mirror of
https://github.com/deepfakes/faceswap
synced 2025-06-09 04:36:50 -04:00
435 lines
18 KiB
Python
435 lines
18 KiB
Python
#!/usr/bin/env python3
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""" Holds the classes for the 3 main Faceswap 'media' objects for
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input (extract) and output (convert) tasks. Those being:
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Images
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Faces
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Alignments"""
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import logging
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import os
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from pathlib import Path
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import cv2
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import imageio
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import numpy as np
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from lib.aligner import Extract as AlignerExtract
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from lib.alignments 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_folder, get_image_paths, set_system_verbosity,
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_video_extensions)
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logger = logging.getLogger(__name__) # pylint: disable=invalid-name
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class Utils():
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""" Holds utility functions that are required by more than one media
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object """
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@staticmethod
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def set_verbosity(loglevel):
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""" Set the system output verbosity """
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set_system_verbosity(loglevel)
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@staticmethod
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def finalize(images_found, num_faces_detected, verify_output):
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""" Finalize the image processing """
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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 main alignments class for extract """
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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,
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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 for the alignments file"""
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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 parent loader to handle skip existing on extract """
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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 = bool(hasattr(self.args, 'skip_existing')
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and self.args.skip_existing)
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skip_faces = bool(hasattr(self.args, 'skip_faces')
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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 algnments 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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""" Holds the full frames/images """
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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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def count_images(self):
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""" Number of images or frames """
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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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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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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 video file that is to be processed """
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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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""" Load an image and yield it with it's filename """
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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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""" Load frames from disk """
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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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""" Return frames from a video file """
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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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""" load requested image """
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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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""" Load a single frame from a video file """
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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():
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""" Optional post processing tasks """
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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 actions to be performed into a list """
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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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""" Set the post processing actions """
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postprocess_items = dict()
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# Debug Landmarks
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if (hasattr(self.args, 'debug_landmarks')
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and self.args.debug_landmarks):
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postprocess_items["DebugLandmarks"] = None
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# Blurry Face
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if hasattr(self.args, 'blur_thresh') and self.args.blur_thresh:
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kwargs = {"blur_thresh": self.args.blur_thresh}
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postprocess_items["BlurryFaceFilter"] = {"kwargs": kwargs}
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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, output_item):
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""" Perform the requested post-processing actions """
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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(output_item)
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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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Usuable in Extract or Convert or both
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depending on context """
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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 params passed in to disable
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logger.debug("Initialized base class %s", self.__class__.__name__)
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def process(self, output_item):
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""" Override for specific post processing action """
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raise NotImplementedError
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class BlurryFaceFilter(PostProcessAction): # pylint: disable=too-few-public-methods
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""" Move blurry faces to a different folder
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Extract Only """
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.blur_thresh = kwargs["blur_thresh"]
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logger.debug("Initialized %s", self.__class__.__name__)
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def process(self, output_item):
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""" Detect and move blurry face """
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extractor = AlignerExtract()
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for idx, detected_face in enumerate(output_item["detected_faces"]):
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frame_name = detected_face["file_location"].parts[-1]
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face = detected_face.aligned_face
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logger.trace("Checking for blurriness. Frame: '%s', Face: %s", frame_name, idx)
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aligned_landmarks = face.aligned_landmarks
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resized_face = face.aligned_face
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size = face.aligned["size"]
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padding = int(size * 0.1875)
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feature_mask = extractor.get_feature_mask(
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aligned_landmarks / size,
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size, padding)
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feature_mask = cv2.blur(feature_mask, (10, 10))
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isolated_face = cv2.multiply(feature_mask, resized_face.astype(float)).astype(np.uint8)
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blurry, focus_measure = self.is_blurry(isolated_face)
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if blurry:
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blur_folder = detected_face["file_location"].parts[:-1]
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blur_folder = get_folder(Path(*blur_folder) / Path("blurry"))
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detected_face["file_location"] = blur_folder / Path(frame_name)
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logger.verbose("%s's focus measure of %s was below the blur threshold, "
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"moving to 'blurry'", frame_name, "{0:.2f}".format(focus_measure))
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def is_blurry(self, image):
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""" Convert to grayscale, and compute the focus measure of the image using the
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Variance of Laplacian method """
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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focus_measure = self.variance_of_laplacian(gray)
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# if the focus measure is less than the supplied threshold,
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# then the image should be considered "blurry"
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retval = (focus_measure < self.blur_thresh, focus_measure)
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logger.trace("Returning: (is_blurry: %s, focus_measure %s)", retval[0], retval[1])
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return retval
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@staticmethod
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def variance_of_laplacian(image):
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""" Compute the Laplacian of the image and then return the focus
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measure, which is simply the variance of the Laplacian """
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retval = cv2.Laplacian(image, cv2.CV_64F).var()
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logger.trace("Returning: %s", retval)
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return retval
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class DebugLandmarks(PostProcessAction): # pylint: disable=too-few-public-methods
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""" Draw debug landmarks on face
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Extract Only """
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def process(self, output_item):
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""" Draw landmarks on image """
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for idx, detected_face in enumerate(output_item["detected_faces"]):
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face = detected_face.aligned_face
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logger.trace("Drawing Landmarks. Frame: '%s'. Face: %s",
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detected_face["file_location"].parts[-1], idx)
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aligned_landmarks = face.aligned_landmarks
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for (pos_x, pos_y) in aligned_landmarks:
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cv2.circle(face.aligned_face, (pos_x, pos_y), 2, (0, 0, 255), -1)
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class FaceFilter(PostProcessAction):
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""" Filter in or out faces based on input image(s)
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Extract or Convert """
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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logger.info("Extracting and aligning face for Face Filter...")
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self.filter = self.load_face_filter(**kwargs)
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logger.debug("Initialized %s", self.__class__.__name__)
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def load_face_filter(self, filter_lists, ref_threshold, aligner, detector,
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multiprocess):
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""" Load faces to filter out of images """
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if not any(val for val in filter_lists.values()):
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return None
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facefilter = None
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filter_files = [self.set_face_filter(f_type, filter_lists[f_type])
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for f_type in ("filter", "nfilter")]
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if any(filters for filters in filter_files):
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facefilter = FilterFunc(filter_files[0],
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filter_files[1],
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detector,
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aligner,
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multiprocess,
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ref_threshold)
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logger.debug("Face filter: %s", facefilter)
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else:
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self.valid = False
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return facefilter
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@staticmethod
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def set_face_filter(f_type, f_args):
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""" Set the required filters """
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if not f_args:
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return list()
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logger.info("%s: %s", f_type.title(), f_args)
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filter_files = f_args if isinstance(f_args, list) else [f_args]
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filter_files = list(filter(lambda fpath: Path(fpath).exists(), filter_files))
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if not filter_files:
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logger.warning("Face %s files were requested, but no files could be found. This "
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"filter will not be applied.", f_type)
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logger.debug("Face Filter files: %s", filter_files)
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return filter_files
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def process(self, output_item):
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""" Filter in/out wanted/unwanted faces """
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if not self.filter:
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return
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ret_faces = list()
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for idx, detect_face in enumerate(output_item["detected_faces"]):
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check_item = detect_face["face"] if isinstance(detect_face, dict) else detect_face
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check_item.load_aligned(output_item["image"])
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if not self.filter.check(check_item):
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logger.verbose("Skipping not recognized face: (Frame: %s Face %s)",
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output_item["filename"], idx)
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continue
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logger.trace("Accepting recognised face. Frame: %s. Face: %s",
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output_item["filename"], idx)
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ret_faces.append(detect_face)
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output_item["detected_faces"] = ret_faces
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