Created legacy mixin for preprocessor and extractors

.gitignore

@@ -13,3 +13,4 @@ sphinx
 dist
 build
 record.txt
+.DS_Store

bob/bio/base/config/baselines/pca_atnt_legacy.py

+# from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector, AlgorithmAdaptor
+
+import bob.db.atnt
+from bob.bio.base.pipelines.vanilla_biometrics.legacy import DatabaseConnector
+
+database = DatabaseConnector(bob.db.atnt.Database(), protocol="Default")
+
+from sklearn.pipeline import Pipeline, make_pipeline
+from sklearn.decomposition import PCA
+
+from bob.pipelines.mixins import CheckpointMixin, SampleMixin
+from bob.bio.base.mixins import CheckpointSampleLinearize
+from bob.bio.base.mixins.legacy import LegacyProcessorMixin
+
+
+class CheckpointSamplePCA(CheckpointMixin, SampleMixin, PCA):
+    """
+    Enables SAMPLE and CHECKPOINTIN handling for https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
+    """
+
+    pass
+
+
+#### PREPROCESSOR LEGACY ###
+import functools
+
+# 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)
+import bob.bio.face
+
+face_cropper = functools.partial(
+    bob.bio.face.preprocessor.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},
+)
+
+
+from bob.pipelines.mixins import mix_me_up
+
+preprocessor = mix_me_up((CheckpointMixin, SampleMixin), LegacyProcessorMixin)
+
+from bob.pipelines.mixins import dask_it
+
+extractor = Pipeline(
+    steps=[
+        ("0", preprocessor(callable=face_cropper, features_dir="./example/extractor0")),
+        ("1", CheckpointSampleLinearize(features_dir="./example/extractor1")),
+        (
+            "2",
+            CheckpointSamplePCA(
+                features_dir="./example/extractor2", model_path="./example/pca.pkl"
+            ),
+        ),
+    ]
+)
+# extractor = dask_it(extractor)
+
+from bob.bio.base.pipelines.vanilla_biometrics.biometric_algorithm import (
+    Distance,
+    BiometricAlgorithmCheckpointMixin,
+)
+
+
+class CheckpointDistance(BiometricAlgorithmCheckpointMixin, Distance):
+    pass
+
+
+algorithm = CheckpointDistance(features_dir="./example/")
+# algorithm = Distance()

bob/bio/base/mixins/legacy.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+
+"""
+Mixins to handle legacy components
+"""
+
+from bob.pipelines.mixins import CheckpointMixin, SampleMixin
+from sklearn.base import TransformerMixin
+from sklearn.utils.validation import check_array
+
+
+class LegacyProcessorMixin(TransformerMixin):
+    """Class that wraps :py:class:`bob.bio.base.preprocessor.Preprocessor` and
+    :py:class:`bob.bio.base.extractor.Extractors`
+
+
+    Example
+    -------
+
+        Wrapping preprocessor with functtools
+        >>> from bob.bio.base.mixins.legacy import LegacyProcessorMixin
+        >>> from bob.bio.face.preprocessor import FaceCrop
+        >>> import functools
+        >>> transformer = LegacyProcessorMixin(functools.partial(FaceCrop, cropped_image_size=(10,10)))
+
+    Example
+    -------
+        Wrapping extractor 
+        >>> from bob.bio.base.mixins.legacy import LegacyProcessorMixin
+        >>> from bob.bio.face.extractor import Linearize
+        >>> transformer = LegacyProcessorMixin(Linearize)
+
+
+    Parameters
+    ----------
+      callable: callable
+         Calleble function that instantiates the scikit estimator
+
+    """
+
+    def __init__(self, callable=None):
+        self.callable = callable
+        self.instance = None
+
+    def fit(self, X, y=None, **fit_params):
+        return self
+
+    def transform(self, X):
+
+        X = check_array(X, allow_nd=True)
+
+        # Instantiates and do the "real" transform
+        if self.instance is None:
+            self.instance = self.callable()
+        return [self.instance(x) for x in X]

bob/bio/base/script/vanilla_biometrics.py

@@ -201,8 +201,8 @@ def vanilla_biometrics(
                     else:
                         raise TypeError("The output of the pipeline is not writeble")
 
-    dask_client.shutdown()
-
+    if dask_client is not None:
+        dask_client.shutdown()
 
 
 @click.command()