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Formatting
This commit is contained in:
Jeremy Hummel
parent
9a41965063
commit
0f0df28b9f
2 changed files with 232 additions and 163 deletions
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@ -12,7 +12,6 @@ from utils.pickle_utils import AntiPickler
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from .Converter import Converter
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'''
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default_mode = {1:'overlay',
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2:'hist-match',
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@ -21,25 +20,27 @@ default_mode = {1:'overlay',
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5:'seamless-hist-match',
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6:'raw'}
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'''
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class ConverterMasked(Converter):
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#override
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# override
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def __init__(self, predictor_func,
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predictor_input_size=0,
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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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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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force_mask_mode=-1):
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super().__init__(predictor_func, Converter.TYPE_FACE)
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#dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
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predictor_func ( np.zeros ( (predictor_input_size,predictor_input_size,3), dtype=np.float32 ) )
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# dummy predict and sleep, tensorflow caching kernels. If remove it, conversion speed will be x2 slower
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predictor_func(np.zeros((predictor_input_size, predictor_input_size, 3), dtype=np.float32))
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time.sleep(2)
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predictor_func_host, predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
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@ -51,22 +52,25 @@ class ConverterMasked(Converter):
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self.face_type = face_type
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self.clip_hborder_mask_per = clip_hborder_mask_per
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mode = io.input_int ("Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) raw. Default - %d : " % (default_mode) , default_mode)
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mode = io.input_int(
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"Choose mode: (1) overlay, (2) hist match, (3) hist match bw, (4) seamless, (5) raw. Default - %d : " % (
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default_mode), default_mode)
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mode_dict = {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:'raw'}
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mode_dict = {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: 'raw'}
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self.mode = mode_dict.get (mode, mode_dict[default_mode] )
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self.mode = mode_dict.get(mode, mode_dict[default_mode])
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if self.mode == 'raw':
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mode = io.input_int ("Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted 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',
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4:'predicted-only'}.get (mode, 'rgb-mask')
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mode = io.input_int(
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"Choose raw mode: (1) rgb, (2) rgb+mask (default), (3) mask only, (4) predicted 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',
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4: 'predicted-only'}.get(mode, 'rgb-mask')
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if self.mode != 'raw':
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@ -78,207 +82,238 @@ class ConverterMasked(Converter):
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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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self.hist_match_threshold = np.clip(io.input_int("Hist match threshold [0..255] (skip:255) : ", 255),
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0, 255)
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if force_mask_mode != -1:
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self.mask_mode = force_mask_mode
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else:
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if face_type == FaceType.FULL:
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self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : " % (1) , 1, help_message="If you learned 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."), 1, 6 )
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self.mask_mode = np.clip(io.input_int(
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"Mask mode: (1) learned, (2) dst, (3) FAN-prd, (4) FAN-dst , (5) FAN-prd*FAN-dst (6) learned*FAN-prd*FAN-dst (?) help. Default - %d : " % (
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1), 1,
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help_message="If you learned 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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1, 6)
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else:
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self.mask_mode = np.clip ( io.input_int ("Mask mode: (1) learned, (2) dst . Default - %d : " % (1) , 1), 1, 2 )
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self.mask_mode = np.clip(io.input_int("Mask mode: (1) learned, (2) dst . Default - %d : " % (1), 1), 1,
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2)
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if self.mask_mode >= 3 and self.mask_mode <= 6:
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self.fan_seg = None
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if self.mode != 'raw':
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self.erode_mask_modifier = base_erode_mask_modifier + np.clip ( io.input_int ("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier), default_erode_mask_modifier), -200, 200)
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self.blur_mask_modifier = base_blur_mask_modifier + np.clip ( io.input_int ("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier), default_blur_mask_modifier), -200, 200)
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self.erode_mask_modifier = base_erode_mask_modifier + np.clip(
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io.input_int("Choose erode mask modifier [-200..200] (skip:%d) : " % (default_erode_mask_modifier),
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default_erode_mask_modifier), -200, 200)
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self.blur_mask_modifier = base_blur_mask_modifier + np.clip(
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io.input_int("Choose blur mask modifier [-200..200] (skip:%d) : " % (default_blur_mask_modifier),
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default_blur_mask_modifier), -200, 200)
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self.output_face_scale = np.clip ( 1.0 + io.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(
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1.0 + io.input_int("Choose output face scale modifier [-50..50] (skip:0) : ", 0) * 0.01, 0.5, 1.5)
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if self.mode != 'raw':
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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 = io.input_str(
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"Apply color transfer to predicted face? Choose mode ( rct/lct skip:None ) : ", None, ['rct', 'lct'])
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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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self.super_resolution = io.input_bool("Apply super resolution? (y/n ?:help skip:n) : ", False,
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help_message="Enhance details by applying DCSCN network.")
