更新产品xml格式
tian jiax
parent
82de4b4dc2
commit
db14c51ee7
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<Root>
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<ProductBasicInfo>
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<ProductName>后向散射系数</ProductName>
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<ProductIdentifier>BackScattering</ProductIdentifier>
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<ProductLevel>5</ProductLevel>
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<ProductResolution> </ProductResolution>
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<ProductDate> </ProductDate>
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<ProductFormat> </ProductFormat>
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<CompressionMethod> </CompressionMethod>
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<ProductSize> </ProductSize>
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<SpatialCoverageInformation>
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<TopLeftLatitude> </TopLeftLatitude>
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<TopLeftLongitude> </TopLeftLongitude>
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<TopRightLatitude> </TopRightLatitude>
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<TopRightLongitude> </TopRightLongitude>
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<BottomRightLatitude> </BottomRightLatitude>
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<BottomRightLongitude> </BottomRightLongitude>
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<BottomLeftLatitude> </BottomLeftLatitude>
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<BottomLeftLongitude> </BottomLeftLongitude>
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<CenterLatitude> </CenterLatitude>
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<CenterLongitude> </CenterLongitude>
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</SpatialCoverageInformation>
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<TimeCoverageInformation>
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<StartTime> </StartTime>
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<EndTime> </EndTime>
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<CenterTime> </CenterTime>
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</TimeCoverageInformation>
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<CoordinateReferenceSystemInformation>
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<MapProjection> </MapProjection>
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<EarthEllipsoid> </EarthEllipsoid>
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<ZoneNo> </ZoneNo>
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</CoordinateReferenceSystemInformation>
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<MetaInfo>
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<Unit> </Unit>
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<UnitDes> </UnitDes>
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</MetaInfo>
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</ProductBasicInfo>
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<ProductProductionInfo>
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<DataSources number="1">
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<DataSource>
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<Satellite> </Satellite>
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<Sensor> </Sensor>
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</DataSource>
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</DataSources>
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<ObservationGeometry>
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<SatelliteAzimuth> </SatelliteAzimuth>
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<SatelliteRange> </SatelliteRange>
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</ObservationGeometry>
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<BandSelection>1</BandSelection>
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<DataSourceDescription>None</DataSourceDescription>
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<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
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<ProductionDate> </ProductionDate>
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<AuxiliaryDataDescription> </AuxiliaryDataDescription>
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</ProductProductionInfo>
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<ProductPublishInfo>
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<Processor>德清</Processor>
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<DistributionUnit> </DistributionUnit>
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<ContactInformation> </ContactInformation>
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</ProductPublishInfo>
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</Root>
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#
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# 样本处理的相关的库
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#
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from tool.algorithm.image.ImageHandle import ImageHandler
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import math
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import numpy as np
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import random
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import scipy
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# 最小二乘求解非线性方程组
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from scipy.optimize import leastsq,fsolve,root
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from osgeo import gdal,gdalconst
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import pandas as pds
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from scipy import interpolate
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from multiprocessing import pool
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# 常量声明区域
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imageHandler=ImageHandler()
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# python 的函数类
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def read_sample_csv(csv_path):
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""" 读取样本的csv
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Args:
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csv_path (string): 样本csv的地址，绝对路径
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return:
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[
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['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数"],
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['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数"],......
