def transformNormals(normals: numpy.ndarray,
transformation: Matrix) -> numpy.ndarray:
"""Transform an array of normals using a matrix
:param normals: :type{numpy.ndarray} array of 3D normals
:param transformation: a 4x4 matrix
:return: :type{numpy.ndarray} the transformed normals
:note This assumes the normals are untranslated unit normals, and returns the same.
"""
data = numpy.pad(normals, ((0, 0), (0, 1)),
"constant",
constant_values=(0.0, 0.0))
# Get the translation from the transformation so we can cancel it later.
translation = transformation.getTranslation()
# Transform the normals so they get the proper rotation
data = data.dot(transformation.getTransposed().getData())
data += transformation.getData()[:, 3]
data = data[:, 0:3]
# Cancel the translation since normals should always go from origin to a point on the unit sphere.
data[:] -= translation.getData()
# Re-normalize the normals, since the transformation can contain scaling.
lengths = numpy.linalg.norm(data, axis=1)
lengths[lengths == 0] = 1
data[:, 0] /= lengths
data[:, 1] /= lengths
data[:, 2] /= lengths
return data
def transformNormals(normals: numpy.ndarray, transformation: Matrix) -> numpy.ndarray:
data = numpy.pad(normals, ((0, 0), (0, 1)), "constant", constant_values=(0.0, 0.0))
# Get the translation from the transformation so we can cancel it later.
translation = transformation.getTranslation()
# Transform the normals so they get the proper rotation
data = data.dot(transformation.getTransposed().getData())
data += transformation.getData()[:, 3]
data = data[:, 0:3]
# Cancel the translation since normals should always go from origin to a point on the unit sphere.
data[:] -= translation.getData()
# Re-normalize the normals, since the transformation can contain scaling.
lengths = numpy.linalg.norm(data, axis = 1)
data[:, 0] /= lengths
data[:, 1] /= lengths
data[:, 2] /= lengths
return data
def transformNormals(normals: numpy.ndarray, transformation: Matrix) -> numpy.ndarray:
data = numpy.pad(normals, ((0, 0), (0, 1)), "constant", constant_values=(0.0, 0.0))
# Get the translation from the transformation so we can cancel it later.
translation = transformation.getTranslation()
# Transform the normals so they get the proper rotation
data = data.dot(transformation.getTransposed().getData())
data += transformation.getData()[:, 3]
data = data[:, 0:3]
# Cancel the translation since normals should always go from origin to a point on the unit sphere.
data[:] -= translation.getData()
# Re-normalize the normals, since the transformation can contain scaling.
lengths = numpy.linalg.norm(data, axis = 1)
data[:, 0] /= lengths
data[:, 1] /= lengths
data[:, 2] /= lengths
return data
def test_translate(self):
matrix = Matrix()
matrix.translate(Vector(1, 1, 1))
assert matrix.getTranslation() == Vector(1, 1, 1)
matrix.translate(Vector(2, 3, 4))
assert matrix.getTranslation() == Vector(3, 4, 5)
def test_translate(self):
matrix = Matrix()
matrix.translate(Vector(1, 1, 1))
assert matrix.getTranslation() == Vector(1, 1, 1)
matrix.translate(Vector(2, 3, 4))
assert matrix.getTranslation() == Vector(3, 4, 5)
