diff --git a/bob/bio/face/helpers.py b/bob/bio/face/helpers.py
index 8cd4e5b9b1a195debdbed2709cbdaf656b310a06..d74e7a4208d7b522b1f8de579c09711d0f6aa711 100644
--- a/bob/bio/face/helpers.py
+++ b/bob/bio/face/helpers.py
@@ -1,37 +1,31 @@
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
-from bob.bio.face.preprocessor import FaceDetect, FaceCrop, Scale
-from skimage.transform import resize
-import numpy as np
+from bob.bio.face.preprocessor import FaceCrop, Scale
+
def face_crop_solver(
cropped_image_size,
color_channel="rgb",
cropped_positions=None,
fixed_positions=None,
- use_face_detector=False,
- dtype=np.uint8
+ annotator=None,
+ dtype="uint8",
):
"""
Decide which face cropper to use.
"""
-
- if use_face_detector:
- return FaceDetect(
- face_cropper="face-crop-eyes", use_flandmark=True
- )
+ # If there's not cropped positions, just resize
+ if cropped_positions is None:
+ return Scale(cropped_image_size)
else:
- # If there's not cropped positions, just resize
- if cropped_positions is None:
- return Scale(cropped_image_size)
- else:
- # Detects the face and crops it without eye detection
- return FaceCrop(
- cropped_image_size=cropped_image_size,
- cropped_positions=cropped_positions,
- color_channel=color_channel,
- fixed_positions=fixed_positions,
- dtype=dtype
- )
+ # Detects the face and crops it without eye detection
+ return FaceCrop(
+ cropped_image_size=cropped_image_size,
+ cropped_positions=cropped_positions,
+ color_channel=color_channel,
+ fixed_positions=fixed_positions,
+ dtype=dtype,
+ annotator=annotator,
+ )
diff --git a/bob/bio/face/preprocessor/FaceDetect.py b/bob/bio/face/preprocessor/FaceDetect.py
deleted file mode 100644
index 479a0eab61e96cb160c7f5bef498392ece2e5813..0000000000000000000000000000000000000000
--- a/bob/bio/face/preprocessor/FaceDetect.py
+++ /dev/null
@@ -1,241 +0,0 @@
-import math
-import numpy
-
-import bob.ip.facedetect
-import bob.ip.flandmark
-
-import bob.ip.base
-import numpy
-
-from .Base import Base
-from .utils import load_cropper_only
-from sklearn.utils import check_array
-from bob.pipelines.sample import SampleBatch
-import logging
-
-logger = logging.getLogger("bob.bio.face")
-
-
-class FaceDetect(Base):
- """Performs a face detection (and facial landmark localization) in the given image and crops the face.
-
- This class is designed to perform a geometric normalization of the face based on the detected face.
- Face detection is performed using :ref:`bob.ip.facedetect <bob.ip.facedetect>`.
- Particularly, the function :py:func:`bob.ip.facedetect.detect_single_face` is executed, which will *always* return *exactly one* bounding box, even if the image contains more than one face, or no face at all.
- The speed of the face detector can be regulated using the ``cascade``, ``distance` ``scale_base`` and ``lowest_scale`` parameters.
- The number of overlapping detected bounding boxes that should be joined can be selected by ``detection_overlap``.
- Please see the documentation of :ref:`bob.ip.facedetect <bob.ip.facedetect>` for more details about these parameters.
-
- Additionally, facial landmarks can be detected using the :ref:`bob.ip.flandmark`.
- If enabled using ``use_flandmark = True`` in the constructor, it is tried to obtain the facial landmarks inside the detected facial area.
- If landmarks are found, these are used to geometrically normalize the face.
- Otherwise, the eye locations are estimated based on the bounding box.
- This is also applied, when ``use_flandmark = False.``
-
- The face cropping itself is done by the given ``face_cropper``.
