              if not item.detected_faces:
                  self._queues["out"].put(item)
                  continue
              for f_idx, face in enumerate(item.detected_faces):
                  feed_face = AlignedFace(face.landmarks_xy,
                                          image=image,
                                          centering=self._storage_centering,
                                          size=self.input_size,
                                          coverage_ratio=self.coverage_ratio,
 
</s> remove                                         image=item.get_image_copy(self.color_format),
 </s> add                                         image=image, </s> remove 
 </s> add             if "source_frame_dims" not in metadata["source"]:
                logger.error("The faces need to be re-extracted as at least some of them do not "
                             "contain information required to correctly generate masks.")
                logger.error("You can re-extract the face-set by using the Alignments Tool's "
                             "Extract job.")
                break </s> remove         for mask, face, feed_face in zip(batch["prediction"],
                                         batch["detected_faces"],
                                         batch["feed_faces"]):
 </s> add         for mask, face, feed_face, roi_mask in zip(batch["prediction"],
                                                   batch["detected_faces"],
                                                   batch["feed_faces"],
                                                   batch["roi_masks"]):
            self._crop_out_of_bounds(mask, roi_mask) </s> remove                 offset -= self.pose.offset["head"]
                offset *= (self._head_size - (self._head_size * _EXTRACT_RATIOS["head"]))
 </s> add                 adjusted = offset - self.pose.offset["head"]
                adjusted *= (self._head_size - (self._head_size * _EXTRACT_RATIOS["head"]))
                image = item.get_image_copy(self.color_format)
                roi = np.ones((*item.image_size[:2], 1), dtype="float32")

                if not self._image_is_aligned:
                    # Add the ROI mask to image so we can get the ROI mask with a single warp
                    image = np.concatenate([image, roi], axis=-1)

              if not item.detected_faces:
                  self._queues["out"].put(item)
                  continue
              for f_idx, face in enumerate(item.detected_faces):
                image = item.get_image_copy(self.color_format)
                roi = np.ones((*item.image_size[:2], 1), dtype="float32")

                if not self._image_is_aligned:
                    # Add the ROI mask to image so we can get the ROI mask with a single warp
                    image = np.concatenate([image, roi], axis=-1)

                  feed_face = AlignedFace(face.landmarks_xy,
                                          image=image,
                                          centering=self._storage_centering,
                                          size=self.input_size,
                                          coverage_ratio=self.coverage_ratio,
keep keep keep add keep keep keep keep keep
 extract: mask - Delete any mask from outside of frame boundaries 
plugins/extract/mask/_base.py