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if self.mode != 'raw':
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self.final_image_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.final_image_color_degrade_power = np.clip(
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io.input_int("Degrade color power of final image [0..100] (skip:0) : ", 0), 0, 100)
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self.alpha = io.input_bool("Export png with alpha channel? (y/n skip:n) : ", False)
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io.log_info ("")
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io.log_info("")
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if self.super_resolution:
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host_proc, dc_upscale = SubprocessFunctionCaller.make_pair( imagelib.DCSCN().upscale )
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host_proc, dc_upscale = SubprocessFunctionCaller.make_pair(imagelib.DCSCN().upscale)
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self.dc_host = AntiPickler(host_proc)
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self.dc_upscale = dc_upscale
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else:
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self.dc_host = None
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#overridable
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# overridable
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def on_host_tick(self):
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self.predictor_func_host.obj.process_messages()
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if self.dc_host is not None:
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self.dc_host.obj.process_messages()
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#overridable
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# overridable
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def on_cli_initialize(self):
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if (self.mask_mode >= 3 and self.mask_mode <= 6) and self.fan_seg == None:
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self.fan_seg = FANSegmentator(256, FaceType.toString( self.face_type ) )
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self.fan_seg = FANSegmentator(256, FaceType.toString(self.face_type))
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#override
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def cli_convert_face (self, img_bgr, img_face_landmarks, debug, **kwargs):
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# override
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def cli_convert_face(self, img_bgr, img_face_landmarks, debug, **kwargs):
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if debug:
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debugs = [img_bgr.copy()]
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask (img_bgr.shape, img_face_landmarks)
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img_face_mask_a = LandmarksProcessor.get_image_hull_mask(img_bgr.shape, img_face_landmarks)
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output_size = self.predictor_input_size
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if self.super_resolution:
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output_size *= 2
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=self.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=self.face_type, scale=self.output_face_scale)
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face_mat = LandmarksProcessor.get_transform_mat(img_face_landmarks, output_size, face_type=self.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat(img_face_landmarks, output_size,
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face_type=self.face_type, scale=self.output_face_scale)
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4 )
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4 )
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dst_face_bgr = cv2.warpAffine(img_bgr, face_mat, (output_size, output_size), flags=cv2.INTER_LANCZOS4)
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dst_face_mask_a_0 = cv2.warpAffine(img_face_mask_a, face_mat, (output_size, output_size),
