merge the two helpers and rename to utils

bob/bio/face/__init__.py

@@ -4,6 +4,7 @@ from . import algorithm
 from . import script
 from . import database
 from . import annotator
+from . import utils
 
 from . import test
 

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

 from bob.bio.face.embeddings.mxnet_models import ArcFaceInsightFace
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import arcface_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(

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

@@ -5,7 +5,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
     VanillaBiometricsPipeline,
 )
 from bob.pipelines.transformers import SampleLinearize
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database()
 

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

 from bob.bio.face.embeddings.tf2_inception_resnet import (
     FaceNetSanderberg_20170512_110547,
 )
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(

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

@@ -3,7 +3,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
     VanillaBiometricsPipeline,
     BioAlgorithmLegacy,
 )
-from bob.bio.face.config.baseline.helpers import (
+from bob.bio.face.utils import (
     lookup_config_from_database,
     legacy_default_cropping,
     make_cropper,

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

 from bob.bio.face.embeddings.tf2_inception_resnet import (
     InceptionResnetv1_Casia_CenterLoss_2018,
 )
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(

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

 from bob.bio.face.embeddings.tf2_inception_resnet import (
     InceptionResnetv1_MsCeleb_CenterLoss_2018,
 )
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import facenet_baseline
 
 

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

 from bob.bio.face.embeddings.tf2_inception_resnet import (
     InceptionResnetv2_Casia_CenterLoss_2018,
 )
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import facenet_baseline
 
 

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

 from bob.bio.face.embeddings.tf2_inception_resnet import (
     InceptionResnetv2_MsCeleb_CenterLoss_2018,
 )
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(

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

@@ -3,7 +3,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
     VanillaBiometricsPipeline,
     BioAlgorithmLegacy,
 )
-from bob.bio.face.config.baseline.helpers import (
+from bob.bio.face.utils import (
     lookup_config_from_database,
     legacy_default_cropping,
     make_cropper,

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

@@ -3,7 +3,7 @@ from bob.bio.base.pipelines.vanilla_biometrics import (
     VanillaBiometricsPipeline,
     BioAlgorithmLegacy,
 )
-from bob.bio.face.config.baseline.helpers import (
+from bob.bio.face.utils import (
     lookup_config_from_database,
     legacy_default_cropping,
     make_cropper,

bob/bio/face/config/baseline/mobilenetv2_msceleb_arcface_2021.py

 from bob.bio.face.embeddings.mobilenet_v2 import MobileNetv2_MsCeleb_ArcFace_2021
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import arcface_baseline
 
 

bob/bio/face/config/baseline/resnet50_msceleb_arcface_2021.py

 from bob.bio.face.embeddings.resnet50 import Resnet50_MsCeleb_ArcFace_2021
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import arcface_baseline
 
 

bob/bio/face/config/baseline/resnet50_vgg2_arcface_2021.py

 from bob.bio.face.embeddings.resnet50 import Resnet50_VGG2_ArcFace_2021
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import arcface_baseline
 
 

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

-from bob.bio.face.config.baseline.helpers import (
+from bob.bio.face.utils import (
     dnn_default_cropping,
     embedding_transformer,
 )

bob/bio/face/config/baseline/tf2_inception_resnet.py

 from bob.extension import rc
 from bob.bio.face.embeddings.tf2_inception_resnet import InceptionResnetv2
-from bob.bio.face.config.baseline.helpers import lookup_config_from_database
+from bob.bio.face.utils import lookup_config_from_database
 from bob.bio.face.config.baseline.templates import facenet_baseline
 
 annotation_type, fixed_positions, memory_demanding = lookup_config_from_database(

bob/bio/face/helpers.py

-from bob.bio.face.preprocessor import FaceCrop, MultiFaceCrop, Scale
-import bob.bio.face.config.baseline.helpers as helpers
-
-def face_crop_solver(
-    cropped_image_size,
-    cropped_positions=None,
-    color_channel="rgb",
-    fixed_positions=None,
-    annotator=None,
-    dtype="uint8",
-):
-    """
-    Decide which face cropper to use.
-    """
-    # If there's not cropped positions, just resize
-    if cropped_positions is None:
-        return Scale(cropped_image_size)
-    else:
-        # Detects the face and crops it without eye detection
-        if isinstance(cropped_positions, list):
-            return MultiFaceCrop(
-                cropped_image_size=cropped_image_size,
-                cropped_positions_list=cropped_positions,
-                fixed_positions_list=fixed_positions,
-                color_channel=color_channel,
-                dtype=dtype,
-                annotator=annotator,
-            )
-        else:
-            return FaceCrop(
-                cropped_image_size=cropped_image_size,
-                cropped_positions=cropped_positions,
-                color_channel=color_channel,
-                fixed_positions=fixed_positions,
-                dtype=dtype,
-                annotator=annotator,
-            )
-
-
-def get_default_cropped_positions(mode, cropped_image_size, annotation_type):
-    """
-    Computes the default cropped positions for the FaceCropper,
-    proportionally to the target image size
-
-
-    Parameters
-    ----------
-    mode: str
-        Which default cropping to use. Available modes are : `legacy` (legacy baselines), `facenet`, `arcface`,
-        and `pad`.
-
-    cropped_image_size : tuple
-        A tuple (HEIGHT, WIDTH) describing the target size of the cropped image.
-
-    annotation_type: str
-        Type of annotations. Possible values are: `bounding-box`, `eyes-center` and None, or a combination of those as a list
-
-    Returns
-    -------
-
-    cropped_positions:
-        The dictionary of cropped positions that will be feeded to the FaceCropper, or a list of such dictionaries if
-        ``annotation_type`` is a list
-    """
-    if mode == "legacy":
-        return helpers.legacy_default_cropping(cropped_image_size, annotation_type)
-    elif mode in ["dnn", "facenet", "arcface"]:
-        return helpers.dnn_default_cropping(cropped_image_size, annotation_type)
-    elif mode == "pad":
-        return helpers.pad_default_cropping(cropped_image_size, annotation_type)
-    else:
-        raise ValueError("Unknown default cropping mode `{}`".format(mode))

