湖南省新邵县建设局网站,php网站开发示例代码,一个网站两个数据库,工程建设的信息网站遥感图像地物分类流程
1. 制作标签
使用arcgis pro或者arcgis或者envi#xff0c;画标签#xff0c;保存为tiff格式
2. 处理标签数据
用python gdal库安装 osgdal库#xff0c;如果安装失败就需要下载 对应库得 .whl去安装#xff0c;网站具体搞忘了#xff0c;可以百…遥感图像地物分类流程
1. 制作标签
使用arcgis pro或者arcgis或者envi画标签保存为tiff格式
2. 处理标签数据
用python gdal库安装 osgdal库如果安装失败就需要下载 对应库得 .whl去安装网站具体搞忘了可以百度
或者rasterio库
2.1 读入tif数据
def readTif(fileName):dataset包含了tif文件得属性比如波段数高宽数据dataset rasterio.open(fileName)if dataset None:print(fileName 文件无法打开)return None# print(dataset.width)return dataset
2.2 处理数据
import csv
# 提取栅格图像信息,制作数据
ori_dataset readTif(orgin_path)
label_dataset readTif(sample_path)width ori_dataset.width # 宽
height ori_dataset.height # 高bands ori_dataset.count # 波段数
# ori_data for k in range(bands)label_matri label_dataset.read(1) #读出标签的矩阵
data_matri ori_dataset.read() #原始图像的矩阵count np.count_nonzero(label_matri) #非零就是标签 有多少非零的就代表样本像素是多少
print(count)
train_data np.zeros((count, 8), dtypedata_matri.dtype) # 新建一个count*8的numpy数组第8维度是原始图像的某一像素点对应的标签0~6代表这一个像素点对应的7ge波段landsata影像
nonzero_indices np.nonzero(label_matri) #非零索引 返回的是(row:array([ 30, 31, 31, ..., 390, 390, 390], dtypeint64), col:array([166, 165, 166, ..., 186, 187, 188], dtypeint64))print(nonzero_indices)
# 写入数据csv, 提取训练数据
# 将 train_data 写入 CSV 文件
csv_file open(csv_filename, modew, newline)
csv_writer csv.writer(csv_file)
# 写入 CSV 文件的标题行包括 Label 和 LabelName
csv_writer.writerow(csv_head_name)for i in range(count):print(i)row, col nonzero_indices[0][i], nonzero_indices[1][i]train_data[i, :7] data_matri[:, row, col]train_data[i, 7] label_matri[row, col]label int(train_data[i, 7])row_data train_data[i]row_data np.append(row_data, labels_name[label]) # 在数据行中添加 LabelNamecsv_writer.writerow(row_data)print(f已将数据写入 CSV 文件: {csv_filename})
csv_file.close()2.3 数据格式
生成的数据格式如下
Band1,Band2,Band3,Band4,Band5,Band6,Band7,Label,LabelName
812,774,969,1111,1152,1146,1069,2,building
801,755,846,1016,1177,1411,1472,2,building
794,748,949,1179,1202,1399,1383,2,building
605,567,691,877,1537,1880,2070,2,building
602,556,768,994,1506,1625,1607,2,building
613,570,768,1045,1394,1483,1460,2,building
465,408,562,772,963,1035,990,2,building
549,484,648,828,969,1096,1028,2,building3. 训练
from sklearn.ensemble import RandomForestClassifier
from sklearn import model_selection
import pickleX train_data[:, :7]
Y train_data[:, 7]
# print(X.shape)
# print(Y.shape)
X_train, X_test, y_train, y_test model_selection.train_test_split(X, Y, test_size0.1, random_state42, stratifyY)
print(y_train)
# 3.用100个树来创建随机森林模型训练随机森林
classifier RandomForestClassifier(n_estimators100,bootstrap True,max_features sqrt)
classifier.fit(X_train, y_train)# 4.计算随机森林的准确率
print(训练集,classifier.score(X_train,y_train))
print(测试集,classifier.score(X_test,y_test))pred_test_y classifier.predict(X_test)
cfm CFM(5, labels_name)
cfm.update(pred_test_y, y_test)
acc, comment_numpy cfm.get_cfm()
print(comment_numpy)
cfm.plot()file open(model_path, wb)
#将模型写入文件
pickle.dump(classifier, file)
#最后关闭文件
file.close()4. 使用模型预测
pred_dataset readTif(pred_path)
pred_width pred_dataset.width
pred_height pred_dataset.height
pred_bands pred_dataset.count
pred_geotrans pred_dataset.transform
pred_crs pred_dataset.crsprint(pred_geotrans)
print(pred_crs)file open(model_path, rb)
# 把模型从文件中读取出来
rf_model pickle.load(file)
# 关闭文件
file.close()pred_martix pred_dataset.read()
data np.zeros((pred_martix.shape[0], pred_martix.shape[1] * pred_martix.shape[2]))# print(pred_martix.shape)
# print(pred_martix[0])
for i in range(pred_martix.shape[0]):# 第i个波段一维数组data[i] pred_martix[i].flatten()
# 转换下维度
pred_x data.swapaxes(0, 1)pred_y rf_model.predict(pred_x)
