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New script:

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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
parent e5bad483ca
commit 6d3607a13d
30 changed files with 279 additions and 1520 deletions
Download patch file Download diff file Expand all files Collapse all files

77
merger/MergerConfig.py
View file

@ -83,34 +83,14 @@ mode_str_dict = {}
for key in mode_dict.keys():
mode_str_dict[ mode_dict[key] ] = key
"""
whole_face_mask_mode_dict = {1:'learned',
2:'dst',
3:'FAN-prd',
4:'FAN-dst',
5:'FAN-prd*FAN-dst',
6:'learned*FAN-prd*FAN-dst'
}
"""
whole_face_mask_mode_dict = {1:'learned',
2:'dst',
8:'XSeg-prd',
9:'XSeg-dst',
10:'XSeg-prd*XSeg-dst',
11:'learned*XSeg-prd*XSeg-dst'
}
mask_mode_dict = {1:'learned',
2:'dst',
3:'XSeg-prd',
4:'XSeg-dst',
5:'XSeg-prd*XSeg-dst',
6:'learned*XSeg-prd*XSeg-dst'
}
full_face_mask_mode_dict = {1:'learned',
2:'dst',
3:'FAN-prd',
4:'FAN-dst',
5:'FAN-prd*FAN-dst',
6:'learned*FAN-prd*FAN-dst'}
half_face_mask_mode_dict = {1:'learned',
2:'dst',
4:'FAN-dst',
7:'learned*FAN-dst'}
ctm_dict = { 0: "None", 1:"rct", 2:"lct", 3:"mkl", 4:"mkl-m", 5:"idt", 6:"idt-m", 7:"sot-m", 8:"mix-m" }
ctm_str_dict = {None:0, "rct":1, "lct":2, "mkl":3, "mkl-m":4, "idt":5, "idt-m":6, "sot-m":7, "mix-m":8 }
@ -176,12 +156,7 @@ class MergerConfigMasked(MergerConfig):
self.hist_match_threshold = np.clip ( self.hist_match_threshold+diff , 0, 255)
def toggle_mask_mode(self):
if self.face_type == FaceType.WHOLE_FACE:
a = list( whole_face_mask_mode_dict.keys() )
elif self.face_type == FaceType.FULL:
a = list( full_face_mask_mode_dict.keys() )
else:
a = list( half_face_mask_mode_dict.keys() )
a = list( mask_mode_dict.keys() )
self.mask_mode = a[ (a.index(self.mask_mode)+1) % len(a) ]
def add_erode_mask_modifier(self, diff):
@ -227,26 +202,11 @@ class MergerConfigMasked(MergerConfig):
if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold", 255, add_info="0..255"), 0, 255)
if self.face_type == FaceType.WHOLE_FACE:
s = """Choose mask mode: \n"""
for key in whole_face_mask_mode_dict.keys():
s += f"""({key}) {whole_face_mask_mode_dict[key]}\n"""
io.log_info(s)
self.mask_mode = io.input_int ("", 1, valid_list=whole_face_mask_mode_dict.keys() )
elif self.face_type == FaceType.FULL:
s = """Choose mask mode: \n"""
for key in full_face_mask_mode_dict.keys():
s += f"""({key}) {full_face_mask_mode_dict[key]}\n"""
io.log_info(s)
self.mask_mode = io.input_int ("", 1, valid_list=full_face_mask_mode_dict.keys(), help_message="If you learned the mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images. 'FAN-prd' - using super smooth mask by pretrained FAN-model from predicted face. 'FAN-dst' - using super smooth mask by pretrained FAN-model from dst face. 'FAN-prd*FAN-dst' or 'learned*FAN-prd*FAN-dst' - using multiplied masks.")
else:
s = """Choose mask mode: \n"""
for key in half_face_mask_mode_dict.keys():
s += f"""({key}) {half_face_mask_mode_dict[key]}\n"""
io.log_info(s)
self.mask_mode = io.input_int ("", 1, valid_list=half_face_mask_mode_dict.keys(), help_message="If you learned the mask, then option 1 should be choosed. 'dst' mask is raw shaky mask from dst aligned images.")
s = """Choose mask mode: \n"""
for key in mask_mode_dict.keys():
s += f"""({key}) {mask_mode_dict[key]}\n"""
io.log_info(s)
self.mask_mode = io.input_int ("", 1, valid_list=mask_mode_dict.keys() )
if 'raw' not in self.mode:
self.erode_mask_modifier = np.clip ( io.input_int ("Choose erode mask modifier", 0, add_info="-400..400"), -400, 400)
@ -302,14 +262,9 @@ class MergerConfigMasked(MergerConfig):
if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
r += f"""hist_match_threshold: {self.hist_match_threshold}\n"""
if self.face_type == FaceType.WHOLE_FACE:
r += f"""mask_mode: { whole_face_mask_mode_dict[self.mask_mode] }\n"""
elif self.face_type == FaceType.FULL:
r += f"""mask_mode: { full_face_mask_mode_dict[self.mask_mode] }\n"""
else:
r += f"""mask_mode: { half_face_mask_mode_dict[self.mask_mode] }\n"""
r += f"""mask_mode: { mask_mode_dict[self.mask_mode] }\n"""
if 'raw' not in self.mode:
r += (f"""erode_mask_modifier: {self.erode_mask_modifier}\n"""
f"""blur_mask_modifier: {self.blur_mask_modifier}\n"""