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]
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"""
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lai_csv=pds.read_csv(csv_path)# 代码测试区域
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lai_csv=lai_csv.loc[:,['id','lon','lat','leaf',"cal"]]
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result=[]
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for i in range(len(lai_csv)):
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result.append([
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0,
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lai_csv.loc[i,'id'],
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lai_csv.loc[i,'lon'], # lon,x
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lai_csv.loc[i,'lat'], # lat,y
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lai_csv.loc[i,'leaf'],
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10**(float(lai_csv.loc[i,'cal'])/10),
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])
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return result
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def read_tiff(tiff_path):
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""" 从文件中读取影像
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Args:
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tiff_path (string): 文件影像路径
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"""
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im_proj, im_geotrans, im_arr=imageHandler.read_img(tiff_path)
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return {
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'proj':im_proj,
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'geotrans':im_geotrans,
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'data':im_arr
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}
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def ReprojectImages2(in_tiff_path,ref_tiff_path,out_tiff_path,resampleAlg=gdalconst.GRA_Bilinear):
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""" 将输入影像重采样到参考影像的范围内
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Args:
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in_tiff_path (string): 输入影像
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ref_tiff_path (string): 参考影像
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out_tiff_path (string): 输出地址
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resampleAlg (gadlconst): 插值方法
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"""
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# 若采用gdal.Warp()方法进行重采样
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# 获取输出影像信息
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inputrasfile = gdal.Open(in_tiff_path, gdal.GA_ReadOnly)
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inputProj = inputrasfile.GetProjection()
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# 获取参考影像信息
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referencefile = gdal.Open(ref_tiff_path, gdal.GA_ReadOnly)
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referencefileProj = referencefile.GetProjection()
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referencefileTrans = referencefile.GetGeoTransform()
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bandreferencefile = referencefile.GetRasterBand(1)
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x = referencefile.RasterXSize
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y = referencefile.RasterYSize
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nbands = referencefile.RasterCount
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# 创建重采样输出文件（设置投影及六参数）
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driver = gdal.GetDriverByName('GTiff')
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output = driver.Create(out_tiff_path, x, y, nbands, bandreferencefile.DataType)
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output.SetGeoTransform(referencefileTrans)
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output.SetProjection(referencefileProj)
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options = gdal.WarpOptions(srcSRS=inputProj, dstSRS=referencefileProj, resampleAlg=gdalconst.GRA_Bilinear)
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gdal.Warp(output, in_tiff_path, options=options)
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def combine_sample_attr(sample_list,attr_tiff):
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""" 构建样本
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Args:
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sample_list (list): 原样本
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attr_tiff (string): 添加的属性数据
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Returns:
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list:[sample,new_attr]
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"""
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result=[]
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# 因为soil_tiff 的影像的 影像分辨率较低
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inv_gt=gdal.InvGeoTransform(attr_tiff['geotrans'])
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for sample_item in sample_list:
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sample_lon=sample_item[2]
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sample_lat=sample_item[3]
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sample_in_tiff_x=inv_gt[0]+inv_gt[1]*sample_lon+inv_gt[2]*sample_lat # x
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sample_in_tiff_y=inv_gt[3]+inv_gt[4]*sample_lon+inv_gt[5]*sample_lat # y
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x_min=int(np.floor(sample_in_tiff_x))
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x_max=int(np.ceil(sample_in_tiff_x))
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y_min=int(np.floor(sample_in_tiff_y))
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y_max=int(np.ceil(sample_in_tiff_y))
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if x_min<0 or y_min<0 or x_max>=attr_tiff['data'].shape[1] or y_max>=attr_tiff['data'].shape[0]:
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continue
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#
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"""
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f = interpolate.interp2d([0,0,1,1], [0,1,1,0],
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[attr_tiff['data'][y_min,x_min],
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attr_tiff['data'][y_max,x_min],
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attr_tiff['data'][y_max,x_max],
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attr_tiff['data'][y_min,x_min]
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], kind='linear')
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interp_value=f(sample_in_tiff_x-x_min,sample_in_tiff_y-y_min)
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sample_item.append(interp_value[0])
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"""
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# 9x9
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x_min=x_min-4 if x_min-9>=0 else 0
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y_min=y_min-4 if y_min-9>=0 else 0
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x_max=x_max+4 if x_max+4<attr_tiff['data'].shape[1] else attr_tiff['data'].shape[1]
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y_max=y_max+4 if y_max+4<attr_tiff['data'].shape[0] else attr_tiff['data'].shape[0]
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interp_value=np.mean(attr_tiff['data'][y_min:y_max,x_min:x_max])
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sample_item.append(interp_value)
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result.append(sample_item)
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return result
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def check_sample(sample_list):
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""" 检查样本值
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Args:
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sample_list (list): 样本值：[ ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量','入射角','后向散射系数'] ]
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Returns:
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list : 处理之后的样本值
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"""
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result=[]
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for item in sample_list:
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if len(item)==10:
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sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma,sample_NDVI=item
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else:
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sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma=item
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if sample_sigma<=0:
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continue
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if (sample_inc*180/np.pi)>90:
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continue
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if sample_soil<=0 or sample_soil>=1:
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continue
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if sample_lai<=0 or sample_lai>=20:
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continue
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result.append(item)
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# 绘制分布图
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# lai=[]
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# sigma=[]
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# csv_sigmas=[]
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# text_label=[]
