  
  def get_align_mat(face, size, should_align_eyes):
      """ Return the alignment Matrix """
      logger.trace("size: %s, should_align_eyes: %s", size, should_align_eyes)
      mat_umeyama = umeyama(np.array(face.landmarks_as_xy[17:]), True)[0:2]
  
      if should_align_eyes is False:
          return mat_umeyama
  
      mat_umeyama = mat_umeyama * size
 
</s> remove     landmarks = np.matrix(face.landmarks_as_xy)
 </s> add     landmarks = np.matrix(face.landmarks_xy)
 </s> remove             padding = int(size * self.extract_ratio) // 2
 </s> add             padding = int(size * self._extract_ratio) // 2 </s> remove     def padding_from_coverage(self, size, coverage_ratio):
 </s> add     def _padding_from_coverage(self, size, coverage_ratio): </s> remove         adjusted_ratio = coverage_ratio - (1 - self.extract_ratio)
 </s> add         adjusted_ratio = coverage_ratio - (1 - self._extract_ratio)
    mat_umeyama = umeyama(np.array(face.landmarks_xy[17:]), True)[0:2]

  
  def get_align_mat(face, size, should_align_eyes):
      """ Return the alignment Matrix """
      logger.trace("size: %s, should_align_eyes: %s", size, should_align_eyes)
    mat_umeyama = umeyama(np.array(face.landmarks_xy[17:]), True)[0:2]
  
      if should_align_eyes is False:
          return mat_umeyama
  
      mat_umeyama = mat_umeyama * size
keep keep keep keep replace keep keep keep keep keep
 De-Multiprocess Extract (#871)

* requirements.txt: - Pin opencv to 4.1.1 (fixes cv2-dnn error)

* lib.face_detect.DetectedFace: change LandmarksXY to landmarks_xy. Add left, right, top, bottom attributes

* lib.model.session: Session manager for loading models into different graphs (for Nvidia + CPU)

* plugins.extract._base: New parent class for all extract plugins

* plugins.extract.pipeline. Remove MultiProcessing. Dynamically limit batchsize for Nvidia cards. Remove loglevel input

* S3FD + FAN plugins. Standardise to Keras version for all backends

* Standardize all extract plugins to new threaded codebase

* Documentation. Start implementing Numpy style docstrings for Sphinx Documentation

* Remove s3fd_amd. Change convert OTF to expect DetectedFace object

* faces_detect - clean up and documentation

* Remove PoolProcess

* Migrate manual tool to new extract workflow

* Remove AMD specific extractor code from cli and plugins

* Sort tool to new extract workflow

* Remove multiprocessing from project

* Remove multiprocessing queues from QueueManager

* Remove multiprocessing support from logger

* Move face_filter to new extraction pipeline

* Alignments landmarksXY > landmarks_xy and legacy handling

* Intercept get_backend for sphinx doc build

# Add Sphinx documentation 
lib/aligner.py