[utils.transform] better input validation in normalize_mirror and apply_transform

masque/test/test_utils.py

@@ -5,7 +5,7 @@ from numpy.testing import assert_equal, assert_allclose
 from numpy import pi
 import pytest
 
-from ..utils import remove_duplicate_vertices, remove_colinear_vertices, poly_contains_points, rotation_matrix_2d, apply_transforms, DeferredDict
+from ..utils import remove_duplicate_vertices, remove_colinear_vertices, poly_contains_points, rotation_matrix_2d, apply_transforms, normalize_mirror, DeferredDict
 from ..file.utils import tmpfile
 from ..utils.curves import bezier
 from ..error import PatternError
@@ -94,6 +94,28 @@ def test_apply_transforms_advanced() -> None:
     assert_allclose(combined[0], [0, 10, pi / 2, 1, 1], atol=1e-10)
 
 
+def test_apply_transforms_empty_inputs() -> None:
+    empty_outer = apply_transforms(numpy.empty((0, 5)), [[1, 2, 0, 0, 1]])
+    assert empty_outer.shape == (0, 5)
+
+    empty_inner = apply_transforms([[1, 2, 0, 0, 1]], numpy.empty((0, 5)))
+    assert empty_inner.shape == (0, 5)
+
+    both_empty_tensor = apply_transforms(numpy.empty((0, 5)), numpy.empty((0, 5)), tensor=True)
+    assert both_empty_tensor.shape == (0, 0, 5)
+
+
+def test_normalize_mirror_validates_length() -> None:
+    with pytest.raises(ValueError, match='2-item sequence'):
+        normalize_mirror(())
+
+    with pytest.raises(ValueError, match='2-item sequence'):
+        normalize_mirror((True,))
+
+    with pytest.raises(ValueError, match='2-item sequence'):
+        normalize_mirror((True, False, True))
+
+
 def test_bezier_validates_weight_length() -> None:
     with pytest.raises(PatternError, match='one entry per control point'):
         bezier([[0, 0], [1, 1]], [0, 0.5, 1], weights=[1])

masque/utils/transform.py

@@ -50,7 +50,10 @@ def normalize_mirror(mirrored: Sequence[bool]) -> tuple[bool, float]:
         `angle_to_rotate` in radians
     """
 
-    mirrored_x, mirrored_y = mirrored
+    if len(mirrored) != 2:
+        raise ValueError(f'mirrored must be a 2-item sequence, got length {len(mirrored)}')
+
+    mirrored_x, mirrored_y = (bool(value) for value in mirrored)
     mirror_x = (mirrored_x != mirrored_y)  # XOR
     angle = numpy.pi if mirrored_y else 0
     return mirror_x, angle
@@ -111,6 +114,11 @@ def apply_transforms(
     if inner.shape[1] == 4:
         inner = numpy.pad(inner, ((0, 0), (0, 1)), constant_values=1.0)
 
+    if outer.shape[0] == 0 or inner.shape[0] == 0:
+        if tensor:
+            return numpy.empty((outer.shape[0], inner.shape[0], 5))
+        return numpy.empty((0, 5))
+
     # If mirrored, flip y's
     xy_mir = numpy.tile(inner[:, :2], (outer.shape[0], 1, 1))   # dims are outer, inner, xyrm
     xy_mir[outer[:, 3].astype(bool), :, 1] *= -1