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_padfield_data():
""" Masked translation registration should behave like in the original
publication """
# Test translated from MATLABimplementation `MaskedFFTRegistrationTest`
# file. You can find the source code here:
# http://www.dirkpadfield.com/Home/MaskedFFTRegistrationCode.zip
shifts = [(75, 75), (-130, 130), (130, 130)]
for xi, yi in shifts:
fixed_image = imread(
fetch('registration/tests/data/OriginalX{:d}Y{:d}.png'
''.format(xi, yi)))
moving_image = imread(
fetch('registration/tests/data/TransformedX{:d}Y{:d}.png'
''.format(xi, yi)))
# Valid pixels are 1
fixed_mask = (fixed_image != 0)
moving_mask = (moving_image != 0)
# Note that shifts in x and y and shifts in cols and rows
shift_y, shift_x = masked_register_translation(
fixed_image,
moving_image,
reference_mask=fixed_mask,
moving_mask=moving_mask,
overlap_ratio=0.1)
# Note: by looking at the test code from Padfield's
# MaskedFFTRegistrationCode repository, the
# shifts were not xi and yi, but xi and -yi
assert_equal((shift_x, shift_y), (-xi, yi))
def test_masked_registration_vs_phase_cross_correlation():
"""masked_register_translation should give the same results as
phase_cross_correlation 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, *_ = phase_cross_correlation(reference_image, shifted)
masked_result = masked_register_translation(reference_image,
shifted,
reference_mask=trivial_mask,
overlap_ratio=1 / 10)
assert_equal(nonmasked_result, masked_result)
def test_masked_registration_3d_contiguous_mask():
"""masked_register_translation should be able to register translations
between volumes with contiguous masks."""
ref_vol = brain()[:, ::2, ::2]
offset = (1, -5, 10)
# create square mask
ref_mask = np.zeros_like(ref_vol, dtype=bool)
ref_mask[:-2, 75:100, 75:100] = True
ref_shifted = real_shift(ref_vol, offset)
measured_offset = masked_register_translation(
ref_vol, ref_shifted, reference_mask=ref_mask, moving_mask=ref_mask
)
assert_equal(offset, -np.array(measured_offset))
