                             interpolation=cv2.INTER_AREA) * 255.0).astype("uint8")
          self._mask = compress(mask)
  
      def _adjust_affine_matrix(self, mask_size, affine_matrix):
          """ Adjust the affine matrix for the mask's storage size
  
          Parameters
          ----------
 
</s> remove     def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None):
 </s> add     def load_feed_face(self, image, size=64, coverage_ratio=0.625, dtype=None,
                       is_aligned_face=False): </s> remove     def get_available_extractors(extractor_type):
 </s> add     def get_available_extractors(extractor_type, add_none=False): </s> remove 
 </s> add         add_none: bool, optional
            Append "none" to the list of returned plugins. Default: False </s> remove         # TODO Migrate these settings to retrieval rather than storage
        # if self.blur_kernel is not None:
        #    predicted = np.array([cv2.GaussianBlur(mask, (self.blur_kernel, self.blur_kernel), 0)
        #                          for mask in batch["prediction"]])
        # else:
        #    predicted = batch["prediction"]
        # predicted[predicted < 0.04] = 0.0
        # predicted[predicted > 0.96] = 1.0
 </s> add  </s> remove     Attributes
    ----------
    blur_kernel, int
        The size of the kernel for applying gaussian blur to the output of the mask

 </s> add 
    def set_blur_kernel_and_threshold(self, blur_kernel=0, threshold=0):
        """ Set the internal blur kernel and threshold amount for returned masks

        Parameters
        ----------
        blur_kernel: int, optional
            The kernel size, in pixels to apply gaussian blurring to the mask. Set to 0 for no
            blurring. Default: 0
        threshold: int, optional
            The threshold amount to minimize/maximize mask values to 0 and 100. Percentage value.
            Default: 0
        """
        logger.trace("blur_kernel: %s, threshold: %s", blur_kernel, threshold)
        self._blur_kernel = blur_kernel
        self._threshold = (threshold / 100.0) * 255.0

                             interpolation=cv2.INTER_AREA) * 255.0).astype("uint8")
          self._mask = compress(mask)
  
    def set_blur_kernel_and_threshold(self, blur_kernel=0, threshold=0):
        """ Set the internal blur kernel and threshold amount for returned masks

        Parameters
        ----------
        blur_kernel: int, optional
            The kernel size, in pixels to apply gaussian blurring to the mask. Set to 0 for no
            blurring. Default: 0
        threshold: int, optional
            The threshold amount to minimize/maximize mask values to 0 and 100. Percentage value.
            Default: 0
        """
        logger.trace("blur_kernel: %s, threshold: %s", blur_kernel, threshold)
        self._blur_kernel = blur_kernel
        self._threshold = (threshold / 100.0) * 255.0

      def _adjust_affine_matrix(self, mask_size, affine_matrix):
          """ Adjust the affine matrix for the mask's storage size
  
          Parameters
          ----------
keep keep add keep keep keep keep keep
 Extract - Mask updates

- Remove none mask plugin
- Make pipeline more flexible
- Add support for pre-aligned faces to masker plugin
- Migrate blur and threshold settings to mask output 
lib/faces_detect.py