              self.batchers[side].set_timelapse_feed(image_files, batchsize)
          logger.debug("Set up timelapse")
  
      def output_timelapse(self):
          """ Set the timelapse dictionary """
          logger.debug("Ouputting timelapse")
          image = self.samples.show_sample()
          if image is None:
              return
          filename = os.path.join(self.output_file, str(int(time.time())) + ".jpg")
  
          cv2.imwrite(filename, image)  # pylint: disable=no-member
 
</s> remove         filename = os.path.join(self.output_file, str(int(time.time())) + ".jpg")
 </s> add         filename = os.path.join(self._output_file, str(int(time.time())) + ".jpg") </s> remove             self.batchers[side].set_timelapse_feed(image_files, batchsize)
        logger.debug("Set up timelapse")
 </s> add             self._batchers[side].set_timelapse_feed(image_files, batchsize)
        logger.debug("Set up time-lapse") </s> remove         cv2.imwrite(filename, image)  # pylint: disable=no-member
        logger.debug("Created timelapse: '%s'", filename)
 </s> add         cv2.imwrite(filename, image)
        logger.debug("Created time-lapse: '%s'", filename) </s> remove                         self.preview_images)
 </s> add                         self._num_images) </s> remove             output = str(get_folder(os.path.join(str(self.model.model_dir),
                                                 "{}_timelapse".format(self.model.name))))
        self.output_file = str(output)
        logger.debug("Timelapse output set to '%s'", self.output_file)
 </s> add             output = str(get_folder(os.path.join(str(self._model.model_dir),
                                                 "{}_timelapse".format(self._model.name))))
        self._output_file = str(output)
        logger.debug("Time-lapse output set to '%s'", self._output_file)
        """ Write the created time-lapse to the specified output folder. """
        logger.debug("Ouputting time-lapse")
        image = self._samples.show_sample()
              self.batchers[side].set_timelapse_feed(image_files, batchsize)
          logger.debug("Set up timelapse")
  
      def output_timelapse(self):
        """ Write the created time-lapse to the specified output folder. """
        logger.debug("Ouputting time-lapse")
        image = self._samples.show_sample()
        """ Write the created time-lapse to the specified output folder. """
        logger.debug("Ouputting time-lapse")
        image = self._samples.show_sample()
        """ Write the created time-lapse to the specified output folder. """
        logger.debug("Ouputting time-lapse")
        image = self._samples.show_sample()
          if image is None:
              return
          filename = os.path.join(self.output_file, str(int(time.time())) + ".jpg")
  
          cv2.imwrite(filename, image)  # pylint: disable=no-member
keep keep keep keep replace replace replace keep keep keep keep keep
 Smart Masks - Training Implementation (#914)

* Smart Masks - Training

- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code

* Update mask helptext in cli.py

* Fix Warp to Landmarks
Remove SHA1 hashing from training data

* Update mask training config

* Capture missing masks at training init

* lib.image.read_image_batch - Return filenames with batch for ordering

* scripts.train - Documentation

* plugins.train.trainer - documentation

* Ensure backward compatibility.
Fix convert for new predicted masks

* Update removed masks to components for legacy models. 
plugins/train/trainer/_base.py