DeepFaceLab/samplelib/PackedFaceset.py
Colombo 7386a9d6fd optimized face sample generator, CPU load is significantly reduced
SAEHD:

added new option
GAN power 0.0 .. 10.0
	Train the network in Generative Adversarial manner.
	Forces the neural network to learn small details of the face.
	You can enable/disable this option at any time,
	but better to enable it when the network is trained enough.
	Typical value is 1.0
	GAN power with pretrain mode will not work.

Example of enabling GAN on 81k iters +5k iters
https://i.imgur.com/OdXHLhU.jpg
https://i.imgur.com/CYAJmJx.jpg

dfhd: default Decoder dimensions are now 48
the preview for 256 res is now correctly displayed

fixed model naming/renaming/removing

Improvements for those involved in post-processing in AfterEffects:

Codec is reverted back to x264 in order to properly use in AfterEffects and video players.

Merger now always outputs the mask to workspace\data_dst\merged_mask

removed raw modes except raw-rgb
raw-rgb mode now outputs selected face mask_mode (before square mask)

'export alpha mask' button is replaced by 'show alpha mask'.
You can view the alpha mask without recompute the frames.

8) 'merged *.bat' now also output 'result_mask.' video file.
8) 'merged lossless' now uses x264 lossless codec (before PNG codec)
result_mask video file is always lossless.

Thus you can use result_mask video file as mask layer in the AfterEffects.
2020-01-28 12:24:45 +04:00

151 lines
5.1 KiB
Python

import pickle
import shutil
import struct
from pathlib import Path
import samplelib.SampleLoader
from core.interact import interact as io
from samplelib import Sample
from core import pathex
packed_faceset_filename = 'faceset.pak'
class PackedFaceset():
VERSION = 1
@staticmethod
def pack(samples_path):
samples_dat_path = samples_path / packed_faceset_filename
if samples_dat_path.exists():
io.log_info(f"{samples_dat_path} : file already exists !")
io.input("Press enter to continue and overwrite.")
as_person_faceset = False
dir_names = pathex.get_all_dir_names(samples_path)
if len(dir_names) != 0:
as_person_faceset = io.input_bool(f"{len(dir_names)} subdirectories found, process as person faceset?", True)
if as_person_faceset:
image_paths = []
for dir_name in dir_names:
image_paths += pathex.get_image_paths(samples_path / dir_name)
else:
image_paths = pathex.get_image_paths(samples_path)
samples = samplelib.SampleLoader.load_face_samples(image_paths)
samples_len = len(samples)
samples_configs = []
for sample in io.progress_bar_generator (samples, "Processing"):
sample_filepath = Path(sample.filename)
sample.filename = sample_filepath.name
if as_person_faceset:
sample.person_name = sample_filepath.parent.name
samples_configs.append ( sample.get_config() )
samples_bytes = pickle.dumps(samples_configs, 4)
of = open(samples_dat_path, "wb")
of.write ( struct.pack ("Q", PackedFaceset.VERSION ) )
of.write ( struct.pack ("Q", len(samples_bytes) ) )
of.write ( samples_bytes )
del samples_bytes #just free mem
del samples_configs
sample_data_table_offset = of.tell()
of.write ( bytes( 8*(samples_len+1) ) ) #sample data offset table
data_start_offset = of.tell()
offsets = []
for sample in io.progress_bar_generator(samples, "Packing"):
try:
if sample.person_name is not None:
sample_path = samples_path / sample.person_name / sample.filename
else:
sample_path = samples_path / sample.filename
with open(sample_path, "rb") as f:
b = f.read()
offsets.append ( of.tell() - data_start_offset )
of.write(b)
except:
raise Exception(f"error while processing sample {sample_path}")
offsets.append ( of.tell() )
of.seek(sample_data_table_offset, 0)
for offset in offsets:
of.write ( struct.pack("Q", offset) )
of.seek(0,2)
of.close()
for filename in io.progress_bar_generator(image_paths, "Deleting files"):
Path(filename).unlink()
if as_person_faceset:
for dir_name in io.progress_bar_generator(dir_names, "Deleting dirs"):
dir_path = samples_path / dir_name
try:
shutil.rmtree(dir_path)
except:
io.log_info (f"unable to remove: {dir_path} ")
@staticmethod
def unpack(samples_path):
samples_dat_path = samples_path / packed_faceset_filename
if not samples_dat_path.exists():
io.log_info(f"{samples_dat_path} : file not found.")
return
samples = PackedFaceset.load(samples_path)
for sample in io.progress_bar_generator(samples, "Unpacking"):
person_name = sample.person_name
if person_name is not None:
person_path = samples_path / person_name
person_path.mkdir(parents=True, exist_ok=True)
target_filepath = person_path / sample.filename
else:
target_filepath = samples_path / sample.filename
with open(target_filepath, "wb") as f:
f.write( sample.read_raw_file() )
samples_dat_path.unlink()
@staticmethod
def load(samples_path):
samples_dat_path = samples_path / packed_faceset_filename
if not samples_dat_path.exists():
return None
f = open(samples_dat_path, "rb")
version, = struct.unpack("Q", f.read(8) )
if version != PackedFaceset.VERSION:
raise NotImplementedError
sizeof_samples_bytes, = struct.unpack("Q", f.read(8) )
samples_configs = pickle.loads ( f.read(sizeof_samples_bytes) )
samples = []
for sample_config in samples_configs:
sample_config = pickle.loads(pickle.dumps (sample_config))
samples.append ( Sample (**sample_config) )
offsets = [ struct.unpack("Q", f.read(8) )[0] for _ in range(len(samples)+1) ]
data_start_offset = f.tell()
f.close()
for i, sample in enumerate(samples):
start_offset, end_offset = offsets[i], offsets[i+1]
sample.set_filename_offset_size( str(samples_dat_path), data_start_offset+start_offset, end_offset-start_offset )
return samples