  from ._base import Masker, logger
  
  
  class Mask(Masker):
      """ Perform transformation to align and get landmarks """
      def __init__(self, **kwargs):
          git_model_id = 8
          model_filename = "Nirkin_300_softmax_v1.h5"
          super().__init__(git_model_id=git_model_id, model_filename=model_filename, **kwargs)
          self.name = "VGG Clear"
 
</s> remove     """ Perform transformation to align and get landmarks """
 </s> add     """ Neural network to process face image into a segmentation mask of the face """ </s> remove     """ Perform transformation to align and get landmarks """
 </s> add     """ Neural network to process face image into a segmentation mask of the face """ </s> remove                 item = np.concatenate(item)
                hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
 </s> add                 item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item) </s> remove                 item = np.concatenate(item)
                hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
 </s> add                 item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item) </s> remove             landmarks = np.array(landmarks, dtype="int32")
 </s> add             landmarks = np.array(landmarks, dtype="float32")
    """ Neural network to process face image into a segmentation mask of the face """
  from ._base import Masker, logger
  
  
  class Mask(Masker):
    """ Neural network to process face image into a segmentation mask of the face """
      def __init__(self, **kwargs):
          git_model_id = 8
          model_filename = "Nirkin_300_softmax_v1.h5"
          super().__init__(git_model_id=git_model_id, model_filename=model_filename, **kwargs)
          self.name = "VGG Clear"
keep keep keep keep replace keep keep keep keep keep
 Landmarks stored and used as floating point numbers (#928)

* remove and fix int adjustments
* masking rounding 
plugins/extract/mask/vgg_clear.py