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# for item in result:
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# if len(item)==10:
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# sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma,sample_NDVI=item
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# else:
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# sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma=item
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# text_label.append(sample_code)
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# lai.append(sample_lai)
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# sigma.append(sample_sigma)
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# csv_sigmas.append(csv_sigma)
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# from matplotlib import pyplot as plt
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# plt.scatter(np.array(lai),np.array(sigma),label="lai-tiff_sigma")
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# for i in range(len(sigma)):
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# plt.annotate(text_label[i], xy = (lai[i], sigma[i])) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
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#
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# plt.scatter(np.array(lai),np.array(csv_sigmas),label="lai-csv_sigmas")
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# for i in range(len(csv_sigmas)):
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# plt.annotate(text_label[i], xy = (lai[i],csv_sigmas[i])) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
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# plt.legend()
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# plt.show()
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return result
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def split_sample_list(sample_list,train_ratio):
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""" 切分样本比值
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Args:
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sample_list (list): 样本列表
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train_ratio (double): 训练样本的比重
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Returns:
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list: [sample_train,sample_test]
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"""
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sample_train=[]
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sample_test=[]
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n=len(sample_list)
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for i in range(n):
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if random.random()<=train_ratio:
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sample_train.append(sample_list[i])
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else:
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sample_test.append(sample_list[i])
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return [sample_train,sample_test]
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@ -249,7 +249,7 @@ class MoistureMain:
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bp = BlockProcess()
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# block_size = bp.get_block_size(self.__rows, self.__cols,block_size_config)
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block_size = bp.get_block_size(self.__rows, self.__cols)
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bp.cut(self.__workspace_preprocessed_path, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
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bp.cut_new(self.__workspace_preprocessed_path, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
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logger.info('blocking tifs success!')
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img_dir, img_name = bp.get_file_names(self.__workspace_block_tif_path, ['tif'])
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@ -339,7 +339,7 @@ class MoistureMain:
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h = ImageHandler.get_img_height(self.__preprocessed_paras['HH'])
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out_path = self.__workspace_processing_path[0:-1]
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tif_type = bp.get_tif_dtype(self.__preprocessed_paras['HH'])
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bp.combine(data_dir, w, h, out_path, file_type=['tif'], datetype=tif_type)
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bp.combine_new(data_dir, w, h, out_path, file_type=['tif'], datetype=tif_type)
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# 添加地理信息
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soil_moisture_path = self.__workspace_processing_path + 'soil_moisture.tif'
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<Root>
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<ProductBasicInfo>
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<ProductName>后向散射系数</ProductName>
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<ProductIdentifier>BackScattering</ProductIdentifier>
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<ProductLevel>5</ProductLevel>
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<ProductResolution> </ProductResolution>
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<ProductDate> </ProductDate>
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<ProductFormat> </ProductFormat>
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<CompressionMethod> </CompressionMethod>
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<ProductSize> </ProductSize>
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<SpatialCoverageInformation>
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<TopLeftLatitude> </TopLeftLatitude>
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<TopLeftLongitude> </TopLeftLongitude>
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<TopRightLatitude> </TopRightLatitude>
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<TopRightLongitude> </TopRightLongitude>
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<BottomRightLatitude> </BottomRightLatitude>
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<BottomRightLongitude> </BottomRightLongitude>
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<BottomLeftLatitude> </BottomLeftLatitude>
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<BottomLeftLongitude> </BottomLeftLongitude>
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<CenterLatitude> </CenterLatitude>
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<CenterLongitude> </CenterLongitude>
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</SpatialCoverageInformation>
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<TimeCoverageInformation>
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<StartTime> </StartTime>
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<EndTime> </EndTime>
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<CenterTime> </CenterTime>
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</TimeCoverageInformation>
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<CoordinateReferenceSystemInformation>
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<MapProjection> </MapProjection>
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<EarthEllipsoid> </EarthEllipsoid>
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<ZoneNo> </ZoneNo>
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</CoordinateReferenceSystemInformation>
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<MetaInfo>
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<Unit> </Unit>
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<UnitDes> </UnitDes>
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</MetaInfo>
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</ProductBasicInfo>
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<ProductProductionInfo>
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<DataSources number="1">
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<DataSource>
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<Satellite> </Satellite>
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<Sensor> </Sensor>
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</DataSource>
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</DataSources>
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<ObservationGeometry>
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<SatelliteAzimuth> </SatelliteAzimuth>
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<SatelliteRange> </SatelliteRange>
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</ObservationGeometry>
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<BandSelection>1</BandSelection>
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<DataSourceDescription>None</DataSourceDescription>
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<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
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<ProductionDate> </ProductionDate>
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<AuxiliaryDataDescription> </AuxiliaryDataDescription>
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</ProductProductionInfo>
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<ProductPublishInfo>
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<Processor>德清</Processor>
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<DistributionUnit> </DistributionUnit>
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<ContactInformation> </ContactInformation>
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</ProductPublishInfo>
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</Root>
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Binary file not shown.
