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https://github.com/iperov/DeepFaceLab.git
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add morph power option to interactive merger, side effect overwrites key bindings of image color degrade!
This commit is contained in:
J\Jan
parent
62f1d57871
commit
cfc3ad8878
6 changed files with 466 additions and 8 deletions
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@ -14,7 +14,7 @@ from core.joblib import MPClassFuncOnDemand, MPFunc
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from core.leras import nn
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from DFLIMG import DFLIMG
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from facelib import FaceEnhancer, FaceType, LandmarksProcessor, XSegNet
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from merger import FrameInfo, InteractiveMergerSubprocessor, MergerConfig
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from merger import FrameInfo, InteractiveMergerSubprocessor, MergerConfig, MergerConfigMaskedMorph
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def main (model_class_name=None,
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@ -78,7 +78,7 @@ def main (model_class_name=None,
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input_path_image_paths = pathex.get_image_paths(input_path)
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if cfg.type == MergerConfig.TYPE_MASKED:
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if cfg.type == MergerConfig.TYPE_MASKED or cfg.type == MergerConfig.TYPE_MASKED_MORPH:
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if not aligned_path.exists():
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io.log_err('Aligned directory not found. Please ensure it exists.')
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return
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@ -11,7 +11,7 @@ from core import imagelib, pathex
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from core.cv2ex import *
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from core.interact import interact as io
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from core.joblib import Subprocessor
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from merger import MergeFaceAvatar, MergeMasked, MergerConfig
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from merger import MergeFaceAvatar, MergeMasked, MergeMaskedMorph, MergerConfig
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from .MergerScreen import Screen, ScreenManager
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@ -105,6 +105,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
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else:
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if cfg.type == MergerConfig.TYPE_MASKED:
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try:
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final_img = MergeMasked (self.predictor_func, self.predictor_input_shape,
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face_enhancer_func=self.face_enhancer_func,
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xseg_256_extract_func=self.xseg_256_extract_func,
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@ -116,6 +117,21 @@ class InteractiveMergerSubprocessor(Subprocessor):
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raise Subprocessor.SilenceException
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else:
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raise Exception( f'Error while merging file [{filepath}]: {e_str}' )
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elif cfg.type == MergerConfig.TYPE_MASKED_MORPH:
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try:
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final_img = MergeMaskedMorph (self.predictor_func, self.predictor_input_shape,
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face_enhancer_func=self.face_enhancer_func,
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xseg_256_extract_func=self.xseg_256_extract_func,
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cfg=cfg,
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frame_info=frame_info)
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except Exception as e:
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e_str = traceback.format_exc()
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if 'MemoryError' in e_str:
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raise Subprocessor.SilenceException
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else:
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raise Exception( f'Error while merging file [{filepath}]: {e_str}' )
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elif cfg.type == MergerConfig.TYPE_FACE_AVATAR:
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final_img = MergeFaceAvatar (self.predictor_func, self.predictor_input_shape,
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@ -290,6 +306,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
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help_images = {
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MergerConfig.TYPE_MASKED : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_masked.jpg') ),
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MergerConfig.TYPE_FACE_AVATAR : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_face_avatar.jpg') ),
