diff --git a/bob/bio/face/annotator/__init__.py b/bob/bio/face/annotator/__init__.py
index 1434c9b48b046726a65280502fa0336b5056526d..b058d897018835424f7b6c4d999c85d289df513a 100644
--- a/bob/bio/face/annotator/__init__.py
+++ b/bob/bio/face/annotator/__init__.py
@@ -1,5 +1,6 @@
import bob.ip.facedetect
+
def bounding_box_to_annotations(bbx):
"""Converts :any:`bob.ip.facedetect.BoundingBox` to dictionary annotations.
diff --git a/bob/bio/face/annotator/bobiptinyface.py b/bob/bio/face/annotator/bobiptinyface.py
index d2790c826d61e4510cfa1e2a61d51166f9d1e270..fc06d274b7b23bf52058e15574a9af8d5c6d9c3a 100644
--- a/bob/bio/face/annotator/bobiptinyface.py
+++ b/bob/bio/face/annotator/bobiptinyface.py
@@ -26,10 +26,7 @@ class BobIpTinyface(Base):
Annotations with (topleft, bottomright) keys (or None).
"""
- annotations = self.tinyface.detect(image)
-
if annotations is not None:
- r = annotations[0]
- return {"topleft": (r[0], r[1]), "bottomright": (r[2], r[3])}
+ return annotations[0]
else:
return None
diff --git a/bob/bio/face/config/annotator/tinyface.py b/bob/bio/face/config/annotator/tinyface.py
index 2274bfeff38e836c34e34cb1846915b58a6d769e..bf223ea9802174d27ca2b7c0a41c6da61fb56f2e 100644
--- a/bob/bio/face/config/annotator/tinyface.py
+++ b/bob/bio/face/config/annotator/tinyface.py
@@ -1,3 +1,3 @@
from bob.bio.face.annotator import BobIpTinyface
-annotator = BobIpTinyface()
+annotator = BobIpTinyface()
\ No newline at end of file
diff --git a/bob/bio/face/config/baseline/mxnet_pipe.py b/bob/bio/face/config/baseline/mxnet_pipe.py
index eeb062004bc0e55b3db402dfa8e39be42d9ed3cf..b28ccd595c0e4a22aa7be65827055d5d8fbaf0fd 100644
--- a/bob/bio/face/config/baseline/mxnet_pipe.py
+++ b/bob/bio/face/config/baseline/mxnet_pipe.py
@@ -1,6 +1,6 @@
import bob.bio.base
from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.extractor import mxnet_model
+from bob.bio.face.extractor import MxNetModel
from bob.bio.base.algorithm import Distance
from bob.bio.base.pipelines.vanilla_biometrics.legacy import BioAlgorithmLegacy
import scipy.spatial
@@ -39,7 +39,7 @@ transform_extra_arguments = (
)
-extractor_transformer = mxnet_model()
+extractor_transformer = MxNetModel()
algorithm = Distance(
distance_function=scipy.spatial.distance.cosine, is_distance_function=True
diff --git a/bob/bio/face/config/baseline/opencv_pipe.py b/bob/bio/face/config/baseline/opencv_pipe.py
index aac2d3f1c45559e35545fb66a5c02ca97aa76790..8d8c63f8dc409f59990792c31a401166ad5a4e16 100644
--- a/bob/bio/face/config/baseline/opencv_pipe.py
+++ b/bob/bio/face/config/baseline/opencv_pipe.py
@@ -35,6 +35,15 @@ preprocessor_transformer = FaceCrop(
fixed_positions=fixed_positions,
)
+cropped_positions = {"leye": (100, 140), "reye": (100, 95)}
+# Preprocessor
+preprocessor_transformer = FaceCrop(
+ cropped_image_size=(224, 224),
+ cropped_positions={"leye": (100, 140), "reye": (100, 95)},
+ color_channel="rgb",
+ fixed_positions=fixed_positions,
+)
+
transform_extra_arguments = (
None
if (cropped_positions is None or fixed_positions is not None)
diff --git a/bob/bio/face/config/baseline/pytorch_pipe_v1.py b/bob/bio/face/config/baseline/pytorch_pipe_v1.py
index ec5c4d9d619110d81c0c576088112aee10d6d282..843e53818a851b7f1cee481c7c011eeff8f4c8c8 100644
--- a/bob/bio/face/config/baseline/pytorch_pipe_v1.py
+++ b/bob/bio/face/config/baseline/pytorch_pipe_v1.py
@@ -1,6 +1,6 @@
import bob.bio.base
from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.extractor import pytorch_loaded_model
+from bob.bio.face.extractor import PyTorchLoadedModel
from bob.bio.base.algorithm import Distance
from bob.bio.base.pipelines.vanilla_biometrics.legacy import BioAlgorithmLegacy
import scipy.spatial
@@ -25,9 +25,11 @@ else:
cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
+cropped_positions = {"leye": (110, 144), "reye": (110, 96)}
+
preprocessor_transformer = FaceCrop(
