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with XSeg model you can train your own mask segmentator of dst(and src) faces that will be used in merger for whole_face. Instead of using a pretrained model (which does not exist), you control which part of faces should be masked. Workflow is not easy, but at the moment it is the best solution for obtaining the best quality of whole_face's deepfakes using minimum effort without rotoscoping in AfterEffects. new scripts: XSeg) data_dst edit.bat XSeg) data_dst merge.bat XSeg) data_dst split.bat XSeg) data_src edit.bat XSeg) data_src merge.bat XSeg) data_src split.bat XSeg) train.bat Usage: unpack dst faceset if packed run XSeg) data_dst split.bat this scripts extracts (previously saved) .json data from jpg faces to use in label tool. run XSeg) data_dst edit.bat new tool 'labelme' is used use polygon (CTRL-N) to mask the face name polygon "1" (one symbol) as include polygon name polygon "0" (one symbol) as exclude polygon 'exclude polygons' will be applied after all 'include polygons' Hot keys: ctrl-N create polygon ctrl-J edit polygon A/D navigate between frames ctrl + mousewheel image zoom mousewheel vertical scroll alt+mousewheel horizontal scroll repeat for 10/50/100 faces, you don't need to mask every frame of dst, only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using polygon with name "0". run XSeg) data_dst merge.bat this script merges .json data of polygons into jpg faces, therefore faceset can be sorted or packed as usual. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: split run edit find these glitchy faces and mask them merge train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: label tool: https://i.imgur.com/aY6QGw1.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
373 lines
15 KiB
Python
373 lines
15 KiB
Python
import numpy as np
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import copy
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from facelib import FaceType
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from core.interact import interact as io
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class MergerConfig(object):
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TYPE_NONE = 0
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TYPE_MASKED = 1
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TYPE_FACE_AVATAR = 2
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####
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TYPE_IMAGE = 3
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TYPE_IMAGE_WITH_LANDMARKS = 4
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def __init__(self, type=0,
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sharpen_mode=0,
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blursharpen_amount=0,
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**kwargs
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):
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self.type = type
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self.sharpen_dict = {0:"None", 1:'box', 2:'gaussian'}
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#default changeable params
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self.sharpen_mode = sharpen_mode
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self.blursharpen_amount = blursharpen_amount
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def copy(self):
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return copy.copy(self)
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#overridable
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def ask_settings(self):
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s = """Choose sharpen mode: \n"""
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for key in self.sharpen_dict.keys():
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s += f"""({key}) {self.sharpen_dict[key]}\n"""
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io.log_info(s)
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self.sharpen_mode = io.input_int ("", 0, valid_list=self.sharpen_dict.keys(), help_message="Enhance details by applying sharpen filter.")
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if self.sharpen_mode != 0:
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self.blursharpen_amount = np.clip ( io.input_int ("Choose blur/sharpen amount", 0, add_info="-100..100"), -100, 100 )
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def toggle_sharpen_mode(self):
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a = list( self.sharpen_dict.keys() )
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self.sharpen_mode = a[ (a.index(self.sharpen_mode)+1) % len(a) ]
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def add_blursharpen_amount(self, diff):
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self.blursharpen_amount = np.clip ( self.blursharpen_amount+diff, -100, 100)
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#overridable
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def get_config(self):
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d = self.__dict__.copy()
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d.pop('type')
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return d
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#overridable
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def __eq__(self, other):
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#check equality of changeable params
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if isinstance(other, MergerConfig):
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return self.sharpen_mode == other.sharpen_mode and \
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self.blursharpen_amount == other.blursharpen_amount
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return False
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#overridable
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def to_string(self, filename):
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r = ""
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r += f"sharpen_mode : {self.sharpen_dict[self.sharpen_mode]}\n"
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r += f"blursharpen_amount : {self.blursharpen_amount}\n"
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return r
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mode_dict = {0:'original',
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1:'overlay',
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2:'hist-match',
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3:'seamless',
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4:'seamless-hist-match',
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5:'raw-rgb',}
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mode_str_dict = {}
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for key in mode_dict.keys():
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mode_str_dict[ mode_dict[key] ] = key
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"""
