def sort_face_yaw(self):
""" Sort by estimated face yaw angle """
logger.info("Sorting by estimated face yaw angle..")
self._loader = FacesLoader(
self._args.input_dir) # TODO This should be set in init
filenames = []
yaws = []
for filename, image, metadata in tqdm(self._loader.load(),
desc="Classifying Faces...",
total=self._loader.count,
leave=False):
if not metadata:
msg = (
"The images to be sorted do not contain alignment data. Images must have "
"been generated by Faceswap's Extract process.\nIf you are sorting an "
"older faceset, then you should re-extract the faces from your source "
"alignments file to generate this data.")
raise FaceswapError(msg)
alignments = metadata["alignments"]
aligned_face = AlignedFace(np.array(alignments["landmarks_xy"],
dtype="float32"),
image=image,
centering="legacy",
is_aligned=True)
filenames.append(filename)
yaws.append(aligned_face.pose.yaw)
logger.info("Sorting...")
matched_list = list(zip(filenames, yaws))
img_list = sorted(matched_list,
key=operator.itemgetter(1),
reverse=True)
return img_list
def sort_face(self):
""" Sort by identity similarity """
logger.info("Sorting by identity similarity...")
self._loader = FacesLoader(
self._args.input_dir) # TODO This should be set in init
filenames = []
preds = []
for filename, image, metadata in tqdm(self._loader.load(),
desc="Classifying Faces...",
total=self._loader.count,
leave=False):
if not metadata:
msg = (
"The images to be sorted do not contain alignment data. Images must have "
"been generated by Faceswap's Extract process.\nIf you are sorting an "
"older faceset, then you should re-extract the faces from your source "
"alignments file to generate this data.")
raise FaceswapError(msg)
alignments = metadata["alignments"]
face = AlignedFace(np.array(alignments["landmarks_xy"],
dtype="float32"),
image=image,
centering="legacy",
size=self._vgg_face.input_size,
is_aligned=True).face
filenames.append(filename)
preds.append(self._vgg_face.predict(face))
logger.info("Sorting by ward linkage...")
indices = self._vgg_face.sorted_similarity(np.array(preds),
method="ward")
img_list = np.array(filenames)[indices]
return img_list
def sort_face(self):
""" Sort by identity similarity """
logger.info("Sorting by identity similarity...")
self._loader = FacesLoader(self._args.input_dir) # TODO This should be set in init
ratio = _EXTRACT_RATIOS["legacy"] / _EXTRACT_RATIOS["head"]
filenames = []
preds = []
no_hash = 0
for filename, image, hsh in tqdm(self._loader.load(),
desc="Classifying Faces...",
total=self._loader.count):
if self._alignments is not None and self._alignments.version != 1.0:
face = self._alignments.hashes_to_alignment.get(hsh)
if face:
image = AlignedFace(face["landmarks_xy"],
image=image,
centering="legacy",
size=self._vgg_face.input_size,
is_aligned=True).face
elif image.shape[0] != image.shape[1]:
logger.warning("Skipping image '%s' as it is not square (probably not a "
"face)", filename)
continue
else: # Center crop the image and add count to warning count
center = image.shape[0] // 2
crop = slice(center - int(center * ratio), center + int(center * ratio))
image = image[crop, crop, :]
no_hash += 1
filenames.append(filename)
preds.append(self._vgg_face.predict(image))
logger.info("Sorting by ward linkage...")
indices = self._vgg_face.sorted_similarity(np.array(preds), method="ward")
img_list = np.array(filenames)[indices]
if no_hash:
logger.warning("%s image(s) were not found in the alignments file. This will likely "
"result in sub-par sorting results, so you should check the output "
"carefully", no_hash)
return img_list
def __init__(self, arguments):
self._args = arguments
self.changes = None
self.serializer = None
self._vgg_face = None
self._loader = FacesLoader(self._args.input_dir)