diff --git a/bob/bio/face/config/baseline/facenet_sanderberg.py b/bob/bio/face/config/baseline/facenet_sanderberg.py
index cf33486b93617e2fc5a3d47ce893f3bfb6124c72..3c989a92b5286b120ff571de8f7398e1f462fbd3 100644
--- a/bob/bio/face/config/baseline/facenet_sanderberg.py
+++ b/bob/bio/face/config/baseline/facenet_sanderberg.py
@@ -1,4 +1,4 @@
-from bob.bio.face.embeddings import FaceNetSanderberg
+from bob.bio.face.embeddings import FaceNetSanderberg_20170512_110547
from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
from bob.bio.base.pipelines.vanilla_biometrics import (
Distance,
@@ -17,7 +17,7 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- FaceNetSanderberg(), annotation_type, fixed_positions
+ FaceNetSanderberg_20170512_110547(), annotation_type, fixed_positions
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/gabor_graph.py b/bob/bio/face/config/baseline/gabor_graph.py
index 2798fb5e07a76d21444f688ec0d67dc75f4cb1f4..b5fca41de57728b39a1bfb71687e9d332c9fde65 100644
--- a/bob/bio/face/config/baseline/gabor_graph.py
+++ b/bob/bio/face/config/baseline/gabor_graph.py
@@ -24,15 +24,14 @@ else:
annotation_type = None
fixed_positions = None
-
-def load(annotation_type, fixed_positions=None):
- ####### SOLVING THE FACE CROPPER TO BE USED ##########
-
+def get_cropper(annotation_type, fixed_positions=None):
# Cropping
face_cropper, transform_extra_arguments = crop_80x64(
annotation_type, fixed_positions, color_channel="gray"
)
+ return face_cropper, transform_extra_arguments
+def get_pipeline(face_cropper, transform_extra_arguments):
preprocessor = bob.bio.face.preprocessor.INormLBP(
face_cropper=face_cropper, dtype=np.float64
)
@@ -79,6 +78,11 @@ def load(annotation_type, fixed_positions=None):
algorithm = BioAlgorithmLegacy(gabor_jet, base_dir=tempdir)
return VanillaBiometricsPipeline(transformer, algorithm)
+def load(annotation_type, fixed_positions=None):
+ ####### SOLVING THE FACE CROPPER TO BE USED ##########
+ face_cropper, transform_extra_arguments = get_cropper(annotation_type, fixed_positions)
+ return get_pipeline(face_cropper, transform_extra_arguments)
+
pipeline = load(annotation_type, fixed_positions)
transformer = pipeline.transformer
diff --git a/bob/bio/face/config/baseline/helpers.py b/bob/bio/face/config/baseline/helpers.py
index 0cd136b1ce3199cfef0593e94ef324427b9c7a11..910f1d8867c28a72826b578bd7683d6eb21a2f2e 100644
--- a/bob/bio/face/config/baseline/helpers.py
+++ b/bob/bio/face/config/baseline/helpers.py
@@ -5,55 +5,131 @@ from bob.pipelines import wrap
from bob.bio.face.helpers import face_crop_solver
import numpy as np
+def embedding_transformer_default_cropping(cropped_image_size, annotation_type):
+ """
+ Computes the default cropped positions for the FaceCropper used with Facenet-like
+ Embedding extractors, proportionally to the target image size
-def embedding_transformer_160x160(embedding, annotation_type, fixed_positions):
+
+ Parameters
+ ----------
+ 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`, 'left-profile',
+ 'right-profile' and None
+
+ Returns
+ -------
+
+ cropped_positions:
+ The dictionary of cropped positions that will be feeded to the FaceCropper.
"""
- Creates a pipeline composed by and FaceCropper and an Embedding extractor.
- This transformer is suited for Facenet based architectures
-
- .. warning::
- This will resize images to :math:`160 \times 160`
+ CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH = cropped_image_size
+
+ if annotation_type == "bounding-box":
+
+ TOP_LEFT_POS = (0, 0)
+ BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+ cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS}
+
+ elif annotation_type == "eyes-center":
+
+ RIGHT_EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(1/3*CROPPED_IMAGE_WIDTH))
+ LEFT_EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(2/3*CROPPED_IMAGE_WIDTH))
+ cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS}
+
+ elif annotation_type == "left-profile":
+
+ EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(3/8*CROPPED_IMAGE_WIDTH))
+ MOUTH_POS = (round(5/7*CROPPED_IMAGE_HEIGHT), round(3/8*CROPPED_IMAGE_WIDTH))
+ cropped_positions={'leye': EYE_POS, 'mouth': MOUTH_POS}
+
+ elif annotation_type == "right-profile":
+
+ EYE_POS = (round(2/7*CROPPED_IMAGE_HEIGHT), round(5/8*CROPPED_IMAGE_WIDTH))
+ MOUTH_POS = (round(5/7*CROPPED_IMAGE_HEIGHT), round(5/8*CROPPED_IMAGE_WIDTH))
+ cropped_positions={'reye': EYE_POS, 'mouth': MOUTH_POS}
+ else:
+
+ cropped_positions = None
+
+ return cropped_positions
+
+def legacy_default_cropping(cropped_image_size, annotation_type):
"""
+ Computes the default cropped positions for the FaceCropper used with legacy extractors,
+ proportionally to the target image size
- # 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
+ Parameters
+ ----------
+ 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`, 'left-profile',
+ 'right-profile' and None
+
+ Returns
+ -------
+
+ cropped_positions:
+ The dictionary of cropped positions that will be feeded to the FaceCropper.
+ """
+ CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH = cropped_image_size
+
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,
- )
+ cropped_positions={"topleft": TOP_LEFT_POS, "bottomright": BOTTOM_RIGHT_POS}
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(
+ RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
+ LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
+ cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS}
+
+ elif annotation_type == "left-profile":
+ # Main reference https://gitlab.idiap.ch/bob/bob.chapter.FRICE/-/blob/master/bob/chapter/FRICE/script/pose.py
+ EYE_POS = (CROPPED_IMAGE_HEIGHT//5, CROPPED_IMAGE_WIDTH // 7 * 3 - 2)
+ MOUTH_POS = (CROPPED_IMAGE_HEIGHT//3 * 2, CROPPED_IMAGE_WIDTH // 7 * 3 - 2)
+ cropped_positions={'leye': EYE_POS, 'mouth': MOUTH_POS}
+
+ elif annotation_type == "right-profile":
+ # Main reference https://gitlab.idiap.ch/bob/bob.chapter.FRICE/-/blob/master/bob/chapter/FRICE/script/pose.py
+ EYE_POS = (CROPPED_IMAGE_HEIGHT//5, CROPPED_IMAGE_WIDTH // 7 * 4 + 2)
+ MOUTH_POS = (CROPPED_IMAGE_HEIGHT//3 * 2, CROPPED_IMAGE_WIDTH // 7 * 4 + 2)
+ cropped_positions={'reye': EYE_POS, 'mouth': MOUTH_POS}
+
+ else:
+
+ cropped_positions = None
+
+ return cropped_positions
+
+def embedding_transformer(cropped_image_size, embedding, annotation_type, cropped_positions, fixed_positions=None):
+ """
+ Creates a pipeline composed by and FaceCropper and an Embedding extractor.
