          max_length_image = max(self.height, self.width)
          self.input_scale = max_length_scale / max_length_image
  
  
  class Align(object):
      """ Perform transformation to align and get landmarks """
      def __init__(self, image, detected_faces, keras_model, verbose):
          self.verbose = verbose
          self.image = image
          self.detected_faces = detected_faces
 
</s> remove     def __init__(self, input_image_bgr, detector, mtcnn_kwargs=None,
                 verbose=False, input_is_predetected_face=False):
 </s> add     def __init__(self, input_image_bgr, detector, dlib_buffer=64,
                 mtcnn_kwargs=None, verbose=False,
                 input_is_predetected_face=False): </s> remove class DetectedFace(object):
 </s> add class DetectedFace(): </s> remove class Alignments(object):
 </s> add class Alignments(): </s> remove class Frame(object):
 </s> add class Frame(): </s> remove class Faces(object):
 </s> add class Faces():
class Align():
          max_length_image = max(self.height, self.width)
          self.input_scale = max_length_scale / max_length_image
  
  
class Align():
      """ Perform transformation to align and get landmarks """
      def __init__(self, image, detected_faces, keras_model, verbose):
          self.verbose = verbose
          self.image = image
          self.detected_faces = detected_faces
keep keep keep keep replace keep keep keep keep keep
 Add option to change dlib-cnn buffer size 
lib/face_alignment/extractor.py