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whole_face_mask_mode_dict = {1:'learned',
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2:'dst',
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3:'FAN-prd',
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4:'FAN-dst',
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5:'FAN-prd*FAN-dst',
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6:'learned*FAN-prd*FAN-dst'
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}
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"""
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whole_face_mask_mode_dict = {1:'learned',
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2:'dst',
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8:'XSeg-prd',
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9:'XSeg-dst',
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10:'XSeg-prd*XSeg-dst',
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11:'learned*XSeg-prd*XSeg-dst'
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}
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full_face_mask_mode_dict = {1:'learned',
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2:'dst',
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3:'FAN-prd',
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4:'FAN-dst',
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5:'FAN-prd*FAN-dst',
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6:'learned*FAN-prd*FAN-dst'}
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half_face_mask_mode_dict = {1:'learned',
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2:'dst',
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4:'FAN-dst',
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7:'learned*FAN-dst'}
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ctm_dict = { 0: "None", 1:"rct", 2:"lct", 3:"mkl", 4:"mkl-m", 5:"idt", 6:"idt-m", 7:"sot-m", 8:"mix-m" }
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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 }
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class MergerConfigMasked(MergerConfig):
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def __init__(self, face_type=FaceType.FULL,
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default_mode = 'overlay',
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mode='overlay',
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masked_hist_match=True,
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hist_match_threshold = 238,
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mask_mode = 1,
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erode_mask_modifier = 0,
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blur_mask_modifier = 0,
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motion_blur_power = 0,
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output_face_scale = 0,
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super_resolution_power = 0,
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color_transfer_mode = ctm_str_dict['rct'],
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image_denoise_power = 0,
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bicubic_degrade_power = 0,
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color_degrade_power = 0,
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**kwargs
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):
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super().__init__(type=MergerConfig.TYPE_MASKED, **kwargs)
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self.face_type = face_type
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if self.face_type not in [FaceType.HALF, FaceType.MID_FULL, FaceType.FULL, FaceType.WHOLE_FACE ]:
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raise ValueError("MergerConfigMasked does not support this type of face.")
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self.default_mode = default_mode
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#default changeable params
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if mode not in mode_str_dict:
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mode = mode_dict[1]
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self.mode = mode
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self.masked_hist_match = masked_hist_match
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self.hist_match_threshold = hist_match_threshold
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self.mask_mode = mask_mode
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self.erode_mask_modifier = erode_mask_modifier
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self.blur_mask_modifier = blur_mask_modifier
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self.motion_blur_power = motion_blur_power
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self.output_face_scale = output_face_scale
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self.super_resolution_power = super_resolution_power
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self.color_transfer_mode = color_transfer_mode
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self.image_denoise_power = image_denoise_power
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self.bicubic_degrade_power = bicubic_degrade_power
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self.color_degrade_power = color_degrade_power
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def copy(self):
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return copy.copy(self)
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def set_mode (self, mode):
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self.mode = mode_dict.get (mode, self.default_mode)
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def toggle_masked_hist_match(self):
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if self.mode == 'hist-match':
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self.masked_hist_match = not self.masked_hist_match
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def add_hist_match_threshold(self, diff):
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if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
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self.hist_match_threshold = np.clip ( self.hist_match_threshold+diff , 0, 255)
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def toggle_mask_mode(self):
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if self.face_type == FaceType.WHOLE_FACE:
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a = list( whole_face_mask_mode_dict.keys() )
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elif self.face_type == FaceType.FULL:
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a = list( full_face_mask_mode_dict.keys() )
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else:
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a = list( half_face_mask_mode_dict.keys() )
