                    "output": timelapse_output}
          logger.debug("Timelapse enabled: %s", kwargs)
          return kwargs
  
      def get_images(self):
          """ Check the image folders exist, contain images and return the image
          objects """
          logger.debug("Getting image paths")
          images = dict()
          for side in ("a", "b"):
              image_dir = getattr(self.args, "input_{}".format(side))
              if not os.path.isdir(image_dir):
 
</s> remove             image_dir = getattr(self.args, "input_{}".format(side))
 </s> add             image_dir = getattr(self._args, "input_{}".format(side)) </s> remove         kwargs = {"input_a": self.args.timelapse_input_a,
                  "input_b": self.args.timelapse_input_b,
 </s> add         kwargs = {"input_a": self._args.timelapse_input_a,
                  "input_b": self._args.timelapse_input_b, </s> remove     @property
    def alignments_paths(self):
        """ Set the alignments path to input folder if not provided """
        alignments_paths = dict()
        for side in ("a", "b"):
            alignments_path = getattr(self.args, "alignments_path_{}".format(side))
            if not alignments_path:
                image_path = getattr(self.args, "input_{}".format(side))
                alignments_path = os.path.join(image_path, "alignments.fsa")
            alignments_paths[side] = alignments_path
        logger.debug("Alignments paths: %s", alignments_paths)
        return alignments_paths
 </s> add         Parameters
        ----------
        model: :file:`plugins.train.model` plugin
            The requested model plugin </s> remove     def set_timelapse(self):
        """ Set time-lapse paths if requested """
        if (not self.args.timelapse_input_a and
                not self.args.timelapse_input_b and
                not self.args.timelapse_output):
 </s> add     @property
    def _image_size(self):
        """ int: The training image size. Reads the first image in the training folder and returns
        the size. """
        image = read_image(self._images["a"][0], raise_error=True)
        size = image.shape[0]
        logger.debug("Training image size: %s", size)
        return size

    @property
    def _alignments_paths(self):
        """ dict: The alignments paths for each of the source and destination faces. Key is the
            side, value is the path to the alignments file """
        alignments_paths = dict()
        for side in ("a", "b"):
            alignments_path = getattr(self._args, "alignments_path_{}".format(side))
            if not alignments_path:
                image_path = getattr(self._args, "input_{}".format(side))
                alignments_path = os.path.join(image_path, "alignments.fsa")
            alignments_paths[side] = alignments_path
        logger.debug("Alignments paths: %s", alignments_paths)
        return alignments_paths

    def _set_timelapse(self):
        """ Set time-lapse paths if requested.

        Returns
        -------
        dict
            The time-lapse keyword arguments for passing to the trainer

        """
        if (not self._args.timelapse_input_a and
                not self._args.timelapse_input_b and
                not self._args.timelapse_output): </s> remove             images = [self.overlay_foreground(frame, image) for image in images]
        if self.scaling != 1.0:
            new_size = int(full_size * self.scaling)
            images = [self.resize_sample(side, image, new_size) for image in images]
 </s> add             images = [self._overlay_foreground(frame, image) for image in images]
        if self._scaling != 1.0:
            new_size = int(full_size * self._scaling)
            images = [self._resize_sample(side, image, new_size) for image in images]
    def _get_images(self):
        """ Check the image folders exist and contains images and obtain image paths.

        Returns
        -------
        dict
            The image paths for each side. The key is the side, the value is the list of paths
            for that side.
        """
                    "output": timelapse_output}
          logger.debug("Timelapse enabled: %s", kwargs)
          return kwargs
  
    def _get_images(self):
        """ Check the image folders exist and contains images and obtain image paths.

        Returns
        -------
        dict
            The image paths for each side. The key is the side, the value is the list of paths
            for that side.
        """
    def _get_images(self):
        """ Check the image folders exist and contains images and obtain image paths.

        Returns
        -------
        dict
            The image paths for each side. The key is the side, the value is the list of paths
            for that side.
        """
    def _get_images(self):
        """ Check the image folders exist and contains images and obtain image paths.

        Returns
        -------
        dict
            The image paths for each side. The key is the side, the value is the list of paths
            for that side.
        """
          logger.debug("Getting image paths")
          images = dict()
          for side in ("a", "b"):
              image_dir = getattr(self.args, "input_{}".format(side))
              if not os.path.isdir(image_dir):
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. 
scripts/train.py