mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 19:05:02 -04:00
810bd0bce7
* Clearer requirements for each platform
* Refactoring of old plugins (Model_Original + Extract_Align) + Cleanups
* Adding GAN128
* Update GAN to v2
* Create instance_normalization.py
* Fix decoder output
* Revert "Fix decoder output"
This reverts commit 3a8ecb8957.
* Fix convert
* Enable all options except perceptual_loss by default
* Disable instance norm
* Update Model.py
* Update Trainer.py
* Match GAN128 to shaoanlu's latest v2
* Add first_order to GAN128
* Disable `use_perceptual_loss`
* Fix call to `self.first_order`
* Switch to average loss in output
* Constrain average to last 100 iterations
* Fix math, constrain average to intervals of 100
* Fix math averaging again
* Remove math and simplify this damn averagin
* Add gan128 conversion
* Update convert.py
* Use non-warped images in masked preview
* Add K.set_learning_phase(1) to gan64
* Add K.set_learning_phase(1) to gan128
* Add missing keras import
* Use non-warped images in masked preview for gan128
* Exclude deleted faces from conversion
* --input-aligned-dir defaults to "{input_dir}/aligned"
* Simplify map operation
* port 'face_alignment' from PyTorch to Keras. It works x2 faster, but initialization takes 20secs.
2DFAN-4.h5 and mmod_human_face_detector.dat included in lib\FaceLandmarksExtractor
fixed dlib vs tensorflow conflict: dlib must do op first, then load keras model, otherwise CUDA OOM error
if face location not found by CNN, its try to find by HOG.
removed this:
- if face.landmarks == None:
- print("Warning! landmarks not found. Switching to crop!")
- return cv2.resize(face.image, (size, size))
because DetectedFace always has landmarks
* Enabled masked converter for GAN models
* Histogram matching, cli option for perceptual loss
* Fix init() positional args error
* Add backwards compatibility for aligned filenames
* Fix masked converter
* Remove GAN converters
240 lines
9.1 KiB
Python
240 lines
9.1 KiB
Python
import argparse
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import os
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import sys
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import time
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from pathlib import Path
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from lib.FaceFilter import FaceFilter
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from lib.faces_detect import detect_faces, DetectedFace
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from lib.utils import get_image_paths, get_folder
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from lib import Serializer
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class FullPaths(argparse.Action):
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"""Expand user- and relative-paths"""
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def __call__(self, parser, namespace, values, option_string=None):
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setattr(namespace, self.dest, os.path.abspath(
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os.path.expanduser(values)))
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class FullHelpArgumentParser(argparse.ArgumentParser):
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"""
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Identical to the built-in argument parser, but on error
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it prints full help message instead of just usage information
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"""
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def error(self, message):
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self.print_help(sys.stderr)
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args = {'prog': self.prog, 'message': message}
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self.exit(2, '%(prog)s: error: %(message)s\n' % args)
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class DirectoryProcessor(object):
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'''
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Abstract class that processes a directory of images
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and writes output to the specified folder
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'''
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arguments = None
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parser = None
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input_dir = None
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output_dir = None
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images_found = 0
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num_faces_detected = 0
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faces_detected = dict()
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verify_output = False
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def __init__(self, subparser, command, description='default'):
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self.create_parser(subparser, command, description)
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self.parse_arguments(description, subparser, command)
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def process_arguments(self, arguments):
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self.arguments = arguments
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print("Input Directory: {}".format(self.arguments.input_dir))
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print("Output Directory: {}".format(self.arguments.output_dir))
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self.serializer = None
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if self.arguments.serializer is None and self.arguments.alignments_path is not None:
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ext = os.path.splitext(self.arguments.alignments_path)[-1]
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self.serializer = Serializer.get_serializer_fromext(ext)
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print(self.serializer, self.arguments.alignments_path)
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else:
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self.serializer = Serializer.get_serializer(self.arguments.serializer or "json")
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print("Using {} serializer".format(self.serializer.ext))
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print('Starting, this may take a while...')
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try:
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if self.arguments.skip_existing:
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self.already_processed = get_image_paths(self.arguments.output_dir)
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except AttributeError:
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pass
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self.output_dir = get_folder(self.arguments.output_dir)
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try:
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try:
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if self.arguments.skip_existing:
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self.input_dir = get_image_paths(self.arguments.input_dir, self.already_processed)
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print('Excluding %s files' % len(self.already_processed))
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else:
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self.input_dir = get_image_paths(self.arguments.input_dir)
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except AttributeError:
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self.input_dir = get_image_paths(self.arguments.input_dir)
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except:
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print('Input directory not found. Please ensure it exists.')
