class_docstr: This is an auxiliary class that enables Video Stabilization for vidgear with minimalistic latency, and at the expense
  of little to no additional computational requirements.
  
  The basic idea behind it is to tracks and save the salient feature array for the given number of frames and then uses
  these anchor point to cancel out all perturbations relative to it for the incoming frames in the queue. This class relies
  heavily on **Threaded Queue mode** for error-free & ultra-fast frame handling.
docstr: An internal method that applies *normalized linear box filter* to path w.r.t averaging window
  
  Parameters:
  
  * path (numpy.ndarray): a cumulative sum of transformations
  * window_size (int): averaging window size
code: def __box_filter_convolve(self, path, window_size):
      """
      An internal method that applies *normalized linear box filter* to path w.r.t averaging window
  
      Parameters:
  
      * path (numpy.ndarray): a cumulative sum of transformations
      * window_size (int): averaging window size
      """
      # pad path to size of averaging window
      path_padded = np.pad(path, (window_size, window_size), "median")
      # apply linear box filter to path
      path_smoothed = np.convolve(
          path_padded, self.__box_filter, mode="same"
      )
      # crop the smoothed path to original path
      path_smoothed = path_smoothed[window_size:-window_size]
      # assert if cropping is completed
      assert path.shape == path_smoothed.shape
      # return smoothed path
      return path_smoothed
  
file_path: vidgear/vidgear/gears/stabilizer.py
imports: import cv2
  import numpy
  import logging
  from collections import deque
  from .helper import logger_handler, check_CV_version, retrieve_best_interpolation, logcurr_vidgear_ver
label_desc: Use image node_id 4 for calling the Stabilizer obj's underlying member method code with example usage: obj.__box_filter_convolve(path, window_size) and returns: path_smoothed