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flags=cv2.INTER_LANCZOS4)
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predictor_input_bgr = cv2.resize (dst_face_bgr, (self.predictor_input_size,self.predictor_input_size))
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predictor_input_bgr = cv2.resize(dst_face_bgr, (self.predictor_input_size, self.predictor_input_size))
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if self.predictor_masked:
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prd_face_bgr, prd_face_mask_a_0 = self.predictor_func (predictor_input_bgr)
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prd_face_bgr, prd_face_mask_a_0 = self.predictor_func(predictor_input_bgr)
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prd_face_bgr = np.clip (prd_face_bgr, 0, 1.0 )
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prd_face_mask_a_0 = np.clip (prd_face_mask_a_0, 0.0, 1.0)
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prd_face_bgr = np.clip(prd_face_bgr, 0, 1.0)
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prd_face_mask_a_0 = np.clip(prd_face_mask_a_0, 0.0, 1.0)
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else:
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predicted = self.predictor_func (predictor_input_bgr)
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prd_face_bgr = np.clip (predicted, 0, 1.0 )
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (self.predictor_input_size,self.predictor_input_size))
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predicted = self.predictor_func(predictor_input_bgr)
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prd_face_bgr = np.clip(predicted, 0, 1.0)
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prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (self.predictor_input_size, self.predictor_input_size))
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if self.super_resolution:
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if debug:
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tmp = cv2.resize (prd_face_bgr, (output_size,output_size), cv2.INTER_CUBIC)
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debugs += [ np.clip( cv2.warpAffine( tmp, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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tmp = cv2.resize(prd_face_bgr, (output_size, output_size), cv2.INTER_CUBIC)
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debugs += [np.clip(cv2.warpAffine(tmp, face_output_mat, img_size, img_bgr.copy(),
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cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0,
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1.0)]
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prd_face_bgr = self.dc_upscale(prd_face_bgr)
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if debug:
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debugs += [ np.clip( cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, img_bgr.copy(), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT ), 0, 1.0) ]
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debugs += [np.clip(cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, img_bgr.copy(),
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cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT), 0,
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1.0)]
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if self.predictor_masked:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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prd_face_mask_a_0 = cv2.resize(prd_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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else:
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 2: #dst
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prd_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 2: # dst
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prd_face_mask_a_0 = cv2.resize(dst_face_mask_a_0, (output_size, output_size), cv2.INTER_CUBIC)
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elif self.mask_mode >= 3 and self.mask_mode <= 6:
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if self.mask_mode == 3 or self.mask_mode == 5 or self.mask_mode == 6:
