mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
ce2aa6a7b7
* Code refactor * Console Added * Add graphing with matplotlib * Add GUI preview support * Improve import handling. * Add GUI integration for tools.py (Launch tools.py with the gui command) * Reformat to conform to PEP8. * Add Tooltips, * Make panels user adjustable * Fix convert.py Sharpen default value
251 lines
14 KiB
Python
251 lines
14 KiB
Python
import cv2
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import re
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import os
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from pathlib import Path
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from tqdm import tqdm
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from lib.cli import DirectoryProcessor, FullPaths
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from lib.utils import BackgroundGenerator, get_folder, get_image_paths, rotate_image
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from plugins.PluginLoader import PluginLoader
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class ConvertImage(DirectoryProcessor):
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filename = ''
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def create_parser(self, subparser, command, description):
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self.optional_arguments = self.get_optional_arguments()
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self.parser = subparser.add_parser(
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command,
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help="Convert a source image to a new one with the face swapped.",
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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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@staticmethod
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def get_optional_arguments():
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''' Put the arguments in a list so that they are accessible from both argparse and gui '''
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argument_list = []
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argument_list.append({ "opts": ('-m', '--model-dir'),
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"action": FullPaths,
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"dest": "model_dir",
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"default": "models",
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"help": "Model directory. A directory containing the trained model \
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you wish to process. Defaults to 'models'"})
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argument_list.append({ "opts": ('-a', '--input-aligned-dir'),
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"action": FullPaths,
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"dest": "input_aligned_dir",
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"default": None,
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"help": "Input \"aligned directory\". A directory that should contain the \
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aligned faces extracted from the input files. If you delete faces from \
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this folder, they'll be skipped during conversion. If no aligned dir is \
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specified, all faces will be converted."})
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argument_list.append({ "opts": ('-t', '--trainer'),
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"type": str,
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"choices": PluginLoader.get_available_models(), # case sensitive because this is used to load a plug-in.
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"default": PluginLoader.get_default_model(),
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"help": "Select the trainer that was used to create the model."})
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argument_list.append({ "opts": ('-s', '--swap-model'),
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"action": "store_true",
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"dest": "swap_model",
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"default": False,
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"help": "Swap the model. Instead of A -> B, swap B -> A."})
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argument_list.append({ "opts": ('-c', '--converter'),
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"type": str,
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"choices": ("Masked", "Adjust"), # case sensitive because this is used to load a plugin.
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"default": "Masked",
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"help": "Converter to use."})
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argument_list.append({ "opts": ('-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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argument_list.append({ "opts": ('-fr', '--frame-ranges'),
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"nargs": "+",
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"type": str,
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"help": "frame ranges to apply transfer to e.g. For frames 10 to 50 and 90 to 100 use --frame-ranges 10-50 90-100. \
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Files must have the frame-number as the last number in the name!"})
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argument_list.append({ "opts": ('-d', '--discard-frames'),
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"action": "store_true",
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"dest": "discard_frames",
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"default": False,
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"help": "When used with --frame-ranges discards frames that are not processed instead of writing them out unchanged."})
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argument_list.append({ "opts": ('-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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argument_list.append({ "opts": ('-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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argument_list.append({ "opts": ('-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 images for the person you want to process. Should be a front portrait"})
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argument_list.append({ "opts": ('-b', '--blur-size'),
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"type": int,
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"default": 2,
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"help": "Blur size. (Masked converter only)"})
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argument_list.append({ "opts": ('-S', '--seamless'),
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"action": "store_true",
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"dest": "seamless_clone",
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"default": False,
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"help": "Use cv2's seamless clone. (Masked converter only)"})
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argument_list.append({ "opts": ('-M', '--mask-type'),
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"type": str.lower, #lowercase this, because its just a string later on.
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"dest": "mask_type",
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"choices": ["rect", "facehull", "facehullandrect"],
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"default": "facehullandrect",
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"help": "Mask to use to replace faces. (Masked converter only)"})
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argument_list.append({ "opts": ('-e', '--erosion-kernel-size'),
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"dest": "erosion_kernel_size",
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"type": int,
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"default": None,
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"help": "Erosion kernel size. (Masked converter only). Positive values apply erosion which reduces the edge of the swapped face. Negative values apply dilation which allows the swapped face to cover more space."})
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argument_list.append({ "opts": ('-mh', '--match-histgoram'),
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"action": "store_true",
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"dest": "match_histogram",
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"default": False,
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"help": "Use histogram matching. (Masked converter only)"})
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argument_list.append({ "opts": ('-sh', ),
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"type": str.lower,
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"dest": "sharpen_image",
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"choices": ["bsharpen", "gsharpen"],
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"default": None,
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"help": "Use Sharpen Image - bsharpen = Box Blur, gsharpen = Gaussian Blur (Masked converter only)"})
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argument_list.append({ "opts": ('-sm', '--smooth-mask'),
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"action": "store_true",
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"dest": "smooth_mask",
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"default": True,
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"help": "Smooth mask (Adjust converter only)"})
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argument_list.append({ "opts": ('-aca', '--avg-color-adjust'),
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"action": "store_true",
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"dest": "avg_color_adjust",
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"default": True,
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"help": "Average color adjust. (Adjust converter only)"})
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argument_list.append({ "opts": ('-g', '--gpus'),
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"type": int,
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"default": 1,
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"help": "Number of GPUs to use for conversion"})
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return argument_list
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def process(self):
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# Original & LowMem models go with Adjust or Masked converter
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# Note: GAN prediction outputs a mask + an image, while other predicts only an image
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model_name = self.arguments.trainer
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conv_name = self.arguments.converter
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self.input_aligned_dir = None
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model = PluginLoader.get_model(model_name)(get_folder(self.arguments.model_dir), self.arguments.gpus)
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if not model.load(self.arguments.swap_model):
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print('Model Not Found! A valid model must be provided to continue!')
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exit(1)
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input_aligned_dir = Path(self.arguments.input_dir)/Path('aligned')
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if self.arguments.input_aligned_dir is not None:
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input_aligned_dir = self.arguments.input_aligned_dir
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try:
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self.input_aligned_dir = [Path(path) for path in get_image_paths(input_aligned_dir)]
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if len(self.input_aligned_dir) == 0:
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print('Aligned directory is empty, no faces will be converted!')
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elif len(self.input_aligned_dir) <= len(self.input_dir)/3:
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print('Aligned directory contains an amount of images much less than the input, are you sure this is the right directory?')
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except:
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print('Aligned directory not found. All faces listed in the alignments file will be converted.')
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converter = PluginLoader.get_converter(conv_name)(model.converter(False),
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trainer=self.arguments.trainer,
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blur_size=self.arguments.blur_size,
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seamless_clone=self.arguments.seamless_clone,
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sharpen_image=self.arguments.sharpen_image,
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mask_type=self.arguments.mask_type,
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erosion_kernel_size=self.arguments.erosion_kernel_size,
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match_histogram=self.arguments.match_histogram,
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smooth_mask=self.arguments.smooth_mask,
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avg_color_adjust=self.arguments.avg_color_adjust
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)
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batch = BackgroundGenerator(self.prepare_images(), 1)
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# frame ranges stuff...
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self.frame_ranges = None
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# split out the frame ranges and parse out "min" and "max" values
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minmax = {
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"min": 0, # never any frames less than 0
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"max": float("inf")
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}
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if self.arguments.frame_ranges:
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self.frame_ranges = [tuple(map(lambda q: minmax[q] if q in minmax.keys() else int(q), v.split("-"))) for v in self.arguments.frame_ranges]
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# last number regex. I know regex is hacky, but its reliablyhacky(tm).
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self.imageidxre = re.compile(r'(\d+)(?!.*\d)')
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for item in batch.iterator():
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self.convert(converter, item)
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def check_skipframe(self, filename):
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try:
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idx = int(self.imageidxre.findall(filename)[0])
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return not any(map(lambda b: b[0]<=idx<=b[1], self.frame_ranges))
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except:
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return False
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def check_skipface(self, filename, face_idx):
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aligned_face_name = '{}_{}{}'.format(Path(filename).stem, face_idx, Path(filename).suffix)
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aligned_face_file = Path(self.arguments.input_aligned_dir) / Path(aligned_face_name)
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# TODO: Remove this temporary fix for backwards compatibility of filenames
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bk_compat_aligned_face_name = '{}{}{}'.format(Path(filename).stem, face_idx, Path(filename).suffix)
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bk_compat_aligned_face_file = Path(self.arguments.input_aligned_dir) / Path(bk_compat_aligned_face_name)
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return aligned_face_file not in self.input_aligned_dir and bk_compat_aligned_face_file not in self.input_aligned_dir
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def convert(self, converter, item):
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try:
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(filename, image, faces) = item
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skip = self.check_skipframe(filename)
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if self.arguments.discard_frames and skip:
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return
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if not skip: # process frame as normal
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for idx, face in faces:
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if self.input_aligned_dir is not None and self.check_skipface(filename, idx):
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print ('face {} for frame {} was deleted, skipping'.format(idx, os.path.basename(filename)))
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continue
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# Check for image rotations and rotate before mapping face
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if face.r != 0:
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height, width = image.shape[:2]
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image = rotate_image(image, face.r)
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image = converter.patch_image(image, face, 64 if "128" not in self.arguments.trainer else 128)
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# TODO: This switch between 64 and 128 is a hack for now. We should have a separate cli option for size
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image = rotate_image(image, face.r * -1, rotated_width=width, rotated_height=height)
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else:
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image = converter.patch_image(image, face, 64 if "128" not in self.arguments.trainer else 128)
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# TODO: This switch between 64 and 128 is a hack for now. We should have a separate cli option for size
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output_file = get_folder(self.output_dir) / Path(filename).name
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cv2.imwrite(str(output_file), image)
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except Exception as e:
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print('Failed to convert image: {}. Reason: {}'.format(filename, e))
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def prepare_images(self):
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self.read_alignments()
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is_have_alignments = self.have_alignments()
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for filename in tqdm(self.read_directory()):
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image = cv2.imread(filename)
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if is_have_alignments:
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if self.have_face(filename):
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faces = self.get_faces_alignments(filename, image)
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else:
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tqdm.write ('no alignment found for {}, skipping'.format(os.path.basename(filename)))
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continue
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else:
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faces = self.get_faces(image)
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yield filename, image, faces
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