new_contents: from FaceAlignment import FaceAlignment
  import numpy as np
  import cv2
  import utils
  
  model = FaceAlignment(112, 112, 1, 1, True)
  model.loadNetwork("../data/DAN-Menpo-tracking.npz")
  
  cascade = cv2.CascadeClassifier("../data/haarcascade_frontalface_alt.xml")
  
  color_img = cv2.imread("../data/jk.jpg")
  if len(color_img.shape) > 2:
      gray_img = np.mean(color_img, axis=2).astype(np.uint8)
  else:
      gray_img = color_img.astype(np.uint8)
  
  # reset = True
  landmarks = None
  
  # if reset:
  rects = cascade.detectMultiScale(gray_img, scaleFactor=1.2, minNeighbors=3, minSize=(50, 50))
  
  for rect in rects:
      tl_x = rect[0]
      tl_y = rect[1]
      br_x = tl_x + rect[2]
      br_y = tl_y + rect[3]
  
      cv2.rectangle(color_img, (tl_x, tl_y), (br_x, br_y), (255, 0, 0))
  
      initLandmarks = utils.bestFitRect(None, model.initLandmarks, [tl_x, tl_y, br_x, br_y])
  
      if model.confidenceLayer:
          landmarks, confidence = model.processImg(gray_img[np.newaxis], initLandmarks)
          if confidence < 0.1:
              reset = True
      else:
          landmarks = model.processImg(gray_img[np.newaxis], initLandmarks)
  
      landmarks = landmarks.astype(np.int32)
      for i in range(landmarks.shape[0]):
          cv2.circle(color_img, (landmarks[i, 0], landmarks[i, 1]), 2, (0, 255, 0))
  
  cv2.imshow("image", color_img)
  
  key = cv2.waitKey(0)
  
subject: Add a sample code for still images