          image: numpy.ndarray
              The image that contains the face to be aligned
          size: int
              The size of the output face in pixels
          align_eyes: bool, optional
              Optionally perform additional alignment to align eyes. Default: `False`
          dtype: str, optional
              Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None``
  
          Notes
          -----
 
</s> remove         * **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> add         * **landmarks** (`numpy.ndarray`, `optional`). Required if :attr:`warp_to_landmarks` is \
        ``True``. The 68 point face landmarks from an alignments file. </s> remove     def _extract_ratio(self):
        """ float: The ratio of padding to add for training images """
        return 0.375
 </s> add     def training_coverage(self):
        """ The coverage ratio to add for training images """
        return 1.0 </s> remove         * **mask_type** (`str`) - The mask type to be used (as defined in \
        :mod:`lib.model.masks`). If not ``None`` then the additional key ``landmarks`` must be \
        provided.

 </s> add  </s> remove             padding = int(size * self._extract_ratio) // 2
 </s> add  </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
        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
 </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
        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
        coverage_ratio: float
            The metric determining the field of view of the returned face
          image: numpy.ndarray
              The image that contains the face to be aligned
          size: int
              The size of the output face in pixels
        coverage_ratio: float
            The metric determining the field of view of the returned face
        coverage_ratio: float
            The metric determining the field of view of the returned face
          dtype: str, optional
              Optionally set a ``dtype`` for the final face to be formatted in. Default: ``None``
  
          Notes
          -----
keep keep keep keep replace replace keep keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
lib/faces_detect.py