mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 04:52:13 -07:00
438213e97c
sort by final : now you can specify target number of images, converter: fix seamless mask and exception, huge refactoring
157 lines
No EOL
10 KiB
Python
157 lines
No EOL
10 KiB
Python
import os
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import sys
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import time
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import argparse
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from utils import Path_utils
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from utils import os_utils
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from pathlib import Path
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if sys.version_info[0] < 3 or (sys.version_info[0] == 3 and sys.version_info[1] < 2):
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raise Exception("This program requires at least Python 3.2")
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class fixPathAction(argparse.Action):
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def __call__(self, parser, namespace, values, option_string=None):
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setattr(namespace, self.dest, os.path.abspath(os.path.expanduser(values)))
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if __name__ == "__main__":
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os_utils.set_process_lowest_prio()
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parser = argparse.ArgumentParser()
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subparsers = parser.add_subparsers()
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def process_extract(arguments):
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from mainscripts import Extractor
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Extractor.main( arguments.input_dir,
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arguments.output_dir,
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arguments.debug,
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arguments.detector,
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arguments.manual_fix,
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arguments.manual_output_debug_fix,
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arguments.manual_window_size,
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face_type=arguments.face_type,
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device_args={'cpu_only' : arguments.cpu_only,
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'multi_gpu' : arguments.multi_gpu,
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}
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)
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extract_parser = subparsers.add_parser( "extract", help="Extract the faces from a pictures.")
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extract_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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extract_parser.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the extracted files will be stored.")
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extract_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Writes debug images to [output_dir]_debug\ directory.")
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extract_parser.add_argument('--face-type', dest="face_type", choices=['half_face', 'full_face', 'head', 'avatar', 'mark_only'], default='full_face', help="Default 'full_face'. Don't change this option, currently all models uses 'full_face'")
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extract_parser.add_argument('--detector', dest="detector", choices=['dlib','mt','manual'], default='dlib', help="Type of detector. Default 'dlib'. 'mt' (MTCNNv1) - faster, better, almost no jitter, perfect for gathering thousands faces for src-set. It is also good for dst-set, but can generate false faces in frames where main face not recognized! In this case for dst-set use either 'dlib' with '--manual-fix' or '--detector manual'. Manual detector suitable only for dst-set.")
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extract_parser.add_argument('--multi-gpu', action="store_true", dest="multi_gpu", default=False, help="Enables multi GPU.")
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extract_parser.add_argument('--manual-fix', action="store_true", dest="manual_fix", default=False, help="Enables manual extract only frames where faces were not recognized.")
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extract_parser.add_argument('--manual-output-debug-fix', action="store_true", dest="manual_output_debug_fix", default=False, help="Performs manual reextract input-dir frames which were deleted from [output_dir]_debug\ dir.")
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extract_parser.add_argument('--manual-window-size', type=int, dest="manual_window_size", default=1368, help="Manual fix window size. Default: 1368.")
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extract_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Extract on CPU. Forces to use MT extractor.")
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extract_parser.set_defaults (func=process_extract)
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def process_sort(arguments):
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from mainscripts import Sorter
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Sorter.main (input_path=arguments.input_dir, sort_by_method=arguments.sort_by_method)
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sort_parser = subparsers.add_parser( "sort", help="Sort faces in a directory.")
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sort_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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sort_parser.add_argument('--by', required=True, dest="sort_by_method", choices=("blur", "face", "face-dissim", "face-yaw", "face-pitch", "hist", "hist-dissim", "brightness", "hue", "black", "origname", "oneface", "final", "test"), help="Method of sorting. 'origname' sort by original filename to recover original sequence." )
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sort_parser.set_defaults (func=process_sort)
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def process_util(arguments):
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from mainscripts import Util
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if arguments.convert_png_to_jpg:
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Util.convert_png_to_jpg_folder (input_path=arguments.input_dir)
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if arguments.add_landmarks_debug_images:
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Util.add_landmarks_debug_images (input_path=arguments.input_dir)
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util_parser = subparsers.add_parser( "util", help="Utilities.")
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util_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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util_parser.add_argument('--convert-png-to-jpg', action="store_true", dest="convert_png_to_jpg", default=False, help="Convert DeepFaceLAB PNG files to JPEG.")
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util_parser.add_argument('--add-landmarks-debug-images', action="store_true", dest="add_landmarks_debug_images", default=False, help="Add landmarks debug image for aligned faces.")
