mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-03-12 20:42:45 -07:00
377 lines
16 KiB
Python
377 lines
16 KiB
Python
import numpy as np
|
|
import copy
|
|
|
|
from facelib import FaceType
|
|
from interact import interact as io
|
|
|
|
|
|
class ConverterConfig(object):
|
|
TYPE_NONE = 0
|
|
TYPE_MASKED = 1
|
|
TYPE_FACE_AVATAR = 2
|
|
####
|
|
|
|
TYPE_IMAGE = 3
|
|
TYPE_IMAGE_WITH_LANDMARKS = 4
|
|
|
|
def __init__(self, type=0,
|
|
|
|
super_resolution_mode=0,
|
|
sharpen_mode=0,
|
|
blursharpen_amount=0,
|
|
**kwargs
|
|
):
|
|
self.type = type
|
|
|
|
self.superres_func = None
|
|
self.blursharpen_func = None
|
|
self.fanseg_input_size = None
|
|
self.fanseg_extract_func = None
|
|
|
|
self.fanchq_input_size = None
|
|
self.fanchq_extract_func = None
|
|
self.ebs_ct_func = None
|
|
|
|
self.super_res_dict = {0:"None", 1:'RankSRGAN'}
|
|
self.sharpen_dict = {0:"None", 1:'box', 2:'gaussian'}
|
|
|
|
#default changeable params
|
|
self.super_resolution_mode = super_resolution_mode
|
|
self.sharpen_mode = sharpen_mode
|
|
self.blursharpen_amount = blursharpen_amount
|
|
|
|
def copy(self):
|
|
return copy.copy(self)
|
|
|
|
#overridable
|
|
def ask_settings(self):
|
|
s = """Choose sharpen mode: \n"""
|
|
for key in self.sharpen_dict.keys():
|
|
s += f"""({key}) {self.sharpen_dict[key]}\n"""
|
|
s += f"""?:help Default: {list(self.sharpen_dict.keys())[0]} : """
|
|
self.sharpen_mode = io.input_int (s, 0, valid_list=self.sharpen_dict.keys(), help_message="Enhance details by applying sharpen filter.")
|
|
|
|
if self.sharpen_mode != 0:
|
|
self.blursharpen_amount = np.clip ( io.input_int ("Choose blur/sharpen amount [-100..100] (skip:0) : ", 0), -100, 100 )
|
|
|
|
s = """Choose super resolution mode: \n"""
|
|
for key in self.super_res_dict.keys():
|
|
s += f"""({key}) {self.super_res_dict[key]}\n"""
|
|
s += f"""?:help Default: {list(self.super_res_dict.keys())[0]} : """
|
|
self.super_resolution_mode = io.input_int (s, 0, valid_list=self.super_res_dict.keys(), help_message="Enhance details by applying superresolution network.")
|
|
|
|
def toggle_sharpen_mode(self):
|
|
a = list( self.sharpen_dict.keys() )
|
|
self.sharpen_mode = a[ (a.index(self.sharpen_mode)+1) % len(a) ]
|
|
|
|
def add_blursharpen_amount(self, diff):
|
|
self.blursharpen_amount = np.clip ( self.blursharpen_amount+diff, -100, 100)
|
|
|
|
def toggle_super_resolution_mode(self):
|
|
a = list( self.super_res_dict.keys() )
|
|
self.super_resolution_mode = a[ (a.index(self.super_resolution_mode)+1) % len(a) ]
|
|
|
|
#overridable
|
|
def get_config(self):
|
|
d = self.__dict__.copy()
|
|
d.pop('type')
|
|
return d
|
|
return {'sharpen_mode':self.sharpen_mode,
|
|
'blursharpen_amount':self.blursharpen_amount,
|
|
'super_resolution_mode':self.super_resolution_mode
|
|
}
|
|
|
|
#overridable
|
|
def __eq__(self, other):
|
|
#check equality of changeable params
|
|
|
|
if isinstance(other, ConverterConfig):
|
|
return self.sharpen_mode == other.sharpen_mode and \
|
|
self.blursharpen_amount == other.blursharpen_amount and \
|
|
self.super_resolution_mode == other.super_resolution_mode
|
|
|
|
return False
|
|
|
|
#overridable
|
|
def to_string(self, filename):
|
|
r = ""
|
|
