              mat[:,2] += padding
  
          new_face = self.get_new_face(image,mat,size)
  
          image_mask = self.get_image_mask( image, new_face, face_detected.landmarks_as_xy(), mat, image_size )
  
          return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
  
      @staticmethod
      def convert_transparent(image, new_face, image_mask, image_size):
 
</s> remove         new_face = self.get_new_face(image,mat,size)
 </s> add         new_face = self.get_new_face(image, mat, size)
 </s> remove             mat[:,2] += padding
 </s> add             mat[:, 2] += padding
 </s> remove         face_mask = numpy.zeros(image.shape,dtype=float)
 </s> add         face_mask = numpy.zeros(image.shape, dtype=float)
 </s> remove         base_image = numpy.copy( image )
        new_image = numpy.copy( image )
 </s> add         base_image = numpy.copy(image)
        new_image = numpy.copy(image)
 </s> remove             face_src = numpy.ones(new_face.shape,dtype=float)
            cv2.warpAffine( face_src, mat, image_size, face_mask, cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT )

        hull_mask = numpy.zeros(image.shape,dtype=float)
 </s> add             face_src = numpy.ones(new_face.shape, dtype=float)
            cv2.warpAffine(face_src,
                           mat,
                           image_size,
                           face_mask,
                           cv2.WARP_INVERSE_MAP | cv2.INTER_CUBIC, cv2.BORDER_TRANSPARENT)

        hull_mask = numpy.zeros(image.shape, dtype=float)

        image_mask = self.get_image_mask(image,
                                         new_face,
                                         face_detected.landmarks_as_xy,
                                         mat,
                                         image_size)

              mat[:,2] += padding
  
          new_face = self.get_new_face(image,mat,size)
  
        image_mask = self.get_image_mask(image,
                                         new_face,
                                         face_detected.landmarks_as_xy,
                                         mat,
                                         image_size)
  
          return self.apply_new_face(image, new_face, image_mask, mat, image_size, size)
  
      @staticmethod
      def convert_transparent(image, new_face, image_mask, image_size):
keep keep keep keep replace keep keep keep keep keep
 Logging (#541)

* Convert prints to logger. Further logging improvements. Tidy  up

* Fix system verbosity. Allow SystemExit

* Fix reload extract bug

* Child Traceback handling

* Safer shutdown procedure

* Add shutdown event to queue manager

* landmarks_as_xy > property. GUI notes + linting. Aligner bugfix

* fix FaceFilter. Enable nFilter when no Filter is supplied

* Fix blurry face filter

* Continue on IO error. Better error handling

* Explicitly print stack trace tocrash log

* Windows Multiprocessing bugfix

* Add git info and conda version to crash log

* Windows/Anaconda mp bugfix

* Logging fixes for training 
plugins/convert/Convert_Masked.py