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,
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, 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, clm, gd_algorithm_cls=RegularisedLandmarkMeanShift,
n_shape=None):
self._model = clm
self._gd_algorithms_cls = checks.check_algorithm_cls(
gd_algorithm_cls, self.n_scales, GradientDescentCLMAlgorithm)
self._check_n_shape(n_shape)
self.algorithms = []
for i in range(self.clm.n_scales):
algorithm = self._gd_algorithms_cls[i](
self.clm.expert_ensembles[i],
self.clm.shape_models[i])
self.algorithms.append(algorithm)
def __init__(self,
clm,
gd_algorithm_cls=RegularisedLandmarkMeanShift,
n_shape=None):
self._model = clm
self._gd_algorithms_cls = checks.check_algorithm_cls(
gd_algorithm_cls, self.n_scales, GradientDescentCLMAlgorithm)
self._check_n_shape(n_shape)
self.algorithms = []
for i in range(self.clm.n_scales):
algorithm = self._gd_algorithms_cls[i](
self.clm.expert_ensembles[i], self.clm.shape_models[i])
self.algorithms.append(algorithm)
