import logging
from .Base import Base
from .croppers import FaceCropBoundingBox, FaceEyesNorm
logger = logging.getLogger("bob.bio.face")
from bob.bio.base import load_resource
class FaceCrop(Base):
"""
Crops the face according to the given annotations.
This class is designed to perform a geometric normalization of the face based
on the eye locations, using :py:class:`bob.bio.face.preprocessor.croppers.FaceEyesNorm`. Usually,
when executing the :py:meth:`crop_face` function, the image and the eye
locations have to be specified. There, the given image will be transformed
such that the eye locations will be placed at specific locations in the
resulting image. These locations, as well as the size of the cropped image,
need to be specified in the constructor of this class, as
``cropped_positions`` and ``cropped_image_size``.
Some image databases do not provide eye locations, but rather bounding boxes.
This is not a problem at all.
Simply define the coordinates, where you want your ``cropped_positions`` to
be in the cropped image, by specifying the same keys in the dictionary that
will be given as ``annotations`` to the :py:meth:`crop_face` function.
.. note::
These locations can even be outside of the cropped image boundary, i.e.,
when the crop should be smaller than the annotated bounding boxes.
Sometimes, databases provide pre-cropped faces, where the eyes are located at
(almost) the same position in all images. Usually, the cropping does not
conform with the cropping that you like (i.e., image resolution is wrong, or
too much background information). However, the database does not provide eye
locations (since they are almost identical for all images). In that case, you
can specify the ``fixed_positions`` in the constructor, which will be taken
instead of the ``annotations`` inside the :py:meth:`crop_face` function (in
which case the ``annotations`` are ignored).
Parameters
----------
cropped_image_size : (int, int)
The resolution of the cropped image, in order (HEIGHT,WIDTH); if not given,
no face cropping will be performed
cropped_positions : dict
The coordinates in the cropped image, where the annotated points should be
put to. This parameter is a dictionary with usually two elements, e.g.,
``{'reye':(RIGHT_EYE_Y, RIGHT_EYE_X) , 'leye':(LEFT_EYE_Y, LEFT_EYE_X)}``.
However, also other parameters, such as ``{'topleft' : ..., 'bottomright' :
...}`` are supported, as long as the ``annotations`` in the `__call__`
function are present.
fixed_positions : dict or None
If specified, ignore the annotations from the database and use these fixed
positions throughout.
allow_upside_down_normalized_faces: bool, optional
If ``False`` (default), a ValueError is raised when normalized faces are going to be
upside down compared to input image. This allows you to catch wrong annotations in
your database easily. If you are sure about your input, you can set this flag to
``True``.
annotator : :any:`bob.bio.base.annotator.Annotator`
If provided, the annotator will be used if the required annotations are
missing.
cropper:
Pointer to a function that will crops using the annotations
kwargs
Remaining keyword parameters passed to the :py:class:`Base` constructor,
such as ``color_channel`` or ``dtype``.
"""
def __init__(
self,
cropped_image_size,
cropped_positions=None,
cropper=None,
fixed_positions=None,
annotator=None,
allow_upside_down_normalized_faces=False,
**kwargs,
):
# call base class constructor
Base.__init__(self, **kwargs)
# Patching image size
if isinstance(cropped_image_size, int):
cropped_image_size = (cropped_image_size, cropped_image_size)
# SEssion the cropper
self.allow_upside_down_normalized_faces = (
allow_upside_down_normalized_faces
)
if cropper is None:
cropper = FaceEyesNorm(
cropped_positions,
cropped_image_size,
allow_upside_down_normalized_faces=allow_upside_down_normalized_faces,
)
self.cropper = cropper
# check parameters
# copy parameters
self.cropped_image_size = cropped_image_size
self.cropped_positions = cropped_positions
# self.cropped_keys = sorted(cropped_positions.keys())
self.fixed_positions = fixed_positions
if isinstance(annotator, str):
annotator = load_resource(annotator, "annotator")
self.annotator = annotator
# create objects required for face cropping
self.cropper = cropper
class MultiFaceCrop(Base):
"""Wraps around FaceCrop to enable a dynamical cropper that can handle several annotation types.
Initialization and usage is similar to the FaceCrop, but the main difference here is that one specifies
a *list* of cropped_positions, and optionally a *list* of associated fixed positions.
For each set of cropped_positions in the list, a new FaceCrop will be instantiated that handles this
exact set of annotations.
When calling the *transform* method, the MultiFaceCrop matches each sample to its associated cropper
based on the received annotation, then performs the cropping of each subset, and finally gathers the results.
If there is more than one cropper matching with the annotations, the **first valid** cropper will be taken.
In case none of the croppers match with the received annotations, a ``ValueError`` is raised.
Parameters
----------
croppers_list : list
A list of :py:class:`FaceCrop` that crops the face
"""
def __init__(
self,
croppers_list,
):
assert isinstance(croppers_list, list)
for cl in croppers_list:
assert isinstance(cl, FaceCrop)
self.croppers_list = croppers_list
class BoundingBoxAnnotatorCrop(Base):
"""
This face cropper uses a 2 stage strategy to crop and align faces in case `annotation_type` has a bounding-box.
In the first stage, it crops the face using the {`topleft`, `bottomright`} parameters and expands them using a `margin` factor.
In the second stage, it uses the `annotator` to estimate {`leye` and `reye`} to make the crop using :py:class:`bob.bio.face.preprocessor.croppers.FaceEyesNorm`.
In case the annotator doesn't work, it returns the cropped face using the `bounding-box` coordinates.
.. warning::
`cropped_positions` must be set with `leye`, `reye`, `topleft` and `bottomright` positions
Parameters
----------
eyes_cropper: :py:class:`bob.bio.face.preprocessor.croppers.FaceEyesNorm`
This is the cropper that will be used to crop the face using eyes positions
annotator : :any:`bob.bio.base.annotator.Annotator`
This is the annotator that will be used to detect faces in the cropped images.
"""
def __init__(
self,
eyes_cropper,
annotator,
margin=0.5,
):
self.eyes_cropper = eyes_cropper
self.margin = margin
self.face_cropper = FaceCropBoundingBox(
final_image_size=self.eyes_cropper.final_image_size, margin=margin
)
if isinstance(annotator, str):
annotator = load_resource(annotator, "annotator")
self.annotator = annotator