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@ -0,0 +1,60 @@
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<Root>
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<ProductBasicInfo>
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<ProductName>后向散射系数</ProductName>
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<ProductIdentifier>BackScattering</ProductIdentifier>
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<ProductLevel>5</ProductLevel>
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<ProductResolution> </ProductResolution>
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<ProductDate> </ProductDate>
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<ProductFormat> </ProductFormat>
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<CompressionMethod> </CompressionMethod>
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<ProductSize> </ProductSize>
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<SpatialCoverageInformation>
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<TopLeftLatitude> </TopLeftLatitude>
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<TopLeftLongitude> </TopLeftLongitude>
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<TopRightLatitude> </TopRightLatitude>
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<TopRightLongitude> </TopRightLongitude>
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<BottomRightLatitude> </BottomRightLatitude>
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<BottomRightLongitude> </BottomRightLongitude>
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<BottomLeftLatitude> </BottomLeftLatitude>
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<BottomLeftLongitude> </BottomLeftLongitude>
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<CenterLatitude> </CenterLatitude>
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<CenterLongitude> </CenterLongitude>
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</SpatialCoverageInformation>
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<TimeCoverageInformation>
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<StartTime> </StartTime>
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<EndTime> </EndTime>
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<CenterTime> </CenterTime>
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</TimeCoverageInformation>
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<CoordinateReferenceSystemInformation>
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<MapProjection> </MapProjection>
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<EarthEllipsoid> </EarthEllipsoid>
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<ZoneNo> </ZoneNo>
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</CoordinateReferenceSystemInformation>
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<MetaInfo>
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<Unit> </Unit>
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<UnitDes> </UnitDes>
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</MetaInfo>
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</ProductBasicInfo>
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<ProductProductionInfo>
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<DataSources number="1">
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<DataSource>
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<Satellite> </Satellite>
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<Sensor> </Sensor>
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</DataSource>
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</DataSources>
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<ObservationGeometry>
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<SatelliteAzimuth> </SatelliteAzimuth>
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<SatelliteRange> </SatelliteRange>
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</ObservationGeometry>
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<BandSelection>1</BandSelection>
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<DataSourceDescription>None</DataSourceDescription>
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<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
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<ProductionDate> </ProductionDate>
