def __init__(self, images, group=None, verbose=False, reference_shape=None,
holistic_features=no_op,
transform=DifferentiablePiecewiseAffine, diagonal=None,
scales=(0.5, 1.0), max_shape_components=None,
max_appearance_components=None, batch_size=None):
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
n_scales = len(scales)
holistic_features = checks.check_features(holistic_features, n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_scales, 'max_appearance_components')
self.holistic_features = holistic_features
self.transform = transform
self.diagonal = diagonal
self.scales = scales
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.reference_shape = reference_shape
self.shape_models = []
self.appearance_models = []
# Train AAM
self._train(images, increment=False, group=group, verbose=verbose,
batch_size=batch_size)
def __init__(self,
features=igo,
patch_shape=(16, 16),
normalization_diagonal=None,
n_levels=3,
downscale=2,
scaled_shape_models=True,
max_shape_components=None,
max_appearance_components=None,
boundary=3):
# check parameters
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
checks.check_boundary(boundary)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_levels, 'max_appearance_components')
features = checks.check_features(features, n_levels)
# store parameters
self.features = features
self.patch_shape = patch_shape
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.boundary = boundary
# patch-based AAMs can only work with TPS transform
self.transform = DifferentiableThinPlateSplines
def __init__(self, features=igo, patch_shape=(16, 16),
normalization_diagonal=None, n_levels=3, downscale=2,
scaled_shape_models=True, max_shape_components=None,
max_appearance_components=None, boundary=3):
# check parameters
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
checks.check_boundary(boundary)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_levels, 'max_appearance_components')
features = checks.check_features(features, n_levels)
# store parameters
self.features = features
self.patch_shape = patch_shape
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.boundary = boundary
# patch-based AAMs can only work with TPS transform
self.transform = DifferentiableThinPlateSplines
def __init__(self,
features=igo,
transform=DifferentiablePiecewiseAffine,
trilist=None,
normalization_diagonal=None,
n_levels=3,
downscale=2,
scaled_shape_models=True,
max_shape_components=None,
max_appearance_components=None,
boundary=3):
# check parameters
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
checks.check_boundary(boundary)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_levels, 'max_appearance_components')
features = checks.check_features(features, n_levels)
# store parameters
self.features = features
self.transform = transform
self.trilist = trilist
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.boundary = boundary
def __init__(self, features=igo, transform=DifferentiablePiecewiseAffine,
trilist=None, normalization_diagonal=None, n_levels=3,
downscale=2, scaled_shape_models=True,
max_shape_components=None, max_appearance_components=None,
boundary=3):
# check parameters
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
checks.check_boundary(boundary)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_levels, 'max_appearance_components')
features = checks.check_features(features, n_levels)
# store parameters
self.features = features
self.transform = transform
self.trilist = trilist
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.boundary = boundary
def __init__(self, images, group=None, verbose=False, reference_shape=None,
holistic_features=no_op,
transform=DifferentiablePiecewiseAffine, diagonal=None,
scales=(0.5, 1.0), shape_model_cls=OrthoPDM,
max_shape_components=None, max_appearance_components=None,
batch_size=None):
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
n_scales = len(scales)
holistic_features = checks.check_callable(holistic_features, n_scales)
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_scales, 'max_appearance_components')
self.holistic_features = holistic_features
self.transform = transform
self.diagonal = diagonal
self.scales = scales
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.reference_shape = reference_shape
self._shape_model_cls = shape_model_cls
self.shape_models = []
self.appearance_models = []
# Train AAM
self._train(images, increment=False, group=group, verbose=verbose,
