parameters: (self, vert, deg, axis=[0, 1, 0], cam=None, texture=None)
return_statement: return self.__call__(new_vert, cams=cam, texture=texture)
docstring: vert is N x 3, torch FloatTensor (or Variable)
docstring_summary: vert is N x 3, torch FloatTensor (or Variable)
docstring_tokens: vert is N x 3 torch FloatTensor ( or Variable )
function: def rotated(self, vert, deg, axis=[0, 1, 0], cam=None, texture=None):
          """
          vert is N x 3, torch FloatTensor (or Variable)
          """
          import cv2
          new_rot = cv2.Rodrigues(np.deg2rad(deg) * np.array(axis))[0]
          new_rot = convert_as(torch.FloatTensor(new_rot), vert)
  
          center = vert.mean(0)
          new_vert = torch.t(torch.matmul(new_rot,
                                          torch.t(vert - center))) + center
          # new_vert = torch.matmul(vert - center, new_rot) + center
  
          return self.__call__(new_vert, cams=cam, texture=texture)
function_tokens: def rotated ( self , vert , deg , axis = [ 0 , 1 , 0 ] , cam = None , texture = None ) : import cv2 new_rot = cv2 . Rodrigues ( np . deg2rad ( deg ) * np . array ( axis ) ) [ 0 ] new_rot = convert_as ( torch . FloatTensor ( new_rot ) , vert ) center = vert . mean ( 0 ) new_vert = torch . t ( torch . matmul ( new_rot , torch . t ( vert - center ) ) ) + center # new_vert = torch.matmul(vert - center, new_rot) + center return self . __call__ ( new_vert , cams = cam , texture = texture )