DeepFaceLab/mainscripts/Util.py
Colombo 76ca79216e Upgraded to TF version 1.13.2
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
2020-01-25 21:58:19 +04:00

230 lines
7.2 KiB
Python

import pickle
from pathlib import Path
import cv2
from DFLIMG import *
from facelib import LandmarksProcessor
from core.imagelib import IEPolys
from core.interact import interact as io
from core import pathex
from core.cv2ex import *
def save_faceset_metadata_folder(input_path):
input_path = Path(input_path)
metadata_filepath = input_path / 'meta.dat'
io.log_info (f"Saving metadata to {str(metadata_filepath)}\r\n")
d = {}
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
dfl_dict = dflimg.getDFLDictData()
d[filepath.name] = ( dflimg.get_shape(), dfl_dict )
try:
with open(metadata_filepath, "wb") as f:
f.write ( pickle.dumps(d) )
except:
raise Exception( 'cannot save %s' % (filename) )
io.log_info("Now you can edit images.")
io.log_info("!!! Keep same filenames in the folder.")
io.log_info("You can change size of images, restoring process will downscale back to original size.")
io.log_info("After that, use restore metadata.")
def restore_faceset_metadata_folder(input_path):
input_path = Path(input_path)
metadata_filepath = input_path / 'meta.dat'
io.log_info (f"Restoring metadata from {str(metadata_filepath)}.\r\n")
if not metadata_filepath.exists():
io.log_err(f"Unable to find {str(metadata_filepath)}.")
try:
with open(metadata_filepath, "rb") as f:
d = pickle.loads(f.read())
except:
raise FileNotFoundError(filename)
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
filepath = Path(filepath)
shape, dfl_dict = d.get(filepath.name, None)
img = cv2_imread (str(filepath))
if img.shape != shape:
img = cv2.resize (img, (shape[1], shape[0]), cv2.INTER_LANCZOS4 )
if filepath.suffix == '.png':
cv2_imwrite (str(filepath), img)
elif filepath.suffix == '.jpg':
cv2_imwrite (str(filepath), img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
if filepath.suffix == '.png':
DFLPNG.embed_dfldict( str(filepath), dfl_dict )
elif filepath.suffix == '.jpg':
DFLJPG.embed_dfldict( str(filepath), dfl_dict )
else:
continue
metadata_filepath.unlink()
def remove_ie_polys_file (filepath):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None:
io.log_err ("%s is not a dfl image file" % (filepath.name) )
return
dflimg.remove_ie_polys()
dflimg.embed_and_set( str(filepath) )
def remove_ie_polys_folder(input_path):
input_path = Path(input_path)
io.log_info ("Removing ie_polys...\r\n")
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Removing"):
filepath = Path(filepath)
remove_ie_polys_file(filepath)
def remove_fanseg_file (filepath):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None:
io.log_err ("%s is not a dfl image file" % (filepath.name) )
return
dflimg.remove_fanseg_mask()
dflimg.embed_and_set( str(filepath) )
def remove_fanseg_folder(input_path):
input_path = Path(input_path)
io.log_info ("Removing fanseg mask...\r\n")
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Removing"):
filepath = Path(filepath)
remove_fanseg_file(filepath)
def convert_png_to_jpg_file (filepath):
filepath = Path(filepath)
if filepath.suffix != '.png':
return
dflpng = DFLPNG.load (str(filepath) )
if dflpng is None:
io.log_err ("%s is not a dfl png image file" % (filepath.name) )
return
dfl_dict = dflpng.getDFLDictData()
img = cv2_imread (str(filepath))
new_filepath = str(filepath.parent / (filepath.stem + '.jpg'))
cv2_imwrite ( new_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
DFLJPG.embed_data( new_filepath,
face_type=dfl_dict.get('face_type', None),
landmarks=dfl_dict.get('landmarks', None),
ie_polys=dfl_dict.get('ie_polys', None),
source_filename=dfl_dict.get('source_filename', None),
source_rect=dfl_dict.get('source_rect', None),
source_landmarks=dfl_dict.get('source_landmarks', None) )
filepath.unlink()
def convert_png_to_jpg_folder (input_path):
input_path = Path(input_path)
io.log_info ("Converting PNG to JPG...\r\n")
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Converting"):
filepath = Path(filepath)
convert_png_to_jpg_file(filepath)
def add_landmarks_debug_images(input_path):
io.log_info ("Adding landmarks debug images...")
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
filepath = Path(filepath)
img = cv2_imread(str(filepath))
dflimg = DFLIMG.load (filepath)
if dflimg is None:
io.log_err ("%s is not a dfl image file" % (filepath.name) )
continue
if img is not None:
face_landmarks = dflimg.get_landmarks()
LandmarksProcessor.draw_landmarks(img, face_landmarks, transparent_mask=True, ie_polys=IEPolys.load(dflimg.get_ie_polys()) )
output_file = '{}{}'.format( str(Path(str(input_path)) / filepath.stem), '_debug.jpg')
cv2_imwrite(output_file, img, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
def recover_original_aligned_filename(input_path):
io.log_info ("Recovering original aligned filename...")
files = []
for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
filepath = Path(filepath)
dflimg = DFLIMG.load (filepath)
if dflimg is None:
io.log_err ("%s is not a dfl image file" % (filepath.name) )
continue
files += [ [filepath, None, dflimg.get_source_filename(), False] ]
files_len = len(files)
for i in io.progress_bar_generator( range(files_len), "Sorting" ):
fp, _, sf, converted = files[i]
if converted:
continue
sf_stem = Path(sf).stem
files[i][1] = fp.parent / ( sf_stem + '_0' + fp.suffix )
files[i][3] = True
c = 1
for j in range(i+1, files_len):
fp_j, _, sf_j, converted_j = files[j]
if converted_j:
continue
if sf_j == sf:
files[j][1] = fp_j.parent / ( sf_stem + ('_%d' % (c)) + fp_j.suffix )
files[j][3] = True
c += 1
for file in io.progress_bar_generator( files, "Renaming", leave=False ):
fs, _, _, _ = file
dst = fs.parent / ( fs.stem + '_tmp' + fs.suffix )
try:
fs.rename (dst)
except:
io.log_err ('fail to rename %s' % (fs.name) )
for file in io.progress_bar_generator( files, "Renaming" ):
fs, fd, _, _ = file
fs = fs.parent / ( fs.stem + '_tmp' + fs.suffix )
try:
fs.rename (fd)
except:
io.log_err ('fail to rename %s' % (fs.name) )