def test_affreg_defaults():
# Test all default arguments with an arbitrary transform
# Select an arbitrary transform (all of them are already tested
# in test_affreg_all_transforms)
transform_name = 'TRANSLATION'
dim = 2
ttype = (transform_name, dim)
aff_options = ['mass', 'voxel-origin', 'centers', None, np.eye(dim + 1)]
for starting_affine in aff_options:
if dim == 2:
nslices = 1
else:
nslices = 45
factor = factors[ttype][0]
sampling_pc = factors[ttype][1]
transform = regtransforms[ttype]
id_param = transform.get_identity_parameters()
static, moving, static_grid2world, moving_grid2world, smask, mmask, T = \
setup_random_transform(transform, factor, nslices, 1.0)
# Sum of absolute differences
start_sad = np.abs(static - moving).sum()
metric = None
x0 = None
sigmas = None
scale_factors = None
level_iters = None
static_grid2world = None
moving_grid2world = None
for ss_sigma_factor in [1.0, None]:
affreg = imaffine.AffineRegistration(metric,
level_iters,
sigmas,
scale_factors,
'L-BFGS-B',
ss_sigma_factor,
options=None)
affine_map = affreg.optimize(static, moving, transform, x0,
static_grid2world, moving_grid2world,
starting_affine)
transformed = affine_map.transform(moving)
# Sum of absolute differences
end_sad = np.abs(static - transformed).sum()
reduction = 1 - end_sad / start_sad
print("%s>>%f" % (ttype, reduction))
assert (reduction > 0.9)
transformed_inv = affine_map.transform_inverse(static)
# Sum of absolute differences
end_sad = np.abs(moving - transformed_inv).sum()
reduction = 1 - end_sad / start_sad
print("%s>>%f" % (ttype, reduction))
assert (reduction > 0.9)
def test_affreg_defaults():
# Test all default arguments with an arbitrary transform
# Select an arbitrary transform (all of them are already tested
# in test_affreg_all_transforms)
transform_name = 'TRANSLATION'
dim = 2
ttype = (transform_name, dim)
aff_options = ['mass', 'voxel-origin', 'centers', None, np.eye(dim+1)]
for starting_affine in aff_options:
if dim == 2:
nslices = 1
else:
nslices = 45
factor = factors[ttype][0]
sampling_pc = factors[ttype][1]
transform = regtransforms[ttype]
id_param = transform.get_identity_parameters()
static, moving, static_grid2world, moving_grid2world, smask, mmask, T = \
setup_random_transform(transform, factor, nslices, 1.0)
# Sum of absolute differences
start_sad = np.abs(static - moving).sum()
metric = None
x0 = None
sigmas = None
scale_factors = None
level_iters = None
static_grid2world = None
moving_grid2world = None
for ss_sigma_factor in [1.0, None]:
affreg = imaffine.AffineRegistration(metric,
level_iters,
sigmas,
scale_factors,
'L-BFGS-B',
ss_sigma_factor,
options=None)
affine_map = affreg.optimize(static, moving, transform, x0,
static_grid2world, moving_grid2world,
starting_affine)
transformed = affine_map.transform(moving)
# Sum of absolute differences
end_sad = np.abs(static - transformed).sum()
reduction = 1 - end_sad / start_sad
print("%s>>%f"%(ttype, reduction))
assert(reduction > 0.9)
transformed_inv = affine_map.transform_inverse(static)
# Sum of absolute differences
end_sad = np.abs(moving - transformed_inv).sum()
reduction = 1 - end_sad / start_sad
print("%s>>%f"%(ttype, reduction))
assert(reduction > 0.9)
def test_mi_gradient():
np.random.seed(2022966)
# Test the gradient of mutual information
h = 1e-5
# Make sure dictionary entries are processed in the same order regardless
# of the platform. Otherwise any random numbers drawn within the loop would
# make the test non-deterministic even if we fix the seed before the loop:
# in this case the samples are drawn with `np.random.randn` below
for ttype in sorted(factors):
transform = regtransforms[ttype]
dim = ttype[1]
if dim == 2:
nslices = 1
else:
nslices = 45
factor = factors[ttype][0]
sampling_proportion = factors[ttype][1]
theta = factors[ttype][2]
# Start from a small rotation
start = regtransforms[('ROTATION', dim)]
nrot = start.get_number_of_parameters()
starting_affine = start.param_to_matrix(0.25 * np.random.randn(nrot))
# Get data (pair of images related to each other by an known transform)
static, moving, static_g2w, moving_g2w, smask, mmask, M = \