# print(pred_y, pred_y.shape)# 将标签还原为图像的二维矩阵
pred_image pred_y.reshape(pred_martix.shape[1], pred_martix.shape[2])
height_, width_ pred_image.shape
tif_data np.zeros((height_, width_, 3), dtypenp.int64)
for label, color in color_mapping.items():tif_data[pred_image label] colortif_data np.transpose(tif_data, (2, 0, 1))im_bands, im_height, im_width tif_data.shape
driver gdal.GetDriverByName(GTiff)
dataset driver.Create(pred_result_tif_path, im_width, im_height, im_bands, gdal.GDT_Byte)
for i in range(im_bands):dataset.GetRasterBand(i 1).WriteArray(tif_data[i])
# if dataset ! None:
# #将栅格数据和地理坐标系统关联起来
# dataset.SetProjection(pred_crs) # 写入投影
# dataset.SetGeoTransform(pred_geotrans) # 写入仿射变换参数dataset None5. other
import numpy as np
import matplotlib.pyplot as plt
from prettytable import PrettyTableclass CFM:混淆矩阵类返回精度和混淆举证def __init__(self, num_classes: int, labels: list):self.matrix np.zeros((num_classes, num_classes))self.num_classes num_classesself.labels labelsdef plot(self):matrix self.matrixprint(matrix)plt.imshow(matrix, cmapplt.cm.Blues)# 设置x轴坐标labelplt.xticks(range(self.num_classes), self.labels, rotation45)# 设置y轴坐标labelplt.yticks(range(self.num_classes), self.labels)# 显示colorbarplt.colorbar()plt.xlabel(True Labels)plt.ylabel(Predicted Labels)plt.title(Confusion matrix)# 在图中标注数量/概率信息thresh matrix.max() / 2for x in range(self.num_classes):for y in range(self.num_classes):# 注意这里的matrix[y, x]不是matrix[x, y]info int(matrix[y, x])plt.text(x, y, info,verticalalignmentcenter,horizontalalignmentcenter,colorwhite if info thresh else black)plt.tight_layout()plt.show()def update(self, preds, labels):_summary_Args:preds (_type_): _description_labels (_type_): _description_preds:预测值labels:真实值confusion martixlabel0 label1 label2 label3pred0pred1pred2pred3for p, t in zip(preds, labels):self.matrix[p, t] 1print(confusion matrix, self.matrix)def get_cfm(self):Accuarcy: 正确样本占总样本数量的比例Percision: 精度PrecisionRecall: 召回率Specificaity: 特异性sum_true 0for i in range(self.num_classes):sum_true self.matrix[i, i]acc sum_true / np.sum(self.matrix)print(the model accuracy is , acc)comment_labels [categeory, Precision, Recall, Specificity]tabel PrettyTable()tabel.field_names comment_labelscomment_numpy np.zeros((self.num_classes, 3))for i in range(self.num_classes):# 第i个分类的精确率 召回率 特异度TP self.matrix[i, i]FP np.sum(self.matrix[i, :]) - TPFN np.sum(self.matrix[:, i]) - TPTN np.sum(self.matrix) - TP - FN - FP# 保留三位小数 如果 TP FN 不等于零就计算并将结果四舍五入到小数点后三位否则率设置为0。Precision round(TP / (TP FP), 3) if TP FP ! 0 else 0.Recall round(TP / (TP FN), 3) if TP FN ! 0 else 0.Specificity round(TN / (TN FP), 3) if TN FP ! 0 else 0.tabel.add_row([self.labels[i], Precision, Recall, Specificity])comment_numpy[i] [Precision, Recall, Specificity]print(tabel)return acc, comment_numpyif __name__ __main__:cfm CFM(2, [cat, dog])actual [1, 0, 1, 1, 0, 1, 0, 0, 1, 0]predicted [1, 0, 1, 0, 0, 1, 1, 1, 1, 0]cfm.update(predicted, actual)acc, comment_numpy cfm.get_cfm()print(comment_numpy)cfm.plot()变量名代表得含义
sample_path ../sample/sample.tif #标签图
orgin_path ../datasets/landsat.tif #原始图
pred_path ../datasets/landsat.tif #需要预测的图
txt_Path ./result/label_data.txt #无
labels_name [, tudi, building, veg, water] # 样本名字分类的类别
csv_filename ../result/train_data.csv # 生成训练数据的存放路径
csv_head_name [Band1, Band2, Band3, Band4, Band5, Band6, Band7, Label, LabelName] # 存放格式
model_path ../model/myrnf.pickle # 最终保存的模型路径
pred_result_tif_path ../result/pred_landsat.tif # 用训练的模型保存的路径
color_mapping {1: (255, 255, 0),2: (255, 0, 0),3: (0, 255, 0),4: (0, 0, 255)
}
# 颜色映射从2D标签映射到3D
