Implement quality score tracking and batch Frigate file mapping

- Track laplacian blur score through quality filtering pipeline
  (quality.py: blur_score on QualityResult; diversity.py: store on asset;
   executor.py: read via quality_score key)
- Replace per-file polling with post-person batch reconciliation:
  after all uploads for a person complete, poll Frigate (up to 15s)
  until the expected number of new files appear, then map by filename
  timestamp order (Frigate FIFO queue = upload order = timestamp order)
- Document race condition limitation: concurrent external uploads cause
  the batch to be skipped entirely (safe but files go unmapped); noted
  in code as requiring a Frigate API fix (return filename on upload)
- Add two assess_quality integration tests for blur_score

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

winnow/quality.py

@@ -20,6 +20,7 @@ class QualityResult:
 
     passed: bool
     reasons: list[str] = field(default_factory=list)
+    blur_score: float | None = None
 
     @property
     def reason(self) -> str:
@@ -113,9 +114,12 @@ def assess_quality(
     img_np = np.asarray(img)
     reasons = []
 
-    # Run all checks, collect failures
+    # Compute laplacian variance once (used by check_blur and stored as blur_score)
+    gray = cv2.cvtColor(img_np, cv2.COLOR_RGB2GRAY) if img_np.ndim == 3 else img_np
+    blur_score = float(cv2.Laplacian(gray, cv2.CV_64F).var())
+
     checks = [
-        check_blur(img_np, blur_threshold),
+        (blur_score >= blur_threshold, f"Blurry (laplacian={blur_score:.1f}, threshold={blur_threshold})" if blur_score < blur_threshold else ""),
         check_grayscale(img_np),
         check_exposure(img_np),
         check_confidence(confidence, min_confidence),
@@ -129,5 +133,5 @@ def assess_quality(
         if not passed:
             reasons.append(reason)
 
-    return QualityResult(passed=len(reasons) == 0, reasons=reasons)
+    return QualityResult(passed=len(reasons) == 0, reasons=reasons, blur_score=blur_score)