Test/clean matte boundaries

CorridorKeyModule/core/color_utils.py

@@ -251,6 +251,11 @@ def clean_matte(alpha_np: np.ndarray, area_threshold: int = 300, dilation: int =
     """
     Cleans up small disconnected components (like tracking markers) from a predicted alpha matte.
     alpha_np: Numpy array [H, W] or [H, W, 1] float (0.0 - 1.0)
+
+    Note: this function is not idempotent at default settings. The dilation and Gaussian blur
+    post-processing expand the feathered edge of surviving regions on each call, so running
+    clean_matte twice on the same matte produces slightly different output. The connected-
+    components filter alone (dilation=0, blur_size=0) is idempotent.
     """
     # Needs to be 2D
     is_3d = False

tests/test_color_utils.py

@@ -366,6 +366,42 @@ def test_3d_input_preserved(self):
         assert result.ndim == 3
         assert result.shape[2] == 1
 
+    def test_all_zero_matte_returns_zeros(self):
+        """An all-zero matte must return all zeros without crashing.
+
+        The connected-components pass on an empty matte must not produce
+        spurious output. Source: README 'Auto-Cleanup' boundary condition.
+        """
+        matte = np.zeros((100, 100), dtype=np.float32)
+        result = cu.clean_matte(matte, area_threshold=300)
+        np.testing.assert_array_equal(result, np.zeros_like(matte))
+
+    def test_all_opaque_matte_preserved(self):
+        """An all-opaque matte must be returned intact.
+
+        The single large component exceeds any reasonable threshold — cleanup
+        must not zero it out. Source: README 'Auto-Cleanup' boundary condition.
+        """
+        matte = np.ones((100, 100), dtype=np.float32)
+        result = cu.clean_matte(matte, area_threshold=300)
+        assert result.min() > 0.9
+
+    def test_idempotent(self):
+        """Running clean_matte twice produces the same result as running it once.
+
+        Tested with dilation=0 and blur_size=0 to isolate the connected-
+        components logic — the pure topological filter is the stable core.
+        Dilation + blur are not idempotent by design (they expand surviving
+        blobs, so a second pass would further expand feathered edges).
+        Source: README 'Auto-Cleanup' boundary condition.
+        """
+        matte = np.zeros((100, 100), dtype=np.float32)
+        matte[20:80, 20:80] = 1.0  # large blob kept
+        matte[5:8, 5:8] = 1.0  # small blob removed on first pass
+        first = cu.clean_matte(matte.copy(), area_threshold=300, dilation=0, blur_size=0)
+        second = cu.clean_matte(first.copy(), area_threshold=300, dilation=0, blur_size=0)
+        np.testing.assert_allclose(first, second, atol=1e-6)
+
 
 # ---------------------------------------------------------------------------
 # create_checkerboard