cropped_image_size=(224, 224),
- cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+ cropped_positions={"leye": (110, 144), "reye": (110, 96)},
color_channel="rgb",
fixed_positions=fixed_positions,
)
@@ -38,8 +40,14 @@ transform_extra_arguments = (
else (("annotations", "annotations"),)
)
+transform_extra_arguments = (
+ None
+ if (cropped_positions is None or fixed_positions is not None)
+ else (("annotations", "annotations"),)
+)
+
-extractor_transformer = pytorch_loaded_model()
+extractor_transformer = PyTorchLoadedModel()
algorithm = Distance(
distance_function=scipy.spatial.distance.cosine, is_distance_function=True
diff --git a/bob/bio/face/config/baseline/pytorch_pipe_v2.py b/bob/bio/face/config/baseline/pytorch_pipe_v2.py
index 630609e6fb22085783fa8c977ddc9702615d3364..90788963d6440c828778638cbc258eaa702a1376 100644
--- a/bob/bio/face/config/baseline/pytorch_pipe_v2.py
+++ b/bob/bio/face/config/baseline/pytorch_pipe_v2.py
@@ -1,6 +1,6 @@
import bob.bio.base
from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.extractor import pytorch_library_model
+from bob.bio.face.extractor import PyTorchLibraryModel
from facenet_pytorch import InceptionResnetV1
from bob.bio.base.algorithm import Distance
from bob.bio.base.pipelines.vanilla_biometrics.legacy import BioAlgorithmLegacy
@@ -26,9 +26,11 @@ else:
cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
+cropped_positions = {"leye": (110, 144), "reye": (110, 96)}
+
preprocessor_transformer = FaceCrop(
cropped_image_size=(224, 224),
- cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+ cropped_positions={"leye": (110, 144), "reye": (110, 96)},
color_channel="rgb",
fixed_positions=fixed_positions,
)
@@ -40,8 +42,15 @@ transform_extra_arguments = (
)
+transform_extra_arguments = (
+ None
+ if (cropped_positions is None or fixed_positions is not None)
+ else (("annotations", "annotations"),)
+)
+
+
model = InceptionResnetV1(pretrained="vggface2").eval()
-extractor_transformer = pytorch_library_model(model=model)
+extractor_transformer = PyTorchLibraryModel(model=model)
algorithm = Distance(
diff --git a/bob/bio/face/config/baseline/tf_pipe.py b/bob/bio/face/config/baseline/tf_pipe.py
index 22c24d95abcf88bf1acb9c9fe9f587fbd7abeccd..c4ea24a68baad4ff57d57d8f773f249284a01b6b 100644
--- a/bob/bio/face/config/baseline/tf_pipe.py
+++ b/bob/bio/face/config/baseline/tf_pipe.py
@@ -1,6 +1,6 @@
import bob.bio.base
from bob.bio.face.preprocessor import FaceCrop
-from bob.bio.face.extractor import tf_model
+from bob.bio.face.extractor import TensorFlowModel
from bob.bio.base.algorithm import Distance
from bob.bio.base.pipelines.vanilla_biometrics.legacy import BioAlgorithmLegacy
import scipy.spatial
@@ -24,11 +24,11 @@ else:
# Preprocessor
-cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
+cropped_positions = {"leye": (80, 100), "reye": (80, 60)}
preprocessor_transformer = FaceCrop(
cropped_image_size=(160, 160),
- cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+ cropped_positions={"leye": (80, 100), "reye": (80, 60)},
color_channel="rgb",
fixed_positions=fixed_positions,
)
@@ -41,7 +41,7 @@ transform_extra_arguments = (
# Extractor
-extractor_transformer = tf_model()
+extractor_transformer = TensorFlowModel()
# Algorithm
algorithm = Distance(
diff --git a/bob/bio/face/extractor/MxNetModel.py b/bob/bio/face/extractor/MxNetModel.py
index 84e856862ee5a2e02eb2c832e4f7fcb4ad4bc83d..dd3ca8da0c8151b6a131d17b28f8e38347230177 100644
--- a/bob/bio/face/extractor/MxNetModel.py
+++ b/bob/bio/face/extractor/MxNetModel.py
@@ -12,11 +12,9 @@ import mxnet as mx
from mxnet import gluon
import warnings
from bob.extension import rc
-
mxnet_resnet_directory = rc["bob.extractor_model.mxnet"]
mxnet_weight_directory = rc["bob.extractor_weights.mxnet"]
-
class MxNetModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under MxNet Interfaces.
@@ -59,10 +57,8 @@ class MxNetModel(TransformerMixin, BaseEstimator):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- deserialized_net = gluon.nn.SymbolBlock.imports(
- mxnet_resnet_directory, ["data"], mxnet_weight_directory, ctx=ctx
- )
-
+ deserialized_net = gluon.nn.SymbolBlock.imports(mxnet_resnet_directory, ['data'], mxnet_weight_directory, ctx=ctx)
+
self.model = deserialized_net
def transform(self, X):
@@ -80,7 +76,7 @@ class MxNetModel(TransformerMixin, BaseEstimator):
feature : 2D, 3D, or 4D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
+
if self.model is None:
self._load_model()
@@ -89,6 +85,7 @@ class MxNetModel(TransformerMixin, BaseEstimator):
return self.model(X,).asnumpy()
+
def __getstate__(self):
# Handling unpicklable objects
@@ -97,4 +94,4 @@ class MxNetModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
+ return {"stateless": True, "requires_fit": False}
\ No newline at end of file
diff --git a/bob/bio/face/extractor/mxnet_resnet.py b/bob/bio/face/extractor/MxNetModel.py~HEAD
similarity index 93%
rename from bob/bio/face/extractor/mxnet_resnet.py
rename to bob/bio/face/extractor/MxNetModel.py~HEAD
index 213c6971e388137112ec7734060f8db7b4c437e3..84e856862ee5a2e02eb2c832e4f7fcb4ad4bc83d 100644
--- a/bob/bio/face/extractor/mxnet_resnet.py
+++ b/bob/bio/face/extractor/MxNetModel.py~HEAD
@@ -12,10 +12,12 @@ import mxnet as mx
from mxnet import gluon
import warnings
from bob.extension import rc
+
mxnet_resnet_directory = rc["bob.extractor_model.mxnet"]
mxnet_weight_directory = rc["bob.extractor_weights.mxnet"]
-class mxnet_model(TransformerMixin, BaseEstimator):
+
+class MxNetModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under MxNet Interfaces.
@@ -57,8 +59,10 @@ class mxnet_model(TransformerMixin, BaseEstimator):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
- deserialized_net = gluon.nn.SymbolBlock.imports(mxnet_resnet_directory, ['data'], mxnet_weight_directory, ctx=ctx)
-
+ deserialized_net = gluon.nn.SymbolBlock.imports(
+ mxnet_resnet_directory, ["data"], mxnet_weight_directory, ctx=ctx
+ )
+
self.model = deserialized_net
def transform(self, X):
@@ -76,7 +80,7 @@ class mxnet_model(TransformerMixin, BaseEstimator):
feature : 2D, 3D, or 4D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
+
if self.model is None:
self._load_model()
@@ -85,7 +89,6 @@ class mxnet_model(TransformerMixin, BaseEstimator):
return self.model(X,).asnumpy()
-
def __getstate__(self):
# Handling unpicklable objects
diff --git a/bob/bio/face/extractor/OpenCVModel.py b/bob/bio/face/extractor/OpenCVModel.py
index 728b2b7ca94c451ada6b09411e9627964bc8df5d..628a98ce954e051a81edc446ad9c85e42a865996 100644
--- a/bob/bio/face/extractor/OpenCVModel.py
+++ b/bob/bio/face/extractor/OpenCVModel.py
@@ -41,6 +41,7 @@ class OpenCVModel(TransformerMixin, BaseEstimator):
This structure only can be used for CAFFE pretrained model.
