          self.x = alignment["x"]
          self.w = alignment["w"]
          self.y = alignment["y"]
          self.h = alignment["h"]
          self.landmarksXY = alignment["landmarksXY"]
          # Manual tool does not know the final hash so default to None
          self.hash = alignment.get("hash", None)
          if image is not None and image.any():
              self.image_to_face(image)
          logger.trace("Created from alignment: (x: %s, w: %s, y: %s. h: %s, "
 
</s> remove             self.image_to_face(image)
 </s> add             self._image_to_face(image) </s> remove         alignment["landmarksXY"] = self.landmarksXY
 </s> add         alignment["landmarks_xy"] = self.landmarks_xy </s> remove     def landmarks_as_xy(self):
        """ Landmarks as XY """
        return self.landmarksXY

    def to_bounding_box_dict(self):
        """ Return Bounding Box as a bounding box dixt """
        retval = dict(left=self.x, top=self.y, right=self.x + self.w, bottom=self.y + self.h)
        logger.trace("Returning: %s", retval)
        return retval

    def from_bounding_box_dict(self, bounding_box_dict, image=None):
        """ Set Bounding Box from a bounding box dict """
        logger.trace("Creating from bounding box dict: %s", bounding_box_dict)
        if not isinstance(bounding_box_dict, dict):
            raise ValueError("Supplied Bounding Box is not a dictionary.")
        self.x = bounding_box_dict["left"]
        self.w = bounding_box_dict["right"] - bounding_box_dict["left"]
        self.y = bounding_box_dict["top"]
        self.h = bounding_box_dict["bottom"] - bounding_box_dict["top"]
        if image is not None and image.any():
            self.image_to_face(image)
        logger.trace("Created from bounding box dict: (x: %s, w: %s, y: %s. h: %s)",
                     self.x, self.w, self.y, self.h)
 </s> add     def top(self):
        """int: Top point (in pixels) of face detection bounding box within the parent image """
        return self.y </s> remove                      self.x, self.w, self.y, self.h, self.landmarksXY)
 </s> add                      self.x, self.w, self.y, self.h, self.landmarks_xy)

    def _image_to_face(self, image):
        """ set self.image to be the cropped face from detected bounding box """
        logger.trace("Cropping face from image")
        self.image = image[self.top: self.bottom,
                           self.left: self.right] </s> remove     def image_to_face(self, image):
        """ Crop an image around bounding box to the face
            and capture it's dimensions """
        logger.trace("Cropping face from image")
        self.image = image[self.y: self.y + self.h,
                           self.x: self.x + self.w]
 </s> add     @property
    def right(self):
        """int: Right point (in pixels) of face detection bounding box within the parent image """
        return self.x + self.w

    @property
    def bottom(self):
        """int: Bottom point (in pixels) of face detection bounding box within the parent image """
        return self.y + self.h

    @property
    def _extract_ratio(self):
        """ float: The ratio of padding to add for training images """
        return 0.375
        self.landmarks_xy = alignment["landmarks_xy"]
          self.x = alignment["x"]
          self.w = alignment["w"]
          self.y = alignment["y"]
          self.h = alignment["h"]
        self.landmarks_xy = alignment["landmarks_xy"]
          # Manual tool does not know the final hash so default to None
          self.hash = alignment.get("hash", None)
          if image is not None and image.any():
              self.image_to_face(image)
          logger.trace("Created from alignment: (x: %s, w: %s, y: %s. h: %s, "
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/faces_detect.py