Source code for bob.bio.face.preprocessor.HistogramEqualization
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Manuel Guenther <Manuel.Guenther@idiap.ch>
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
import cv2
import numpy
import numpy as np
from .Base import Base
from .utils import load_cropper
class HistogramEqualization(Base):
"""Crops the face (if desired) and performs histogram equalization to photometrically enhance the image.
Parameters
----------
face_cropper : str or :py:class:`bob.bio.face.preprocessor.FaceCrop` or :py:class:`bob.bio.face.preprocessor.FaceDetect` or ``None``
The face image cropper that should be applied to the image.
If ``None`` is selected, no face cropping is performed.
Otherwise, the face cropper might be specified as a registered resource, a configuration file, or an instance of a preprocessor.
.. note:: The given class needs to contain a ``crop_face`` method.
kwargs
Remaining keyword parameters passed to the :py:class:`Base` constructor, such as ``color_channel`` or ``dtype``.
"""
def __init__(self, face_cropper, **kwargs):
Base.__init__(self, **kwargs)
self.face_cropper = (face_cropper,)
self.cropper = load_cropper(face_cropper)
[docs] def equalize_histogram(self, image):
"""equalize_histogram(image) -> equalized
Performs the histogram equalization on the given image.
Parameters
----------
image : 2D :py:class:`numpy.ndarray`
The image to berform histogram equalization with.
The image will be transformed to type ``uint8`` before computing the histogram.
Returns
-------
equalized : 2D :py:class:`numpy.ndarray` (float)
The photometrically enhanced image.
"""
return cv2.equalizeHist(np.round(image).astype(numpy.uint8))
[docs] def transform(self, X, annotations=None):
"""
Aligns the given image according to the given annotations.
First, the desired color channel is extracted from the given image.
Afterward, the face is eventually cropped using the ``face_cropper`` specified in the constructor.
Then, the image is photometrically enhanced using histogram equalization.
Finally, the resulting face is converted to the desired data type.
Parameters
----------
X : 2D or 3D :py:class:`numpy.ndarray`
The face image to be processed.
annotations : dict or ``None``
The annotations that fit to the given image.
Might be ``None``, when the ``face_cropper`` is ``None`` or of type :py:class:`FaceDetect`.
Returns
-------
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:
# TODO: USE THE TAG `ALLOW_ANNOTATIONS`
image = (
self.cropper.transform([image])
if annotations is None
else self.cropper.transform([image], [annotations])
)
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:
return [_crop(data) for data in X]
else:
return [_crop(data, annot) for data, annot in zip(X, annotations)]