          self._vgg_face = None
          # TODO set this as FacesLoader in init. Need to move all processes to use it
          self._loader = None
  
      def _get_alignments(self):
          """ Obtain the alignments data and validate for methods which require it.
  
          Returns
          -------
          :class:`lib.align.Alignments`
              The alignments object pertaining to the data to be sorted. Returns ``None`` if an
              alignments file can't be found or is not required.
          """
          required_methods = ["face"]
          if self._args.sort_method not in required_methods:
              return None
          if self._args.alignments_path is None:
              path = os.path.join(self._args.input_dir, "alignments.fsa")
          else:
              path = self._args.alignments_path
  
          if not os.path.isfile(path):
              logger.warning("Alignments file not found at '%s'. Not using an alignments file will "
                             "lead to vastly inferior results.", path)
              logger.warning("It is highly recommended that you use an alignments file for sorting "
                             "by '%s'.", self._args.sort_method)
              return None
  
          return Alignments(*os.path.split(path))
  
      def process(self):
          """ Main processing function of the sort tool """
  
          # Setting default argument values that cannot be set by argparse
  
 
</s> remove         self._alignments = self._get_alignments()
 </s> add  </s> remove     def _get_aligned_faces(self, input_sizes):
        """ Pre-generate aligned faces as they are needed for all training functions.
 </s> add         # Validate image is the same size as the other images for the side
        if width != self._image_sizes[side]:
            msg = ("All training images for each side must be of the same size.\nImages "
                   "in side '{}' have mismatched sizes {} and {}.\nPlease double check "
                   "your dataset".format(side.upper(), self._image_sizes[side], width))
            raise FaceswapError(msg)

    def _update_legacy_facesets(self):
        """ Update the png header data for legacy face sets that do not contain the meta data in
        the exif header.
        """
        if self._validate_metadata(output_warning=False):
            logger.debug("All faces contain valid header information")
            return

        for side, png_meta in self._detected_faces.items():
            if all(png_meta.values()):
                continue
            filenames = [filename for filename, meta in png_meta.items() if not meta]
            logger.info("Legacy faces discovered for side '%s'. Updating %s images...",
                        side.upper(), len(filenames))
            alignments = Alignments(*os.path.split(self._get_alignments_path(side)))
            self._alignments_version.setdefault(side, set()).add(alignments.version)

            executor = futures.ThreadPoolExecutor()
            with executor:
                images = {executor.submit(update_legacy_png_header, filename, alignments): filename
                          for filename in filenames}

                for future in tqdm(
                        futures.as_completed(images),
                        desc="Updating legacy training images ({})".format(side.upper()),
                        total=len(filenames),
                        leave=False):
                    detected_face = DetectedFace()
                    detected_face.from_png_meta(future.result())
                    png_meta[images[future]] = detected_face

    def _get_alignments_path(self, side):
        """ Obtain the path to an alignments file for the given training side.

        Used for updating legacy facesets to contain the meta information within the image header

        Parameters
        ----------
        side: ["a" or "b"]
            The training side to obtain the alignments file for.

        Returns
        -------
        str
            The full path to the training alignments file

        Raises
        ------
        FaceswapError
            If an alignments file cannot be located
        """
        alignments_path = getattr(self._args, "alignments_path_{}".format(side))
        if not alignments_path:
            image_path = getattr(self._args, "input_{}".format(side))
            alignments_path = os.path.join(image_path, "alignments.fsa")
        if not os.path.exists(alignments_path):
            msg = ("You are using a legacy faceset that does not contain embedded "
                   "meta-information. An alignments file must be provided so that these files can "
                   "be updated.\n"
                   f"Alignments file does not exist: '{alignments_path}'")
            raise FaceswapError(msg)
        return alignments_path

    # <<< VALIDATE LOADED DETECTED FACE INFORMATION >>>
    def _validity_check(self):
        """ Check the validity of the finally loaded data.

        Ensure that each side has consistent alignments versions.
        Ensure that each side has a full compliment of metadata.
        """
        invalid = [side.upper()
                   for side, version in self._alignments_version.items() if len(version) > 1]
        if invalid:
            raise FaceswapError("Mixing legacy and full head extracted facesets is not supported. "
                                "The following side(s) contain a mix of extracted face "
                                "types: {}".format(invalid))
        # Replace check alignments version sets with actual floats
        self._alignments_version = {key: val.pop()
                                    for key, val in self._alignments_version.items()}

        if 1.0 in self._alignments_version.values() and self._config["centering"] != "legacy":
            logger.warning("You are using legacy extracted faces but have selected '%s' "
                           "centering which is incompatible. Switching centering to 'legacy'",
                           self._config["centering"])
            self._config["centering"] = "legacy"

        self._validate_metadata(output_warning=True)
        self._validate_masks()

