diff --git a/bob/bio/base/algorithm/Algorithm.py b/bob/bio/base/algorithm/Algorithm.py
index 91802e411eeb87a0173cbc4707c363a7c8b93743..b508cd48603b085efad470bc748751b2782b9bfa 100644
--- a/bob/bio/base/algorithm/Algorithm.py
+++ b/bob/bio/base/algorithm/Algorithm.py
@@ -214,10 +214,11 @@ class Algorithm (object):
score : float
The fused similarity between the given ``models`` and the ``probe``.
"""
+
if isinstance(models, list):
- return self.model_fusion_function([self.score(model, probe) for model in models])
+ return [self.score(model, probe) for model in models]
elif isinstance(models, numpy.ndarray):
- return self.model_fusion_function([self.score(models[i,:], probe) for i in range(models.shape[0])])
+ return [self.score(models[i,:], probe) for i in range(models.shape[0])]
else:
raise ValueError("The model does not have the desired format (list, array, ...)")
diff --git a/bob/bio/base/config/examples/isv_atnt_legacy_all_legacy.py b/bob/bio/base/config/examples/isv_atnt_legacy_all_legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..41ee5f2c30a74561cca1b3f3dec90345ffc07bfd
--- /dev/null
+++ b/bob/bio/base/config/examples/isv_atnt_legacy_all_legacy.py
@@ -0,0 +1,75 @@
+from bob.bio.face.database import AtntBioDatabase
+from bob.bio.gmm.algorithm import ISV
+from bob.bio.face.preprocessor import FaceCrop
+from sklearn.pipeline import make_pipeline
+from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector, Preprocessor, AlgorithmAsTransformer, AlgorithmAsBioAlg, Extractor
+import functools
+from bob.bio.base.pipelines.vanilla_biometrics.implemented import (
+ Distance,
+ CheckpointDistance,
+)
+import os
+
+# DATABASE
+database = DatabaseConnector(
+ AtntBioDatabase(original_directory="./atnt", protocol="Default"),
+)
+database.allow_scoring_with_all_biometric_references = True
+
+base_dir = "example/isv"
+
+# PREPROCESSOR LEGACY
+
+# Cropping
+CROPPED_IMAGE_HEIGHT = 80
+CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
+
+# eye positions for frontal images
+RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
+LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
+
+
+# RANDOM EYES POSITIONS
+# I JUST MADE UP THESE NUMBERS
+FIXED_RIGHT_EYE_POS = (30, 30)
+FIXED_LEFT_EYE_POS = (20, 50)
+
+face_cropper = functools.partial(
+ FaceCrop,
+ cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
+ cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
+ fixed_positions={"leye": FIXED_LEFT_EYE_POS, "reye": FIXED_RIGHT_EYE_POS},
+)
+
+
+import bob.bio.face
+
+extractor = functools.partial(
+ bob.bio.face.extractor.DCTBlocks,
+ block_size=12,
+ block_overlap=11,
+ number_of_dct_coefficients=45,
+)
+
+
+
+# ALGORITHM LEGACY
+isv = functools.partial(ISV, subspace_dimension_of_u=10, number_of_gaussians=2)
+
+model_path=os.path.join(base_dir, "ubm_u.hdf5")
+transformer = make_pipeline(
+ Preprocessor(callable=face_cropper, features_dir=os.path.join(base_dir,"face_crop")),
+ Extractor(extractor, features_dir=os.path.join(base_dir, "dcts")),
+ AlgorithmAsTransformer(
+ callable=isv, features_dir=os.path.join(base_dir,"isv"), model_path=model_path
+ ),
+)
+
+
+algorithm = AlgorithmAsBioAlg(callable=isv, features_dir=base_dir, model_path=model_path)
+
+
+from bob.bio.base.pipelines.vanilla_biometrics import VanillaBiometrics, dask_vanilla_biometrics
+
+#pipeline = VanillaBiometrics(transformer, algorithm)
+pipeline = dask_vanilla_biometrics(VanillaBiometrics(transformer, algorithm))
diff --git a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
index a1beb9ce0595a371a3fb8aa55e8e30f78b6429c7..bcf9840bd6e7c8dc7b570cb48756526730b89236 100644
--- a/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
+++ b/bob/bio/base/pipelines/vanilla_biometrics/legacy.py
@@ -20,6 +20,7 @@ from bob.pipelines.sample import DelayedSample, SampleSet, Sample
from bob.pipelines.utils import is_picklable
from sklearn.base import TransformerMixin, BaseEstimator
import logging
+import copy
logger = logging.getLogger("bob.bio.base")
@@ -72,9 +73,7 @@ class DatabaseConnector(Database):
model training. See, e.g., :py:func:`.pipelines.first`.