+ This transformer is suited for Facenet based architectures
+
+ .. warning::
+ This will resize images to the requested `image_size`
+
+ """
+ color_channel = "rgb"
+
+ face_cropper = face_crop_solver(
cropped_image_size,
color_channel=color_channel,
- cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
+ cropped_positions=cropped_positions,
fixed_positions=fixed_positions,
)
- else:
- transform_extra_arguments = None
- # DEFAULT TO FACE SIMPLE RESIZE
- face_cropper = face_crop_solver(cropped_image_size)
+ transform_extra_arguments = None if cropped_positions is None else (("annotations", "annotations"),)
transformer = make_pipeline(
wrap(
@@ -66,66 +142,39 @@ def embedding_transformer_160x160(embedding, annotation_type, fixed_positions):
return transformer
-
-def embedding_transformer_112x112(embedding, annotation_type, fixed_positions):
+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 resize images to :math:`112 \times 112`
+ This will resize images to :math:`160 \times 160`
"""
+ cropped_positions = embedding_transformer_default_cropping((160, 160), annotation_type)
- # This is the size of the image that this model expects
- CROPPED_IMAGE_HEIGHT = 112
- CROPPED_IMAGE_WIDTH = 112
- cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
- color_channel = "rgb"
+ return embedding_transformer((160, 160), embedding, annotation_type, cropped_positions, fixed_positions)
- #### 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 = (32, 34)
- LEFT_EYE_POS = (32, 77)
-
- # 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,
- )
+def embedding_transformer_112x112(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 resize images to :math:`112 \times 112`
+
+ """
+ cropped_image_size = (112, 112)
+ if annotation_type == "eyes-center":
+ # Hard coding eye positions for backward consistency
+ cropped_positions = {'leye': (32, 77), 'reye': (32, 34)}
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),
- )
+ # Will use default
+ cropped_positions = embedding_transformer_default_cropping(cropped_image_size, annotation_type)
- return transformer
+ return embedding_transformer(cropped_image_size, embedding, annotation_type, cropped_positions, fixed_positions)
def crop_80x64(annotation_type, fixed_positions=None, color_channel="gray"):
@@ -156,49 +205,25 @@ def crop_80x64(annotation_type, fixed_positions=None, color_channel="gray"):
The parameters to the transformer
"""
+ color_channel = color_channel
+ dtype = np.float64
# 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)
-
cropped_image_size = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
- color_channel = color_channel
- dtype = np.float64
- if annotation_type == "bounding-box":
- transform_extra_arguments = (("annotations", "annotations"),)
- TOP_LEFT_POS = (0, 0)
- BOTTOM_RIGHT_POS = (CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH)
+ cropped_positions = legacy_default_cropping(cropped_image_size, annotation_type)
- # 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,
- dtype=dtype
- )
-
- elif annotation_type == "eyes-center":
- transform_extra_arguments = (("annotations", "annotations"),)
- # eye positions for frontal images
- # Detects the face and crops it without eye detection
- face_cropper = face_crop_solver(
+ face_cropper = face_crop_solver(
cropped_image_size,
color_channel=color_channel,
- cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
+ cropped_positions=cropped_positions,
fixed_positions=fixed_positions,
dtype=dtype
)
-
- else:
- transform_extra_arguments = None
- # DEFAULT TO FACE SIMPLE RESIZE
- face_cropper = face_crop_solver(cropped_image_size)
+
+ transform_extra_arguments = None if cropped_positions is None else (("annotations", "annotations"),)
return face_cropper, transform_extra_arguments
diff --git a/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py b/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py
index 041034c3e1bcd8035d49233e123538440761e984..7b8bfc4a23c926f998fe01a77fd0b12734345b95 100644
--- a/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py
+++ b/bob/bio/face/config/baseline/inception_resnetv1_casiawebface.py
@@ -1,4 +1,4 @@
-from bob.bio.face.embeddings import InceptionResnetv1_CasiaWebFace
+from bob.bio.face.embeddings import InceptionResnetv1_Casia_CenterLoss_2018
from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
from bob.bio.base.pipelines.vanilla_biometrics import (
Distance,
@@ -15,7 +15,7 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv1_CasiaWebFace(), annotation_type, fixed_positions
+ InceptionResnetv1_Casia_CenterLoss_2018(), annotation_type, fixed_positions
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py b/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py
index 087eff8f719d13e1c9e9a9851c3eb0558e417aec..b09ade9be0e785b9678bcb1354cee3c441b13f46 100644
--- a/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py
+++ b/bob/bio/face/config/baseline/inception_resnetv1_msceleb.py
@@ -1,4 +1,4 @@
-from bob.bio.face.embeddings import InceptionResnetv1_MsCeleb
+from bob.bio.face.embeddings import InceptionResnetv1_MsCeleb_CenterLoss_2018
from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
from bob.bio.base.pipelines.vanilla_biometrics import (
Distance,
@@ -16,7 +16,7 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv1_MsCeleb(), annotation_type, fixed_positions
+ InceptionResnetv1_MsCeleb_CenterLoss_2018(), annotation_type, fixed_positions
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py b/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py
index d5b91c11769cf810360eee8811f20d9d1476e323..82fc0eb843a2e8ad13c46bcce818a05840ae3b9c 100644
--- a/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py
+++ b/bob/bio/face/config/baseline/inception_resnetv2_casiawebface.py
@@ -1,4 +1,4 @@
-from bob.bio.face.embeddings import InceptionResnetv2_CasiaWebFace
+from bob.bio.face.embeddings import InceptionResnetv2_Casia_CenterLoss_2018
from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
from bob.bio.base.pipelines.vanilla_biometrics import (
Distance,
@@ -16,7 +16,7 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv2_CasiaWebFace(), annotation_type, fixed_positions
+ InceptionResnetv2_Casia_CenterLoss_2018(), annotation_type, fixed_positions
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py b/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py
index 7db50cd6ee2f3ea61b7b6b41376dc167e198e435..6c28239830e7cea1b95d41a76dc2b9e4bd4f27cb 100644
--- a/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py
+++ b/bob/bio/face/config/baseline/inception_resnetv2_msceleb.py
@@ -1,4 +1,4 @@
-from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb
+from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb_CenterLoss_2018
from bob.bio.face.config.baseline.helpers import embedding_transformer_160x160
from bob.bio.base.pipelines.vanilla_biometrics import (
Distance,
@@ -16,7 +16,7 @@ else:
def load(annotation_type, fixed_positions=None):
transformer = embedding_transformer_160x160(
- InceptionResnetv2_MsCeleb(), annotation_type, fixed_positions
+ InceptionResnetv2_MsCeleb_CenterLoss_2018(), annotation_type, fixed_positions
)
algorithm = Distance()
diff --git a/bob/bio/face/config/baseline/lgbphs.py b/bob/bio/face/config/baseline/lgbphs.py
index e906159b276e46e1f4c44ccef0a40d8c6e250c42..8b2ee18103d15d43ef515970888337d11c3242c6 100644
--- a/bob/bio/face/config/baseline/lgbphs.py
+++ b/bob/bio/face/config/baseline/lgbphs.py
@@ -21,15 +21,14 @@ else:
annotation_type = None
fixed_positions = None
-
-def load(annotation_type, fixed_positions=None):
- ####### SOLVING THE FACE CROPPER TO BE USED ##########
-
+def get_cropper(annotation_type, fixed_positions=None):
# Cropping
face_cropper, transform_extra_arguments = crop_80x64(
annotation_type, fixed_positions, color_channel="gray"
)
+ return face_cropper, transform_extra_arguments
+def get_pipeline(face_cropper, transform_extra_arguments):
preprocessor = bob.bio.face.preprocessor.TanTriggs(
face_cropper=face_cropper, dtype=np.float64
)
@@ -56,7 +55,6 @@ def load(annotation_type, fixed_positions=None):
)
-
### BIOMETRIC ALGORITHM
histogram = bob.bio.face.algorithm.Histogram(
distance_function = bob.math.histogram_intersection,
@@ -69,5 +67,10 @@ def load(annotation_type, fixed_positions=None):
return VanillaBiometricsPipeline(transformer, algorithm)
+def load(annotation_type, fixed_positions=None):
+ ####### SOLVING THE FACE CROPPER TO BE USED ##########
+ face_cropper, transform_extra_arguments = get_cropper(annotation_type, fixed_positions)
+ return get_pipeline(face_cropper, transform_extra_arguments)
+
pipeline = load(annotation_type, fixed_positions)
transformer = pipeline.transformer
\ No newline at end of file
diff --git a/bob/bio/face/config/baseline/tf2_inception_resnet.py b/bob/bio/face/config/baseline/tf2_inception_resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d68f0bad2ec215b6663edbace89665cea0c5ec6
--- /dev/null
+++ b/bob/bio/face/config/baseline/tf2_inception_resnet.py
@@ -0,0 +1,44 @@
+from bob.extension import rc
+from bob.bio.face.embeddings.tf2_inception_resnet import InceptionResnetv2
+from bob.bio.face.preprocessor import FaceCrop
+from bob.bio.face.config.baseline.helpers import (
+ embedding_transformer_default_cropping,
+ embedding_transformer
+)
+
+from sklearn.pipeline import make_pipeline
+from bob.pipelines.wrappers import wrap
+from bob.bio.base.pipelines.vanilla_biometrics import (
+ Distance,
+ VanillaBiometricsPipeline,
+)
+
+
+if "database" in locals():
+ annotation_type = database.annotation_type
+ fixed_positions = database.fixed_positions
+else:
+ annotation_type = None
+ fixed_positions = None
+
+def load(annotation_type, fixed_positions=None):
+ CROPPED_IMAGE_SIZE = (160, 160)
+ CROPPED_POSITIONS = embedding_transformer_default_cropping(CROPPED_IMAGE_SIZE,
+ annotation_type=annotation_type)
+
+ extractor_path = rc['bob.bio.face.tf2.casia-webface-inception-v2']
+
+ embedding = InceptionResnetv2(checkpoint_path=extractor_path)
+
+ transformer = embedding_transformer(CROPPED_IMAGE_SIZE,
+ embedding,
+ annotation_type,
+ CROPPED_POSITIONS,
+ fixed_positions)
+
+ algorithm = Distance()
+
+ return VanillaBiometricsPipeline(transformer, algorithm)
+
+pipeline = load(annotation_type, fixed_positions)
+transformer = pipeline.transformer
diff --git a/bob/bio/face/embeddings/__init__.py b/bob/bio/face/embeddings/__init__.py
index b34ea58693dc9de51979efa8c7c4d732e93c4faa..05228099435a7d31268b27fb9c2199224d3692a3 100644
--- a/bob/bio/face/embeddings/__init__.py
+++ b/bob/bio/face/embeddings/__init__.py
@@ -1,13 +1,38 @@
-from .facenet_sanderberg import FaceNetSanderberg
-from .idiap_inception_resnet import (
- InceptionResnetv2_MsCeleb,
- InceptionResnetv2_CasiaWebFace,
- InceptionResnetv1_MsCeleb,
- InceptionResnetv1_CasiaWebFace
-)
+import os
+import bob.extension.download
+
+def download_model(model_path, urls, zip_file="model.tar.gz"):
+ """
+ Download and unzip a model from some URL.
