mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-08 20:13:52 -04:00
05018f6119
* Extract - Implement aligner re-feeding - Add extract type to pipeline.ExtractMedia - Add pose annotation to debug * Convert - implement centering - remove usage of feed and reference face properties - Remove distributed option from convert - Force update of alignments file on legacy receive * Train - Resize preview image to model output size - Force legacy centering if centering does not exist in model's state file - Enable training on legacy face sets * Alignments Tool - Update draw to include head/pose - Remove DFL drop + linting - Remove remove-frames job - remove align-eyes option - Update legacy masks to new extract type - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files - Re-generate thumbnails on legacy upgrade * Mask Tool - Update for new extract + bugfix full frame * Manual Tool - Update to new extraction method - Disable legacy alignments, - extract box bugfix - extract faces - size to 512 and center on head * Preview Tool - Display based on model centering * Sort Tool - Use alignments for sort by face * lib.aligner - Add Pose Class - Add AlignedFace Class - center _MEAN_FACE on x - Add meta information with versioning to alignments file - lib.aligner.get_align_matrix to use landmarks not face - Refactor aligned faces in lib.faces_detect * lib.logger - larger file log padding * lib.config - Fix global changeable_items * lib.face_filter - Use new extracted face images * lib.image - bump thumbnail default size to 96px
641 lines
23 KiB
Python
641 lines
23 KiB
Python
#!/usr/bin/env python3
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""" Helper functions for :mod:`~scripts.extract` and :mod:`~scripts.convert`.
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Holds the classes for the 2 main Faceswap 'media' objects: Images and Alignments.
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Holds optional pre/post processing functions for convert and extract.
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"""
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import logging
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import os
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import sys
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from pathlib import Path
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import cv2
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import numpy as np
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import imageio
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from lib.align import Alignments as AlignmentsBase
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from lib.face_filter import FaceFilter as FilterFunc
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from lib.image import count_frames, read_image
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from lib.utils import (camel_case_split, get_image_paths, _video_extensions)
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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def finalize(images_found, num_faces_detected, verify_output):
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""" Output summary statistics at the end of the extract or convert processes.
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Parameters
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----------
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images_found: int
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The number of images/frames that were processed
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num_faces_detected: int
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The number of faces that have been detected
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verify_output: bool
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``True`` if multiple faces were detected in frames otherwise ``False``.
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"""
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logger.info("-------------------------")
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logger.info("Images found: %s", images_found)
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logger.info("Faces detected: %s", num_faces_detected)
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logger.info("-------------------------")
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if verify_output:
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logger.info("Note:")
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logger.info("Multiple faces were detected in one or more pictures.")
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logger.info("Double check your results.")
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logger.info("-------------------------")
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logger.info("Process Succesfully Completed. Shutting Down...")
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class Alignments(AlignmentsBase):
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""" Override :class:`lib.align.Alignments` to add custom loading based on command
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line arguments.
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Parameters
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----------
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arguments: :class:`argparse.Namespace`
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The command line arguments that were passed to Faceswap
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is_extract: bool
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``True`` if the process calling this class is extraction otherwise ``False``
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input_is_video: bool, optional
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``True`` if the input to the process is a video, ``False`` if it is a folder of images.
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Default: False
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"""
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def __init__(self, arguments, is_extract, input_is_video=False):
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logger.debug("Initializing %s: (is_extract: %s, input_is_video: %s)",
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self.__class__.__name__, is_extract, input_is_video)
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self._args = arguments
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self._is_extract = is_extract
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folder, filename = self._set_folder_filename(input_is_video)
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super().__init__(folder, filename=filename)
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logger.debug("Initialized %s", self.__class__.__name__)
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def _set_folder_filename(self, input_is_video):
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""" Return the folder and the filename for the alignments file.
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If the input is a video, the alignments file will be stored in the same folder
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as the video, with filename `<videoname>_alignments`.
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If the input is a folder of images, the alignments file will be stored in folder with
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the images and just be called 'alignments'
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Parameters
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----------
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input_is_video: bool, optional
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``True`` if the input to the process is a video, ``False`` if it is a folder of images.
