def build_all_models_frgc(images,
ref_frame_path,
subject_id,
out_path='/vol/atlas/homes/pts08/',
transform_class=ThinPlateSplines,
square_mask=False):
print "Beginning model creation for {0}".format(subject_id)
# Build reference frame
ref_frame = mio.import_image(ref_frame_path)
labeller([ref_frame], 'PTS', ibug_68_closed_mouth)
ref_frame.crop_to_landmarks(boundary=2,
group='ibug_68_closed_mouth',
label='all')
if not square_mask:
ref_frame.constrain_mask_to_landmarks(group='ibug_68_closed_mouth',
label='all')
reference_shape = ref_frame.landmarks['ibug_68_closed_mouth'].lms
# Extract all shapes
labeller(images, 'PTS', ibug_68_closed_mouth)
shapes = [img.landmarks['ibug_68_closed_mouth'].lms for img in images]
# Warp each of the images to the reference image
print "Warping all frgc shapes to reference frame of {0}".format(
subject_id)
tps_transforms = [
transform_class(reference_shape, shape) for shape in shapes
]
warped_images = [
img.warp_to(ref_frame.mask, t)
for img, t in zip(images, tps_transforms)
]
# Calculate the normal matrix
print 'Extracting all normals'
normal_matrix = extract_normals(warped_images)
# Save memory by deleting all the images since we don't need them any more.
# Keep one around that we can query for it's size etc
example_image = deepcopy(warped_images[0])
del warped_images[:]
# Normals
print 'Computing normal feature space'
normal_images = create_feature_space(normal_matrix,
example_image,
'normals',
subject_id,
out_path=out_path)
# Spherical
print 'Computing spherical feature space'
spherical_matrix = Spherical().logmap(normal_matrix)
spherical_images = create_feature_space(spherical_matrix,
example_image,
'spherical',
subject_id,
out_path=out_path)
# AEP
print 'Computing AEP feature space'
mean_normals = normalise_vector(np.mean(normal_matrix, 0))
aep_matrix = AEP(mean_normals).logmap(normal_matrix)
aep_images = create_feature_space(aep_matrix,
example_image,
'aep',
subject_id,
out_path=out_path)
# PGA
print 'Computing PGA feature space'
mu = intrinsic_mean(normal_matrix, PGA, max_iters=50)
pga_matrix = PGA(mu).logmap(normal_matrix)
pga_images = create_feature_space(pga_matrix,
example_image,
'pga',
subject_id,
out_path=out_path)
# PCA models
n_components = 200
print 'Computing PCA models ({} components)'.format(n_components)
template = ref_frame
normal_model = PCAModel(normal_images, center=True)
normal_model.trim_components(200)
cosine_model = PCAModel(normal_images, center=False)
cosine_model.trim_components(200)
spherical_model = PCAModel(spherical_images, center=False)
spherical_model.trim_components(200)
aep_model = PCAModel(aep_images, center=False)
aep_model.trim_components(200)
pga_model = PCAModel(pga_images, center=False)
pga_model.trim_components(200)
mean_normals_image = normal_model.mean
mu_image = mean_normals_image.from_vector(mu)
# Save out models
pickle_model(out_path, subject_id, 'normal', normal_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'cosine', cosine_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'spherical', spherical_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'aep', aep_model, template,
mean_normals)
pickle_model(out_path,
subject_id,
'pga',
pga_model,
template,
mean_normals,
intrinsic_means=mu_image)
# <codecell> from logmap_utils import partial_logmap from aep import AEP aep_matrix = AEP(mean_normals).logmap(normal_matrix) aep_images = create_feature_space(aep_matrix, warped_images[0], 'aep') # <markdowncell> # ## Calculate the PGA feature space # <codecell> from pga import PGA, intrinsic_mean mu = intrinsic_mean(normal_matrix, PGA, max_iters=50) # <codecell> pga_matrix = PGA(mu).logmap(normal_matrix) pga_images = create_feature_space(pga_matrix, warped_images[0], 'pga') # <markdowncell> # ## Calculate the PCA for LS, Spherical, Cosine and AEP # <codecell> # Create the template image template = ref_frame
# <codecell> from logmap_utils import partial_logmap from aep import AEP aep_matrix = AEP(mean_normals).logmap(normal_matrix) aep_images = create_feature_space(aep_matrix, warped_images[0], 'aep') # <markdowncell> # ## Calculate the PGA feature space # <codecell> from pga import PGA, intrinsic_mean mu = intrinsic_mean(normal_matrix, PGA, max_iters=50) # <codecell> pga_matrix = PGA(mu).logmap(normal_matrix) pga_images = create_feature_space(pga_matrix, warped_images[0], 'pga') # <markdowncell> # ## Calculate the PCA for LS, Spherical, Cosine and AEP # <codecell> # Create the template image template = ref_frame
def build_all_models_frgc(images, ref_frame_path, subject_id,