setup_random_transform(transform, factor, nslices, 2.0)
# Prepare a MutualInformationMetric instance
mi_metric = imaffine.MutualInformationMetric(32, sampling_proportion)
mi_metric.setup(
transform,
static,
moving,
starting_affine=starting_affine)
# Compute the gradient with the implementation under test
actual = mi_metric.gradient(theta)
# Compute the gradient using finite-diferences
n = transform.get_number_of_parameters()
expected = np.empty(n, dtype=np.float64)
val0 = mi_metric.distance(theta)
for i in range(n):
dtheta = theta.copy()
dtheta[i] += h
val1 = mi_metric.distance(dtheta)
expected[i] = (val1 - val0) / h
dp = expected.dot(actual)
enorm = npl.norm(expected)
anorm = npl.norm(actual)
nprod = dp / (enorm * anorm)
assert(nprod >= 0.99)
def test_mi_gradient():
np.random.seed(2022966)
# Test the gradient of mutual information
h = 1e-5
# Make sure dictionary entries are processed in the same order regardless
# of the platform. Otherwise any random numbers drawn within the loop would
# make the test non-deterministic even if we fix the seed before the loop:
# in this case the samples are drawn with `np.random.randn` below
for ttype in sorted(factors):
transform = regtransforms[ttype]
dim = ttype[1]
if dim == 2:
nslices = 1
else:
nslices = 45
factor = factors[ttype][0]
sampling_proportion = factors[ttype][1]
theta = factors[ttype][2]
# Start from a small rotation
start = regtransforms[('ROTATION', dim)]
nrot = start.get_number_of_parameters()
starting_affine = start.param_to_matrix(0.25 * np.random.randn(nrot))
# Get data (pair of images related to each other by an known transform)
static, moving, static_g2w, moving_g2w, smask, mmask, M = \
setup_random_transform(transform, factor, nslices, 2.0)
# Prepare a MutualInformationMetric instance
mi_metric = imaffine.MutualInformationMetric(32, sampling_proportion)
mi_metric.setup(
transform,
static,
moving,
starting_affine=starting_affine)
# Compute the gradient with the implementation under test
actual = mi_metric.gradient(theta)
# Compute the gradient using finite-diferences
n = transform.get_number_of_parameters()
expected = np.empty(n, dtype=np.float64)
val0 = mi_metric.distance(theta)
for i in range(n):
dtheta = theta.copy()
dtheta[i] += h
val1 = mi_metric.distance(dtheta)
expected[i] = (val1 - val0) / h
dp = expected.dot(actual)
enorm = npl.norm(expected)
anorm = npl.norm(actual)
nprod = dp / (enorm * anorm)
assert(nprod >= 0.99)
def test_affreg_all_transforms():
# Test affine registration using all transforms with typical settings
# Make sure dictionary entries are processed in the same order regardless
# of the platform.
# Otherwise any random numbers drawn within the loop would make
# the test non-deterministic even if we fix the seed before the loop.
# Right now, this test does not draw any samples,
# but we still sort the entries
# to prevent future related failures.
for ttype in sorted(factors):
dim = ttype[1]
if dim == 2:
nslices = 1
else:
nslices = 45
factor = factors[ttype][0]
sampling_pc = factors[ttype][1]
transform = regtransforms[ttype]
static, moving, static_grid2world, moving_grid2world, smask, mmask, T = \
setup_random_transform(transform, factor, nslices, 1.0)
# Sum of absolute differences
start_sad = np.abs(static - moving).sum()
metric = imaffine.MutualInformationMetric(32, sampling_pc)
affreg = imaffine.AffineRegistration(metric,
[1000, 100, 50],
[3, 1, 0],
[4, 2, 1],
'L-BFGS-B',
None,
options=None)
x0 = transform.get_identity_parameters()
affine_map = affreg.optimize(static, moving, transform, x0,
static_grid2world, moving_grid2world)
transformed = affine_map.transform(moving)
# Sum of absolute differences
end_sad = np.abs(static - transformed).sum()
reduction = 1 - end_sad / start_sad
print("%s>>%f" % (ttype, reduction))
assert(reduction > 0.9)
# Verify that exception is raised if level_iters is empty
metric = imaffine.MutualInformationMetric(32)
assert_raises(ValueError, imaffine.AffineRegistration, metric, [])
def test_affreg_all_transforms():
