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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-11 07:37:03 -07:00
added converter --seamless-erode-mask-modifier . This mask used to define area for opencv seamless cloning function. So if you erode it little bit , you can decrease flickering effect on some frames.
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iperov
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bc5ca1ab59
commit
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2 changed files with 47 additions and 36 deletions
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@ -12,8 +12,6 @@ class ConverterMasked(ConverterBase):
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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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erode_mask = True,
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blur_mask = True,
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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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@ -21,6 +19,7 @@ class ConverterMasked(ConverterBase):
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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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@ -33,25 +32,18 @@ class ConverterMasked(ConverterBase):
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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.erode_mask = erode_mask
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self.blur_mask = blur_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.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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if self.erode_mask_modifier != 0 and not self.erode_mask:
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print ("Erode mask modifier not used in this model.")
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if self.blur_mask_modifier != 0 and not self.blur_mask:
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print ("Blur modifier not used in this model.")
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self.alpha = alpha
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#override
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def get_mode(self):
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@ -125,34 +117,44 @@ class ConverterMasked(ConverterBase):
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if debug:
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print ("lowest_len = %f" % (lowest_len) )
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
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blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
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if debug:
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print ("erode_size = %d, blur_size = %d" % (ero, blur) )
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img_mask_blurry_aaa = img_face_mask_aaa
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if self.erode_mask:
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if self.erode_mask_modifier != 0:
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
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if debug:
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print ("erode_size = %d" % (ero) )
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if ero > 0:
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img_mask_blurry_aaa = cv2.erode(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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img_mask_blurry_aaa = cv2.erode(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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img_mask_blurry_aaa = cv2.dilate(img_mask_blurry_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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if self.blur_mask and blur > 0:
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if self.seamless_erode_mask_modifier != 0:
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ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.seamless_erode_mask_modifier )
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if ero > 0:
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img_face_mask_flatten_aaa = cv2.erode(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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img_face_mask_flatten_aaa = cv2.dilate(img_face_mask_flatten_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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if debug:
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print ("seamless_erode_size = %d" % (ero) )
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if self.blur_mask_modifier > 0:
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blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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if debug:
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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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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.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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@ -199,12 +201,12 @@ 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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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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#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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