mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-07 21:42:08 -07:00
added 'raw' mode to convertermasked
This commit is contained in:
iperov
parent
1c601dce4a
commit
42f0e438f3
1 changed files with 158 additions and 143 deletions
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@ -24,31 +24,44 @@ class ConverterMasked(ConverterBase):
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self.face_type = face_type
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self.face_type = face_type
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self.TFLabConverter = None
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self.TFLabConverter = None
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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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mode = input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless (default), (5) seamless hist match, (6) raw : ", 4)
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self.mode = {1:'overlay',
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self.mode = {1:'overlay',
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2:'hist-match',
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2:'hist-match',
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3:'hist-match-bw',
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3:'hist-match-bw',
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4:'seamless',
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4:'seamless',
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5:'seamless-hist-match'}.get (mode, 'seamless')
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5:'seamless-hist-match',
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6:'raw'}.get (mode, 'seamless')
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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if self.mode == 'raw':
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self.masked_hist_match = input_bool("Masked hist match? (y/n skip:y) : ", True)
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mode = input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only : ", 2)
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self.raw_mode = {1:'rgb',
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2:'rgb-mask',
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3:'mask-only'}.get (mode, 'rgb-mask')
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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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if self.mode != 'raw':
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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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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.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 != 'raw':
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if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
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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.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.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.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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if self.mode != 'raw':
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self.alpha = input_bool("Export png with alpha channel? (y/n skip:n) : ", False)
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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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print ("")
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#override
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#override
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@ -107,134 +120,136 @@ class ConverterMasked(ConverterBase):
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maxregion = np.argwhere(img_face_mask_flatten_aaa==1.0)
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maxregion = np.argwhere(img_face_mask_flatten_aaa==1.0)
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out_img = img_bgr.copy()
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out_img = img_bgr.copy()
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if maxregion.size != 0:
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miny,minx = maxregion.min(axis=0)[:2]
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maxy,maxx = maxregion.max(axis=0)[:2]
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if debug:
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print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy ) )
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lenx = maxx - minx
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leny = maxy - miny
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if lenx >= 4 and leny >= 4:
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masky = int(minx+(lenx//2))
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maskx = int(miny+(leny//2))
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lowest_len = min (lenx, leny)
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if debug:
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print ("lowest_len = %f" % (lowest_len) )
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img_mask_blurry_aaa = img_face_mask_aaa
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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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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.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 debug:
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print ("seamless_erode_size = %d" % (ero) )
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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 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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if debug:
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print ("blur_size = %d" % (blur) )
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if blur > 0:
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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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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prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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if debug:
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debugs += [ cv2.warpAffine( prd_face_bgr, 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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hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=prd_face_bgr.dtype)
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if self.masked_hist_match:
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hist_mask_a *= prd_face_mask_a
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hist_match_1 = prd_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_1[ hist_match_1 > 1.0 ] = 1.0
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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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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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if self.mode == 'raw':
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if self.raw_mode == 'rgb':
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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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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 self.raw_mode == 'rgb-mask':
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out_img = np.concatenate ( [out_img, np.expand_dims (img_face_mask_aaa[:,:,0],-1)], -1 )
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if self.raw_mode == 'mask-only':
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out_img = img_face_mask_aaa
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else:
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if maxregion.size != 0:
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miny,minx = maxregion.min(axis=0)[:2]
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maxy,maxx = maxregion.max(axis=0)[:2]
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if debug:
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if debug:
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debugs += [out_img.copy()]
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print ("maxregion.size: %d, minx:%d, maxx:%d miny:%d, maxy:%d" % (maxregion.size, minx, maxx, miny, maxy ) )
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debugs += [img_mask_blurry_aaa.copy()]
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if self.mode == 'overlay':
