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5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat Copies faces containing XSeg polygons to aligned_xseg\ dir. Useful only if you want to collect labeled faces and reuse them in other fakes. Now you can use trained XSeg mask in the SAEHD training process. It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model. use 5.XSeg.optional) trained mask for data_dst/data_src - apply.bat 5.XSeg.optional) trained mask for data_dst/data_src - remove.bat Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions. XSeg trainer : now you can choose type of face XSeg trainer : now you can restart training in “override settings” Merger: XSeg-* modes now can be used with all types of faces. Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed, because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
from enum import IntEnum
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from pathlib import Path
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import cv2
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import numpy as np
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from core.cv2ex import *
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from DFLIMG import *
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from facelib import LandmarksProcessor
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from core.imagelib import SegIEPolys
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class SampleType(IntEnum):
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IMAGE = 0 #raw image
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FACE_BEGIN = 1
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FACE = 1 #aligned face unsorted
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FACE_PERSON = 2 #aligned face person
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FACE_TEMPORAL_SORTED = 3 #sorted by source filename
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FACE_END = 3
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QTY = 4
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class Sample(object):
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__slots__ = ['sample_type',
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'filename',
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'face_type',
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'shape',
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'landmarks',
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'seg_ie_polys',
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'xseg_mask',
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'eyebrows_expand_mod',
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'source_filename',
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'person_name',
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'pitch_yaw_roll',
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'_filename_offset_size',
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]
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def __init__(self, sample_type=None,
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filename=None,
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face_type=None,
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shape=None,
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landmarks=None,
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seg_ie_polys=None,
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xseg_mask=None,
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eyebrows_expand_mod=None,
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source_filename=None,
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person_name=None,
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pitch_yaw_roll=None,
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**kwargs):
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self.sample_type = sample_type if sample_type is not None else SampleType.IMAGE
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self.filename = filename
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self.face_type = face_type
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self.shape = shape
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self.landmarks = np.array(landmarks) if landmarks is not None else None
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if isinstance(seg_ie_polys, SegIEPolys):
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self.seg_ie_polys = seg_ie_polys
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else:
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self.seg_ie_polys = SegIEPolys.load(seg_ie_polys)
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self.xseg_mask = xseg_mask
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self.eyebrows_expand_mod = eyebrows_expand_mod if eyebrows_expand_mod is not None else 1.0
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self.source_filename = source_filename
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self.person_name = person_name
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self.pitch_yaw_roll = pitch_yaw_roll
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self._filename_offset_size = None
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def get_pitch_yaw_roll(self):
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if self.pitch_yaw_roll is None:
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self.pitch_yaw_roll = LandmarksProcessor.estimate_pitch_yaw_roll(self.landmarks, size=self.shape[1])
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return self.pitch_yaw_roll
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def set_filename_offset_size(self, filename, offset, size):
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self._filename_offset_size = (filename, offset, size)
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def read_raw_file(self, filename=None):
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if self._filename_offset_size is not None:
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filename, offset, size = self._filename_offset_size
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with open(filename, "rb") as f:
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f.seek( offset, 0)
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return f.read (size)
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else:
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with open(filename, "rb") as f:
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return f.read()
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def load_bgr(self):
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img = cv2_imread (self.filename, loader_func=self.read_raw_file).astype(np.float32) / 255.0
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return img
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def get_config(self):
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return {'sample_type': self.sample_type,
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'filename': self.filename,
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'face_type': self.face_type,
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'shape': self.shape,
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'landmarks': self.landmarks.tolist(),
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'seg_ie_polys': self.seg_ie_polys.dump(),
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'xseg_mask' : self.xseg_mask,
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'eyebrows_expand_mod': self.eyebrows_expand_mod,
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'source_filename': self.source_filename,
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'person_name': self.person_name
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}
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