+
+ Parameters
+ ----------
+
+ model_path: str
+ Path where the model is supposed to be stored
+
+ urls: list
+ List of paths where the model is stored
-from .arface import ArcFace_InsightFaceTF
+ zip_file: str
+ File name after the download
+ """
+
+ if not os.path.exists(model_path):
+ os.makedirs(model_path, exist_ok=True)
+ zip_file = os.path.join(model_path, zip_file)
+ bob.extension.download.download_and_unzip(urls, zip_file)
+
+
+from .tf2_inception_resnet import (
+ InceptionResnet,
+ InceptionResnetv2_MsCeleb_CenterLoss_2018,
+ InceptionResnetv2_Casia_CenterLoss_2018,
+ InceptionResnetv1_MsCeleb_CenterLoss_2018,
+ InceptionResnetv1_Casia_CenterLoss_2018,
+ FaceNetSanderberg_20170512_110547
+)
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
@@ -26,11 +51,11 @@ def __appropriate__(*args):
__appropriate__(
- FaceNetSanderberg,
- InceptionResnetv2_MsCeleb,
- InceptionResnetv2_CasiaWebFace,
- InceptionResnetv1_MsCeleb,
- InceptionResnetv1_CasiaWebFace,
- ArcFace_InsightFaceTF
+ InceptionResnet,
+ InceptionResnetv2_MsCeleb_CenterLoss_2018,
+ InceptionResnetv1_MsCeleb_CenterLoss_2018,
+ InceptionResnetv2_Casia_CenterLoss_2018,
+ InceptionResnetv1_Casia_CenterLoss_2018,
+ FaceNetSanderberg_20170512_110547
)
__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/bob/bio/face/embeddings/facenet_sanderberg.py b/bob/bio/face/embeddings/facenet_sanderberg.py
deleted file mode 100644
index e0a48dc2c648960b2a3f520b73e9592d28c7d031..0000000000000000000000000000000000000000
--- a/bob/bio/face/embeddings/facenet_sanderberg.py
+++ /dev/null
@@ -1,242 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-"""
-Wrapper for the free FaceNet variant:
- https://github.com/davidsandberg/facenet
-
-
- Model 20170512-110547
-"""
-
-from __future__ import division
-
-from sklearn.base import TransformerMixin, BaseEstimator
-
-import os
-import re
-import logging
-import numpy as np
-from bob.ip.color import gray_to_rgb
-from bob.io.image import to_matplotlib
-from bob.extension import rc
-import bob.extension.download
-import bob.io.base
-from sklearn.utils import check_array
-
-logger = logging.getLogger(__name__)
-
-FACENET_MODELPATH_KEY = "bob.bio.face.facenet_sanderberg_modelpath"
-
-
-def prewhiten(img):
- mean = np.mean(img)
- std = np.std(img)
- std_adj = np.maximum(std, 1.0 / np.sqrt(img.size))
- y = np.multiply(np.subtract(img, mean), 1 / std_adj)
- return y
-
-
-def get_model_filenames(model_dir):
- # code from https://github.com/davidsandberg/facenet
- files = os.listdir(model_dir)
- meta_files = [s for s in files if s.endswith(".meta")]
- if len(meta_files) == 0:
- raise ValueError("No meta file found in the model directory (%s)" % model_dir)
- elif len(meta_files) > 1:
- raise ValueError(
- "There should not be more than one meta file in the model "
- "directory (%s)" % model_dir
- )
- meta_file = meta_files[0]
- max_step = -1
- for f in files:
- step_str = re.match(r"(^model-[\w\- ]+.ckpt-(\d+))", f)
- if step_str is not None and len(step_str.groups()) >= 2:
- step = int(step_str.groups()[1])
- if step > max_step:
- max_step = step
- ckpt_file = step_str.groups()[0]
- return meta_file, ckpt_file
-
-
-class FaceNetSanderberg(TransformerMixin, BaseEstimator):
- """Wrapper for the free FaceNet variant:
- https://github.com/davidsandberg/facenet
-
- And for a preprocessor you can use::
-
- from bob.bio.face.preprocessor import FaceCrop
- # This is the size of the image that this model expects
- CROPPED_IMAGE_HEIGHT = 160
- CROPPED_IMAGE_WIDTH = 160
- # eye positions for frontal images
- RIGHT_EYE_POS = (46, 53)
- LEFT_EYE_POS = (46, 107)
- # Crops the face using eye annotations
- preprocessor = FaceCrop(
- cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
- cropped_positions={'leye': LEFT_EYE_POS, 'reye': RIGHT_EYE_POS},
- color_channel='rgb'
- )
-
- """
-
- def __init__(
- self,
- model_path=rc[FACENET_MODELPATH_KEY],
- image_size=160,
- layer_name="embeddings:0",
- **kwargs,
- ):
- super(FaceNetSanderberg, self).__init__()
- self.model_path = model_path
- self.image_size = image_size
- self.layer_name = layer_name
- self.loaded = False
- self._clean_unpicklables()
-
- def _clean_unpicklables(self):
- self.session = None
- self.embeddings = None
- self.graph = None
- self.images_placeholder = None
- self.phase_train_placeholder = None
-
- def _check_feature(self, img):
- img = check_array(img, allow_nd=True)
-
- def _convert(img):
- assert img.shape[-2] == self.image_size
- assert img.shape[-3] == self.image_size
- img = prewhiten(img)
- return img
-
- if img.ndim == 3:
- if img.shape[0]==3:
- img = np.moveaxis(img, 0, -1)
- return _convert(img)[None, ...] # Adding another axis
- elif img.ndim == 4:
- if img.shape[1]==3:
- img = np.moveaxis(img, 1, -1)
- return _convert(img)
- else:
- raise ValueError(f"Image shape {img.shape} not supported")
-
- def load_model(self):
- import tensorflow as tf
-
- tf.compat.v1.reset_default_graph()
-
- session_conf = tf.compat.v1.ConfigProto(
- intra_op_parallelism_threads=1, inter_op_parallelism_threads=1
- )
- self.graph = tf.Graph()
- self.session = tf.compat.v1.Session(graph=self.graph, config=session_conf)
-
- if self.model_path is None:
- self.model_path = self.get_modelpath()
- if not os.path.exists(self.model_path):
- bob.io.base.create_directories_safe(FaceNetSanderberg.get_modelpath())
- zip_file = os.path.join(
- FaceNetSanderberg.get_modelpath(), "20170512-110547.zip"
- )
- urls = [
- # This link only works in Idiap CI to save bandwidth.
