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317 lines
No EOL
13 KiB
Python
317 lines
No EOL
13 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 interact import interact as io
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class ConverterConfig(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, predictor_func=None,
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predictor_input_shape=None):
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self.type = type
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self.predictor_func = predictor_func
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self.predictor_input_shape = predictor_input_shape
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self.dcscn_upscale_func = None
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self.fanseg_input_size = None
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self.fanseg_extract_func = None
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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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pass
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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, ConverterConfig):
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return True
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return False
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#overridable
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def __str__(self):
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return "ConverterConfig: ."
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class ConverterConfigMasked(ConverterConfig):
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def __init__(self, predictor_func=None,
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predictor_input_shape=None,
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predictor_masked=True,
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face_type=FaceType.FULL,
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default_mode = 4,
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base_erode_mask_modifier = 0,
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base_blur_mask_modifier = 0,
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default_erode_mask_modifier = 0,
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default_blur_mask_modifier = 0,
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clip_hborder_mask_per = 0,
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):
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super().__init__(type=ConverterConfig.TYPE_MASKED,
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predictor_func=predictor_func,
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predictor_input_shape=predictor_input_shape,
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)
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if len(predictor_input_shape) != 3:
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raise ValueError("ConverterConfigMasked: predictor_input_shape must be rank 3.")
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if predictor_input_shape[0] != predictor_input_shape[1]:
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raise ValueError("ConverterConfigMasked: predictor_input_shape must be a square.")
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self.predictor_masked = predictor_masked
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self.face_type = face_type
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if self.face_type not in [FaceType.FULL, FaceType.HALF]:
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raise ValueError("ConverterConfigMasked supports only full or half face masks.")
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self.default_mode = default_mode
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self.base_erode_mask_modifier = base_erode_mask_modifier
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self.base_blur_mask_modifier = base_blur_mask_modifier
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self.default_erode_mask_modifier = default_erode_mask_modifier
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self.default_blur_mask_modifier = default_blur_mask_modifier
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self.clip_hborder_mask_per = clip_hborder_mask_per
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#default changeable params
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self.mode = 'overlay'
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self.masked_hist_match = True
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self.hist_match_threshold = 238
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self.mask_mode = 1
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self.erode_mask_modifier = 0
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self.blur_mask_modifier = 0
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self.motion_blur_power = 0
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self.output_face_scale = 0
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self.color_transfer_mode = 0
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self.super_resolution = False
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self.color_degrade_power = 0
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self.export_mask_alpha = False
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self.mode_dict = {0:'original',
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1:'overlay',
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2:'hist-match',
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3:'hist-match-bw',
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4:'seamless',
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5:'seamless-hist-match',
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6:'raw-rgb',
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7:'raw-rgb-mask',
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8:'raw-mask-only',
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9:'raw-predicted-only'}
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self.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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self.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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self.ctm_dict = { 0: "None", 1:"rct", 2:"lct" }
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self.ctm_str_dict = {None:0, "rct":1, "lct": 2 }
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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 = self.mode_dict.get (mode, self.mode_dict[self.default_mode] )
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def toggle_masked_hist_match(self):
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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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 == 'hist-match-bw' 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.FULL:
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a = list( self.full_face_mask_mode_dict.keys() )
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else:
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a = list( self.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 , -200, 200)
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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 , -200, 200)
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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) % 3
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def toggle_super_resolution(self):
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self.super_resolution = not self.super_resolution
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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 toggle_export_mask_alpha(self):
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self.export_mask_alpha = not self.export_mask_alpha
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def ask_settings(self):
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s = """Choose mode: \n"""
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for key in self.mode_dict.keys():
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s += f"""({key}) {self.mode_dict[key]}\n"""
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s += f"""Default: {self.default_mode} : """
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mode = io.input_int (s, self.default_mode)
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self.mode = self.mode_dict.get (mode, self.mode_dict[self.default_mode] )
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if 'raw' not in self.mode:
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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self.masked_hist_match = io.input_bool("Masked hist match? (y/n skip:y) : ", True)
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw' or self.mode == 'seamless-hist-match':
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self.hist_match_threshold = np.clip ( io.input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255)
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if self.face_type == FaceType.FULL:
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s = """Choose mask mode: \n"""
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for key in self.full_face_mask_mode_dict.keys():
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s += f"""({key}) {self.full_face_mask_mode_dict[key]}\n"""
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s += f"""?:help Default: 1 : """
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self.mask_mode = io.input_int (s, 1, valid_list=self.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 self.half_face_mask_mode_dict.keys():
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s += f"""({key}) {self.half_face_mask_mode_dict[key]}\n"""
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s += f"""?:help , Default: 1 : """
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self.mask_mode = io.input_int (s, 1, valid_list=self.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 = self.base_erode_mask_modifier + np.clip ( io.input_int ("Choose erode mask modifier [-200..200] (skip:%d) : " % (self.default_erode_mask_modifier), self.default_erode_mask_modifier), -200, 200)
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self.blur_mask_modifier = self.base_blur_mask_modifier + np.clip ( io.input_int ("Choose blur mask modifier [-200..200] (skip:%d) : " % (self.default_blur_mask_modifier), self.default_blur_mask_modifier), -200, 200)
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self.motion_blur_power = np.clip ( io.input_int ("Choose motion blur power [0..100] (skip:%d) : " % (0), 0), 0, 100)
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self.output_face_scale = np.clip (io.input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0), -50, 50)
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if 'raw' not in self.mode:
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self.color_transfer_mode = io.input_str ("Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct','lct'])
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self.color_transfer_mode = self.ctm_str_dict[self.color_transfer_mode]
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self.super_resolution = io.input_bool("Apply super resolution? (y/n ?:help skip:n) : ", False, help_message="Enhance details by applying DCSCN network.")
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if 'raw' not in self.mode:
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self.color_degrade_power = np.clip ( io.input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
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self.export_mask_alpha = io.input_bool("Export png with alpha channel of the mask? (y/n skip:n) : ", False)
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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, ConverterConfigMasked):
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return 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 == other.super_resolution and \
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self.color_degrade_power == other.color_degrade_power and \
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self.export_mask_alpha == other.export_mask_alpha
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return False
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def __str__(self):
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r = (
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"""ConverterConfig:\n"""
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f"""Mode: {self.mode}\n"""
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)
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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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 == 'hist-match-bw' 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.FULL:
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r += f"""mask_mode: { self.full_face_mask_mode_dict[self.mask_mode] }\n"""
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else:
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r += f"""mask_mode: { self.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: { self.ctm_dict[self.color_transfer_mode]}\n"""
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r += f"""super_resolution: {self.super_resolution}\n"""
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if 'raw' not in self.mode:
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r += (f"""color_degrade_power: {self.color_degrade_power}\n"""
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f"""export_mask_alpha: {self.export_mask_alpha}\n""")
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r += "================"
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return r
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class ConverterConfigFaceAvatar(ConverterConfig):
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def __init__(self, predictor_func=None,
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predictor_input_shape=None,
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temporal_face_count=0
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):
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super().__init__(type=ConverterConfig.TYPE_FACE_AVATAR,
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predictor_func=predictor_func,
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predictor_input_shape=predictor_input_shape
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)
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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 = False
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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? (y/n ?:help skip:n) : ", False, help_message="Add source image for comparison.")
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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, ConverterConfigFaceAvatar):
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return self.add_source_image == other.add_source_image
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return False
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#override
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def __str__(self):
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return ("ConverterConfig: \n"
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f"add_source_image : {self.add_source_image}\n"
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"================"
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) |