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https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
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
230 lines
7.2 KiB
Python
230 lines
7.2 KiB
Python
import pickle
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from pathlib import Path
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import cv2
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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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from core.interact import interact as io
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from core import pathex
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from core.cv2ex import *
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def save_faceset_metadata_folder(input_path):
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input_path = Path(input_path)
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metadata_filepath = input_path / 'meta.dat'
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io.log_info (f"Saving metadata to {str(metadata_filepath)}\r\n")
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d = {}
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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dfl_dict = dflimg.getDFLDictData()
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d[filepath.name] = ( dflimg.get_shape(), dfl_dict )
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try:
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with open(metadata_filepath, "wb") as f:
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f.write ( pickle.dumps(d) )
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except:
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raise Exception( 'cannot save %s' % (filename) )
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io.log_info("Now you can edit images.")
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io.log_info("!!! Keep same filenames in the folder.")
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io.log_info("You can change size of images, restoring process will downscale back to original size.")
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io.log_info("After that, use restore metadata.")
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def restore_faceset_metadata_folder(input_path):
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input_path = Path(input_path)
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metadata_filepath = input_path / 'meta.dat'
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io.log_info (f"Restoring metadata from {str(metadata_filepath)}.\r\n")
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if not metadata_filepath.exists():
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io.log_err(f"Unable to find {str(metadata_filepath)}.")
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try:
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with open(metadata_filepath, "rb") as f:
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d = pickle.loads(f.read())
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except:
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raise FileNotFoundError(filename)
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
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filepath = Path(filepath)
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shape, dfl_dict = d.get(filepath.name, None)
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img = cv2_imread (str(filepath))
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if img.shape != shape:
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img = cv2.resize (img, (shape[1], shape[0]), cv2.INTER_LANCZOS4 )
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if filepath.suffix == '.png':
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cv2_imwrite (str(filepath), img)
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elif filepath.suffix == '.jpg':
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cv2_imwrite (str(filepath), img, [int(cv2.IMWRITE_JPEG_QUALITY), 100] )
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if filepath.suffix == '.png':
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DFLPNG.embed_dfldict( str(filepath), dfl_dict )
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elif filepath.suffix == '.jpg':
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DFLJPG.embed_dfldict( str(filepath), dfl_dict )
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else:
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continue
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metadata_filepath.unlink()
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def remove_ie_polys_file (filepath):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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return
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dflimg.remove_ie_polys()
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dflimg.embed_and_set( str(filepath) )
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def remove_ie_polys_folder(input_path):
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input_path = Path(input_path)
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io.log_info ("Removing ie_polys...\r\n")
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Removing"):
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filepath = Path(filepath)
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remove_ie_polys_file(filepath)
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def remove_fanseg_file (filepath):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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return
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dflimg.remove_fanseg_mask()
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dflimg.embed_and_set( str(filepath) )
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def remove_fanseg_folder(input_path):
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input_path = Path(input_path)
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io.log_info ("Removing fanseg mask...\r\n")
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Removing"):
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filepath = Path(filepath)
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remove_fanseg_file(filepath)
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def convert_png_to_jpg_file (filepath):
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filepath = Path(filepath)
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if filepath.suffix != '.png':
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return
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dflpng = DFLPNG.load (str(filepath) )
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if dflpng is None:
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io.log_err ("%s is not a dfl png image file" % (filepath.name) )
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return
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dfl_dict = dflpng.getDFLDictData()
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img = cv2_imread (str(filepath))
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new_filepath = str(filepath.parent / (filepath.stem + '.jpg'))
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cv2_imwrite ( new_filepath, img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])
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DFLJPG.embed_data( new_filepath,
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face_type=dfl_dict.get('face_type', None),
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landmarks=dfl_dict.get('landmarks', None),
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ie_polys=dfl_dict.get('ie_polys', None),
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source_filename=dfl_dict.get('source_filename', None),
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source_rect=dfl_dict.get('source_rect', None),
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source_landmarks=dfl_dict.get('source_landmarks', None) )
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filepath.unlink()
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def convert_png_to_jpg_folder (input_path):
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input_path = Path(input_path)
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io.log_info ("Converting PNG to JPG...\r\n")
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Converting"):
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filepath = Path(filepath)
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convert_png_to_jpg_file(filepath)
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def add_landmarks_debug_images(input_path):
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io.log_info ("Adding landmarks debug images...")
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
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filepath = Path(filepath)
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img = cv2_imread(str(filepath))
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dflimg = DFLIMG.load (filepath)
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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continue
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if img is not None:
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face_landmarks = dflimg.get_landmarks()
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LandmarksProcessor.draw_landmarks(img, face_landmarks, transparent_mask=True, ie_polys=IEPolys.load(dflimg.get_ie_polys()) )
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output_file = '{}{}'.format( str(Path(str(input_path)) / filepath.stem), '_debug.jpg')
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cv2_imwrite(output_file, img, [int(cv2.IMWRITE_JPEG_QUALITY), 50] )
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def recover_original_aligned_filename(input_path):
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io.log_info ("Recovering original aligned filename...")
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files = []
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for filepath in io.progress_bar_generator( pathex.get_image_paths(input_path), "Processing"):
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filepath = Path(filepath)
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dflimg = DFLIMG.load (filepath)
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if dflimg is None:
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io.log_err ("%s is not a dfl image file" % (filepath.name) )
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continue
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files += [ [filepath, None, dflimg.get_source_filename(), False] ]
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files_len = len(files)
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for i in io.progress_bar_generator( range(files_len), "Sorting" ):
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fp, _, sf, converted = files[i]
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if converted:
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continue
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sf_stem = Path(sf).stem
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files[i][1] = fp.parent / ( sf_stem + '_0' + fp.suffix )
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files[i][3] = True
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c = 1
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for j in range(i+1, files_len):
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fp_j, _, sf_j, converted_j = files[j]
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if converted_j:
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continue
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if sf_j == sf:
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files[j][1] = fp_j.parent / ( sf_stem + ('_%d' % (c)) + fp_j.suffix )
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files[j][3] = True
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c += 1
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for file in io.progress_bar_generator( files, "Renaming", leave=False ):
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fs, _, _, _ = file
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dst = fs.parent / ( fs.stem + '_tmp' + fs.suffix )
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try:
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fs.rename (dst)
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except:
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io.log_err ('fail to rename %s' % (fs.name) )
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for file in io.progress_bar_generator( files, "Renaming" ):
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fs, fd, _, _ = file
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fs = fs.parent / ( fs.stem + '_tmp' + fs.suffix )
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try:
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fs.rename (fd)
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except:
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io.log_err ('fail to rename %s' % (fs.name) )
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