              self._cache["crop_size"] = size
          size = self._cache["crop_size"]
  
          landmarks = np.array([face.landmarks for face in aligned])
          cropped = np.zeros((batch.shape[0], size, size, batch.shape[3]), dtype=batch.dtype)
  
          for out, align, img in zip(cropped, aligned, batch):
              slices = align.get_cropped_slices(self._config["centering"])
              out[slices["out"][0], slices["out"][1], :] = img[slices["in"][0], slices["in"][1], :]
          return cropped, landmarks
  
      def _apply_mask(self, filenames, batch, side):
          """ Applies the mask to the 4th channel of the image. If masks are not being used
          applies a dummy all ones mask.
 
</s> remove             size = aligned[0].get_cropped_size(self._config["centering"])
 </s> add             size = aligned[0].size </s> remove 
        # Revert back to the correct centering and size and reset the cache
        self._centering = old_centering
        self._size = old_size
        self._cache = self._set_cache()
 </s> add  </s> remove         src_size = self.size - (self._size * _EXTRACT_RATIOS[self._centering])
        head_size = 2 * int(np.rint(src_size / (1 - _EXTRACT_RATIOS["head"]) / 2))
        if head_size != image.shape[0]:
            interp = cv2.INTER_CUBIC if image.shape[0] < head_size else cv2.INTER_AREA
            image = cv2.resize(image, (head_size, head_size), interpolation=interp)

        # store requested size + centering whilst temporary converting to full head extract
        old_centering = self._centering
        old_size = self.size
        self._centering = "head"
        self._size = image.shape[0]

        # crop the requested centering from image
        size = self.get_cropped_size(old_centering)
        out = np.zeros((size, size, image.shape[-1]), dtype=image.dtype)
        slices = self.get_cropped_slices(old_centering)
 </s> add         logger.trace("head_size: %s, image_size: %s, target_size: %s",
                     self._head_size, image.shape[0], self.size)
        if self._head_size != image.shape[0]:
            interp = cv2.INTER_CUBIC if image.shape[0] < self._head_size else cv2.INTER_AREA
            image = cv2.resize(image, (self._head_size, self._head_size), interpolation=interp)

        out = np.zeros((self.size, self.size, image.shape[-1]), dtype=image.dtype)
        slices = self._get_cropped_slices() </s> remove         if self._alignments_version[side] == 1.0:
            centering = "legacy"
            size = self._training_sizes[side]
        else:
            centering = self._config["centering"]
            size = list(self._aligned_faces[side].values())[0].get_cropped_size(centering)
 </s> add         size = list(self._aligned_faces[side].values())[0].size </s> remove     def get_cropped_size(self, centering):
        """ Obtain the size of a cropped face from a full head centered image.

        Parameters
        ----------
        centering: ["legacy", "face"]
            The type of centering to obtain the region of interest for. "legacy" places the nose
            in the center of the image (the original method for aligning). "face" aligns for the
            nose to be in the center of the face (top to bottom) but the center of the skull for
            left to right.

        Returns
        -------
        int
           The pixel size of a sub-crop image from a full head aligned image

        Notes
        -----
        The ROI in relation to the source image is calculated by rounding the padding of one side
        to the nearest integer then applying this padding to the center of the crop, so the size
        is calculated in the same way.
        """
        if self._centering != "head":
            raise ValueError("Sub ROI can only be obtained from an aligned face with 'head' "
                             "centering")
        with self._cache["cropped_size"][1]:
            if not self._cache["cropped_size"][0].get(centering):
                src_size = self.size - (self._size * _EXTRACT_RATIOS["head"])
                size = 2 * int(np.rint(src_size / (1 - _EXTRACT_RATIOS[centering]) / 2))
                logger.trace("centering: %s, size: %s, crop_size: %s", centering, self._size, size)
                self._cache["cropped_size"][0][centering] = size
        return self._cache["cropped_size"][0][centering]

    def get_cropped_slices(self, centering):
 </s> add     def _get_cropped_slices(self):
        cropped = np.array([align.extract_face(img) for align, img in zip(aligned, batch)])
              self._cache["crop_size"] = size
          size = self._cache["crop_size"]
  
          landmarks = np.array([face.landmarks for face in aligned])
        cropped = np.array([align.extract_face(img) for align, img in zip(aligned, batch)])
        cropped = np.array([align.extract_face(img) for align, img in zip(aligned, batch)])
        cropped = np.array([align.extract_face(img) for align, img in zip(aligned, batch)])
        cropped = np.array([align.extract_face(img) for align, img in zip(aligned, batch)])
        cropped = np.array([align.extract_face(img) for align, img in zip(aligned, batch)])
          return cropped, landmarks
  
      def _apply_mask(self, filenames, batch, side):
          """ Applies the mask to the 4th channel of the image. If masks are not being used
          applies a dummy all ones mask.
keep keep keep keep replace replace replace replace replace keep keep keep keep keep
 Bugfix - Warp to landmarks 
lib/training_data.py