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 = cp.asarray(camera())
shift = (-7, 12)
shifted = cp.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])
reference_image = cp.asarray(reference_image)
shifted = cp.asarray(shifted)
measured_shift = masked_register_translation(
reference_image,
shifted,
cp.ones(ref_mask.shape, dtype=ref_mask.dtype),
cp.ones(shifted_mask.shape, dtype=shifted_mask.dtype),
)
cp.testing.assert_array_equal(measured_shift, -cp.asarray(shift))
def test_4d_input_subpixel():
phantom = img_as_float(cp.asarray(binary_blobs(length=32, n_dim=4)))
reference_image = fft.fftn(phantom)
subpixel_shift = (-2.3, 1.7, 5.4, -3.2)
shifted_image = fourier_shift(reference_image, subpixel_shift)
result, error, diffphase = phase_cross_correlation(
reference_image, shifted_image, upsample_factor=10, space="fourier"
)
assert_allclose(result, -cp.asarray(subpixel_shift), atol=0.05)
def test_3d_input():
phantom = img_as_float(cp.asarray(binary_blobs(length=32, n_dim=3)))
reference_image = fft.fftn(phantom)
shift = (-2.0, 1.0, 5.0)
shifted_image = fourier_shift(reference_image, shift)
result, error, diffphase = phase_cross_correlation(
reference_image, shifted_image, space="fourier"
)
assert_allclose(result, -cp.asarray(shift), atol=0.05)
# subpixel precision now available for 3-D data
subpixel_shift = (-2.3, 1.7, 5.4)
shifted_image = fourier_shift(reference_image, subpixel_shift)
result, error, diffphase = phase_cross_correlation(
reference_image, shifted_image, upsample_factor=100, space="fourier"
)
assert_allclose(result, -cp.asarray(subpixel_shift), atol=0.05)
def test_subpixel_precision():
reference_image = fft.fftn(cp.asarray(camera()))
subpixel_shift = (-2.4, 1.32)
shifted_image = fourier_shift(reference_image, subpixel_shift)
# subpixel precision
result, error, diffphase = phase_cross_correlation(
reference_image, shifted_image, upsample_factor=100, space="fourier"
)
assert_allclose(result[:2], -cp.asarray(subpixel_shift), atol=0.05)
def test_size_one_dimension_input():
# take a strip of the input image
reference_image = fft.fftn(cp.asarray(camera())[:, 15]).reshape((-1, 1))
subpixel_shift = (-2.4, 4)
shifted_image = fourier_shift(reference_image, subpixel_shift)
# subpixel precision
result, error, diffphase = phase_cross_correlation(
reference_image, shifted_image, upsample_factor=20, space="fourier"
)
assert_allclose(result[:2], -cp.asarray((-2.4, 0)), atol=0.05)
def test_correlation():
reference_image = fft.fftn(cp.asarray(camera()))
shift = (-7, 12)
shifted_image = fourier_shift(
reference_image, shift
) # TODO: replace with CUDA fourier_shift
# pixel precision
result, error, diffphase = phase_cross_correlation(
reference_image, shifted_image, space="fourier"
)
assert_allclose(result[:2], -cp.asarray(shift))
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 = cp.asarray(camera())
shift = (-7, 12)
shifted = cp.real(
fft.ifft2(fourier_shift(fft.fft2(reference_image), shift)))
trivial_mask = cp.ones_like(reference_image)
nonmasked_result, *_ = phase_cross_correlation(reference_image, shifted)
masked_result = masked_register_translation(reference_image,
shifted,
trivial_mask,
overlap_ratio=0.1)
cp.testing.assert_array_equal(nonmasked_result, masked_result)
def test_masked_registration_random_masks():
"""masked_register_translation should be able to register translations
between images even with random masks."""
# See random number generator for reproducible results
np.random.seed(23)
reference_image = cp.asarray(camera())
shift = (-7, 12)
shifted = np.real(
fft.ifft2(fourier_shift(fft.fft2(reference_image), shift)))
# 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])
ref_mask = cp.asarray(ref_mask)
shifted_mask = cp.asarray(shifted_mask)
measured_shift = masked_register_translation(reference_image, shifted,
ref_mask, shifted_mask)
cp.testing.assert_array_equal(measured_shift, -cp.asarray(shift))