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, 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,
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