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MergerConfig.TYPE_MASKED_MORPH : cv2_imread ( str(Path(__file__).parent / 'gfx' / 'help_merger_masked.jpg') ),
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}
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self.main_screen = Screen(initial_scale_to_width=1368, image=None, waiting_icon=True)
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@ -332,7 +349,17 @@ class InteractiveMergerSubprocessor(Subprocessor):
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'c' : lambda cfg,shift_pressed: cfg.toggle_color_transfer_mode(),
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'n' : lambda cfg,shift_pressed: cfg.toggle_sharpen_mode(),
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}
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#TODO remove side effects
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self.masked_morph_keys_funcs = self.masked_keys_funcs.copy()
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self.masked_morph_keys_funcs.update({
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'p' : lambda cfg,shift_pressed: cfg.add_morph_power(1 if not shift_pressed else 5),
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';' : lambda cfg,shift_pressed: cfg.add_morph_power(-1),
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':' : lambda cfg,shift_pressed: cfg.add_morph_power(-5)})
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self.masked_keys = list(self.masked_keys_funcs.keys())
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self.masked_morph_keys = list(self.masked_morph_keys_funcs.keys())
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#overridable optional
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def on_clients_finalized(self):
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@ -417,7 +444,7 @@ class InteractiveMergerSubprocessor(Subprocessor):
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self.is_interactive_quitting = True
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elif self.screen_manager.get_current() is self.main_screen:
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if self.merger_config.type == MergerConfig.TYPE_MASKED and chr_key in self.masked_keys:
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if (self.merger_config.type == MergerConfig.TYPE_MASKED and chr_key in self.masked_keys) or (self.merger_config.type == MergerConfig.TYPE_MASKED_MORPH and chr_key in self.masked_morph_keys):
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self.process_remain_frames = False
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if cur_frame is not None:
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@ -426,6 +453,9 @@ class InteractiveMergerSubprocessor(Subprocessor):
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if cfg.type == MergerConfig.TYPE_MASKED:
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self.masked_keys_funcs[chr_key](cfg, shift_pressed)
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if cfg.type == MergerConfig.TYPE_MASKED_MORPH:
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self.masked_morph_keys_funcs[chr_key](cfg, shift_pressed)
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if prev_cfg != cfg:
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io.log_info ( cfg.to_string(cur_frame.frame_info.filepath.name) )
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@ -351,3 +351,344 @@ def MergeMasked (predictor_func,
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final_img = np.concatenate ( [final_img, final_mask], -1)
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return (final_img*255).astype(np.uint8)
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def MergeMaskedMorph (predictor_func,
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predictor_input_shape,
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face_enhancer_func,
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xseg_256_extract_func,
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cfg,
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frame_info):
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img_bgr_uint8 = cv2_imread(frame_info.filepath)
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img_bgr_uint8 = imagelib.normalize_channels (img_bgr_uint8, 3)
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img_bgr = img_bgr_uint8.astype(np.float32) / 255.0
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outs = []
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for face_num, img_landmarks in enumerate( frame_info.landmarks_list ):
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out_img, out_img_merging_mask = MergeMaskedFaceMorph (predictor_func, predictor_input_shape, face_enhancer_func, xseg_256_extract_func, cfg, frame_info, img_bgr_uint8, img_bgr, img_landmarks)
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outs += [ (out_img, out_img_merging_mask) ]
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#Combining multiple face outputs
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final_img = None
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final_mask = None
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for img, merging_mask in outs:
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h,w,c = img.shape
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if final_img is None:
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final_img = img
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final_mask = merging_mask