    def _validate_metadata(self, output_warning=True):
        """ Validate that all images to be trained on have associated alignments data. If not
        generate a warning. </s> remove             The alignments paths for each of the source and destination faces. Key is the
            side, value is the path to the alignments file
 </s> add             For keys "a" and "b" the values are a ``dict`` with the key being the filename of the
            training image and the value being the :class:`lib.align.DetectedFace` object
        """
        metadata = dict()
        for side, filelist in image_list.items():
            meta_side = dict()
            logger.debug("side: %s, file count: %s", side, len(filelist))
            for filename, meta in tqdm(read_image_meta_batch(filelist),
                                       desc="Reading training images ({})".format(side.upper()),
                                       total=len(filelist),
                                       leave=False):

                self._validate_image_size(side, filename, meta["width"], meta["height"])

                if "itxt" not in meta or "alignments" not in meta["itxt"]:
                    meta_side[filename] = None
                else:
                    alignments_version = meta["itxt"]["source"]["alignments_version"]
                    self._alignments_version.setdefault(side, set()).add(alignments_version)
                    detected_face = DetectedFace()
                    detected_face.from_png_meta(meta["itxt"]["alignments"])
                    meta_side[filename] = detected_face
            metadata[side] = meta_side
        return metadata

    def _validate_image_size(self, side, filename, width, height):
        """ Validate that the images are square and that the sizes for all image in a side are
        the same.

        Parameters
        ----------
        side: ["a" or "b"]
            The training side that is being processed
        filename: str
            The filename of the image that is being validated
        width: int
            The width of the image to be validated
        height: int
            The height of the image to be validated </s> remove class Merge():  # pylint:disable=too-few-public-methods
    """ Merge multiple alignments files into one.

    Parameters
    ----------
    alignments: :class:`tools.lib_alignments.media.AlignmentData`
        The alignments data loaded from an alignments file for this rename job
    arguments: :class:`argparse.Namespace`
        The :mod:`argparse` arguments as passed in from :mod:`tools.py`
    """
    def __init__(self, alignments, arguments):
        self._alignments = alignments
        self._check_versions()
        self._faces = self._get_faces(arguments)
        self._final_alignments = alignments[0]
        self._process_alignments = alignments[1:]
        self._hashes_to_frame = None

    def _check_versions(self):
        """ Ensure all alignments files are compatible versions. If not, exit with error. """
        versions = [al.version for al in self._alignments]
        logger.debug(versions)
        if any(vers < 2.0 for vers in versions) and any(vers >= 2.0 for vers in versions):
            logger.error("You have selected incompatible alignments files for merging. You cannot "
                         "merge alignments files for legacy extracted faces with aligments files "
                         "for full-head extracted faces.")
            logger.info("You can update legacy alignments files by using the Extract job in the "
                        "Alignments tool to re-extract the faces in full-head format.")
            sys.exit(0)

    @staticmethod
    def _get_faces(arguments):
        """ If faces argument is specified, load the faces folder otherwise return ``None``.

        Parameters
        ----------
        arguments: :class:`argparse.Namespace`
            The :mod:`argparse` arguments as passed in from :mod:`tools.py`

        Returns
        -------
        :class:`tools.alignments.media.Faces` or ``None``
            The faces object or ``None`` if not faces folder specified
        """
        if not hasattr(arguments, "faces_dir") or not arguments.faces_dir:
            return None
        return Faces(arguments.faces_dir)

    def process(self):
        """Run the alignments file merging process. """
        logger.info("[MERGE ALIGNMENTS]")  # Tidy up cli output
        if self._faces is not None:
            self._remove_faces()
        self._hashes_to_frame = self._final_alignments.hashes_to_frame
        skip_count = 0
        merge_count = 0
        total_count = sum([alignments.frames_count for alignments in self._process_alignments])

        with tqdm(desc="Merging Alignments", total=total_count) as pbar:
            for alignments in self._process_alignments:
                for _, src_alignments, _, frame in alignments.yield_faces():
                    for idx, alignment in enumerate(src_alignments):
                        if not alignment.get("hash", None):
                            logger.warning("Alignment '%s':%s has no Hash! Skipping", frame, idx)
                            skip_count += 1
                            continue
                        if self._check_exists(frame, alignment, idx):
                            skip_count += 1
                            continue
                        self._merge_alignment(frame, alignment, idx)
                        merge_count += 1
                    pbar.update(1)
        logger.info("Alignments Merged: %s", merge_count)
        logger.info("Alignments Skipped: %s", skip_count)
        if merge_count != 0:
            self._set_destination_filename()
            self._final_alignments.save()

    def _remove_faces(self):
        """ Removes faces from the alignments file if a faces folder has been provided and the
        faces do not exist within each alignments file. """
        face_hashes = list(self._faces.items.keys())
        del_faces_count = 0
        del_frames_count = 0
        if not face_hashes:
            logger.error("No face hashes. This would remove all faces from your alignments file.")
            return
        for alignments in tqdm(self._alignments, desc="Filtering out faces"):
            pre_face_count = alignments.faces_count
            pre_frames_count = alignments.frames_count
            alignments.filter_hashes(face_hashes, filter_out=False)
            # Remove frames with no faces
            frames = list(alignments.data.keys())
            for frame in frames:
                if not alignments.frame_has_faces(frame):
                    del alignments.data[frame]
            post_face_count = alignments.faces_count
            post_frames_count = alignments.frames_count
            removed_faces = pre_face_count - post_face_count
            removed_frames = pre_frames_count - post_frames_count
            del_faces_count += removed_faces
            del_frames_count += removed_frames
            logger.verbose("Removed %s faces and %s frames from %s",
                           removed_faces, removed_frames, os.path.basename(alignments.file))
        logger.info("Total removed - faces: %s, frames: %s", del_faces_count, del_frames_count)

    def _check_exists(self, frame, alignment, index):
        """ Remove duplicate faces from the alignments file when an instance of the face already
        exists.

        Parameters
        ----------
        frame: str
            The frame name for the current frame being processed
        alignment: dict
            The alignment dictionary for the current face in the frame
        index: int
            The face index for the current face in the frame

        Returns
        -------
        bool
            ``True`` if the face has been already been seen, otherwise ``False``
        """
        existing_frame = self._hashes_to_frame.get(alignment["hash"], None)
        if not existing_frame:
            return False
        if frame in existing_frame.keys():
            logger.verbose("Face '%s': %s already exists in destination at position %s. "
                           "Skipping", frame, index, existing_frame[frame])
        elif frame not in existing_frame.keys():
            logger.verbose("Face '%s': %s exists in destination as: %s. "
                           "Skipping", frame, index, existing_frame)
        return True

    def _merge_alignment(self, frame, alignment, idx):
        """ Merge the source alignment into the destination final alignments dictionary

        Parameters
        ----------
        frame: str
            The frame name for the current frame being processed
        alignment: dict
            The alignment dictionary for the current face in the frame
        index: int
            The face index for the current face in the frame
        """
        logger.debug("Merging alignment: (frame: %s, src_idx: %s, hash: %s)",
                     frame, idx, alignment["hash"])
        self._hashes_to_frame.setdefault(alignment["hash"], dict())[frame] = idx
        self._final_alignments.data.setdefault(frame,
                                               dict()).setdefault("faces", []).append(alignment)

    def _set_destination_filename(self):
        """ Set the destination filename """
        folder = os.path.split(self._final_alignments.file)[0]
        ext = os.path.splitext(self._final_alignments.file)[1]
        now = datetime.now().strftime("%Y%m%d_%H%M%S")
        filename = os.path.join(folder, "alignments_merged_{}{}".format(now, ext))
        logger.debug("Output set to: '%s'", filename)
        self._final_alignments.set_filename(filename)


 </s> add  </s> remove     def _load_alignments(self):
        """ Loads the given alignments file(s) prior to running the selected job.

        Returns
        -------
        :class:`~tools.alignments.media.AlignmentData` or list
            The alignments data formatted for use by the alignments tool. If multiple alignments
            files have been selected, then this will be a list of
            :class:`~tools.alignments.media.AlignmentData` objects
        """
        logger.debug("Loading alignments")
        if len(self.args.alignments_file) > 1 and self.args.job != "merge":
            logger.error("Multiple alignments files are only permitted for merging")
            sys.exit(0)
        if len(self.args.alignments_file) == 1 and self.args.job == "merge":
            logger.error("More than one alignments file required for merging")
            sys.exit(0)

        if len(self.args.alignments_file) == 1:
            retval = AlignmentData(self.args.alignments_file[0])
        else:
            retval = [AlignmentData(a_file) for a_file in self.args.alignments_file]
        logger.debug("Alignments: %s", retval)
        return retval

 </s> add 
          self._vgg_face = None
          # TODO set this as FacesLoader in init. Need to move all processes to use it
          self._loader = None
  


























      def process(self):
          """ Main processing function of the sort tool """
  
          # Setting default argument values that cannot be set by argparse
  
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Update extracted faces to use PNG EXIF data (#1123)

Documentation
  - Update Usage.md, align.rst and image.rst
lib.image.py
  - read_image - Remove hash return, add metadata return
  - Remove read_image_hash functions
  - Add read_image_meta functios
  - Replace encode_image_with_hash with encode_image (to store metadata)
  - Add png meta reading and writing functions
  - Update Image Loaders/Savers to handle metadata rather than hashes
lib.training_data
  - Naming updates to remove references to hashes
lib.align.Alignments
  - Add versioning notes
  - Increment alignments version to 2.1
  - Deprecate hashing lookup functions
  - Replace filter_hashes with filter_faces
lib.align.detected_face
  - DetectedFace
    - Remove hash property
    - Add png header data serializing/deserializing functions
  - Mask
    - Add png header data serializing/deserializing functions
  - add update_legacy_png_header function to update png meta data
lib.cli.args - Deprecate alignments files for training
- plugins.train.trainer
  - Update alignments/mask code to read png header data
- scripts.convert
  - Aligned images folder - read data from png headers
- scripts.extract
  - Write png header information and no longer store hash of face
- tools.alignments
  - remove leftover-faces, merge and update-hashes jobs
  - Update jobs to use png meta data rather than hashes
- tools.manual
  - Update extract code to output png meta data and don't store hashes
  - Perform check on launch that tool is not pointing at a faces folder
tools.mask
  - Update to use png meta data
tools.sort
  - Update to use png meta data 
tools/sort/sort.py