          for filename, face in self.filters.items():
              logger.debug("Loading aligned face: '%s'", filename)
              image = face["image"]
              detected_face = face["detected_face"]
              detected_face.load_aligned(image, size=224)
              face["face"] = detected_face.aligned_face
              del face["image"]
              logger.debug("Loaded aligned face: ('%s', shape: %s)",
                           filename, face["face"].shape)
  
      def get_filter_encodings(self):
 
</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         preds = np.empty((self._loader.count, 512), dtype="float32")
        for idx, (filename, image) in enumerate(tqdm(self._loader.load(),
                                                     desc="Classifying Faces...",
                                                     total=self._loader.count)):
 </s> add         preds = []
        no_hash = 0
        for filename, image, hsh in tqdm(self._loader.load(),
                                         desc="Classifying Faces...",
                                         total=self._loader.count):
            if self._alignments is not None and self._alignments.version != 1.0:
                face = self._alignments.hashes_to_alignment.get(hsh)
                if face:
                    image = AlignedFace(face["landmarks_xy"],
                                        image=image,
                                        centering="legacy",
                                        size=self._vgg_face.input_size,
                                        is_aligned=True).face
                elif image.shape[0] != image.shape[1]:
                    logger.warning("Skipping image '%s' as it is not square (probably not a "
                                   "face)", filename)
                    continue
                else:  # Center crop the image and add count to warning count
                    center = image.shape[0] // 2
                    crop = slice(center - int(center * ratio), center + int(center * ratio))
                    image = image[crop, crop, :]
                    no_hash += 1
 </s> remove 
    @staticmethod
    def align_eyes(face, image):
        """ Re-extract a face with the pupils forced to be absolutely horizontally aligned """
        umeyama_landmarks = face.aligned_landmarks
        left_eye_center = umeyama_landmarks[42:48].mean(axis=0)
        right_eye_center = umeyama_landmarks[36:42].mean(axis=0)
        d_y = right_eye_center[1] - left_eye_center[1]
        d_x = right_eye_center[0] - left_eye_center[0]
        theta = np.pi - np.arctan2(d_y, d_x)
        rot_cos = np.cos(theta)
        rot_sin = np.sin(theta)
        rotation_matrix = np.array([[rot_cos, -rot_sin, 0.],
                                    [rot_sin, rot_cos, 0.],
                                    [0., 0., 1.]])

        mat_umeyama = np.concatenate((face.aligned["matrix"], np.array([[0., 0., 1.]])), axis=0)
        corrected_mat = np.dot(rotation_matrix, mat_umeyama)
        face.aligned["matrix"] = corrected_mat[:2]
        face.aligned["face"] = AlignerExtract().transform(image,
                                                          face.aligned["matrix"],
                                                          face.aligned["size"],
                                                          int(face.aligned["size"] * 0.375) // 2)
        logger.trace("Adjusted matrix: %s", face.aligned["matrix"])
        return face
 </s> add  </s> remove             face.feed = dict()
 </s> add         del batch["feed_faces"]
            detected_face.load_aligned(image, centering="legacy", size=224)
            face["face"] = detected_face.aligned.face

          for filename, face in self.filters.items():
              logger.debug("Loading aligned face: '%s'", filename)
              image = face["image"]
              detected_face = face["detected_face"]
            detected_face.load_aligned(image, centering="legacy", size=224)
            face["face"] = detected_face.aligned.face
            detected_face.load_aligned(image, centering="legacy", size=224)
            face["face"] = detected_face.aligned.face
              del face["image"]
              logger.debug("Loaded aligned face: ('%s', shape: %s)",
                           filename, face["face"].shape)
  
      def get_filter_encodings(self):
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 
lib/face_filter.py