def test_center_image():
# BASEX sample image, Gaussians at 10, 15, 20, 70,85, 100, 145, 150, 155
# image width, height n = 361, origin = (180, 180)
IM = abel.tools.analytical.SampleImage(n=361, name="dribinski").image
# artificially displace origin, now at (179, 182)
IMx = shift(IM, (-1, 2))
true_origin = (179, 182)
# find_origin using 'slice' method
origin = find_origin(IMx, method="slice")
assert_allclose(origin, true_origin, atol=1)
# find_origin using 'com' method
origin = find_origin(IMx, method="com")
assert_allclose(origin, true_origin, atol=1)
# check single axis - vertical
# center shifted image IMx in the vertical direction only
IMc = center_image(IMx, method="com", axes=1)
# determine the origin
origin = find_origin(IMc, method="com")
assert_allclose(origin, (179, 180), atol=1)
# check single axis - horizontal
# center shifted image IMx in the horizontal direction only
IMc = center_image(IMx, method="com", axes=0)
origin = find_origin(IMc, method="com")
assert_allclose(origin, (180, 182), atol=1)
# check even image size returns odd
# drop off one column, to make an even column image
IM = IM[:, :-1]
m, n = IM.shape
IMy = center_image(IM, method="slice", odd_size=True)
assert_allclose(IMy.shape, (m, n - 1))
def test_centering_function_shape():
# ni -> original shape
# n -> result of the centering function
for (y, x) in [(20, 11), # crop image
(21, 11),
(5, 11), # pad image
(4, 11)]:
data = np.zeros((y, x))
res = center_image(data, (y//2, x//2))
assert_equal( res.shape, (y, x),
'Centering preserves shapes for ni={}, n={}'.format(y, x))
def test_centering_function_shape():
# ni -> original shape
# n -> result of the centering function
for (y, x) in [
(20, 11), # crop image
(21, 11),
(5, 11), # pad image
(4, 11)
]:
data = np.zeros((y, x))
res = center_image(data, (y // 2, x // 2))
assert_equal(res.shape, (y, x),
'Centering preserves shapes for ni={}, n={}'.format(y, x))
def test_centering_function():
# ni -> original shape of the data is (ni, ni)
# n_c -> the image center is (n_c, n_c)
for (ni, n_c) in [(10, 5),
(10, 5),
]:
arr = np.zeros((ni, ni))
# arr[n_c-1:n_c+2,n_c-1:n_c+2] = 1
# # else:
arr[n_c-1:n_c+1,n_c-1:n_c+1] = 1.0
res = center_image(arr, (n_c, n_c), odd_size=False)
# The print statements below can be commented after we fix the centering issue
# print('Original array')
# print(arr)
# print('Centered array')
# print(res)
assert_equal( is_symmetric(res), True,\
'Validating the centering function for ni={}, n_c={}'.format(ni, n_c))
def test_centering_function():
# ni -> original shape of the data is (ni, ni)
# n_c -> the image center is (n_c, n_c)
for (ni, n_c) in [
(10, 5),
(10, 5),
]:
arr = np.zeros((ni, ni))
# arr[n_c-1:n_c+2,n_c-1:n_c+2] = 1
# # else:
arr[n_c - 1:n_c + 1, n_c - 1:n_c + 1] = 1.0
res = center_image(arr, (n_c, n_c), odd_size=False)
# The print statements below can be commented after we fix the centering issue
# print('Original array')
# print(arr)
# print('Centered array')
# print(res)
assert_equal( is_symmetric(res), True,\
'Validating the centering function for ni={}, n_c={}'.format(ni, n_c))
