mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-08 03:26:47 -04:00
83c36b9489
* Improving performance of extraction. Two main changes to improve the most recent modifications to extract: 1st FaceLandmarkExtractor would try to use cnn first, then try hog. The problem was that this reduced the speed by 4 for images where cnn didn't find anything, and most of the times hog wouldn't find anything either or it would be a bad extract. For me it wasn't worth it. With this you can specify on input -D if you want to use hog, cnn, or all. 'all' will try cnn, then hog like FaceLandmarkExtractor was doing. cnn or hog will just use 1 detection method. 2nd change is a rehaul of the verbose parameter. Now warnings when a face is not detected will just be shown if indicated by -v or --verbose. This restores the verbose function to what it once was. With this change I was able to process 1,000 per each 4 minutes regardless if faces were detected or not. Performance improvement just applies to not detected images but I normally will have lots of images without clear faces in my set, so I figured it would impact others. Also the introduction of 'all' would allow trying other models together more easily in the future. * Update faces_detect.py * Update extract.py * Update FaceLandmarksExtractor.py * spacing fix
152 lines
6.4 KiB
Python
152 lines
6.4 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, rotate_image
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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", "all"), # 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('-l', '--ref_threshold',
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type=float,
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dest="ref_threshold",
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default=0.6,
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help="Threshold for positive face recognition"
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)
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parser.add_argument('-n', '--nfilter',
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type=str,
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dest="nfilter",
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nargs='+',
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default="nfilter.jpg",
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help="Reference image for the persons you do not want to process. Should be a front portrait"
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)
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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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nargs='+',
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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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parser.add_argument('-r', '--rotate-images',
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type=str,
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dest="rotate_images",
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choices=("on", "off"),
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default="off",
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help="If a face isn't found, rotate the images through 90 degree "
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"iterations to try to find a face. Can find more faces at the "
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"cost of extraction speed.")
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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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for filename in tqdm(self.read_directory()):
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try:
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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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if self.arguments.verbose:
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print('Failed to extract from image: {}. Reason: {}'.format(filename, e))
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pass
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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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if self.arguments.verbose:
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print('Failed to extract from image: {}. Reason: {}'.format(filename, e))
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pass
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return filename, []
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def imageRotator(self, image):
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''' rotates the image through 90 degree iterations to find a face '''
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angle = 90
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while angle <= 270:
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rotated_image = rotate_image(image, angle)
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faces = self.get_faces(rotated_image, rotation=angle)
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rotated_faces = [(idx, face) for idx, face in faces]
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if len(rotated_faces) != 0:
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if self.arguments.verbose:
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print('found face(s) by rotating image {} degrees'.format(angle))
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break
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angle += 90
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return rotated_faces, rotated_image
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def handleImage(self, image, filename):
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faces = self.get_faces(image)
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process_faces = [(idx, face) for idx, face in faces]
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# Run image rotator if requested and no faces found
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if self.arguments.rotate_images.lower() == 'on' and len(process_faces) == 0:
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process_faces, image = self.imageRotator(image)
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rvals = []
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for idx, face in process_faces:
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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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"r": face.r,
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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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