                  faces_count = len(item["detected_faces"])
  
                  # Safety measure. If a large stream of frames appear that do not have faces,
                  # these will stack up into RAM. Keep a count of consecutive frames with no faces.
                  # If self.batchsize number of frames appear, force the current batch through
                  # to clear RAM.
                  consecutive_no_faces = consecutive_no_faces + 1 if faces_count == 0 else 0
                  self.faces_count += faces_count
                  if faces_count > 1:
                      self.verify_output = True
 
</s> remove                 self.faces_count += faces_count
 </s> add                 self._faces_count += faces_count </s> remove             item = self.in_queue.get()
 </s> add             item = self._in_queue.get() </s> remove                     self.verify_output = True
 </s> add                     self._verify_output = True </s> remove     def get_filelist(self):
        """ Return a list of files, filtering out those frames which do not contain faces """
 </s> add     def _get_filelist(self):
        """ Get a list of files for the input, filtering out those frames which do
        not contain faces.

        Returns
        -------
        list
            A list of filenames of frames that contain faces.
        """ </s> remove             if item != "EOF" and (faces_seen < self.batchsize and
                                  consecutive_no_faces < self.batchsize):
 </s> add             if item != "EOF" and (faces_seen < self._batchsize and
                                  consecutive_no_faces < self._batchsize):
                # If self._batchsize number of frames appear, force the current batch through
                  faces_count = len(item["detected_faces"])
  
                  # Safety measure. If a large stream of frames appear that do not have faces,
                  # these will stack up into RAM. Keep a count of consecutive frames with no faces.
                # If self._batchsize number of frames appear, force the current batch through
                  # to clear RAM.
                  consecutive_no_faces = consecutive_no_faces + 1 if faces_count == 0 else 0
                  self.faces_count += faces_count
                  if faces_count > 1:
                      self.verify_output = True
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