Fixed issue with the annotations

bob/bio/face/preprocessor/HistogramEqualization.py

@@ -96,7 +96,12 @@ class HistogramEqualization(Base):
         def _crop(image, annotations):
             image = self.change_color_channel(image)
             if self.cropper is not None:
-                image = self.cropper.transform([image], [annotations])[0]
+                # TODO: USE THE TAG `ALLOW_ANNOTATIONS`
+                image = (
+                    self.cropper.transform([image])[0]
+                    if annotations is None
+                    else self.cropper.transform([image], [annotations])[0]
+                )
             image = self.equalize_histogram(image)
             return self.data_type(image)
 

bob/bio/face/preprocessor/INormLBP.py

@@ -25,6 +25,7 @@ from .utils import load_cropper
 from sklearn.utils import check_array
 from bob.pipelines.sample import SampleBatch
 
+
 class INormLBP(Base):
     """Performs I-Norm LBP on the given image"""
 
@@ -120,10 +121,15 @@ class INormLBP(Base):
         def _crop(image, annotations=None):
             image = self.change_color_channel(image)
             if self.cropper is not None:
-                image = self.cropper.transform([image], annotations=[annotations])[0]
+                # TODO: USE THE TAG `ALLOW_ANNOTATIONS`
+                image = (
+                    self.cropper.transform([image])[0]
+                    if annotations is None
+                    else self.cropper.transform([image], [annotations])[0]
+                )
             image = self.lbp_extractor(image)
             return self.data_type(image)
-                
+
         if annotations is None:
             return [_crop(data) for data in X]
         else:

bob/bio/face/preprocessor/Scale.py

@@ -27,7 +27,7 @@ def scale(images, target_img_size):
 
     # images are always batched
     output_shape = tuple(target_img_size)
-    output_shape = tuple(images.shape[0:1]) + output_shape
+    output_shape = tuple(images.shape[-1:-2]) + output_shape
     images = resize(images, output_shape=output_shape)
 
     return to_bob(images)

bob/bio/face/preprocessor/SelfQuotientImage.py

@@ -25,6 +25,7 @@ from .Base import Base
 from .utils import load_cropper
 from bob.pipelines.sample import SampleBatch
 
+
 class SelfQuotientImage(Base):
     """Crops the face (if desired) and applies self quotient image algorithm [WLW04]_ to photometrically enhance the image.
 
@@ -49,9 +50,8 @@ class SelfQuotientImage(Base):
         Base.__init__(self, **kwargs)
 
         # call base class constructor with its set of parameters
-        self.face_cropper=face_cropper
-        self.sigma=sigma
-
+        self.face_cropper = face_cropper
+        self.sigma = sigma
 
         self.cropper = load_cropper(face_cropper)
 
@@ -90,10 +90,16 @@ class SelfQuotientImage(Base):
     face : 2D :py:class:`numpy.ndarray`
       The cropped and photometrically enhanced face.
     """
+
         def _crop(image, annotations):
             image = self.change_color_channel(image)
             if self.cropper is not None:
-                image = self.cropper.transform([image], [annotations])[0]
+                # TODO: USE THE TAG `ALLOW_ANNOTATIONS`
+                image = (
+                    self.cropper.transform([image])[0]
+                    if annotations is None
+                    else self.cropper.transform([image], [annotations])[0]
+                )
             image = self.self_quotient(image)
             return self.data_type(image)
 

bob/bio/face/preprocessor/TanTriggs.py

@@ -24,6 +24,7 @@ from .Base import Base
 from .utils import load_cropper
 from bob.pipelines.sample import SampleBatch
 
+
 class TanTriggs(Base):
     """Crops the face (if desired) and applies Tan&Triggs algorithm [TT10]_ to photometrically enhance the image.
 
@@ -110,9 +111,13 @@ class TanTriggs(Base):
         def _crop(image, annotations=None):
             image = self.change_color_channel(image)
             if self.cropper is not None:
-                image = self.cropper.transform([image], [annotations])[0]
+                # TODO: USE THE TAG `ALLOW_ANNOTATIONS`
+                image = (
+                    self.cropper.transform([image])[0]
+                    if annotations is None
+                    else self.cropper.transform([image], [annotations])[0]
+                )
             image = self.tan_triggs(image)
-
             return self.data_type(image)
 
         if annotations is None:
@@ -120,7 +125,6 @@ class TanTriggs(Base):
         else:
             return [_crop(data, annot) for data, annot in zip(X, annotations)]
 
-
     def __getstate__(self):
         d = dict(self.__dict__)
         d.pop("tan_triggs")