  
  import cv2
  import numpy as np
  
  from lib.model import masks as model_masks
  from ._base import Adjustment, BlurMask, logger
  
  
  class Mask(Adjustment):
      """ Return the requested mask """
      def __init__(self, mask_type, output_size, predicted_available, **kwargs):
 
</s> remove import cv2
 </s> add  </s> remove from lib.model import masks as model_masks
 </s> add  </s> remove """ Parent class for mask adjustments for faceswap.py converter """
 </s> add """ Base class for Faceswap :mod:`~plugins.convert.mask` Plugins """ </s> remove from cv2 import imwrite  # pylint:disable=no-name-in-module
 </s> add import cv2 </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)
from ._base import Adjustment, logger
  
  import cv2
  import numpy as np
  
from ._base import Adjustment, logger
from ._base import Adjustment, logger
  
  
  class Mask(Adjustment):
      """ Return the requested mask """
      def __init__(self, mask_type, output_size, predicted_available, **kwargs):
keep keep keep keep replace replace 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 
plugins/convert/mask/mask_blend.py