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import numpy as np
################### metrics ###################
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.initialized = False
self.val = None
self.avg = None
self.sum = None
self.count = None
def initialize(self, val, weight):
self.val = val
self.avg = val
self.sum = val * weight
self.count = weight
self.initialized = True
def update(self, val, weight=1):
if not self.initialized:
self.initialize(val, weight)
else:
self.add(val, weight)
def add(self, val, weight):
self.val = val
self.sum += val * weight
self.count += weight
self.avg = self.sum / self.count
def value(self):
return self.val
def average(self):
return self.avg
def get_scores(self):
scores_dict = cm2score(self.sum)
return scores_dict
def clear(self):
self.initialized = False
################### cm metrics ###################
class ConfuseMatrixMeter(AverageMeter):
"""Computes and stores the average and current value"""
def __init__(self, n_class):
super(ConfuseMatrixMeter, self).__init__()
self.n_class = n_class
def update_cm(self, pr, gt, weight=1):
"""获得当前混淆矩阵，并计算当前F1得分，并更新混淆矩阵"""
val = get_confuse_matrix(num_classes=self.n_class, label_gts=gt, label_preds=pr)
self.update(val, weight)
current_score = cm2F1(val)
return current_score
def get_scores(self):
scores_dict = cm2score(self.sum)
return scores_dict
def harmonic_mean(xs):
harmonic_mean = len(xs) / sum((x + 1e-6) ** -1 for x in xs)
return harmonic_mean
def cm2F1(confusion_matrix):
hist = confusion_matrix
tp = hist[1, 1]
fn = hist[1, 0]
fp = hist[0, 1]
tn = hist[0, 0]
# recall
recall = tp / (tp + fn + np.finfo(np.float32).eps)
# precision
precision = tp / (tp + fp + np.finfo(np.float32).eps)
# F1 score
f1 = 2 * recall * precision / (recall + precision + np.finfo(np.float32).eps)
return f1
def cm2score(confusion_matrix):
hist = confusion_matrix
tp = hist[1, 1]
fn = hist[1, 0]
fp = hist[0, 1]
tn = hist[0, 0]
# acc
oa = (tp + tn) / (tp + fn + fp + tn + np.finfo(np.float32).eps)
# recall
recall = tp / (tp + fn + np.finfo(np.float32).eps)
# precision
precision = tp / (tp + fp + np.finfo(np.float32).eps)
# F1 score
f1 = 2 * recall * precision / (recall + precision + np.finfo(np.float32).eps)
# IoU
iou = tp / (tp + fp + fn + np.finfo(np.float32).eps)
# pre
pre = ((tp + fn) * (tp + fp) + (tn + fp) * (tn + fn)) / (tp + fp + tn + fn) ** 2
# kappa
kappa = (oa - pre) / (1 - pre)
score_dict = {'Kappa': kappa, 'IoU': iou, 'F1': f1, 'OA': oa, 'recall': recall, 'precision': precision, 'Pre': pre}
return score_dict
def get_confuse_matrix(num_classes, label_gts, label_preds):
"""计算一组预测的混淆矩阵"""
def __fast_hist(label_gt, label_pred):
"""
Collect values for Confusion Matrix
For reference, please see: https://en.wikipedia.org/wiki/Confusion_matrix
:param label_gt: <np.array> ground-truth
:param label_pred: <np.array> prediction
:return: <np.ndarray> values for confusion matrix
"""
mask = (label_gt >= 0) & (label_gt < num_classes)
hist = np.bincount(num_classes * label_gt[mask].astype(int) + label_pred[mask],
minlength=num_classes ** 2).reshape(num_classes, num_classes)
return hist
confusion_matrix = np.zeros((num_classes, num_classes))
for lt, lp in zip(label_gts, label_preds):
confusion_matrix += __fast_hist(lt.flatten(), lp.flatten())
return confusion_matrix
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