feat: post-upload quality gate via FRIGATE_SCORE_THRESHOLD

When FRIGATE_SCORE_THRESHOLD > 0, images that score below the threshold
after upload are deleted from Frigate and removed from the tracker.
Skipped when pre_run_count == 0 (cold start — no class mean to compare
against yet). Deletion happens after reconciliation so the Frigate
filename is known. Disabled by default (0.0).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.env.example

@@ -30,6 +30,7 @@ STRATEGY=auto
 # MIN_CONFIDENCE=0.7          # Minimum face detection confidence (default: 0.7)
 # BLUR_THRESHOLD=120.0        # Laplacian blur threshold; lower = accept more blur (default: 120.0)
 # MAX_AUTO_IMAGES=80          # Hard cap on auto-diversity selection (default: 80)
+# FRIGATE_SCORE_THRESHOLD=0.0 # Remove uploaded images scoring below this after upload (0 = disabled; skipped on cold start)
 
 # ── Caching & Models ──────────────────────────────────────────────────────────
 # FORCE_CPU=true              # Disable GPU, fall back to CPU

winnow/config.py

@@ -31,6 +31,7 @@ class _Config:
     MIN_CONFIDENCE: float = 0.7
     MAX_AUTO_IMAGES: int = 80
     QUALITY_REPLACEMENT: bool = True
+    FRIGATE_SCORE_THRESHOLD: float = 0.0
 
     # People filtering
     MIN_FACE_COUNT: int = 0
@@ -62,6 +63,7 @@ class _Config:
         self.MIN_CONFIDENCE = float(os.getenv("MIN_CONFIDENCE", "0.7"))
         self.MAX_AUTO_IMAGES = int(os.getenv("MAX_AUTO_IMAGES", "80"))
         self.QUALITY_REPLACEMENT = os.getenv("QUALITY_REPLACEMENT", "true").lower() in ("true", "1", "yes")
+        self.FRIGATE_SCORE_THRESHOLD = float(os.getenv("FRIGATE_SCORE_THRESHOLD", "0.0"))
         self.FACE_MARGIN = float(os.getenv("FACE_MARGIN", "0.15"))
         self.USE_FULL_RESOLUTION = os.getenv("USE_FULL_RESOLUTION", "true").lower() in ("true", "1", "yes")
         self.ENABLE_FACE_ALIGNMENT = os.getenv("ENABLE_FACE_ALIGNMENT", "true").lower() in ("true", "1", "yes")

winnow/executor.py

@@ -19,6 +19,7 @@ from .immich_api import fetch_face_data, fetch_full_image
 from .log_config import console
 from .quality import assess_quality
 from .upload_tracker import (
+    get_frigate_filename_for_asset,
     get_lowest_quality_mapped_file,
     get_tracked_frigate_file_count,
     get_tracked_frigate_filenames,
@@ -356,9 +357,11 @@ def upload_to_frigate(jobs: list[dict]) -> None:
             else:
                 known_frigate_files_at_start: set[str] = set(_snapshot)
             effective_count = get_tracked_frigate_file_count(name)
+            pre_run_count = effective_count
             quality_replacement = job.get("config", {}).get("quality_replacement", False)
             actually_uploaded: list[tuple[str, str | None]] = []
             failed_deletes: set[str] = set()
+            quality_gate_failed: set[str] = set()
             min_quality_score_for_slot: float | None = None
 
             for fname in person_files:
@@ -455,6 +458,22 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                 )
                                 actually_uploaded.append((fname, asset_id))
 
+                                # Flag for post-reconcile removal if below threshold.
+                                # We don't know the Frigate filename yet — reconcile maps
+                                # it first, then we delete using the mapped name.
+                                threshold = Config.FRIGATE_SCORE_THRESHOLD
+                                if (
+                                    threshold > 0
+                                    and pre_run_count > 0
+                                    and post_fscore is not None
+                                    and post_fscore < threshold
+                                ):
+                                    quality_gate_failed.add(asset_id)
+                                    progress.console.print(
+                                        f"    [yellow]⚠  {fname}: Frigate score {post_fscore:.2f}"
+                                        f" < threshold {threshold:.2f}, will remove after mapping[/yellow]"
+                                    )
+
                             break
                         else:
                             if attempt < max_retries:
@@ -520,6 +539,26 @@ def upload_to_frigate(jobs: list[dict]) -> None:
             if actually_uploaded:
                 _reconcile_frigate_mappings(name, known_frigate_files_at_start, actually_uploaded)
 
+            # Post-reconcile quality gate: filenames are now mapped, so we can delete.
+            if quality_gate_failed:
+                removed = 0
+                for asset_id in quality_gate_failed:
+                    frigate_fn = get_frigate_filename_for_asset(name, asset_id)
+                    if frigate_fn and delete_frigate_person_files(name, [frigate_fn]):
+                        remove_frigate_file(name, frigate_fn)
+                        effective_count -= 1
+                        removed += 1
+                    else:
+                        logger.warning(
+                            f"{name}: could not remove low-score file for {asset_id}"
+                            " — no Frigate filename mapped (reconciliation race?)"
+                        )
+                if removed:
+                    progress.console.print(
+                        f"  [yellow]🗑  {name}: removed {removed} image(s) below"
+                        f" Frigate score threshold ({Config.FRIGATE_SCORE_THRESHOLD:.2f})[/yellow]"
+                    )
+
             # Per-person summary
             if person_failed == 0:
                 progress.console.print(

winnow/upload_tracker.py

@@ -250,6 +250,16 @@ def get_lowest_quality_mapped_file(
     return min(candidates, key=lambda x: x[2])
 
 
+def get_frigate_filename_for_asset(person_name: str, asset_id: str) -> str | None:
+    """Return the Frigate training filename mapped to this asset ID, or None."""
+    data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
+    for frigate_filename, aid in entry["frigate_files"].items():
+        if aid == asset_id:
+            return frigate_filename
+    return None
+
+
 def find_by_crop_dimension(size: int) -> list[dict]:
     """Return all tracked crops whose width or height matches `size` pixels.