          The 68 point landmarks as discovered in :mod:`plugins.extract.align`. Should be a ``list``
          of 68 `(x, y)` ``tuples`` with each of the landmark co-ordinates.
      mask: dict
          The generated mask(s) for the face as generated in :mod:`plugins.extract.mask`. Must be a
          dict of `{name (str): mask (numpy.ndarray)}
      """
      def __init__(self, image=None, x=None, w=None, y=None, h=None,
                   landmarks_xy=None, mask=None, filename=None):
          logger.trace("Initializing %s: (image: %s, x: %s, w: %s, y: %s, h:%s, "
                       "landmarks_xy: %s, filename: %s)",
 
</s> remove                      "landmarks_xy: %s, filename: %s)",
 </s> add                      "landmarks_xy: %s, mask: %s, filename: %s)", </s> remove         * **landmarks** (`numpy.ndarray`, `optional`). Required if :attr:`warp_to_landmarks` is \
        ``True``. The 68 point face landmarks from an alignments file.
 </s> add         * **landmarks** (`numpy.ndarray`, `optional`). Required if using a :attr:`mask_type` is \
        not ``None`` or :attr:`warp_to_landmarks` is ``True``. The 68 point face landmarks from \
        an alignments file. </s> remove     Paramaters
 </s> add     Parameters </s> remove         config item ``warp_to_landmarks`` is ``True``. If the landmarks for an image cannot be
        found, then an error is raised. """
 </s> add         config item ``warp_to_landmarks`` is ``True`` or if :attr:`mask_type` is not ``None``. If
        the landmarks for an image cannot be found, then an error is raised. """ </s> remove         alignments:             dict containing paths to alignments files for keys 'a' and 'b'
        preview_scaling:        How much to scale the preview out by
        training_size:          Size of the training images
        coverage_ratio:         Ratio of face to be cropped out for training
        replicate_input_mask:   Replicate input masks with additional model dedicated layers
        no_logs:                Disable tensorboard logging
        snapshot_interval:      Interval for saving model snapshots
        warp_to_landmarks:      Use random_warp_landmarks instead of random_warp
        augment_color:          Perform random shifting of L*a*b* colors
        no_flip:                Don't perform a random flip on the image
        pingpong:               Train each side seperately per save iteration rather than together
 </s> add         alignments:         dict containing paths to alignments files for keys 'a' and 'b'
        preview_scaling:    How much to scale the preview out by
        training_size:      Size of the training images
        coverage_ratio:     Ratio of face to be cropped out for training
        mask_type:          Type of mask to use. See lib.model.masks for valid mask names.
                            Set to None for not used
        no_logs:            Disable tensorboard logging
        snapshot_interval:  Interval for saving model snapshots
        warp_to_landmarks:  Use random_warp_landmarks instead of random_warp
        augment_color:      Perform random shifting of L*a*b* colors
        no_flip:            Don't perform a random flip on the image
        pingpong:           Train each side seperately per save iteration rather than together
        dict of {**name** (`str`): :class:`Mask`}.
          The 68 point landmarks as discovered in :mod:`plugins.extract.align`. Should be a ``list``
          of 68 `(x, y)` ``tuples`` with each of the landmark co-ordinates.
      mask: dict
          The generated mask(s) for the face as generated in :mod:`plugins.extract.mask`. Must be a
        dict of {**name** (`str`): :class:`Mask`}.
      """
      def __init__(self, image=None, x=None, w=None, y=None, h=None,
                   landmarks_xy=None, mask=None, filename=None):
          logger.trace("Initializing %s: (image: %s, x: %s, w: %s, y: %s, h:%s, "
                       "landmarks_xy: %s, filename: %s)",
keep keep keep keep replace keep keep keep keep keep
 Serialize masks to alignments file

- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version 
lib/faces_detect.py