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evaluation.py
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def two_cls_access(reference,result):
# for Hermiston dataset
# reference:change_value=1;unchange_value=0
# result: predicted map:change_value=1;unchange_value=0
# 对二类变化检测的结果进行精度评价，指标为kappad系数和OA值
# 输入：
# reference：二元变化reference(二值图，H*W)
# resultz:算法检测得到的二类变化结果图(二值图，H*W)]
oa_kappa = []
m,n = reference.shape
if reference.shape != result.shape:
print('the size of reference shoulf be equal to that of result')
return oa_kappa
reference = np.reshape(reference, -1)
result = np.reshape(result, -1)
label_0 = np.where(reference == 0)
label_1 = np.where(reference == 1)
predict_0 = np.where(result == 0)
predict_1 = np.where(result == 1)
label_0 = label_0[0]
label_1 = label_1[0]
predict_0 = predict_0[0]
predict_1 = predict_1[0]
tp = set(label_1).intersection(set(predict_1)) # True Positive
tn = set(label_0).intersection(set(predict_0)) # False Positive
fp = set(label_0).intersection(set(predict_1)) # False Positive
fn = set(label_1).intersection(set(predict_0)) # False Negative
precision = len(tp) / (len(tp) + len(fp))
recall = len(tp) / (len(tp) + len(fn))
precision = round(precision, 4)
recall = round(recall, 4)
F1 = 2 * (precision * recall) / (precision + recall)
F1 = round(F1, 4)
print('F1= ' + str(F1))
print('recall= ' + str(recall))
print('precision= ' + str(precision))
oa = (len(tp)+len(tn))/m/n # Overall precision
pe = (len(label_1)*len(predict_1)+len(label_0)*len(predict_0))/m/n/m/n
kappa = (oa-pe)/(1-pe)
oa = round(oa, 4)
kappa = round(kappa, 4)
oa_kappa.append('OA')
oa_kappa.append(oa)
oa_kappa.append('kappa')
oa_kappa.append(kappa)
oa_kappa.append('F1')
oa_kappa.append(F1)
oa_kappa.append('recall')
oa_kappa.append(recall)
oa_kappa.append('precision')
oa_kappa.append(precision)
print('OA: ' + str(oa) + ' ' + 'kappa: ' + str(kappa))
return oa_kappa
def two_cls_access_for_Bay_Barbara(reference,result):
# for Bay & Barbra datasets
# reference:change_value=1;unchange_value=2
# result: predicted map:change_value=1;unchange_value=0
# 对二类变化检测的结果进行精度评价，指标为kappad系数和OA值
# 输入：
# reference：二元变化reference(二值图，H*W), change=1; unchanged=2;uncertain=0
# resultz:算法检测得到的二类变化结果图(二值图，H*W)]
oa_kappa = []
# m,n = reference.shape
if reference.shape != result.shape:
print('the size of reference shoulf be equal to that of result')
return oa_kappa
reference = np.reshape(reference, -1)
result = np.reshape(result, -1)
label_0 = np.where(reference == 2) # Unchanged
label_1 = np.where(reference == 1) # Changed
predict_0 = np.where(result == 0) # Unchanged
predict_1 = np.where(result == 1) # Changed
label_0 = label_0[0]
label_1 = label_1[0]
predict_0 = predict_0[0]
predict_1 = predict_1[0]
tp = set(label_1).intersection(set(predict_1)) # True Positive
tn = set(label_0).intersection(set(predict_0)) # True Negative
fp = set(label_0).intersection(set(predict_1)) # False Positive
fn = set(label_1).intersection(set(predict_0)) # False Negative
precision = len(tp) / (len(tp) + len(fp)) # (预测为1且正确预测的样本数) / (所有真实情况为1的样本数)
recall = len(tp) / (len(tp) + len(fn)) # (预测为1且正确预测的样本数) / (所有真实情况为1的样本数)
precision = round(precision, 4)
recall = round(recall, 4)
F1 = 2 * (precision * recall) / (precision + recall)
F1 = round(F1, 4)
print('F1= ' + str(F1))
print('recall= ' + str(recall))
print('precision= ' + str(precision))
total_num = len(label_0) +len(label_1)
oa = (len(tp) + len(tn)) / total_num # Overall precision
pe = ((len(tp)+len(fn))*(len(tp)+len(fp)) +(len(fp)+len(tn))*(len(fn)+len(tn)))/ total_num / total_num
kappa = (oa-pe)/(1-pe)
oa = round(oa, 4)
kappa = round(kappa, 4)
oa_kappa.append('OA')
oa_kappa.append(oa)
oa_kappa.append('kappa')
oa_kappa.append(kappa)
oa_kappa.append('F1')
oa_kappa.append(F1)
oa_kappa.append('recall')
oa_kappa.append(recall)
oa_kappa.append('precision')
oa_kappa.append(precision)
print('OA: ' + str(oa) + ' ' + 'kappa: ' + str(kappa))
# print('whole OA is' + str(oa))
# print('whole kappa is' + str(kappa))
return oa_kappa