- "http://www.idiap.ch/private/wheels/gitlab/"
- "facenet_model2_20170512-110547.zip",
- # this link to dropbox would work for everybody
- "https://www.dropbox.com/s/"
- "k7bhxe58q7d48g7/facenet_model2_20170512-110547.zip?dl=1",
- ]
- bob.extension.download.download_and_unzip(urls, zip_file)
-
- # code from https://github.com/davidsandberg/facenet
- model_exp = os.path.expanduser(self.model_path)
-
- with self.graph.as_default():
- if os.path.isfile(model_exp):
- logger.info("Model filename: %s" % model_exp)
- with tf.compat.v1.gfile.FastGFile(model_exp, "rb") as f:
- graph_def = tf.compat.v1.GraphDef()
- graph_def.ParseFromString(f.read())
- tf.import_graph_def(graph_def, name="")
- else:
- logger.info("Model directory: %s" % model_exp)
- meta_file, ckpt_file = get_model_filenames(model_exp)
-
- logger.info("Metagraph file: %s" % meta_file)
- logger.info("Checkpoint file: %s" % ckpt_file)
-
- saver = tf.compat.v1.train.import_meta_graph(
- os.path.join(model_exp, meta_file)
- )
- saver.restore(self.session, os.path.join(model_exp, ckpt_file))
- # Get input and output tensors
- self.images_placeholder = self.graph.get_tensor_by_name("input:0")
- self.embeddings = self.graph.get_tensor_by_name(self.layer_name)
- self.phase_train_placeholder = self.graph.get_tensor_by_name("phase_train:0")
- logger.info("Successfully loaded the model.")
- self.loaded = True
-
- def transform(self, X, **kwargs):
- def _transform(X):
-
- images = self._check_feature(X)
- if not self.loaded:
- self.load_model()
-
- feed_dict = {
- self.images_placeholder: images,
- self.phase_train_placeholder: False,
- }
- features = self.session.run(self.embeddings, feed_dict=feed_dict)
- return features
-
- return [_transform(i) for i in X]
-
- @staticmethod
- def get_modelpath():
- """
- Get default model path.
-
- First we try the to search this path via Global Configuration System.
- If we can not find it, we set the path in the directory
- `<project>/data`
- """
-
- # Priority to the RC path
- model_path = rc[FACENET_MODELPATH_KEY]
-
- if model_path is None:
- import pkg_resources
-
- model_path = pkg_resources.resource_filename(
- __name__, "data/FaceNet/20170512-110547"
- )
-
- return model_path
-
- def __setstate__(self, d):
- # Handling unpicklable objects
- self.__dict__ = d
-
- def __getstate__(self):
- import tensorflow as tf
-
- # Handling unpicklable objects
- d = self.__dict__
- d.pop("session") if "session" in d else None
- d.pop("embeddings") if "embeddings" in d else None
- d.pop("graph") if "graph" in d else None
- d.pop("images_placeholder") if "images_placeholder" in d else None
- d.pop("phase_train_placeholder") if "phase_train_placeholder" in d else None
- tf.compat.v1.reset_default_graph()
- self.loaded = False
- return d
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
-
- def fit(self, X, y=None):
- return self
diff --git a/bob/bio/face/embeddings/idiap_inception_resnet.py b/bob/bio/face/embeddings/idiap_inception_resnet.py
deleted file mode 100644
index 5398b24e24df95d459d33d26a184c905f99e3685..0000000000000000000000000000000000000000
--- a/bob/bio/face/embeddings/idiap_inception_resnet.py
+++ /dev/null
@@ -1,123 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-import os
-from sklearn.base import TransformerMixin, BaseEstimator
-from .tensorflow_compat_v1 import TensorflowCompatV1
-
-
-class InceptionResnetv2_MsCeleb(TensorflowCompatV1):
- """
- Inception Restnet v2 model from https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/1e40a68bfbbb3dd8813c48d50b2f23ff7a399956/bob/learn/tensorflow/network/InceptionResnetV2.py
-
- This model was trained using the MsCeleb 1M dataset
-
- The input shape of this model is :math:`3 \times 160 \times 160`
- The output embedding is :math:`n \times 128`, where :math:`n` is the number of samples
-
- """
-
- def __init__(self):
- from bob.learn.tensorflow.network import inception_resnet_v2_batch_norm
-
- bob_rc_variable = "bob.bio.face.idiap_inception_resnet_v2_path"
- urls = ["https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/inception-v2_batchnorm_rgb.tar.gz"]
- model_subdirectory = "idiap_inception_resnet_v2_path"
-
-
- checkpoint_filename = self.get_modelpath(bob_rc_variable, model_subdirectory)
- self.download_model(checkpoint_filename, urls)
-
- input_shape = (1, 160, 160, 3)
- architecture_fn = inception_resnet_v2_batch_norm
-
- super().__init__(checkpoint_filename, input_shape, architecture_fn)
-
-
-class InceptionResnetv2_CasiaWebFace(TensorflowCompatV1):
- """
- Inception Restnet v2 model from https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/1e40a68bfbbb3dd8813c48d50b2f23ff7a399956/bob/learn/tensorflow/network/InceptionResnetV2.py
-
- This model was trained using the Casia WebFace
-
- The input shape of this model is :math:`3 \times 160 \times 160`
- The output embedding is :math:`n \times 128`, where :math:`n` is the number of samples
-
- """
-
- def __init__(self):
- """Loads the tensorflow model
- """
- from bob.learn.tensorflow.network import inception_resnet_v2_batch_norm
-
- bob_rc_variable = "bob.bio.face.idiap_inception_resnet_v2_casiawebface_path"
- urls = ["https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/idiap_inception_resnet_v2_casiawebface_path.tar.gz"]
- model_subdirectory = "idiap_inception_resnet_v2_casiawebface_path"
-
-
- checkpoint_filename = self.get_modelpath(bob_rc_variable, model_subdirectory)
- self.download_model(checkpoint_filename, urls)
-
- input_shape = (1, 160, 160, 3)
- architecture_fn = inception_resnet_v2_batch_norm
-
- super().__init__(checkpoint_filename, input_shape, architecture_fn)
-
-
-class InceptionResnetv1_MsCeleb(TensorflowCompatV1):
- """
- Inception Restnet v1 model from https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/1e40a68bfbbb3dd8813c48d50b2f23ff7a399956/bob/learn/tensorflow/network/InceptionResnetV1.py
-
- This model was trained using the MsCeleb 1M dataset
-
- The input shape of this model is :math:`3 \times 160 \times 160`
- The output embedding is :math:`n \times 128`, where :math:`n` is the number of samples
-
- """
-
- def __init__(self):
- from bob.learn.tensorflow.network import inception_resnet_v1_batch_norm
-
- bob_rc_variable = "bob.bio.face.idiap_inception_resnet_v1_msceleb_path"
- urls = ["https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/idiap_inception_resnet_v1_msceleb_path.tar.gz"]
- model_subdirectory = "idiap_inception_resnet_v1_msceleb_path"
-
-
- checkpoint_filename = self.get_modelpath(bob_rc_variable, model_subdirectory)
- self.download_model(checkpoint_filename, urls)
-
- input_shape = (1, 160, 160, 3)
- architecture_fn = inception_resnet_v1_batch_norm
-
- super().__init__(checkpoint_filename, input_shape, architecture_fn)
-
-
-
-class InceptionResnetv1_CasiaWebFace(TensorflowCompatV1):
- """
- Inception Restnet v1 model from https://gitlab.idiap.ch/bob/bob.learn.tensorflow/-/blob/1e40a68bfbbb3dd8813c48d50b2f23ff7a399956/bob/learn/tensorflow/network/InceptionResnetV1.py
-
- This model was trained using the Casia WebFace
-
- The input shape of this model is :math:`3 \times 160 \times 160`
- The output embedding is :math:`n \times 128`, where :math:`n` is the number of samples
-
- """
-
- def __init__(self):
- """Loads the tensorflow model
- """
- from bob.learn.tensorflow.network import inception_resnet_v1_batch_norm
-
- bob_rc_variable = "bob.bio.face.idiap_inception_resnet_v1_casiawebface_path"
- urls = ["https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/idiap_inception_resnet_v1_casiawebface_path.tar.gz"]
- model_subdirectory = "idiap_inception_resnet_v1_casiawebface_path"
-
-
- checkpoint_filename = self.get_modelpath(bob_rc_variable, model_subdirectory)
- self.download_model(checkpoint_filename, urls)
-
- input_shape = (1, 160, 160, 3)
- architecture_fn = inception_resnet_v1_batch_norm
-
- super().__init__(checkpoint_filename, input_shape, architecture_fn)
diff --git a/bob/bio/face/embeddings/tensorflow_compat_v1.py b/bob/bio/face/embeddings/tensorflow_compat_v1.py
deleted file mode 100644
index 216d1cc84377b191ce5f43f3257eeefa12ac5c11..0000000000000000000000000000000000000000
--- a/bob/bio/face/embeddings/tensorflow_compat_v1.py
+++ /dev/null
@@ -1,187 +0,0 @@
-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-
-import os
-import pkg_resources
-import bob.extension.download
-from bob.extension import rc
-from sklearn.base import TransformerMixin, BaseEstimator
-import numpy as np
-import logging
-from sklearn.utils import check_array
-from bob.pipelines.sample import SampleBatch
-import copy
-logger = logging.getLogger(__name__)
-
-
-class TensorflowCompatV1(TransformerMixin, BaseEstimator):
- """
- Tensorflow v1 compatible set of transformers.