batch_size=batch_size)
def __init__(self,
images,
group=None,
holistic_features=no_op,
reference_shape=None,
diagonal=None,
scales=(0.5, 1.0),
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
patch_shape=(17, 17),
context_shape=(34, 34),
sample_offsets=None,
transform=DifferentiablePiecewiseAffine,
shape_model_cls=OrthoPDM,
max_shape_components=None,
max_appearance_components=None,
sigma=None,
boundary=3,
response_covariance=2,
patch_normalisation=no_op,
cosine_mask=True,
verbose=False):
# Check parameters
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
n_scales = len(scales)
holistic_features = checks.check_callable(holistic_features, n_scales)
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_scales, 'max_appearance_components')
# Assign attributes
self.expert_ensemble_cls = checks.check_callable(
expert_ensemble_cls, n_scales)
self.expert_ensembles = []
self.patch_shape = checks.check_patch_shape(patch_shape, n_scales)
self.context_shape = checks.check_patch_shape(context_shape, n_scales)
self.holistic_features = holistic_features
self.transform = transform
self.diagonal = diagonal
self.scales = scales
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.reference_shape = reference_shape
self.shape_model_cls = shape_model_cls
self.sigma = sigma
self.boundary = boundary
self.sample_offsets = sample_offsets
self.response_covariance = response_covariance
self.patch_normalisation = patch_normalisation
self.cosine_mask = cosine_mask
self.shape_models = []
self.appearance_models = []
self.expert_ensembles = []
self._train(images=images, group=group, verbose=verbose)
def __init__(self, images, group=None, appearance_graph=None,
shape_graph=None, deformation_graph=None,
holistic_features=no_op, reference_shape=None, diagonal=None,
scales=(0.5, 1.0), patch_shape=(17, 17),
patch_normalisation=no_op, use_procrustes=True,
precision_dtype=np.float32, max_shape_components=None,
n_appearance_components=None, can_be_incremented=False,
verbose=False, batch_size=None):
# Check parameters
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
n_scales = len(scales)
holistic_features = checks.check_callable(holistic_features, n_scales)
patch_shape = checks.check_patch_shape(patch_shape, n_scales)
patch_normalisation = checks.check_callable(patch_normalisation,
n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
n_appearance_components = checks.check_max_components(
n_appearance_components, n_scales, 'n_appearance_components')
# Assign attributes
self.diagonal = diagonal
self.scales = scales
self.holistic_features = holistic_features
self.patch_shape = patch_shape
self.patch_normalisation = patch_normalisation
self.reference_shape = reference_shape
self.use_procrustes = use_procrustes
self.is_incremental = can_be_incremented
self.precision_dtype = precision_dtype
self.max_shape_components = max_shape_components
self.n_appearance_components = n_appearance_components
# Check provided graphs
self.appearance_graph = checks.check_graph(
appearance_graph, UndirectedGraph, 'appearance_graph', n_scales)
self.shape_graph = checks.check_graph(shape_graph, UndirectedGraph,
'shape_graph', n_scales)
self.deformation_graph = checks.check_graph(
deformation_graph, [DirectedGraph, Tree], 'deformation_graph',
n_scales)
# Initialize models' lists
self.shape_models = []
self.appearance_models = []
self.deformation_models = []
# Train APS
self._train(images, increment=False, group=group, batch_size=batch_size,
verbose=verbose)
def __init__(self, images, group=None, verbose=False, batch_size=None,
diagonal=None, scales=(0.5, 1), holistic_features=no_op,
shape_model_cls=OrthoPDM,
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
max_shape_components=None, reference_shape=None):
scales = checks.check_scales(scales)
n_scales = len(scales)
self.diagonal = checks.check_diagonal(diagonal)
self.scales = scales
self.holistic_features = checks.check_callable(holistic_features,
self.n_scales)
self.expert_ensemble_cls = checks.check_algorithm_cls(
expert_ensemble_cls, self.n_scales, ExpertEnsemble)
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
self.max_shape_components = checks.check_max_components(
max_shape_components, self.n_scales, 'max_shape_components')
self.reference_shape = reference_shape
self.shape_models = []
self._shape_model_cls = shape_model_cls
self.expert_ensembles = []
# Train CLM
self._train(images, increment=False, group=group, verbose=verbose,
