def load_aligned(self, image, size=256, align_eyes=False, dtype=None):
""" No need to load aligned information for all uses of this
class, so only call this to load the information for easy
reference to aligned properties for this face """
# Don't reload an already aligned face:
if self.aligned:
logger.trace(
"Skipping alignment calculation for already aligned face")
else:
logger.trace(
"Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)",
size, align_eyes, dtype)
padding = int(size * self.extract_ratio) // 2
self.aligned["size"] = size
self.aligned["padding"] = padding
self.aligned["align_eyes"] = align_eyes
self.aligned["matrix"] = get_align_mat(self, size, align_eyes)
self.aligned["face"] = None
if image is not None and self.aligned["face"] is None:
logger.trace("Getting aligned face")
face = AlignerExtract().transform(image, self.aligned["matrix"],
size, padding)
self.aligned["face"] = face if dtype is None else face.astype(
dtype)
logger.trace(
"Loaded aligned face: %s",
{key: val
for key, val in self.aligned.items() if key != "face"})
def load_reference_face(self,
image,
size=64,
coverage_ratio=0.625,
dtype=None):
""" Return a face in the correct dimensions for reference to the output from a NN
Coverage ratio should be the ratio of the extracted image that was used for
training """
logger.trace(
"Loading reference face: (size: %s, coverage_ratio: %s, dtype: %s)",
size, coverage_ratio, dtype)
self.reference["size"] = size
self.reference["padding"] = self.padding_from_coverage(
size, coverage_ratio)
self.reference["matrix"] = get_align_mat(self,
size,
should_align_eyes=False)
face = np.clip(
AlignerExtract().transform(image, self.reference["matrix"], size,
self.reference["padding"])[:, :, :3] /
255.0, 0.0, 1.0)
self.reference["face"] = face if dtype is None else face.astype(dtype)
logger.trace("Loaded reference face. (face_shape: %s, matrix: %s)",
self.reference_face.shape, self.reference_matrix)
def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None):
""" Align a face in the correct dimensions for feeding into a model.
Parameters
----------
image: numpy.ndarray
The image that contains the face to be aligned
size: int
The size of the face in pixels to be fed into the model
coverage_ratio: float, optional
the ratio of the extracted image that was used for training. Default: `0.625`
dtype: str, optional
Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None``
Notes
-----
This method must be executed to get access to the following `properties`:
- :func:`feed_face`
- :func:`feed_interpolators`
"""
logger.trace("Loading feed face: (size: %s, coverage_ratio: %s, dtype: %s)",
size, coverage_ratio, dtype)
self.feed["size"] = size
self.feed["padding"] = self._padding_from_coverage(size, coverage_ratio)
self.feed["matrix"] = get_align_mat(self)
face = AlignerExtract().transform(image, self.feed["matrix"], size, self.feed["padding"])
face = np.clip(face[:, :, :3] / 255., 0., 1.)
self.feed["face"] = face if dtype is None else face.astype(dtype)
logger.trace("Loaded feed face. (face_shape: %s, matrix: %s)",
self.feed_face.shape, self._feed_matrix)
def extract(self, image, face, size):
if face.landmarks == None:
print("Warning! landmarks not found. Switching to crop!")
return cv2.resize(face.image, (size, size))
alignment = get_align_mat( face )
return self.transform( image, alignment, size, 48 )
def extract(self, image, face, size):
if face.landmarks == None:
print("Warning! landmarks not found. Switching to crop!")
return cv2.resize(face.image, (size, size))
alignment = get_align_mat(face)
return self.transform(image, alignment, size, 48)
def load_aligned(self, image, size=256, padding=48, align_eyes=False):
""" No need to load aligned information for all uses of this
class, so only call this to load the information for easy
reference to aligned properties for this face """
self.aligned["size"] = size
self.aligned["padding"] = padding
self.aligned["align_eyes"] = align_eyes
self.aligned["matrix"] = get_align_mat(self, size, align_eyes)
self.aligned["face"] = AlignerExtract().transform(
image, self.aligned["matrix"], size, padding)
def load_aligned(self, image, size=256, align_eyes=False, dtype=None):
""" Align a face from a given image.
Aligning a face is a relatively expensive task and is not required for all uses of
the :class:`~lib.faces_detect.DetectedFace` object, so call this function explicitly to
load an aligned face.
