  
          width = 4
          side_cols = width // 2
          if side_cols != 1:
              headers = self.duplicate_headers(headers, side_cols)
  
          header = np.concatenate([headers["a"], headers["b"]], axis=1)
          figure = np.concatenate([figures["a"], figures["b"]], axis=0)
          height = int(figure.shape[0] / width)
          figure = figure.reshape((width, height) + figure.shape[1:])
 
</s> remove         figure = stack_images(figure)
        figure = np.vstack((header, figure))
 </s> add         figure = _stack_images(figure)
        figure = np.concatenate((header, figure), axis=0) </s> remove         height = int(64 * self.scaling)
 </s> add         height = int(64 * self._scaling) </s> remove     def get_headers(self, side, width):
        """ Set headers for images """
 </s> add     def _get_headers(self, side, width):
        """ Set header row for the final preview frame

        Parameters
        ----------
        side: {"a" or "b"}
            The side that the headers should be generated for
        width: int
            The width of each column in the preview frame

        Returns
        -------
        :class:`numpy.ndarray`
            The column headings for the given side
        """ </s> remove             if self.model.input_shape[0] / faces.shape[1] != 1.0:
                feeds[side] = self.resize_sample(side, faces, self.model.input_shape[0])
                feeds[side] = feeds[side].reshape((-1, ) + self.model.input_shape)
 </s> add             if self._model.input_shape[0] / faces.shape[1] != 1.0:
                feeds[side] = self._resize_sample(side, faces, self._model.input_shape[0])
                feeds[side] = feeds[side].reshape((-1, ) + self._model.input_shape) </s> remove             display = self.to_full_frame(side, samples, predictions)
            headers[side] = self.get_headers(side, display[0].shape[1])
 </s> add             display = self._to_full_frame(side, samples, predictions)
            headers[side] = self._get_headers(side, display[0].shape[1])
            headers = self._duplicate_headers(headers, side_cols)
  
          width = 4
          side_cols = width // 2
          if side_cols != 1:
            headers = self._duplicate_headers(headers, side_cols)
  
          header = np.concatenate([headers["a"], headers["b"]], axis=1)
          figure = np.concatenate([figures["a"], figures["b"]], axis=0)
          height = int(figure.shape[0] / width)
          figure = figure.reshape((width, height) + figure.shape[1:])
keep keep keep keep 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