taken specifically for recognition, in controlled conditions, uploaded directly through Frigate's UI. For people you can do that for, do it. winnow is for everyone else: people in your library you want Frigate to recognise but don't have dedicated training photos for. It mines your existing Immich library for the most diverse spread of real-world appearances and fills the gap.
 
@@ -257,7 +257,7 @@ When run with a terminal attached, winnow starts an interactive session: select
 ## Requirements
 
 - **Immich** v1.106+
-- **Frigate** v0.16+ (face mode only — object mode has no Frigate dependency)
+- **Frigate** v0.16+
 - **GPU** recommended: NVIDIA (CUDA), AMD (ROCm), or Intel (Arc / iGPU via OpenVINO)
 - **Python** 3.13+
 

winnow/embeddings.py

@@ -205,25 +205,17 @@ def get_face_embedding(img_pil: Image.Image) -> np.ndarray | None:
 def get_embedding(
     img_pil: Image.Image,
     asset_id: str | None = None,
-    immich_embedding: np.ndarray | None = None,
 ) -> np.ndarray | None:
     """Get embedding for a face image.
 
-    Priority:
-    1. Pre-fetched Immich embedding (if provided)
-    2. Disk cache (if enabled and asset_id provided)
-    3. Local InsightFace computation
+    Checks disk cache first (if enabled and asset_id provided),
+    then falls back to local InsightFace computation.
     """
     from .config import Config
 
     use_cache = Config.ENABLE_CACHE and asset_id is not None
     cache = get_cache(Config.CACHE_DIR) if use_cache else None
 
-    if immich_embedding is not None:
-        if cache:
-            cache.put(asset_id, immich_embedding, "insightface")
-        return immich_embedding
-
     if cache:
         cached = cache.get(asset_id, "insightface")
         if cached is not None:

winnow/image_processing.py

@@ -2,6 +2,7 @@
 
 import logging
 import os
+import warnings
 
 import numpy as np
 from PIL import Image
@@ -113,7 +114,9 @@ def process_face_mode(
                 min(img_h, y2 + pad_y),
             )
             search_crop = img.crop(search_box)
-            detected = insightface_app.get(np.asarray(search_crop))
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", message=".*estimate.*is deprecated", category=FutureWarning)
+                detected = insightface_app.get(np.asarray(search_crop))
             if detected:
                 cx, cy = search_crop.width / 2, search_crop.height / 2
                 best = min(

winnow/immich_api.py

@@ -5,7 +5,6 @@ from dataclasses import dataclass
 from datetime import datetime, timedelta, timezone
 from io import BytesIO
 
-import numpy as np
 import requests
 from PIL import Image, ImageOps
 
@@ -21,7 +20,6 @@ _MAX_ASSETS_PER_PERSON = 5000  # Stop fetching after this many — diversity poo
 class FaceData:
     """Pre-computed face data from Immich."""
 
-    embedding: np.ndarray | None
     bbox: tuple[float, float, float, float]  # (x1, y1, x2, y2)
     confidence: float | None
     image_width: int
@@ -120,7 +118,7 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
         person_id: Optional person ID to match the specific face
 
     Returns:
-        FaceData with embedding, bbox, and confidence, or None if unavailable
+        FaceData with bbox and confidence, or None if unavailable
     """
     try:
         resp = requests.get(
@@ -148,12 +146,6 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
         if face is None:
             face = faces[0]  # Fall back to first/largest face
 
-        # Extract embedding if available
-        embedding = None
-        if "embedding" in face:
-            embedding = np.array(face["embedding"], dtype=np.float32)
-
-        # Extract bounding box
         bbox = (
             face.get("boundingBoxX1", 0),
             face.get("boundingBoxY1", 0),
@@ -163,7 +155,6 @@ def fetch_face_data(asset_id: str, person_id: str | None = None) -> FaceData | N
 
         score = face.get("score")
         return FaceData(
-            embedding=embedding,
             bbox=bbox,
             confidence=score if score is not None else face.get("confidence"),
             image_width=face.get("imageWidth", 0),