r += f"sharpen_mode : {self.sharpen_dict[self.sharpen_mode]}\n"
|
|
r += f"blursharpen_amount : {self.blursharpen_amount}\n"
|
|
r += f"super_resolution_mode : {self.super_res_dict[self.super_resolution_mode]}\n"
|
|
return r
|
|
|
|
mode_dict = {0:'original',
|
|
1:'overlay',
|
|
2:'hist-match',
|
|
3:'seamless',
|
|
4:'seamless-hist-match',
|
|
5:'raw-rgb',
|
|
6:'raw-rgb-mask',
|
|
7:'raw-mask-only',
|
|
8:'raw-predicted-only'}
|
|
|
|
mode_str_dict = {}
|
|
|
|
for key in mode_dict.keys():
|
|
mode_str_dict[ mode_dict[key] ] = key
|
|
|
|
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 }
|
|
|
|
class ConverterConfigMasked(ConverterConfig):
|
|
|
|
def __init__(self, face_type=FaceType.FULL,
|
|
default_mode = 'overlay',
|
|
clip_hborder_mask_per = 0,
|
|
|
|
mode='overlay',
|
|
masked_hist_match=True,
|
|
hist_match_threshold = 238,
|
|
mask_mode = 1,
|
|
erode_mask_modifier = 50,
|
|
blur_mask_modifier = 50,
|
|
motion_blur_power = 0,
|
|
output_face_scale = 0,
|
|
color_transfer_mode = ctm_str_dict['rct'],
|
|
image_denoise_power = 0,
|
|
bicubic_degrade_power = 0,
|
|
color_degrade_power = 0,
|
|
export_mask_alpha = False,
|
|
**kwargs
|
|
):
|
|
|
|
super().__init__(type=ConverterConfig.TYPE_MASKED, **kwargs)
|
|
|
|
self.face_type = face_type
|
|
if self.face_type not in [FaceType.HALF, FaceType.MID_FULL, FaceType.FULL ]:
|
|
raise ValueError("ConverterConfigMasked does not support this type of face.")
|
|
|
|
self.default_mode = default_mode
|
|
self.clip_hborder_mask_per = clip_hborder_mask_per
|
|
|
|
#default changeable params
|
|
self.mode = mode
|
|
self.masked_hist_match = masked_hist_match
|
|
self.hist_match_threshold = hist_match_threshold
|
|
self.mask_mode = mask_mode
|
|
self.erode_mask_modifier = erode_mask_modifier
|
|
self.blur_mask_modifier = blur_mask_modifier
|
|
self.motion_blur_power = motion_blur_power
|
|
self.output_face_scale = output_face_scale
|
|
self.color_transfer_mode = color_transfer_mode
|
|
self.image_denoise_power = image_denoise_power
|
|
self.bicubic_degrade_power = bicubic_degrade_power
|
|
self.color_degrade_power = color_degrade_power
|
|
self.export_mask_alpha = export_mask_alpha
|
|
|
|
def copy(self):
|
|
return copy.copy(self)
|
|
|
|
def set_mode (self, mode):
|
|
self.mode = mode_dict.get (mode, self.default_mode)
|
|
|
|
def toggle_masked_hist_match(self):
|
|
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
|
|
self.masked_hist_match = not self.masked_hist_match
|
|
|
|
def add_hist_match_threshold(self, diff):
|
|
if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
|
|
self.hist_match_threshold = np.clip ( self.hist_match_threshold+diff , 0, 255)
|
|
|
|
def toggle_mask_mode(self):
|
|
if self.face_type == FaceType.FULL:
|
|
a = list( full_face_mask_mode_dict.keys() )
|
|
else:
|
|
a = list( half_face_mask_mode_dict.keys() )
|
|
self.mask_mode = a[ (a.index(self.mask_mode)+1) % len(a) ]
|
|
|
|
def add_erode_mask_modifier(self, diff):
|
|
self.erode_mask_modifier = np.clip ( self.erode_mask_modifier+diff , -400, 400)
|
|
|
|
def add_blur_mask_modifier(self, diff):
|
|
self.blur_mask_modifier = np.clip ( self.blur_mask_modifier+diff , -400, 400)
|
|
|
|
def add_motion_blur_power(self, diff):
|
|
