def test_cross_correlate_masked_output_shape():
"""Masked normalized cross-correlation should return a shape
of N + M + 1 for each transform axis."""
shape1 = (15, 4, 5)
shape2 = (6, 12, 7)
expected_full_shape = tuple(np.array(shape1) + np.array(shape2) - 1)
expected_same_shape = shape1
arr1 = cp.zeros(shape1)
arr2 = cp.zeros(shape2)
# Trivial masks
m1 = cp.ones_like(arr1)
m2 = cp.ones_like(arr2)
full_xcorr = cross_correlate_masked(arr1,
arr2,
m1,
m2,
axes=(0, 1, 2),
mode="full")
assert full_xcorr.dtype.kind != "c" # grlee77: output should be real
assert full_xcorr.shape == expected_full_shape
same_xcorr = cross_correlate_masked(arr1,
arr2,
m1,
m2,
axes=(0, 1, 2),
mode="same")
assert same_xcorr.shape == expected_same_shape
def test_cross_correlate_masked_test_against_mismatched_dimensions():
"""Masked normalized cross-correlation should raise an error if array
dimensions along non-transformation axes are mismatched."""
shape1 = (23, 1, 1)
shape2 = (6, 2, 2)
arr1 = cp.zeros(shape1)
arr2 = cp.zeros(shape2)
# Trivial masks
m1 = cp.ones_like(arr1)
m2 = cp.ones_like(arr2)
with pytest.raises(ValueError):
cross_correlate_masked(arr1, arr2, m1, m2, axes=(1, 2))
def test_cross_correlate_masked_autocorrelation_trivial_masks():
"""Masked normalized cross-correlation between identical arrays
should reduce to an autocorrelation even with random masks."""
# See random number generator for reproducible results
np.random.seed(23)
arr1 = cp.asarray(camera())
# Random masks with 75% of pixels being valid
m1 = np.random.choice([True, False], arr1.shape, p=[3 / 4, 1 / 4])
m2 = np.random.choice([True, False], arr1.shape, p=[3 / 4, 1 / 4])
m1 = cp.asarray(m1)
m2 = cp.asarray(m2)
xcorr = cross_correlate_masked(arr1,
arr1,
m1,
m2,
axes=(0, 1),
mode="same",
overlap_ratio=0).real
max_index = cp.unravel_index(cp.argmax(xcorr), xcorr.shape)
max_index = tuple(map(int, max_index))
# Autocorrelation should have maximum in center of array
# CuPy Backend: uint8 inputs will be processed in float32, so reduce
# decimal to 5
assert_almost_equal(float(xcorr.max()), 1, decimal=5)
np.testing.assert_array_equal(max_index, np.array(arr1.shape) / 2)
def test_cross_correlate_masked_output_range():
"""Masked normalized cross-correlation should return between 1 and -1."""
# See random number generator for reproducible results
np.random.seed(23)
# Array dimensions must match along non-transformation axes, in
# this case
# axis 0
shape1 = (15, 4, 5)
shape2 = (15, 12, 7)
# Initial array ranges between -5 and 5
arr1 = 10 * np.random.random(shape1) - 5
arr2 = 10 * np.random.random(shape2) - 5
# random masks
m1 = np.random.choice([True, False], arr1.shape)
m2 = np.random.choice([True, False], arr2.shape)
arr1 = cp.asarray(arr1)
arr2 = cp.asarray(arr2)
m1 = cp.asarray(m1)
m2 = cp.asarray(m2)
xcorr = cross_correlate_masked(arr1, arr2, m1, m2, axes=(1, 2))
# No assert array less or equal, so we add an eps
# Also could not find an `assert_array_greater`, Use (-xcorr) instead
eps = np.finfo(np.float64).eps
cp.testing.assert_array_less(xcorr, 1 + eps)
cp.testing.assert_array_less(-xcorr, 1 + eps)
def test_cross_correlate_masked_side_effects():
"""Masked normalized cross-correlation should not modify the inputs."""
shape1 = (2, 2, 2)
shape2 = (2, 2, 2)
arr1 = cp.zeros(shape1)
arr2 = cp.zeros(shape2)
# Trivial masks
m1 = cp.ones_like(arr1)
m2 = cp.ones_like(arr2)
# for arr in (arr1, arr2, m1, m2):
# arr.setflags(write=False)
arr1c, arr2c, m1c, m2c = [a.copy() for a in (arr1, arr2, m1, m2)]
cross_correlate_masked(arr1, arr2, m1, m2)
cp.testing.assert_array_equal(arr1, arr1c)
cp.testing.assert_array_equal(arr2, arr2c)
cp.testing.assert_array_equal(m1, m1c)
cp.testing.assert_array_equal(m2, m2c)
def test_cross_correlate_masked_over_axes():
"""Masked normalized cross-correlation over axes should be
equivalent to a loop over non-transform axes."""
# See random number generator for reproducible results
np.random.seed(23)
arr1 = np.random.random((8, 8, 5))
arr2 = np.random.random((8, 8, 5))
m1 = np.random.choice([True, False], arr1.shape)
m2 = np.random.choice([True, False], arr2.shape)
arr1 = cp.asarray(arr1)
arr2 = cp.asarray(arr2)
m1 = cp.asarray(m1)
m2 = cp.asarray(m2)
# Loop over last axis
with_loop = cp.empty_like(arr1, dtype=np.complex)
for index in range(arr1.shape[-1]):
with_loop[:, :, index] = cross_correlate_masked(
arr1[:, :, index],
arr2[:, :, index],
m1[:, :, index],
m2[:, :, index],
axes=(0, 1),
mode="same",
)
over_axes = cross_correlate_masked(arr1,
arr2,
m1,
m2,
axes=(0, 1),
mode="same")
cp.testing.assert_array_almost_equal(with_loop, over_axes)
