text: # -*-coding:utf-8 -*-
  
  import os
  from sklearn.metrics import confusion_matrix
  import matplotlib.pyplot as plt
  import numpy as np
  
  # 制作混淆矩阵
  with open('label.json', 'r'):
      label_id_name_dict  = json.load(f)
  
  labels = list(label_id_name_dict.keys())
  
  y_true = np.loadtxt('./re_label.txt')
  y_pred = np.loadtxt('./pr_label.txt')
  
  tick_marks = np.array(range(len(labels))) + 0.5
  
  def plot_confusion_matrix(cm, title='Confusion Matrix', cmap=plt.cm.binary):
      plt.imshow(cm, interpolation='nearest', cmap=cmap)
      plt.title(title)
      plt.colorbar()
      xlocations = np.array(range(len(labels)))
      plt.xticks(xlocations, labels, rotation=90)
      plt.yticks(xlocations, labels)
      plt.ylabel('True label')
      plt.xlabel('Predicted label')
  
  
  cm = confusion_matrix(y_true, y_pred)
  np.set_printoptions(precision=2)
  cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
  print(cm_normalized)
  plt.figure(figsize=(10,10), dpi=500)
  
  ind_array = np.arange(len(labels))
  x, y = np.meshgrid(ind_array, ind_array)
  
  for x_val, y_val in zip(x.flatten(), y.flatten()):
      c = cm_normalized[y_val][x_val]
      if c > 0.01:
          plt.text(x_val, y_val, "%0.2f" % (c,), color='red', fontsize=7, va='center', ha='center')
  # offset the tick
  plt.gca().set_xticks(tick_marks, minor=True)
  plt.gca().set_yticks(tick_marks, minor=True)
  plt.gca().xaxis.set_ticks_position('none')
  plt.gca().yaxis.set_ticks_position('none')
  plt.grid(True, which='minor', linestyle='-')
  plt.gcf().subplots_adjust(bottom=0.15)
  
  plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
  # show confusion matrix
  plt.savefig('./confusion_matrix.png', format='png')
  plt.show()
  
  
  
  