          self._interpolator = None
  
          self._blur_kernel = 0
          self._threshold = 0.0
  
      @property
      def mask(self):
          """ numpy.ndarray: The mask at the size of :attr:`stored_size` with any requested blurring
 
</s> remove         if self._threshold != 0.0 or self._blur_kernel != 0:
 </s> add         if self._threshold != 0.0 or self._blur["kernel"] != 0: </s> remove         if self._blur_kernel != 0:
            mask = cv2.GaussianBlur(mask, (self._blur_kernel, self._blur_kernel), 0)[..., None]
 </s> add         if self._blur["kernel"] != 0:
            mask = BlurMask(self._blur["type"],
                            mask,
                            self._blur["kernel"],
                            passes=self._blur["passes"]).blurred </s> remove         if blur_kernel == 0 or blur_kernel % 2 == 1:
            self._blur_kernel = blur_kernel
        else:
            self._blur_kernel = blur_kernel + 1
 </s> add         if blur_type is not None:
            blur_kernel += 0 if blur_kernel == 0 or blur_kernel % 2 == 1 else 1
            self._blur["kernel"] = blur_kernel
            self._blur["type"] = blur_type
            self._blur["passes"] = blur_passes </s> remove     """ Return the requested mask """
    def __init__(self, mask_type, output_size, predicted_available, **kwargs):
        super().__init__(mask_type, output_size, predicted_available, **kwargs)
        self.do_erode = self.config.get("erosion", 0) != 0
        self.do_blend = self.config.get("type", None) is not None

    def process(self, detected_face, predicted_mask=None):
        """ Return mask and perform processing """
        mask = self.get_mask(detected_face, predicted_mask)
 </s> add     """ Manipulations to perform to the mask that is to be applied to the output of the Faceswap
    model.

    Parameters
    ----------
    mask_type: str
        The mask type to use for this plugin
    output_size: int
        The size of the output from the Faceswap model.
    coverage_ratio: float
        The coverage ratio that the Faceswap model was trained at.
    **kwargs: dict, optional
        See the parent :class:`~plugins.convert.mask._base` for additional keyword arguments.
    """
    def __init__(self, mask_type, output_size, coverage_ratio, **kwargs):
        super().__init__(mask_type, output_size, **kwargs)
        self._do_erode = self.config.get("erosion", 0) != 0
        self._coverage_ratio = coverage_ratio

    def process(self, detected_face, predicted_mask=None):  # pylint:disable=arguments-differ
        """ Obtain the requested mask type and perform any defined mask manipulations.

        Parameters
        ----------
        detected_face: :class:`lib.faces_detect.DetectedFace`
            The DetectedFace object as returned from :class:`scripts.convert.Predictor`.
        predicted_mask: :class:`numpy.ndarray`, optional
            The predicted mask as output from the Faceswap Model, if the model was trained
            with a mask, otherwise ``None``. Default: ``None``.

        Returns
        -------
        mask: :class:`numpy.ndarray`
            The mask with all requested manipulations applied
        raw_mask: :class:`numpy.ndarray`
            The mask with no erosion/dilation applied
        """
        mask = self._get_mask(detected_face, predicted_mask) </s> remove         return mask, raw_mask
 </s> add         return mask.astype("float32") / 255.0, raw_mask.astype("float32") / 255.0

    def _get_mask(self, detected_face, predicted_mask):
        """ Return the requested mask with any requested blurring applied.
        self._blur = dict()
          self._interpolator = None
  
        self._blur = dict()
          self._blur_kernel = 0
          self._threshold = 0.0
  
      @property
      def mask(self):
          """ numpy.ndarray: The mask at the size of :attr:`stored_size` with any requested blurring
keep add keep keep keep keep keep keep
 Smart Masks to Convert (#957)

- scripts.convert - Use Smart Masks for Convert
    * Make on-the-fly conversion an explicit option

- Move BlurMask to lib.faces_detect

- tools.preview - Fix for smart masks
    * Subclass from tk.Tk
    * Options to lib.gui.control_helper
    *variable cleanup

- lib.logger - Demote more tensorflow deprecation messages

- Documentation:
    * lib.faces_detect.BlurMask
    * plugins.convert.mask
    * lib.convert
    * scripts.convert
    * scripts.fsmedia
    * tools.preview 
lib/faces_detect.py