"""
-
objects = self.database.training_files()
-
return [_biofile_to_delayed_sample(k, self.database) for k in objects]
def references(self, group="dev"):
@@ -376,7 +375,9 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
"""
- def __init__(self, callable, features_dir, extension=".hdf5", **kwargs):
+ def __init__(
+ self, callable, features_dir, extension=".hdf5", model_path=None, **kwargs
+ ):
super().__init__(callable, **kwargs)
self.features_dir = features_dir
self.biometric_reference_dir = os.path.join(
@@ -384,11 +385,49 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
)
self.score_dir = os.path.join(self.features_dir, "scores")
self.extension = extension
+ self.model_path = model_path
+ self.is_projector_loaded = False
def _enroll_sample_set(self, sampleset):
# Enroll
return self.enroll(sampleset)
+ def _load_projector(self):
+ """
+ Run :py:meth:`bob.bio.base.algorithm.Algorithm.load_projector` if necessary by
+ :py:class:`bob.bio.base.algorithm.Algorithm`
+ """
+ if self.instance.performs_projection and not self.is_projector_loaded:
+ if self.model_path is None:
+ raise ValueError(
+ "Algorithm " + f"{self. instance} performs_projection. Hence, "
+ "`model_path` needs to passed in `AlgorithmAsBioAlg.__init__`"
+ )
+ else:
+ # Loading model
+ self.instance.load_projector(self.model_path)
+ self.is_projector_loaded = True
+
+ def _restore_state_of_ref(self, ref):
+ """
+ There are some algorithms that :py:meth:`bob.bio.base.algorithm.Algorithm.read_model` or
+ :py:meth:`bob.bio.base.algorithm.Algorithm.read_feature` depends
+ on the state of `self` to be properly loaded.
+ In these cases, it's not possible to rely only in the unbounded method extracted by
+ :py:func:`_get_pickable_method`.
+
+ This function replaces the current state of these objects (that are not)
+ by bounding them with `self.instance`
+ """
+
+ if isinstance(ref, DelayedSample):
+ new_ref = copy.copy(ref)
+
+ new_ref.load = functools.partial(ref.load.func, self.instance, ref.load.args[1])
+ return new_ref
+ else:
+ return ref
+
def _score_sample_set(
self,
sampleset,
@@ -408,20 +447,31 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
data = make_four_colums_score(ref.subject, probe.subject, probe.path, score)
return Sample(data, parent=ref)
+ self._load_projector()
retval = []
+
for subprobe_id, s in enumerate(sampleset.samples):
# Creating one sample per comparison
subprobe_scores = []
if allow_scoring_with_all_biometric_references:
if self.stacked_biometric_references is None:
+
+ if self.instance.performs_projection:
+ # Hydrating the state of biometric references
+ biometric_references = [
+ self._restore_state_of_ref(ref)
+ for ref in biometric_references
+ ]
+
self.stacked_biometric_references = [
ref.data for ref in biometric_references
]
+
+ s = self._restore_state_of_ref(s)
scores = self.score_multiple_biometric_references(
self.stacked_biometric_references, s.data
)
-
# Wrapping the scores in samples
for ref, score in zip(biometric_references, scores):
subprobe_scores.append(_write_sample(ref, sampleset, score))
@@ -430,6 +480,7 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
for ref in [
r for r in biometric_references if r.key in sampleset.references
]:
+ ref = self._restore_state_of_ref(ref)
score = self.score(ref.data, s.data)
subprobe_scores.append(_write_sample(ref, sampleset, score))
@@ -458,6 +509,7 @@ class AlgorithmAsBioAlg(_NonPickableWrapper, BioAlgorithm):
path = os.path.join(
self.biometric_reference_dir, str(enroll_features.key) + self.extension
)
+ self._load_projector()
if path is None or not os.path.isfile(path):
# Enrolling
data = [s.data for s in enroll_features.samples]