Source code for bob.bio.face.preprocessor.TanTriggs

#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Manuel Guenther <Manuel.Guenther@idiap.ch>
# @date: Thu May 24 10:41:42 CEST 2012
#
# Copyright (C) 2011-2012 Idiap Research Institute, Martigny, Switzerland
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

import bob.ip.base

import numpy
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.

  **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.

  gamma, sigma0, sigma1, size, threshold, alpha
    Please refer to the [TT10]_ original paper (see :py:class:`bob.ip.base.TanTriggs` documentation).

  kwargs
    Remaining keyword parameters passed to the :py:class:`Base` constructor, such as ``color_channel`` or ``dtype``.
  """

    def __init__(
        self,
        face_cropper,
        gamma=0.2,
        sigma0=1,
        sigma1=2,
        size=5,
        threshold=10.0,
        alpha=0.1,
        **kwargs
    ):

        Base.__init__(self, **kwargs)

        # call base class constructor with its set of parameters

        self.face_cropper = face_cropper
        self.gamma = gamma
        self.sigma0 = sigma0
        self.sigma1 = sigma1
        self.size = size
        self.threshold = threshold
        self.alpha = alpha

        self.gamma = gamma
        self.sigma0 = sigma0
        self.sigma1 = sigma1
        self.size = size
        self.threshold = threshold
        self.alpha = alpha

        self.cropper = load_cropper(face_cropper)
        self._init_non_pickables()

    def _init_non_pickables(self):
        self.tan_triggs = bob.ip.base.TanTriggs(
            self.gamma, self.sigma0, self.sigma1, self.size, self.threshold, self.alpha
        )

    def transform(self, X, annotations=None):
        """__call__(image, annotations = None) -> face

    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 the Tan&Triggs algorithm [TT10]_.
    Finally, the resulting face is converted to the desired data type.

    **Parameters:**

    image : 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_one_sample(image, annotations=None):

            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])
                )
                # We change the color channel *after* cropping : some croppers use MTCNN internally, that works on multichannel images
                image = self.change_color_channel(image[0])
                image = self.tan_triggs(image)
            else:
                # Handle with the cropper is None
                image = self.change_color_channel(image)
                image = self.tan_triggs(image)

            return self.data_type(image)

        if annotations is None:
            return [_crop_one_sample(data) for data in X]
        else:
            return [
                _crop_one_sample(data, annot) for data, annot in zip(X, annotations)
            ]

    def __getstate__(self):
        d = dict(self.__dict__)
        d.pop("tan_triggs")
        return d

    def __setstate__(self, d):
        self.__dict__ = d
        self._init_non_pickables()