# Test affine registration using all transforms with typical settings
# Make sure dictionary entries are processed in the same order regardless
# of the platform.
# Otherwise any random numbers drawn within the loop would make
# the test non-deterministic even if we fix the seed before the loop.
# Right now, this test does not draw any samples,
# but we still sort the entries
# to prevent future related failures.
for ttype in sorted(factors):
dim = ttype[1]
if dim == 2:
nslices = 1
else:
nslices = 45
factor = factors[ttype][0]
sampling_pc = factors[ttype][1]
transform = regtransforms[ttype]
static, moving, static_grid2world, moving_grid2world, smask, mmask, T = \
setup_random_transform(transform, factor, nslices, 1.0)
# Sum of absolute differences
start_sad = np.abs(static - moving).sum()
metric = imaffine.MutualInformationMetric(32, sampling_pc)
affreg = imaffine.AffineRegistration(metric,
[1000, 100, 50],
[3, 1, 0],
[4, 2, 1],
'L-BFGS-B',
None,
options=None)
x0 = transform.get_identity_parameters()
affine_map = affreg.optimize(static, moving, transform, x0,
static_grid2world, moving_grid2world)
transformed = affine_map.transform(moving)
# Sum of absolute differences
end_sad = np.abs(static - transformed).sum()
reduction = 1 - end_sad / start_sad
print("%s>>%f" % (ttype, reduction))
assert(reduction > 0.9)
# Verify that exception is raised if level_iters is empty
metric = imaffine.MutualInformationMetric(32)
assert_raises(ValueError, imaffine.AffineRegistration, metric, [])
def test_image_registration():
with TemporaryDirectory() as temp_out_dir:
static, moving, static_g2w, moving_g2w, smask, mmask, M\
= setup_random_transform(transform=regtransforms[('AFFINE', 3)],
rfactor=0.1)
save_nifti(pjoin(temp_out_dir, 'b0.nii.gz'),
data=static,
affine=static_g2w)
save_nifti(pjoin(temp_out_dir, 't1.nii.gz'),
data=moving,
affine=moving_g2w)
static_image_file = pjoin(temp_out_dir, 'b0.nii.gz')
moving_image_file = pjoin(temp_out_dir, 't1.nii.gz')
image_registration_flow = ImageRegistrationFlow()
def read_distance(qual_fname):
temp_val = 0
with open(pjoin(temp_out_dir, qual_fname), 'r') as f:
temp_val = f.readlines()[-1]
return float(temp_val)
def test_com():
out_moved = pjoin(temp_out_dir, "com_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "com_affine.txt")
image_registration_flow._force_overwrite = True
image_registration_flow.run(static_image_file,
moving_image_file,
transform='com',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine)
check_existence(out_moved, out_affine)
def test_translation():
out_moved = pjoin(temp_out_dir, "trans_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "trans_affine.txt")
image_registration_flow._force_overwrite = True
image_registration_flow.run(static_image_file,
moving_image_file,
transform='trans',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='trans_q.txt')
dist = read_distance('trans_q.txt')
npt.assert_almost_equal(float(dist), -0.3953547764454917, 1)
check_existence(out_moved, out_affine)
def test_rigid():
out_moved = pjoin(temp_out_dir, "rigid_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "rigid_affine.txt")
image_registration_flow._force_overwrite = True
image_registration_flow.run(static_image_file,
moving_image_file,
transform='rigid',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='rigid_q.txt')
dist = read_distance('rigid_q.txt')
npt.assert_almost_equal(dist, -0.6900534794005155, 1)
check_existence(out_moved, out_affine)
def test_rigid_isoscaling():
out_moved = pjoin(temp_out_dir, "rigid_isoscaling_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "rigid_isoscaling_affine.txt")
image_registration_flow._force_overwrite = True
image_registration_flow.run(static_image_file,
moving_image_file,