batch_size=batch_size)
def __init__(self,
classifier_trainers=linear_svm_lr,
patch_shape=(5, 5),
features=sparse_hog,
normalization_diagonal=None,
n_levels=3,
downscale=1.1,
scaled_shape_models=True,
max_shape_components=None,
boundary=3):
# general deformable model checks
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
checks.check_boundary(boundary)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
features = checks.check_features(features, n_levels)
# CLM specific checks
classifier_trainers = check_classifier_trainers(
classifier_trainers, n_levels)
patch_shape = check_patch_shape(patch_shape)
# store parameters
self.classifier_trainers = classifier_trainers
self.patch_shape = patch_shape
self.features = features
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.boundary = boundary
def __init__(self, images, group=None, verbose=False, batch_size=None,
diagonal=None, scales=(0.5, 1), holistic_features=no_op,
# shape_model_cls=build_normalised_pca_shape_model,
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
max_shape_components=None, reference_shape=None,
shape_forgetting_factor=1.0):
self.diagonal = checks.check_diagonal(diagonal)
self.scales = checks.check_scales(scales)
self.holistic_features = checks.check_features(holistic_features,
self.n_scales)
# self.shape_model_cls = checks.check_algorithm_cls(
# shape_model_cls, self.n_scales, ShapeModel)
self.expert_ensemble_cls = checks.check_algorithm_cls(
expert_ensemble_cls, self.n_scales, ExpertEnsemble)
self.max_shape_components = checks.check_max_components(
max_shape_components, self.n_scales, 'max_shape_components')
self.shape_forgetting_factor = shape_forgetting_factor
self.reference_shape = reference_shape
self.shape_models = []
self.expert_ensembles = []
# Train CLM
self._train(images, increment=False, group=group, verbose=verbose,
batch_size=batch_size)
def __init__(self, template, shapes, group=None, holistic_features=no_op,
reference_shape=None, diagonal=None, scales=(0.5, 1.0),
transform=DifferentiablePiecewiseAffine,
shape_model_cls=OrthoPDM, max_shape_components=None,
verbose=False, batch_size=None):
# Check arguments
checks.check_diagonal(diagonal)
n_scales = len(scales)
scales = checks.check_scales(scales)
holistic_features = checks.check_callable(holistic_features, n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
# Assign attributes
self.holistic_features = holistic_features
self.transform = transform
self.diagonal = diagonal
self.scales = scales
self.max_shape_components = max_shape_components
self.reference_shape = reference_shape
self.shape_models = []
self.warped_templates = []
self._shape_model_cls = shape_model_cls
# Train ATM
self._train(template, shapes, increment=False, group=group,
verbose=verbose, batch_size=batch_size)
def __init__(self,
images,
group=None,
verbose=False,
batch_size=None,
diagonal=None,
scales=(0.5, 1),
holistic_features=no_op,
shape_model_cls=OrthoPDM,
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
max_shape_components=None,
reference_shape=None):
scales = checks.check_scales(scales)
n_scales = len(scales)
self.diagonal = checks.check_diagonal(diagonal)
self.scales = scales
self.holistic_features = checks.check_callable(holistic_features,
self.n_scales)
self.expert_ensemble_cls = checks.check_algorithm_cls(
expert_ensemble_cls, self.n_scales, ExpertEnsemble)
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
self.max_shape_components = checks.check_max_components(
max_shape_components, self.n_scales, 'max_shape_components')
self.reference_shape = reference_shape
self.shape_models = []
self._shape_model_cls = shape_model_cls
self.expert_ensembles = []
# Train CLM
self._train(images,
increment=False,
group=group,
verbose=verbose,
batch_size=batch_size)
def __init__(self, classifier_trainers=linear_svm_lr, patch_shape=(5, 5),
features=sparse_hog, normalization_diagonal=None,
n_levels=3, downscale=1.1, scaled_shape_models=True,
max_shape_components=None, boundary=3):
# general deformable model checks
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
checks.check_boundary(boundary)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
features = checks.check_features(features, n_levels)
# CLM specific checks
classifier_trainers = check_classifier_trainers(classifier_trainers, n_levels)
patch_shape = check_patch_shape(patch_shape)
# store parameters
self.classifier_trainers = classifier_trainers
self.patch_shape = patch_shape