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<AuxiliaryDataDescription> </AuxiliaryDataDescription>
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</ProductProductionInfo>
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<ProductPublishInfo>
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<Processor>德清</Processor>
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<DistributionUnit> </DistributionUnit>
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<ContactInformation> </ContactInformation>
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</ProductPublishInfo>
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</Root>
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@ -0,0 +1,60 @@
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<Root>
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<ProductBasicInfo>
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<ProductName>后向散射系数</ProductName>
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<ProductIdentifier>BackScattering</ProductIdentifier>
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<ProductLevel>5</ProductLevel>
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<ProductResolution> </ProductResolution>
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<ProductDate> </ProductDate>
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<ProductFormat> </ProductFormat>
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<CompressionMethod> </CompressionMethod>
|
||||
<ProductSize> </ProductSize>
|
||||
<SpatialCoverageInformation>
|
||||
<TopLeftLatitude> </TopLeftLatitude>
|
||||
<TopLeftLongitude> </TopLeftLongitude>
|
||||
<TopRightLatitude> </TopRightLatitude>
|
||||
<TopRightLongitude> </TopRightLongitude>
|
||||
<BottomRightLatitude> </BottomRightLatitude>
|
||||
<BottomRightLongitude> </BottomRightLongitude>
|
||||
<BottomLeftLatitude> </BottomLeftLatitude>
|
||||
<BottomLeftLongitude> </BottomLeftLongitude>
|
||||
<CenterLatitude> </CenterLatitude>
|
||||
<CenterLongitude> </CenterLongitude>
|
||||
</SpatialCoverageInformation>
|
||||
<TimeCoverageInformation>
|
||||
<StartTime> </StartTime>
|
||||
<EndTime> </EndTime>
|
||||
<CenterTime> </CenterTime>
|
||||
</TimeCoverageInformation>
|
||||
<CoordinateReferenceSystemInformation>
|
||||
<MapProjection> </MapProjection>
|
||||
<EarthEllipsoid> </EarthEllipsoid>
|
||||
<ZoneNo> </ZoneNo>
|
||||
</CoordinateReferenceSystemInformation>
|
||||
<MetaInfo>
|
||||
<Unit> </Unit>
|
||||
<UnitDes> </UnitDes>
|
||||
</MetaInfo>
|
||||
</ProductBasicInfo>
|
||||
<ProductProductionInfo>
|
||||
<DataSources number="1">
|
||||
<DataSource>
|
||||
<Satellite> </Satellite>
|
||||
<Sensor> </Sensor>
|
||||
</DataSource>
|
||||
</DataSources>
|
||||
<ObservationGeometry>
|
||||
<SatelliteAzimuth> </SatelliteAzimuth>
|
||||
<SatelliteRange> </SatelliteRange>
|
||||
</ObservationGeometry>
|
||||
<BandSelection>1</BandSelection>
|
||||
<DataSourceDescription>None</DataSourceDescription>
|
||||
<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
|
||||
<ProductionDate> </ProductionDate>
|
||||
<AuxiliaryDataDescription> </AuxiliaryDataDescription>
|
||||
</ProductProductionInfo>
|
||||
<ProductPublishInfo>
|
||||
<Processor>德清</Processor>
|
||||
<DistributionUnit> </DistributionUnit>
|
||||
<ContactInformation> </ContactInformation>
|
||||
</ProductPublishInfo>
|
||||
</Root>
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
<Root>
|
||||
<ProductBasicInfo>
|
||||
<ProductName>后向散射系数</ProductName>
|
||||
<ProductIdentifier>BackScattering</ProductIdentifier>
|
||||
<ProductLevel>5</ProductLevel>
|
||||
<ProductResolution> </ProductResolution>
|
||||
<ProductDate> </ProductDate>
|
||||
<ProductFormat> </ProductFormat>
|
||||
<CompressionMethod> </CompressionMethod>
|
||||
<ProductSize> </ProductSize>
|
||||
<SpatialCoverageInformation>
|
||||
<TopLeftLatitude> </TopLeftLatitude>
|
||||
<TopLeftLongitude> </TopLeftLongitude>
|
||||
<TopRightLatitude> </TopRightLatitude>
|
||||
<TopRightLongitude> </TopRightLongitude>
|
||||
<BottomRightLatitude> </BottomRightLatitude>
|
||||
<BottomRightLongitude> </BottomRightLongitude>
|
||||
<BottomLeftLatitude> </BottomLeftLatitude>
|
||||
<BottomLeftLongitude> </BottomLeftLongitude>
|
||||
<CenterLatitude> </CenterLatitude>
|
||||
<CenterLongitude> </CenterLongitude>
|
||||
</SpatialCoverageInformation>
|
||||
<TimeCoverageInformation>
