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5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat
Copies faces containing XSeg polygons to aligned_xseg\ dir.
Useful only if you want to collect labeled faces and reuse them in other fakes.

Now you can use trained XSeg mask in the SAEHD training process.
It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model.
use
5.XSeg.optional) trained mask for data_dst/data_src - apply.bat
5.XSeg.optional) trained mask for data_dst/data_src - remove.bat

Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions.

XSeg trainer : now you can choose type of face
XSeg trainer : now you can restart training in “override settings”
Merger: XSeg-* modes now can be used with all types of faces.

Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed,
because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.
This commit is contained in:
Colombo 2020-03-30 14:00:40 +04:00
commit 6d3607a13d
30 changed files with 279 additions and 1520 deletions
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33
samplelib/Sample.py
View file

@ -7,7 +7,7 @@ import numpy as np
from core.cv2ex import *
from DFLIMG import *
from facelib import LandmarksProcessor
from core.imagelib import IEPolys, SegIEPolys
from core.imagelib import SegIEPolys
class SampleType(IntEnum):
IMAGE = 0 #raw image
@ -26,8 +26,8 @@ class Sample(object):
'face_type',
'shape',
'landmarks',
'ie_polys',
'seg_ie_polys',
'xseg_mask',
'eyebrows_expand_mod',
'source_filename',
'person_name',
@ -40,8 +40,8 @@ class Sample(object):
face_type=None,
shape=None,
landmarks=None,
ie_polys=None,
seg_ie_polys=None,
xseg_mask=None,
eyebrows_expand_mod=None,
source_filename=None,
person_name=None,
@ -53,8 +53,13 @@ class Sample(object):
self.face_type = face_type
self.shape = shape
self.landmarks = np.array(landmarks) if landmarks is not None else None
self.ie_polys = IEPolys.load(ie_polys)
self.seg_ie_polys = SegIEPolys.load(seg_ie_polys)
if isinstance(seg_ie_polys, SegIEPolys):
self.seg_ie_polys = seg_ie_polys
else:
self.seg_ie_polys = SegIEPolys.load(seg_ie_polys)
self.xseg_mask = xseg_mask
self.eyebrows_expand_mod = eyebrows_expand_mod if eyebrows_expand_mod is not None else 1.0
self.source_filename = source_filename
self.person_name = person_name
@ -90,25 +95,9 @@ class Sample(object):
'face_type': self.face_type,
'shape': self.shape,
'landmarks': self.landmarks.tolist(),
'ie_polys': self.ie_polys.dump(),
'seg_ie_polys': self.seg_ie_polys.dump(),
'xseg_mask' : self.xseg_mask,
'eyebrows_expand_mod': self.eyebrows_expand_mod,
'source_filename': self.source_filename,
'person_name': self.person_name
}
"""
def copy_and_set(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, fanseg_mask=None, person_name=None):
return Sample(
sample_type=sample_type if sample_type is not None else self.sample_type,
filename=filename if filename is not None else self.filename,
face_type=face_type if face_type is not None else self.face_type,
shape=shape if shape is not None else self.shape,
landmarks=landmarks if landmarks is not None else self.landmarks.copy(),
ie_polys=ie_polys if ie_polys is not None else self.ie_polys,
pitch_yaw_roll=pitch_yaw_roll if pitch_yaw_roll is not None else self.pitch_yaw_roll,
eyebrows_expand_mod=eyebrows_expand_mod if eyebrows_expand_mod is not None else self.eyebrows_expand_mod,
source_filename=source_filename if source_filename is not None else self.source_filename,
person_name=person_name if person_name is not None else self.person_name)
"""