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else:
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final_img = final_img*(1-merging_mask) + img*merging_mask
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final_mask = np.clip (final_mask + merging_mask, 0, 1 )
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final_img = np.concatenate ( [final_img, final_mask], -1)
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return (final_img*255).astype(np.uint8)
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def MergeMaskedFaceMorph (predictor_func, predictor_input_shape,
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face_enhancer_func,
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xseg_256_extract_func,
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cfg, frame_info, img_bgr_uint8, img_bgr, img_face_landmarks):
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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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input_size = predictor_input_shape[0]
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mask_subres_size = input_size*4
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output_size = input_size
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if cfg.super_resolution_power != 0:
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output_size *= 4
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type)
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face_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, output_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
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if mask_subres_size == output_size:
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face_mask_output_mat = face_output_mat
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else:
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face_mask_output_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, mask_subres_size, face_type=cfg.face_type, scale= 1.0 + 0.01*cfg.output_face_scale)
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dst_face_bgr = cv2.warpAffine( img_bgr , face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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dst_face_bgr = np.clip(dst_face_bgr, 0, 1)
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dst_face_mask_a_0 = cv2.warpAffine( img_face_mask_a, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
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dst_face_mask_a_0 = np.clip(dst_face_mask_a_0, 0, 1)
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predictor_input_bgr = cv2.resize (dst_face_bgr, (input_size,input_size) )
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predicted = predictor_func (predictor_input_bgr, (cfg.morph_power / 100.0))
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prd_face_bgr = np.clip (predicted[0], 0, 1.0)
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prd_face_mask_a_0 = np.clip (predicted[1], 0, 1.0)
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prd_face_dst_mask_a_0 = np.clip (predicted[2], 0, 1.0)
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if cfg.super_resolution_power != 0:
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prd_face_bgr_enhanced = face_enhancer_func(prd_face_bgr, is_tanh=True, preserve_size=False)
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mod = cfg.super_resolution_power / 100.0
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prd_face_bgr = cv2.resize(prd_face_bgr, (output_size,output_size))*(1.0-mod) + prd_face_bgr_enhanced*mod
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prd_face_bgr = np.clip(prd_face_bgr, 0, 1)
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if cfg.super_resolution_power != 0:
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prd_face_mask_a_0 = cv2.resize (prd_face_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
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prd_face_dst_mask_a_0 = cv2.resize (prd_face_dst_mask_a_0, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
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if cfg.mask_mode == 0: #full
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wrk_face_mask_a_0 = np.ones_like(dst_face_mask_a_0)
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elif cfg.mask_mode == 1: #dst
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wrk_face_mask_a_0 = cv2.resize (dst_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
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elif cfg.mask_mode == 2: #learned-prd
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wrk_face_mask_a_0 = prd_face_mask_a_0
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elif cfg.mask_mode == 3: #learned-dst
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wrk_face_mask_a_0 = prd_face_dst_mask_a_0
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elif cfg.mask_mode == 4: #learned-prd*learned-dst
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wrk_face_mask_a_0 = prd_face_mask_a_0*prd_face_dst_mask_a_0
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elif cfg.mask_mode == 5: #learned-prd+learned-dst
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wrk_face_mask_a_0 = np.clip( prd_face_mask_a_0+prd_face_dst_mask_a_0, 0, 1)
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elif cfg.mask_mode >= 6 and cfg.mask_mode <= 9: #XSeg modes