self.motion_blur_power = np.clip ( self.motion_blur_power+diff, 0, 100)
|
|
|
|
def add_output_face_scale(self, diff):
|
|
self.output_face_scale = np.clip ( self.output_face_scale+diff , -50, 50)
|
|
|
|
def toggle_color_transfer_mode(self):
|
|
self.color_transfer_mode = (self.color_transfer_mode+1) % ( max(ctm_dict.keys())+1 )
|
|
|
|
def add_color_degrade_power(self, diff):
|
|
self.color_degrade_power = np.clip ( self.color_degrade_power+diff , 0, 100)
|
|
|
|
def add_image_denoise_power(self, diff):
|
|
self.image_denoise_power = np.clip ( self.image_denoise_power+diff, 0, 500)
|
|
|
|
def add_bicubic_degrade_power(self, diff):
|
|
self.bicubic_degrade_power = np.clip ( self.bicubic_degrade_power+diff, 0, 100)
|
|
|
|
def toggle_export_mask_alpha(self):
|
|
self.export_mask_alpha = not self.export_mask_alpha
|
|
|
|
def ask_settings(self):
|
|
s = """Choose mode: \n"""
|
|
for key in mode_dict.keys():
|
|
s += f"""({key}) {mode_dict[key]}\n"""
|
|
s += f"""Default: { mode_str_dict.get(self.default_mode, 1) } : """
|
|
|
|
mode = io.input_int (s, mode_str_dict.get(self.default_mode, 1) )
|
|
|
|
self.mode = mode_dict.get (mode, self.default_mode )
|
|
|
|
if 'raw' not in self.mode:
|
|
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
|
|
self.masked_hist_match = io.input_bool("Masked hist match? (y/n skip:y) : ", True)
|
|
|
|
if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
|
|
self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255)
|
|
|
|
if 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"""
|
|
s += f"""?:help Default: 1 : """
|
|
|
|
self.mask_mode = io.input_int (s, 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"""
|
|
s += f"""?:help , Default: 1 : """
|
|
self.mask_mode = io.input_int (s, 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.")
|
|
|
|
if 'raw' not in self.mode:
|
|
self.erode_mask_modifier = np.clip ( io.input_int ("Choose erode mask modifier [-400..400] (skip:%d) : " % 0, 0), -400, 400)
|
|
self.blur_mask_modifier = np.clip ( io.input_int ("Choose blur mask modifier [-400..400] (skip:%d) : " % 0, 0), -400, 400)
|
|
self.motion_blur_power = np.clip ( io.input_int ("Choose motion blur power [0..100] (skip:%d) : " % (0), 0), 0, 100)
|
|
|
|
self.output_face_scale = np.clip (io.input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0), -50, 50)
|
|
|
|
if 'raw' not in self.mode:
|
|
self.color_transfer_mode = io.input_str ( f"Apply color transfer to predicted face? Choose mode ( {' / '.join ([str(x) for x in list(ctm_str_dict.keys())])} skip:None ) : ", None, ctm_str_dict.keys() )
|
|
self.color_transfer_mode = ctm_str_dict[self.color_transfer_mode]
|
|
|
|
super().ask_settings()
|
|
|
|
if 'raw' not in self.mode:
|
|
self.image_denoise_power = np.clip ( io.input_int ("Choose image degrade by denoise power [0..500] (skip:%d) : " % (0), 0), 0, 500)
|
|
self.bicubic_degrade_power = np.clip ( io.input_int ("Choose image degrade by bicubic rescale power [0..100] (skip:%d) : " % (0), 0), 0, 100)
|
|
self.color_degrade_power = np.clip ( io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
|
|
self.export_mask_alpha = io.input_bool("Export png with alpha channel of the mask? (y/n skip:n) : ", False)
|
|
|
|
io.log_info ("")
|
|
|
|
