基于Keras和Unet算法的多波段遥感图像语义分割python实现源码.zip

# -*- coding: utf-8 -*- """ Created on Tue Jul 21 15:29:02 2020 @author: 12624 """ import numpy as np import cv2 import os """ 混淆矩阵 P\L P N P TP FP N FN TN """ # 获取颜色字典 # labelFolder 标签文件夹,之所以遍历文件夹是因为一张标签可能不包含所有类别颜色 # classNum 类别总数(含背景) def color_dict(labelFolder, classNum): colorDict = [] # 获取文件夹内的文件名 ImageNameList = os.listdir(labelFolder) for i in range(len(ImageNameList)): ImagePath = labelFolder + "/" + ImageNameList[i] img = cv2.imread(ImagePath).astype(np.uint32) # 如果是灰度，转成RGB if(len(img.shape) == 2): img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB).astype(np.uint32) # 为了提取唯一值，将RGB转成一个数 img_new = img[:,:,0] * 1000000 + img[:,:,1] * 1000 + img[:,:,2] unique = np.unique(img_new) # 将第i个像素矩阵的唯一值添加到colorDict中 for j in range(unique.shape[0]): colorDict.append(unique[j]) # 对目前i个像素矩阵里的唯一值再取唯一值 colorDict = sorted(set(colorDict)) # 若唯一值数目等于总类数(包括背景)ClassNum，停止遍历剩余的图像 if(len(colorDict) == classNum): break # 存储颜色的BGR字典，用于预测时的渲染结果 colorDict_BGR = [] for k in range(len(colorDict)): # 对没有达到九位数字的结果进行左边补零(eg:5,201,111->005,201,111) color = str(colorDict[k]).rjust(9, '0') # 前3位B,中3位G,后3位R color_BGR = [int(color[0 : 3]), int(color[3 : 6]), int(color[6 : 9])] colorDict_BGR.append(color_BGR) # 转为numpy格式 colorDict_BGR = np.array(colorDict_BGR) # 存储颜色的GRAY字典，用于预处理时的onehot编码 colorDict_GRAY = colorDict_BGR.reshape((colorDict_BGR.shape[0], 1 ,colorDict_BGR.shape[1])).astype(np.uint8) colorDict_GRAY = cv2.cvtColor(colorDict_GRAY, cv2.COLOR_BGR2GRAY) return colorDict_BGR, colorDict_GRAY def ConfusionMatrix(numClass, imgPredict, Label): # 返回混淆矩阵 mask = (Label >= 0) & (Label < numClass) label = numClass * Label[mask] + imgPredict[mask] count = np.bincount(label, minlength = numClass**2) confusionMatrix = count.reshape(numClass, numClass) return confusionMatrix def OverallAccuracy(confusionMatrix): # 返回所有类的整体像素精度OA # acc = (TP + TN) / (TP + TN + FP + TN) OA = np.diag(confusionMatrix).sum() / confusionMatrix.sum() return OA def Precision(confusionMatrix): # 返回所有类别的精确率precision precision = np.diag(confusionMatrix) / confusionMatrix.sum(axis = 1) return precision def Recall(confusionMatrix): # 返回所有类别的召回率recall recall = np.diag(confusionMatrix) / confusionMatrix.sum(axis = 0) return recall def F1Score(confusionMatrix): precision = np.diag(confusionMatrix) / confusionMatrix.sum(axis = 1) recall = np.diag(confusionMatrix) / confusionMatrix.sum(axis = 0) f1score = 2 * precision * recall / (precision + recall) return f1score def IntersectionOverUnion(confusionMatrix): # 返回交并比IoU intersection = np.diag(confusionMatrix) union = np.sum(confusionMatrix, axis = 1) + np.sum(confusionMatrix, axis = 0) - np.diag(confusionMatrix) IoU = intersection / union return IoU def MeanIntersectionOverUnion(confusionMatrix): # 返回平均交并比mIoU intersection = np.diag(confusionMatrix) union = np.sum(confusionMatrix, axis = 1) + np.sum(confusionMatrix, axis = 0) - np.diag(confusionMatrix) IoU = intersection / union mIoU = np.nanmean(IoU) return mIoU def Frequency_Weighted_Intersection_over_Union(confusionMatrix): # 返回频权交并比FWIoU freq = np.sum(confusionMatrix, axis=1) / np.sum(confusionMatrix) iu = np.diag(confusionMatrix) / ( np.sum(confusionMatrix, axis = 1) + np.sum(confusionMatrix, axis = 0) - np.diag(confusionMatrix)) FWIoU = (freq[freq > 0] * iu[freq > 0]).sum() return FWIoU ################################################################# # 标签图像文件夹 LabelPath = r"Data\test\label1" # 预测图像文件夹 PredictPath = r"Data\test\predict1" # 类别数目(包括背景) classNum = 3 ################################################################# # 获取类别颜色字典 colorDict_BGR, colorDict_GRAY = color_dict(LabelPath, classNum) # 获取文件夹内所有图像 labelList = os.listdir(LabelPath) PredictList = os.listdir(PredictPath) # 读取第一个图像，后面要用到它的shape Label0 = cv2.imread(LabelPath + "//" + labelList[0], 0) # 图像数目 label_num = len(labelList) # 把所有图像放在一个数组里 label_all = np.zeros((label_num, ) + Label0.shape, np.uint8) predict_all = np.zeros((label_num, ) + Label0.shape, np.uint8) for i in range(label_num): Label = cv2.imread(LabelPath + "//" + labelList[i]) Label = cv2.cvtColor(Label, cv2.COLOR_BGR2GRAY) label_all[i] = Label Predict = cv2.imread(PredictPath + "//" + PredictList[i]) Predict = cv2.cvtColor(Predict, cv2.COLOR_BGR2GRAY) predict_all[i] = Predict # 把颜色映射为0,1,2,3... for i in range(colorDict_GRAY.shape[0]): label_all[label_all == colorDict_GRAY[i][0]] = i predict_all[predict_all == colorDict_GRAY[i][0]] = i # 拉直成一维 label_all = label_all.flatten() predict_all = predict_all.flatten() # 计算混淆矩阵及各精度参数 confusionMatrix = ConfusionMatrix(classNum, predict_all, label_all) precision = Precision(confusionMatrix) recall = Recall(confusionMatrix) OA = OverallAccuracy(confusionMatrix) IoU = IntersectionOverUnion(confusionMatrix) FWIOU = Frequency_Weighted_Intersection_over_Union(confusionMatrix) mIOU = MeanIntersectionOverUnion(confusionMatrix) f1ccore = F1Score(confusionMatrix) for i in range(colorDict_BGR.shape[0]): # 输出类别颜色,需要安装webcolors,直接pip install webcolors try: import webcolors rgb = colorDict_BGR[i] rgb[0], rgb[2] = rgb[2], rgb[0] print(webcolors.rgb_to_name(rgb), end = " ") # 不安装的话,输出灰度值 except: print(colorDict_GRAY[i][0], end = " ") print("") print("混淆矩阵:") print(confusionMatrix) print("精确度:") print(precision) print("召回率:") print(recall) print("F1-Score:") print(f1ccore) print("整体精度:") print(OA) print("IoU:") print(IoU) print("mIoU:") print(mIOU) print("FWIoU:") print(FWIOU)