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prd_face_bgr_256 = cv2.resize (prd_face_bgr, (256,256) )
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prd_face_bgr_256_mask = self.fan_seg.extract( prd_face_bgr_256 )
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FAN_prd_face_mask_a_0 = cv2.resize (prd_face_bgr_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
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prd_face_bgr_256 = cv2.resize(prd_face_bgr, (256, 256))
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prd_face_bgr_256_mask = self.fan_seg.extract(prd_face_bgr_256)
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FAN_prd_face_mask_a_0 = cv2.resize(prd_face_bgr_256_mask, (output_size, output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 4 or self.mask_mode == 5 or self.mask_mode == 6:
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face_256_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, 256, face_type=FaceType.FULL)
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dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4 )
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dst_face_256_mask = self.fan_seg.extract( dst_face_256_bgr )
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FAN_dst_face_mask_a_0 = cv2.resize (dst_face_256_mask, (output_size,output_size), cv2.INTER_CUBIC)
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face_256_mat = LandmarksProcessor.get_transform_mat(img_face_landmarks, 256, face_type=FaceType.FULL)
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dst_face_256_bgr = cv2.warpAffine(img_bgr, face_256_mat, (256, 256), flags=cv2.INTER_LANCZOS4)
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dst_face_256_mask = self.fan_seg.extract(dst_face_256_bgr)
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FAN_dst_face_mask_a_0 = cv2.resize(dst_face_256_mask, (output_size, output_size), cv2.INTER_CUBIC)
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if self.mask_mode == 3: #FAN-prd
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if self.mask_mode == 3: # FAN-prd
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prd_face_mask_a_0 = FAN_prd_face_mask_a_0
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elif self.mask_mode == 4: #FAN-dst
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elif self.mask_mode == 4: # FAN-dst
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prd_face_mask_a_0 = FAN_dst_face_mask_a_0
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elif self.mask_mode == 5:
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prd_face_mask_a_0 = FAN_prd_face_mask_a_0 * FAN_dst_face_mask_a_0
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elif self.mask_mode == 6:
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prd_face_mask_a_0 = prd_face_mask_a_0 * FAN_prd_face_mask_a_0 * FAN_dst_face_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_0[prd_face_mask_a_0 < 0.001] = 0.0
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prd_face_mask_a = prd_face_mask_a_0[...,np.newaxis]
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prd_face_mask_aaa = np.repeat (prd_face_mask_a, (3,), axis=-1)
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prd_face_mask_a = prd_face_mask_a_0[..., np.newaxis]
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prd_face_mask_aaa = np.repeat(prd_face_mask_a, (3,), axis=-1)
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img_face_mask_aaa = cv2.warpAffine( prd_face_mask_aaa, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 )
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img_face_mask_aaa = np.clip (img_face_mask_aaa, 0.0, 1.0)
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img_face_mask_aaa [ img_face_mask_aaa <= 0.1 ] = 0.0 #get rid of noise
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img_face_mask_aaa = cv2.warpAffine(prd_face_mask_aaa, face_output_mat, img_size,
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np.zeros(img_bgr.shape, dtype=np.float32),
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flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4)
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img_face_mask_aaa = np.clip(img_face_mask_aaa, 0.0, 1.0)