transform='rigid_isoscaling',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='rigid_isoscaling_q.txt')
dist = read_distance('rigid_isoscaling_q.txt')
npt.assert_almost_equal(dist, -0.6960044668271375, 1)
check_existence(out_moved, out_affine)
def test_rigid_scaling():
out_moved = pjoin(temp_out_dir, "rigid_scaling_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "rigid_scaling_affine.txt")
image_registration_flow._force_overwrite = True
image_registration_flow.run(static_image_file,
moving_image_file,
transform='rigid_scaling',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='rigid_scaling_q.txt')
dist = read_distance('rigid_scaling_q.txt')
npt.assert_almost_equal(dist, -0.698688892993124, 1)
check_existence(out_moved, out_affine)
def test_affine():
out_moved = pjoin(temp_out_dir, "affine_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "affine_affine.txt")
image_registration_flow._force_overwrite = True
image_registration_flow.run(static_image_file,
moving_image_file,
transform='affine',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='affine_q.txt')
dist = read_distance('affine_q.txt')
npt.assert_almost_equal(dist, -0.7670650775914811, 1)
check_existence(out_moved, out_affine)
# Creating the erroneous behavior
def test_err():
image_registration_flow._force_overwrite = True
npt.assert_raises(ValueError,
image_registration_flow.run,
static_image_file,
moving_image_file,
transform='notransform')
image_registration_flow._force_overwrite = True
npt.assert_raises(ValueError,
image_registration_flow.run,
static_image_file,
moving_image_file,
metric='wrong_metric')
def check_existence(movedfile, affine_mat_file):
assert os.path.exists(movedfile)
assert os.path.exists(affine_mat_file)
return True
test_com()
test_translation()
test_rigid()
test_rigid_isoscaling()
test_rigid_scaling()
test_affine()
test_err()
def test_apply_affine_transform():
with TemporaryDirectory() as temp_out_dir:
factors = {
('TRANSLATION', 3): (2.0, None, np.array([2.3, 4.5, 1.7])),
('RIGID', 3):
(0.1, None, np.array([0.1, 0.15, -0.11, 2.3, 4.5, 1.7])),
('RIGIDISOSCALING', 3):
(0.1, None, np.array([0.1, 0.15, -0.11, 2.3, 4.5, 1.7, 0.8])),
('RIGIDSCALING', 3):
(0.1, None,
np.array([0.1, 0.15, -0.11, 2.3, 4.5, 1.7, 0.8, 0.9, 1.1])),
('AFFINE', 3): (0.1, None,
np.array([
0.99, -0.05, 0.03, 1.3, 0.05, 0.99, -0.10, 2.5,
-0.07, 0.10, 0.99, -1.4
]))
}
image_registration_flow = ImageRegistrationFlow()
apply_trans = ApplyTransformFlow()
for i in factors.keys():
static, moving, static_g2w, moving_g2w, smask, mmask, M = \
setup_random_transform(transform=regtransforms[i],
rfactor=factors[i][0])
stat_file = str(i[0]) + '_static.nii.gz'
mov_file = str(i[0]) + '_moving.nii.gz'
save_nifti(pjoin(temp_out_dir, stat_file),
data=static,
affine=static_g2w)
save_nifti(pjoin(temp_out_dir, mov_file),
data=moving,
affine=moving_g2w)
static_image_file = pjoin(temp_out_dir,
str(i[0]) + '_static.nii.gz')
moving_image_file = pjoin(temp_out_dir,
str(i[0]) + '_moving.nii.gz')
out_moved = pjoin(temp_out_dir, str(i[0]) + "_moved.nii.gz")
out_affine = pjoin(temp_out_dir, str(i[0]) + "_affine.txt")
if str(i[0]) == "TRANSLATION":
transform_type = "trans"
elif str(i[0]) == "RIGIDISOSCALING":
transform_type = "rigid_isoscaling"
elif str(i[0]) == "RIGIDSCALING":
transform_type = "rigid_scaling"
else:
transform_type = str(i[0]).lower()
image_registration_flow.run(static_image_file,
moving_image_file,
transform=transform_type,
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
level_iters=[1, 1, 1],
save_metric=False)
# Checking for the created moved file.
assert os.path.exists(out_moved)
assert os.path.exists(out_affine)
images = pjoin(temp_out_dir, '*moving*')
apply_trans.run(static_image_file,
images,
out_dir=temp_out_dir,
transform_map_file=out_affine)
