def pre_fill(self, filenames, side):
""" When warp to landmarks is enabled, the cache must be pre-filled, as each side needs
access to the other side's alignments.
Parameters
----------
filenames: list
The list of full paths to the images to load the metadata from
side: str
`"a"` or `"b"`. The side of the model being cached. Used for info output
"""
with self._lock:
for filename, meta in tqdm(
read_image_meta_batch(filenames),
desc="WTL: Caching Landmarks ({})".format(side.upper()),
total=len(filenames),
leave=False):
if "itxt" not in meta or "alignments" not in meta["itxt"]:
raise FaceswapError(
f"Invalid face image found. Aborting: '{filename}'")
size = meta["width"]
meta = meta["itxt"]
# Version Check
self._validate_version(meta, filename)
detected_face = self._add_aligned_face(filename,
meta["alignments"],
size)
self._cache[os.path.basename(
filename)]["detected_face"] = detected_face
self._partial_load = True
def sort_distance(self):
""" Sort by comparison of face landmark points to mean face by average distance of core
landmarks. """
logger.info("Sorting by average distance of landmarks...")
filenames = []
distances = []
filelist = [os.path.join(self._loader.location, fname)
for fname in os.listdir(self._loader.location)
if os.path.splitext(fname)[-1] == ".png"]
for filename, metadata in tqdm(read_image_meta_batch(filelist),
total=len(filelist),
desc="Calculating Distances"):
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["itxt"]["alignments"]
aligned_face = AlignedFace(np.array(alignments["landmarks_xy"], dtype="float32"))
filenames.append(filename)
distances.append(aligned_face.average_distance)
logger.info("Sorting...")
matched_list = list(zip(filenames, distances))
img_list = sorted(matched_list, key=operator.itemgetter(1))
return img_list
def _get_face_metadata(self):
""" Check for the existence of an aligned directory for identifying which faces in the
target frames should be swapped. If it exists, scan the folder for face's metadata
Returns
-------
dict
Dictionary of source frame names with a list of associated face indices to be skipped
"""
retval = dict()
input_aligned_dir = self._args.input_aligned_dir
if input_aligned_dir is None:
logger.verbose(
"Aligned directory not specified. All faces listed in the "
"alignments file will be converted")
return retval
if not os.path.isdir(input_aligned_dir):
logger.warning(
"Aligned directory not found. All faces listed in the "
"alignments file will be converted")
return retval
log_once = False
filelist = get_image_paths(input_aligned_dir)
for fullpath, metadata in tqdm(read_image_meta_batch(filelist),
total=len(filelist),
desc="Reading Face Data",
leave=False):
if "itxt" not in metadata or "source" not in metadata["itxt"]:
# UPDATE LEGACY FACES FROM ALIGNMENTS FILE
if not log_once:
logger.warning(
"Legacy faces discovered in '%s'. These faces will be updated",
input_aligned_dir)
log_once = True
data = update_legacy_png_header(fullpath, self._alignments)
if not data:
raise FaceswapError(
"Some of the faces being passed in from '{}' could not be matched to the "
"alignments file '{}'\nPlease double check your sources and try "
"again.".format(input_aligned_dir,
self._alignments.file))
meta = data["source"]
else:
meta = metadata["itxt"]["source"]
retval.setdefault(meta["source_filename"],
list()).append(meta["face_index"])
if not retval:
raise FaceswapError(
"Aligned directory is empty, no faces will be converted!")
if len(retval) <= len(self._input_images) / 3:
logger.warning(
"Aligned directory contains far fewer images than the input "
"directory, are you sure this is the right folder?")
return retval
def process_folder(self):
""" Iterate through the faces folder pulling out various information for each face.
Yields
------
dict
A dictionary for each face found containing the keys returned from
:class:`lib.image.read_image_meta_batch`
"""
logger.info("Loading file list from %s", self.folder)
if self._alignments is not None: # Legacy updating
filelist = [
os.path.join(self.folder, face)
for face in os.listdir(self.folder)
if self.valid_extension(face)
]
else:
filelist = [
os.path.join(self.folder, face)
for face in os.listdir(self.folder)
if os.path.splitext(face)[-1] == ".png"
]
log_once = False
for fullpath, metadata in tqdm(read_image_meta_batch(filelist),
total=len(filelist),
desc="Reading Face Data"):
if "itxt" not in metadata or "source" not in metadata["itxt"]:
if self._alignments is None: # Can't update legacy
raise FaceswapError(
f"The folder '{self.folder}' contains images that do not include Faceswap "
"metadata.\nAll images in the provided folder should contain faces "
"generated from Faceswap's extraction process.\nPlease double check the "
"source and try again.")
if not log_once:
logger.warning(
"Legacy faces discovered. These faces will be updated")
log_once = True
data = update_legacy_png_header(fullpath, self._alignments)
if not data:
raise FaceswapError(
"Some of the faces being passed in from '{}' could not be matched to the "
"alignments file '{}'\nPlease double check your sources and try "
"again.".format(self.folder, self._alignments.file))
retval = data["source"]
else:
retval = metadata["itxt"]["source"]
retval["current_filename"] = os.path.basename(fullpath)
yield retval