- This cropper can either be an instance of :py:class:`FaceCrop` (or any other class that provides a similar ``crop_face`` function), or it can be the resource name of a face cropper, such as ``'face-crop-eyes'``.
-
- **Parameters:**
-
- face_cropper : :py:class:`bob.bio.face.preprocessor.FaceCrop` or str
- The face cropper to be used to crop the detected face.
- Might be an instance of a :py:class:`FaceCrop` or the name of a face cropper resource.
-
- cascade : str or ``None``
- The file name, where a face detector cascade can be found.
- If ``None``, the default cascade for frontal faces :py:func:`bob.ip.facedetect.default_cascade` is used.
-
- use_flandmark : bool
- If selected, :py:class:`bob.ip.flandmark.Flandmark` is used to detect the eye locations.
- Otherwise, the eye locations are estimated based on the detected bounding box.
-
- detection_overlap : float
- See :py:func:`bob.ip.facedetect.detect_single_face`.
-
- distance : int
- See the Sampling section in the :ref:`Users Guide of bob.ip.facedetect <bob.ip.facedetect>`.
-
- scale_base : float
- See the Sampling section in the :ref:`Users Guide of bob.ip.facedetect <bob.ip.facedetect>`.
-
- lowest_scale : float
- See the Sampling section in the :ref:`Users Guide of bob.ip.facedetect <bob.ip.facedetect>`.
-
- kwargs
- Remaining keyword parameters passed to the :py:class:`Base` constructor, such as ``color_channel`` or ``dtype``.
- """
-
- def __init__(
- self,
- face_cropper,
- cascade=None,
- use_flandmark=False,
- detection_overlap=0.2,
- distance=2,
- scale_base=math.pow(2.0, -1.0 / 16.0),
- lowest_scale=0.125,
- **kwargs
- ):
- # call base class constructors
- Base.__init__(self, **kwargs)
-
- self.face_cropper = face_cropper
- self.cascade=cascade
- self.use_flandmark=use_flandmark
- self.detection_overlap=detection_overlap
- self.distance=distance
- self.scale_base=scale_base
- self.lowest_scale=lowest_scale
-
- assert face_cropper is not None
-
- self.scale_base = scale_base
- self.lowest_scale = lowest_scale
- self.distance = distance
- self.cascade = cascade
- self.use_flandmark = use_flandmark
-
- self.detection_overlap = detection_overlap
- self.quality = None
-
- self.cropper = load_cropper_only(face_cropper)
-
- self._init_non_pickables()
-
- def _init_non_pickables(self):
- self.sampler = bob.ip.facedetect.Sampler(
- scale_factor=self.scale_base,
- lowest_scale=self.lowest_scale,
- distance=self.distance,
- )
-
- if self.cascade is None:
- self.cascade_classifier = bob.ip.facedetect.default_cascade()
- else:
- self.cascade_classifier = bob.ip.facedetect.Cascade(
- bob.io.base.HDF5File(self.cascade)
- )
-
- self.flandmark = bob.ip.flandmark.Flandmark() if self.use_flandmark else None
-
- def _landmarks(self, image, bounding_box):
- """Try to detect the landmarks in the given bounding box, and return the eye locations."""
- # get the landmarks in the face
- if self.flandmark is not None:
- # use the flandmark detector
-
- # make the bounding box square shape by extending the horizontal position by 2 pixels times width/20
- bb = bob.ip.facedetect.BoundingBox(
- topleft=(
- bounding_box.top_f,
- bounding_box.left_f - bounding_box.size[1] / 10.0,
- ),
- size=bounding_box.size,
- )
-
- top = max(bb.top, 0)
- left = max(bb.left, 0)
- bottom = min(bb.bottom, image.shape[0])
- right = min(bb.right, image.shape[1])
- landmarks = self.flandmark.locate(
- image, top, left, bottom - top, right - left
- )
-
- if landmarks is not None and len(landmarks):
- return {
- "reye": (
- (landmarks[1][0] + landmarks[5][0]) / 2.0,
- (landmarks[1][1] + landmarks[5][1]) / 2.0,
- ),
- "leye": (
- (landmarks[2][0] + landmarks[6][0]) / 2.0,
- (landmarks[2][1] + landmarks[6][1]) / 2.0,
- ),
- }
- else:
- logger.warn("Could not detect landmarks -- using estimated landmarks")
-
- # estimate from default locations
- return bob.ip.facedetect.expected_eye_positions(bounding_box)
-
- def crop_face(self, image, annotations=None):
- """crop_face(image, annotations = None) -> face
-
- Detects the face (and facial landmarks), and used the ``face_cropper`` given in the constructor to crop the face.
