fix: stale __version__, scheduler import order, quality test coverage

- __init__.py: derive __version__ from importlib.metadata instead of
  a hardcoded "0.1.0" that was six releases out of date
- scheduler.py: move winnow.cli import to module top (no more noqa);
  clean up redundant bool variables in check_models
- tests/test_quality.py: 17 tests covering all five quality check
  functions individually plus assess_quality integration cases

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

scheduler.py

@@ -11,6 +11,10 @@ except ImportError:
     print("croniter not installed. Run: uv add croniter")
     sys.exit(1)
 
+# Imported at module level so models loaded during the first run stay
+# resident in memory across all subsequent scheduled runs.
+from winnow.cli import main
+
 SCHEDULE = os.environ["CRON_SCHEDULE"]
 MODELS_DIR = os.environ.get("HF_HOME", "/models/huggingface")
 INSIGHTFACE_BASE = os.environ.get("INSIGHTFACE_HOME", "/models")
@@ -21,18 +25,12 @@ logger = logging.getLogger(__name__)
 def check_models() -> None:
     buffalo = Path(INSIGHTFACE_BASE) / ".insightface" / "models" / "buffalo_l"
     hf_hub = Path(MODELS_DIR) / "hub"
-    buffalo_ok = buffalo.exists()
+    if not buffalo.exists():
-    hf_ok = hf_hub.exists() and any(hf_hub.iterdir())
-    if not buffalo_ok:
         print("  InsightFace Buffalo_L not found — will download on first run", flush=True)
-    if not hf_ok:
+    if not (hf_hub.exists() and any(hf_hub.iterdir())):
         print("  HuggingFace models not found — will download on first run", flush=True)
 
 
-# Import once — models loaded during the first run stay resident in memory
-# for all subsequent scheduled runs, avoiding repeated multi-GB load times.
-from winnow.cli import main  # noqa: E402
-
 NOW = time.time()
 cron = croniter(SCHEDULE, NOW)
 next_run = cron.get_next(float)

