refactor: cleanup audit findings — dedup helpers, prune orphan scores, cache has_frigate_scores

- upload_tracker: extract _pick_mapped_file() private helper; get_lowest_quality_mapped_file
  and get_most_redundant_mapped_file are now one-liners over the same body
- upload_tracker: remove_frigate_file now also prunes the corresponding frigate_scores entry,
  preventing unbounded accumulation of orphaned score entries across replacement cycles
- frigate_api: get_frigate_face_counts delegates to get_all_frigate_person_files, eliminating
  the duplicated "name != 'train' and isinstance(files, list)" filter body
- executor: cache has_frigate_scores(name) as person_has_fscores before the per-file loop;
  refresh it after each remove_frigate_file call and after each scored upload, eliminating
  two redundant disk reads per at-cap file iteration
- executor: casefold() both sides of the recognize_face person-name comparison so a Frigate
  casing normalization or manual-registration casing mismatch does not silently suppress scoring

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

winnow/executor.py

@@ -395,6 +395,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
             actually_uploaded: list[tuple[str, str | None]] = []
             failed_deletes: set[str] = set()
             min_quality_score_for_slot: float | None = None
+            person_has_fscores: bool = has_frigate_scores(name)
 
             for fname in person_files:
                 fpath = os.path.join(person_dir, fname)
@@ -427,9 +428,9 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                 # handles this conservatively by skipping that candidate until the next run.
                 pre_fscore: float | None = None
                 if Config.ENABLE_FRIGATE_SCORES and pre_run_count > 0:
-                    if not at_cap or has_frigate_scores(name):
+                    if not at_cap or person_has_fscores:
                         _result = recognize_face(fpath)
-                        if _result is not None and _result[0] == name:
+                        if _result is not None and (_result[0] or "").casefold() == name.casefold():
                             pre_fscore = _result[1]
 
                 # Ceiling check: skip if the existing training set already covers this
@@ -450,7 +451,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         progress.advance(upload_task)
                         continue
 
-                    using_fscore = has_frigate_scores(name) and Config.ENABLE_FRIGATE_SCORES
+                    using_fscore = person_has_fscores and Config.ENABLE_FRIGATE_SCORES
                     if using_fscore:
                         candidate_score = pre_fscore
                         if candidate_score is None:
@@ -476,6 +477,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         )
                         if delete_frigate_person_files(name, [target_frigate_file]):
                             remove_frigate_file(name, target_frigate_file)
+                            person_has_fscores = has_frigate_scores(name)
                             effective_count -= 1
                             min_quality_score_for_slot = None  # clear any blur-mode slot floor — Frigate uses a different score metric
                         else:
@@ -507,6 +509,7 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                         )
                         if delete_frigate_person_files(name, [target_frigate_file]):
                             remove_frigate_file(name, target_frigate_file)
+                            person_has_fscores = has_frigate_scores(name)
                             effective_count -= 1
                             min_quality_score_for_slot = score_map.get(fname)
                         else:
@@ -538,6 +541,8 @@ def upload_to_frigate(jobs: list[dict]) -> None:
                                     crop_dims=dims_map.get(fname),
                                     frigate_score=pre_fscore,
                                 )
+                                if pre_fscore is not None:
+                                    person_has_fscores = True
                                 actually_uploaded.append((fname, asset_id))
 
                             break

winnow/frigate_api.py

@@ -22,24 +22,6 @@ def _get_faces_data() -> dict | None:
         return None
 
 
-def get_frigate_face_counts() -> dict[str, int] | None:
-    """Return {person_name: training_image_count} from Frigate's train directory.
-
-    Returns None if FRIGATE_URL is not set or the API is unreachable, so callers
-    can distinguish "API unavailable" from "person has 0 images."
-    """
-    data = _get_faces_data()
-    if data is None:
-        return None
-    # Response: {person_name: [file, ...], "train": [...], ...}
-    # "train" is a flat pending list, not a person — skip it.
-    return {
-        name: len(files)
-        for name, files in data.items()
-        if name != "train" and isinstance(files, list)
-    }
-
-
 def get_all_frigate_person_files() -> dict[str, list[str]] | None:
     """Return {person_name: [filename, ...]} for every person in Frigate.
 
