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https://github.com/iperov/DeepFaceLab.git
synced 2025-08-14 02:37:00 -07:00
added new model U-net Face Morpher.
removed AVATAR - useless model was just for demo removed MIAEF128 - use UFM insted removed LIAEF128YAW - use model option sort by yaw on start for any model All models now ask some options on start. Session options (such as target epoch, batch_size, write_preview_history etc) can be overrided by special command arg. Converter now always ask options and no more support to define options via command line. fix bug when ConverterMasked always used not predicted mask. SampleGenerator now always generate samples with replicated border, exclude mask samples. refactorings
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iperov
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29 changed files with 673 additions and 1013 deletions
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@ -4,47 +4,53 @@ from facelib import FaceType
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import cv2
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import numpy as np
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from utils import image_utils
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from utils.console_utils import *
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class ConverterMasked(ConverterBase):
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#override
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def __init__(self, predictor,
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predictor_input_size=0,
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output_size=0,
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face_type=FaceType.FULL,
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clip_border_mask_per = 0,
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masked_hist_match = True,
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hist_match_threshold = 255,
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mode='seamless',
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use_predicted_mask = True,
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erode_mask_modifier=0,
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blur_mask_modifier=0,
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seamless_erode_mask_modifier=0,
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output_face_scale_modifier=0.0,
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transfercolor=False,
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final_image_color_degrade_power=0,
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alpha=False,
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face_type=FaceType.FULL,
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base_erode_mask_modifier = 0,
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base_blur_mask_modifier = 0,
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**in_options):
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super().__init__(predictor)
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self.predictor_input_size = predictor_input_size
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self.output_size = output_size
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self.face_type = face_type
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self.use_predicted_mask = use_predicted_mask
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self.clip_border_mask_per = clip_border_mask_per
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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.mode = 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.seamless_erode_mask_modifier = seamless_erode_mask_modifier
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self.output_face_scale = np.clip(1.0 + output_face_scale_modifier*0.01, 0.5, 1.5)
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self.transfercolor = transfercolor
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self.face_type = face_type
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self.TFLabConverter = None
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self.final_image_color_degrade_power = np.clip (final_image_color_degrade_power, 0, 100)
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self.alpha = alpha
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mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless (default), (5) seamless hist match : ", 4)
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self.mode = {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'}.get (mode, 'seamless')
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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self.masked_hist_match = 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 ( input_int("Hist match threshold [0..255] (skip:255) : ", 255), 0, 255)
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self.use_predicted_mask = input_bool("Use predicted mask? (y/n skip:y) : ", True)
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self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( input_int ("Choose erode mask modifier [-200..200] (skip:0) : ", 0), -200, 200)
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self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( input_int ("Choose blur mask modifier [-200..200] (skip:0) : ", 0), -200, 200)
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self.seamless_erode_mask_modifier = 0
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if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
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self.seamless_erode_mask_modifier = np.clip ( input_int ("Choose seamless erode mask modifier [-100..100] (skip:0) : ", 0), -100, 100)
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self.output_face_scale = np.clip ( 1.0 + input_int ("Choose output face scale modifier [-50..50] (skip:0) : ", 0)*0.01, 0.5, 1.5)
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self.transfercolor = input_bool("Transfer color from dst face to converted final face? (y/n skip:n) : ", False)
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self.final_image_color_degrade_power = np.clip ( input_int ("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
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self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False)
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print ("")
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#override
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def get_mode(self):
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return ConverterBase.MODE_FACE
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@ -79,7 +85,7 @@ class ConverterMasked(ConverterBase):
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if not self.use_predicted_mask:
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prd_face_mask_a_0 = predictor_input_mask_a_0
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prd_face_mask_a_0[ prd_face_mask_a_0 < 0.001 ] = 0.0
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prd_face_mask_a = np.expand_dims (prd_face_mask_a_0, axis=-1)
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@ -145,16 +151,6 @@ class ConverterMasked(ConverterBase):
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print ("blur_size = %d" % (blur) )
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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#if self.clip_border_mask_per > 0:
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# prd_border_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=prd_face_mask_a.dtype)
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# prd_border_size = int ( prd_border_rect_mask_a.shape[1] * self.clip_border_mask_per )
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#
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# prd_border_rect_mask_a[0:prd_border_size,:,:] = 0
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# prd_border_rect_mask_a[-prd_border_size:,:,:] = 0
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# prd_border_rect_mask_a[:,0:prd_border_size,:] = 0
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# prd_border_rect_mask_a[:,-prd_border_size:,:] = 0
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# prd_border_rect_mask_a = np.expand_dims(cv2.blur(prd_border_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
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if self.mode == 'hist-match-bw':
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prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY)
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@ -174,22 +170,21 @@ class ConverterMasked(ConverterBase):
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hist_match_2 = dst_face_bgr*hist_mask_a + (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=prd_face_bgr.dtype)
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hist_match_2[ hist_match_1 > 1.0 ] = 1.0
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new_prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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prd_face_bgr = new_prd_face_bgr
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prd_face_bgr = image_utils.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
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if self.mode == 'hist-match-bw':
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prd_face_bgr = prd_face_bgr.astype(np.float32)
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out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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if debug:
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debugs += [out_img.copy()]
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debugs += [img_mask_blurry_aaa.copy()]
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if self.mode == 'overlay':
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pass
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if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
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out_img = np.clip( img_bgr*(1-img_face_mask_aaa) + (out_img*img_face_mask_aaa) , 0, 1.0 )
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if debug:
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@ -200,14 +195,7 @@ class ConverterMasked(ConverterBase):
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if debug:
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debugs += [out_img.copy()]
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#if self.clip_border_mask_per > 0:
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# img_prd_border_rect_mask_a = cv2.warpAffine( prd_border_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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# img_prd_border_rect_mask_a = np.expand_dims (img_prd_border_rect_mask_a, -1)
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#
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# out_img = out_img * img_prd_border_rect_mask_a + img_bgr * (1.0 - img_prd_border_rect_mask_a)
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# img_mask_blurry_aaa *= img_prd_border_rect_mask_a
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
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if self.mode == 'seamless-hist-match':
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