tests/test_quality.py

@@ -0,0 +1,151 @@
+"""Tests for image quality filtering functions."""
+
+import numpy as np
+from PIL import Image
+
+
+def _rgb_image(r, g, b, size=(100, 100)) -> Image.Image:
+    arr = np.full((*size, 3), [r, g, b], dtype=np.uint8)
+    return Image.fromarray(arr, "RGB")
+
+
+def _noisy_color_image(size=(100, 100)) -> Image.Image:
+    """Noisy image with a strong red channel so grayscale check passes."""
+    rng = np.random.default_rng(0)
+    arr = rng.integers(0, 256, (*size, 3), dtype=np.uint8)
+    arr[:, :, 0] = np.clip(arr[:, :, 0].astype(int) + 80, 0, 255).astype(np.uint8)
+    arr[:, :, 2] = np.clip(arr[:, :, 2].astype(int) - 80, 0, 255).astype(np.uint8)
+    return Image.fromarray(arr, "RGB")
+
+
+# ── check_blur ────────────────────────────────────────────────────────────────
+
+def test_blur_rejects_flat_image():
+    from winnow.quality import check_blur
+    flat = np.full((100, 100, 3), 128, dtype=np.uint8)
+    passed, reason = check_blur(flat, threshold=100.0)
+    assert not passed
+    assert "Blurry" in reason
+
+
+def test_blur_passes_noisy_color_image():
+    from winnow.quality import check_blur
+    img = _noisy_color_image()
+    passed, _ = check_blur(np.asarray(img), threshold=100.0)
+    assert passed
+
+
+# ── check_grayscale ───────────────────────────────────────────────────────────
+
+def test_grayscale_rejects_ir_image():
+    from winnow.quality import check_grayscale
+    gray = np.full((100, 100, 3), 128, dtype=np.uint8)
+    passed, reason = check_grayscale(gray)
+    assert not passed
+    assert "Grayscale" in reason
+
+
+def test_grayscale_passes_color_image():
+    from winnow.quality import check_grayscale
+    color = np.zeros((100, 100, 3), dtype=np.uint8)
+    color[:, :, 0] = 200  # strong red channel
+    passed, _ = check_grayscale(color)
+    assert passed
+
+
+def test_grayscale_rejects_single_channel():
+    from winnow.quality import check_grayscale
+    single = np.full((100, 100, 1), 128, dtype=np.uint8)
+    passed, reason = check_grayscale(single)
+    assert not passed
+
+
+# ── check_exposure ────────────────────────────────────────────────────────────
+
+def test_exposure_rejects_black_image():
+    from winnow.quality import check_exposure
+    black = np.zeros((100, 100, 3), dtype=np.uint8)
+    passed, reason = check_exposure(black)
+    assert not passed
+    assert "Underexposed" in reason
+
+
+def test_exposure_rejects_white_image():
+    from winnow.quality import check_exposure
+    white = np.full((100, 100, 3), 255, dtype=np.uint8)
+    passed, reason = check_exposure(white)
+    assert not passed
+    assert "Overexposed" in reason
+
+
+def test_exposure_passes_normal_image():
+    from winnow.quality import check_exposure
+    mid = np.full((100, 100, 3), 128, dtype=np.uint8)
+    passed, _ = check_exposure(mid)
+    assert passed
+
+
+# ── check_face_size ───────────────────────────────────────────────────────────
+
+def test_face_size_rejects_small_face():
+    from winnow.quality import check_face_size
+    passed, reason = check_face_size(30, 30, min_px=50)
+    assert not passed
+    assert "small" in reason
+
+
+def test_face_size_passes_adequate_face():
+    from winnow.quality import check_face_size
+    passed, _ = check_face_size(100, 100, min_px=50)
+    assert passed
+
+
+def test_face_size_rejects_if_either_dimension_small():
+    from winnow.quality import check_face_size
+    passed, _ = check_face_size(100, 30, min_px=50)
+    assert not passed
+
+
+# ── check_confidence ──────────────────────────────────────────────────────────
+
+def test_confidence_rejects_low_score():
+    from winnow.quality import check_confidence
+    passed, reason = check_confidence(0.5, min_conf=0.7)
+    assert not passed
+    assert "confidence" in reason.lower()
+
+
+def test_confidence_passes_high_score():
+    from winnow.quality import check_confidence
+    passed, _ = check_confidence(0.95, min_conf=0.7)
+    assert passed
+
+
+def test_confidence_passes_none_score():
+    from winnow.quality import check_confidence
+    passed, _ = check_confidence(None, min_conf=0.7)
+    assert passed
+
+
+# ── assess_quality (integration) ─────────────────────────────────────────────
+
+def test_assess_quality_passes_good_image():
+    from winnow.quality import assess_quality
+    img = _noisy_color_image()
+    result = assess_quality(img, face_bbox=(10, 10, 110, 110), confidence=0.9)
+    assert result.passed
+
+
+def test_assess_quality_collects_multiple_failures():
+    from winnow.quality import assess_quality
+    black = _rgb_image(0, 0, 0)
+    result = assess_quality(black, face_bbox=(0, 0, 10, 10), confidence=0.3)
+    assert not result.passed
+    assert len(result.reasons) >= 2
+
+
+def test_assess_quality_skips_face_size_without_bbox():
+    from winnow.quality import assess_quality
+    img = _noisy_color_image()
+    result = assess_quality(img, face_bbox=None, confidence=0.9)
+    assert result.passed

winnow/__init__.py

@@ -5,4 +5,9 @@ Immich library for Frigate's Face Recognition (ArcFace) and Object/State
 Classification models.
 """
 
-__version__ = "0.1.0"
+from importlib.metadata import PackageNotFoundError, version
+
+try:
+    __version__ = version("winnow")
+except PackageNotFoundError:
+    __version__ = "unknown"