code: def cut_patch(img, landmarks, height, width, threshold=5):
      center_x, center_y = np.mean(landmarks, axis=0)
      # Check for too much bias in height and width
      if abs(center_y - img.shape[0] / 2) > height + threshold:
          return None
      if abs(center_x - img.shape[1] / 2) > width + threshold:
          return None
      # Calculate bounding box coordinates
      y_min = int(round(np.clip(center_y - height, 0, img.shape[0])))
      y_max = int(round(np.clip(center_y + height, 0, img.shape[0])))
      x_min = int(round(np.clip(center_x - width, 0, img.shape[1])))
      x_max = int(round(np.clip(center_x + width, 0, img.shape[1])))
      # Cut the image
      cutted_img = np.copy(img[y_min:y_max, x_min:x_max])
      return cutted_img, y_min, x_min
  
file_path: espnet/egs2/lrs3/avsr1/local/face_alignment.py
imports: import os
  import cv2
  import numpy
  from skimage import transform
  from transforms import *
label_desc: Use image node_id 4 for calling a global function with example usage: cut_patch(img, landmarks, height, width, threshold) and returns: cutted_img,  y_min,  x_min, None, None