          self.faces["dst"] = list()
          destination = self.destination if self.destination else [np.ones_like(src["image"])
                                                                   for src in self.source]
          for idx, image in enumerate(destination):
              self.faces["dst"].append(AlignerExtract().transform(
                  image,
                  self.faces["matrix"][idx],
                  self.size,
                  self.padding))
          logger.debug("Updated destination faces")
 
</s> remove         self.faces["dst"] = list()
 </s> add         self._faces["dst"] = list() </s> remove     def crop_destination_faces(self):
        """ Update the main faces dict with new destination faces based on source matrices """
 </s> add     def _crop_destination_faces(self):
        """ Extract the swapped faces from the swapped frames using the source face destination
        matrices. """ </s> remove                 self.faces["matrix"][idx],
                self.size,
                self.padding))
 </s> add                 self._faces["matrix"][idx],
                self._size,
                self._padding)) </s> remove                 self.size,
                self.padding))
 </s> add                 self._size,
                self._padding)) </s> remove     def header_text(self):
        """ Create header text for output image """
        font_scale = self.size / 640
        height = self.size // 8
 </s> add     def _header_text(self):
        """ Create the header text displaying the frame name for each preview column.

        Returns
        -------
        :class:`numpy.ndarray`
            The header row of the preview image containing the frame names for each column
        """
        font_scale = self._size / 640
        height = self._size // 8
            self._faces["dst"].append(AlignerExtract().transform(
          self.faces["dst"] = list()
          destination = self.destination if self.destination else [np.ones_like(src["image"])
                                                                   for src in self.source]
          for idx, image in enumerate(destination):
            self._faces["dst"].append(AlignerExtract().transform(
                  image,
                  self.faces["matrix"][idx],
                  self.size,
                  self.padding))
          logger.debug("Updated destination faces")
keep keep keep keep 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 
tools/preview.py