self.features = features
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.boundary = boundary
def __init__(
self,
template,
shapes,
group=None,
verbose=False,
reference_shape=None,
holistic_features=no_op,
shape_model_cls=OrthoPDM,
transform=DifferentiablePiecewiseAffine,
diagonal=None,
scales=(0.5, 1.0),
max_shape_components=None,
batch_size=None,
):
checks.check_diagonal(diagonal)
n_scales = len(scales)
scales = checks.check_scales(scales)
holistic_features = checks.check_callable(holistic_features, n_scales)
max_shape_components = checks.check_max_components(max_shape_components, n_scales, "max_shape_components")
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
self.holistic_features = holistic_features
self.transform = transform
self.diagonal = diagonal
self.scales = scales
self.max_shape_components = max_shape_components
self.reference_shape = reference_shape
self.shape_models = []
self.warped_templates = []
self._shape_model_cls = shape_model_cls
# Train ATM
self._train(template, shapes, increment=False, group=group, verbose=verbose, batch_size=batch_size)
def __init__(self, images, group=None, holistic_features=no_op,
reference_shape=None, diagonal=None, scales=(0.5, 1),
patch_shape=(17, 17), patch_normalisation=no_op,
context_shape=(34, 34), cosine_mask=True, sample_offsets=None,
shape_model_cls=OrthoPDM,
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
max_shape_components=None, verbose=False, batch_size=None):
self.scales = checks.check_scales(scales)
n_scales = len(scales)
self.diagonal = checks.check_diagonal(diagonal)
self.holistic_features = checks.check_callable(holistic_features,
n_scales)
self.expert_ensemble_cls = checks.check_callable(expert_ensemble_cls,
n_scales)
self._shape_model_cls = checks.check_callable(shape_model_cls,
n_scales)
self.max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
self.reference_shape = reference_shape
self.patch_shape = checks.check_patch_shape(patch_shape, n_scales)
self.patch_normalisation = patch_normalisation
self.context_shape = checks.check_patch_shape(context_shape, n_scales)
self.cosine_mask = cosine_mask
self.sample_offsets = sample_offsets
self.shape_models = []
self.expert_ensembles = []
# Train CLM
self._train(images, increment=False, group=group, verbose=verbose,
batch_size=batch_size)
def __init__(self, images, group=None, holistic_features=no_op,
reference_shape=None, diagonal=None, scales=(0.5, 1.0),
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
patch_shape=(17, 17), context_shape=(34, 34),
sample_offsets=None, transform=DifferentiablePiecewiseAffine,
shape_model_cls=OrthoPDM, max_shape_components=None,
max_appearance_components=None, sigma=None, boundary=3,
response_covariance=2, patch_normalisation=no_op,
cosine_mask=True, verbose=False):
# Check parameters
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
n_scales = len(scales)
holistic_features = checks.check_callable(holistic_features, n_scales)
shape_model_cls = checks.check_callable(shape_model_cls, n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
max_appearance_components = checks.check_max_components(
max_appearance_components, n_scales, 'max_appearance_components')
# Assign attributes
self.expert_ensemble_cls = checks.check_callable(expert_ensemble_cls,
n_scales)
self.expert_ensembles = []
self.patch_shape = checks.check_patch_shape(patch_shape, n_scales)
self.context_shape = checks.check_patch_shape(context_shape, n_scales)
self.holistic_features = holistic_features
self.transform = transform
self.diagonal = diagonal
self.scales = scales
self.max_shape_components = max_shape_components
self.max_appearance_components = max_appearance_components
self.reference_shape = reference_shape
self.shape_model_cls = shape_model_cls
self.sigma = sigma
self.boundary = boundary
self.sample_offsets = sample_offsets
self.response_covariance = response_covariance
self.patch_normalisation = patch_normalisation
self.cosine_mask = cosine_mask
self.shape_models = []
self.appearance_models = []
self.expert_ensembles = []
self._train(images=images, group=group, verbose=verbose)
def __init__(self, adjacency_array_appearance=None, gaussian_per_patch=True,
adjacency_array_deformation=None, root_vertex_deformation=None,
adjacency_array_shape=None, features=no_op,
patch_shape=(17, 17), normalization_diagonal=None, n_levels=2,
downscale=2, scaled_shape_models=False, use_procrustes=True,
max_shape_components=None, n_appearance_parameters=None):
