text: #!/usr/bin/python
  import numpy as np
  import cv2
  
  
  def imgLabeling(img1, img2, img3, img4, maskSize, xoffsetL, xoffsetR,
                  minloc_old=None):
      if len(img1.shape) == 3:
          errL = np.sum(np.square(img1.astype(np.float64) -
                                  img2.astype(np.float64)), axis=2)
          errR = np.sum(np.square(img3.astype(np.float64) -
                                  img4.astype(np.float64)), axis=2)
      else:
          errL = np.square(img1.astype(np.float64) - img2.astype(np.float64))
          errR = np.square(img3.astype(np.float64) - img4.astype(np.float64))
      EL = np.zeros(errL.shape, np.float64)
      ER = np.zeros(errR.shape, np.float64)
      EL[0] = errL[0]
      ER[0] = errR[0]
      for i in range(1, maskSize[1]):
          EL[i, 0] = errL[i, 0] + min(EL[i - 1, 0], EL[i - 1, 1])
          ER[i, 0] = errR[i, 0] + min(ER[i - 1, 0], ER[i - 1, 1])
          for j in range(1, EL.shape[1] - 1):
              EL[i, j] = errL[i, j] + \
                  min(EL[i - 1, j - 1], EL[i - 1, j], EL[i - 1, j + 1])
              ER[i, j] = errR[i, j] + \
                  min(ER[i - 1, j - 1], ER[i - 1, j], ER[i - 1, j + 1])
          EL[i, -1] = errL[i, -1] + min(EL[i - 1, -1], EL[i - 1, -2])
          ER[i, -1] = errR[i, -1] + min(ER[i - 1, -1], ER[i - 1, -2])
      minlocL = np.argmin(EL, axis=1) + xoffsetL
      minlocR = np.argmin(ER, axis=1) + xoffsetR
      if minloc_old is None:
          minloc_old = [minlocL, minlocR, minlocL, minlocR]
      minlocL_fin = np.int32(0.4 * minlocL + 0.3 *
                             minloc_old[0] + 0.3 * minloc_old[2])
      minlocR_fin = np.int32(0.4 * minlocR + 0.3 *
                             minloc_old[1] + 0.3 * minloc_old[3])
      mask = np.ones((maskSize[1], maskSize[0], 3), np.float64)
      for i in range(maskSize[1]):
          mask[i, minlocL_fin[i]:minlocR_fin[i]] = 0
          mask[i, minlocL_fin[i]] = 0.5
          mask[i, minlocR_fin[i]] = 0.5
      # cv2.imshow('mask', mask.astype(np.float32))
      return mask, [minlocL, minlocR, minlocL_fin, minlocR_fin]
  