This method plugs into :mod:`lib.aligner` to perform face alignment based on this face's
``landmarks_xy``. If the face has already been aligned, then this function will return
having performed no action.
Parameters
----------
image: numpy.ndarray
The image that contains the face to be aligned
size: int
The size of the output face in pixels
align_eyes: bool, optional
Optionally perform additional alignment to align eyes. Default: `False`
dtype: str, optional
Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None``
Notes
-----
This method must be executed to get access to the following `properties`:
- :func:`original_roi`
- :func:`aligned_landmarks`
- :func:`aligned_face`
- :func:`adjusted_interpolators`
"""
if self.aligned:
# Don't reload an already aligned face
logger.trace(
"Skipping alignment calculation for already aligned face")
else:
logger.trace(
"Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)",
size, align_eyes, dtype)
padding = int(size * self._extract_ratio) // 2
self.aligned["size"] = size
self.aligned["padding"] = padding
self.aligned["align_eyes"] = align_eyes
self.aligned["matrix"] = get_align_mat(self, size, align_eyes)
self.aligned["face"] = None
if image is not None and self.aligned["face"] is None:
logger.trace("Getting aligned face")
face = AlignerExtract().transform(image, self.aligned["matrix"],
size, padding)
self.aligned["face"] = face if dtype is None else face.astype(
dtype)
logger.trace(
"Loaded aligned face: %s",
{key: val
for key, val in self.aligned.items() if key != "face"})
def patch_image( self, image, face_detected):
size = 64
image_size = image.shape[1], image.shape[0]
mat = numpy.array(get_align_mat(face_detected)).reshape(2,3) * size
new_face = self.get_new_face(image,mat,size)
image_mask = self.get_image_mask( image, new_face, face_detected, mat, image_size )
return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def patch_image( self, image, face_detected ):
size = 64
image_size = image.shape[1], image.shape[0]
mat = numpy.array(get_align_mat(face_detected)).reshape(2,3) * size
new_face = self.get_new_face(image,mat,size)
image_mask = self.get_image_mask( image, new_face, face_detected, mat, image_size )
return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def load_feed_face(self,
image,
size=64,
coverage_ratio=0.625,
dtype=None,
is_aligned_face=False):
""" Align a face in the correct dimensions for feeding into a model.
Parameters
----------
image: numpy.ndarray
The image that contains the face to be aligned
size: int
The size of the face in pixels to be fed into the model
coverage_ratio: float, optional
the ratio of the extracted image that was used for training. Default: `0.625`
dtype: str, optional
Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None``
is_aligned_face: bool, optional
Indicates that the :attr:`image` is an aligned face rather than a frame.
Default: ``False``
Notes
-----
This method must be executed to get access to the following `properties`:
- :func:`feed_face`
- :func:`feed_interpolators`
"""
logger.trace(
"Loading feed face: (size: %s, coverage_ratio: %s, dtype: %s, "
"is_aligned_face: %s)", size, coverage_ratio, dtype,
is_aligned_face)
self.feed["size"] = size
self.feed["padding"] = self._padding_from_coverage(
size, coverage_ratio)
self.feed["matrix"] = get_align_mat(self)
if is_aligned_face:
original_size = image.shape[0]
interp = cv2.INTER_CUBIC if original_size < size else cv2.INTER_AREA
face = cv2.resize(image, (size, size), interpolation=interp)
else:
face = AlignerExtract().transform(image, self.feed["matrix"], size,
self.feed["padding"])
self.feed["face"] = face if dtype is None else face.astype(dtype)
logger.trace("Loaded feed face. (face_shape: %s, matrix: %s)",
self.feed_face.shape, self.feed_matrix)
def load_aligned(self, image, size=256, padding=48, align_eyes=False):
""" No need to load aligned information for all uses of this
class, so only call this to load the information for easy
reference to aligned properties for this face """
logger.trace(
"Loading aligned face: (size: %s, padding: %s, align_eyes: %s)",
size, padding, align_eyes)
self.aligned["size"] = size
self.aligned["padding"] = padding
self.aligned["align_eyes"] = align_eyes