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Returns
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-------
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folder: str
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The folder where the alignments file will be stored
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filename: str
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The filename of the alignments file
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"""
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if self._args.alignments_path:
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logger.debug("Alignments File provided: '%s'", self._args.alignments_path)
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folder, filename = os.path.split(str(self._args.alignments_path))
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elif input_is_video:
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logger.debug("Alignments from Video File: '%s'", self._args.input_dir)
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folder, filename = os.path.split(self._args.input_dir)
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filename = "{}_alignments".format(os.path.splitext(filename)[0])
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else:
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logger.debug("Alignments from Input Folder: '%s'", self._args.input_dir)
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folder = str(self._args.input_dir)
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filename = "alignments"
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logger.debug("Setting Alignments: (folder: '%s' filename: '%s')", folder, filename)
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return folder, filename
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def _load(self):
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""" Override the parent :func:`~lib.align.Alignments._load` to handle skip existing
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frames and faces on extract.
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If skip existing has been selected, existing alignments are loaded and returned to the
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calling script.
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Returns
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-------
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dict
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Any alignments that have already been extracted if skip existing has been selected
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otherwise an empty dictionary
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"""
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data = dict()
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if not self._is_extract:
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if not self.have_alignments_file:
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return data
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data = super()._load()
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return data
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skip_existing = hasattr(self._args, 'skip_existing') and self._args.skip_existing
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skip_faces = hasattr(self._args, 'skip_faces') and self._args.skip_faces
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if not skip_existing and not skip_faces:
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logger.debug("No skipping selected. Returning empty dictionary")
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return data
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if not self.have_alignments_file and (skip_existing or skip_faces):
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logger.warning("Skip Existing/Skip Faces selected, but no alignments file found!")
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return data
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data = self._serializer.load(self.file)
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if skip_faces:
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# Remove items from alignments that have no faces so they will
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# be re-detected
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del_keys = [key for key, val in data.items() if not val]
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logger.debug("Frames with no faces selected for redetection: %s", len(del_keys))
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for key in del_keys:
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if key in data:
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logger.trace("Selected for redetection: '%s'", key)
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del data[key]
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return data
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class Images():
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""" Handles the loading of frames from a folder of images or a video file for extract
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and convert processes.
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Parameters
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----------
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arguments: :class:`argparse.Namespace`
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The command line arguments that were passed to Faceswap
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"""
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def __init__(self, arguments):
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logger.debug("Initializing %s", self.__class__.__name__)
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self._args = arguments
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self._is_video = self._check_input_folder()
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self._input_images = self._get_input_images()
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self._images_found = self._count_images()
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logger.debug("Initialized %s", self.__class__.__name__)
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@property
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def is_video(self):
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"""bool: ``True`` if the input is a video file otherwise ``False``. """
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return self._is_video
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@property
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def input_images(self):
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"""str or list: Path to the video file if the input is a video otherwise list of
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image paths. """
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return self._input_images
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@property
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def images_found(self):
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"""int: The number of frames that exist in the video file, or the folder of images. """
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return self._images_found
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def _count_images(self):
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""" Get the number of Frames from a video file or folder of images.
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Returns
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-------
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int
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The number of frames in the image source
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"""
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if self._is_video:
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retval = int(count_frames(self._args.input_dir, fast=True))
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else:
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retval = len(self._input_images)
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return retval
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def _check_input_folder(self):
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""" Check whether the input is a folder or video.
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Returns
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-------
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bool
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``True`` if the input is a video otherwise ``False``
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"""
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if not os.path.exists(self._args.input_dir):
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logger.error("Input location %s not found.", self._args.input_dir)
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sys.exit(1)
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if (os.path.isfile(self._args.input_dir) and
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os.path.splitext(self._args.input_dir)[1].lower() in _video_extensions):
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logger.info("Input Video: %s", self._args.input_dir)
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retval = True
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else:
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logger.info("Input Directory: %s", self._args.input_dir)
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retval = False
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return retval
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def _get_input_images(self):
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""" Return the list of images or path to video file that is to be processed.
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Returns
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-------
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str or list
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Path to the video file if the input is a video otherwise list of image paths.
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"""
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if self._is_video:
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input_images = self._args.input_dir
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else:
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input_images = get_image_paths(self._args.input_dir)
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return input_images
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def load(self):
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""" Generator to load frames from a folder of images or from a video file.
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Yields
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------
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filename: str
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The filename of the current frame
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image: :class:`numpy.ndarray`
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A single frame
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"""
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iterator = self._load_video_frames if self._is_video else self._load_disk_frames
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for filename, image in iterator():
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yield filename, image
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def _load_disk_frames(self):
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""" Generator to load frames from a folder of images.