-
- Parameters
- ----------
- checkpoint_filename: str
- Path of your checkpoint. If the .meta file is providede the last checkpoint will be loaded.
-
- input_shape: tuple
- input_shape: Input shape for the tensorflow neural network
-
- architecture_fn :
- A tf.Tensor containing the operations to be executed
- """
-
- def __init__(self, checkpoint_filename, input_shape, architecture_fn):
- """Loads the tensorflow model
- """
-
- self.checkpoint_filename = checkpoint_filename
- self.input_shape = input_shape
- self.architecture_fn = architecture_fn
- self.loaded = False
-
- def transform(self, X):
- """
- Forward the data with the loaded neural network
-
- Parameters
- ----------
- X : numpy.ndarray
- Input Data
-
- Returns
- -------
- numpy.ndarray
- The features.
-
- """
-
- def _transform(data):
- data = check_array(data, allow_nd=True)
-
- # THE INPUT SHAPE FOR THESE MODELS
- # ARE `N x C x H x W`
-
- # If ndim==3 we add another axis
- if data.ndim == 3:
- data = data[None, ...]
-
- # Making sure it's channels last and has three channels
- if data.ndim == 4:
- # Just swiping the second dimension if bob format NxCxHxH
- if data.shape[1] == 3:
- data = np.moveaxis(data, 1, -1)
-
- if data.shape != self.input_shape:
- raise ValueError(
- f"Image shape {data.shape} not supported. Expected {self.input_shape}"
- )
-
- if not self.loaded:
- self.load_model()
-
- return self.session.run(self.embedding, feed_dict={self.input_tensor: data},)
-
- return [_transform(x) for x in X]
-
- def load_model(self):
- import tensorflow as tf
-
- logger.info(f"Loading model `{self.checkpoint_filename}`")
-
- tf.compat.v1.reset_default_graph()
-
- self.input_tensor = tf.placeholder(tf.float32, shape=self.input_shape)
-
- # Taking the embedding
- prelogits = self.architecture_fn(
- tf.stack(
- [
- tf.image.per_image_standardization(i)
- for i in tf.unstack(self.input_tensor)
- ]
- ),
- mode=tf.estimator.ModeKeys.PREDICT,
- )[0]
- self.embedding = tf.nn.l2_normalize(prelogits, dim=1, name="embedding")
-
- # Initializing the variables of the current architecture_fn
- self.session = tf.compat.v1.Session()
- self.session.run(tf.compat.v1.global_variables_initializer())
-
- # Loading the last checkpoint and overwriting the current variables
- saver = tf.compat.v1.train.Saver()
- if os.path.splitext(self.checkpoint_filename)[1] == ".meta":
- saver.restore(
- self.session,
- tf.train.latest_checkpoint(os.path.dirname(self.checkpoint_filename)),
- )
- elif os.path.isdir(self.checkpoint_filename):
- saver.restore(
- self.session, tf.train.latest_checkpoint(self.checkpoint_filename)
- )
- else:
- saver.restore(self.session, self.checkpoint_filename)
-
- self.loaded = True
-
- def __setstate__(self, d):
- # Handling unpicklable objects
- self.__dict__ = d
-
- def __getstate__(self):
- # Handling unpicklable objects
- d = self.__dict__.copy()
- d.pop("session", None)
- d.pop("input_tensor", None)
- d.pop("embedding", None)
- d["loaded"] = False
- return d
-
- def get_modelpath(self, bob_rc_variable, model_subdirectory):
- """
- Get default model path.
-
- First we try the to search this path via Global Configuration System.
- If we can not find it, we set the path in the directory
- `<project>/data`
- """
-
- # Priority to the RC path
- model_path = rc[bob_rc_variable]
-
- if model_path is None:
-
- model_path = pkg_resources.resource_filename(
- __name__, os.path.join("data", model_subdirectory)
- )
-
- return model_path
-
- def download_model(self, model_path, urls, zip_file="model.tar.gz"):
- """
- Download and unzip a model from some URL.
-
- Parameters
- ----------
-
- model_path: str
- Path where the model is supposed to be stored
-
- urls: list
- List of paths where the model is stored
-
- zip_file: str
- File name after the download
-
- """
-
- if not os.path.exists(model_path):
- bob.io.base.create_directories_safe(model_path)
- zip_file = os.path.join(model_path, zip_file)
- bob.extension.download.download_and_unzip(urls, zip_file)
-
- def fit(self, X, y=None):
- return self
-
- def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
diff --git a/bob/bio/face/embeddings/tf2_inception_resnet.py b/bob/bio/face/embeddings/tf2_inception_resnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..a80e15652b312d379a960dc0beeddb5bc093c439
--- /dev/null
+++ b/bob/bio/face/embeddings/tf2_inception_resnet.py
@@ -0,0 +1,256 @@
+import tensorflow as tf
+from bob.learn.tensorflow.utils.image import to_channels_last
+from sklearn.base import TransformerMixin, BaseEstimator
+from sklearn.utils import check_array
+
+from tensorflow.keras import Sequential
+from tensorflow.keras.layers.experimental import preprocessing
+from bob.extension import rc
+from functools import partial
+import pkg_resources
+import os
+from bob.bio.face.embeddings import download_model
+
+
+def sanderberg_rescaling():
+ # FIXED_STANDARDIZATION from https://github.com/davidsandberg/facenet
+ # [-0.99609375, 0.99609375]
+ preprocessor = preprocessing.Rescaling(scale=1 / 128, offset=-127.5 / 128)
+ return preprocessor
+
+
+class InceptionResnet(TransformerMixin, BaseEstimator):
+ """
+ Base Transformer for InceptionResnet architectures.
+
+ Szegedy, Christian, et al. "Inception-v4, inception-resnet and the impact of residual connections on learning." arXiv preprint arXiv:1602.07261 (2016).
+
+ Parameters
+ ----------
+
+ checkpoint_path: str
+ Path containing the checkpoint
+
+ preprocessor:
+ Preprocessor function
+
+ """
+
+ def __init__(self, checkpoint_path, preprocessor=None, **kwargs):
+ super().__init__(**kwargs)
+ self.checkpoint_path = checkpoint_path
+ self.model = None
+ self.preprocessor = preprocessor
+
+ def load_model(self):
+ self.model = tf.keras.models.load_model(self.checkpoint_path)
+
+ def inference(self, X):
+ if self.preprocessor is not None:
+ X = self.preprocessor(tf.cast(X, "float32"))
+
+ prelogits = self.model(X, training=False)
+ embeddings = tf.math.l2_normalize(prelogits, axis=-1)
+ return embeddings
+
+ def transform(self, X):
+ if self.model is None:
+ self.load_model()
+
+ X = check_array(X, allow_nd=True)
+ X = tf.convert_to_tensor(X)
+ X = to_channels_last(X)
+
+ if X.shape[-3:] != self.model.input_shape[-3:]:
+ raise ValueError(
+ f"Image shape {X.shape} not supported. Expected {self.model.input_shape}"
+ )
+
+ return self.inference(X).numpy()
+
+ def __setstate__(self, d):
+ self.__dict__ = d
+
+ def __getstate__(self):
+ # Handling unpicklable objects
+ d = self.__dict__.copy()
+ d["model"] = None
+ return d
+
+ def _more_tags(self):
+ return {"stateless": True, "requires_fit": False}
+
+
+class InceptionResnetv2_MsCeleb_CenterLoss_2018(InceptionResnet):
+ """
+ InceptionResnet v2 model trained in 2018 using the MSCeleb dataset in the context of the work:
+
+ Freitas Pereira, Tiago, André Anjos, and Sébastien Marcel. "Heterogeneous face recognition using domain specific units." IEEE Transactions on Information Forensics and Security 14.7 (2018): 1803-1816.
