Created proper baselines

bob/bio/face/config/baseline/facenet.py

-import bob.bio.face
-
-from sklearn.pipeline import make_pipeline
-from bob.bio.base.wrappers import wrap_sample_preprocessor
-from bob.pipelines import wrap
 from bob.bio.face.embeddings import FaceNetSanderberg
-from bob.bio.face.helpers import face_crop_solver
+from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
 
-# This is the size of the image that this model expects
-CROPPED_IMAGE_HEIGHT = 160
-CROPPED_IMAGE_WIDTH = 160
-cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
-color_channel = "rgb"
 
 if "database" in locals():
     annotation_type = database.annotation_type
     fixed_positions = database.fixed_positions
 else:
-    annotation_type = "eyes-center"
+    annotation_type = None
     fixed_positions = None
 
-#### SOLVING THE FACE CROPPER TO BE USED
-if annotation_type == "bounding-box":
-    transform_extra_arguments = (("annotations", "annotations"),)
-    TOP_LEFT_POS = (0, 0)
-    BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
-
-    # Detects the face and crops it without eye detection
-    face_cropper = face_crop_solver(
-        cropped_image_size,
-        color_channel=color_channel,
-        cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS},
-        fixed_positions=fixed_positions,
-    )
-
-elif annotation_type == "eyes-center":
-    transform_extra_arguments = (("annotations", "annotations"),)
-    # eye positions for frontal images
-    RIGHT_EYE_POS = (46, 53)
-    LEFT_EYE_POS = (46, 107)
-
-    # Detects the face and crops it without eye detection
-    face_cropper = face_crop_solver(
-        cropped_image_size,
-        color_channel=color_channel,
-        cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
-        fixed_positions=fixed_positions,
-    )
-
-else:
-    transform_extra_arguments = None
-    # DEFAULT TO FACE SIMPLE RESIZE
-    face_cropper = face_crop_solver(cropped_image_size)
-
-
-embedding = FaceNetSanderberg()
 
-transformer = make_pipeline(
-    wrap(
-        ["sample"],
-        face_cropper,
-        transform_extra_arguments=transform_extra_arguments,
-    ),
-    wrap(["sample"], embedding),
-)
+transformer = embedding_transformer_160x160(FaceNetSanderberg(), annotation_type, fixed_positions)
\ No newline at end of file

bob/bio/face/config/baseline/helpers.py

+import bob.bio.face
+from sklearn.pipeline import make_pipeline
+from bob.bio.base.wrappers import wrap_sample_preprocessor
+from bob.pipelines import wrap
+from bob.bio.face.helpers import face_crop_solver
+
+
+def embedding_transformer_160x160(embedding, annotation_type, fixed_positions):
+    """
+    Creates a pipeline composed by and FaceCropper and an Embedding extractor.
+    This transformer is suited for Facenet based architectures
+    
+    .. warning::
+       This will redirect images to :math:`160 \times 160`
+    
+    """
+
+    # This is the size of the image that this model expects
+    CROPPED_IMAGE_HEIGHT = 160
+    CROPPED_IMAGE_WIDTH = 160
+    cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+    color_channel = "rgb"
+
+    #### SOLVING THE FACE CROPPER TO BE USED
+    if annotation_type == "bounding-box":
+        transform_extra_arguments = (("annotations", "annotations"),)
+        TOP_LEFT_POS = (0, 0)
+        BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+
+        # Detects the face and crops it without eye detection
+        face_cropper = face_crop_solver(
+            cropped_image_size,
+            color_channel=color_channel,
+            cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS},
+            fixed_positions=fixed_positions,
+        )
+
+    elif annotation_type == "eyes-center":
+        transform_extra_arguments = (("annotations", "annotations"),)
+        # eye positions for frontal images
+        RIGHT_EYE_POS = (46, 53)
+        LEFT_EYE_POS = (46, 107)
+
+        # Detects the face and crops it without eye detection
+        face_cropper = face_crop_solver(
+            cropped_image_size,
+            color_channel=color_channel,
+            cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
+            fixed_positions=fixed_positions,
+        )
+
+    else:
+        transform_extra_arguments = None
+        # DEFAULT TO FACE SIMPLE RESIZE
+        face_cropper = face_crop_solver(cropped_image_size)
+
+    transformer = make_pipeline(
+        wrap(
+            ["sample"],
+            face_cropper,
+            transform_extra_arguments=transform_extra_arguments,
+        ),
+        wrap(["sample"], embedding),
+    )
+
+    return transformer

bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py

+from bob.bio.face.embeddings import InceptionResnetv1_CasiaWebFace
+from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
+
+
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+
+transformer = embedding_transformer_160x160(InceptionResnetv1_CasiaWebFace(), annotation_type, fixed_positions)

bob/bio/face/config/baseline/inception_resnetv1_msceleb.py

+from bob.bio.face.embeddings import InceptionResnetv1_MsCeleb
+from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
+
+
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+
+transformer = embedding_transformer_160x160(InceptionResnetv1_MsCeleb(), annotation_type, fixed_positions)

bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py

+from bob.bio.face.embeddings import InceptionResnetv2_CasiaWebFace
+from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
+
+
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+
+transformer = embedding_transformer_160x160(InceptionResnetv2_CasiaWebFace(), annotation_type, fixed_positions)

bob/bio/face/config/baseline/inception_resnetv2_msceleb.py

+from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb
+from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
+
+
+if "database" in locals():
+    annotation_type = database.annotation_type
+    fixed_positions = database.fixed_positions
+else:
+    annotation_type = None
+    fixed_positions = None
+
+
+transformer = embedding_transformer_160x160(InceptionResnetv2_MsCeleb(), annotation_type, fixed_positions)
\ No newline at end of file

bob/bio/face/config/database/mobio.py

 #!/usr/bin/env python
 
 from bob.bio.face.database import MobioBioDatabase
+from bob.bio.base.pipelines.vanilla_biometrics import DatabaseConnector
+from bob.extension import rc
 
-mobio_image_directory = "[YOUR_MOBIO_IMAGE_DIRECTORY]"
-mobio_annotation_directory = "[YOUR_MOBIO_ANNOTATION_DIRECTORY]"
+database = DatabaseConnector(
+    MobioBioDatabase(
+        original_directory=rc["bob.db.mobio.directory"],
+        annotation_directory=rc["bob.db.mobio.annotation_directory"],
+        original_extension=".png",
+        protocol="mobile0-male",
+    )
+)
+database.allow_scoring_with_all_biometric_references = True
 
-mobio_image = MobioBioDatabase(
-    original_directory=mobio_image_directory,
-    original_extension=".png",
-    annotation_directory=mobio_annotation_directory,
-    annotation_type="eyecenter",
 
-    protocol='male',
-    models_depend_on_protocol=True,
-)
+mobio_image_directory = rc["bob.db.mobio.directory"]
+mobio_annotation_directory = rc["bob.db.mobio.annotation_directory"]
 
-mobio_male = MobioBioDatabase(
-    original_directory=mobio_image_directory,
-    original_extension=".png",
-    annotation_directory=mobio_annotation_directory,
-    annotation_type="eyecenter",
+allow_scoring_with_all_biometric_references = True
+annotation_type = "eyes-center"
+fixed_positions = None
 
-    protocol='male',
-    models_depend_on_protocol=True,
 
-    all_files_options={'gender': 'male'},
-    extractor_training_options={'gender': 'male'},
-    projector_training_options={'gender': 'male'},
-    enroller_training_options={'gender': 'male'},
-    z_probe_options={'gender': 'male'}
+mobio_image = DatabaseConnector(
+    MobioBioDatabase(
+        original_directory=mobio_image_directory,
+        original_extension=".png",
+        annotation_directory=mobio_annotation_directory,
+        annotation_type="eyecenter",
+        protocol="male",
+        models_depend_on_protocol=True,
+    ),
+    allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
+    annotation_type=annotation_type,
+    fixed_positions=fixed_positions,
 )
 
