          self._interpolator = None
  
      @property
      def mask(self):
          """ numpy.ndarray: The mask at the size of :attr:`stored_size` """
          return np.frombuffer(decompress(self._mask),
                               dtype="uint8").reshape((self.stored_size, self.stored_size, 1))
  
      @property
      def full_frame_mask(self):
          """ numpy.ndarray: The mask affined to the original full frame """
          frame = np.zeros(self._frame_dims + (1, ), dtype="uint8")
 
</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 
 </s> add         add_none: bool, optional
            Append "none" to the list of returned plugins. Default: False
        """ numpy.ndarray: The mask at the size of :attr:`stored_size` with any requested blurring
        and threshold amount applied."""
        dims = (self.stored_size, self.stored_size, 1)
        mask = np.frombuffer(decompress(self._mask), dtype="uint8").reshape(dims)
        if self._threshold != 0.0:
            mask[mask < self._threshold] = 0.0
            mask[mask > 255.0 - self._threshold] = 255.0
        if self._blur_kernel != 0:
            mask = cv2.GaussianBlur(mask, (self._blur_kernel, self._blur_kernel), 0)[..., None]
        logger.trace("mask shape: %s", mask.shape)
        return mask
          self._interpolator = None
  
      @property
      def mask(self):
        """ numpy.ndarray: The mask at the size of :attr:`stored_size` with any requested blurring
        and threshold amount applied."""
        dims = (self.stored_size, self.stored_size, 1)
        mask = np.frombuffer(decompress(self._mask), dtype="uint8").reshape(dims)
        if self._threshold != 0.0:
            mask[mask < self._threshold] = 0.0
            mask[mask > 255.0 - self._threshold] = 255.0
        if self._blur_kernel != 0:
            mask = cv2.GaussianBlur(mask, (self._blur_kernel, self._blur_kernel), 0)[..., None]
        logger.trace("mask shape: %s", mask.shape)
        return mask
        """ numpy.ndarray: The mask at the size of :attr:`stored_size` with any requested blurring
        and threshold amount applied."""
        dims = (self.stored_size, self.stored_size, 1)
        mask = np.frombuffer(decompress(self._mask), dtype="uint8").reshape(dims)
        if self._threshold != 0.0:
            mask[mask < self._threshold] = 0.0
            mask[mask > 255.0 - self._threshold] = 255.0
        if self._blur_kernel != 0:
            mask = cv2.GaussianBlur(mask, (self._blur_kernel, self._blur_kernel), 0)[..., None]
        logger.trace("mask shape: %s", mask.shape)
        return mask
        """ numpy.ndarray: The mask at the size of :attr:`stored_size` with any requested blurring
        and threshold amount applied."""
        dims = (self.stored_size, self.stored_size, 1)
        mask = np.frombuffer(decompress(self._mask), dtype="uint8").reshape(dims)
        if self._threshold != 0.0:
            mask[mask < self._threshold] = 0.0
            mask[mask > 255.0 - self._threshold] = 255.0
        if self._blur_kernel != 0:
            mask = cv2.GaussianBlur(mask, (self._blur_kernel, self._blur_kernel), 0)[..., None]
        logger.trace("mask shape: %s", mask.shape)
        return mask
  
      @property
      def full_frame_mask(self):
          """ numpy.ndarray: The mask affined to the original full frame """
          frame = np.zeros(self._frame_dims + (1, ), dtype="uint8")
keep keep keep keep replace replace replace 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