+
+ """
+
+ def __init__(self):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "inceptionresnetv2_msceleb_centerloss_2018"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.InceptionResnetv2_MsCeleb_CenterLoss_2018"]
+ is None
+ else rc["bob.bio.face.models.InceptionResnetv2_MsCeleb_CenterLoss_2018"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/inceptionresnetv2_msceleb_centerloss_2018.tar.gz"
+ ]
+
+ download_model(
+ checkpoint_path, urls, "inceptionresnetv2_msceleb_centerloss_2018.tar.gz"
+ )
+
+ super(InceptionResnetv2_MsCeleb_CenterLoss_2018, self).__init__(
+ checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ )
+
+
+class InceptionResnetv2_Casia_CenterLoss_2018(InceptionResnet):
+ """
+ InceptionResnet v2 model trained in 2018 using the CasiaWebFace dataset in the context of the work:
+
+ Freitas Pereira, Tiago, André Anjos, and Sébastien Marcel. "Heterogeneous face recognition using domain specific units." IEEE Transactions on Information Forensics and Security 14.7 (2018): 1803-1816.
+
+ """
+
+ def __init__(self):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "inceptionresnetv2_casia_centerloss_2018"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.InceptionResnetv2_Casia_CenterLoss_2018"] is None
+ else rc["bob.bio.face.models.InceptionResnetv2_Casia_CenterLoss_2018"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/inceptionresnetv2_casia_centerloss_2018.tar.gz"
+ ]
+
+ download_model(
+ checkpoint_path, urls, "inceptionresnetv2_casia_centerloss_2018.tar.gz"
+ )
+
+ super(InceptionResnetv2_Casia_CenterLoss_2018, self).__init__(
+ checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ )
+
+
+
+class InceptionResnetv1_Casia_CenterLoss_2018(InceptionResnet):
+ """
+ InceptionResnet v1 model trained in 2018 using the CasiaWebFace dataset in the context of the work:
+
+ Freitas Pereira, Tiago, André Anjos, and Sébastien Marcel. "Heterogeneous face recognition using domain specific units." IEEE Transactions on Information Forensics and Security 14.7 (2018): 1803-1816.
+
+ """
+
+ def __init__(self):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "inceptionresnetv1_casia_centerloss_2018"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.InceptionResnetv1_Casia_CenterLoss_2018"] is None
+ else rc["bob.bio.face.models.InceptionResnetv1_Casia_CenterLoss_2018"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/inceptionresnetv1_casia_centerloss_2018.tar.gz"
+ ]
+
+ download_model(
+ checkpoint_path, urls, "inceptionresnetv1_casia_centerloss_2018.tar.gz"
+ )
+
+ super(InceptionResnetv1_Casia_CenterLoss_2018, self).__init__(
+ checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ )
+
+class InceptionResnetv1_MsCeleb_CenterLoss_2018(InceptionResnet):
+ """
+ InceptionResnet v1 model trained in 2018 using the MsCeleb dataset in the context of the work:
+
+ Freitas Pereira, Tiago, André Anjos, and Sébastien Marcel. "Heterogeneous face recognition using domain specific units." IEEE Transactions on Information Forensics and Security 14.7 (2018): 1803-1816.
+
+ """
+
+ def __init__(self):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "inceptionresnetv1_msceleb_centerloss_2018"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.InceptionResnetv1_MsCeleb_CenterLoss_2018"] is None
+ else rc["bob.bio.face.models.InceptionResnetv1_MsCeleb_CenterLoss_2018"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/inceptionresnetv1_msceleb_centerloss_2018.tar.gz"
+ ]
+
+ download_model(
+ checkpoint_path, urls, "inceptionresnetv1_msceleb_centerloss_2018.tar.gz"
+ )
+
+ super(InceptionResnetv1_MsCeleb_CenterLoss_2018, self).__init__(
+ checkpoint_path, preprocessor=tf.image.per_image_standardization,
+ )
+
+
+class FaceNetSanderberg_20170512_110547(InceptionResnet):
+ """
+ Wrapper for the free FaceNet from David Sanderberg model 20170512_110547:
+ https://github.com/davidsandberg/facenet
+
+ And for a preprocessor you can use::
+
+ from bob.bio.face.preprocessor import FaceCrop
+ # This is the size of the image that this model expects
+ CROPPED_IMAGE_HEIGHT = 160
+ CROPPED_IMAGE_WIDTH = 160
+ # eye positions for frontal images
+ RIGHT_EYE_POS = (46, 53)
+ LEFT_EYE_POS = (46, 107)
+ # Crops the face using eye annotations
+ preprocessor = FaceCrop(
+ cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
+ cropped_positions={'leye': LEFT_EYE_POS, 'reye': RIGHT_EYE_POS},
+ color_channel='rgb'
+ )
+ """
+
+ def __init__(self):
+ internal_path = pkg_resources.resource_filename(
+ __name__, os.path.join("data", "facenet_sanderberg_20170512_110547"),
+ )
+
+ checkpoint_path = (
+ internal_path
+ if rc["bob.bio.face.models.facenet_sanderberg_20170512_110547"] is None
+ else rc["bob.bio.face.models.facenet_sanderberg_20170512_110547"]
+ )
+
+ urls = [
+ "https://www.idiap.ch/software/bob/data/bob/bob.bio.face/master/tf2/facenet_sanderberg_20170512_110547.tar.gz"
+ ]
+
+ download_model(
+ checkpoint_path, urls, "facenet_sanderberg_20170512_110547.tar.gz"
+ )
+
+ super(FaceNetSanderberg_20170512_110547, self).__init__(
+ checkpoint_path, tf.image.per_image_standardization,
+ )
\ No newline at end of file
diff --git a/bob/bio/face/extractor/GridGraph.py b/bob/bio/face/extractor/GridGraph.py
index 313c2445219a10d07f7bbb655eeb5bf5369d70f8..ce7a8a37a515f5ecdea25dc2c96234573085acdc 100644
--- a/bob/bio/face/extractor/GridGraph.py
+++ b/bob/bio/face/extractor/GridGraph.py
@@ -211,7 +211,6 @@ class GridGraph(Extractor):
The list of Gabor jets extracted from the image.
The 2D location of the jet's nodes is not returned.