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if cfg.mask_mode == 6 or cfg.mask_mode == 8 or cfg.mask_mode == 9:
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# obtain XSeg-prd
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prd_face_xseg_bgr = cv2.resize (prd_face_bgr, (xseg_input_size,)*2, interpolation=cv2.INTER_CUBIC)
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prd_face_xseg_mask = xseg_256_extract_func(prd_face_xseg_bgr)
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X_prd_face_mask_a_0 = cv2.resize ( prd_face_xseg_mask, (output_size, output_size), interpolation=cv2.INTER_CUBIC)
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if cfg.mask_mode >= 7 and cfg.mask_mode <= 9:
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# obtain XSeg-dst
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xseg_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, xseg_input_size, face_type=cfg.face_type)
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dst_face_xseg_bgr = cv2.warpAffine(img_bgr, xseg_mat, (xseg_input_size,)*2, flags=cv2.INTER_CUBIC )
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dst_face_xseg_mask = xseg_256_extract_func(dst_face_xseg_bgr)
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X_dst_face_mask_a_0 = cv2.resize (dst_face_xseg_mask, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
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if cfg.mask_mode == 6: #'XSeg-prd'
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wrk_face_mask_a_0 = X_prd_face_mask_a_0
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elif cfg.mask_mode == 7: #'XSeg-dst'
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wrk_face_mask_a_0 = X_dst_face_mask_a_0
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elif cfg.mask_mode == 8: #'XSeg-prd*XSeg-dst'
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wrk_face_mask_a_0 = X_prd_face_mask_a_0 * X_dst_face_mask_a_0
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elif cfg.mask_mode == 9: #learned-prd*learned-dst*XSeg-prd*XSeg-dst
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wrk_face_mask_a_0 = prd_face_mask_a_0 * prd_face_dst_mask_a_0 * X_prd_face_mask_a_0 * X_dst_face_mask_a_0
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wrk_face_mask_a_0[ wrk_face_mask_a_0 < (1.0/255.0) ] = 0.0 # get rid of noise
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# resize to mask_subres_size
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if wrk_face_mask_a_0.shape[0] != mask_subres_size:
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wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (mask_subres_size, mask_subres_size), interpolation=cv2.INTER_CUBIC)
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# process mask in local predicted space
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if 'raw' not in cfg.mode:
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# add zero pad
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wrk_face_mask_a_0 = np.pad (wrk_face_mask_a_0, input_size)
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ero = cfg.erode_mask_modifier
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blur = cfg.blur_mask_modifier
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if ero > 0:
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wrk_face_mask_a_0 = cv2.erode(wrk_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(ero,ero)), iterations = 1 )
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elif ero < 0:
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wrk_face_mask_a_0 = cv2.dilate(wrk_face_mask_a_0, cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(-ero,-ero)), iterations = 1 )
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# clip eroded/dilated mask in actual predict area
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# pad with half blur size in order to accuratelly fade to zero at the boundary
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clip_size = input_size + blur // 2
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wrk_face_mask_a_0[:clip_size,:] = 0
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wrk_face_mask_a_0[-clip_size:,:] = 0
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wrk_face_mask_a_0[:,:clip_size] = 0
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wrk_face_mask_a_0[:,-clip_size:] = 0
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if blur > 0:
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blur = blur + (1-blur % 2)
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wrk_face_mask_a_0 = cv2.GaussianBlur(wrk_face_mask_a_0, (blur, blur) , 0)
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wrk_face_mask_a_0 = wrk_face_mask_a_0[input_size:-input_size,input_size:-input_size]
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wrk_face_mask_a_0 = np.clip(wrk_face_mask_a_0, 0, 1)
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img_face_mask_a = cv2.warpAffine( wrk_face_mask_a_0, face_mask_output_mat, img_size, np.zeros(img_bgr.shape[0:2], dtype=np.float32), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )[...,None]
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img_face_mask_a = np.clip (img_face_mask_a, 0.0, 1.0)
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img_face_mask_a [ img_face_mask_a < (1.0/255.0) ] = 0.0 # get rid of noise
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if wrk_face_mask_a_0.shape[0] != output_size:
