”new”

bob/bio/face/annotator/__init__.py

 import bob.ip.facedetect
 
-
 def bounding_box_to_annotations(bbx):
     """Converts :any:`bob.ip.facedetect.BoundingBox` to dictionary annotations.
 

bob/bio/face/annotator/bobiptinyface.py

@@ -2,14 +2,14 @@ import bob.ip.facedetect.tinyface
 from . import Base
 import cv2 as cv
 
+
 class BobIpTinyface(Base):
     """Annotator using tinyface in bob.ip.facedetect"""
-    
+
     def __init__(self, **kwargs):
         super(BobIpTinyface, self).__init__(**kwargs)
         self.tinyface = bob.ip.facedetect.tinyface.TinyFacesDetector(prob_thresh=0.5)
 
-
     def annotate(self, image, **kwargs):
         """Annotates an image using tinyface
 
@@ -25,15 +25,11 @@ class BobIpTinyface(Base):
         dict
             Annotations with (topleft, bottomright) keys (or None).
         """
-  
-        annotations = self.tinyface.detect(image)
 
+        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 {"topleft": (r[0], r[1]), "bottomright": (r[2], r[3])}
         else:
             return None
-
-
-

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

@@ -23,25 +23,36 @@ else:
     fixed_positions = None
 
 
+cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
 
-cropped_positions={'leye':(49,72), 'reye':(49,38)}
-
-preprocessor_transformer = FaceCrop(cropped_image_size=(112,112), cropped_positions={'leye':(49,72), 'reye':(49,38)}, color_channel='rgb',fixed_positions=fixed_positions)
-
-transform_extra_arguments = (None if (cropped_positions is None or fixed_positions is not None) else (("annotations", "annotations"),))
-
-
+preprocessor_transformer = FaceCrop(
+    cropped_image_size=(112, 112),
+    cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+    color_channel="rgb",
+    fixed_positions=fixed_positions,
+)
 
+transform_extra_arguments = (
+    None
+    if (cropped_positions is None or fixed_positions is not None)
+    else (("annotations", "annotations"),)
+)
 
 
 extractor_transformer = mxnet_model()
 
-algorithm = Distance(distance_function = scipy.spatial.distance.cosine,is_distance_function = True)
+algorithm = Distance(
+    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
+)
 
 
 # Chain the Transformers together
 transformer = make_pipeline(
-    wrap(["sample"], preprocessor_transformer,transform_extra_arguments=transform_extra_arguments),
+    wrap(
+        ["sample"],
+        preprocessor_transformer,
+        transform_extra_arguments=transform_extra_arguments,
+    ),
     wrap(["sample"], extractor_transformer)
     # Add more transformers here if needed
 )
@@ -50,5 +61,3 @@ transformer = make_pipeline(
 # Assemble the Vanilla Biometric pipeline and execute
 pipeline = VanillaBiometricsPipeline(transformer, algorithm)
 transformer = pipeline.transformer
-
-

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

@@ -26,27 +26,41 @@ else:
     fixed_positions = None
 
 
+cropped_positions = {"leye": (98, 144), "reye": (98, 76)}
+# Preprocessor
+preprocessor_transformer = FaceCrop(
+    cropped_image_size=(224, 224),
+    cropped_positions={"leye": (98, 144), "reye": (98, 76)},
+    color_channel="rgb",
+    fixed_positions=fixed_positions,
+)
 
-cropped_positions={"leye": (98, 144), "reye": (98, 76)}
-#Preprocessor
-preprocessor_transformer = FaceCrop(cropped_image_size=(224,224), cropped_positions={"leye": (98, 144), "reye": (98, 76)}, color_channel='rgb',fixed_positions=fixed_positions)
-
-transform_extra_arguments = (None if (cropped_positions is None or fixed_positions is not None) else (("annotations", "annotations"),))
+transform_extra_arguments = (
+    None
+    if (cropped_positions is None or fixed_positions is not None)
+    else (("annotations", "annotations"),)
+)
 
 
-#Extractor
+# Extractor
 extractor_transformer = opencv_model()
 
 
-#Algorithm
-algorithm = Distance(distance_function = scipy.spatial.distance.cosine,is_distance_function = True)
+# Algorithm
+algorithm = Distance(
+    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
+)
 
 ## Creation of the pipeline
 
 
 # Chain the Transformers together
 transformer = make_pipeline(
-    wrap(["sample"], preprocessor_transformer,transform_extra_arguments=transform_extra_arguments),
+    wrap(
+        ["sample"],
+        preprocessor_transformer,
+        transform_extra_arguments=transform_extra_arguments,
+    ),
     wrap(["sample"], extractor_transformer)
     # Add more transformers here if needed
 )

bob/bio/face/config/baseline/pytorch_pipe_v1.py

@@ -23,23 +23,36 @@ else:
     fixed_positions = None
 
 
+cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
 
-cropped_positions={'leye':(49,72), 'reye':(49,38)}
-
-preprocessor_transformer = FaceCrop(cropped_image_size=(224,224), cropped_positions={'leye':(49,72), 'reye':(49,38)}, color_channel='rgb',fixed_positions=fixed_positions)
-
-transform_extra_arguments = (None if (cropped_positions is None or fixed_positions is not None) else (("annotations", "annotations"),))
+preprocessor_transformer = FaceCrop(
+    cropped_image_size=(224, 224),
+    cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+    color_channel="rgb",
+    fixed_positions=fixed_positions,
+)
 
+transform_extra_arguments = (
+    None
+    if (cropped_positions is None or fixed_positions is not None)
+    else (("annotations", "annotations"),)
+)
 
 
 extractor_transformer = pytorch_loaded_model()
 
-algorithm = Distance(distance_function = scipy.spatial.distance.cosine,is_distance_function = True)
+algorithm = Distance(
+    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
+)
 
 
 # Chain the Transformers together
 transformer = make_pipeline(
-    wrap(["sample"], preprocessor_transformer,transform_extra_arguments=transform_extra_arguments),
+    wrap(
+        ["sample"],
+        preprocessor_transformer,
+        transform_extra_arguments=transform_extra_arguments,
+    ),
     wrap(["sample"], extractor_transformer)
     # Add more transformers here if needed
 )
@@ -48,60 +61,3 @@ transformer = make_pipeline(
 # Assemble the Vanilla Biometric pipeline and execute
 pipeline = VanillaBiometricsPipeline(transformer, algorithm)
 transformer = pipeline.transformer
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bob/bio/face/config/baseline/pytorch_pipe_v2.py

@@ -54,6 +54,7 @@ transformer = make_pipeline(
 # Assemble the Vanilla Biometric pipeline and execute
 pipeline = VanillaBiometricsPipeline(transformer, algorithm)
 transformer = pipeline.transformer
+<<<<<<< HEAD
 
 
 
@@ -111,3 +112,5 @@ transformer = pipeline.transformer
 
 
 
+=======
+>>>>>>> ”new”

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

@@ -24,23 +24,38 @@ else:
 
 
 # Preprocessor
-cropped_positions={'leye':(49,72), 'reye':(49,38)}
+cropped_positions = {"leye": (49, 72), "reye": (49, 38)}
 
-preprocessor_transformer = FaceCrop(cropped_image_size=(160,160), cropped_positions={'leye':(49,72), 'reye':(49,38)}, color_channel='rgb',fixed_positions=fixed_positions)
+preprocessor_transformer = FaceCrop(
+    cropped_image_size=(160, 160),
+    cropped_positions={"leye": (49, 72), "reye": (49, 38)},
+    color_channel="rgb",
+    fixed_positions=fixed_positions,
+)
 
-transform_extra_arguments = (None if (cropped_positions is None or fixed_positions is not None) else (("annotations", "annotations"),))
+transform_extra_arguments = (
+    None
+    if (cropped_positions is None or fixed_positions is not None)
+    else (("annotations", "annotations"),)
+)
 
 
 # Extractor
 extractor_transformer = tf_model()
 
 # Algorithm
-algorithm = Distance(distance_function = scipy.spatial.distance.cosine,is_distance_function = True)
+algorithm = Distance(
+    distance_function=scipy.spatial.distance.cosine, is_distance_function=True
+)
 
 
 # Chain the Transformers together
 transformer = make_pipeline(
-    wrap(["sample"], preprocessor_transformer,transform_extra_arguments=transform_extra_arguments),
+    wrap(
+        ["sample"],
+        preprocessor_transformer,
+        transform_extra_arguments=transform_extra_arguments,
+    ),
     wrap(["sample"], extractor_transformer)
     # Add more transformers here if needed
 )
@@ -49,60 +64,3 @@ transformer = make_pipeline(
 # Assemble the Vanilla Biometric pipeline and execute
 pipeline = VanillaBiometricsPipeline(transformer, algorithm)
 transformer = pipeline.transformer
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bob/bio/face/extractor/__init__.py

@@ -9,7 +9,7 @@ from .opencv_caffe import opencv_model
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):
-  """Says object was actually declared here, and not in the import module.
+    """Says object was actually declared here, and not in the import module.
   Fixing sphinx warnings of not being able to find classes, when path is shortened.
   Parameters:
 
@@ -19,7 +19,9 @@ def __appropriate__(*args):
   <https://github.com/sphinx-doc/sphinx/issues/3048>`
   """
 
-  for obj in args: obj.__module__ = __name__
+    for obj in args:
+        obj.__module__ = __name__
+
 
 __appropriate__(
     DCTBlocks,
@@ -30,5 +32,5 @@ __appropriate__(
     pytorch_library_model,
     tf_model,
     opencv_model,
-    )
-__all__ = [_ for _ in dir() if not _.startswith('_')]
+)
+__all__ = [_ for _ in dir() if not _.startswith("_")]

bob/bio/face/extractor/mxnet_resnet.py

@@ -12,9 +12,11 @@ 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):
 
     """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):
@@ -76,16 +80,15 @@ 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()
+            self._load_model()
 
         X = check_array(X, allow_nd=True)
         X = mx.nd.array(X)
 
         return self.model(X,).asnumpy()
 
-
     def __getstate__(self):
         # Handling unpicklable objects
 

bob/bio/face/extractor/opencv_caffe.py

@@ -44,7 +44,6 @@ class opencv_model(TransformerMixin, BaseEstimator):
   use_gpu: True or False.
     """
 
-
     def __init__(self, use_gpu=False, **kwargs):
         super().__init__(**kwargs)
         self.model = None
@@ -64,7 +63,7 @@ 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
 
@@ -83,16 +82,15 @@ 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()
+            self._load_model()
 
         img = np.array(X)
 
         self.model.setInput(img)
-     
-        return self.model.forward()
 
+        return self.model.forward()
 
     def __getstate__(self):
         # Handling unpicklable objects

bob/bio/face/extractor/pytorch_model.py

@@ -16,6 +16,7 @@ import imp
 pytorch_model_directory = rc["bob.extractor_model.pytorch"]
 pytorch_weight_directory = rc["bob.extractor_weights.pytorch"]
 
+
 class pytorch_loaded_model(TransformerMixin, BaseEstimator):
     """Extracts features using deep face recognition models under PyTorch Interface, especially for the models and weights that need to load by hand.
     
@@ -30,7 +31,7 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
 
   **Parameters:**
   use_gpu: True or False.
-    """ 
+    """
 
     def __init__(self, use_gpu=False, **kwargs):
         super().__init__(**kwargs)
@@ -52,12 +53,12 @@ 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
@@ -77,7 +78,7 @@ 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()
 
@@ -85,7 +86,6 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
 
         return self.model(X).detach().numpy()
 
-
     def __getstate__(self):
         # Handling unpicklable objects
 
@@ -95,14 +95,8 @@ class pytorch_loaded_model(TransformerMixin, BaseEstimator):
 
     def _more_tags(self):
         return {"stateless": True, "requires_fit": False}
-        
-        
-        
-        
-        
-        
-        
-        
+
+
 class pytorch_library_model(TransformerMixin, BaseEstimator):
     """Extracts features using deep face recognition with registered model frames in the PyTorch Library. 
     
@@ -153,7 +147,6 @@ class pytorch_library_model(TransformerMixin, BaseEstimator):
 
         return self.model(X).detach().numpy()
 
-
     def __getstate__(self):
         # Handling unpicklable objects
 

bob/bio/face/extractor/tf_model.py

@@ -17,6 +17,7 @@ from tensorflow import keras
 
 tf_model_directory = rc["bob.extractor_model.tf"]
 
+
 class tf_model(TransformerMixin, BaseEstimator):
     """Extracts features using deep face recognition models under TensorFlow Interface.
 
@@ -71,7 +72,7 @@ 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()
 
@@ -80,10 +81,8 @@ class tf_model(TransformerMixin, BaseEstimator):
         X = to_channels_last(X)
         predict = self.model.predict(X)
 
-
         return predict
 
-
     def __getstate__(self):
         # Handling unpicklable objects
 

requirements.txt

@@ -22,4 +22,4 @@ mxnet
 opencv-python
 six
 scikit-image
-scikit-learn # for pipelines Tranformers
+scikit-learn # for pipelines Tranformers
\ No newline at end of file