def test_linear_coefficients_reconstruction_opposite_images(self, size):
"""Tests that the matte can be reconstructed by using the coefficients ."""
tensor_shape = np.random.randint(size, 6, size=3).tolist()
image = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
_, pseudo_inverse = matting.build_matrices(image, size=size)
a, b = matting.linear_coefficients(1.0 - image, pseudo_inverse)
reconstructed = matting.reconstruct(image, a, b)
self.assertAllClose(1.0 - image, reconstructed, atol=1e-4)
def test_reconstruct_jacobian_random(self, channels):
"""Tests the Jacobian of the reconstruct function."""
tensor_shape = np.random.randint(1, 5, size=3).tolist()
image_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [channels])
mul_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [channels])
add_init = np.random.uniform(0.0, 1.0, size=tensor_shape + [1])
image = tf.convert_to_tensor(value=image_init)
mul = tf.convert_to_tensor(value=mul_init)
add = tf.convert_to_tensor(value=add_init)
reconstruction = matting.reconstruct(image, mul, add)
with self.subTest(name="image"):
self.assert_jacobian_is_correct(image, image_init, reconstruction)
with self.subTest(name="coeff_mul"):
self.assert_jacobian_is_correct(mul, mul_init, reconstruction)
with self.subTest(name="coeff_add"):
self.assert_jacobian_is_correct(add, add_init, reconstruction)