 
-mobio_female = MobioBioDatabase(
-    original_directory=mobio_image_directory,
-    original_extension=".png",
-    annotation_directory=mobio_annotation_directory,
-    annotation_type="eyecenter",
+mobio_male = DatabaseConnector(
+    MobioBioDatabase(
+        original_directory=mobio_image_directory,
+        original_extension=".png",
+        annotation_directory=mobio_annotation_directory,
+        annotation_type="eyecenter",
+        protocol="male",
+        models_depend_on_protocol=True,
+        all_files_options={"gender": "male"},
+        extractor_training_options={"gender": "male"},
+        projector_training_options={"gender": "male"},
+        enroller_training_options={"gender": "male"},
+        z_probe_options={"gender": "male"},
+    ),
+    allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
+    annotation_type=annotation_type,
+    fixed_positions=fixed_positions,
+)
 
-    protocol='female',
-    models_depend_on_protocol=True,
 
-    all_files_options={'gender': 'female'},
-    extractor_training_options={'gender': 'female'},
-    projector_training_options={'gender': 'female'},
-    enroller_training_options={'gender': 'female'},
-    z_probe_options={'gender': 'female'}
+mobio_female = DatabaseConnector(
+    MobioBioDatabase(
+        original_directory=mobio_image_directory,
+        original_extension=".png",
+        annotation_directory=mobio_annotation_directory,
+        annotation_type="eyecenter",
+        protocol="female",
+        models_depend_on_protocol=True,
+        all_files_options={"gender": "female"},
+        extractor_training_options={"gender": "female"},
+        projector_training_options={"gender": "female"},
+        enroller_training_options={"gender": "female"},
+        z_probe_options={"gender": "female"},
+    ),
+    allow_scoring_with_all_biometric_references=allow_scoring_with_all_biometric_references,
+    annotation_type=annotation_type,
+    fixed_positions=fixed_positions,
 )

bob/bio/face/test/test_baseline.py

@@ -19,10 +19,58 @@ def test_facenet_baseline():
 
     fake_sample = get_fake_sample()
 
-    # transformed_sample = transformer.transform([fake_sample])[0].data
+    transformed_sample = transformer.transform([fake_sample])[0]
+    transformed_data = transformed_sample.data
+    assert transformed_sample.data.size == 128
+
+
+def test_inception_resnetv2_msceleb():
+    config_name = pkg_resources.resource_filename(
+        "bob.bio.face", "config/baseline/inception_resnetv2_msceleb.py"
+    )
+    transformer = load([config_name]).transformer
+
+    fake_sample = get_fake_sample()
+
+    transformed_sample = transformer.transform([fake_sample])[0]
+    transformed_data = transformed_sample.data
+    assert transformed_sample.data.size == 128
+
+
+def test_inception_resnetv2_casiawebface():
+    config_name = pkg_resources.resource_filename(
+        "bob.bio.face", "config/baseline/inception_resnetv2_casiawebface.py"
+    )
+    transformer = load([config_name]).transformer
+
+    fake_sample = get_fake_sample()
 
     transformed_sample = transformer.transform([fake_sample])[0]
     transformed_data = transformed_sample.data
     assert transformed_sample.data.size == 128
 
-    pass
+
+def test_inception_resnetv1_msceleb():
+    config_name = pkg_resources.resource_filename(
+        "bob.bio.face", "config/baseline/inception_resnetv1_msceleb.py"
+    )
+    transformer = load([config_name]).transformer
+
+    fake_sample = get_fake_sample()
+
+    transformed_sample = transformer.transform([fake_sample])[0]
+    transformed_data = transformed_sample.data
+    assert transformed_sample.data.size == 128
+
+
+def test_inception_resnetv1_casiawebface():
+    config_name = pkg_resources.resource_filename(
+        "bob.bio.face", "config/baseline/inception_resnetv1_casiawebface.py"
+    )
+    transformer = load([config_name]).transformer
+
+    fake_sample = get_fake_sample()
+
+    transformed_sample = transformer.transform([fake_sample])[0]
+    transformed_data = transformed_sample.data
+    assert transformed_sample.data.size == 128