def test_masked_registration_random_masks_non_equal_sizes():
"""masked_register_translation should be able to register
translations between images that are not the same size even
with random masks."""
# See random number generator for reproducible results
np.random.seed(23)
reference_image = camera()
shift = (-7, 12)
shifted = np.real(
fft.ifft2(fourier_shift(fft.fft2(reference_image), shift)))
# Crop the shifted image
shifted = shifted[64:-64, 64:-64]
# Random masks with 75% of pixels being valid
ref_mask = np.random.choice([True, False],
reference_image.shape,
p=[3 / 4, 1 / 4])
shifted_mask = np.random.choice([True, False],
shifted.shape,
p=[3 / 4, 1 / 4])
measured_shift = masked_register_translation(
reference_image,
shifted,
reference_mask=np.ones_like(ref_mask),
moving_mask=np.ones_like(shifted_mask))
assert_equal(measured_shift, -np.array(shift))
def test_masked_registration_vs_register_translation():
"""masked_register_translation should give the same results as
register_translation in the case of trivial masks."""
reference_image = camera()
shift = (-7, 12)
shifted = np.real(fft.ifft2(fourier_shift(
fft.fft2(reference_image), shift)))
trivial_mask = np.ones_like(reference_image)
nonmasked_result, *_ = register_translation(reference_image, shifted)
masked_result = masked_register_translation(
reference_image, shifted, trivial_mask, overlap_ratio=1 / 10)
assert_equal(nonmasked_result, masked_result)