"""
+
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.model = None
@@ -59,7 +60,7 @@ class OpenCVModel(TransformerMixin, BaseEstimator):
def _load_model(self):
- net = cv2.dnn.readNetFromCaffe(opencv_model_prototxt, opencv_model_directory)
+ net = cv2.dnn.readNetFromCaffe(opencv_model_prototxt,opencv_model_directory)
self.model = net
@@ -78,17 +79,18 @@ class OpenCVModel(TransformerMixin, BaseEstimator):
feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
+
if self.model is None:
self._load_model()
img = np.array(X)
- img = img / 255
+ img = img/255
self.model.setInput(img)
-
+
return self.model.forward()
+
def __getstate__(self):
# Handling unpicklable objects
@@ -97,4 +99,4 @@ class OpenCVModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
+ return {"stateless": True, "requires_fit": False}
\ No newline at end of file
diff --git a/bob/bio/face/extractor/opencv_caffe.py b/bob/bio/face/extractor/OpenCVModel.py~HEAD
similarity index 93%
rename from bob/bio/face/extractor/opencv_caffe.py
rename to bob/bio/face/extractor/OpenCVModel.py~HEAD
index eb520e6dc6fc963becd48620c3916044e86cbbc0..728b2b7ca94c451ada6b09411e9627964bc8df5d 100644
--- a/bob/bio/face/extractor/opencv_caffe.py
+++ b/bob/bio/face/extractor/OpenCVModel.py~HEAD
@@ -25,7 +25,7 @@ opencv_model_directory = rc["bob.extractor_model.opencv"]
opencv_model_prototxt = rc["bob.extractor_weights.opencv"]
-class opencv_model(TransformerMixin, BaseEstimator):
+class OpenCVModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under OpenCV Interface
Users can download the pretrained face recognition models with OpenCV Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model frame to :py:class:`bob.extractor_model.opencv`, and set config of the parameters to :py:class:`bob.extractor_weights.opencv`.
@@ -39,15 +39,11 @@ class opencv_model(TransformerMixin, BaseEstimator):
.. note::
This structure only can be used for CAFFE pretrained model.
-
-
"""
-
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.model = None
-
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "opencv_model"),
@@ -63,7 +59,7 @@ class opencv_model(TransformerMixin, BaseEstimator):
def _load_model(self):
- net = cv2.dnn.readNetFromCaffe(opencv_model_prototxt,opencv_model_directory)
+ net = cv2.dnn.readNetFromCaffe(opencv_model_prototxt, opencv_model_directory)
self.model = net
@@ -82,17 +78,16 @@ class opencv_model(TransformerMixin, BaseEstimator):
feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
+
if self.model is None:
-
self._load_model()
img = np.array(X)
+ img = img / 255
self.model.setInput(img)
-
- return self.model.forward()
+ return self.model.forward()
def __getstate__(self):
# Handling unpicklable objects
@@ -102,5 +97,4 @@ class opencv_model(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
-
return {"stateless": True, "requires_fit": False}
diff --git a/bob/bio/face/extractor/PyTorchModel.py b/bob/bio/face/extractor/PyTorchModel.py
index 24e02bc3013e81463c799b8ca95ab6bf0fb5d5f7..cf46de660a246e2d7beda9d13c0b255882d9eb62 100644
--- a/bob/bio/face/extractor/PyTorchModel.py
+++ b/bob/bio/face/extractor/PyTorchModel.py
@@ -16,7 +16,6 @@ import imp
pytorch_model_directory = rc["bob.extractor_model.pytorch"]
pytorch_weight_directory = rc["bob.extractor_weights.pytorch"]
-
class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under PyTorch Interface, especially for the models and weights that need to load by hand.
@@ -28,7 +27,7 @@ class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
$ bob config set bob.extractor_weights.pytorch /PATH/TO/WEIGHTS/
The extracted features can be combined with different the algorithms.
- """
+ """
def __init__(self, **kwargs):
super().__init__(**kwargs)
@@ -49,12 +48,12 @@ class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
def _load_model(self):
- MainModel = imp.load_source("MainModel", pytorch_model_directory)
+ MainModel = imp.load_source('MainModel', pytorch_model_directory)
network = torch.load(pytorch_weight_directory)
network.eval()
-
- self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
+
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
network.to(self.device)
self.model = network
@@ -78,10 +77,11 @@ class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
self._load_model()
X = torch.Tensor(X)
- X = X / 255
+ X = X/255
return self.model(X).detach().numpy()
+
def __getstate__(self):
# Handling unpicklable objects
@@ -91,8 +91,14 @@ class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
def _more_tags(self):
return {"stateless": True, "requires_fit": False}
-
-
+
+
+
+
+
+
+
+
class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition with registered model frames in the PyTorch Library.
