mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-07 19:05:02 -04:00
120535eb11
* Pytorch and face-alignment * Skip processed frames when extracting faces. * Reset to master version * Reset to master * Added --skip-existing argument to Extract script. Default is to NOT skip already processed frames. Added logic to write_alignments to append new alignments (and preserve existing ones) to existing alignments file when the skip-existing option is used. * Fixed exception for --skip-existing when using the convert script * Sync with upstream * Fixed error when using Convert script. * Bug fix * Merges alignments only if --skip-existing is used. * Creates output dir when not found, even when using --skip-existing.
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(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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