                               len(batch), faces_seen)
                  detected_batch = [detected_face for item in batch
                                    for detected_face in item["detected_faces"]]
                  if faces_seen != 0:
                      feed_faces = self.compile_feed_faces(detected_batch)
                      batch_size = None
                      if is_plaidml and feed_faces.shape[0] != self.batchsize:
                          logger.verbose("Fallback to BS=1")
                          batch_size = 1
                      predicted = self.predict(feed_faces, batch_size)
 
</s> remove             item = self.in_queue.get()
 </s> add             item = self._in_queue.get() </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     def queue_out_frames(self, batch, swapped_faces):
        """ Compile the batch back to original frames and put to out_queue """
 </s> add     def _queue_out_frames(self, batch, swapped_faces):
        """ Compile the batch back to original frames and put to the Out Queue.

        For batching, faces are split away from their frames. This compiles all detected faces
        back to their parent frame before putting each frame to the out queue in batches.

        Parameters
        ----------
        batch: dict
            The batch that was used as the input for the model predict function
        swapped_faces: :class:`numpy.ndarray`
            The predictions returned from the model's predict function
        """ </s> remove                     new_opt = options["value"]  # Keep saved item for other sections
 </s> add                     new_opt = options.value  # Keep saved item for other sections </s> remove             for item, options in self.config.defaults[config_section].items():
 </s> add             for item, options in self._config_dicts[config_section].items():
                    feed_faces = self._compile_feed_faces(detected_batch)
                               len(batch), faces_seen)
                  detected_batch = [detected_face for item in batch
                                    for detected_face in item["detected_faces"]]
                  if faces_seen != 0:
                    feed_faces = self._compile_feed_faces(detected_batch)
                      batch_size = None
                      if is_plaidml and feed_faces.shape[0] != self.batchsize:
                          logger.verbose("Fallback to BS=1")
                          batch_size = 1
                      predicted = self.predict(feed_faces, batch_size)
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