          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
          for mask, face in zip(batch["feed"], batch["detected_faces"]):
              parts = self.parse_parts(np.array(face.feed_landmarks))
              for item in parts:
                  item = np.rint(np.concatenate(item)).astype("int32")
                  hull = cv2.convexHull(item)
                  cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)
          batch["prediction"] = batch["feed"]
 
</s> remove         for mask, face in zip(batch["feed"], batch["detected_faces"]):
            parts = self.parse_parts(np.array(face.feed_landmarks))
 </s> add         for mask, face in zip(batch["feed"], batch["feed_faces"]):
            parts = self.parse_parts(np.array(face.landmarks)) </s> remove         batch["feed"] = np.array([face.feed_face[..., :3]
                                  for face in batch["detected_faces"]], dtype="float32") / 255.0
 </s> add         batch["feed"] = np.array([feed.face[..., :3]
                                  for feed in batch["feed_faces"]], dtype="float32") / 255.0 </s> remove         input_ = [face.feed_face[..., :3] for face in batch["detected_faces"]]
 </s> add         input_ = [feed.face[..., :3] for feed in batch["feed_faces"]] </s> remove         input_ = np.array([face.feed_face[..., :3]
                           for face in batch["detected_faces"]], dtype="float32")
 </s> add         input_ = np.array([feed.face[..., :3]
                           for feed in batch["feed_faces"]], dtype="float32") </s> remove         logger.trace("Input batch shape: %s, side: %s", batch.shape, side)
 </s> add         logger.trace("Input filenames: %s, batch shape: %s, side: %s",
                     filenames, batch.shape, side)
        for mask, face in zip(batch["feed"], batch["feed_faces"]):
            parts = self.parse_parts(np.array(face.landmarks))
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
        for mask, face in zip(batch["feed"], batch["feed_faces"]):
            parts = self.parse_parts(np.array(face.landmarks))
        for mask, face in zip(batch["feed"], batch["feed_faces"]):
            parts = self.parse_parts(np.array(face.landmarks))
              for item in parts:
                  item = np.rint(np.concatenate(item)).astype("int32")
                  hull = cv2.convexHull(item)
                  cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)
          batch["prediction"] = batch["feed"]
keep keep keep keep replace replace keep keep keep keep keep
 Extract - Increase area and move centering (#1095)

* Extract
  - Implement aligner re-feeding
  - Add extract type to pipeline.ExtractMedia
  - Add pose annotation to debug
* Convert
  - implement centering
  - remove usage of feed and reference face properties
  - Remove distributed option from convert
  - Force update of alignments file on legacy receive
* Train
  - Resize preview image to model output size
  - Force legacy centering if centering does not exist in model's state file
  - Enable training on legacy face sets

* Alignments Tool
  - Update draw to include head/pose
  - Remove DFL drop + linting
  - Remove remove-frames job
  - remove align-eyes option
  - Update legacy masks to new extract type
  - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
  - Re-generate thumbnails on legacy upgrade
* Mask Tool
  - Update for new extract + bugfix full frame
* Manual Tool
  - Update to new extraction method
   - Disable legacy alignments,
   - extract box bugfix
   - extract faces - size to 512 and center on head
* Preview Tool
  - Display based on model centering
* Sort Tool
  - Use alignments for sort by face

* lib.aligner
  - Add Pose Class
  - Add AlignedFace Class
  - center _MEAN_FACE on x
  - Add meta information with versioning to alignments file
  - lib.aligner.get_align_matrix to use landmarks not face
  - Refactor aligned faces in lib.faces_detect
* lib.logger
  - larger file log padding
* lib.config
  - Fix global changeable_items
* lib.face_filter
  - Use new extracted face images
* lib.image
  - bump thumbnail default size to 96px 
plugins/extract/mask/components.py