def __init__(self, images, group=None, bounding_box_group_glob=None,
reference_shape=None, sd_algorithm_cls=Newton,
holistic_features=no_op, patch_features=no_op,
patch_shape=(17, 17), diagonal=None, scales=(0.5, 1.0),
n_iterations=6, n_perturbations=30,
perturb_from_gt_bounding_box=noisy_shape_from_bounding_box,
batch_size=None, verbose=False):
# check parameters
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
n_scales = len(scales)
patch_features = checks.check_features(patch_features, n_scales)
holistic_features = checks.check_features(holistic_features, n_scales)
patch_shape = checks.check_patch_shape(patch_shape, n_scales)
# set parameters
self.algorithms = []
self.reference_shape = reference_shape
self._sd_algorithm_cls = sd_algorithm_cls
self.holistic_features = holistic_features
self.patch_features = patch_features
self.patch_shape = patch_shape
self.diagonal = diagonal
self.scales = scales
self.n_perturbations = n_perturbations
self.n_iterations = checks.check_max_iters(n_iterations, n_scales)
self._perturb_from_gt_bounding_box = perturb_from_gt_bounding_box
# set up algorithms
self._setup_algorithms()
# Now, train the model!
self._train(images,increment=False, group=group,
bounding_box_group_glob=bounding_box_group_glob,
verbose=verbose, batch_size=batch_size)
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, images, group=None, bounding_box_group_glob=None,
verbose=False, reference_shape=None, diagonal=None,
scales=(0.5, 1.0), n_perturbations=30, n_dlib_perturbations=1,
perturb_from_gt_bounding_box=noisy_shape_from_bounding_box,
n_iterations=10, feature_padding=0, n_pixel_pairs=400,
distance_prior_weighting=0.1, regularisation_weight=0.1,
n_split_tests=20, n_trees=500, n_tree_levels=5):
checks.check_diagonal(diagonal)
self.diagonal = diagonal
self.scales = checks.check_scales(scales)
self.holistic_features = checks.check_features(no_op, self.n_scales)
self.reference_shape = reference_shape
self.n_perturbations = n_perturbations
self.n_iterations = checks.check_max_iters(n_iterations, self.n_scales)
self._perturb_from_gt_bounding_box = perturb_from_gt_bounding_box
self._setup_dlib_options(feature_padding, n_pixel_pairs,
distance_prior_weighting,
regularisation_weight, n_split_tests, n_trees,
n_dlib_perturbations, n_tree_levels)
self._setup_algorithms()
# Train DLIB over multiple scales
self._train(images, group=group,
bounding_box_group_glob=bounding_box_group_glob,
verbose=verbose)
def __init__(self, template, shapes, group=None, verbose=False,
reference_shape=None, holistic_features=no_op,
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_features(holistic_features, n_scales)
max_shape_components = checks.check_max_components(
max_shape_components, n_scales, 'max_shape_components')
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 = []
# Train ATM
self._train(template, shapes, 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, group=None, holistic_features=no_op,
transform_cls=DifferentiableAlignmentAffine, diagonal=None,
scales=(0.5, 1.0), algorithm_cls=InverseCompositional,
residual_cls=SSD):
checks.check_diagonal(diagonal)
scales = checks.check_scales(scales)
holistic_features = checks.check_features(holistic_features,
len(scales))
self.holistic_features = holistic_features
self.transform_cls = transform_cls
self.diagonal = diagonal
self.scales = list(scales)
# Make template masked for warping
template = template.as_masked(copy=False)
if self.diagonal:
template = template.rescale_landmarks_to_diagonal_range(
self.diagonal, group=group)
self.reference_shape = template.landmarks[group].lms
self.templates, self.sources = self._prepare_template(template,
group=group)
self._set_up(algorithm_cls, residual_cls)
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,
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, 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, 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, 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
