from bob.bio.face.embeddings.pytorch import iresnet50
from bob.bio.face.utils import lookup_config_from_database
(
annotation_type,
fixed_positions,
memory_demanding,
) = lookup_config_from_database(locals().get("database"))
def load(annotation_type, fixed_positions=None, memory_demanding=False):
return iresnet50(annotation_type, fixed_positions, memory_demanding)
pipeline = load(annotation_type, fixed_positions, memory_demanding)
transformer = pipeline.transformer
transformer = pipeline.transformer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import FunctionTransformer
from bob.bio.base.algorithm import Distance
from bob.bio.base.pipelines import PipelineSimple
from bob.bio.face.utils import lookup_config_from_database
from bob.pipelines import wrap
(
annotation_type,
fixed_positions,
memory_demanding,
) = lookup_config_from_database()
from sklearn.base import BaseEstimator, TransformerMixin
from bob.bio.face.color import rgb_to_gray
class ToGray(TransformerMixin, BaseEstimator):
def transform(self, X, annotations=None):
return [rgb_to_gray(data)[0:10, 0:10] for data in X]
def _more_tags(self):
return {"requires_fit": False}
def fit(self, X, y=None):
return self
def load(annotation_type, fixed_positions=None):