self.aligned["matrix"] = get_align_mat(self, size, align_eyes)
self.aligned["face"] = AlignerExtract().transform(
image, self.aligned["matrix"], size, padding)
logger.trace(
"Loaded aligned face: %s",
{key: val
for key, val in self.aligned.items() if key != "face"})
def patch_image(self, image, face_detected, size):
image_size = image.shape[1], image.shape[0]
mat = numpy.array(
get_align_mat(face_detected, size,
should_align_eyes=False)).reshape(2, 3)
mat = mat * size
new_face = self.get_new_face(image, mat, size)
image_mask = self.get_image_mask(image, new_face,
face_detected.landmarksAsXY(), mat,
image_size)
return self.apply_new_face(image, new_face, image_mask, mat,
image_size, size)
def patch_image( self, image, face_detected, size ):
image_size = image.shape[1], image.shape[0]
mat = numpy.array(get_align_mat(face_detected, size, should_align_eyes=False)).reshape(2,3)
if "GAN" not in self.trainer:
mat = mat * size
else:
padding = int(48/256*size)
mat = mat * (size - 2 * padding)
mat[:,2] += padding
new_face = self.get_new_face(image,mat,size)
image_mask = self.get_image_mask( image, new_face, face_detected.landmarks_as_xy(), mat, image_size )
return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
def load_reference_face(self, image, size=64, coverage_ratio=0.625, dtype=None):
""" Return a face in the correct dimensions for reference to the output from a NN
Coverage ratio should be the ratio of the extracted image that was used for
training """
logger.trace("Loading reference face: (size: %s, coverage_ratio: %s, dtype: %s)",
size, coverage_ratio, dtype)
self.reference["size"] = size
self.reference["padding"] = self.padding_from_coverage(size, coverage_ratio)
self.reference["matrix"] = get_align_mat(self, size, should_align_eyes=False)
face = np.clip(AlignerExtract().transform(image,
self.reference["matrix"],
size,
self.reference["padding"])[:, :, :3] / 255.0,
0.0, 1.0)
self.reference["face"] = face if dtype is None else face.astype(dtype)
logger.trace("Loaded reference face. (face_shape: %s, matrix: %s)",
self.reference_face.shape, self.reference_matrix)
def patch_image(self, image, face_detected, size):
image_size = image.shape[1], image.shape[0]
mat = numpy.array(
get_align_mat(face_detected, size,
should_align_eyes=False)).reshape(2, 3)
if "GAN" not in self.trainer:
mat = mat * size
else:
padding = int(48 / 256 * size)
mat = mat * (size - 2 * padding)
mat[:, 2] += padding
new_face = self.get_new_face(image, mat, size)
image_mask = self.get_image_mask(image, new_face,
face_detected.landmarks_as_xy(), mat,
image_size)
return self.apply_new_face(image, new_face, image_mask, mat,
image_size, size)
def load_aligned(self, image, size=256, align_eyes=False, dtype=None):
""" No need to load aligned information for all uses of this
class, so only call this to load the information for easy
reference to aligned properties for this face """
logger.trace("Loading aligned face: (size: %s, align_eyes: %s, dtype: %s)",
size, align_eyes, dtype)
padding = int(size * self.extract_ratio) // 2
self.aligned["size"] = size
self.aligned["padding"] = padding
self.aligned["align_eyes"] = align_eyes
self.aligned["matrix"] = get_align_mat(self, size, align_eyes)
if image is None:
self.aligned["face"] = None
else:
face = AlignerExtract().transform(
image,
self.aligned["matrix"],
size,
padding)
self.aligned["face"] = face if dtype is None else face.astype(dtype)
logger.trace("Loaded aligned face: %s", {key: val
for key, val in self.aligned.items()
if key != "face"})
def extract(self, image, face, size, align_eyes):
""" Extract a face from an image """
alignment = get_align_mat(face, size, align_eyes)
extracted = self.transform(image, alignment, size, 48)
return extracted, alignment
def extract(self, image, face, size):
if face.landmarks == None:
return cv2.resize(face.image, (size, size))
alignment = get_align_mat(face)
return self.transform(image, alignment, size, 48)
def extract(self, image, face, size):
alignment = get_align_mat(face)
return self.transform(image, alignment, size, 48)
def extract(self, image, face, size, align_eyes):
alignment = get_align_mat(face, size, align_eyes)
extracted = self.transform(image, alignment, size, 48)
return extracted, alignment