# check parameters
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
features = checks.check_features(features, n_levels)
n_appearance_parameters = _check_n_parameters(
n_appearance_parameters, n_levels, 'n_appearance_parameters')
# appearance graph
if adjacency_array_appearance is None:
self.graph_appearance = None
elif adjacency_array_appearance == 'yorgos':
self.graph_appearance = 'yorgos'
else:
self.graph_appearance = UndirectedGraph(adjacency_array_appearance)
# shape graph
if adjacency_array_shape is None:
self.graph_shape = None
else:
self.graph_shape = UndirectedGraph(adjacency_array_shape)
# check adjacency_array_deformation, root_vertex_deformation
if adjacency_array_deformation is None:
self.graph_deformation = None
if root_vertex_deformation is None:
self.root_vertex = 0
else:
if root_vertex_deformation is None:
self.graph_deformation = DirectedGraph(adjacency_array_deformation)
else:
self.graph_deformation = Tree(adjacency_array_deformation,
root_vertex_deformation)
# store parameters
self.features = features
self.patch_shape = patch_shape
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.n_appearance_parameters = n_appearance_parameters
self.use_procrustes = use_procrustes
self.gaussian_per_patch = gaussian_per_patch
def __init__(
self,
images,
group=None,
holistic_features=no_op,
reference_shape=None,
diagonal=None,
scales=(1, ), # scales=(0.5, 1)
patch_shape=(8, 8),
patch_normalisation=no_op,
context_shape=(8, 8),
cosine_mask=True,
sample_offsets=None,
shape_model_cls=OrthoPDM,
expert_ensemble_cls=CorrelationFilterExpertEnsemble,
max_shape_components=None,
verbose=False,
batch_size=None):
self.scales = checks.check_scales(scales)
n_scales = len(scales)
self.diagonal = checks.check_diagonal(diagonal)
self.holistic_features = checks.check_callable(holistic_features,
n_scales)
self.expert_ensemble_cls = checks.check_callable(
expert_ensemble_cls, n_scales)
self._shape_model_cls = checks.check_callable(shape_model_cls,
n_scales)
self.max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
self.reference_shape = reference_shape
self.patch_shape = checks.check_patch_shape(patch_shape, n_scales)
self.patch_normalisation = patch_normalisation
self.context_shape = checks.check_patch_shape(context_shape, n_scales)
self.cosine_mask = cosine_mask
self.sample_offsets = sample_offsets
self.shape_models = []
self.expert_ensembles = []
# Train CLM
self._train(images,
increment=False,
group=group,
verbose=verbose,
batch_size=batch_size)
def __init__(self,
adjacency_array_appearance=None,
gaussian_per_patch=True,
adjacency_array_deformation=None,
root_vertex_deformation=None,
adjacency_array_shape=None,
features=no_op,
patch_shape=(17, 17),
normalization_diagonal=None,
n_levels=2,
downscale=2,
scaled_shape_models=False,
use_procrustes=True,
max_shape_components=None,
n_appearance_parameters=None):
# check parameters
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
checks.check_normalization_diagonal(normalization_diagonal)
max_shape_components = checks.check_max_components(
max_shape_components, n_levels, 'max_shape_components')
features = checks.check_features(features, n_levels)
n_appearance_parameters = _check_n_parameters(
n_appearance_parameters, n_levels, 'n_appearance_parameters')
# appearance graph
if adjacency_array_appearance is None:
self.graph_appearance = None
elif adjacency_array_appearance == 'yorgos':
self.graph_appearance = 'yorgos'
else:
self.graph_appearance = UndirectedGraph(adjacency_array_appearance)
# shape graph
if adjacency_array_shape is None:
self.graph_shape = None
else:
self.graph_shape = UndirectedGraph(adjacency_array_shape)
# check adjacency_array_deformation, root_vertex_deformation
if adjacency_array_deformation is None:
self.graph_deformation = None
if root_vertex_deformation is None:
self.root_vertex = 0
else:
if root_vertex_deformation is None:
self.graph_deformation = DirectedGraph(
adjacency_array_deformation)
else:
self.graph_deformation = Tree(adjacency_array_deformation,
root_vertex_deformation)
# store parameters
self.features = features
self.patch_shape = patch_shape
self.normalization_diagonal = normalization_diagonal
self.n_levels = n_levels
self.downscale = downscale
self.scaled_shape_models = scaled_shape_models
self.max_shape_components = max_shape_components
self.n_appearance_parameters = n_appearance_parameters
self.use_procrustes = use_procrustes
self.gaussian_per_patch = gaussian_per_patch