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self.mask_mode = a[ (a.index(self.mask_mode)+1) % len(a) ]
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def add_erode_mask_modifier(self, diff):
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self.erode_mask_modifier = np.clip ( self.erode_mask_modifier+diff , -400, 400)
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def add_blur_mask_modifier(self, diff):
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self.blur_mask_modifier = np.clip ( self.blur_mask_modifier+diff , 0, 400)
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def add_motion_blur_power(self, diff):
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self.motion_blur_power = np.clip ( self.motion_blur_power+diff, 0, 100)
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def add_output_face_scale(self, diff):
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self.output_face_scale = np.clip ( self.output_face_scale+diff , -50, 50)
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def toggle_color_transfer_mode(self):
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self.color_transfer_mode = (self.color_transfer_mode+1) % ( max(ctm_dict.keys())+1 )
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def add_super_resolution_power(self, diff):
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self.super_resolution_power = np.clip ( self.super_resolution_power+diff , 0, 100)
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def add_color_degrade_power(self, diff):
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self.color_degrade_power = np.clip ( self.color_degrade_power+diff , 0, 100)
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def add_image_denoise_power(self, diff):
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self.image_denoise_power = np.clip ( self.image_denoise_power+diff, 0, 500)
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def add_bicubic_degrade_power(self, diff):
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self.bicubic_degrade_power = np.clip ( self.bicubic_degrade_power+diff, 0, 100)
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def ask_settings(self):
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s = """Choose mode: \n"""
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for key in mode_dict.keys():
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s += f"""({key}) {mode_dict[key]}\n"""
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io.log_info(s)
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mode = io.input_int ("", mode_str_dict.get(self.default_mode, 1) )
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self.mode = mode_dict.get (mode, self.default_mode )
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if 'raw' not in self.mode:
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if self.mode == 'hist-match':
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self.masked_hist_match = io.input_bool("Masked hist match?", True)
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if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
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self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold", 255, add_info="0..255"), 0, 255)
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if self.face_type == FaceType.WHOLE_FACE:
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s = """Choose mask mode: \n"""
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for key in whole_face_mask_mode_dict.keys():
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s += f"""({key}) {whole_face_mask_mode_dict[key]}\n"""
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io.log_info(s)
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self.mask_mode = io.input_int ("", 1, valid_list=whole_face_mask_mode_dict.keys() )
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elif self.face_type == FaceType.FULL:
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s = """Choose mask mode: \n"""
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for key in full_face_mask_mode_dict.keys():
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s += f"""({key}) {full_face_mask_mode_dict[key]}\n"""
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io.log_info(s)
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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.")
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else:
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s = """Choose mask mode: \n"""
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for key in half_face_mask_mode_dict.keys():
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s += f"""({key}) {half_face_mask_mode_dict[key]}\n"""
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io.log_info(s)
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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.")
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if 'raw' not in self.mode:
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self.erode_mask_modifier = np.clip ( io.input_int ("Choose erode mask modifier", 0, add_info="-400..400"), -400, 400)
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self.blur_mask_modifier = np.clip ( io.input_int ("Choose blur mask modifier", 0, add_info="0..400"), 0, 400)
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self.motion_blur_power = np.clip ( io.input_int ("Choose motion blur power", 0, add_info="0..100"), 0, 100)
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self.output_face_scale = np.clip (io.input_int ("Choose output face scale modifier", 0, add_info="-50..50" ), -50, 50)
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if 'raw' not in self.mode:
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self.color_transfer_mode = io.input_str ( "Color transfer to predicted face", None, valid_list=list(ctm_str_dict.keys())[1:] )
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self.color_transfer_mode = ctm_str_dict[self.color_transfer_mode]
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super().ask_settings()
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self.super_resolution_power = np.clip ( io.input_int ("Choose super resolution power", 0, add_info="0..100", help_message="Enhance details by applying superresolution network."), 0, 100)
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if 'raw' not in self.mode:
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self.image_denoise_power = np.clip ( io.input_int ("Choose image degrade by denoise power", 0, add_info="0..500"), 0, 500)
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self.bicubic_degrade_power = np.clip ( io.input_int ("Choose image degrade by bicubic rescale power", 0, add_info="0..100"), 0, 100)
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self.color_degrade_power = np.clip ( io.input_int ("Degrade color power of final image", 0, add_info="0..100"), 0, 100)
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io.log_info ("")
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def __eq__(self, other):