@@ -122,8 +128,8 @@ class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
self.device = None
def _load_model(self):
-
- self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+ self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.model.to(self.device)
def transform(self, X):
@@ -146,10 +152,11 @@ class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
self._load_model()
X = torch.Tensor(X)
- X = X / 255
+ X = X/255
return self.model(X).detach().numpy()
+
def __getstate__(self):
# Handling unpicklable objects
@@ -158,4 +165,4 @@ class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
+ return {"stateless": True, "requires_fit": False}
\ No newline at end of file
diff --git a/bob/bio/face/extractor/pytorch_model.py b/bob/bio/face/extractor/PyTorchModel.py~HEAD
similarity index 88%
rename from bob/bio/face/extractor/pytorch_model.py
rename to bob/bio/face/extractor/PyTorchModel.py~HEAD
index c940aae6b1fe1043803099e2aeeb844d71820946..24e02bc3013e81463c799b8ca95ab6bf0fb5d5f7 100644
--- a/bob/bio/face/extractor/pytorch_model.py
+++ b/bob/bio/face/extractor/PyTorchModel.py~HEAD
@@ -16,7 +16,8 @@ import imp
pytorch_model_directory = rc["bob.extractor_model.pytorch"]
pytorch_weight_directory = rc["bob.extractor_weights.pytorch"]
-class pytorch_loaded_model(TransformerMixin, BaseEstimator):
+
+class PyTorchLoadedModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under PyTorch Interface, especially for the models and weights that need to load by hand.
Users can download the pretrained face recognition models with PyTorch Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model frame to :py:class:`bob.extractor_model.pytorch`, and set config of the parameters to :py:class:`bob.extractor_weights.pytorch`.
@@ -27,14 +28,11 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
$ bob config set bob.extractor_weights.pytorch /PATH/TO/WEIGHTS/
The extracted features can be combined with different the algorithms.
-
-
- """
+ """
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.model = None
-
internal_path = pkg_resources.resource_filename(
__name__, os.path.join("data", "resnet"),
@@ -51,12 +49,12 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
def _load_model(self):
- MainModel = imp.load_source('MainModel', pytorch_model_directory)
+ MainModel = imp.load_source("MainModel", pytorch_model_directory)
network = torch.load(pytorch_weight_directory)
network.eval()
-
- self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
+
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
network.to(self.device)
self.model = network
@@ -76,15 +74,14 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
if self.model is None:
self._load_model()
X = torch.Tensor(X)
+ X = X / 255
return self.model(X).detach().numpy()
-
def __getstate__(self):
# Handling unpicklable objects
@@ -93,10 +90,10 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
-
return {"stateless": True, "requires_fit": False}
-
-class pytorch_library_model(TransformerMixin, BaseEstimator):
+
+
+class PyTorchLibraryModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition with registered model frames in the PyTorch Library.
Users can import the pretrained face recognition models from PyTorch library. The model should be called in the pipeline. Example: `facenet_pytorch <https://github.com/timesler/facenet-pytorch>`_
@@ -105,7 +102,6 @@ class pytorch_library_model(TransformerMixin, BaseEstimator):
**Parameters:**
model: pytorch model calling from library.
- use_gpu: True or False.
"""
def __init__(self, model=None, **kwargs):
@@ -125,6 +121,11 @@ class pytorch_library_model(TransformerMixin, BaseEstimator):
self.checkpoint_path = checkpoint_path
self.device = None
+ def _load_model(self):
+
+ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ self.model.to(self.device)
+
def transform(self, X):
"""__call__(image) -> feature
@@ -141,11 +142,14 @@ class pytorch_library_model(TransformerMixin, BaseEstimator):
The list of features extracted from the image.
"""
+ if self.model is None:
+ self._load_model()
+
X = torch.Tensor(X)
+ X = X / 255
return self.model(X).detach().numpy()
-
def __getstate__(self):
# Handling unpicklable objects
@@ -154,4 +158,4 @@ class pytorch_library_model(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
\ No newline at end of file
+ return {"stateless": True, "requires_fit": False}
diff --git a/bob/bio/face/extractor/TensorFlowModel.py b/bob/bio/face/extractor/TensorFlowModel.py
index 697afec88bf8bd382390d023744be332ac8c345d..dca1bc9a9104d31dc94835a10d60b780dd84d4ea 100644
--- a/bob/bio/face/extractor/TensorFlowModel.py
+++ b/bob/bio/face/extractor/TensorFlowModel.py
@@ -17,7 +17,6 @@ from tensorflow import keras
tf_model_directory = rc["bob.extractor_model.tf"]
-
class TensorFlowModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under TensorFlow Interface.