# Checking for the transformed file.
assert os.path.exists(pjoin(temp_out_dir, "transformed.nii.gz"))
def test_image_registration():
with TemporaryDirectory() as temp_out_dir:
static, moving, static_g2w, moving_g2w, smask, mmask, M\
= setup_random_transform(transform=regtransforms[('AFFINE', 3)],
rfactor=0.1)
save_nifti(pjoin(temp_out_dir, 'b0.nii.gz'), data=static,
affine=static_g2w)
save_nifti(pjoin(temp_out_dir, 't1.nii.gz'), data=moving,
affine=moving_g2w)
static_image_file = pjoin(temp_out_dir, 'b0.nii.gz')
moving_image_file = pjoin(temp_out_dir, 't1.nii.gz')
image_registeration_flow = ImageRegistrationFlow()
def read_distance(qual_fname):
temp_val = 0
with open(pjoin(temp_out_dir, qual_fname), 'r') as f:
temp_val = f.readlines()[-1]
return float(temp_val)
def test_com():
out_moved = pjoin(temp_out_dir, "com_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "com_affine.txt")
image_registeration_flow._force_overwrite = True
image_registeration_flow.run(static_image_file,
moving_image_file,
transform='com',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine)
check_existence(out_moved, out_affine)
def test_translation():
out_moved = pjoin(temp_out_dir, "trans_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "trans_affine.txt")
image_registeration_flow._force_overwrite = True
image_registeration_flow.run(static_image_file,
moving_image_file,
transform='trans',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='trans_q.txt')
dist = read_distance('trans_q.txt')
npt.assert_almost_equal(float(dist), -0.3953547764454917, 1)
check_existence(out_moved, out_affine)
def test_rigid():
out_moved = pjoin(temp_out_dir, "rigid_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "rigid_affine.txt")
image_registeration_flow._force_overwrite = True
image_registeration_flow.run(static_image_file,
moving_image_file,
transform='rigid',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='rigid_q.txt')
dist = read_distance('rigid_q.txt')
npt.assert_almost_equal(dist, -0.6900534794005155, 1)
check_existence(out_moved, out_affine)
def test_affine():
out_moved = pjoin(temp_out_dir, "affine_moved.nii.gz")
out_affine = pjoin(temp_out_dir, "affine_affine.txt")
image_registeration_flow._force_overwrite = True
image_registeration_flow.run(static_image_file,
moving_image_file,
transform='affine',
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
save_metric=True,
level_iters=[100, 10, 1],
out_quality='affine_q.txt')
dist = read_distance('affine_q.txt')
npt.assert_almost_equal(dist, -0.7670650775914811, 1)
check_existence(out_moved, out_affine)
# Creating the erroneous behavior
def test_err():
image_registeration_flow._force_overwrite = True
npt.assert_raises(ValueError, image_registeration_flow.run,
static_image_file,
moving_image_file,
transform='notransform')
image_registeration_flow._force_overwrite = True
npt.assert_raises(ValueError, image_registeration_flow.run,
static_image_file,
moving_image_file,
metric='wrong_metric')
def check_existence(movedfile, affine_mat_file):
assert os.path.exists(movedfile)
assert os.path.exists(affine_mat_file)
return True
test_com()
test_translation()
test_rigid()
test_affine()
test_err()
def test_apply_affine_transform():
with TemporaryDirectory() as temp_out_dir:
factors = {
('TRANSLATION', 3): (2.0, None, np.array([2.3, 4.5, 1.7])),
('RIGID', 3): (0.1, None, np.array([0.1, 0.15, -0.11, 2.3, 4.5,
1.7])),
('AFFINE', 3): (0.1, None, np.array([0.99, -0.05, 0.03, 1.3,
0.05, 0.99, -0.10, 2.5,
-0.07, 0.10, 0.99, -1.4]))}
image_registeration_flow = ImageRegistrationFlow()
apply_trans = ApplyTransformFlow()
for i in factors.keys():
static, moving, static_g2w, moving_g2w, smask, mmask, M = \
setup_random_transform(transform=regtransforms[i],
rfactor=factors[i][0])
stat_file = str(i[0]) + '_static.nii.gz'
mov_file = str(i[0]) + '_moving.nii.gz'
save_nifti(pjoin(temp_out_dir, stat_file), data=static,
affine=static_g2w)
save_nifti(pjoin(temp_out_dir, mov_file), data=moving,
affine=moving_g2w)
static_image_file = pjoin(temp_out_dir,
str(i[0]) + '_static.nii.gz')
moving_image_file = pjoin(temp_out_dir,
str(i[0]) + '_moving.nii.gz')
out_moved = pjoin(temp_out_dir,
str(i[0]) + "_moved.nii.gz")
out_affine = pjoin(temp_out_dir,
str(i[0]) + "_affine.txt")
if str(i[0]) == "TRANSLATION":
transform_type = "trans"
else:
transform_type = str(i[0]).lower()
image_registeration_flow.run(static_image_file, moving_image_file,
transform=transform_type,
out_dir=temp_out_dir,
out_moved=out_moved,
out_affine=out_affine,
level_iters=[1, 1, 1],
save_metric=False)
# Checking for the created moved file.
assert os.path.exists(out_moved)
assert os.path.exists(out_affine)
images = pjoin(temp_out_dir, '*moving*')
apply_trans.run(static_image_file, images,
out_dir=temp_out_dir,
transform_map_file=out_affine)
# Checking for the transformed file.
assert os.path.exists(pjoin(temp_out_dir, "transformed.nii.gz"))
