      def check(self, detected_face):
          """ Check the extracted Face """
          logger.trace("Checking face with FaceFilter")
          distances = {"filter": list(), "nfilter": list()}
          encodings = self.vgg_face.predict(detected_face.aligned_face)
          for filt in self.filters.values():
              similarity = self.vgg_face.find_cosine_similiarity(filt["encoding"], encodings)
              distances[filt["type"]].append(similarity)
  
          avgs = {key: avg(val) if val else None for key, val in distances.items()}
 
</s> remove     def check(self, detected_face):
        """ Check the extracted Face """
 </s> add     def check(self, image, detected_face):
        """ Check the extracted Face

        Parameters
        ----------
        image: :class:`numpy.ndarray`
            The original frame that contains the face to be checked
        detected_face: :class:`lib.align.DetectedFace`
            The detected face object that contains the face to be checked

        Returns
        -------
        bool
            ``True`` if the face matches a filter otherwise ``False``
        """
 </s> remove                           face.feed_matrix,
                          face.feed_interpolators[1],
 </s> add                           feed_face.adjusted_matrix,
                          feed_face.interpolators[1], </s> remove             face.feed = dict()
 </s> add         del batch["feed_faces"] </s> remove         count = sum(1 for val in self.alignments.data.values() if val["faces"])
 </s> add         count = sum(1 for val in self._alignments.data.values() if val["faces"]) </s> remove         logger.debug({key: {k: len(v) for k, v in val.items()} for key, val in retval.items()})
 </s> add         logger.debug({key: {k: v if isinstance(v, float) else len(v)
                            for k, v in val.items()}
                      for key, val in retval.items()})
        feed = AlignedFace(detected_face.landmarks_xy, image=image, size=224, centering="legacy")
        encodings = self.vgg_face.predict(feed.face)

      def check(self, detected_face):
          """ Check the extracted Face """
          logger.trace("Checking face with FaceFilter")
          distances = {"filter": list(), "nfilter": list()}
        feed = AlignedFace(detected_face.landmarks_xy, image=image, size=224, centering="legacy")
        encodings = self.vgg_face.predict(feed.face)
          for filt in self.filters.values():
              similarity = self.vgg_face.find_cosine_similiarity(filt["encoding"], encodings)
              distances[filt["type"]].append(similarity)
  
          avgs = {key: avg(val) if val else None for key, val in distances.items()}
keep keep keep keep 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 
lib/face_filter.py