-
- **Parameters:**
-
- image : 2D or 3D :py:class:`numpy.ndarray`
- The face image to be processed.
-
- annotations : any
- Ignored.
-
- **Returns:**
-
- face : 2D or 3D :py:class:`numpy.ndarray` (float)
- The detected and cropped face.
- """
- uint8_image = image.astype(numpy.uint8)
- if uint8_image.ndim == 3:
- uint8_image = bob.ip.color.rgb_to_gray(uint8_image)
-
- # detect the face
- bounding_box, self.quality = bob.ip.facedetect.detect_single_face(
- uint8_image, self.cascade_classifier, self.sampler, self.detection_overlap
- )
-
- # get the eye landmarks
- annotations = self._landmarks(uint8_image, bounding_box)
-
- # apply face cropping
- return self.cropper.crop_face(image, annotations)
-
- def transform(self, X, annotations=None):
- """__call__(image, annotations = None) -> face
-
- Aligns the given image according to the detected face bounding box or the detected facial features.
-
- First, the desired color channel is extracted from the given image.
- Afterward, the face is detected and cropped, see :py:meth:`crop_face`.
- 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 : any
- Ignored.
-
- **Returns:**
-
- face : 2D :py:class:`numpy.ndarray`
- The cropped face.
- """
- def _crop(image, annotation):
-
- # convert to the desired color channel
- image = self.color_channel(image)
-
- # detect face and crop it
- image = self.crop_face(image)
-
- # convert data type
- 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)]
-
- def __getstate__(self):
- d = dict(self.__dict__)
- d.pop("sampler")
- d.pop("cascade_classifier")
- d.pop("flandmark")
- return d
-
- def __setstate__(self, d):
- self.__dict__ = d
- self._init_non_pickables()
diff --git a/bob/bio/face/preprocessor/__init__.py b/bob/bio/face/preprocessor/__init__.py
index d7e0bcc4576e74fbfd89fb04de6ffba8c173a386..31e02f0cf4151eb82cc725911c4caebcd6042e89 100644
--- a/bob/bio/face/preprocessor/__init__.py
+++ b/bob/bio/face/preprocessor/__init__.py
@@ -1,6 +1,5 @@
from .Base import Base
from .FaceCrop import FaceCrop
-from .FaceDetect import FaceDetect
from .TanTriggs import TanTriggs
from .INormLBP import INormLBP
diff --git a/bob/bio/face/preprocessor/utils.py b/bob/bio/face/preprocessor/utils.py
index 9ea3eea51e5025b202ee2817458c0438db91d712..ffd5602632cb909a465d2238a3f58e0b74325e0c 100644
--- a/bob/bio/face/preprocessor/utils.py
+++ b/bob/bio/face/preprocessor/utils.py
@@ -4,14 +4,11 @@ import six
def load_cropper(face_cropper):
from .FaceCrop import FaceCrop
- from .FaceDetect import FaceDetect
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, FaceDetect)):
- # cropper = face_cropper
else:
cropper = face_cropper
diff --git a/bob/bio/face/test/test_picklability.py b/bob/bio/face/test/test_picklability.py
index 799d3de8db6c4936bfaad3fa99da05922b00ce26..b5c60ae4c3f7359686a8d18c8afc92642f38fd39 100644
--- a/bob/bio/face/test/test_picklability.py
+++ b/bob/bio/face/test/test_picklability.py
@@ -68,26 +68,6 @@ def test_face_crop():
assert assert_picklable_with_exceptions(cropper)
-def test_face_detect():
- face_cropper = bob.bio.face.preprocessor.FaceCrop(
- cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
- cropped_positions={'leye': LEFT_EYE_POS, 'reye': RIGHT_EYE_POS}
- )
-
- face_detect = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = False
- )
-
- assert_picklable_with_exceptions(face_detect)
-
- face_detect = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = True
- )