out_path='/vol/atlas/homes/pts08/',
transform_class=ThinPlateSplines,
square_mask=False):
print "Beginning model creation for {0}".format(subject_id)
# Build reference frame
ref_frame = mio.import_image(ref_frame_path)
labeller([ref_frame], 'PTS', ibug_68_closed_mouth)
ref_frame.crop_to_landmarks(boundary=2, group='ibug_68_closed_mouth',
label='all')
if not square_mask:
ref_frame.constrain_mask_to_landmarks(group='ibug_68_closed_mouth',
label='all')
reference_shape = ref_frame.landmarks['ibug_68_closed_mouth'].lms
# Extract all shapes
labeller(images, 'PTS', ibug_68_closed_mouth)
shapes = [img.landmarks['ibug_68_closed_mouth'].lms for img in images]
# Warp each of the images to the reference image
print "Warping all frgc shapes to reference frame of {0}".format(subject_id)
tps_transforms = [transform_class(reference_shape, shape) for shape in shapes]
warped_images = [img.warp_to(ref_frame.mask, t)
for img, t in zip(images, tps_transforms)]
# Calculate the normal matrix
print 'Extracting all normals'
normal_matrix = extract_normals(warped_images)
# Save memory by deleting all the images since we don't need them any more.
# Keep one around that we can query for it's size etc
example_image = deepcopy(warped_images[0])
del warped_images[:]
# Normals
print 'Computing normal feature space'
normal_images = create_feature_space(normal_matrix, example_image,
'normals', subject_id,
out_path=out_path)
# Spherical
print 'Computing spherical feature space'
spherical_matrix = Spherical().logmap(normal_matrix)
spherical_images = create_feature_space(spherical_matrix, example_image,
'spherical', subject_id,
out_path=out_path)
# AEP
print 'Computing AEP feature space'
mean_normals = normalise_vector(np.mean(normal_matrix, 0))
aep_matrix = AEP(mean_normals).logmap(normal_matrix)
aep_images = create_feature_space(aep_matrix, example_image, 'aep',
subject_id,
out_path=out_path)
# PGA
print 'Computing PGA feature space'
mu = intrinsic_mean(normal_matrix, PGA, max_iters=50)
pga_matrix = PGA(mu).logmap(normal_matrix)
pga_images = create_feature_space(pga_matrix, example_image, 'pga',
subject_id,
out_path=out_path)
# PCA models
n_components = 200
print 'Computing PCA models ({} components)'.format(n_components)
template = ref_frame
normal_model = PCAModel(normal_images, center=True)
normal_model.trim_components(200)
cosine_model = PCAModel(normal_images, center=False)
cosine_model.trim_components(200)
spherical_model = PCAModel(spherical_images, center=False)
spherical_model.trim_components(200)
aep_model = PCAModel(aep_images, center=False)
aep_model.trim_components(200)
pga_model = PCAModel(pga_images, center=False)
pga_model.trim_components(200)
mean_normals_image = normal_model.mean
mu_image = mean_normals_image.from_vector(mu)
# Save out models
pickle_model(out_path, subject_id, 'normal', normal_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'cosine', cosine_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'spherical', spherical_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'aep', aep_model, template,
mean_normals)
pickle_model(out_path, subject_id, 'pga', pga_model, template,
mean_normals, intrinsic_means=mu_image)
def test_pga_northpole_intrinsic_mean():
mu = intrinsic_mean(small_sd_vectors, PGA, max_iters=5)
assert_allclose(mu, expected_pga_northpole_mu)
def test_aep_intrinsic_mean():
mu = intrinsic_mean(small_sd_vectors, AEP, max_iters=5)
assert_allclose(mu, expected_pga_northpole_mu)
