mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-16 10:03:41 -07:00
Converter:
Session is now saved to the model folder. blur and erode ranges are increased to -400+400 hist-match-bw is now replaced with seamless2 mode. Added 'ebs' color transfer mode (works only on Windows). FANSEG model (used in FAN-x mask modes) is retrained with new model configuration and now produces better precision and less jitter
This commit is contained in:
Colombo
parent
70dada42ea
commit
7ed38a8097
29 changed files with 768 additions and 314 deletions
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@ -73,6 +73,7 @@ class ConvertSubprocessor(Subprocessor):
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self.device_idx = client_dict['device_idx']
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self.device_name = client_dict['device_name']
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self.predictor_func = client_dict['predictor_func']
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self.predictor_input_shape = client_dict['predictor_input_shape']
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self.superres_func = client_dict['superres_func']
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#transfer and set stdin in order to work code.interact in debug subprocess
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@ -115,15 +116,21 @@ class ConvertSubprocessor(Subprocessor):
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return fanseg.extract(*args, **kwargs)
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self.fanseg_extract_func = fanseg_extract
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import ebsynth
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def ebs_ct(*args, **kwargs):
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return ebsynth.color_transfer(*args, **kwargs)
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self.ebs_ct_func = ebs_ct
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return None
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#override
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def process_data(self, pf): #pf=ProcessingFrame
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cfg = pf.cfg.copy()
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cfg.predictor_func = self.predictor_func
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cfg.sharpen_func = self.sharpen_func
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cfg.superres_func = self.superres_func
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cfg.ebs_ct_func = self.ebs_ct_func
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frame_info = pf.frame_info
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@ -152,7 +159,7 @@ class ConvertSubprocessor(Subprocessor):
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cfg.fanseg_extract_func = self.fanseg_extract_func
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try:
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final_img = ConvertMasked (cfg, frame_info)
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final_img = ConvertMasked (self.predictor_func, self.predictor_input_shape, cfg, 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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@ -161,7 +168,8 @@ class ConvertSubprocessor(Subprocessor):
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raise Exception( 'Error while converting file [%s]: %s' % (filename, e_str) )
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elif cfg.type == ConverterConfig.TYPE_FACE_AVATAR:
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final_img = ConvertFaceAvatar (cfg, pf.prev_temporal_frame_infos,
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final_img = ConvertFaceAvatar (self.predictor_func, self.predictor_input_shape,
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cfg, pf.prev_temporal_frame_infos,
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pf.frame_info,
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pf.next_temporal_frame_infos )
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@ -179,21 +187,22 @@ class ConvertSubprocessor(Subprocessor):
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return pf.frame_info.filename
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#override
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def __init__(self, is_interactive, converter_config, frames, output_path, model_iter):
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def __init__(self, is_interactive, converter_session_filepath, predictor_func, predictor_input_shape, converter_config, frames, output_path, model_iter):
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if len (frames) == 0:
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raise ValueError ("len (frames) == 0")
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super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if CONVERTER_DEBUG else 60, io_loop_sleep_time=0.001, initialize_subprocesses_in_serial=False)# if debug == True else 60)
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super().__init__('Converter', ConvertSubprocessor.Cli, 86400 if CONVERTER_DEBUG else 60, io_loop_sleep_time=0.001, initialize_subprocesses_in_serial=False)
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self.is_interactive = is_interactive
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self.converter_session_filepath = Path(converter_session_filepath)
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self.converter_config = converter_config
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#dummy predict and sleep, tensorflow caching kernels. If remove it, sometime conversion speed can be x2 slower
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self.converter_config.predictor_func (dummy_predict=True)
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predictor_func (dummy_predict=True)
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time.sleep(2)
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self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(self.converter_config.predictor_func)
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self.converter_config.predictor_func = None
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self.predictor_func_host, self.predictor_func = SubprocessFunctionCaller.make_pair(predictor_func)
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self.predictor_input_shape = predictor_input_shape
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self.dcscn = None
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self.ranksrgan = None
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@ -211,11 +220,9 @@ class ConvertSubprocessor(Subprocessor):
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self.prefetch_frame_count = self.process_count = min(6,multiprocessing.cpu_count())
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session_data = None
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session_dat_path = self.output_path / 'session.dat'
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if session_dat_path.exists():
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if self.is_interactive and self.converter_session_filepath.exists():
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try:
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with open( str(session_dat_path), "rb") as f:
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with open( str(self.converter_session_filepath), "rb") as f:
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session_data = pickle.loads(f.read())
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except Exception as e:
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pass
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@ -246,7 +253,7 @@ class ConvertSubprocessor(Subprocessor):
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break
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if frames_equal:
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io.log_info ("Using saved session.")
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io.log_info ('Using saved session from ' + '/'.join (self.converter_session_filepath.parts[-2:]) )
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self.frames = s_frames
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self.frames_idxs = s_frames_idxs
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self.frames_done_idxs = s_frames_done_idxs
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@ -292,6 +299,7 @@ class ConvertSubprocessor(Subprocessor):
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yield 'CPU%d' % (i), {}, {'device_idx': i,
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'device_name': 'CPU%d' % (i),
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'predictor_func': self.predictor_func,
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'predictor_input_shape' : self.predictor_input_shape,
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'superres_func': self.superres_func,
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'stdin_fd': sys.stdin.fileno() if CONVERTER_DEBUG else None
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}
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@ -332,10 +340,9 @@ class ConvertSubprocessor(Subprocessor):
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'frames_done_idxs': self.frames_done_idxs,
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'model_iter' : self.model_iter,
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}
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save_path = self.output_path / 'session.dat'
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save_path.write_bytes( pickle.dumps(session_data) )
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self.converter_session_filepath.write_bytes( pickle.dumps(session_data) )
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io.log_info ("Session is saved to " + '/'.join (save_path.parts[-2:]) )
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io.log_info ("Session is saved to " + '/'.join (self.converter_session_filepath.parts[-2:]) )
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cfg_change_keys = ['`','1', '2', '3', '4', '5', '6', '7', '8', '9',
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'q', 'a', 'w', 's', 'e', 'd', 'r', 'f', 't', 'g','y','h','u','j',
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@ -550,7 +557,7 @@ class ConvertSubprocessor(Subprocessor):
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for i in range ( min(len(self.frames_idxs), self.prefetch_frame_count) ):
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frame = self.frames[ self.frames_idxs[i] ]
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if not frame.is_done and not frame.is_processing and frame.cfg is not None:
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if not frame.is_done and not frame.is_processing and frame.cfg is not None:
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frame.is_processing = True
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return ConvertSubprocessor.ProcessingFrame(idx=frame.idx,
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cfg=frame.cfg.copy(),
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@ -592,8 +599,8 @@ def main (args, device_args):
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import models
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model = models.import_model( args['model_name'] )(model_path, device_args=device_args)
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cfg = model.get_ConverterConfig()
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converter_session_filepath = model.get_strpath_storage_for_file('converter_session.dat')
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predictor_func, predictor_input_shape, cfg = model.get_ConverterConfig()
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if not is_interactive:
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cfg.ask_settings()
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@ -717,7 +724,10 @@ def main (args, device_args):
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else:
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ConvertSubprocessor (
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is_interactive = is_interactive,
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converter_config = cfg,
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converter_session_filepath = converter_session_filepath,
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predictor_func = predictor_func,
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predictor_input_shape = predictor_input_shape,
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converter_config = cfg,
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frames = frames,
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output_path = output_path,
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model_iter = model.get_iter()
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@ -320,7 +320,7 @@ class MaskEditor:
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def get_ie_polys(self):
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return self.ie_polys
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def mask_editor_main(input_dir, confirmed_dir=None, skipped_dir=None):
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def mask_editor_main(input_dir, confirmed_dir=None, skipped_dir=None, no_default_mask=False):
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input_path = Path(input_dir)
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confirmed_path = Path(confirmed_dir)
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@ -334,6 +334,11 @@ def mask_editor_main(input_dir, confirmed_dir=None, skipped_dir=None):
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if not skipped_path.exists():
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skipped_path.mkdir(parents=True)
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if not no_default_mask:
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eyebrows_expand_mod = np.clip ( io.input_int ("Default eyebrows expand modifier? (0..400, skip:100) : ", 100), 0, 400 ) / 100.0
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else:
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eyebrows_expand_mod = None
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wnd_name = "MaskEditor tool"
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io.named_window (wnd_name)
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@ -407,7 +412,10 @@ def mask_editor_main(input_dir, confirmed_dir=None, skipped_dir=None):
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if fanseg_mask is not None:
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mask = fanseg_mask
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else:
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mask = LandmarksProcessor.get_image_hull_mask( img.shape, lmrks)
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if no_default_mask:
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mask = np.zeros ( (target_wh,target_wh,3) )
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else:
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mask = LandmarksProcessor.get_image_hull_mask( img.shape, lmrks, eyebrows_expand_mod=eyebrows_expand_mod)
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else:
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img = np.zeros ( (target_wh,target_wh,3) )
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mask = np.ones ( (target_wh,target_wh,3) )
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@ -506,7 +514,7 @@ def mask_editor_main(input_dir, confirmed_dir=None, skipped_dir=None):
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do_save_move_count -= 1
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ed.mask_finish()
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dflimg.embed_and_set (str(filepath), ie_polys=ed.get_ie_polys() )
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dflimg.embed_and_set (str(filepath), ie_polys=ed.get_ie_polys(), eyebrows_expand_mod=eyebrows_expand_mod )
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done_paths += [ confirmed_path / filepath.name ]
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done_images_types[filepath.name] = 2
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@ -517,7 +525,7 @@ def mask_editor_main(input_dir, confirmed_dir=None, skipped_dir=None):
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do_save_count -= 1
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ed.mask_finish()
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dflimg.embed_and_set (str(filepath), ie_polys=ed.get_ie_polys() )
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dflimg.embed_and_set (str(filepath), ie_polys=ed.get_ie_polys(), eyebrows_expand_mod=eyebrows_expand_mod )
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done_paths += [ filepath ]
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done_images_types[filepath.name] = 2
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@ -7,6 +7,33 @@ from utils.cv2_utils import *
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from facelib import LandmarksProcessor
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from interact import interact as io
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def remove_ie_polys_file (filepath):
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filepath = Path(filepath)
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if filepath.suffix == '.png':
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dflimg = DFLPNG.load( str(filepath) )
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elif filepath.suffix == '.jpg':
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dflimg = DFLJPG.load ( str(filepath) )
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else:
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return
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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return
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dflimg.remove_ie_polys()
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dflimg.embed_and_set( str(filepath) )
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def remove_ie_polys_folder(input_path):
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input_path = Path(input_path)
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io.log_info ("Removing ie_polys...\r\n")
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for filepath in io.progress_bar_generator( Path_utils.get_image_paths(input_path), "Removing"):
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filepath = Path(filepath)
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remove_ie_polys_file(filepath)
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def remove_fanseg_file (filepath):
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filepath = Path(filepath)
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