- assert assert_picklable_with_exceptions(face_detect)
-
-
def test_INormLBP():
face_cropper = bob.bio.face.preprocessor.FaceCrop(
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
@@ -126,7 +106,7 @@ def test_SQI():
def test_HistogramEqualization():
-
+
face_cropper = bob.bio.face.preprocessor.FaceCrop(
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={'leye': LEFT_EYE_POS, 'reye': RIGHT_EYE_POS}
diff --git a/bob/bio/face/test/test_preprocessors.py b/bob/bio/face/test/test_preprocessors.py
index 4c88442af87e75f0c0936033f3a753efb1082db2..5ce379fd4fba39786447482796553323a04207e9 100644
--- a/bob/bio/face/test/test_preprocessors.py
+++ b/bob/bio/face/test/test_preprocessors.py
@@ -127,7 +127,7 @@ def test_face_crop():
cropper.cropped_positions,
fixed_positions={"reye": annotation["reye"], "leye": annotation["leye"]},
)
- # result must be identical to the original face cropper (same eyes are used)
+ # result must be identical to the original face cropper (same eyes are used)
_compare(fixed_cropper.transform([image]), reference)
# check color cropping
@@ -152,41 +152,6 @@ def test_face_crop():
cropper.channel = "gray"
-def test_face_detect():
- image, annotation = _image(), None
- face_cropper = bob.bio.face.preprocessor.FaceCrop(
- cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
- cropped_positions={'leye': LEFT_EYE_POS, 'reye': RIGHT_EYE_POS}
- )
-
- cropper = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = False
- )
-
- assert isinstance(cropper, bob.bio.face.preprocessor.FaceDetect)
- assert isinstance(cropper, bob.bio.face.preprocessor.Base)
- assert cropper.flandmark is None
-
- # execute face detector
- reference = pkg_resources.resource_filename(
- "bob.bio.face.test", "data/detected.hdf5"
- )
- _compare(cropper.transform([image], [annotation]), reference)
- assert abs(cropper.quality - 39.209601948013685) < 1e-5
-
- # execute face detector with flandmark
- cropper = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = True
- )
- reference = pkg_resources.resource_filename(
- "bob.bio.face.test", "data/flandmark.hdf5"
- )
- _compare(cropper.transform([image], [annotation]), reference)
- assert abs(cropper.quality - 39.209601948013685) < 1e-5
-
-
def test_tan_triggs():
# read input
image, annotation = _image(), _annotation()
@@ -199,7 +164,7 @@ def test_tan_triggs():
preprocessor = bob.bio.face.preprocessor.TanTriggs(face_cropper=face_cropper)
assert isinstance(preprocessor, bob.bio.face.preprocessor.TanTriggs)
- assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
+ assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute face cropper
@@ -221,14 +186,6 @@ def test_tan_triggs():
)
)
- face_detector = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = True
- )
- preprocessor = bob.bio.face.preprocessor.TanTriggs(face_cropper=face_detector)
- assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
- assert preprocessor.cropper.flandmark is not None
-
def test_inorm_lbp():
# read input
@@ -256,20 +213,9 @@ def test_inorm_lbp():
# load the preprocessor without cropping
preprocessor = bob.bio.face.preprocessor.INormLBP(
- face_cropper = None,
+ face_cropper = None,
)
assert preprocessor.cropper is None
- # load the preprocessor landmark detection
- face_detector = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = True
- )
-
- preprocessor = bob.bio.face.preprocessor.INormLBP(
- face_cropper = face_detector,
- dtype = numpy.float64
- )
- assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
