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| """ | |
| refer to https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/utils/metrics.py | |
| """ | |
| import numpy as np | |
| __all__ = ['SegmentationMetric'] | |
| """ | |
| confusionMetric # 注意：此处横着代表预测值，竖着代表真实值，与之前介绍的相反 | |
| P\L P N | |
| P TP FP | |
| N FN TN | |
| """ | |
| class SegmentationMetric(object): | |
| def __init__(self, numClass): | |
| self.numClass = numClass | |
| self.confusionMatrix = np.zeros((self.numClass,) * 2) # 混淆矩阵（空） | |
| def pixelAccuracy(self): | |
| # return all class overall pixel accuracy 正确的像素占总像素的比例 | |
| # PA = acc = (TP + TN) / (TP + TN + FP + TN) | |
| acc = np.diag(self.confusionMatrix).sum() / self.confusionMatrix.sum() | |
| return acc | |
| def classPixelAccuracy(self): | |
| # return each category pixel accuracy(A more accurate way to call it precision) | |
| # acc = (TP) / TP + FP | |
| classAcc = np.diag(self.confusionMatrix) / self.confusionMatrix.sum(axis=1) | |
| return classAcc # 返回的是一个列表值，如：[0.90, 0.80, 0.96]，表示类别1 2 3各类别的预测准确率 | |
| def meanPixelAccuracy(self): | |
| """ | |
| Mean Pixel Accuracy(MPA，均像素精度)：是PA的一种简单提升，计算每个类内被正确分类像素数的比例，之后求所有类的平均。 | |
| :return: | |
| """ | |
| classAcc = self.classPixelAccuracy() | |
| meanAcc = np.nanmean(classAcc) # np.nanmean 求平均值，nan表示遇到Nan类型，其值取为0 | |
| return meanAcc # 返回单个值，如：np.nanmean([0.90, 0.80, 0.96, nan, nan]) = (0.90 + 0.80 + 0.96） / 3 = 0.89 | |
| def IntersectionOverUnion(self): | |
| # Intersection = TP Union = TP + FP + FN | |
| # IoU = TP / (TP + FP + FN) | |
| intersection = np.diag(self.confusionMatrix) # 取对角元素的值，返回列表 | |
| union = np.sum(self.confusionMatrix, axis=1) + np.sum(self.confusionMatrix, axis=0) - np.diag( | |
| self.confusionMatrix) # axis = 1表示混淆矩阵行的值，返回列表； axis = 0表示取混淆矩阵列的值，返回列表 | |
| IoU = intersection / union # 返回列表，其值为各个类别的IoU | |
| return IoU | |
| def meanIntersectionOverUnion(self): | |
| mIoU = np.nanmean(self.IntersectionOverUnion()) # 求各类别IoU的平均 | |
| return mIoU | |
| def genConfusionMatrix(self, imgPredict, imgLabel): # | |
| """ | |
| 同FCN中score.py的fast_hist()函数,计算混淆矩阵 | |
| :param imgPredict: | |
| :param imgLabel: | |
| :return: 混淆矩阵 | |
| """ | |
| # remove classes from unlabeled pixels in gt image and predict | |
| mask = (imgLabel >= 0) & (imgLabel < self.numClass) | |
| label = self.numClass * imgLabel[mask] + imgPredict[mask] | |
| count = np.bincount(label, minlength=self.numClass ** 2) | |
| confusionMatrix = count.reshape(self.numClass, self.numClass) | |
| # print(confusionMatrix) | |
| return confusionMatrix | |
| def Frequency_Weighted_Intersection_over_Union(self): | |
| """ | |
| FWIoU，频权交并比:为MIoU的一种提升，这种方法根据每个类出现的频率为其设置权重。 | |
| FWIOU = [(TP+FN)/(TP+FP+TN+FN)] *[TP / (TP + FP + FN)] | |
| """ | |
| 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 addBatch(self, imgPredict, imgLabel): | |
| assert imgPredict.shape == imgLabel.shape | |
| self.confusionMatrix += self.genConfusionMatrix(imgPredict, imgLabel) # 得到混淆矩阵 | |
| return self.confusionMatrix | |
| def reset(self): | |
| self.confusionMatrix = np.zeros((self.numClass, self.numClass)) | |