"""
-
assert image.ndim == 2
assert isinstance(image, numpy.ndarray)
image = image.astype(numpy.float64)
diff --git a/bob/bio/face/preprocessor/FaceCrop.py b/bob/bio/face/preprocessor/FaceCrop.py
index 18719c1577e1f75de7d22ead2cb2f1b3363477c3..9b04b9decc79e3099b987b1e67c2843e34a096e0 100644
--- a/bob/bio/face/preprocessor/FaceCrop.py
+++ b/bob/bio/face/preprocessor/FaceCrop.py
@@ -3,6 +3,7 @@ import numpy
import logging
from .Base import Base
+from sklearn.base import TransformerMixin, BaseEstimator
logger = logging.getLogger("bob.bio.face")
from bob.bio.base import load_resource
@@ -370,3 +371,64 @@ class FaceCrop(Base):
def __setstate__(self, d):
self.__dict__ = d
self._init_non_pickables()
+
+
+class MultiFaceCrop(TransformerMixin, BaseEstimator):
+ def __init__(
+ self,
+ cropped_image_size,
+ cropped_positions_list,
+ fixed_positions=None,
+ mask_sigma=None,
+ mask_neighbors=5,
+ mask_seed=None,
+ annotator=None,
+ allow_upside_down_normalized_faces=False,
+ **kwargs,
+ ):
+
+ assert isinstance(cropped_positions_list, list)
+
+ self.croppers = {}
+ for cropped_positions in cropped_positions_list:
+ assert len(cropped_positions) == 2
+ self.croppers[tuple(cropped_positions)] = FaceCrop(
+ cropped_image_size,
+ cropped_positions,
+ fixed_positions,
+ mask_sigma,
+ mask_neighbors,
+ mask_seed,
+ annotator,
+ allow_upside_down_normalized_faces,
+ **kwargs,
+ )
+
+ def transform(self, X, annotations=None):
+ subsets = {k: {"X": [], "annotations": []} for k in self.croppers.keys()}
+
+ def assign(X_elem, annotations_elem):
+ valid_keys = [
+ k
+ for k in self.croppers.keys()
+ if set(k).issubset(set(annotations_elem.keys()))
+ ]
+ assert (
+ len(valid_keys) == 1
+ ), "Cropper selection from the annotations is ambiguous ({} valid croppers)".format(
+ len(valid_keys)
+ )
+ subsets[valid_keys[0]]["X"].append(X_elem)
+ subsets[valid_keys[0]]["annotations"].append(annotations_elem)
+
+ for X_elem, annotations_elem in zip(X, annotations):
+ assign(X_elem, annotations_elem)
+
+ transformed_subsets = {
+ k: self.croppers[k].transform(**subsets[k]) for k in subsets.keys()
+ }
+
+ return [item for sublist in transformed_subsets.values() for item in sublist]
+
+ def fit(self, X, y=None):
+ return self
diff --git a/bob/bio/face/preprocessor/HistogramEqualization.py b/bob/bio/face/preprocessor/HistogramEqualization.py
index 36c497cfbd6fb3ede17058481fc31bb4499109dc..63f7a594bd06b99cf4eb51be8bf396310aebbefa 100644
--- a/bob/bio/face/preprocessor/HistogramEqualization.py
+++ b/bob/bio/face/preprocessor/HistogramEqualization.py
@@ -98,11 +98,15 @@ class HistogramEqualization(Base):
if self.cropper is not None:
# TODO: USE THE TAG `ALLOW_ANNOTATIONS`
image = (
- self.cropper.transform([image])[0]
+ self.cropper.transform([image])
if annotations is None
- else self.cropper.transform([image], [annotations])[0]
+ else self.cropper.transform([image], [annotations])
)
- image = self.equalize_histogram(image)
+ image = self.equalize_histogram(image[0])
+ else:
+ # Handle with the cropper is None
+ image = self.equalize_histogram(image)
+
return self.data_type(image)
if annotations is None:
diff --git a/bob/bio/face/preprocessor/INormLBP.py b/bob/bio/face/preprocessor/INormLBP.py
index c2235e63b74e191b2560b369189268aca9a1d3f4..d3a8bc10b6c94b58a01939d5e112b299893255f5 100644
--- a/bob/bio/face/preprocessor/INormLBP.py
+++ b/bob/bio/face/preprocessor/INormLBP.py
@@ -118,22 +118,28 @@ class INormLBP(Base):
The cropped and photometrically enhanced face.
"""
- def _crop(image, annotations=None):
+ def _crop_one_sample(image, annotations=None):
image = self.change_color_channel(image)
if self.cropper is not None:
# TODO: USE THE TAG `ALLOW_ANNOTATIONS`
image = (
- self.cropper.transform([image])[0]
+ self.cropper.transform([image])
if annotations is None
- else self.cropper.transform([image], [annotations])[0]
+ else self.cropper.transform([image], [annotations])
)
- image = self.lbp_extractor(image)
+
+ # LBP's doesn't work with batches, so we have to work this out
+ image = self.lbp_extractor(image[0])
+ else:
+ # Handle with the cropper is None
+ image = self.lbp_extractor(image)
+
return self.data_type(image)
if annotations is None:
- return [_crop(data) for data in X]
+ return [_crop_one_sample(data) for data in X]
else:
- return [_crop(data, annot) for data, annot in zip(X, annotations)]
+ return [_crop_one_sample(data, annot) for data, annot in zip(X, annotations)]
def __getstate__(self):
d = dict(self.__dict__)
diff --git a/bob/bio/face/preprocessor/Scale.py b/bob/bio/face/preprocessor/Scale.py
index 82ee172e150f032ee98a292bdbed4516c6f62789..53166f42f641dfee017c4011c14702c135f3dfe7 100644
--- a/bob/bio/face/preprocessor/Scale.py
+++ b/bob/bio/face/preprocessor/Scale.py
@@ -2,7 +2,7 @@ from sklearn.preprocessing import FunctionTransformer
from skimage.transform import resize
from sklearn.utils import check_array
from bob.io.image import to_matplotlib, to_bob
-
+import numpy as np
def scale(images, target_img_size):
"""Scales a list of images to the target size
@@ -22,15 +22,21 @@ def scale(images, target_img_size):
if isinstance(target_img_size, int):
target_img_size = (target_img_size, target_img_size)
- images = check_array(images, allow_nd=True)
- images = to_matplotlib(images)
-
# images are always batched
+ images = check_array(images, allow_nd=True)
+
output_shape = tuple(target_img_size)
- output_shape = tuple(images.shape[-1:-2]) + output_shape
- images = resize(images, output_shape=output_shape)
-
- return to_bob(images)
+ output_shape = tuple(images.shape[0:1]) + output_shape
+
+ # If it's Bob batched RGB images
+ if images.ndim > 3:
+ images = to_matplotlib(images)
+ images = resize(images, output_shape=output_shape)
+ return to_bob(images)
+ else:
+ # If it's Bob batched gray scaled images
+ images = resize(images, output_shape=output_shape)
+ return images
def Scale(target_img_size):
diff --git a/bob/bio/face/preprocessor/SelfQuotientImage.py b/bob/bio/face/preprocessor/SelfQuotientImage.py
index d83248b01da44c3c554acfeea40a0c6224b32fc6..35ed158cc4f27e06cc7fbe8b897c2a5cb8e38030 100644
--- a/bob/bio/face/preprocessor/SelfQuotientImage.py
+++ b/bob/bio/face/preprocessor/SelfQuotientImage.py
@@ -96,11 +96,15 @@ class SelfQuotientImage(Base):
if self.cropper is not None:
# TODO: USE THE TAG `ALLOW_ANNOTATIONS`
image = (
- self.cropper.transform([image])[0]
+ self.cropper.transform([image])
if annotations is None
- else self.cropper.transform([image], [annotations])[0]
- )
- image = self.self_quotient(image)
+ else self.cropper.transform([image], [annotations])
+ )
+ image = self.self_quotient(image)[0]
+ else:
+ # Handle with the cropper is None
+ image = self.self_quotient(image)
+
return self.data_type(image)
if annotations is None:
diff --git a/bob/bio/face/preprocessor/TanTriggs.py b/bob/bio/face/preprocessor/TanTriggs.py
index a1abfd38b91ff40078f022cbe28d6887ad995eee..c1344d3db11a1211cdb9de3de45c82c9f9118413 100644
--- a/bob/bio/face/preprocessor/TanTriggs.py
+++ b/bob/bio/face/preprocessor/TanTriggs.py
@@ -108,22 +108,26 @@ class TanTriggs(Base):
The cropped and photometrically enhanced face.
