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)