@@ -67,19 +66,20 @@ class TensorFlowModel(TransformerMixin, BaseEstimator):
feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
+
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)
- X = X / 255
+ X = X/255
predict = self.model.predict(X)
return predict
+
def __getstate__(self):
# Handling unpicklable objects
@@ -88,4 +88,4 @@ class TensorFlowModel(TransformerMixin, BaseEstimator):
return d
def _more_tags(self):
- return {"stateless": True, "requires_fit": False}
+ return {"stateless": True, "requires_fit": False}
\ No newline at end of file
diff --git a/bob/bio/face/extractor/tf_model.py b/bob/bio/face/extractor/TensorFlowModel.py~HEAD
similarity index 97%
rename from bob/bio/face/extractor/tf_model.py
rename to bob/bio/face/extractor/TensorFlowModel.py~HEAD
index a94f0adb867019eaf9f4adef76428562533be98a..697afec88bf8bd382390d023744be332ac8c345d 100644
--- a/bob/bio/face/extractor/tf_model.py
+++ b/bob/bio/face/extractor/TensorFlowModel.py~HEAD
@@ -17,7 +17,8 @@ from tensorflow import keras
tf_model_directory = rc["bob.extractor_model.tf"]
-class tf_model(TransformerMixin, BaseEstimator):
+
+class TensorFlowModel(TransformerMixin, BaseEstimator):
"""Extracts features using deep face recognition models under TensorFlow Interface.
Users can download the pretrained face recognition models with TensorFlow Interface. The path to downloaded models should be specified before running the extractor (usually before running the pipeline file that includes the extractor). That is, set config of the model to :py:class:`bob.extractor_model.tf`.
@@ -27,8 +28,6 @@ class tf_model(TransformerMixin, BaseEstimator):
$ bob config set bob.extractor_model.tf /PATH/TO/MODEL/
The extracted features can be combined with different the algorithms.
-
-
"""
def __init__(self, **kwargs):
@@ -68,19 +67,19 @@ class tf_model(TransformerMixin, BaseEstimator):
feature : 2D or 3D :py:class:`numpy.ndarray` (floats)
The list of features extracted from the image.
"""
-
+
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)
- predict = self.model.predict(X)
+ X = X / 255
+ predict = self.model.predict(X)
return predict
-
def __getstate__(self):
# Handling unpicklable objects
diff --git a/bob/bio/face/extractor/__init__.py b/bob/bio/face/extractor/__init__.py
index 32b97163775852dbca316f538672be6bccf2095d..d98275bc189a792df01b1031277063bfd6ff82a5 100644
--- a/bob/bio/face/extractor/__init__.py
+++ b/bob/bio/face/extractor/__init__.py
@@ -1,11 +1,11 @@
from .DCTBlocks import DCTBlocks
from .GridGraph import GridGraph
from .LGBPHS import LGBPHS
-from .mxnet_resnet import mxnet_model
-from .pytorch_model import pytorch_loaded_model
-from .pytorch_model import pytorch_library_model
-from .tf_model import tf_model
-from .opencv_caffe import opencv_model
+from .MxNetModel import MxNetModel
+from .PyTorchModel import PyTorchLoadedModel
+from .PyTorchModel import PyTorchLibraryModel
+from .TensorFlowModel import TensorFlowModel
+from .OpenCVModel import OpenCVModel
# gets sphinx autodoc done right - don't remove it
def __appropriate__(*args):
@@ -27,10 +27,10 @@ __appropriate__(
DCTBlocks,
GridGraph,
LGBPHS,
- mxnet_model,
- pytorch_loaded_model,
- pytorch_library_model,
- tf_model,
- opencv_model,
+ MxNetModel,
+ PyTorchLoadedModel,
+ PyTorchLibraryModel,
+ TensorFlowModel,
+ OpenCVModel,
)
__all__ = [_ for _ in dir() if not _.startswith("_")]
diff --git a/doc/baselines.rst b/doc/baselines.rst
index 88c42a629cd6a9eb1ccd18fe92146aa3bbfc499c..4f8d9c2da0043f74340d937ba66169613d803863 100644
--- a/doc/baselines.rst
+++ b/doc/baselines.rst
@@ -61,12 +61,14 @@ Deep learning baselines
Deep Learning with different interfaces baselines
=================================================
-* ``mxnet_pipe``: Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