"""
- def _crop(image, annotations=None):
+ def _crop_one_sample(image, annotations=None):
image = self.change_color_channel(image)
if self.cropper is not None:
# TODO: USE THE TAG `ALLOW_ANNOTATIONS`
image = (
- self.cropper.transform([image])[0]
+ self.cropper.transform([image])
if annotations is None
- else self.cropper.transform([image], [annotations])[0]
+ else self.cropper.transform([image], [annotations])
)
- image = self.tan_triggs(image)
+ image = self.tan_triggs(image[0])
+ else:
+ # Handle with the cropper is None
+ image = self.tan_triggs(image)
+
return self.data_type(image)
if annotations is None:
- return [_crop(data) for data in X]
+ return [_crop_one_sample(data) for data in X]
else:
- return [_crop(data, annot) for data, annot in zip(X, annotations)]
+ return [_crop_one_sample(data, annot) for data, annot in zip(X, annotations)]
def __getstate__(self):
d = dict(self.__dict__)
diff --git a/bob/bio/face/test/test_baselines.py b/bob/bio/face/test/test_baselines.py
index 6b46f5ab3e0c5e7d9813bc1fb39b0882e7a814f3..851aad1ec65f904fd56e8ef14d4388c4dd1df1d8 100644
--- a/bob/bio/face/test/test_baselines.py
+++ b/bob/bio/face/test/test_baselines.py
@@ -12,6 +12,7 @@ import os
import bob.io.base
import functools
import copy
+import tensorflow as tf
images = dict()
@@ -70,9 +71,11 @@ def run_baseline(baseline, samples_for_training=[]):
# CHECKPOINTING
with tempfile.TemporaryDirectory() as d:
+ cpy = copy.deepcopy(pipeline)
checkpoint_pipeline = checkpoint_vanilla_biometrics(
- copy.deepcopy(pipeline), base_dir=d
+ cpy, base_dir=d
)
+
checkpoint_scores = checkpoint_pipeline([], biometric_references, probes)
assert len(checkpoint_scores) == 1
assert len(checkpoint_scores[0]) == 1
@@ -85,10 +88,11 @@ def run_baseline(baseline, samples_for_training=[]):
assert "scores" in dirs
# DASK
- with tempfile.TemporaryDirectory() as d:
+ with tempfile.TemporaryDirectory() as d:
+ cpy = copy.deepcopy(pipeline)
dask_pipeline = dask_vanilla_biometrics(
- checkpoint_vanilla_biometrics(copy.deepcopy(pipeline), base_dir=d)
+ checkpoint_vanilla_biometrics(cpy, base_dir=d)
)
dask_scores = dask_pipeline([], biometric_references, probes)
dask_scores = dask_scores.compute(scheduler="single-threaded")
@@ -102,7 +106,6 @@ def run_baseline(baseline, samples_for_training=[]):
assert "samplewrapper-2" in dirs
assert "scores" in dirs
-
def test_facenet_baseline():
run_baseline("facenet-sanderberg")
@@ -122,14 +125,14 @@ def test_inception_resnetv1_msceleb():
def test_inception_resnetv1_casiawebface():
run_baseline("inception-resnetv1-casiawebface")
-
+"""
def test_arcface_insight_tf():
import tensorflow as tf
tf.compat.v1.reset_default_graph()
run_baseline("arcface-insight-tf")
-
+"""
def test_gabor_graph():
run_baseline("gabor_graph")
diff --git a/bob/bio/face/test/test_embeddings.py b/bob/bio/face/test/test_embeddings.py
index 316423881b873de657c29d13a3f4f22b41fd0fd1..0d518b0a35cf2d0328fdf928bb149bff3df390ef 100644
--- a/bob/bio/face/test/test_embeddings.py
+++ b/bob/bio/face/test/test_embeddings.py
@@ -5,35 +5,16 @@ from bob.pipelines import Sample, wrap
import pkg_resources
-def test_facenet():
- from bob.bio.face.embeddings import FaceNetSanderberg
-
- np.random.seed(10)
-
- transformer = FaceNetSanderberg()
- # Raw data
- data = np.random.rand(3, 160, 160).astype("uint8")
- output = transformer.transform([data])[0]
- assert output.size == 128, output.shape
-
- # Sample Batch
- sample = Sample(data)
- transformer_sample = wrap(["sample"], transformer)
- output = [s.data for s in transformer_sample.transform([sample])][0]
- assert output.size == 128, output.shape
-
-
def test_idiap_inceptionv2_msceleb():
- from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb
+ from bob.bio.face.embeddings import InceptionResnetv2_MsCeleb_CenterLoss_2018
reference = bob.io.base.load(
pkg_resources.resource_filename(
"bob.bio.face.test", "data/inception_resnet_v2_rgb.hdf5"
)
)
-
np.random.seed(10)
- transformer = InceptionResnetv2_MsCeleb()
+ transformer = InceptionResnetv2_MsCeleb_CenterLoss_2018()
data = (np.random.rand(3, 160, 160) * 255).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -48,10 +29,10 @@ def test_idiap_inceptionv2_msceleb():
def test_idiap_inceptionv2_casia():
- from bob.bio.face.embeddings import InceptionResnetv2_CasiaWebFace
+ from bob.bio.face.embeddings import InceptionResnetv2_Casia_CenterLoss_2018
np.random.seed(10)
- transformer = InceptionResnetv2_CasiaWebFace()
+ transformer = InceptionResnetv2_Casia_CenterLoss_2018()
data = np.random.rand(3, 160, 160).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -65,10 +46,10 @@ def test_idiap_inceptionv2_casia():
def test_idiap_inceptionv1_msceleb():
- from bob.bio.face.embeddings import InceptionResnetv1_MsCeleb
+ from bob.bio.face.embeddings import InceptionResnetv1_MsCeleb_CenterLoss_2018
np.random.seed(10)
- transformer = InceptionResnetv1_MsCeleb()
+ transformer = InceptionResnetv1_MsCeleb_CenterLoss_2018()
data = np.random.rand(3, 160, 160).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -82,10 +63,10 @@ def test_idiap_inceptionv1_msceleb():
def test_idiap_inceptionv1_casia():
- from bob.bio.face.embeddings import InceptionResnetv1_CasiaWebFace
+ from bob.bio.face.embeddings import InceptionResnetv1_Casia_CenterLoss_2018
np.random.seed(10)
- transformer = InceptionResnetv1_CasiaWebFace()
+ transformer = InceptionResnetv1_Casia_CenterLoss_2018()
data = np.random.rand(3, 160, 160).astype("uint8")
output = transformer.transform([data])[0]
assert output.size == 128, output.shape
@@ -97,7 +78,23 @@ def test_idiap_inceptionv1_casia():
assert output.size == 128, output.shape
+def test_facenet_sanderberg():
+ from bob.bio.face.embeddings import FaceNetSanderberg_20170512_110547
+
+ np.random.seed(10)
+ transformer = FaceNetSanderberg_20170512_110547()
+ data = np.random.rand(3, 160, 160).astype("uint8")
+ output = transformer.transform([data])[0]
+ assert output.size == 128, output.shape
+
+ # Sample Batch
+ sample = Sample(data)
+ transformer_sample = wrap(["sample"], transformer)
+ output = [s.data for s in transformer_sample.transform([sample])][0]
+ assert output.size == 128, output.shape
+
+"""
def test_arface_insight_tf():
import tensorflow as tf
@@ -116,3 +113,4 @@ def test_arface_insight_tf():
transformer_sample = wrap(["sample"], transformer)
output = [s.data for s in transformer_sample.transform([sample])][0]
assert output.size == 512, output.shape
+"""
\ No newline at end of file
diff --git a/bob/bio/face/test/test_transformers.py b/bob/bio/face/test/test_transformers.py
index f4e19bc74ea0b0208215dd1aff54293bcdd851ce..284a48aa9719b452d43b001e7da44cf7d6c6968c 100644
--- a/bob/bio/face/test/test_transformers.py
+++ b/bob/bio/face/test/test_transformers.py
@@ -11,7 +11,7 @@ def get_fake_sample(face_size=(160, 160), eyes={"leye": (46, 107), "reye": (46,
return Sample(data, key="1", annotations=annotations)
-def test_facenet():
+def test_facenet_sanderberg():
transformer = load_resource("facenet-sanderberg", "transformer")
fake_sample = get_fake_sample()
@@ -60,7 +60,7 @@ def test_inception_resnetv1_casiawebface():
transformed_data = transformed_sample.data
assert transformed_sample.data.size == 128
-
+"""
def test_arcface_insight_tf():
import tensorflow as tf
tf.compat.v1.reset_default_graph()
@@ -71,7 +71,7 @@ def test_arcface_insight_tf():
transformed_sample = transformer.transform([fake_sample])[0]
transformed_data = transformed_sample.data
assert transformed_sample.data.size == 512
-
+"""
def test_gabor_graph():
transformer = load_resource("gabor-graph", "transformer")
diff --git a/develop.cfg b/develop.cfg
index 02064b99239b338aaa5873626cb83f642959f689..4ba1def3457b120f4a4b42274acf4491f2037f55 100644
--- a/develop.cfg
+++ b/develop.cfg
@@ -7,7 +7,9 @@ parts = scripts
develop = src/bob.pipelines
src/bob.bio.base
src/bob.ip.gabor
- .
+ src/bob.db.multipie
+ src/bob.learn.tensorflow
+ .
@@ -15,7 +17,8 @@ eggs = bob.bio.face
bob.pipelines
bob.bio.base
bob.ip.gabor
-
+ bob.db.multipie
+ bob.learn.tensorflow
extensions = bob.buildout
mr.developer
@@ -27,8 +30,10 @@ auto-checkout = *
[sources]
bob.pipelines = git git@gitlab.idiap.ch:bob/bob.pipelines
-bob.bio.base = git git@gitlab.idiap.ch:bob/bob.bio.base branch=dask-pipelines
+bob.bio.base = git git@gitlab.idiap.ch:bob/bob.bio.base
bob.ip.gabor = git git@gitlab.idiap.ch:bob/bob.ip.gabor
+bob.db.multipie = git git@gitlab.idiap.ch:bob/bob.db.multipie
+bob.learn.tensorflow = git git@gitlab.idiap.ch/bob/bob.learn.tensorflow
[scripts]
recipe = bob.buildout:scripts