      def add_queues(self):
          """ Add the queues for convert """
          logger.debug("Adding queues. Queue size: %s", self.queue_size)
          for qname in ("convert_in", "convert_out", "patch"):
              queue_manager.add_queue(qname, self.queue_size)
  
      def process(self):
          """ Process the conversion """
          logger.debug("Starting Conversion")
          # queue_manager.debug_monitor(5)
 
</s> remove         """ Process the conversion """
 </s> add         """ The entry point for triggering the Conversion Process.

        Should only be called from  :class:`lib.cli.ScriptExecutor`
        """ </s> remove         output_dir = get_folder(self.args.output_dir)
        logger.info("Output Directory: %s", output_dir)

    def add_queues(self):
        """ Add the queues for convert """
        logger.debug("Adding queues. Queue size: %s", self.queue_size)
 </s> add         if (self._args.mask_type not in ("none", "predicted") and
                not self._alignments.mask_is_valid(self._args.mask_type)):
            msg = ("You have selected the Mask Type `{}` but at least one face does not have this "
                   "mask stored in the Alignments File.\nYou should generate the required masks "
                   "with the Mask Tool or set the Mask Type option to an existing Mask Type.\nA "
                   "summary of existing masks is as follows:\nTotal faces: {}, Masks: "
                   "{}".format(self._args.mask_type, self._alignments.faces_count,
                               self._alignments.mask_summary))
            raise FaceswapError(msg)
        if self._args.mask_type == "predicted" and not self._predictor.has_predicted_mask:
            available_masks = [k for k, v in self._alignments.mask_summary.items()
                               if k != "none" and v == self._alignments.faces_count]
            if not available_masks:
                msg = ("Predicted Mask selected, but the model was not trained with a mask and no "
                       "masks are stored in the Alignments File.\nYou should generate the "
                       "required masks with the Mask Tool or set the Mask Type to `none`.")
                raise FaceswapError(msg)
            mask_type = available_masks[0]
            logger.warning("Predicted Mask selected, but the model was not trained with a "
                           "mask. Selecting first available mask: '%s'", mask_type)
            self._args.mask_type = mask_type

    def _add_queues(self):
        """ Add the queues for in, patch and out. """
        logger.debug("Adding queues. Queue size: %s", self._queue_size) </s> remove             self.convert_images()
            self.disk_io.save_thread.join()
 </s> add             self._convert_images()
            self._disk_io.save_thread.join() </s> remove             self.add_queue(task)
            self.start_thread(task)
 </s> add             self._add_queue(task)
            self._start_thread(task) </s> remove         self.busy_indicator.pack(side=tk.BOTTOM, padx=5, pady=5, fill=tk.X)
        self.busy_indicator.start()
 </s> add         self._busy_indicator.pack(side=tk.LEFT, padx=5, pady=(5, 10), fill=tk.X, expand=True)
        self._busy_indicator.start()

    def _add_actions(self, parent):
        """ Add Action Buttons to the :class:`ActionFrame`
            queue_manager.add_queue(qname, self._queue_size)
      def add_queues(self):
          """ Add the queues for convert """
          logger.debug("Adding queues. Queue size: %s", self.queue_size)
          for qname in ("convert_in", "convert_out", "patch"):
            queue_manager.add_queue(qname, self._queue_size)
  
      def process(self):
          """ Process the conversion """
          logger.debug("Starting Conversion")
          # queue_manager.debug_monitor(5)
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 
scripts/convert.py