+* ``mxnet-pipe``: Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
-* ``pytorch_pipe_v1``: Pytorch network that extracs 1000-dimensional featrues, trained by Manual Gunther, as described in [LGB18]_
+* ``mxnet-tinyface``: Applying `tinyface annoator <https://github.com/chinakook/hr101_mxnet>`_ for the Arcface Resnet Model using MxNet Interfaces from `Insightface <https://github.com/deepinsight/insightface>`_
-* ``pytorch_pipe_v2``: Inception Resnet face recognition model from `facenet_pytorch <https://github.com/timesler/facenet-pytorch>`_
+* ``pytorch-pipe-v1``: Pytorch network that extracts 1000-dimensional features, trained by Manual Gunther, as described in [LGB18]_
-* ``tf_pipe``: Inception Resnet v2 model trained using the MSCeleb dataset in the context of the work published by [TFP18]_
+* ``pytorch-pipe-v2``: Inception Resnet face recognition model from `facenet_pytorch <https://github.com/timesler/facenet-pytorch>`_
-* ``opencv_pipe``: VGG Face descriptor pretrained models, i.e. `Caffe model <https://www.robots.ox.ac.uk/~vgg/software/vgg_face/>`_
+* ``tf-pipe``: Inception Resnet v2 model trained using the MSCeleb dataset in the context of the work published by [TFP18]_
+
+* ``opencv-pipe``: VGG Face descriptor pretrained models, i.e. `Caffe model <https://www.robots.ox.ac.uk/~vgg/software/vgg_face/>`_
diff --git a/doc/deeplearningextractor.rst b/doc/deeplearningextractor.rst
index 378a35d8d292155e9db8790461ac3ded54bdbe95..466a0fa4fa224cb2a47deb96d11e440df48a69b7 100644
--- a/doc/deeplearningextractor.rst
+++ b/doc/deeplearningextractor.rst
@@ -234,7 +234,7 @@ In this baseline, we use :py:class:`bob.bio.face.preprocessor.FaceCrop` with ``
and ``cropped_image_size=(160,160)``
as preprocessor, Inception Resnet v2 in [TFP18]_ as extractor, and ``distance-cosine`` as the algorithm. By testing on LFW database, we get the following ROC plot:
-.. figure:: img/tensorflow_lfw_pipe.png
+.. figure:: img/tensorflow_pipe.png
:figwidth: 75%
:align: center
:alt: Face recognition results of LFW database.
diff --git a/doc/implemented.rst b/doc/implemented.rst
index 75acc7129ef0132eb8ad762c73ddf7e53cb5b204..7a3074a4f92189f275caa5708a094ce8e6b07f75 100644
--- a/doc/implemented.rst
+++ b/doc/implemented.rst
@@ -13,6 +13,7 @@ Databases
.. autosummary::
bob.bio.face.database.ARFaceBioDatabase
bob.bio.face.database.AtntBioDatabase
+ bob.bio.face.database.CasiaAfricaDatabase
bob.bio.face.database.MobioDatabase
bob.bio.face.database.ReplayBioDatabase
bob.bio.face.database.ReplayMobileBioDatabase
@@ -57,11 +58,11 @@ Image Feature Extractors
bob.bio.face.extractor.DCTBlocks
bob.bio.face.extractor.GridGraph
bob.bio.face.extractor.LGBPHS
- bob.bio.face.extractor.mxnet_model
- bob.bio.face.extractor.pytorch_loaded_model
- bob.bio.face.extractor.pytorch_library_model
- bob.bio.face.extractor.tf_model
- bob.bio.face.extractor.opencv_model
+ bob.bio.face.extractor.MxNetModel
+ bob.bio.face.extractor.PyTorchLoadedModel
+ bob.bio.face.extractor.PyTorchLibraryModel
+ bob.bio.face.extractor.TensorFlowModel
+ bob.bio.face.extractor.OpenCVModel
Face Recognition Algorithms
~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff --git a/doc/references.rst b/doc/references.rst
index bbb5417a0b6d3bf20a07c65c115921c57437ff0f..7d6d919cf3fd7011b4165ca381b211d023c93c95 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -17,4 +17,4 @@ References
.. [ZSQ09] *W. Zhang, S. Shan, L. Qing, X. Chen and W. Gao*. **Are Gabor phases really useless for face recognition?** Pattern Analysis & Applications, 12:301-307, 2009.
.. [TFP18] de 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.
.. [HRM06] *G. Heusch, Y. Rodriguez, and S. Marcel*. **Local Binary Patterns as an Image Preprocessing for Face Authentication**. In IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), 2006.
-.. [LGB18] *C. Li, M. Gunther and T. E. Boult*. **ECLIPSE: Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse Clustering**. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 131-140, doi: 10.1109/WACV.2018.00021.
\ No newline at end of file
+.. [LGB18] *C. Li, M. Gunther and T. E. Boult*. **ECLIPSE: Ensembles of Centroids Leveraging Iteratively Processed Spatial Eclipse Clustering**. IEEE Winter Conference on Applications of Computer Vision (WACV), 2018.
diff --git a/setup.py b/setup.py
index 1b89313b5655bf3c32dd43cd65e66430a5862937..be478a7d515bc05e0827ff30169c66b75bbf0403 100644
--- a/setup.py
+++ b/setup.py
@@ -137,9 +137,10 @@ setup(
"lgbphs = bob.bio.face.config.baseline.lgbphs:transformer",
"dummy = bob.bio.face.config.baseline.dummy:transformer",
"mxnet-pipe = bob.bio.face.config.baseline.mxnet_pipe:transformer",
+ "mxnet-tinyface = bob.bio.face.config.baseline.mxnet_tinyface:transformer",
"pytorch-pipe-v1 = bob.bio.face.config.baseline.pytorch_pipe_v1:transformer",
"pytorch-pipe-v2 = bob.bio.face.config.baseline.pytorch_pipe_v2:transformer",
- "tf-pipe = bob.bio.face.config.baseline.ty_pipe:transformer",
+ "tf-pipe = bob.bio.face.config.baseline.tf_pipe:transformer",
"opencv-pipe = bob.bio.face.config.baseline.opencv_pipe:transformer",
],
# baselines
@@ -155,11 +156,13 @@ setup(
"lda = bob.bio.face.config.baseline.lda:pipeline",
"dummy = bob.bio.face.config.baseline.dummy:pipeline",
"resnet50-msceleb-arcface-2021 = bob.bio.face.config.baseline.resnet50_msceleb_arcface_2021:pipeline",
+ "resnet50-vgg2-arcface-2021 = bob.bio.face.config.baseline.resnet50_vgg2_arcface_2021:pipeline",
"mobilenetv2-msceleb-arcface-2021 = bob.bio.face.config.baseline.mobilenetv2_msceleb_arcface_2021",
"mxnet-pipe = bob.bio.face.config.baseline.mxnet_pipe:pipeline",
+ "mxnet-tinyface = bob.bio.face.config.baseline.mxnet_tinyface:pipeline",
"pytorch-pipe-v1 = bob.bio.face.config.baseline.pytorch_pipe_v1:pipeline",
"pytorch-pipe-v2 = bob.bio.face.config.baseline.pytorch_pipe_v2:pipeline",
- "tf-pipe = bob.bio.face.config.baseline.ty_pipe:pipeline",
+ "tf-pipe = bob.bio.face.config.baseline.tf_pipe:pipeline",
"opencv-pipe = bob.bio.face.config.baseline.opencv_pipe:pipeline",
],
"bob.bio.config": [
@@ -173,9 +176,10 @@ setup(
"lgbphs = bob.bio.face.config.baseline.lgbphs",
"lda = bob.bio.face.config.baseline.lda",
"mxnet-pipe = bob.bio.face.config.baseline.mxnet_pipe",
+ "mxnet-tinyface = bob.bio.face.config.baseline.mxnet_tinyface",
"pytorch-pipe-v1 = bob.bio.face.config.baseline.pytorch_pipe_v1",
"pytorch-pipe-v2 = bob.bio.face.config.baseline.pytorch_pipe_v2",
- "tf-pipe = bob.bio.face.config.baseline.ty_pipe",
+ "tf-pipe = bob.bio.face.config.baseline.tf_pipe",
"opencv-pipe = bob.bio.face.config.baseline.opencv_pipe",
"arface = bob.bio.face.config.database.arface",
"atnt = bob.bio.face.config.database.atnt",
@@ -196,6 +200,7 @@ setup(
"casia-africa = bob.bio.face.config.database.casia_africa",
"morph = bob.bio.face.config.database.morph",
"resnet50-msceleb-arcface-2021 = bob.bio.face.config.baseline.resnet50_msceleb_arcface_2021",
+ "resnet50-vgg2-arcface-2021 = bob.bio.face.config.baseline.resnet50_vgg2_arcface_2021",
"mobilenetv2-msceleb-arcface-2021 = bob.bio.face.config.baseline.mobilenetv2_msceleb_arcface_2021",
],
"bob.bio.cli": [