                         "and  landmarks. It is recommended to perfom Extract first for "
                         "superior results")
          extractor = Extractor(detector="cv2-dnn",
                                aligner="cv2-dnn",
                                loglevel=self.args.loglevel,
                                multiprocess=False,
                                rotate_images=None,
                                min_size=20)
          extractor.launch()
          logger.debug("Loaded extractor")
 
</s> remove                 exception = faces.get("exception", False)
                if exception:
                    break
 </s> add  </s> remove     def align_faces(self, detector_name, aligner_name, loglevel, multiprocess):
 </s> add     def align_faces(self, detector_name, aligner_name, multiprocess):
 </s> remove             if exception:
                break
 </s> add  </s> remove         logger.debug("Initializing %s: (detector: %s, aligner: %s, loglevel: %s, configfile: %s, "
 </s> add         logger.debug("Initializing %s: (detector: %s, aligner: %s, configfile: %s, " </s> remove         exception = False
 </s> add 
                         "and  landmarks. It is recommended to perfom Extract first for "
                         "superior results")
          extractor = Extractor(detector="cv2-dnn",
                                aligner="cv2-dnn",

                                multiprocess=False,
                                rotate_images=None,
                                min_size=20)
          extractor.launch()
          logger.debug("Loaded extractor")
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 
scripts/convert.py