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

-from sklearn.pipeline import make_pipeline
-from bob.pipelines import wrap
-from bob.bio.face import helpers
-import numpy as np
 import logging
 
+from .preprocessor import FaceCrop
+from .preprocessor import MultiFaceCrop
+from .preprocessor import Scale
+from bob.pipelines import wrap
+from sklearn.pipeline import make_pipeline
+
 logger = logging.getLogger(__name__)
 
 
@@ -237,7 +239,7 @@ def make_cropper(
     transform_extra_arguments for wrapping the cropper with a SampleWrapper.
 
     """
-    face_cropper = helpers.face_crop_solver(
+    face_cropper = face_crop_solver(
         cropped_image_size=cropped_image_size,
         cropped_positions=cropped_positions,
         fixed_positions=fixed_positions,
@@ -289,3 +291,74 @@ def embedding_transformer(
     )
 
     return transformer
+
+
+def face_crop_solver(
+    cropped_image_size,
+    cropped_positions=None,
+    color_channel="rgb",
+    fixed_positions=None,
+    annotator=None,
+    dtype="uint8",
+):
+    """
+    Decide which face cropper to use.
+    """
+    # If there's not cropped positions, just resize
+    if cropped_positions is None:
+        return Scale(cropped_image_size)
+    else:
+        # Detects the face and crops it without eye detection
+        if isinstance(cropped_positions, list):
+            return MultiFaceCrop(
+                cropped_image_size=cropped_image_size,
+                cropped_positions_list=cropped_positions,
+                fixed_positions_list=fixed_positions,
+                color_channel=color_channel,
+                dtype=dtype,
+                annotator=annotator,
+            )
+        else:
+            return FaceCrop(
+                cropped_image_size=cropped_image_size,
+                cropped_positions=cropped_positions,
+                color_channel=color_channel,
+                fixed_positions=fixed_positions,
+                dtype=dtype,
+                annotator=annotator,
+            )
+
+
+def get_default_cropped_positions(mode, cropped_image_size, annotation_type):
+    """
+    Computes the default cropped positions for the FaceCropper,
+    proportionally to the target image size
+
+
+    Parameters
+    ----------
+    mode: str
+        Which default cropping to use. Available modes are : `legacy` (legacy baselines), `facenet`, `arcface`,
+        and `pad`.
+
+    cropped_image_size : tuple
+        A tuple (HEIGHT, WIDTH) describing the target size of the cropped image.
+
+    annotation_type: str
+        Type of annotations. Possible values are: `bounding-box`, `eyes-center` and None, or a combination of those as a list
+
+    Returns
+    -------
+
+    cropped_positions:
+        The dictionary of cropped positions that will be feeded to the FaceCropper, or a list of such dictionaries if
+        ``annotation_type`` is a list
+    """
+    if mode == "legacy":
+        return legacy_default_cropping(cropped_image_size, annotation_type)
+    elif mode in ["dnn", "facenet", "arcface"]:
+        return dnn_default_cropping(cropped_image_size, annotation_type)
+    elif mode == "pad":
+        return pad_default_cropping(cropped_image_size, annotation_type)
+    else:
+        raise ValueError("Unknown default cropping mode `{}`".format(mode))

doc/faq.rst

@@ -22,7 +22,7 @@ Some face embedding extractors work well on loosely cropped faces, while others
 We provide a few reasonable defaults that are used in our implemented baselines. They are accessible through a function as follows :
 ::
 
-    from bob.bio.face.helpers import get_default_cropped_positions
+    from bob.bio.face.utils import get_default_cropped_positions
     mode = 'legacy'
     cropped_image_size=(160, 160)
     annotation_type='eyes-center'
@@ -31,7 +31,7 @@ We provide a few reasonable defaults that are used in our implemented baselines.
 
 There are currently three available modes :
 
-* :code:`legacy` Tight crop, used in non neural-net baselines such as :code:`gabor-graph`, :code:`lgbphs` or :code:`lda`. 
+* :code:`legacy` Tight crop, used in non neural-net baselines such as :code:`gabor-graph`, :code:`lgbphs` or :code:`lda`.
   It is typically use with a 5:4 aspect ratio for the :code:`cropped_image_size`
 * :code:`dnn` Loose crop, used for neural-net baselines such as the ArcFace or FaceNet models.
 * :code:`pad` Tight crop used in some PAD baselines
@@ -40,4 +40,3 @@ We present hereafter a visual example of those crops for the `eyes-center` annot
 
 .. plot:: plot/default_crops.py
     :include-source: True
-    
\ No newline at end of file

doc/plot/default_crops.py

 import bob.io.image
-from bob.bio.face.helpers import get_default_cropped_positions
+from bob.bio.face.utils import get_default_cropped_positions
 from bob.bio.face.preprocessor import FaceCrop
 import matplotlib.pyplot as plt