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util_parser.set_defaults (func=process_util)
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def process_train(arguments):
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args = {'training_data_src_dir' : arguments.training_data_src_dir,
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'training_data_dst_dir' : arguments.training_data_dst_dir,
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'model_path' : arguments.model_dir,
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'model_name' : arguments.model_name,
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'no_preview' : arguments.no_preview,
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'debug' : arguments.debug,
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}
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device_args = {'cpu_only' : arguments.cpu_only,
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'force_gpu_idx' : arguments.force_gpu_idx,
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}
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from mainscripts import Trainer
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Trainer.main(args, device_args)
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train_parser = subparsers.add_parser( "train", help="Trainer")
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train_parser.add_argument('--training-data-src-dir', required=True, action=fixPathAction, dest="training_data_src_dir", help="Dir of src-set.")
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train_parser.add_argument('--training-data-dst-dir', required=True, action=fixPathAction, dest="training_data_dst_dir", help="Dir of dst-set.")
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train_parser.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
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train_parser.add_argument('--model', required=True, dest="model_name", choices=Path_utils.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Type of model")
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train_parser.add_argument('--no-preview', action="store_true", dest="no_preview", default=False, help="Disable preview window.")
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train_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug samples.")
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train_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Train on CPU.")
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train_parser.add_argument('--force-gpu-idx', type=int, dest="force_gpu_idx", default=-1, help="Force to choose this GPU idx.")
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train_parser.set_defaults (func=process_train)
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def process_convert(arguments):
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args = {'input_dir' : arguments.input_dir,
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'output_dir' : arguments.output_dir,
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'aligned_dir' : arguments.aligned_dir,
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'model_dir' : arguments.model_dir,
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'model_name' : arguments.model_name,
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'debug' : arguments.debug,
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}
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device_args = {'cpu_only' : arguments.cpu_only,
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'force_gpu_idx' : arguments.force_gpu_idx,
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}
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from mainscripts import Converter
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Converter.main (args, device_args)
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convert_parser = subparsers.add_parser( "convert", help="Converter")
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convert_parser.add_argument('--input-dir', required=True, action=fixPathAction, dest="input_dir", help="Input directory. A directory containing the files you wish to process.")
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convert_parser.add_argument('--output-dir', required=True, action=fixPathAction, dest="output_dir", help="Output directory. This is where the converted files will be stored.")
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convert_parser.add_argument('--aligned-dir', action=fixPathAction, dest="aligned_dir", help="Aligned directory. This is where the extracted of dst faces stored. Not used in AVATAR model.")
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convert_parser.add_argument('--model-dir', required=True, action=fixPathAction, dest="model_dir", help="Model dir.")
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convert_parser.add_argument('--model', required=True, dest="model_name", choices=Path_utils.get_all_dir_names_startswith ( Path(__file__).parent / 'models' , 'Model_'), help="Type of model")
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convert_parser.add_argument('--debug', action="store_true", dest="debug", default=False, help="Debug converter.")
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convert_parser.add_argument('--force-gpu-idx', type=int, dest="force_gpu_idx", default=-1, help="Force to choose this GPU idx.")
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convert_parser.add_argument('--cpu-only', action="store_true", dest="cpu_only", default=False, help="Convert on CPU.")
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convert_parser.set_defaults(func=process_convert)
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def bad_args(arguments):
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parser.print_help()
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exit(0)
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parser.set_defaults(func=bad_args)
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arguments = parser.parse_args()
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#os.environ['force_plaidML'] = '1'
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arguments.func(arguments)
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print ("Done.")
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"""
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Suppressing error with keras 2.2.4+ on python exit:
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Exception ignored in: <bound method BaseSession._Callable.__del__ of <tensorflow.python.client.session.BaseSession._Callable object at 0x000000001BDEA9B0>>
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Traceback (most recent call last):
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File "D:\DeepFaceLab\_internal\bin\lib\site-packages\tensorflow\python\client\session.py", line 1413, in __del__
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AttributeError: 'NoneType' object has no attribute 'raise_exception_on_not_ok_status'
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reproduce: https://github.com/keras-team/keras/issues/11751 ( still no solution )
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"""
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outnull_file = open(os.devnull, 'w')
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os.dup2 ( outnull_file.fileno(), sys.stderr.fileno() )
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sys.stderr = outnull_file
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'''
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import code
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code.interact(local=dict(globals(), **locals()))
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''' |