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, regression_type=mlr, regression_features=None,
features=no_op, n_levels=3, downscale=1.2, noise_std=0.04,
rotation=False, n_perturbations=10):
features = checks.check_features(features, n_levels)
DeformableModel.__init__(self, features)
# general deformable model checks
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
# SDM specific checks
regression_type_list = check_regression_type(regression_type,
n_levels)
regression_features = check_regression_features(regression_features,
n_levels)
check_n_permutations(n_perturbations)
# store parameters
self.regression_type = regression_type_list
self.regression_features = regression_features
self.n_levels = n_levels
self.downscale = downscale
self.noise_std = noise_std
self.rotation = rotation
self.n_perturbations = n_perturbations
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,
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')
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.boundary = boundary
# patch-based AAMs can only work with TPS transform
self.transform = ThinPlateSplines
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,
regression_type=mlr,
regression_features=None,
features=no_op,
n_levels=3,
downscale=1.2,
noise_std=0.04,
rotation=False,
n_perturbations=10):
features = checks.check_features(features, n_levels)
DeformableModel.__init__(self, features)
# general deformable model checks
checks.check_n_levels(n_levels)
checks.check_downscale(downscale)
# SDM specific checks
regression_type_list = check_regression_type(regression_type, n_levels)
regression_features = check_regression_features(
regression_features, n_levels)
check_n_permutations(n_perturbations)
# store parameters
self.regression_type = regression_type_list
self.regression_features = regression_features
self.n_levels = n_levels
self.downscale = downscale
self.noise_std = noise_std
self.rotation = rotation
self.n_perturbations = n_perturbations
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,
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, 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,
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