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exit(1)
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self.filter = self.load_filter()
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self.process()
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self.finalize()
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def read_alignments(self):
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fn = os.path.join(str(self.arguments.input_dir),"alignments.{}".format(self.serializer.ext))
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if self.arguments.alignments_path is not None:
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fn = self.arguments.alignments_path
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try:
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print("Reading alignments from: {}".format(fn))
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with open(fn, self.serializer.roptions) as f:
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self.faces_detected = self.serializer.unmarshal(f.read())
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except Exception as e:
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print("{} not read!".format(fn))
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print(str(e))
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self.faces_detected = dict()
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def write_alignments(self):
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fn = os.path.join(str(self.arguments.input_dir), "alignments.{}".format(self.serializer.ext))
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if self.arguments.alignments_path is not None:
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fn = self.arguments.alignments_path
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print("Alignments filepath: %s" % fn)
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if self.arguments.skip_existing:
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if os.path.exists(fn):
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with open(fn, self.serializer.roptions) as inf:
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data = self.serializer.unmarshal(inf.read())
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for k, v in data.items():
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self.faces_detected[k] = v
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else:
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print('Existing alignments file "%s" not found.' % fn)
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try:
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print("Writing alignments to: {}".format(fn))
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with open(fn, self.serializer.woptions) as fh:
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fh.write(self.serializer.marshal(self.faces_detected))
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except Exception as e:
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print("{} not written!".format(fn))
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print(str(e))
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self.faces_detected = dict()
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def read_directory(self):
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self.images_found = len(self.input_dir)
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return self.input_dir
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def have_face(self, filename):
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return os.path.basename(filename) in self.faces_detected
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def have_alignments(self):
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fn = os.path.join(str(self.arguments.input_dir), "alignments.{}".format(self.serializer.ext))
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return os.path.exists(fn)
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def get_faces_alignments(self, filename, image):
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faces_count = 0
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faces = self.faces_detected[os.path.basename(filename)]
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for rawface in faces:
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face = DetectedFace(**rawface)
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face.image = image[face.y : face.y + face.h, face.x : face.x + face.w]
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if self.filter is not None and not self.filter.check(face):
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print('Skipping not recognized face!')
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continue
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yield faces_count, face
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self.num_faces_detected += 1
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faces_count += 1
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if faces_count > 1 and self.arguments.verbose:
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print('Note: Found more than one face in an image! File: %s' % filename)
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self.verify_output = True
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def get_faces(self, image):
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faces_count = 0
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faces = detect_faces(image, self.arguments.detector)
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for face in faces:
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if self.filter is not None and not self.filter.check(face):
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print('Skipping not recognized face!')
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continue
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yield faces_count, face
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self.num_faces_detected += 1
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faces_count += 1
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if faces_count > 1 and self.arguments.verbose:
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self.verify_output = True
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def load_filter(self):
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filter_file = self.arguments.filter
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if Path(filter_file).exists():
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print('Loading reference image for filtering')
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return FaceFilter(filter_file)
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# for now, we limit this class responsability to the read of files. images and faces are processed outside this class
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def process(self):
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# implement your image processing!
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raise NotImplementedError()
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def parse_arguments(self, description, subparser, command):
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self.parser.add_argument('-i', '--input-dir',
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action=FullPaths,
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dest="input_dir",
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default="input",
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help="Input directory. A directory containing the files \
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you wish to process. Defaults to 'input'")
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self.parser.add_argument('-o', '--output-dir',
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action=FullPaths,
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dest="output_dir",
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default="output",
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help="Output directory. This is where the converted files will \
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be stored. Defaults to 'output'")
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self.parser.add_argument('--serializer',
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type=str.lower,
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dest="serializer",
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choices=("yaml", "json", "pickle"),
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help="serializer for alignments file")
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self.parser.add_argument('--alignments',
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type=str,
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dest="alignments_path",
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help="optional path to alignments file.")
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self.parser.add_argument('-v', '--verbose',
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action="store_true",
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dest="verbose",
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default=False,
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help="Show verbose output")
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self.parser = self.add_optional_arguments(self.parser)
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self.parser.set_defaults(func=self.process_arguments)
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def create_parser(self, subparser, command, description):
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parser = subparser.add_parser(
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command,
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description=description,
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epilog="Questions and feedback: \
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https://github.com/deepfakes/faceswap-playground"
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)
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return parser
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def add_optional_arguments(self, parser):
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# Override this for custom arguments
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return parser
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def finalize(self):
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print('-------------------------')
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print('Images found: {}'.format(self.images_found))
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print('Faces detected: {}'.format(self.num_faces_detected))
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print('-------------------------')
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if self.verify_output:
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print('Note:')
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print('Multiple faces were detected in one or more pictures.')
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print('Double check your results.')
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print('-------------------------')
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print('Done!')
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