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img_face_mask_aaa[img_face_mask_aaa <= 0.1] = 0.0 # get rid of noise
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if debug:
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debugs += [img_face_mask_aaa.copy()]
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out_img = img_bgr.copy()
|
||||
|
||||
if self.mode == 'raw':
|
||||
if self.raw_mode == 'rgb' or self.raw_mode == 'rgb-mask':
|
||||
out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
|
||||
out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, out_img,
|
||||
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT)
|
||||
|
||||
if self.raw_mode == 'rgb-mask':
|
||||
out_img = np.concatenate ( [out_img, np.expand_dims (img_face_mask_aaa[:,:,0],-1)], -1 )
|
||||
out_img = np.concatenate([out_img, np.expand_dims(img_face_mask_aaa[:, :, 0], -1)], -1)
|
||||
|
||||
if self.raw_mode == 'mask-only':
|
||||
out_img = img_face_mask_aaa
|
||||
|
||||
if self.raw_mode == 'predicted-only':
|
||||
out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.zeros(out_img.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
|
||||
out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size,
|
||||
np.zeros(out_img.shape, dtype=np.float32),
|
||||
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT)
|
||||
|
||||
else:
|
||||
#averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask
|
||||
# averaging [lenx, leny, maskx, masky] by grayscale gradients of upscaled mask
|
||||
ar = []
|
||||
for i in range(1, 10):
|
||||
maxregion = np.argwhere( img_face_mask_aaa > i / 10.0 )
|
||||
maxregion = np.argwhere(img_face_mask_aaa > i / 10.0)
|
||||
if maxregion.size != 0:
|
||||
miny,minx = maxregion.min(axis=0)[:2]
|
||||
maxy,maxx = maxregion.max(axis=0)[:2]
|
||||
miny, minx = maxregion.min(axis=0)[:2]
|
||||
maxy, maxx = maxregion.max(axis=0)[:2]
|
||||
lenx = maxx - minx
|
||||
leny = maxy - miny
|
||||
if min(lenx,leny) >= 4:
|
||||
ar += [ [ lenx, leny] ]
|
||||
if min(lenx, leny) >= 4:
|
||||
ar += [[lenx, leny]]
|
||||
|
||||
if len(ar) > 0:
|
||||
lenx, leny = np.mean ( ar, axis=0 )
|
||||
lowest_len = min (lenx, leny)
|
||||
lenx, leny = np.mean(ar, axis=0)
|
||||
lowest_len = min(lenx, leny)
|
||||
if debug:
|
||||
io.log_info ("lenx/leny:(%d/%d) " % (lenx, leny ) )
|
||||
io.log_info ("lowest_len = %f" % (lowest_len) )
|
||||
io.log_info("lenx/leny:(%d/%d) " % (lenx, leny))
|
||||
io.log_info("lowest_len = %f" % (lowest_len))
|
||||
|
||||
if self.erode_mask_modifier != 0:
|
||||
ero = int( lowest_len * ( 0.126 - lowest_len * 0.00004551365 ) * 0.01*self.erode_mask_modifier )
|
||||
ero = int(lowest_len * (0.126 - lowest_len * 0.00004551365) * 0.01 * self.erode_mask_modifier)
|
||||
if debug:
|
||||
io.log_info ("erode_size = %d" % (ero) )
|
||||
io.log_info("erode_size = %d" % (ero))
|
||||
if ero > 0:
|
||||
img_face_mask_aaa = cv2.erode(img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
|
||||
img_face_mask_aaa = cv2.erode(img_face_mask_aaa,
|
||||
cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ero, ero)),
|
||||
iterations=1)
|
||||
elif ero < 0:
|
||||
img_face_mask_aaa = cv2.dilate(img_face_mask_aaa, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
|
||||
img_face_mask_aaa = cv2.dilate(img_face_mask_aaa,
|
||||
cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (-ero, -ero)),
|
||||
iterations=1)
|
||||
|
||||
img_mask_blurry_aaa = img_face_mask_aaa
|
||||
|
||||
if self.clip_hborder_mask_per > 0: #clip hborder before blur
|
||||
prd_hborder_rect_mask_a = np.ones ( prd_face_mask_a.shape, dtype=np.float32)
|
||||
prd_border_size = int ( prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per )
|
||||
prd_hborder_rect_mask_a[:,0:prd_border_size,:] = 0
|
||||
prd_hborder_rect_mask_a[:,-prd_border_size:,:] = 0
|
||||
prd_hborder_rect_mask_a = np.expand_dims(cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size) ),-1)
|
||||
if self.clip_hborder_mask_per > 0: # clip hborder before blur
|
||||
prd_hborder_rect_mask_a = np.ones(prd_face_mask_a.shape, dtype=np.float32)
|
||||
prd_border_size = int(prd_hborder_rect_mask_a.shape[1] * self.clip_hborder_mask_per)
|
||||
prd_hborder_rect_mask_a[:, 0:prd_border_size, :] = 0
|
||||
prd_hborder_rect_mask_a[:, -prd_border_size:, :] = 0
|
||||
prd_hborder_rect_mask_a = np.expand_dims(
|
||||
cv2.blur(prd_hborder_rect_mask_a, (prd_border_size, prd_border_size)), -1)
|
||||
|
||||
img_prd_hborder_rect_mask_a = cv2.warpAffine( prd_hborder_rect_mask_a, face_output_mat, img_size, np.zeros(img_bgr.shape, dtype=np.float32), cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4 )
|
||||
img_prd_hborder_rect_mask_a = np.expand_dims (img_prd_hborder_rect_mask_a, -1)
|
||||
img_prd_hborder_rect_mask_a = cv2.warpAffine(prd_hborder_rect_mask_a, face_output_mat, img_size,
|
||||
np.zeros(img_bgr.shape, dtype=np.float32),
|
||||
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4)
|
||||
img_prd_hborder_rect_mask_a = np.expand_dims(img_prd_hborder_rect_mask_a, -1)
|
||||
img_mask_blurry_aaa *= img_prd_hborder_rect_mask_a
|
||||
img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
|
||||
img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0)
|
||||
|
||||
if debug:
|
||||
debugs += [img_mask_blurry_aaa.copy()]
|
||||
|
||||
if self.blur_mask_modifier > 0:
|
||||
blur = int( lowest_len * 0.10 * 0.01*self.blur_mask_modifier )
|
||||
blur = int(lowest_len * 0.10 * 0.01 * self.blur_mask_modifier)
|
||||
if debug:
|
||||
io.log_info ("blur_size = %d" % (blur) )
|
||||
io.log_info("blur_size = %d" % (blur))
|
||||
if blur > 0:
|
||||
img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur) )
|
||||
img_mask_blurry_aaa = cv2.blur(img_mask_blurry_aaa, (blur, blur))
|
||||
|
||||
img_mask_blurry_aaa = np.clip( img_mask_blurry_aaa, 0, 1.0 )
|
||||
face_mask_blurry_aaa = cv2.warpAffine( img_mask_blurry_aaa, face_mat, (output_size, output_size) )
|
||||
img_mask_blurry_aaa = np.clip(img_mask_blurry_aaa, 0, 1.0)
|
||||
face_mask_blurry_aaa = cv2.warpAffine(img_mask_blurry_aaa, face_mat, (output_size, output_size))
|
||||
|
||||
if debug:
|
||||
debugs += [img_mask_blurry_aaa.copy()]
|
||||
|
|
@ -286,57 +321,73 @@ class ConverterMasked(Converter):
|
|||
if 'seamless' not in self.mode and self.color_transfer_mode is not None:
|
||||
if self.color_transfer_mode == 'rct':
|
||||
if debug:
|
||||
debugs += [ np.clip( 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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(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), 0, 1.0)]
|
||||
|
||||
prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (prd_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
prd_face_bgr = imagelib.reinhard_color_transfer(
|
||||
np.clip((prd_face_bgr * 255).astype(np.uint8), 0, 255),
|
||||
np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255),
|
||||
source_mask=prd_face_mask_a, target_mask=prd_face_mask_a)
|
||||
prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
prd_face_bgr = np.clip(prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
|
||||
if debug:
|
||||
debugs += [ np.clip( 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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(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), 0, 1.0)]
|
||||
|
||||
|
||||
elif self.color_transfer_mode == 'lct':
|
||||
if debug:
|
||||
debugs += [ np.clip( 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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(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), 0, 1.0)]
|
||||
|
||||
prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
|
||||
prd_face_bgr = np.clip( prd_face_bgr, 0.0, 1.0)
|
||||
prd_face_bgr = imagelib.linear_color_transfer(prd_face_bgr, dst_face_bgr)
|
||||
prd_face_bgr = np.clip(prd_face_bgr, 0.0, 1.0)
|
||||
|
||||
if debug:
|
||||
debugs += [ np.clip( 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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(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), 0, 1.0)]
|
||||
|
||||
if self.mode == 'hist-match-bw':
|
||||
prd_face_bgr = cv2.cvtColor(prd_face_bgr, cv2.COLOR_BGR2GRAY)
|
||||
prd_face_bgr = np.repeat( np.expand_dims (prd_face_bgr, -1), (3,), -1 )
|
||||
prd_face_bgr = np.repeat(np.expand_dims(prd_face_bgr, -1), (3,), -1)
|
||||
|
||||
if self.mode == 'hist-match' or self.mode == 'hist-match-bw':
|
||||
if debug:
|
||||
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 ) ]
|
||||
