def _build_shape_model(cls, shapes, max_components):
r"""
Builds a shape model given a set of shapes.
Parameters
----------
shapes: list of :map:`PointCloud`
The set of shapes from which to build the model.
max_components: None or int or float
Specifies the number of components of the trained shape model.
If int, it specifies the exact number of components to be retained.
If float, it specifies the percentage of variance to be retained.
If None, all the available components are kept (100% of variance).
Returns
-------
shape_model: :class:`menpo.model.pca`
The PCA shape model.
"""
# centralize shapes
centered_shapes = [Translation(-s.centre).apply(s) for s in shapes]
# align centralized shape using Procrustes Analysis
gpa = GeneralizedProcrustesAnalysis(centered_shapes)
aligned_shapes = [s.aligned_source for s in gpa.transforms]
# build shape model
shape_model = PCAModel(aligned_shapes)
if max_components is not None:
# trim shape model if required
shape_model.trim_components(max_components)
return shape_model
def build_shape_model(shapes, max_components=None, prefix='', verbose=False):
r"""
Builds a shape model given a set of shapes.
Parameters
----------
shapes: list of :map:`PointCloud`
The set of shapes from which to build the model.
max_components: None or int or float
Specifies the number of components of the trained shape model.
If int, it specifies the exact number of components to be retained.
If float, it specifies the percentage of variance to be retained.
If None, all the available components are kept (100% of variance).
Returns
-------
shape_model: :class:`menpo.model.pca`
The PCA shape model.
"""
if verbose:
print_dynamic('{}Building shape model'.format(prefix))
# compute aligned shapes
aligned_shapes = align_shapes(shapes)
# build shape model
shape_model = PCAModel(aligned_shapes)
if max_components is not None:
# trim shape model if required
shape_model.trim_components(max_components)
return shape_model
def build_shape_model(shapes, max_components):
r"""
Builds a shape model given a set of shapes.
Parameters
----------
shapes: list of :map:`PointCloud`
The set of shapes from which to build the model.
max_components: None or int or float
Specifies the number of components of the trained shape model.
If int, it specifies the exact number of components to be retained.
If float, it specifies the percentage of variance to be retained.
If None, all the available components are kept (100% of variance).
Returns
-------
shape_model: :class:`menpo.model.pca`
The PCA shape model.
"""
# centralize shapes
centered_shapes = [Translation(-s.centre()).apply(s) for s in shapes]
# align centralized shape using Procrustes Analysis
gpa = GeneralizedProcrustesAnalysis(centered_shapes)
aligned_shapes = [s.aligned_source() for s in gpa.transforms]
# build shape model
shape_model = PCAModel(aligned_shapes)
if max_components is not None:
# trim shape model if required
shape_model.trim_components(max_components)
return shape_model
def _build_shape_model(cls, shapes, max_components):
r"""
"""
# centralize shapes
centered_shapes = [Translation(-s.centre).apply(s) for s in shapes]
# align centralized shape using Procrustes Analysis
gpa = GeneralizedProcrustesAnalysis(centered_shapes)
aligned_shapes = [s.aligned_source for s in gpa.transforms]
# build shape model
shape_model = PCAModel(aligned_shapes)
if max_components is not None:
# trim shape model if required
shape_model.trim_components(max_components)
return shape_model
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 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)