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lenx = maxx - minx
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pass
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leny = maxy - miny
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if lenx >= 4 and leny >= 4:
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masky = int(minx+(lenx//2))
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maskx = int(miny+(leny//2))
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lowest_len = min (lenx, leny)
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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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if debug:
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debugs += [out_img.copy()]
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print ("lowest_len = %f" % (lowest_len) )
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out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_mask_flatten_aaa*255).astype(np.uint8), (masky,maskx) , cv2.NORMAL_CLONE )
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img_mask_blurry_aaa = img_face_mask_aaa
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out_img = out_img.astype(np.float32) / 255.0
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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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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.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 debug:
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print ("seamless_erode_size = %d" % (ero) )
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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 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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if debug:
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print ("blur_size = %d" % (blur) )
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if blur > 0:
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img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
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img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
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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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prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
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if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
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if debug:
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debugs += [ cv2.warpAffine( prd_face_bgr, 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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hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=prd_face_bgr.dtype)
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if self.masked_hist_match:
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hist_mask_a *= prd_face_mask_a
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hist_match_1 = prd_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_1[ hist_match_1 > 1.0 ] = 1.0
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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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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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if debug:
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debugs += [out_img.copy()]
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debugs += [out_img.copy()]
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debugs += [img_mask_blurry_aaa.copy()]
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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 == 'overlay':
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pass
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if self.mode == 'seamless-hist-match':
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if self.mode == 'seamless' or self.mode == 'seamless-hist-match':
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out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
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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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new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
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if debug:
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new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
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debugs += [out_img.copy()]
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out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
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if self.transfercolor:
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out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_mask_flatten_aaa*255).astype(np.uint8), (masky,maskx) , cv2.NORMAL_CLONE )
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if self.TFLabConverter is None:
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out_img = out_img.astype(np.float32) / 255.0
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self.TFLabConverter = image_utils.TFLabConverter()
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img_lab_l, img_lab_a, img_lab_b = np.split ( self.TFLabConverter.bgr2lab (img_bgr), 3, axis=-1 )
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if debug:
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out_img_lab_l, out_img_lab_a, out_img_lab_b = np.split ( self.TFLabConverter.bgr2lab (out_img), 3, axis=-1 )
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debugs += [out_img.copy()]
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out_img = self.TFLabConverter.lab2bgr ( np.concatenate([out_img_lab_l, img_lab_a, img_lab_b], axis=-1) )
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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.final_image_color_degrade_power != 0:
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if self.mode == 'seamless-hist-match':
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||||||
if debug:
|
out_face_bgr = cv2.warpAffine( out_img, face_mat, (self.output_size, self.output_size) )
|
||||||
debugs += [out_img.copy()]
|
new_out_face_bgr = image_utils.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
|
||||||
out_img_reduced = image_utils.reduce_colors(out_img, 256)
|
new_out = cv2.warpAffine( new_out_face_bgr, face_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
|
||||||
if self.final_image_color_degrade_power == 100:
|
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
|
||||||
out_img = out_img_reduced
|
|
||||||
else:
|
|
||||||
alpha = self.final_image_color_degrade_power / 100.0
|
|
||||||
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
|
||||||
|
|
||||||
if self.alpha:
|
if self.transfercolor:
|
||||||
new_image = out_img.copy()
|
if self.TFLabConverter is None:
|
||||||
new_image = (new_image*255).astype(np.uint8) #convert image to int
|
self.TFLabConverter = image_utils.TFLabConverter()
|
||||||
b_channel, g_channel, r_channel = cv2.split(new_image) #splitting RGB
|
|
||||||
alpha_channel = img_mask_blurry_aaa.copy() #making copy of alpha channel
|
|
||||||
alpha_channel = (alpha_channel*255).astype(np.uint8)
|
|
||||||
alpha_channel, tmp2, tmp3 = cv2.split(alpha_channel) #splitting alpha to three channels, they all same in original alpha channel, we need just one
|
|
||||||
out_img = cv2.merge((b_channel,g_channel, r_channel, alpha_channel)) #mergin RGB with alpha
|
|
||||||
out_img = out_img.astype(np.float32) / 255.0
|
|
||||||
|
|
||||||
|
img_lab_l, img_lab_a, img_lab_b = np.split ( self.TFLabConverter.bgr2lab (img_bgr), 3, axis=-1 )
|
||||||
|
out_img_lab_l, out_img_lab_a, out_img_lab_b = np.split ( self.TFLabConverter.bgr2lab (out_img), 3, axis=-1 )
|
||||||
|
|
||||||
|
out_img = self.TFLabConverter.lab2bgr ( np.concatenate([out_img_lab_l, img_lab_a, img_lab_b], axis=-1) )
|
||||||
|
|
||||||
|
if self.final_image_color_degrade_power != 0:
|
||||||
|
if debug:
|
||||||
|
debugs += [out_img.copy()]
|
||||||
|
out_img_reduced = image_utils.reduce_colors(out_img, 256)
|
||||||
|
if self.final_image_color_degrade_power == 100:
|
||||||
|
out_img = out_img_reduced
|
||||||
|
else:
|
||||||
|
alpha = self.final_image_color_degrade_power / 100.0
|
||||||
|
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
||||||
|
|
||||||
|
if self.alpha:
|
||||||
|
out_img = np.concatenate ( [out_img, np.expand_dims (img_mask_blurry_aaa[:,:,0],-1)], -1 )
|
||||||
|
|
||||||
if debug:
|
if debug:
|
||||||
debugs += [out_img.copy()]
|
debugs += [out_img.copy()]
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue