content: import numpy as np
  
  
  class Evaluator(object):
      def __init__(self, num_class):
          self.num_class = num_class
          self.confusion_matrix = np.zeros((self.num_class,)*2)
  
      def Pixel_Accuracy(self):
          Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum()
          return Acc
  
      def Pixel_Accuracy_Class(self):
          Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1)
          Acc = np.nanmean(Acc)
          return Acc
  
      def Mean_Intersection_over_Union(self):
          MIoU = np.diag(self.confusion_matrix) / (
                      np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) -
                      np.diag(self.confusion_matrix))
          MIoU = np.nanmean(MIoU)
          return MIoU
  
      def Frequency_Weighted_Intersection_over_Union(self):
          freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix)
          iu = np.diag(self.confusion_matrix) / (
                      np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) -
                      np.diag(self.confusion_matrix))
  
          FWIoU = (freq[freq > 0] * iu[freq > 0]).sum()
          return FWIoU
  
      def _generate_matrix(self, gt_image, pre_image):
          mask = (gt_image >= 0) & (gt_image < self.num_class)
          label = self.num_class * gt_image[mask].astype('int') + pre_image[mask]
          count = np.bincount(label, minlength=self.num_class**2)
          confusion_matrix = count.reshape(self.num_class, self.num_class)
          return confusion_matrix
  
      def add_batch(self, gt_image, pre_image):
          assert gt_image.shape == pre_image.shape
          self.confusion_matrix += self._generate_matrix(gt_image, pre_image)
  
      def reset(self):
          self.confusion_matrix = np.zeros((self.num_class,) * 2)
  
  
  
  
  