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Yields
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------
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filename: str
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The filename of the current frame
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image: :class:`numpy.ndarray`
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A single frame
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"""
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logger.debug("Input is separate Frames. Loading images")
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for filename in self._input_images:
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image = read_image(filename, raise_error=False)
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if image is None:
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continue
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yield filename, image
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def _load_video_frames(self):
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""" Generator to load frames from a video file.
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Yields
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------
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filename: str
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The filename of the current frame
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image: :class:`numpy.ndarray`
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A single frame
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"""
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logger.debug("Input is video. Capturing frames")
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vidname = os.path.splitext(os.path.basename(self._args.input_dir))[0]
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reader = imageio.get_reader(self._args.input_dir, "ffmpeg")
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for i, frame in enumerate(reader):
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# Convert to BGR for cv2 compatibility
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frame = frame[:, :, ::-1]
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filename = "{}_{:06d}.png".format(vidname, i + 1)
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logger.trace("Loading video frame: '%s'", filename)
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yield filename, frame
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reader.close()
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def load_one_image(self, filename):
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""" Obtain a single image for the given filename.
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Parameters
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----------
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filename: str
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The filename to return the image for
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Returns
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------
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:class:`numpy.ndarray`
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The image for the requested filename,
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"""
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logger.trace("Loading image: '%s'", filename)
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if self._is_video:
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if filename.isdigit():
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frame_no = filename
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else:
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frame_no = os.path.splitext(filename)[0][filename.rfind("_") + 1:]
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logger.trace("Extracted frame_no %s from filename '%s'", frame_no, filename)
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retval = self._load_one_video_frame(int(frame_no))
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else:
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retval = read_image(filename, raise_error=True)
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return retval
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def _load_one_video_frame(self, frame_no):
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""" Obtain a single frame from a video file.
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Parameters
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----------
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frame_no: int
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The frame index for the required frame
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Returns
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------
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:class:`numpy.ndarray`
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The image for the requested frame index,
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"""
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logger.trace("Loading video frame: %s", frame_no)
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reader = imageio.get_reader(self._args.input_dir, "ffmpeg")
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reader.set_image_index(frame_no - 1)
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frame = reader.get_next_data()[:, :, ::-1]
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reader.close()
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return frame
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class PostProcess(): # pylint:disable=too-few-public-methods
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""" Optional pre/post processing tasks for convert and extract.
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Builds a pipeline of actions that have optionally been requested to be performed
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in this session.
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Parameters
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----------
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arguments: :class:`argparse.Namespace`
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The command line arguments that were passed to Faceswap
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"""
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def __init__(self, arguments):
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logger.debug("Initializing %s", self.__class__.__name__)
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self._args = arguments
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self._actions = self._set_actions()
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logger.debug("Initialized %s", self.__class__.__name__)
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def _set_actions(self):
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""" Compile the requested actions to be performed into a list
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Returns
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-------
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list
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The list of :class:`PostProcessAction` to be performed
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"""
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postprocess_items = self._get_items()
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actions = list()
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for action, options in postprocess_items.items():
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options = dict() if options is None else options
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args = options.get("args", tuple())
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kwargs = options.get("kwargs", dict())
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args = args if isinstance(args, tuple) else tuple()
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kwargs = kwargs if isinstance(kwargs, dict) else dict()
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task = globals()[action](*args, **kwargs)
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if task.valid:
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logger.debug("Adding Postprocess action: '%s'", task)
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actions.append(task)
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for action in actions:
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action_name = camel_case_split(action.__class__.__name__)
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logger.info("Adding post processing item: %s", " ".join(action_name))
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return actions
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def _get_items(self):
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""" Check the passed in command line arguments for requested actions,
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For any requested actions, add the item to the actions list along with
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any relevant arguments and keyword arguments.
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Returns
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-------
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dict
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The name of the action to be performed as the key. Any action specific
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arguments and keyword arguments as the value.