def test_heq():
@@ -285,7 +231,7 @@ def test_heq():
)
assert isinstance(preprocessor, bob.bio.face.preprocessor.HistogramEqualization)
- assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
+ assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(
@@ -302,18 +248,6 @@ def test_heq():
assert preprocessor.cropper is None
# load the preprocessor landmark detection
- face_detector = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = True
- )
-
- preprocessor = bob.bio.face.preprocessor.HistogramEqualization(
- face_cropper = face_detector,
- dtype = numpy.float64
- )
-
- assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
-
def test_sqi():
# read input
@@ -328,7 +262,7 @@ def test_sqi():
)
assert isinstance(preprocessor, bob.bio.face.preprocessor.SelfQuotientImage)
- assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
+ assert isinstance(preprocessor, bob.bio.face.preprocessor.Base)
assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceCrop)
# execute preprocessor
_compare(
@@ -343,14 +277,3 @@ def test_sqi():
face_cropper = None
)
assert preprocessor.cropper is None
- # load the preprocessor landmark detection
- face_detector = bob.bio.face.preprocessor.FaceDetect(
- face_cropper = face_cropper,
- use_flandmark = True
- )
-
- preprocessor = bob.bio.face.preprocessor.SelfQuotientImage(
- face_cropper = face_detector
- )
-
- assert isinstance(preprocessor.cropper, bob.bio.face.preprocessor.FaceDetect)
diff --git a/doc/implementation.rst b/doc/implementation.rst
index 05e023392879e2c351f0bb551140877d369f9fd3..2fd65693d3cdbb145dca86ec8e54a0482bac2dd7 100644
--- a/doc/implementation.rst
+++ b/doc/implementation.rst
@@ -67,14 +67,16 @@ However, we want to use the same algorithms on those images as well, so we have
So, image preprocessing becomes a three stage algorithm.
How to combine the two stages, image alignment and photometric enhancement, we have seen before.
-Fortunately, the same technique can be applied for the :py:class:`bob.bio.face.preprocessor.FaceDetect`.
The face detector takes as an input a ``face_cropper``, where we can use the same options to select a face cropper, just that we cannot pass ``None``.
Interestingly, the face detector itself can be used as a ``face_cropper`` inside the photometric enhancement classes.
Hence, to generate a TanTriggs preprocessor that performs face detection, crops the face and performs photometric enhancement, you can create:
.. code-block:: py
- preprocessor = bob.bio.face.preprocessor.TanTriggs(face_cropper = bob.bio.face.preprocessor.FaceDetect(face_cropper = 'face-crop-eyes', use_flandmark = True) )
+ face_cropper = bob.bio.base.load_resource("face-crop-eyes", "preprocessor")
+ annotator = bob.bio.base.load_resource("facedetect-eye-estimate", "annotator")
+ face_cropper.annotator = annotator
+ preprocessor = bob.bio.face.preprocessor.TanTriggs(face_cropper=face_cropper)
Or simply (using the face detector :ref:`Resource <bob.bio.face.preprocessors>`):
diff --git a/doc/implemented.rst b/doc/implemented.rst
index 9b442a5955dfd7147c91050c11e9374e04c1ec6e..481d8f9ed4c473cdfe126383a601a770d75ded63 100644
--- a/doc/implemented.rst
+++ b/doc/implemented.rst
@@ -43,7 +43,6 @@ Image Preprocessors
.. autosummary::
bob.bio.face.preprocessor.Base
bob.bio.face.preprocessor.FaceCrop
- bob.bio.face.preprocessor.FaceDetect
bob.bio.face.preprocessor.TanTriggs
bob.bio.face.preprocessor.HistogramEqualization