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wrk_face_mask_a_0 = cv2.resize (wrk_face_mask_a_0, (output_size,output_size), interpolation=cv2.INTER_CUBIC)
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wrk_face_mask_a = wrk_face_mask_a_0[...,None]
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out_img = None
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out_merging_mask_a = None
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if cfg.mode == 'original':
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return img_bgr, img_face_mask_a
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elif 'raw' in cfg.mode:
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if cfg.mode == 'raw-rgb':
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out_img_face = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC)
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out_img_face_mask = cv2.warpAffine( np.ones_like(prd_face_bgr), face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC)
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out_img = img_bgr*(1-out_img_face_mask) + out_img_face*out_img_face_mask
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out_merging_mask_a = img_face_mask_a
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elif cfg.mode == 'raw-predict':
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out_img = prd_face_bgr
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out_merging_mask_a = wrk_face_mask_a
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else:
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raise ValueError(f"undefined raw type {cfg.mode}")
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out_img = np.clip (out_img, 0.0, 1.0 )
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else:
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# Process if the mask meets minimum size
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maxregion = np.argwhere( img_face_mask_a >= 0.1 )
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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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lenx = maxx - minx
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leny = maxy - miny
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if min(lenx,leny) >= 4:
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wrk_face_mask_area_a = wrk_face_mask_a.copy()
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wrk_face_mask_area_a[wrk_face_mask_area_a>0] = 1.0
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if 'seamless' not in cfg.mode and cfg.color_transfer_mode != 0:
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if cfg.color_transfer_mode == 1: #rct
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prd_face_bgr = imagelib.reinhard_color_transfer ( np.clip( prd_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
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np.clip( dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8), )
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prd_face_bgr = np.clip( prd_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
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elif cfg.color_transfer_mode == 2: #lct
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prd_face_bgr = imagelib.linear_color_transfer (prd_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 3: #mkl
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prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 4: #mkl-m
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prd_face_bgr = imagelib.color_transfer_mkl (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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elif cfg.color_transfer_mode == 5: #idt
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prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr, dst_face_bgr)
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elif cfg.color_transfer_mode == 6: #idt-m
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prd_face_bgr = imagelib.color_transfer_idt (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
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elif cfg.color_transfer_mode == 7: #sot-m
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||||
prd_face_bgr = imagelib.color_transfer_sot (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30)
|
||||
prd_face_bgr = np.clip (prd_face_bgr, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 8: #mix-m
|
||||
prd_face_bgr = imagelib.color_transfer_mix (prd_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
|
||||
if cfg.mode == 'hist-match':
|
||||
hist_mask_a = np.ones ( prd_face_bgr.shape[:2] + (1,) , dtype=np.float32)
|
||||
|
||||
if cfg.masked_hist_match:
|
||||
hist_mask_a *= wrk_face_mask_area_a
|
||||
|
||||
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_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, cfg.hist_match_threshold ).astype(dtype=np.float32)
|
||||
|
||||
if 'seamless' in cfg.mode:
|
||||
#mask used for cv2.seamlessClone
|
||||
img_face_seamless_mask_a = None
|
||||
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_face_mask_a.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
|
||||
|
||||
out_img = cv2.warpAffine( prd_face_bgr, face_output_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )
|
||||
out_img = np.clip(out_img, 0.0, 1.0)
|
||||
|
||||
if 'seamless' in cfg.mode:
|
||||
try:
|
||||
#calc same bounding rect and center point as in cv2.seamlessClone to prevent jittering (not flickering)