28
samplelib/SampleLoader.py
View file

@ -74,8 +74,8 @@ class SampleLoader:
( face_type,
shape,
landmarks,
ie_polys,
seg_ie_polys,
xseg_mask,
eyebrows_expand_mod,
source_filename,
) in result:
@ -84,35 +84,13 @@ class SampleLoader:
face_type=FaceType.fromString (face_type),
shape=shape,
landmarks=landmarks,
ie_polys=ie_polys,
seg_ie_polys=seg_ie_polys,
xseg_mask=xseg_mask,
eyebrows_expand_mod=eyebrows_expand_mod,
source_filename=source_filename,
))
return sample_list
"""
@staticmethod
def load_face_samples ( image_paths):
sample_list = []
for filename in io.progress_bar_generator (image_paths, desc="Loading"):
dflimg = DFLIMG.load (Path(filename))
if dflimg is None:
io.log_err (f"{filename} is not a dfl image file.")
else:
sample_list.append( Sample(filename=filename,
sample_type=SampleType.FACE,
face_type=FaceType.fromString ( dflimg.get_face_type() ),
shape=dflimg.get_shape(),
landmarks=dflimg.get_landmarks(),
ie_polys=dflimg.get_ie_polys(),
eyebrows_expand_mod=dflimg.get_eyebrows_expand_mod(),
source_filename=dflimg.get_source_filename(),
))
return sample_list
"""
@staticmethod
def upgradeToFaceTemporalSortedSamples( samples ):
new_s = [ (s, s.source_filename) for s in samples]
@ -178,8 +156,8 @@ class FaceSamplesLoaderSubprocessor(Subprocessor):
data = (dflimg.get_face_type(),
dflimg.get_shape(),
dflimg.get_landmarks(),
dflimg.get_ie_polys(),
dflimg.get_seg_ie_polys(),
dflimg.get_xseg_mask(),
dflimg.get_eyebrows_expand_mod(),
dflimg.get_source_filename() )

19
samplelib/SampleProcessor.py
View file

@ -56,8 +56,14 @@ class SampleProcessor(object):
ct_sample_bgr = None
h,w,c = sample_bgr.shape
def get_full_face_mask():
full_face_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
def get_full_face_mask():
if sample.xseg_mask is not None:
full_face_mask = sample.xseg_mask
if full_face_mask.shape[0] != h or full_face_mask.shape[1] != w:
full_face_mask = cv2.resize(full_face_mask, (w,h), interpolation=cv2.INTER_CUBIC)
full_face_mask = imagelib.normalize_channels(full_face_mask, 1)
else:
full_face_mask = LandmarksProcessor.get_image_hull_mask (sample_bgr.shape, sample_landmarks, eyebrows_expand_mod=sample.eyebrows_expand_mod )
return np.clip(full_face_mask, 0, 1)
def get_eyes_mask():
@ -125,19 +131,18 @@ class SampleProcessor(object):
raise Exception ('sample %s type %s does not match model requirement %s. Consider extract necessary type of faces.' % (sample.filename, sample.face_type, face_type) )
if sample_type == SPST.FACE_MASK:
if sample_type == SPST.FACE_MASK:
if face_mask_type == SPFMT.FULL_FACE:
img = get_full_face_mask()
elif face_mask_type == SPFMT.EYES:
img = get_eyes_mask()
elif face_mask_type == SPFMT.FULL_FACE_EYES:
img = get_full_face_mask() + get_eyes_mask()
img = get_full_face_mask()
img += get_eyes_mask()*img
else:
img = np.zeros ( sample_bgr.shape[0:2]+(1,), dtype=np.float32)
if sample.ie_polys is not None:
sample.ie_polys.overlay_mask(img)
if sample_face_type == FaceType.MARK_ONLY:
mat = LandmarksProcessor.get_transform_mat (sample_landmarks, warp_resolution, face_type)

1
samplelib/__init__.py
View file

@ -10,4 +10,5 @@ from .SampleGeneratorImage import SampleGeneratorImage
from .SampleGeneratorImageTemporal import SampleGeneratorImageTemporal
from .SampleGeneratorFaceCelebAMaskHQ import SampleGeneratorFaceCelebAMaskHQ
from .SampleGeneratorFaceXSeg import SampleGeneratorFaceXSeg
from .SampleGeneratorFaceAvatarOperator import SampleGeneratorFaceAvatarOperator
from .PackedFaceset import PackedFaceset