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here new whole_face + XSeg workflow: with XSeg model you can train your own mask segmentator for dst(and/or src) faces that will be used by the merger for whole_face. Instead of using a pretrained segmentator model (which does not exist), you control which part of faces should be masked. new scripts: 5.XSeg) data_dst edit masks.bat 5.XSeg) data_src edit masks.bat 5.XSeg) train.bat Usage: unpack dst faceset if packed run 5.XSeg) data_dst edit masks.bat Read tooltips on the buttons (en/ru/zn languages are supported) mask the face using include or exclude polygon mode. repeat for 50/100 faces, !!! you don't need to mask every frame of dst only frames where the face is different significantly, for example: closed eyes changed head direction changed light the more various faces you mask, the more quality you will get Start masking from the upper left area and follow the clockwise direction. Keep the same logic of masking for all frames, for example: the same approximated jaw line of the side faces, where the jaw is not visible the same hair line Mask the obstructions using exclude polygon mode. run XSeg) train.bat train the model Check the faces of 'XSeg dst faces' preview. if some faces have wrong or glitchy mask, then repeat steps: run edit find these glitchy faces and mask them train further or restart training from scratch Restart training of XSeg model is only possible by deleting all 'model\XSeg_*' files. If you want to get the mask of the predicted face (XSeg-prd mode) in merger, you should repeat the same steps for src faceset. New mask modes available in merger for whole_face: XSeg-prd - XSeg mask of predicted face -> faces from src faceset should be labeled XSeg-dst - XSeg mask of dst face -> faces from dst faceset should be labeled XSeg-prd*XSeg-dst - the smallest area of both if workspace\model folder contains trained XSeg model, then merger will use it, otherwise you will get transparent mask by using XSeg-* modes. Some screenshots: XSegEditor: https://i.imgur.com/7Bk4RRV.jpg trainer : https://i.imgur.com/NM1Kn3s.jpg merger : https://i.imgur.com/glUzFQ8.jpg example of the fake using 13 segmented dst faces : https://i.imgur.com/wmvyizU.gifv
114 lines
No EOL
4.5 KiB
Python
114 lines
No EOL
4.5 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 IEPolys, 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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'ie_polys',
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'seg_ie_polys',
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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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ie_polys=None,
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seg_ie_polys=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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self.ie_polys = IEPolys.load(ie_polys)
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self.seg_ie_polys = SegIEPolys.load(seg_ie_polys)
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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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'ie_polys': self.ie_polys.dump(),
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'seg_ie_polys': self.seg_ie_polys.dump(),
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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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"""
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def copy_and_set(self, sample_type=None, filename=None, face_type=None, shape=None, landmarks=None, ie_polys=None, pitch_yaw_roll=None, eyebrows_expand_mod=None, source_filename=None, fanseg_mask=None, person_name=None):
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return Sample(
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sample_type=sample_type if sample_type is not None else self.sample_type,
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filename=filename if filename is not None else self.filename,
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face_type=face_type if face_type is not None else self.face_type,
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shape=shape if shape is not None else self.shape,
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landmarks=landmarks if landmarks is not None else self.landmarks.copy(),
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ie_polys=ie_polys if ie_polys is not None else self.ie_polys,
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pitch_yaw_roll=pitch_yaw_roll if pitch_yaw_roll is not None else self.pitch_yaw_roll,
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eyebrows_expand_mod=eyebrows_expand_mod if eyebrows_expand_mod is not None else self.eyebrows_expand_mod,
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source_filename=source_filename if source_filename is not None else self.source_filename,
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person_name=person_name if person_name is not None else self.person_name)
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""" |