|
||||
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_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
|
||||
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
|
||||
else:
|
||||
print ("Seamless fail: " + e_str)
|
||||
|
||||
cfg_mp = cfg.motion_blur_power / 100.0
|
||||
|
||||
out_img = img_bgr*(1-img_face_mask_a) + (out_img*img_face_mask_a)
|
||||
|
||||
if ('seamless' in cfg.mode and cfg.color_transfer_mode != 0) or \
|
||||
cfg.mode == 'seamless-hist-match' or \
|
||||
cfg_mp != 0 or \
|
||||
cfg.blursharpen_amount != 0 or \
|
||||
cfg.image_denoise_power != 0 or \
|
||||
cfg.bicubic_degrade_power != 0:
|
||||
|
||||
out_face_bgr = cv2.warpAffine( out_img, face_mat, (output_size, output_size), flags=cv2.INTER_CUBIC )
|
||||
|
||||
if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
|
||||
if cfg.color_transfer_mode == 1:
|
||||
out_face_bgr = imagelib.reinhard_color_transfer ( np.clip(out_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8),
|
||||
np.clip(dst_face_bgr*wrk_face_mask_area_a*255, 0, 255).astype(np.uint8) )
|
||||
out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 2: #lct
|
||||
out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 3: #mkl
|
||||
out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 4: #mkl-m
|
||||
out_face_bgr = imagelib.color_transfer_mkl (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
elif cfg.color_transfer_mode == 5: #idt
|
||||
out_face_bgr = imagelib.color_transfer_idt (out_face_bgr, dst_face_bgr)
|
||||
elif cfg.color_transfer_mode == 6: #idt-m
|
||||
out_face_bgr = imagelib.color_transfer_idt (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
elif cfg.color_transfer_mode == 7: #sot-m
|
||||
out_face_bgr = imagelib.color_transfer_sot (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a, steps=10, batch_size=30)
|
||||
out_face_bgr = np.clip (out_face_bgr, 0.0, 1.0)
|
||||
elif cfg.color_transfer_mode == 8: #mix-m
|
||||
out_face_bgr = imagelib.color_transfer_mix (out_face_bgr*wrk_face_mask_area_a, dst_face_bgr*wrk_face_mask_area_a)
|
||||
|
||||
if cfg.mode == 'seamless-hist-match':
|
||||
out_face_bgr = imagelib.color_hist_match(out_face_bgr, dst_face_bgr, cfg.hist_match_threshold)
|
||||
|
||||
if cfg_mp != 0:
|
||||
k_size = int(frame_info.motion_power*cfg_mp)
|
||||
if k_size >= 1:
|
||||
k_size = np.clip (k_size+1, 2, 50)
|
||||
if cfg.super_resolution_power != 0:
|
||||
k_size *= 2
|
||||
out_face_bgr = imagelib.LinearMotionBlur (out_face_bgr, k_size , frame_info.motion_deg)
|
||||
|
||||
if cfg.blursharpen_amount != 0:
|
||||
out_face_bgr = imagelib.blursharpen ( out_face_bgr, cfg.sharpen_mode, 3, cfg.blursharpen_amount)
|
||||
|
||||
if cfg.image_denoise_power != 0:
|
||||
n = cfg.image_denoise_power
|
||||
while n > 0:
|
||||
img_bgr_denoised = cv2.medianBlur(img_bgr, 5)
|
||||
if int(n / 100) != 0:
|
||||
img_bgr = img_bgr_denoised
|
||||
else:
|
||||
pass_power = (n % 100) / 100.0
|
||||
img_bgr = img_bgr*(1.0-pass_power)+img_bgr_denoised*pass_power
|
||||
n = max(n-10,0)
|
||||
|
||||
if cfg.bicubic_degrade_power != 0:
|
||||
p = 1.0 - cfg.bicubic_degrade_power / 101.0
|
||||
img_bgr_downscaled = cv2.resize (img_bgr, ( int(img_size[0]*p), int(img_size[1]*p ) ), interpolation=cv2.INTER_CUBIC)
|
||||
img_bgr = cv2.resize (img_bgr_downscaled, img_size, interpolation=cv2.INTER_CUBIC)
|
||||
|
||||
new_out = cv2.warpAffine( out_face_bgr, face_mat, img_size, np.empty_like(img_bgr), cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC )
|
||||
|
||||
out_img = np.clip( img_bgr*(1-img_face_mask_a) + (new_out*img_face_mask_a) , 0, 1.0 )
|
||||
|
||||
if cfg.color_degrade_power != 0:
|
||||
out_img_reduced = imagelib.reduce_colors(out_img, 256)
|
||||
if cfg.color_degrade_power == 100:
|
||||
out_img = out_img_reduced
|
||||
else:
|
||||
alpha = cfg.color_degrade_power / 100.0
|
||||
out_img = (out_img*(1.0-alpha) + out_img_reduced*alpha)
|
||||
out_merging_mask_a = img_face_mask_a
|
||||
|
||||
if out_img is None:
|
||||
out_img = img_bgr.copy()
|
||||
|
||||
return out_img, out_merging_mask_a
|
||||
|
|
|
|||
|
|
@ -9,10 +9,12 @@ class MergerConfig(object):
|
|||
TYPE_NONE = 0
|
||||
TYPE_MASKED = 1
|
||||
TYPE_FACE_AVATAR = 2
|
||||
|
||||
####
|
||||
|
||||
TYPE_IMAGE = 3
|
||||
TYPE_IMAGE_WITH_LANDMARKS = 4
|
||||
TYPE_MASKED_MORPH = 5
|
||||
|
||||
def __init__(self, type=0,
|
||||
sharpen_mode=0,
|
||||
|
|
@ -327,3 +329,87 @@ class MergerConfigFaceAvatar(MergerConfig):
|
|||
f"add_source_image : {self.add_source_image}\n") + \
|
||||
super().to_string(filename) + "================"
|
||||
|
||||
|
||||
class MergerConfigMaskedMorph(MergerConfigMasked):
|
||||
|
||||
#override
|
||||
def __init__(self, face_type=FaceType.WHOLE_FACE,
|
||||
default_mode = 'overlay',
|
||||
mode='overlay',
|
||||
masked_hist_match=True,
|
||||
hist_match_threshold = 238,
|
||||
mask_mode = 4,
|
||||
erode_mask_modifier = 0,
|
||||
blur_mask_modifier = 0,
|
||||
motion_blur_power = 0,
|
||||
output_face_scale = 0,
|
||||
super_resolution_power = 0,
|
||||
color_transfer_mode = ctm_str_dict['rct'],
|
||||
image_denoise_power = 0,
|
||||
bicubic_degrade_power = 0,
|
||||
color_degrade_power = 0,
|
||||
morph_power=100,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
MergerConfig.__init__(self, type=MergerConfig.TYPE_MASKED_MORPH, **kwargs)
|
||||
|
||||
self.face_type = face_type
|
||||
if self.face_type not in [FaceType.WHOLE_FACE, FaceType.HEAD ]:
|
||||
raise ValueError("MergerConfigMaskedMorph does not support this type of face.")