def __eq__(self, other):
|
|
#check equality of changeable params
|
|
|
|
if isinstance(other, ConverterConfigMasked):
|
|
return super().__eq__(other) and \
|
|
self.mode == other.mode and \
|
|
self.masked_hist_match == other.masked_hist_match and \
|
|
self.hist_match_threshold == other.hist_match_threshold and \
|
|
self.mask_mode == other.mask_mode and \
|
|
self.erode_mask_modifier == other.erode_mask_modifier and \
|
|
self.blur_mask_modifier == other.blur_mask_modifier and \
|
|
self.motion_blur_power == other.motion_blur_power and \
|
|
self.output_face_scale == other.output_face_scale and \
|
|
self.color_transfer_mode == other.color_transfer_mode and \
|
|
self.image_denoise_power == other.image_denoise_power and \
|
|
self.bicubic_degrade_power == other.bicubic_degrade_power and \
|
|
self.color_degrade_power == other.color_degrade_power and \
|
|
self.export_mask_alpha == other.export_mask_alpha
|
|
|
|
return False
|
|
|
|
def to_string(self, filename):
|
|
r = (
|
|
f"""ConverterConfig {filename}:\n"""
|
|
f"""Mode: {self.mode}\n"""
|
|
)
|
|
|
|
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
|
|
r += f"""masked_hist_match: {self.masked_hist_match}\n"""
|
|
|
|
if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
|
|
r += f"""hist_match_threshold: {self.hist_match_threshold}\n"""
|
|
|
|
if 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"""
|
|
|
|
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"""
|
|
f"""motion_blur_power: {self.motion_blur_power}\n""")
|
|
|
|
r += f"""output_face_scale: {self.output_face_scale}\n"""
|
|
|
|
if 'raw' not in self.mode:
|
|
r += f"""color_transfer_mode: { ctm_dict[self.color_transfer_mode]}\n"""
|
|
|
|
r += super().to_string(filename)
|
|
|
|
if 'raw' not in self.mode:
|
|
r += (f"""image_denoise_power: {self.image_denoise_power}\n"""
|
|
f"""bicubic_degrade_power: {self.bicubic_degrade_power}\n"""
|
|
f"""color_degrade_power: {self.color_degrade_power}\n"""
|
|
f"""export_mask_alpha: {self.export_mask_alpha}\n""")
|
|
|
|
r += "================"
|
|
|
|
return r
|
|
|
|
|
|
class ConverterConfigFaceAvatar(ConverterConfig):
|
|
|
|
def __init__(self, temporal_face_count=0,
|
|
add_source_image=False):
|
|
super().__init__(type=ConverterConfig.TYPE_FACE_AVATAR)
|
|
self.temporal_face_count = temporal_face_count
|
|
|
|
#changeable params
|
|
self.add_source_image = add_source_image
|
|
|
|
def copy(self):
|
|
return copy.copy(self)
|
|
|
|
#override
|
|
def ask_settings(self):
|
|
self.add_source_image = io.input_bool("Add source image? (y/n ?:help skip:n) : ", False, help_message="Add source image for comparison.")
|
|
super().ask_settings()
|
|
|
|
def toggle_add_source_image(self):
|
|
self.add_source_image = not self.add_source_image
|
|
|
|
#override
|
|
def __eq__(self, other):
|
|
#check equality of changeable params
|
|
|
|
if isinstance(other, ConverterConfigFaceAvatar):
|
|
return super().__eq__(other) and \
|
|
self.add_source_image == other.add_source_image
|
|
|
|
return False
|
|
|
|
#override
|
|
def to_string(self, filename):
|
|
return (f"ConverterConfig {filename}:\n"
|
|
f"add_source_image : {self.add_source_image}\n") + \
|
|
super().to_string(filename) + "================"
|
|
|