text: from FDDB import FDDB
  dd = FDDB()
  dd.runFDDB(pred='./FDDB-result/result.txt', result_path=None, index=-1)
  
  from matplotlib import pyplot as plt
  import numpy as np
  
  path_ContROC = "./FDDB-result/ContROC.txt"
  path_DiscROC = "./FDDB-result/DiscROC.txt"
  path_imgSave = "./FDDB-result/result.png"
  
  set_x_lim = 1000
  
  # get data
  with open(path_DiscROC, 'r') as fp:
      discROC = fp.readlines()
  
  # get disc data x, y
  discROC = [line.split() for line in discROC]
  disc_x = [float(x[1]) for x in discROC]
  disc_y = [float(y[0]) for y in discROC]
  
  # get data we need to be print
  count = len(discROC)
  
  ### plot data
  plt.figure()
  
  # set y limite
  plt.ylim((-0.07,1))
  # plt.xlim((-2,set_x_lim))
  # print label
  plt.xlabel('False Positive (FP)')
  plt.ylabel('True Positive Rate (FPR)')
  
  # plot data
  plt.plot(disc_x,disc_y,color = '#007777', linewidth = 3.0)
  
  # print data text
  plt.title('MTCNN-Tensorflow')
  plt.text(disc_x[0] - disc_x[0] / 3,disc_y[0] + 0.03,'Discrete Score: %.3f' %(disc_y[0] * 100) + '%')
  
  # 
  plt.grid()
  
  # save img
  # plt.figure(figsize=(10, 10))
  plt.savefig('./FDDB-result/result.png')
  plt.show()
  
  