|
||||
<StartTime> </StartTime>
|
||||
<EndTime> </EndTime>
|
||||
<CenterTime> </CenterTime>
|
||||
</TimeCoverageInformation>
|
||||
<CoordinateReferenceSystemInformation>
|
||||
<MapProjection> </MapProjection>
|
||||
<EarthEllipsoid> </EarthEllipsoid>
|
||||
<ZoneNo> </ZoneNo>
|
||||
</CoordinateReferenceSystemInformation>
|
||||
<MetaInfo>
|
||||
<Unit> </Unit>
|
||||
<UnitDes> </UnitDes>
|
||||
</MetaInfo>
|
||||
</ProductBasicInfo>
|
||||
<ProductProductionInfo>
|
||||
<DataSources number="1">
|
||||
<DataSource>
|
||||
<Satellite> </Satellite>
|
||||
<Sensor> </Sensor>
|
||||
</DataSource>
|
||||
</DataSources>
|
||||
<ObservationGeometry>
|
||||
<SatelliteAzimuth> </SatelliteAzimuth>
|
||||
<SatelliteRange> </SatelliteRange>
|
||||
</ObservationGeometry>
|
||||
<BandSelection>1</BandSelection>
|
||||
<DataSourceDescription>None</DataSourceDescription>
|
||||
<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
|
||||
<ProductionDate> </ProductionDate>
|
||||
<AuxiliaryDataDescription> </AuxiliaryDataDescription>
|
||||
</ProductProductionInfo>
|
||||
<ProductPublishInfo>
|
||||
<Processor>德清</Processor>
|
||||
<DistributionUnit> </DistributionUnit>
|
||||
<ContactInformation> </ContactInformation>
|
||||
</ProductPublishInfo>
|
||||
</Root>
|
||||
|
|
@ -0,0 +1,79 @@
|
|||
#encoding=utf-8
|
||||
import xml.etree.ElementTree as ET
|
||||
import pandas as pd
|
||||
import csv
|
||||
|
||||
def xml2csv(xmlpath):
|
||||
tree_obj = ET.parse(xmlpath)
|
||||
|
||||
# 得到所有匹配Region 标签的Element对象的list集合
|
||||
list_Region = tree_obj.findall("Region")
|
||||
for Region in list_Region:
|
||||
# 几何面对应的类(phenology_name)在标签<Region name="water" color="255,0,0">
|
||||
Region_dict = Region.attrib
|
||||
phenology_name = Region_dict.get("name")
|
||||
print(phenology_name)
|
||||
list_GeometryDef = Region.findall("GeometryDef")
|
||||
list_Polygon = list_GeometryDef[0].findall("Polygon") # 获得该类下的几何面list
|
||||
for polygon in list_Polygon:
|
||||
# 从polygon list中获取得到<Coordinates>标签的数据 注意是空格分隔的和csv中不同
|
||||
Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
|
||||
# POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
|
||||
print("value")
|
||||
|
||||
# 向csv中写
|
||||
def csvfile(csvpath,data):
|
||||
|
||||
with open(csvpath, 'a', newline='') as file:
|
||||
# 2. step
|
||||
writer = csv.writer(file)
|
||||
# data example
|
||||
#data = ["This", "is", "a", "Test"]
|
||||
writer.writerow(data)
|
||||
|
||||
|
||||
# Define the structure of the data
|
||||
#data示例 student_header = ['name', 'age', 'major', 'minor']
|
||||
def csvcreateTitile(csvpath,data):
|
||||
# 1. Open a new CSV file
|
||||
with open(csvpath, 'w') as file:
|
||||
# 2. Create a CSV writer
|
||||
writer = csv.writer(file)
|
||||
# 3. Write data to the file
|
||||
writer.writerow(data)
|
||||
|
||||
# 将列表中的坐标对转换为字符串
|
||||
def createcsv_roi_polygon(coordinates):
|
||||
coord_str = ', '.join([f'{coordinates[i]} {coordinates[i + 1]}' for i in range(0, len(coordinates), 2)])
|
||||
# 构建最终的POLYGON字符串
|
||||
polygon_str = f'POLYGON(({coord_str}))'
|
||||
print(polygon_str)
|
||||
return polygon_str
|
||||
|
||||
if __name__ == '__main__':
|
||||
xmlpath = r"E:\MicroWorkspace\GF3A_nanjing\input-ortho\test_shp\test.xml"
|
||||
|
||||
tree_obj = ET.parse(xmlpath)
|
||||
csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
|
||||
csvpath = r"E:\MicroWorkspace\GF3A_nanjing\input-ortho\test_shp\test.csv"
|
||||
# csvcreateTitile(csvpath,csv_header)
|
||||
csvfile(csvpath,csv_header)
|
||||
# 得到所有匹配Region 标签的Element对象的list集合
|
||||
list_Region = tree_obj.findall("Region")
|
||||
for Region in list_Region:
|
||||
# 几何面对应的类(phenology_name)在标签<Region name="water" color="255,0,0">
|
||||
Region_dict = Region.attrib
|
||||
phenology_name = Region_dict.get("name")
|
||||
print(phenology_name)
|
||||
# list_GeometryDef = Region.findall("GeometryDef")
|
||||
list_Polygon = Region.findall(".//Polygon") # 获得该类下的几何面list
|
||||
count = 1
|
||||
for polygon in list_Polygon:
|
||||
# 从polygon list中获取得到<Coordinates>标签的数据 注意是空格分隔的和csv中不同
|
||||
Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
|
||||
# 将坐标和ploygon对应的写入到.csv中
|
||||
polygon_str=createcsv_roi_polygon(Coordinates_list)
|
||||
# POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
|
||||
data = ['0', count, phenology_name, polygon_str]
|
||||
csvfile(csvpath,data)
|
||||
count += 1
|
||||
Loading…
Reference in New Issue