|
||||
|
||||
self.default_mode = default_mode
|
||||
|
||||
#default changeable params
|
||||
if mode not in mode_str_dict:
|
||||
mode = mode_dict[1]
|
||||
|
||||
self.mode = mode
|
||||
self.masked_hist_match = masked_hist_match
|
||||
self.hist_match_threshold = hist_match_threshold
|
||||
self.mask_mode = mask_mode
|
||||
self.erode_mask_modifier = erode_mask_modifier
|
||||
self.blur_mask_modifier = blur_mask_modifier
|
||||
self.motion_blur_power = motion_blur_power
|
||||
self.output_face_scale = output_face_scale
|
||||
self.super_resolution_power = super_resolution_power
|
||||
self.color_transfer_mode = color_transfer_mode
|
||||
self.image_denoise_power = image_denoise_power
|
||||
self.bicubic_degrade_power = bicubic_degrade_power
|
||||
self.color_degrade_power = color_degrade_power
|
||||
self.morph_power = morph_power
|
||||
|
||||
|
||||
|
||||
#override
|
||||
def to_string(self, filename):
|
||||
r = super().to_string(filename)
|
||||
if r.endswith('================'):
|
||||
r = r[:-16]
|
||||
|
||||
r += f"""morph_power: {self.morph_power}\n"""
|
||||
|
||||
r += "================"
|
||||
|
||||
return r
|
||||
|
||||
#override
|
||||
def ask_settings(self):
|
||||
|
||||
super().ask_settings()
|
||||
|
||||
self.morph_power = np.clip ( io.input_int ("Morph power in percent (overwrites morph factor)", 0, add_info="0..100"), 0, 100)
|
||||
|
||||
#override
|
||||
def add_morph_power(self, diff):
|
||||
self.morph_power = np.clip ( self.morph_power+diff , 0, 100)
|
||||
|
||||
def __eq__(self, other):
|
||||
#check equality of changeable params
|
||||
|
||||
if isinstance(other, MergerConfigMaskedMorph):
|
||||
return super().__eq__(other) and \
|
||||
self.morph_power == other.morph_power
|
||||
|
||||
return False
|
||||
|
||||
|
|
@ -1,5 +1,6 @@
|
|||
from .FrameInfo import FrameInfo
|
||||
from .MergerConfig import MergerConfig, MergerConfigMasked, MergerConfigFaceAvatar
|
||||
from .MergerConfig import MergerConfig, MergerConfigMasked, MergerConfigFaceAvatar, MergerConfigMaskedMorph
|
||||
from .MergeMasked import MergeMasked
|
||||
from .MergeMasked import MergeMaskedMorph
|
||||
from .MergeAvatar import MergeFaceAvatar
|
||||
from .InteractiveMergerSubprocessor import InteractiveMergerSubprocessor
|
||||
|
|
@ -800,11 +800,11 @@ class AMPModel(ModelBase):
|
|||
def get_MergerConfig(self):
|
||||
morph_factor = np.clip ( io.input_number ("Morph factor", 0.75, add_info="0.0 .. 1.0"), 0.0, 1.0 )
|
||||
|
||||
def predictor_morph(face):
|
||||
return self.predictor_func(face, morph_factor)
|
||||
def predictor_morph(face, func_morph_factor=morph_factor):
|
||||
return self.predictor_func(face, func_morph_factor)
|
||||
|
||||
|
||||
import merger
|
||||
return predictor_morph, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMasked(face_type=self.face_type, default_mode = 'overlay')
|
||||
return predictor_morph, (self.options['resolution'], self.options['resolution'], 3), merger.MergerConfigMaskedMorph(face_type=self.face_type, default_mode = 'overlay')
|
||||
|
||||
Model = AMPModel
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue