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>

tests/test_quality.py

@@ -134,6 +134,16 @@ def test_assess_quality_passes_good_image():
     img = _noisy_color_image()
     result = assess_quality(img, face_bbox=(10, 10, 110, 110), confidence=0.9)
     assert result.passed
+    assert result.blur_score is not None
+    assert result.blur_score > 0
+
+
+def test_assess_quality_blur_score_is_low_for_flat_image():
+    from winnow.quality import assess_quality
+    flat = _rgb_image(128, 128, 128)
+    result = assess_quality(flat)
+    assert result.blur_score is not None
+    assert result.blur_score < 1.0
 
 
 def test_assess_quality_collects_multiple_failures():