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"""
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postprocess_items = dict()
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# Debug Landmarks
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if (hasattr(self._args, 'debug_landmarks') and self._args.debug_landmarks):
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postprocess_items["DebugLandmarks"] = None
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# Face Filter post processing
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if ((hasattr(self._args, "filter") and self._args.filter is not None) or
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(hasattr(self._args, "nfilter") and
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self._args.nfilter is not None)):
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if hasattr(self._args, "detector"):
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detector = self._args.detector.replace("-", "_").lower()
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else:
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detector = "cv2_dnn"
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if hasattr(self._args, "aligner"):
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aligner = self._args.aligner.replace("-", "_").lower()
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else:
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aligner = "cv2_dnn"
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face_filter = dict(detector=detector,
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aligner=aligner,
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multiprocess=not self._args.singleprocess)
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filter_lists = dict()
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if hasattr(self._args, "ref_threshold"):
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face_filter["ref_threshold"] = self._args.ref_threshold
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for filter_type in ('filter', 'nfilter'):
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filter_args = getattr(self._args, filter_type, None)
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filter_args = None if not filter_args else filter_args
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filter_lists[filter_type] = filter_args
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face_filter["filter_lists"] = filter_lists
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postprocess_items["FaceFilter"] = {"kwargs": face_filter}
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logger.debug("Postprocess Items: %s", postprocess_items)
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return postprocess_items
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def do_actions(self, extract_media):
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""" Perform the requested optional post-processing actions on the given image.
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Parameters
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----------
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extract_media: :class:`~plugins.extract.pipeline.ExtractMedia`
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The :class:`~plugins.extract.pipeline.ExtractMedia` object to perform the
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action on.
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Returns
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-------
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:class:`~plugins.extract.pipeline.ExtractMedia`
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The original :class:`~plugins.extract.pipeline.ExtractMedia` with any actions applied
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"""
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for action in self._actions:
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logger.debug("Performing postprocess action: '%s'", action.__class__.__name__)
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action.process(extract_media)
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class PostProcessAction(): # pylint: disable=too-few-public-methods
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""" Parent class for Post Processing Actions.
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Usable in Extract or Convert or both depending on context. Any post-processing actions should
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inherit from this class.
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Parameters
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-----------
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args: tuple
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Varies for specific post process action
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kwargs: dict
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Varies for specific post process action
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"""
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def __init__(self, *args, **kwargs):
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logger.debug("Initializing %s: (args: %s, kwargs: %s)",
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self.__class__.__name__, args, kwargs)
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self._valid = True # Set to False if invalid parameters passed in to disable
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logger.debug("Initialized base class %s", self.__class__.__name__)
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@property
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def valid(self):
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"""bool: ``True`` if the action if the parameters passed in for this action are valid,
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otherwise ``False`` """
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return self._valid
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def process(self, extract_media):
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""" Override for specific post processing action
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Parameters
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----------
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extract_media: :class:`~plugins.extract.pipeline.ExtractMedia`
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The :class:`~plugins.extract.pipeline.ExtractMedia` object to perform the
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action on.
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"""
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raise NotImplementedError
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class DebugLandmarks(PostProcessAction): # pylint: disable=too-few-public-methods
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""" Draw debug landmarks on face output. Extract Only """
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def process(self, extract_media):
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""" Draw landmarks on a face.
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Parameters
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----------
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extract_media: :class:`~plugins.extract.pipeline.ExtractMedia`
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The :class:`~plugins.extract.pipeline.ExtractMedia` object that contains the faces to
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draw the landmarks on to
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Returns
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-------
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:class:`~plugins.extract.pipeline.ExtractMedia`
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The original :class:`~plugins.extract.pipeline.ExtractMedia` with landmarks drawn
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onto the face
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"""
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frame = os.path.splitext(os.path.basename(extract_media.filename))[0]
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for idx, face in enumerate(extract_media.detected_faces):
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logger.trace("Drawing Landmarks. Frame: '%s'. Face: %s", frame, idx)
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# Landmarks
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for (pos_x, pos_y) in face.aligned.landmarks:
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cv2.circle(face.aligned.face, (pos_x, pos_y), 1, (0, 255, 255), -1)
|
|
# Pose
|
|
center = tuple(np.int32((face.aligned.size / 2, face.aligned.size / 2)))
|
|
points = (face.aligned.pose.xyz_2d * face.aligned.size).astype("int32")
|
|
cv2.line(face.aligned.face, center, tuple(points[1]), (0, 255, 0), 1)
|
|
cv2.line(face.aligned.face, center, tuple(points[0]), (255, 0, 0), 1)
|
|
cv2.line(face.aligned.face, center, tuple(points[2]), (0, 0, 255), 1)
|
|
# Face centering
|
|
roi = face.aligned.get_cropped_roi("face")
|
|
cv2.rectangle(face.aligned.face, tuple(roi[:2]), tuple(roi[2:]), (0, 255, 0), 1)
|
|
|
|
|
|
class FaceFilter(PostProcessAction):
|
|
""" Filter in or out faces based on input image(s). Extract or Convert
|
|
|
|
Parameters
|
|
-----------
|
|
args: tuple
|
|
Unused
|
|
kwargs: dict
|
|
Keyword arguments for face filter:
|
|
|
|
* **detector** (`str`) - The detector to use
|
|
|
|
* **aligner** (`str`) - The aligner to use
|
|
|
|
* **multiprocess** (`bool`) - Whether to run the extraction pipeline in single process \
|
|
mode or not
|
|
|
|
* **ref_threshold** (`float`) - The reference threshold for a positive match
|
|
|
|
* **filter_lists** (`dict`) - The filter and nfilter image paths
|
|
"""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
logger.info("Extracting and aligning face for Face Filter...")
|
|
self._filter = self._load_face_filter(**kwargs)
|
|
logger.debug("Initialized %s", self.__class__.__name__)
|
|
|
|
def _load_face_filter(self, filter_lists, ref_threshold, aligner, detector, multiprocess):
|
|
""" Set up and load the :class:`~lib.face_filter.FaceFilter`.
|
|
|
|
Parameters
|
|
----------
|
|
filter_lists: dict
|
|
The filter and nfilter image paths
|
|
ref_threshold: float
|
|
The reference threshold for a positive match
|
|
aligner: str
|
|
The aligner to use
|
|
detector: str
|
|
The detector to use
|
|
multiprocess: bool
|
|
Whether to run the extraction pipeline in single process mode or not
|
|
|
|
Returns
|
|
-------
|
|
:class:`~lib.face_filter.FaceFilter`
|
|
The face filter
|
|
"""
|
|
if not any(val for val in filter_lists.values()):
|
|
return None
|
|
|
|
facefilter = None
|
|
filter_files = [self._set_face_filter(f_type, filter_lists[f_type])
|
|
for f_type in ("filter", "nfilter")]
|
|
|
|
if any(filters for filters in filter_files):
|
|
facefilter = FilterFunc(filter_files[0],
|
|
filter_files[1],
|
|
detector,
|
|
aligner,
|
|
multiprocess,
|
|
ref_threshold)
|
|
logger.debug("Face filter: %s", facefilter)
|
|
else:
|
|
self.valid = False
|
|
return facefilter
|
|
|
|
@staticmethod
|
|
def _set_face_filter(f_type, f_args):
|
|
""" Check filter files exist and add the filter file paths to a list.
|
|
|
|
Parameters
|
|
----------
|
|
f_type: {"filter", "nfilter"}
|
|
The type of filter to create this list for
|
|
f_args: str or list
|
|
The filter image(s) to use
|
|
|
|
Returns
|
|
-------
|
|
list
|
|
The confirmed existing paths to filter files to use
|
|
"""
|
|
if not f_args:
|
|
return list()
|
|
|
|
logger.info("%s: %s", f_type.title(), f_args)
|
|
filter_files = f_args if isinstance(f_args, list) else [f_args]
|
|
filter_files = list(filter(lambda fpath: Path(fpath).exists(), filter_files))
|
|
if not filter_files:
|
|
logger.warning("Face %s files were requested, but no files could be found. This "
|
|
"filter will not be applied.", f_type)
|
|
logger.debug("Face Filter files: %s", filter_files)
|
|
return filter_files
|
|
|
|
def process(self, extract_media):
|
|
""" Filters in or out any wanted or unwanted faces based on command line arguments.
|
|
|
|
Parameters
|
|
----------
|
|
extract_media: :class:`~plugins.extract.pipeline.ExtractMedia`
|
|
The :class:`~plugins.extract.pipeline.ExtractMedia` object to perform the
|
|
face filtering on.
|
|
|
|
Returns
|
|
-------
|
|
:class:`~plugins.extract.pipeline.ExtractMedia`
|
|
The original :class:`~plugins.extract.pipeline.ExtractMedia` with any requested filters
|
|
applied
|
|
"""
|
|
if not self._filter:
|
|
return
|
|
ret_faces = list()
|
|
for idx, detect_face in enumerate(extract_media.detected_faces):
|
|
check_item = detect_face["face"] if isinstance(detect_face, dict) else detect_face
|
|
if not self._filter.check(extract_media.image, check_item):
|
|
logger.verbose("Skipping not recognized face: (Frame: %s Face %s)",
|
|
extract_media.filename, idx)
|
|
continue
|
|
logger.trace("Accepting recognised face. Frame: %s. Face: %s",
|
|
extract_media.filename, idx)
|
|
ret_faces.append(detect_face)
|
|
extract_media.add_detected_faces(ret_faces)
|