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)]
|
||||
|
||||
hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
|
||||
hist_mask_a = np.ones(prd_face_bgr.shape[:2] + (1,), dtype=np.float32)
|
||||
|
||||
if self.masked_hist_match:
|
||||
hist_mask_a *= prd_face_mask_a
|
||||
|
||||
white = (1.0-hist_mask_a)* np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
|
||||
white = (1.0 - hist_mask_a) * np.ones(prd_face_bgr.shape[:2] + (1,), dtype=np.float32)
|
||||
|
||||
hist_match_1 = prd_face_bgr*hist_mask_a + white
|
||||
hist_match_1[ hist_match_1 > 1.0 ] = 1.0
|
||||
hist_match_1 = prd_face_bgr * hist_mask_a + white
|
||||
hist_match_1[hist_match_1 > 1.0] = 1.0
|
||||
|
||||
hist_match_2 = dst_face_bgr*hist_mask_a + white
|
||||
hist_match_2[ hist_match_1 > 1.0 ] = 1.0
|
||||
hist_match_2 = dst_face_bgr * hist_mask_a + white
|
||||
hist_match_2[hist_match_1 > 1.0] = 1.0
|
||||
|
||||
prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold )
|
||||
prd_face_bgr = imagelib.color_hist_match(hist_match_1, hist_match_2, self.hist_match_threshold)
|
||||
|
||||
#if self.masked_hist_match:
|
||||
# if self.masked_hist_match:
|
||||
# prd_face_bgr -= white
|
||||
|
||||
if self.mode == 'hist-match-bw':
|
||||
prd_face_bgr = prd_face_bgr.astype(dtype=np.float32)
|
||||
|
||||
out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, out_img, cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT )
|
||||
out_img = cv2.warpAffine(prd_face_bgr, face_output_mat, img_size, out_img,
|
||||
cv2.WARP_INVERSE_MAP | cv2.INTER_LANCZOS4, cv2.BORDER_TRANSPARENT)
|
||||
out_img = np.clip(out_img, 0.0, 1.0)
|
||||
|
||||
if debug:
|
||||
|
|
@ -346,73 +397,91 @@ class ConverterMasked(Converter):
|
|||
pass
|
||||
|
||||
if 'seamless' in self.mode:
|
||||
#mask used for cv2.seamlessClone
|
||||
# mask used for cv2.seamlessClone
|
||||
img_face_seamless_mask_a = None
|
||||
img_face_mask_a = img_mask_blurry_aaa[...,0:1]
|
||||
for i in range(1,10):
|
||||
img_face_mask_a = img_mask_blurry_aaa[..., 0:1]
|
||||
for i in range(1, 10):
|
||||
a = img_face_mask_a > i / 10.0
|
||||
if len(np.argwhere(a)) == 0:
|
||||
continue
|
||||
img_face_seamless_mask_a = img_mask_blurry_aaa[...,0:1].copy()
|
||||
img_face_seamless_mask_a = img_mask_blurry_aaa[..., 0:1].copy()
|
||||
img_face_seamless_mask_a[a] = 1.0
|
||||
img_face_seamless_mask_a[img_face_seamless_mask_a <= i / 10.0] = 0.0
|
||||
break
|
||||
|
||||
try:
|
||||
#calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering
|
||||
l,t,w,h = cv2.boundingRect( (img_face_seamless_mask_a*255).astype(np.uint8) )
|
||||
s_maskx, s_masky = int(l+w/2), int(t+h/2)
|
||||
# calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering
|
||||
l, t, w, h = cv2.boundingRect((img_face_seamless_mask_a * 255).astype(np.uint8))
|
||||
s_maskx, s_masky = int(l + w / 2), int(t + h / 2)
|
||||
|
||||
out_img = cv2.seamlessClone( (out_img*255).astype(np.uint8), (img_bgr*255).astype(np.uint8), (img_face_seamless_mask_a*255).astype(np.uint8), (s_maskx,s_masky) , cv2.NORMAL_CLONE )
|
||||
out_img = cv2.seamlessClone((out_img * 255).astype(np.uint8), (img_bgr * 255).astype(np.uint8),
|
||||
(img_face_seamless_mask_a * 255).astype(np.uint8),
|
||||
(s_maskx, s_masky), cv2.NORMAL_CLONE)
|
||||
out_img = out_img.astype(dtype=np.float32) / 255.0
|
||||
except Exception as e:
|
||||
#seamlessClone may fail in some cases
|
||||
# seamlessClone may fail in some cases
|
||||
e_str = traceback.format_exc()
|
||||
|
||||
if 'MemoryError' in e_str:
|
||||
raise Exception("Seamless fail: " + e_str) #reraise MemoryError in order to reprocess this data by other processes
|
||||
raise Exception(
|
||||
"Seamless fail: " + e_str) # reraise MemoryError in order to reprocess this data by other processes
|
||||
else:
|
||||
print ("Seamless fail: " + e_str)
|
||||
print("Seamless fail: " + e_str)
|
||||
|
||||
if debug:
|
||||
debugs += [out_img.copy()]
|
||||
|
||||
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (out_img*img_mask_blurry_aaa) , 0, 1.0 )
|
||||
out_img = np.clip(img_bgr * (1 - img_mask_blurry_aaa) + (out_img * img_mask_blurry_aaa), 0, 1.0)
|
||||
|
||||
if 'seamless' in self.mode and self.color_transfer_mode is not None:
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
|
||||
