diff --git a/bob/bio/face/preprocessor/Base.py b/bob/bio/face/preprocessor/Base.py
index 0e296e423f547daf987377aa1e3168fe2396a36d..3170b408733f8e92081782fd5487b5f7c40ac826 100644
--- a/bob/bio/face/preprocessor/Base.py
+++ b/bob/bio/face/preprocessor/Base.py
@@ -1,27 +1,59 @@
-import numpy
import bob.io.image
import bob.ip.color
+import numpy
+from sklearn.base import BaseEstimator
+from sklearn.base import TransformerMixin
+
+
+def change_color_channel(image, color_channel):
+ if image.ndim == 2:
+ if color_channel == "rgb":
+ return bob.ip.color.gray_to_rgb(image)
+ if color_channel != "gray":
+ raise ValueError(
+ "There is no rule to extract a "
+ + color_channel
+ + " image from a gray level image!"
+ )
+ return image
-from sklearn.base import TransformerMixin, BaseEstimator
+ if color_channel == "rgb":
+ return image
+ if color_channel == "gray":
+ return bob.ip.color.rgb_to_gray(image)
+ if color_channel == "red":
+ return image[0, :, :]
+ if color_channel == "green":
+ return image[1, :, :]
+ if color_channel == "blue":
+ return image[2, :, :]
+
+ raise ValueError(
+ "The image channel '%s' is not known or not yet implemented", color_channel
+ )
class Base(TransformerMixin, BaseEstimator):
"""Performs color space adaptations and data type corrections for the given
- image.
+ image.
- **Parameters:**
+ **Parameters:**
- dtype : :py:class:`numpy.dtype` or convertible or ``None``
- The data type that the resulting image will have.
+ dtype : :py:class:`numpy.dtype` or convertible or ``None``
+ The data type that the resulting image will have.
- color_channel : one of ``('gray', 'red', 'gren', 'blue', 'rgb')``
- The specific color channel, which should be extracted from the image.
- """
+ color_channel : one of ``('gray', 'red', 'gren', 'blue', 'rgb')``
+ The specific color channel, which should be extracted from the image.
+ """
def __init__(self, dtype=None, color_channel="gray", **kwargs):
- self.channel = color_channel
+ self.color_channel = color_channel
self.dtype = dtype
+ @property
+ def channel(self):
+ return self.color_channel
+
def _more_tags(self):
return {"stateless": True, "requires_fit": False}
@@ -31,85 +63,62 @@ class Base(TransformerMixin, BaseEstimator):
def color_channel(self, image):
"""color_channel(image) -> channel
- Returns the channel of the given image, which was selected in the
- constructor. Currently, gray, red, green and blue channels are supported.
+ Returns the channel of the given image, which was selected in the
+ constructor. Currently, gray, red, green and blue channels are supported.
- **Parameters:**
+ **Parameters:**
- image : 2D or 3D :py:class:`numpy.ndarray`
- The image to get the specified channel from.
+ image : 2D or 3D :py:class:`numpy.ndarray`
+ The image to get the specified channel from.
- **Returns:**
+ **Returns:**
- channel : 2D or 3D :py:class:`numpy.ndarray`
- The extracted color channel.
- """
+ channel : 2D or 3D :py:class:`numpy.ndarray`
+ The extracted color channel.
+ """
- if image.ndim == 2:
- if self.channel == "rgb":
- return bob.ip.color.gray_to_rgb(image)
- if self.channel != "gray":
- raise ValueError(
- "There is no rule to extract a "
- + self.channel
- + " image from a gray level image!"
- )
- return image
-
- if self.channel == "rgb":
- return image
- if self.channel == "gray":
- return bob.ip.color.rgb_to_gray(image)
- if self.channel == "red":
- return image[0, :, :]
- if self.channel == "green":
- return image[1, :, :]
- if self.channel == "blue":
- return image[2, :, :]
-
- raise ValueError(
- "The image channel '%s' is not known or not yet implemented", self.channel
- )
+ return change_color_channel(image, self.color_channel)
def data_type(self, image):
"""data_type(image) -> image
- Converts the given image into the data type specified in the constructor of
- this class. If no data type was specified, or the ``image`` is ``None``, no
- conversion is performed.
+ Converts the given image into the data type specified in the constructor of
+ this class. If no data type was specified, or the ``image`` is ``None``, no
+ conversion is performed.
- **Parameters:**
+ **Parameters:**
- image : 2D or 3D :py:class:`numpy.ndarray`
- The image to convert.
+ image : 2D or 3D :py:class:`numpy.ndarray`
+ The image to convert.
- **Returns:**
+ **Returns:**
- image : 2D or 3D :py:class:`numpy.ndarray`
- The image converted to the desired data type, if any.
- """
+ image : 2D or 3D :py:class:`numpy.ndarray`
+ The image converted to the desired data type, if any.
+ """
if self.dtype is not None and image is not None:
image = image.astype(self.dtype)
return image
- def transform(self, image, annotations=None):
- """__call__(image, annotations = None) -> image
+ def transform(self, images, annotations=None):
+ """Extracts the desired color channel and converts to the desired data type.
- Extracts the desired color channel and converts to the desired data type.
+ **Parameters:**
- **Parameters:**
+ image : 2D or 3D :py:class:`numpy.ndarray`
+ The image to preprocess.
- image : 2D or 3D :py:class:`numpy.ndarray`
- The image to preprocess.
+ annotations : any
+ Ignored.
- annotations : any
- Ignored.
+ **Returns:**
- **Returns:**
+ image : 2D :py:class:`numpy.ndarray`
+ The image converted converted to the desired color channel and type.
+ """
+ return [self._transform_one_image(img) for img in images]
- image : 2D :py:class:`numpy.ndarray`
- The image converted converted to the desired color channel and type.
- """
+ def _transform_one_image(self, image):
assert isinstance(image, numpy.ndarray) and image.ndim in (2, 3)
# convert to grayscale
image = self.color_channel(image)
diff --git a/bob/bio/face/preprocessor/utils.py b/bob/bio/face/preprocessor/utils.py
index ffd5602632cb909a465d2238a3f58e0b74325e0c..d59c9bd85e8b1c961df11e65909f5515f5ed48c0 100644
--- a/bob/bio/face/preprocessor/utils.py
+++ b/bob/bio/face/preprocessor/utils.py
@@ -1,5 +1,4 @@
import bob.bio.base
-import six
def load_cropper(face_cropper):
@@ -7,25 +6,9 @@ def load_cropper(face_cropper):
if face_cropper is None:
cropper = None
- elif isinstance(face_cropper, six.string_types):
+ elif isinstance(face_cropper, str):
cropper = bob.bio.base.load_resource(face_cropper, "preprocessor")
else:
cropper = face_cropper
return cropper
-
-
-def load_cropper_only(face_cropper):
- from .FaceCrop import FaceCrop
-
- if face_cropper is None:
- cropper = None
- elif isinstance(face_cropper, six.string_types):
- cropper = bob.bio.base.load_resource(face_cropper, "preprocessor")
- elif isinstance(face_cropper, FaceCrop):
- cropper = face_cropper
- else:
- raise ValueError("The given face cropper type is not understood")
-
- assert cropper is None or isinstance(cropper, FaceCrop)
- return cropper