@@ -49,6 +31,8 @@ def get_all_frigate_person_files() -> dict[str, list[str]] | None:
     data = _get_faces_data()
     if data is None:
         return None
+    # Response: {person_name: [file, ...], "train": [...], ...}
+    # "train" is a flat pending list, not a person — skip it.
     return {
         name: files
         for name, files in data.items()
@@ -56,6 +40,18 @@ def get_all_frigate_person_files() -> dict[str, list[str]] | None:
     }
 
 
+def get_frigate_face_counts() -> dict[str, int] | None:
+    """Return {person_name: training_image_count} from Frigate's train directory.
+
+    Returns None if FRIGATE_URL is not set or the API is unreachable, so callers
+    can distinguish "API unavailable" from "person has 0 images."
+    """
+    all_files = get_all_frigate_person_files()
+    if all_files is None:
+        return None
+    return {name: len(files) for name, files in all_files.items()}
+
+
 def get_frigate_person_files(person_name: str) -> list[str] | None:
     """Return the list of training filenames for a person in Frigate.
 

winnow/upload_tracker.py

@@ -167,7 +167,9 @@ def remove_frigate_file(person_name: str, frigate_filename: str) -> None:
     data = _load(UPLOAD_TRACKER_FILE)
     by_person = data.get("by_person", {})
     entry = _migrate_entry(by_person.get(person_name, {}))
-    entry["frigate_files"].pop(frigate_filename, None)
+    asset_id = entry["frigate_files"].pop(frigate_filename, None)
+    if asset_id:
+        entry["frigate_scores"].pop(asset_id, None)
     by_person[person_name] = entry
     _save(UPLOAD_TRACKER_FILE, data)
     logger.debug(f"Removed Frigate file mapping {frigate_filename} ({person_name})")
@@ -204,6 +206,22 @@ def has_frigate_scores(person_name: str) -> bool:
     return any(asset_id in frigate_scores for asset_id in frigate_files.values())
 
 
+def _pick_mapped_file(
+    person_name: str, score_key: str, *, highest: bool, exclude: set[str] | None = None
+) -> tuple[str, str, float] | None:
+    data = _load(UPLOAD_TRACKER_FILE)
+    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
+    scores = entry.get(score_key, {})
+    candidates = [
+        (ff, asset_id, scores[asset_id])
+        for ff, asset_id in entry.get("frigate_files", {}).items()
+        if (exclude is None or ff not in exclude) and asset_id in scores
+    ]
+    if not candidates:
+        return None
+    return max(candidates, key=lambda x: x[2]) if highest else min(candidates, key=lambda x: x[2])
+
+
 def get_lowest_quality_mapped_file(
     person_name: str, exclude: set[str] | None = None
 ) -> tuple[str, str, float] | None:
@@ -213,21 +231,7 @@ def get_lowest_quality_mapped_file(
     Used for quality replacement when no Frigate scores are available.
     Pass `exclude` to skip files that failed to delete this run.
     """
-    data = _load(UPLOAD_TRACKER_FILE)
+    return _pick_mapped_file(person_name, "scores", highest=False, exclude=exclude)
-    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
-    frigate_files = entry.get("frigate_files", {})
-    blur_scores = entry.get("scores", {})
-
-    candidates = [
-        (ff, asset_id, blur_scores[asset_id])
-        for ff, asset_id in frigate_files.items()
-        if (exclude is None or ff not in exclude)
-        and asset_id in blur_scores
-    ]
-
-    if not candidates:
-        return None
-    return min(candidates, key=lambda x: x[2])
 
 
 def get_most_redundant_mapped_file(
@@ -240,21 +244,7 @@ def get_most_redundant_mapped_file(
     = the most redundant file and therefore the best replacement target.
     Pass `exclude` to skip files that failed to delete this run.
     """
-    data = _load(UPLOAD_TRACKER_FILE)
+    return _pick_mapped_file(person_name, "frigate_scores", highest=True, exclude=exclude)
-    entry = _migrate_entry(data.get("by_person", {}).get(person_name, {}))
-    frigate_files = entry.get("frigate_files", {})
-    frigate_scores = entry.get("frigate_scores", {})
-
-    candidates = [
-        (ff, asset_id, frigate_scores[asset_id])
-        for ff, asset_id in frigate_files.items()
-        if (exclude is None or ff not in exclude)
-        and asset_id in frigate_scores
-    ]
-
-    if not candidates:
-        return None
-    return max(candidates, key=lambda x: x[2])
 
 
 def get_frigate_filename_for_asset(person_name: str, asset_id: str) -> str | None: