def test_cc_2d():
r""" Test 2D SyN with CC metric
Register a coronal slice from a T1w brain MRI before and after warping
it under a synthetic invertible map. We verify that the final
registration is of good quality.
"""
fname = get_fnames('t1_coronal_slice')
nslices = 1
b = 0.1
m = 4
image = np.load(fname)
moving, static = get_warped_stacked_image(image, nslices, b, m)
# Configure the metric
sigma_diff = 3.0
radius = 4
metric = metrics.CCMetric(2, sigma_diff, radius)
# Configure and run the Optimizer
level_iters = [15, 5]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
warped = mapping.transform(moving)
starting_energy = np.sum((static - moving)**2)
final_energy = np.sum((static - warped)**2)
reduced = 1.0 - final_energy / starting_energy
assert (reduced > 0.9)
def test_ssd_2d_gauss_newton():
r""" Test 2D SyN with SSD metric, Gauss-Newton optimizer
Classical Circle-To-C experiment for 2D monomodal registration. We
verify that the final registration is of good quality.
"""
fname_moving = get_fnames('reg_o')
fname_static = get_fnames('reg_c')
moving = np.load(fname_moving)
static = np.load(fname_static)
moving = np.array(moving, dtype=floating)
static = np.array(static, dtype=floating)
moving = (moving - moving.min()) / (moving.max() - moving.min())
static = (static - static.min()) / (static.max() - static.min())
# Create the SSD metric
smooth = 4
inner_iter = 5
step_type = 'gauss_newton'
similarity_metric = metrics.SSDMetric(2, smooth, inner_iter, step_type)
# Configure and run the Optimizer
level_iters = [200, 100, 50, 25]
step_length = 0.5
opt_tol = 1e-4
inv_iter = 40
inv_tol = 1e-3
ss_sigma_factor = 0.2
optimizer = imwarp.SymmetricDiffeomorphicRegistration(
similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol,
inv_iter, inv_tol)
# test callback not being called
optimizer.INIT_START_CALLED = 0
optimizer.INIT_END_CALLED = 0
optimizer.OPT_START_CALLED = 0
optimizer.OPT_END_CALLED = 0
optimizer.SCALE_START_CALLED = 0
optimizer.SCALE_END_CALLED = 0
optimizer.ITER_START_CALLED = 0
optimizer.ITER_END_CALLED = 0
optimizer.verbosity = VerbosityLevels.DEBUG
id = np.eye(3)
mapping = optimizer.optimize(static, moving, id, id, id)
m = optimizer.get_map()
assert_equal(mapping, m)
warped = mapping.transform(moving)
starting_energy = np.sum((static - moving)**2)
final_energy = np.sum((static - warped)**2)
reduced = 1.0 - final_energy / starting_energy
assert (reduced > 0.9)
assert_equal(optimizer.OPT_START_CALLED, 0)
assert_equal(optimizer.OPT_END_CALLED, 0)
assert_equal(optimizer.SCALE_START_CALLED, 0)
assert_equal(optimizer.SCALE_END_CALLED, 0)
assert_equal(optimizer.ITER_START_CALLED, 0)
assert_equal(optimizer.ITER_END_CALLED, 0)
def test_em_3d_demons():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of EM in
3D, and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common anatomical
regions. The "working version" of EM in 3D obtains very similar results as
those reported for ANTS on the same database. Any modification that produces
a change in the energy profile should be carefully validated to ensure no
accuracy loss.
'''
moving, static = get_synthetic_warped_circle(30)
moving[...,:8] = 0
moving[...,-1:-9:-1] = 0
static[...,:8] = 0
static[...,-1:-9:-1] = 0
#Create the EM metric
smooth=25.0
inner_iter=20
step_length=0.25
q_levels=256
double_gradient=True
iter_type='demons'
similarity_metric = metrics.EMMetric(
3, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Create the optimizer
level_iters = [10, 5]
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)
if floating is np.float32:
expected_profile = \
np.array([144.0369470764622, 122.39543007604394, 112.43783718421119,
85.46819602604248, 101.15549228031932, 119.62429965589826,
124.00100190950647, 118.94404608675168, 112.57666071129853,
117.84424645441413, 4470.2719430621, 4138.201850019068,
4007.225585554024, 4074.8853855654797, 3833.6272345908865])
else:
expected_profile = \
np.array([144.03695786872666, 121.73922862297613, 107.41132697303448,
90.70731102557508, 97.4295175632117, 112.78404966709469,
103.29910157963684, 111.83865866152108, 121.26265581989485,
118.19913094423933, 4222.003181977351, 4418.042311441615,
4508.671160627819, 4761.251133428944, 4292.8507317299245])
assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_em_3d_gauss_newton():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of EM in
3D, and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common
anatomical regions. The "working version" of EM in 3D obtains very similar
results as those reported for ANTS on the same database. Any modification
that produces a change in the energy profile should be carefully validated
to ensure no accuracy loss.
'''
moving, static = get_synthetic_warped_circle(30)
moving[...,:8] = 0
moving[...,-1:-9:-1] = 0
static[...,:8] = 0
static[...,-1:-9:-1] = 0
#Create the EM metric
smooth=25.0
inner_iter=20
step_length=0.25
q_levels=256
double_gradient=True
iter_type='gauss_newton'
similarity_metric = metrics.EMMetric(
3, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Create the optimizer
level_iters = [10, 5]
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
np.array([144.03694724, 63.06874155, 51.84694887, 39.6374044,
31.84981429, 44.3778833, 37.84961761, 38.00509734,
38.67423812, 38.47003306])
elif USING_GCC_SSE2:
expected_profile = \
np.array([144.03694724, 63.06874148, 51.84694881, 39.63740417,
31.84981481, 44.37788414, 37.84961844, 38.00509881,
38.67423954, 38.47003339])
assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_em_3d_demons():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of EM in
3D, and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common
anatomical regions. The "working version" of EM in 3D obtains very similar
results as those reported for ANTS on the same database. Any modification
that produces a change in the energy profile should be carefully validated
to ensure no accuracy loss.
'''
moving, static = get_synthetic_warped_circle(30)
moving[...,:8] = 0
moving[...,-1:-9:-1] = 0
static[...,:8] = 0
static[...,-1:-9:-1] = 0
#Create the EM metric
smooth=25.0
inner_iter=20
step_length=0.25
q_levels=256
double_gradient=True
iter_type='demons'
similarity_metric = metrics.EMMetric(
3, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Create the optimizer
level_iters = [10, 5]
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
np.array([144.03694708, 122.39512307, 111.31925381, 90.9100989,
93.93705232, 104.22993997, 110.57817867, 140.45262039,
133.87804571, 119.20794977])
elif USING_GCC_SSE2:
expected_profile = \
np.array([144.03694708, 122.39512227, 111.31924572, 90.91010482,
93.93707059, 104.22996918, 110.57822649, 140.45298465,
133.87831302, 119.20826433])
assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_em_3d_gauss_newton():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of EM in
3D, and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common anatomical
regions. The "working version" of EM in 3D obtains very similar results as
those reported for ANTS on the same database. Any modification that produces
a change in the energy profile should be carefully validated to ensure no
accuracy loss.
'''
moving, static = get_synthetic_warped_circle(30)
moving[...,:8] = 0
moving[...,-1:-9:-1] = 0
static[...,:8] = 0
static[...,-1:-9:-1] = 0
#Create the EM metric
smooth=25.0
inner_iter=20
step_length=0.25
q_levels=256
double_gradient=True
iter_type='gauss_newton'
similarity_metric = metrics.EMMetric(
3, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Create the optimizer
level_iters = [10, 5]
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)
if floating is np.float32:
expected_profile = \
np.array([144.03694724, 63.06898905, 51.84577681, 39.75409677,
32.10342869, 44.84663951, 38.48587153, 36.64351228,
37.14853803, 40.07766093, 1686.24351443, 1500.19633766,
1302.04852831, 1148.19549508, 1032.820053])
else:
expected_profile = \
np.array([144.03695787, 63.06894122, 51.84575143, 39.75308705,
32.13062096, 44.15214831, 40.71952511, 37.26523679,
37.86654915, 34.92844873, 1644.56890565, 1408.15872151,
1274.1339093, 1131.38037004, 1004.71854514])
assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_cc_3d():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of CC in
3D, and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common anatomical
regions. The "working version" of CC in 3D obtains very similar results as
those reported for ANTS on the same database with the same number of
iterations. Any modification that produces a change in the energy profile
should be carefully validated to ensure no accuracy loss.
'''
moving, static = moving, static = get_synthetic_warped_circle(20)
#Create the CC metric
sigma_diff = 2.0
radius = 4
similarity_metric = metrics.CCMetric(3, sigma_diff, radius)
#Create the optimizer
level_iters = [20, 10]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None, None, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)*1e-4
if floating is np.float32:
expected_profile = \
[-0.17136006, -0.1927569, -0.20436096, -0.20785162, -0.20389074,
-0.20924245, -0.20703476, -0.20914331, -0.20797673, -0.20894931,
-0.20861396, -0.20884857, -0.20867266, -0.20892447, -0.20721796,
-0.20964756, -3.03669258, -3.08002058, -3.07073973, -3.06754953,
-3.06459596, -3.08409787, -3.08325558, -3.08083075, -3.08227744,
-3.08802501]
else:
expected_profile = \
[-0.17416006, -0.1937569, -0.20776097, -0.21125163, -0.20709075,
-0.21184246, -0.21003478, -0.21214333, -0.21117674, -0.21194933,
-0.21141397, -0.21184858, -0.21147268, -0.21232447, -0.211618,
-0.21264758, -3.13589338, -3.29324761, -3.3906351, -3.46849833,
-3.51429254, -3.51747425, -3.5175145, -3.52346059, -3.51608344,
-3.53157882]
expected_profile = np.asarray(expected_profile)
assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_ssd_3d_gauss_newton():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test is intended to detect regressions
only: we saved the energy profile (the sequence of energy values at each
iteration) of a working version of SSD in 3D using the Gauss-Newton step,
and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common
anatomical regions.
'''
moving, static = get_synthetic_warped_circle(35)
moving[...,:10] = 0
moving[...,-1:-11:-1] = 0
static[...,:10] = 0
static[...,-1:-11:-1] = 0
#Create the SSD metric
smooth = 4
inner_iter = 5
step_type = 'gauss_newton'
similarity_metric = metrics.SSDMetric(3, smooth, inner_iter, step_type)
#Create the optimizer
level_iters = [10, 5]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
np.array([348.3204721, 143.480757, 44.30003405, 8.73624842,
3.13227203, 14.70806563, 6.48360268, 23.52491883,
17.25669088, 48.99709064])
elif USING_GCC_SSE2:
expected_profile = \
np.array([348.3204721, 143.48075646, 44.30003413, 8.73624841,
3.13227181, 14.70806845, 6.48360884, 23.52499421,
17.25667176, 48.997691])
assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_ssd_3d_demons():
r'''
Register a stack of circles ('cylinder') before and after warping them
with a synthetic diffeomorphism. This test is intended to detect
regressions only: we saved the energy profile (the sequence of energy
values at each iteration) of a working version of SSD in 3D using the
Demons step, and this test checks that the current energy profile matches
the saved one. The validation of the "working version" was done by
registering the 18 manually annotated T1 brain MRI database IBSR with each
other and computing the jaccard index for all 31 common anatomical regions.
'''
moving, static = get_synthetic_warped_circle(30)
moving[..., :8] = 0
moving[..., -1:-9:-1] = 0
static[..., :8] = 0
static[..., -1:-9:-1] = 0
#Create the SSD metric
smooth = 4
step_type = 'demons'
similarity_metric = metrics.SSDMetric(3,
smooth=smooth,
step_type=step_type)
#Create the optimizer
level_iters = [10, 5]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(
similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol,
inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
np.array([312.22706987, 154.65556884, 53.88543188, 9.11484007,
36.46592407, 13.20522299, 48.65663399, 14.91579802,
49.82954704, 14.92646254])
elif USING_GCC_SSE2:
expected_profile = \
np.array([312.22706987, 154.65556885, 53.88455398, 9.11770682,
36.48642824, 13.21706748, 48.67710635, 14.91782047,
49.84142899, 14.92531294])
assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_ssd_3d_gauss_newton():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test is intended to detect regressions
only: we saved the energy profile (the sequence of energy values at each
iteration) of a working version of SSD in 3D using the Gauss-Newton step,
and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was
done by registering the 18 manually annotated T1 brain MRI database IBSR
with each other and computing the jaccard index for all 31 common anatomical
regions.
'''
moving, static = get_synthetic_warped_circle(35)
moving[...,:10] = 0
moving[...,-1:-11:-1] = 0
static[...,:10] = 0
static[...,-1:-11:-1] = 0
#Create the SSD metric
smooth = 4
inner_iter = 5
step_type = 'gauss_newton'
similarity_metric = metrics.SSDMetric(3, smooth, inner_iter, step_type)
#Create the optimizer
level_iters = [10, 5]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)
if floating is np.float32:
expected_profile = \
np.array([348.32047209927373, 143.49111863234222, 44.328151771258206,
8.759564367010988, 3.1378191742723662, 14.846951961939153,
6.405154727081836, 20.437036950018083, 17.399044912417597,
49.072929423423496, 269.2553956858318, 80.72079256138973,
200.242742072974, 68.21238489882822, 208.28730597575378])
else:
expected_profile = \
np.array([348.32049916992855, 143.49111631974688, 44.328145727486174,
8.759562612294948, 3.137819214539283, 14.846929648490525,
6.405154001052728, 20.437123731745928, 17.39892098642616,
49.07339619667625, 269.2533380585498, 80.72162511785703,
200.24505477294477, 68.21183884286609, 208.29025925025073])
assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_cc_3d():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of CC in
3D, and this test checks that the current energy profile matches the saved
one. The validation of the "working version" was done by registering the
18 manually annotated T1 brain MRI database IBSR with each other and
computing the jaccard index for all 31 common anatomical regions. The
"working version" of CC in 3D obtains very similar results as
those reported for ANTS on the same database with the same number of
iterations. Any modification that produces a change in the energy profile
should be carefully validated to ensure no accuracy loss.
'''
moving, static = moving, static = get_synthetic_warped_circle(20)
#Create the CC metric
sigma_diff = 2.0
radius = 4
similarity_metric = metrics.CCMetric(3, sigma_diff, radius)
#Create the optimizer
level_iters = [20, 10]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(
similarity_metric, level_iters, step_length, ss_sigma_factor, opt_tol,
inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None, None, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(optimizer.full_energy_profile,
10) * 1e-4
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
[-0.17336006, -0.20516197, -0.20448353, -0.20630727, -0.20652892,
-0.2073403, -3.0046531, -3.43771429, -3.47262116, -3.51383381]
elif USING_GCC_SSE2:
expected_profile = \
[-0.17136006, -0.20632291, -0.2038927, -0.20688352, -0.20821154,
-0.20909298, -0.20872891, -0.20933514, -3.06861497, -3.07851062]
expected_profile = np.asarray(expected_profile)
assert_array_almost_equal(energy_profile, expected_profile, decimal=4)
def test_em_3d_demons():
r""" Test 3D SyN with EM metric, demons-like optimizer
Register a volume created by stacking copies of a coronal slice from
a T1w brain MRI before and after warping it under a synthetic
invertible map. We verify that the final registration is of good
quality.
"""
fname = get_fnames('t1_coronal_slice')
nslices = 21
b = 0.1
m = 4
image = np.load(fname)
moving, static = get_warped_stacked_image(image, nslices, b, m)
# Create the EM metric
smooth = 2.0
inner_iter = 20
step_length = 0.25
q_levels = 256
double_gradient = True
iter_type = 'demons'
similarity_metric = metrics.EMMetric(
3, smooth, inner_iter, q_levels, double_gradient, iter_type)
# Create the optimizer
level_iters = [20, 5]
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 1.0
optimizer = imwarp.SymmetricDiffeomorphicRegistration(
similarity_metric,
level_iters,
step_length,
ss_sigma_factor,
opt_tol,
inv_iter,
inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
warped = mapping.transform(moving)
starting_energy = np.sum((static - moving)**2)
final_energy = np.sum((static - warped)**2)
reduced = 1.0 - final_energy / starting_energy
assert(reduced > 0.9)
def test_optimizer_exceptions():
#An arbitrary valid metric
metric = metrics.SSDMetric(2)
# The metric must not be None
assert_raises(ValueError, imwarp.SymmetricDiffeomorphicRegistration, None)
# The iterations list must not be empty
assert_raises(ValueError, imwarp.SymmetricDiffeomorphicRegistration, metric, [])
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, None)
#Verify the default iterations list
assert_array_equal(optimizer.level_iters, [100,100,25])
#Verify exception thrown when attepting to fit the energy profile without enough data
assert_raises(ValueError, optimizer._get_energy_derivative)
def test_ssd_3d_demons():
r'''
Register a stack of circles ('cylinder') before and after warping them with
a synthetic diffeomorphism. This test is intended to detect regressions
only: we saved the energy profile (the sequence of energy values at each
iteration) of a working version of SSD in 3D using the Demons step, and this
test checks that the current energy profile matches the saved one. The
validation of the "working version" was done by registering the 18 manually
annotated T1 brain MRI database IBSR with each other and computing the
jaccard index for all 31 common anatomical regions.
'''
moving, static = get_synthetic_warped_circle(30)
moving[...,:8] = 0
moving[...,-1:-9:-1] = 0
static[...,:8] = 0
static[...,-1:-9:-1] = 0
#Create the SSD metric
smooth = 4
step_type = 'demons'
similarity_metric = metrics.SSDMetric(3, smooth=smooth, step_type=step_type)
#Create the optimizer
level_iters = [10, 5]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)
if floating is np.float32:
expected_profile = \
np.array([312.22706987, 154.65556885, 53.88594419, 9.22626825,
36.50370933, 13.54829978, 49.57619437, 15.71122527,
53.45897119, 15.62018739, 521.95785712, 158.16217928,
182.49116432, 144.91081752, 176.6810387])
else:
expected_profile = \
np.array([312.22709468, 154.65706498, 53.8856324, 8.90160898,
34.91911552, 12.66043296, 49.61341791, 15.14198327,
52.25467529, 18.88243845, 490.48088231, 149.29027701,
192.26219053, 137.5291187, 187.2795753])
assert_array_almost_equal(energy_profile, expected_profile, decimal=6)
def test_cc_3d():
r""" Test 3D SyN with CC metric
Register a volume created by stacking copies of a coronal slice from
a T1w brain MRI before and after warping it under a synthetic
invertible map. We verify that the final registration is of good
quality.
"""
fname = get_fnames('t1_coronal_slice')
nslices = 21
b = 0.1
m = 4
image = np.load(fname)
moving, static = get_warped_stacked_image(image, nslices, b, m)
# Create the CC metric
sigma_diff = 2.0
radius = 2
similarity_metric = metrics.CCMetric(3, sigma_diff, radius)
# Create the optimizer
level_iters = [20, 5]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.2
optimizer = imwarp.SymmetricDiffeomorphicRegistration(
similarity_metric,
level_iters,
step_length,
ss_sigma_factor,
opt_tol,
inv_iter,
inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None, None, None)
m = optimizer.get_map()
assert_equal(mapping, m)
warped = mapping.transform(moving)
starting_energy = np.sum((static - moving)**2)
final_energy = np.sum((static - warped)**2)
reduced = 1.0 - final_energy / starting_energy
assert(reduced > 0.9)
def test_ssd_3d_gauss_newton():
r""" Test 3D SyN with SSD metric, Gauss-Newton optimizer
Register a stack of circles ('cylinder') before and after warping them
with a synthetic diffeomorphism. We verify that the final registration
is of good quality.
"""
moving, static = get_synthetic_warped_circle(35)
moving[..., :10] = 0
moving[..., -1:-11:-1] = 0
static[..., :10] = 0
static[..., -1:-11:-1] = 0
# Create the SSD metric
smooth = 4
inner_iter = 5
step_type = 'gauss_newton'
similarity_metric = metrics.SSDMetric(3, smooth, inner_iter, step_type)
# Create the optimizer
level_iters = [10, 10]
step_length = 0.1
opt_tol = 1e-4
inv_iter = 20
inv_tol = 1e-3
ss_sigma_factor = 0.5
optimizer = imwarp.SymmetricDiffeomorphicRegistration(
similarity_metric,
level_iters,
step_length,
ss_sigma_factor,
opt_tol,
inv_iter,
inv_tol)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
warped = mapping.transform(moving)
starting_energy = np.sum((static - moving)**2)
final_energy = np.sum((static - warped)**2)
reduced = 1.0 - final_energy / starting_energy
assert(reduced > 0.9)
def test_em_2d_demons():
r'''
Register a circle to itself after warping it under a synthetic invertible
map. This test is intended to detect regressions only: we saved the energy
profile (the sequence of energy values at each iteration) of a working
version of EM in 2D, and this test checks that the current energy profile
matches the saved one.
'''
moving, static = get_synthetic_warped_circle(1)
#Configure the metric
smooth=25.0
inner_iter=20
q_levels=256
double_gradient=False
iter_type='demons'
metric = metrics.EMMetric(
2, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Configure and run the Optimizer
level_iters = [40, 20, 10]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
[2.50773393, 3.26942324, 1.81684393, 5.44878881, 40.0195918,
31.87030788, 25.15710409, 29.82206485, 196.33114499, 213.86419995]
elif USING_GCC_SSE2:
expected_profile = \
[2.50773393, 3.26942352, 1.8168445, 5.44879264, 40.01956373,
31.65616398, 32.43115903, 35.24130742, 192.89072697, 195.456909]
assert_array_almost_equal(energy_profile, np.array(expected_profile),
decimal=5)
def test_em_2d_gauss_newton():
r'''
Register a circle to itself after warping it under a synthetic invertible
map. This test is intended to detect regressions only: we saved the energy
profile (the sequence of energy values at each iteration) of a working
version of EM in 2D, and this test checks that the current energy profile
matches the saved one.
'''
moving, static = get_synthetic_warped_circle(1)
#Configure the metric
smooth=25.0
inner_iter=20
q_levels=256
double_gradient=False
iter_type='gauss_newton'
metric = metrics.EMMetric(
2, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Configure and run the Optimizer
level_iters = [40, 20, 10]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
[2.50773392, 0.41762978, 0.30900322, 0.14818498, 0.44620725,
1.53134054, 1.42115728, 1.66358267, 1.184265, 46.13635772]
elif USING_GCC_SSE2:
expected_profile = \
[2.50773392, 0.41763383, 0.30908578, 0.06241115, 0.11573476,
2.48475885, 1.10053769, 0.9270271, 49.37186785, 44.72643467]
assert_array_almost_equal(energy_profile, np.array(expected_profile),
decimal=5)
def test_em_2d_demons():
r'''
Register a circle to itself after warping it under a synthetic invertible
map. This test is intended to detect regressions only: we saved the energy
profile (the sequence of energy values at each iteration) of a working
version of EM in 2D, and this test checks that the current energy profile
matches the saved one.
'''
moving, static = get_synthetic_warped_circle(1)
#Configure the metric
smooth=25.0
inner_iter=20
q_levels=256
double_gradient=False
iter_type='demons'
metric = metrics.EMMetric(
2, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Configure and run the Optimizer
level_iters = [40, 20, 10]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)[::2]
if floating is np.float32:
expected_profile = \
[2.50773393, 4.59842633, 3.94307794, 3.09777134, 2.57982865,
3.24937725, 0.42507437, 2.59523238, 29.8114103, 34.94621044,
27.49480758, 38.64567224, 28.14442977, 25.34123425, 36.95076494,
192.36444764, 202.90168694, 188.44310016, 199.73662253, 193.81159141]
else:
expected_profile = \
[2.50773436, 4.59843299, 3.94307817, 3.09777401, 2.57983375,
3.24936765, 0.42506361, 2.5952175, 29.81143768, 33.42148555,
29.04341476, 29.44541313, 27.39435491, 27.62029669, 187.34889413,
206.57998934, 198.48724278, 188.65410869, 177.83943006]
assert_array_almost_equal(energy_profile, np.array(expected_profile))
def test_em_2d_gauss_newton():
r'''
Register a circle to itself after warping it under a synthetic invertible
map. This test is intended to detect regressions only: we saved the energy
profile (the sequence of energy values at each iteration) of a working
version of EM in 2D, and this test checks that the current energy profile
matches the saved one.
'''
moving, static = get_synthetic_warped_circle(1)
#Configure the metric
smooth=25.0
inner_iter=20
q_levels=256
double_gradient=False
iter_type='gauss_newton'
metric = metrics.EMMetric(
2, smooth, inner_iter, q_levels, double_gradient, iter_type)
#Configure and run the Optimizer
level_iters = [40, 20, 10]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)[::4]
if floating is np.float32:
expected_profile = \
[2.50773392e+00, 1.19082175e+00, 3.44192871e-01, 4.26320783e-01,
3.77910892e-02, 3.34404847e-01, 3.00400618e+00, 2.56292691e+00,
2.10458398e+00, 2.45479897e+00, 6.14513257e+01, 5.38091115e+01,
5.27868250e+01]
else:
expected_profile = \
[2.50773436, 1.19082577, 0.34422934, 0.19543193, 0.23659461,
0.41145348, 3.56414698, 3.02325691, 1.74649377, 1.8172007,
2.09930208, 53.06513917, 49.4088898 ]
assert_array_almost_equal(energy_profile, np.array(expected_profile))
def test_em_2d_gauss_newton():
r""" Test 2D SyN with EM metric, Gauss-Newton optimizer
Register a coronal slice from a T1w brain MRI before and after warping
it under a synthetic invertible map. We verify that the final
registration is of good quality.
"""
fname = get_fnames('t1_coronal_slice')
nslices = 1
b = 0.1
m = 4
image = np.load(fname)
moving, static = get_warped_stacked_image(image, nslices, b, m)
# Configure the metric
smooth = 5.0
inner_iter = 20
q_levels = 256
double_gradient = False
iter_type = 'gauss_newton'
metric = metrics.EMMetric(2, smooth, inner_iter, q_levels, double_gradient,
iter_type)
# Configure and run the Optimizer
level_iters = [40, 20, 10]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
warped = mapping.transform(moving)
starting_energy = np.sum((static - moving)**2)
final_energy = np.sum((static - warped)**2)
reduced = 1.0 - final_energy / starting_energy
assert (reduced > 0.9)
def test_cc_2d():
r'''
Register a circle to itself after warping it under a synthetic invertible
map. This test is intended to detect regressions only: we saved the energy
profile (the sequence of energy values at each iteration) of a working
version of CC in 2D, and this test checks that the current energy profile
matches the saved one.
'''
moving, static = get_synthetic_warped_circle(1)
#Configure the metric
sigma_diff = 3.0
radius = 4
metric = metrics.CCMetric(2, sigma_diff, radius)
#Configure and run the Optimizer
level_iters = [10, 5]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(energy_profile)
if USING_VC_SSE2:
expected_profile = \
[-681.02276193, -910.57721051, -1012.76781394, -1021.24181308,
-1016.97233745, -977.35458126, -1013.90114894, -989.04516449,
-1021.72431465, -988.46698723]
elif USING_GCC_SSE2:
expected_profile = \
[-681.02276236, -920.57714783, -1008.82241171, -1021.91021701,
-994.86961164, -1026.52978164, -1015.83587405, -1020.02780802,
-993.8576053, -1026.4369566 ]
expected_profile = np.asarray(expected_profile)
assert_array_almost_equal(energy_profile, expected_profile, decimal=5)
def test_cc_2d():
r'''
Register a circle to itself after warping it under a synthetic invertible
map. This test is intended to detect regressions only: we saved the energy
profile (the sequence of energy values at each iteration) of a working
version of CC in 2D, and this test checks that the current energy profile
matches the saved one.
'''
moving, static = get_synthetic_warped_circle(1)
#Configure the metric
sigma_diff = 3.0
radius = 4
metric = metrics.CCMetric(2, sigma_diff, radius)
#Configure and run the Optimizer
level_iters = [10, 5]
optimizer = imwarp.SymmetricDiffeomorphicRegistration(metric, level_iters)
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
energy_profile = np.array(optimizer.full_energy_profile)
if floating is np.float32:
expected_profile = \
[-681.02276236, -920.57714783, -1006.81657463, -1025.83920278,
-1018.48108319, -1018.45000627, -1005.09703188, -1012.69584487,
-1016.42673146, -998.88289898, -2788.28110913, -2829.93521983,
-2823.50737329, -2840.70676978, -2805.36486487]
else:
expected_profile = \
[-685.02275452, -928.57719958, -1018.81655195, -1025.39899865,
-1002.72673624, -1009.50734392, -1017.17915308, -999.55456055,
-1025.76154939, -994.94755395, -2784.70111646, -2820.40702267,
-2801.45275665, -2822.69658715, -2813.39864684]
expected_profile = np.asarray(expected_profile)
assert_array_almost_equal(energy_profile, expected_profile)
def compute_morph_map(img_m, img_s=None, niter_affine=(100, 100, 10),
niter_sdr=(5, 5, 3)):
# get Static to world transform
img_s_grid2world = img_s.affine
# output Static as ndarray
img_s = img_s.dataobj[:, :, :]
# normalize values
img_s = img_s.astype('float') / img_s.max()
# get Moving to world transform
img_m_grid2world = img_m.affine
# output Moving as ndarray
img_m = img_m.dataobj[:, :, :]
# normalize values
img_m = img_m.astype('float') / img_m.max()
# compute center of mass
c_of_mass = imaffine.transform_centers_of_mass(img_s, img_s_grid2world,
img_m, img_m_grid2world)
nbins = 32
# set up Affine Registration
affreg = imaffine.AffineRegistration(
metric=imaffine.MutualInformationMetric(nbins, None),
level_iters=list(niter_affine),
sigmas=[3.0, 1.0, 0.0],
factors=[4, 2, 1])
# translation
translation = affreg.optimize(img_s, img_m,
transforms.TranslationTransform3D(), None,
img_s_grid2world, img_m_grid2world,
starting_affine=c_of_mass.affine)
# rigid body transform (translation + rotation)
rigid = affreg.optimize(img_s, img_m,
transforms.RigidTransform3D(), None,
img_s_grid2world, img_m_grid2world,
starting_affine=translation.affine)
# affine transform (translation + rotation + scaling)
affine = affreg.optimize(img_s, img_m,
transforms.AffineTransform3D(), None,
img_s_grid2world, img_m_grid2world,
starting_affine=rigid.affine)
# apply affine transformation
img_m_affine = affine.transform(img_m)
# set up Symmetric Diffeomorphic Registration (metric, iterations)
sdr = imwarp.SymmetricDiffeomorphicRegistration(
metrics.CCMetric(3), list(niter_sdr))
# compute mapping
mapping = sdr.optimize(img_s, img_m_affine)
return mapping, affine
def _compute_morph_sdr(mri_from, mri_to, niter_affine, niter_sdr, zooms):
"""Get a matrix that morphs data from one subject to another."""
import nibabel as nib
with np.testing.suppress_warnings():
from dipy.align import imaffine, imwarp, metrics, transforms
from dipy.align.reslice import reslice
logger.info('Computing nonlinear Symmetric Diffeomorphic Registration...')
# reslice mri_from
mri_from_res, mri_from_res_affine = reslice(
_get_img_fdata(mri_from), mri_from.affine,
mri_from.header.get_zooms()[:3], zooms)
with warnings.catch_warnings(): # nibabel<->numpy warning
mri_from = nib.Nifti1Image(mri_from_res, mri_from_res_affine)
# reslice mri_to
mri_to_res, mri_to_res_affine = reslice(
_get_img_fdata(mri_to), mri_to.affine, mri_to.header.get_zooms()[:3],
zooms)
with warnings.catch_warnings(): # nibabel<->numpy warning
mri_to = nib.Nifti1Image(mri_to_res, mri_to_res_affine)
affine = mri_to.affine
mri_to = _get_img_fdata(mri_to) # to ndarray
mri_to /= mri_to.max()
mri_from_affine = mri_from.affine # get mri_from to world transform
mri_from = _get_img_fdata(mri_from) # to ndarray
mri_from /= mri_from.max() # normalize
# compute center of mass
c_of_mass = imaffine.transform_centers_of_mass(
mri_to, affine, mri_from, mri_from_affine)
# set up Affine Registration
affreg = imaffine.AffineRegistration(
metric=imaffine.MutualInformationMetric(nbins=32),
level_iters=list(niter_affine),
sigmas=[3.0, 1.0, 0.0],
factors=[4, 2, 1])
# translation
logger.info('Optimizing translation:')
with wrapped_stdout(indent=' '):
translation = affreg.optimize(
mri_to, mri_from, transforms.TranslationTransform3D(), None,
affine, mri_from_affine, starting_affine=c_of_mass.affine)
# rigid body transform (translation + rotation)
logger.info('Optimizing rigid-body:')
with wrapped_stdout(indent=' '):
rigid = affreg.optimize(
mri_to, mri_from, transforms.RigidTransform3D(), None,
affine, mri_from_affine, starting_affine=translation.affine)
# affine transform (translation + rotation + scaling)
logger.info('Optimizing full affine:')
with wrapped_stdout(indent=' '):
pre_affine = affreg.optimize(
mri_to, mri_from, transforms.AffineTransform3D(), None,
affine, mri_from_affine, starting_affine=rigid.affine)
# compute mapping
sdr = imwarp.SymmetricDiffeomorphicRegistration(
metrics.CCMetric(3), list(niter_sdr))
logger.info('Optimizing SDR:')
with wrapped_stdout(indent=' '):
sdr_morph = sdr.optimize(mri_to, pre_affine.transform(mri_from))
shape = tuple(sdr_morph.domain_shape) # should be tuple of int
return shape, zooms, affine, pre_affine, sdr_morph
def test_ssd_2d_demons():
r'''
Classical Circle-To-C experiment for 2D Monomodal registration. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of SSD in
2D using the Demons step, and this test checks that the current energy
profile matches the saved one.
'''
fname_moving = get_data('reg_o')
fname_static = get_data('reg_c')
moving = np.load(fname_moving)
static = np.load(fname_static)
moving = np.array(moving, dtype=floating)
static = np.array(static, dtype=floating)
moving = (moving-moving.min())/(moving.max() - moving.min())
static = (static-static.min())/(static.max() - static.min())
#Create the SSD metric
smooth = 4
step_type = 'demons'
similarity_metric = metrics.SSDMetric(2, smooth=smooth, step_type=step_type)
#Configure and run the Optimizer
level_iters = [200, 100, 50, 25]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 40
inv_tol = 1e-3
ss_sigma_factor = 0.2
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
#test callback being called
optimizer.INIT_START_CALLED = 0
optimizer.INIT_END_CALLED = 0
optimizer.OPT_START_CALLED = 0
optimizer.OPT_END_CALLED = 0
optimizer.SCALE_START_CALLED = 0
optimizer.SCALE_END_CALLED = 0
optimizer.ITER_START_CALLED = 0
optimizer.ITER_END_CALLED = 0
optimizer.callback_counter_test = 0
optimizer.callback = simple_callback
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
subsampled_energy_profile = np.array(optimizer.full_energy_profile[::10])
if floating is np.float32:
expected_profile = \
np.array([312.6813333, 162.57756447, 99.2766679, 77.38698935,
61.75415204, 55.37420428, 46.36872571, 41.81811505,
36.38683617, 33.03952963, 30.91409901, 54.41447237,
23.40232241, 12.75092466, 10.19231733, 9.21058037,
57.4636143, 38.94004856, 36.26093212, 108.0136453,
81.35521049, 74.61956833])
else:
expected_profile = \
np.array([312.68133361, 162.57744066, 99.27669798, 77.38683186,
61.75391429, 55.3740711, 46.36870776, 41.81809239,
36.3898153, 32.78365961, 30.69843811, 53.67073767,
21.74630524, 11.98102583, 11.51086685, 55.30707781,
39.88467545, 34.29444978, 33.10822964, 122.64743831,
84.18144073, 75.60088687])
assert_array_almost_equal(subsampled_energy_profile, expected_profile)
assert_equal(optimizer.OPT_START_CALLED, 1)
assert_equal(optimizer.OPT_END_CALLED, 1)
assert_equal(optimizer.SCALE_START_CALLED, 1)
assert_equal(optimizer.SCALE_END_CALLED, 1)
assert_equal(optimizer.ITER_START_CALLED, 1)
assert_equal(optimizer.ITER_END_CALLED, 1)
def test_ssd_2d_gauss_newton():
r'''
Classical Circle-To-C experiment for 2D Monomodal registration. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of SSD
in 2D using the Gauss Newton step, and this test checks that the current
energy profile matches the saved one.
'''
fname_moving = get_data('reg_o')
fname_static = get_data('reg_c')
moving = np.load(fname_moving)
static = np.load(fname_static)
moving = np.array(moving, dtype=floating)
static = np.array(static, dtype=floating)
moving = (moving-moving.min())/(moving.max() - moving.min())
static = (static-static.min())/(static.max() - static.min())
#Create the SSD metric
smooth = 4
inner_iter = 5
step_type = 'gauss_newton'
similarity_metric = metrics.SSDMetric(2, smooth, inner_iter, step_type)
#Configure and run the Optimizer
level_iters = [200, 100, 50, 25]
step_length = 0.5
opt_tol = 1e-4
inv_iter = 40
inv_tol = 1e-3
ss_sigma_factor = 0.2
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
#test callback not being called
optimizer.INIT_START_CALLED = 0
optimizer.INIT_END_CALLED = 0
optimizer.OPT_START_CALLED = 0
optimizer.OPT_END_CALLED = 0
optimizer.SCALE_START_CALLED = 0
optimizer.SCALE_END_CALLED = 0
optimizer.ITER_START_CALLED = 0
optimizer.ITER_END_CALLED = 0
optimizer.verbosity = VerbosityLevels.DEBUG
id = np.eye(3)
mapping = optimizer.optimize(static, moving, id, id, id)
m = optimizer.get_map()
assert_equal(mapping, m)
subsampled_energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(subsampled_energy_profile)
if USING_VC_SSE2:
expected_profile = \
np.array([312.68133316, 70.17782995, 21.38508088, 96.41054776,
49.990781, 43.11867579, 24.53952718, 51.0786643,
143.24848252, 150.48349573])
elif USING_GCC_SSE2:
expected_profile = \
np.array([312.68133316, 70.17782938, 21.26798507, 96.51765054,
51.1495088, 37.86204803, 21.62425293, 49.44868302,
121.6643917, 137.91427228])
assert_array_almost_equal(subsampled_energy_profile, expected_profile,
decimal = 5)
assert_equal(optimizer.OPT_START_CALLED, 0)
assert_equal(optimizer.OPT_END_CALLED, 0)
assert_equal(optimizer.SCALE_START_CALLED, 0)
assert_equal(optimizer.SCALE_END_CALLED, 0)
assert_equal(optimizer.ITER_START_CALLED, 0)
assert_equal(optimizer.ITER_END_CALLED, 0)
def test_ssd_2d_demons():
r'''
Classical Circle-To-C experiment for 2D Monomodal registration. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of SSD in
2D using the Demons step, and this test checks that the current energy
profile matches the saved one.
'''
fname_moving = get_data('reg_o')
fname_static = get_data('reg_c')
moving = np.load(fname_moving)
static = np.load(fname_static)
moving = np.array(moving, dtype=floating)
static = np.array(static, dtype=floating)
moving = (moving-moving.min())/(moving.max() - moving.min())
static = (static-static.min())/(static.max() - static.min())
#Create the SSD metric
smooth = 4
step_type = 'demons'
similarity_metric = metrics.SSDMetric(2, smooth=smooth, step_type=step_type)
#Configure and run the Optimizer
level_iters = [200, 100, 50, 25]
step_length = 0.25
opt_tol = 1e-4
inv_iter = 40
inv_tol = 1e-3
ss_sigma_factor = 0.2
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
#test callback being called
optimizer.INIT_START_CALLED = 0
optimizer.INIT_END_CALLED = 0
optimizer.OPT_START_CALLED = 0
optimizer.OPT_END_CALLED = 0
optimizer.SCALE_START_CALLED = 0
optimizer.SCALE_END_CALLED = 0
optimizer.ITER_START_CALLED = 0
optimizer.ITER_END_CALLED = 0
optimizer.callback_counter_test = 0
optimizer.callback = simple_callback
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, None)
m = optimizer.get_map()
assert_equal(mapping, m)
subsampled_energy_profile = subsample_profile(
optimizer.full_energy_profile, 10)
print(subsampled_energy_profile)
if USING_VC_SSE2:
expected_profile = \
np.array([312.6813333, 80.74625551, 49.43591374, 34.08871301,
25.18286981, 17.78955273, 25.91334939, 20.16932281,
43.86083145, 79.0966558 ])
elif USING_GCC_SSE2:
expected_profile = \
np.array([312.6813333, 98.17321941, 60.98300837, 47.75387157,
34.11067498, 122.91901409, 19.75599298, 14.28763847,
36.33599718, 88.62426913])
assert_array_almost_equal(subsampled_energy_profile,
expected_profile, decimal=5)
assert_equal(optimizer.OPT_START_CALLED, 1)
assert_equal(optimizer.OPT_END_CALLED, 1)
assert_equal(optimizer.SCALE_START_CALLED, 1)
assert_equal(optimizer.SCALE_END_CALLED, 1)
assert_equal(optimizer.ITER_START_CALLED, 1)
assert_equal(optimizer.ITER_END_CALLED, 1)
def test_ssd_2d_gauss_newton():
r'''
Classical Circle-To-C experiment for 2D Monomodal registration. This test
is intended to detect regressions only: we saved the energy profile (the
sequence of energy values at each iteration) of a working version of SSD in
2D using the Gauss Newton step, and this test checks that the current energy
profile matches the saved one.
'''
fname_moving = get_data('reg_o')
fname_static = get_data('reg_c')
moving = np.load(fname_moving)
static = np.load(fname_static)
moving = np.array(moving, dtype=floating)
static = np.array(static, dtype=floating)
moving = (moving-moving.min())/(moving.max() - moving.min())
static = (static-static.min())/(static.max() - static.min())
#Create the SSD metric
smooth = 4
inner_iter = 5
step_type = 'gauss_newton'
similarity_metric = metrics.SSDMetric(2, smooth, inner_iter, step_type)
#Configure and run the Optimizer
level_iters = [200, 100, 50, 25]
step_length = 0.5
opt_tol = 1e-4
inv_iter = 40
inv_tol = 1e-3
ss_sigma_factor = 0.2
optimizer = imwarp.SymmetricDiffeomorphicRegistration(similarity_metric,
level_iters, step_length, ss_sigma_factor, opt_tol, inv_iter, inv_tol)
#test callback not being called
optimizer.INIT_START_CALLED = 0
optimizer.INIT_END_CALLED = 0
optimizer.OPT_START_CALLED = 0
optimizer.OPT_END_CALLED = 0
optimizer.SCALE_START_CALLED = 0
optimizer.SCALE_END_CALLED = 0
optimizer.ITER_START_CALLED = 0
optimizer.ITER_END_CALLED = 0
optimizer.verbosity = VerbosityLevels.DEBUG
mapping = optimizer.optimize(static, moving, np.eye(3), np.eye(3), np.eye(3))
m = optimizer.get_map()
assert_equal(mapping, m)
subsampled_energy_profile = np.array(optimizer.full_energy_profile[::10])
if floating is np.float32:
expected_profile = \
np.array([312.68133316, 79.40404517, 23.3715698, 125.02700267,
59.79982213, 34.64971733, 23.37131446, 171.28250576,
62.22266377, 125.24392168])
else:
expected_profile = \
np.array([312.68133361, 79.40404354, 23.34588446, 124.3247997,
61.69601973, 38.15047181, 23.53315113, 80.0791295,
57.21700113, 143.73270476])
assert_array_almost_equal(subsampled_energy_profile, expected_profile)
assert_equal(optimizer.OPT_START_CALLED, 0)
assert_equal(optimizer.OPT_END_CALLED, 0)
assert_equal(optimizer.SCALE_START_CALLED, 0)
assert_equal(optimizer.SCALE_END_CALLED, 0)
assert_equal(optimizer.ITER_START_CALLED, 0)
assert_equal(optimizer.ITER_END_CALLED, 0)
def _compute_morph_sdr(mri_from, mri_to, niter_affine=(100, 100, 10),
niter_sdr=(5, 5, 3), zooms=(5., 5., 5.)):
"""Get a matrix that morphs data from one subject to another."""
_check_dep(nibabel='2.1.0', dipy='0.10.1')
import nibabel as nib
with np.testing.suppress_warnings():
from dipy.align import imaffine, imwarp, metrics, transforms
from dipy.align.reslice import reslice
logger.info('Computing nonlinear Symmetric Diffeomorphic Registration...')
# use voxel size of mri_from
if zooms is None:
zooms = mri_from.header.get_zooms()[:3]
zooms = np.atleast_1d(zooms).astype(float)
if zooms.shape == (1,):
zooms = np.repeat(zooms, 3)
if zooms.shape != (3,):
raise ValueError('zooms must be None, a singleton, or have shape (3,),'
' got shape %s' % (zooms.shape,))
# reslice mri_from
mri_from_res, mri_from_res_affine = reslice(
mri_from.get_data(), mri_from.affine, mri_from.header.get_zooms()[:3],
zooms)
with warnings.catch_warnings(): # nibabel<->numpy warning
mri_from = nib.Nifti1Image(mri_from_res, mri_from_res_affine)
# reslice mri_to
mri_to_res, mri_to_res_affine = reslice(
mri_to.get_data(), mri_to.affine, mri_to.header.get_zooms()[:3],
zooms)
with warnings.catch_warnings(): # nibabel<->numpy warning
mri_to = nib.Nifti1Image(mri_to_res, mri_to_res_affine)
affine = mri_to.affine
mri_to = np.array(mri_to.dataobj, float) # to ndarray
mri_to /= mri_to.max()
mri_from_affine = mri_from.affine # get mri_from to world transform
mri_from = np.array(mri_from.dataobj, float) # to ndarray
mri_from /= mri_from.max() # normalize
# compute center of mass
c_of_mass = imaffine.transform_centers_of_mass(
mri_to, affine, mri_from, affine)
# set up Affine Registration
affreg = imaffine.AffineRegistration(
metric=imaffine.MutualInformationMetric(nbins=32),
level_iters=list(niter_affine),
sigmas=[3.0, 1.0, 0.0],
factors=[4, 2, 1])
# translation
translation = affreg.optimize(
mri_to, mri_from, transforms.TranslationTransform3D(), None, affine,
mri_from_affine, starting_affine=c_of_mass.affine)
# rigid body transform (translation + rotation)
rigid = affreg.optimize(
mri_to, mri_from, transforms.RigidTransform3D(), None,
affine, mri_from_affine, starting_affine=translation.affine)
# affine transform (translation + rotation + scaling)
pre_affine = affreg.optimize(
mri_to, mri_from, transforms.AffineTransform3D(), None,
affine, mri_from_affine, starting_affine=rigid.affine)
# compute mapping
sdr = imwarp.SymmetricDiffeomorphicRegistration(
metrics.CCMetric(3), list(niter_sdr))
sdr_morph = sdr.optimize(mri_to, pre_affine.transform(mri_from))
shape = tuple(sdr_morph.domain_shape) # should be tuple of int
logger.info('done.')
return shape, zooms, affine, pre_affine, sdr_morph
