Make auto_threshold test robust across OpenCV builds

ccoeff_normed scores span [-1, 1], so the Otsu cut-off can legitimately be
negative on some OpenCV builds (match_auto clamps it with floor anyway). Assert
the threshold is below a perfect match and use a relative separability check
(bimodal > flat) instead of absolute bounds.

Author: JE-Chen

test/unit_test/headless/test_match_autothresh_batch.py

@@ -28,8 +28,14 @@ def _haystack(*tops_lefts):
 def test_auto_threshold_reports_metrics():
     info = auto_threshold(_template(), haystack=_haystack((20, 30), (20, 150)))
     assert set(info) == {"threshold", "separability", "n_above"}
-    assert 0.0 < info["threshold"] < 1.0
-    assert info["separability"] > 0.3   # clearly bimodal: matches vs background
+    # ccoeff_normed spans [-1, 1]; the cut-off just has to sit below a perfect
+    # match (its exact value depends on the OpenCV build's score distribution).
+    assert info["threshold"] < 1.0
+    assert info["n_above"] >= 2
+    # a clearly bimodal surface (matches vs background) is more separable than a
+    # flat one — a relative check that is stable across OpenCV builds
+    blank = auto_threshold(_template(), haystack=np.zeros((160, 220), np.uint8))
+    assert info["separability"] > blank["separability"]
 
 
 def test_match_auto_finds_both_occurrences():