out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size))
|
||||
|
||||
if self.color_transfer_mode == 'rct':
|
||||
if debug:
|
||||
debugs += [ np.clip( cv2.warpAffine( out_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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(cv2.warpAffine(out_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), 0, 1.0)]
|
||||
|
||||
new_out_face_bgr = imagelib.reinhard_color_transfer ( np.clip( (out_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
np.clip( (dst_face_bgr*255).astype(np.uint8), 0, 255),
|
||||
new_out_face_bgr = imagelib.reinhard_color_transfer(
|
||||
np.clip((out_face_bgr * 255).astype(np.uint8), 0, 255),
|
||||
np.clip((dst_face_bgr * 255).astype(np.uint8), 0, 255),
|
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source_mask=face_mask_blurry_aaa, target_mask=face_mask_blurry_aaa)
|
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new_out_face_bgr = np.clip( new_out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
new_out_face_bgr = np.clip(new_out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
|
||||
if debug:
|
||||
debugs += [ np.clip( cv2.warpAffine( new_out_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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(cv2.warpAffine(new_out_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), 0, 1.0)]
|
||||
|
||||
|
||||
elif self.color_transfer_mode == 'lct':
|
||||
if debug:
|
||||
debugs += [ np.clip( cv2.warpAffine( out_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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(cv2.warpAffine(out_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), 0, 1.0)]
|
||||
|
||||
new_out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
|
||||
new_out_face_bgr = np.clip( new_out_face_bgr, 0.0, 1.0)
|
||||
new_out_face_bgr = imagelib.linear_color_transfer(out_face_bgr, dst_face_bgr)
|
||||
new_out_face_bgr = np.clip(new_out_face_bgr, 0.0, 1.0)
|
||||
|
||||
if debug:
|
||||
debugs += [ np.clip( cv2.warpAffine( new_out_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 ), 0, 1.0) ]
|
||||
debugs += [np.clip(cv2.warpAffine(new_out_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), 0, 1.0)]
|
||||
|
||||
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 )
|
||||
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
|
||||
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)
|
||||
out_img = np.clip(img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0)
|
||||
|
||||
if self.mode == 'seamless-hist-match':
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size) )
|
||||
out_face_bgr = cv2.warpAffine(out_img, face_mat, (output_size, output_size))
|
||||
new_out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, self.hist_match_threshold)
|
||||
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 )
|
||||
out_img = np.clip( img_bgr*(1-img_mask_blurry_aaa) + (new_out*img_mask_blurry_aaa) , 0, 1.0 )
|
||||
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)
|
||||
out_img = np.clip(img_bgr * (1 - img_mask_blurry_aaa) + (new_out * img_mask_blurry_aaa), 0, 1.0)
|
||||
|
||||
if self.final_image_color_degrade_power != 0:
|
||||
if debug:
|
||||
|
|
@ -422,12 +491,12 @@ class ConverterMasked(Converter):
|
|||
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)
|
||||
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 )
|
||||
out_img = np.concatenate([out_img, np.expand_dims(img_mask_blurry_aaa[:, :, 0], -1)], -1)
|
||||
|
||||
out_img = np.clip (out_img, 0.0, 1.0 )
|
||||
out_img = np.clip(out_img, 0.0, 1.0)
|
||||
|
||||
if debug:
|
||||
debugs += [out_img.copy()]
|
||||
|
|
|
|||
|
|
@ -224,9 +224,9 @@ class SampleProcessor(object):
|
|||
if ct_sample_bgr is None:
|
||||
ct_sample_bgr = ct_sample.load_bgr()
|
||||
|
||||
ct_sample_bgr_resized = cv2.resize(ct_sample_bgr, (resolution, resolution), cv2.INTER_LINEAR)
|
||||
# ct_sample_bgr_resized = cv2.resize(ct_sample_bgr, (resolution, resolution), cv2.INTER_LINEAR)
|
||||
|
||||
img_bgr = imagelib.linear_color_transfer(img_bgr, ct_sample_bgr_resized)
|
||||
img_bgr = imagelib.reinhard_color_transfer(img_bgr, ct_sample_bgr[..., 0:3])
|
||||
img_bgr = np.clip(img_bgr, 0.0, 1.0)
|
||||
|
||||
if normalize_std_dev:
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue