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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
76ca79216e
Removed the wait at first launch for most graphics cards. Increased speed of training by 10-20%, but you have to retrain all models from scratch. SAEHD: added option 'use float16' Experimental option. Reduces the model size by half. Increases the speed of training. Decreases the accuracy of the model. The model may collapse or not train. Model may not learn the mask in large resolutions. true_face_training option is replaced by "True face power". 0.0000 .. 1.0 Experimental option. Discriminates the result face to be more like the src face. Higher value - stronger discrimination. Comparison - https://i.imgur.com/czScS9q.png
110 lines
No EOL
4.2 KiB
Python
110 lines
No EOL
4.2 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
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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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'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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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.eyebrows_expand_mod = eyebrows_expand_mod
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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(landmarks)
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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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'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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""" |