              predicts only an image. """
          Utils.set_verbosity(self.args.verbose)
  
          if not self.alignments.have_alignments_file:
              self.generate_alignments()
  
          self.faces.faces_detected = self.alignments.read_alignments()
  
          model = self.load_model()
          converter = self.load_converter(model)
  
          batch = BackgroundGenerator(self.prepare_images(), 1)
 
</s> remove             self.timelapse = Timelapse.CreateTimelapse(self.args.timelapse_input_A,
                                                       self.args.timelapse_input_B,
                                                       self.args.timelapse_output,
                                                       trainer)
 </s> add             self.timelapse = Timelapse.create_timelapse(
                self.args.timelapse_input_A,
                self.args.timelapse_input_B,
                self.args.timelapse_output,
                trainer) </s> remove         batch = lib.utils.BackgroundGenerator(self.minibatch(images, batchsize, doShuffle), 1)
 </s> add         batch = BackgroundGenerator(self.minibatch(images, batchsize, doShuffle), 1)
 </s> remove         model = PluginLoader.get_model(self.trainer_name)(model_dir, self.args.gpus)
 </s> add         model = PluginLoader.get_model(self.trainer_name)(model_dir,
                                                          self.args.gpus) </s> remove         generator = lib.training_data.TrainingDataGenerator(random_transform_args, 160, zoom)
        batch = generator.minibatchAB(input_images, batch_size, doShuffle=False)
 </s> add         generator = TrainingDataGenerator(random_transform_args, 160, zoom)
        batch = generator.minibatchAB(input_images, batch_size,
                                      doShuffle=False) </s> remove                 self.write_alignments()
 </s> add                 self.alignments.save()
            self.load_extractor()
              predicts only an image. """
          Utils.set_verbosity(self.args.verbose)
  
          if not self.alignments.have_alignments_file:
            self.load_extractor()
            self.load_extractor()
            self.load_extractor()
  
          model = self.load_model()
          converter = self.load_converter(model)
  
          batch = BackgroundGenerator(self.prepare_images(), 1)
keep keep keep keep replace replace replace keep keep keep keep keep
 Extraction - Speed improvements (#522) (#523)

* Extraction - Speed improvements (#522)

* Initial Plugin restructure

* Detectors to plugins. Detector speed improvements

* Re-implement dlib aligner, remove models, FAN to TF. Parallel processing

* Update manual, update convert, implement parallel/serial switching

* linting + fix cuda check (setup.py). requirements update keras 2.2.4

* Add extract size option. Fix dlib hog init

* GUI: Increase tooltip width

* Update alignment tool to support new DetectedFace

* Add skip existing faces option

* Fix sort tool to new plugin structure

* remove old align plugin

* fix convert -skip faces bug

* Fix convert skipping no faces frames

* Convert - draw onto transparent layer

* Fix blur threshold bug

* fix skip_faces convert bug

* Fix training 
scripts/convert.py