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#check equality of changeable params
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if isinstance(other, MergerConfigMasked):
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return super().__eq__(other) and \
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self.mode == other.mode and \
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self.masked_hist_match == other.masked_hist_match and \
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self.hist_match_threshold == other.hist_match_threshold and \
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self.mask_mode == other.mask_mode and \
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self.erode_mask_modifier == other.erode_mask_modifier and \
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self.blur_mask_modifier == other.blur_mask_modifier and \
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self.motion_blur_power == other.motion_blur_power and \
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self.output_face_scale == other.output_face_scale and \
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self.color_transfer_mode == other.color_transfer_mode and \
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self.super_resolution_power == other.super_resolution_power and \
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self.image_denoise_power == other.image_denoise_power and \
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self.bicubic_degrade_power == other.bicubic_degrade_power and \
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self.color_degrade_power == other.color_degrade_power
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return False
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def to_string(self, filename):
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r = (
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f"""MergerConfig {filename}:\n"""
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f"""Mode: {self.mode}\n"""
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)
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if self.mode == 'hist-match':
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r += f"""masked_hist_match: {self.masked_hist_match}\n"""
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if self.mode == 'hist-match' or self.mode == 'seamless-hist-match':
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r += f"""hist_match_threshold: {self.hist_match_threshold}\n"""
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if self.face_type == FaceType.WHOLE_FACE:
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r += f"""mask_mode: { whole_face_mask_mode_dict[self.mask_mode] }\n"""
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elif self.face_type == FaceType.FULL:
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r += f"""mask_mode: { full_face_mask_mode_dict[self.mask_mode] }\n"""
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else:
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r += f"""mask_mode: { half_face_mask_mode_dict[self.mask_mode] }\n"""
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if 'raw' not in self.mode:
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r += (f"""erode_mask_modifier: {self.erode_mask_modifier}\n"""
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f"""blur_mask_modifier: {self.blur_mask_modifier}\n"""
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f"""motion_blur_power: {self.motion_blur_power}\n""")
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r += f"""output_face_scale: {self.output_face_scale}\n"""
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if 'raw' not in self.mode:
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r += f"""color_transfer_mode: {ctm_dict[self.color_transfer_mode]}\n"""
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r += super().to_string(filename)
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r += f"""super_resolution_power: {self.super_resolution_power}\n"""
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if 'raw' not in self.mode:
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r += (f"""image_denoise_power: {self.image_denoise_power}\n"""
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f"""bicubic_degrade_power: {self.bicubic_degrade_power}\n"""
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f"""color_degrade_power: {self.color_degrade_power}\n""")
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r += "================"
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return r
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class MergerConfigFaceAvatar(MergerConfig):
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def __init__(self, temporal_face_count=0,
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add_source_image=False):
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super().__init__(type=MergerConfig.TYPE_FACE_AVATAR)
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self.temporal_face_count = temporal_face_count
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#changeable params
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self.add_source_image = add_source_image
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def copy(self):
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return copy.copy(self)
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#override
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def ask_settings(self):
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self.add_source_image = io.input_bool("Add source image?", False, help_message="Add source image for comparison.")
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super().ask_settings()
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def toggle_add_source_image(self):
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self.add_source_image = not self.add_source_image
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#override
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def __eq__(self, other):
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#check equality of changeable params
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if isinstance(other, MergerConfigFaceAvatar):
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return super().__eq__(other) and \
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self.add_source_image == other.add_source_image
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return False
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#override
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def to_string(self, filename):
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return (f"MergerConfig {filename}:\n"
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f"add_source_image : {self.add_source_image}\n") + \
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super().to_string(filename) + "================"
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