mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-08 11:53:26 -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
106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
import cv2
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from pathlib import Path
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from tqdm import tqdm
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import os
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from lib.cli import DirectoryProcessor
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from lib.utils import get_folder
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from lib.multithreading import pool_process
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from plugins.PluginLoader import PluginLoader
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class ExtractTrainingData(DirectoryProcessor):
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def create_parser(self, subparser, command, description):
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self.parser = subparser.add_parser(
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command,
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help="Extract the faces from a pictures.",
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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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def add_optional_arguments(self, parser):
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parser.add_argument('-D', '--detector',
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type=str,
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choices=("hog", "cnn"), # case sensitive because this is used to load a plugin.
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default="hog",
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help="Detector to use. 'cnn' detects much more angles but will be much more resource intensive and may fail on large files.")
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parser.add_argument('-f', '--filter',
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type=str,
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dest="filter",
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default="filter.jpg",
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help="Reference image for the person you want to process. Should be a front portrait"
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)
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parser.add_argument('-j', '--processes',
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type=int,
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default=1,
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help="Number of processes to use.")
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parser.add_argument('-s', '--skip-existing',
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action='store_true',
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dest='skip_existing',
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default=False,
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help="Skips frames already extracted.")
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parser.add_argument('-dl', '--debug-landmarks',
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action="store_true",
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dest="debug_landmarks",
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default=False,
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help="Draw landmarks for debug.")
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return parser
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def process(self):
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extractor_name = "Align" # TODO Pass as argument
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self.extractor = PluginLoader.get_extractor(extractor_name)()
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processes = self.arguments.processes
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try:
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if processes != 1:
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files = list(self.read_directory())
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for filename, faces in tqdm(pool_process(self.processFiles, files, processes=processes), total = len(files)):
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self.num_faces_detected += 1
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self.faces_detected[os.path.basename(filename)] = faces
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else:
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try:
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for filename in tqdm(self.read_directory()):
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image = cv2.imread(filename)
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self.faces_detected[os.path.basename(filename)] = self.handleImage(image, filename)
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except Exception as e:
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print('Failed to extract from image: {}. Reason: {}'.format(filename, e))
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finally:
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self.write_alignments()
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def processFiles(self, filename):
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try:
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image = cv2.imread(filename)
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return filename, self.handleImage(image, filename)
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except Exception as e:
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print('Failed to extract from image: {}. Reason: {}'.format(filename, e))
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def handleImage(self, image, filename):
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count = 0
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faces = self.get_faces(image)
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rvals = []
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for idx, face in faces:
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count = idx
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# Draws landmarks for debug
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if self.arguments.debug_landmarks:
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for (x, y) in face.landmarksAsXY():
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cv2.circle(image, (x, y), 2, (0, 0, 255), -1)
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resized_image = self.extractor.extract(image, face, 256)
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output_file = get_folder(self.output_dir) / Path(filename).stem
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cv2.imwrite('{}_{}{}'.format(str(output_file), str(idx), Path(filename).suffix), resized_image)
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f = {
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"x": face.x,
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"w": face.w,
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"y": face.y,
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"h": face.h,
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"landmarksXY": face.landmarksAsXY()
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}
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rvals.append(f)
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return rvals
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