205 lines
11 KiB
Python
205 lines
11 KiB
Python
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from xml.etree.ElementTree import ElementTree, Element
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import shutil
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from tool.algorithm.image.ImageHandle import ImageHandler
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from tool.algorithm.algtools.PreProcess import PreProcess as pp
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from osgeo import gdal
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import numpy as np
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import datetime
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import os
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import glob
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os.environ['PROJ_LIB'] = r"E:\soft\Anaconda\envs\micro\Lib\site-packages\osgeo\data\proj"
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class CreateMetaDict:
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def __init__(self, image_path, origin_xml, pack_path, out_path1, out_path2):
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self.ImageHandler = ImageHandler()
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self.image_path = image_path
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self.origin_xml = origin_xml
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self.pack_path = pack_path
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self.file_size = self.get_file_size()
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self.out_path1 = out_path1
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self.out_path2 = out_path2
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self.timeDict = self.get_productTime()
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pass
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def calu_nature(self):
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"""
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将productinfo节点需要填写的信息存入字典中
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image_path:影像路径
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image_pair:输入的压缩包中的极化对 例：hh,hv,vh,vv=【1，1，1，1】
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out_path1：地理转平面的输出路径
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out_path2：平面转地理的输出路径
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"""
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para_dict = {}
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proj = self.ImageHandler.get_projection(self.image_path) # 输出的影像若是投影坐标系则先转成地理坐标系
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keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass
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if keyword == "GEOGCS":
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pass
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elif keyword == "PROJCS":
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pp.trans_projcs2geogcs(self.out_path2, self.image_path)
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image_path = self.out_path2
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elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
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raise Exception('image projection is missing!')
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pp.trans_geogcs2projcs(self.out_path1, self.image_path) # 坐标投影, 地理转平面投影坐标
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imageinfo_widthspace = self.ImageHandler.get_geotransform(self.out_path1)[1] # 投影后的分辨率
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# imageinfo_heightspace = -self.ImageHandler.get_geotransform(out_path1)[5] # 投影后的分辨率
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# para_dict.update({"imageinfo_widthspace": imageinfo_widthspace})
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# para_dict.update({"imageinfo_heightspace": imageinfo_heightspace})
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para_dict.update({"imageinfo_ProductResolution": imageinfo_widthspace})
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para_dict.update({"imageinfo_ProductFormat": "GEOTIFF"})
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para_dict.update({"imageinfo_CompressionMethod": "None"})
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para_dict.update({"imageinfo_ProductSize": str(self.file_size) + "MB"}) #todo 产品总大小
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get_scope = self.ImageHandler.get_scope(self.image_path)
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point_upleft, point_upright, point_downleft, point_downright = get_scope[0], get_scope[1], get_scope[2], get_scope[3]
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para_dict.update({"SpatialCoverageInformation_TopLeftLatitude": point_upleft[1]})
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para_dict.update({"SpatialCoverageInformation_TopLeftLongitude": point_upleft[0]})
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para_dict.update({"SpatialCoverageInformation_TopRightLatitude": point_upright[1]})
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para_dict.update({"SpatialCoverageInformation_TopRightLongitude": point_upright[0]})
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para_dict.update({"SpatialCoverageInformation_BottomLeftLatitude": point_downleft[1]})
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para_dict.update({"SpatialCoverageInformation_BottomLeftLongitude": point_downleft[0]})
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para_dict.update({"SpatialCoverageInformation_BottomRightLatitude": point_downright[1]})
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para_dict.update({"SpatialCoverageInformation_BottomRightLongitude": point_downright[0]})
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longitude_max = np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).max()
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longitude_min = np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).min()
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latitude_max = np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).max()
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latitude_min = np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).min()
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imageinfo_center_latitude = (latitude_max + latitude_min) / 2
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imageinfo_center_longitude = (longitude_max + longitude_min) / 2
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para_dict.update({"SpatialCoverageInformation_CenterLatitude": imageinfo_center_latitude})
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para_dict.update({"SpatialCoverageInformation_CenterLongitude": imageinfo_center_longitude})
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para_dict.update({"TimeCoverageInformation_StartTime": self.timeDict.get("startTime")})
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para_dict.update({"TimeCoverageInformation_CenterTime": self.timeDict.get("centerTime")})
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para_dict.update({"TimeCoverageInformation_EndTime": self.timeDict.get("endTime")})
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para_dict.update({"CoordinateReferenceSystemInformation_EarthEllipsoid": "WGS84"})
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para_dict.update({"CoordinateReferenceSystemInformation_MapProjection": "UTM"})
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para_dict.update({"CoordinateReferenceSystemInformation_ZoneNo": "None"})
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para_dict.update({"MetaInfo_Unit": "none"}) # 设置单位
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para_dict.update({"MetaInfo_UnitDes": "无量纲"}) # 设置单位
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# 补充ProductProductionInfo节信息
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data_name = os.path.basename(self.image_path)
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strs = data_name.split("_")
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para_dict.update({"DataSources_DataSource_Satellite": strs[0]})
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para_dict.update({"DataSources_DataSource_Sensor": strs[0]})
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para_dict.update({"ObservationGeometry_SatelliteAzimuth": "None"})
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para_dict.update({"ObservationGeometry_SatelliteRange": "None"})
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para_dict.update({"ProductProductionInfo_BandSelection": "1"})
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para_dict.update({"ProductProductionInfo_DataSourceDescription": "None"})
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para_dict.update({"ProductProductionInfo_DataSourceProcessingDescription": "参考产品介绍PDF"})
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productGentime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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para_dict.update({"ProductProductionInfo_ProductionDate": productGentime})
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para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": ""})
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# para_dict.update({"ProductPublishInfo_Processor": "德清院"}) # 生产者
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# para_dict.update({"ProductPublishInfo_DistributionUnit": "none"}) # 分发单位
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# para_dict.update({"ProductPublishInfo_ContactInformation": "none"}) # 联系信息
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return para_dict
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def get_productTime(self):
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time_dict = {}
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tree = ElementTree()
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tree.parse(self.origin_xml)
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root = tree.getroot()
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platform = root.find("platform")
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if platform is None:
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centerTime = " "
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else:
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centerTime = platform.find("CenterTime").text.split(".")[0]
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productInfo = root.find("imageinfo")
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imagingTime = productInfo.find("imagingTime")
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if imagingTime is None:
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startTime = " "
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endTime = " "
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else:
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startTime = imagingTime.find("start").text.split(".")[0]
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endTime = imagingTime.find("end").text.split(".")[0]
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time_dict.update({"startTime": startTime})
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time_dict.update({"centerTime": centerTime})
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time_dict.update({"endTime": endTime})
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return time_dict
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def get_file_size(self):
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in_tif_paths = list(glob.glob(os.path.join(self.pack_path, '*.tif')))
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in_tif_paths1 = list(glob.glob(os.path.join(self.pack_path, '*.tiff')))
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in_tif_paths += in_tif_paths1
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size = 0
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for file in in_tif_paths:
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fsize = os.path.getsize(file) # 返回的是字节大小
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size += fsize
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return round(size / float(1024*1024), 2)
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class CreateProductXml:
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def __init__(self, par_dict, model_path, xml_path):
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self.par_dict = par_dict
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self.xml_path = xml_path
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shutil.copy(model_path, xml_path)
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pass
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def create_standard_xml(self):
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"""将字典中的信息写入到copy的xml文件中"""
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tree = ElementTree()
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tree.parse(self.xml_path) # 影像头文件
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root = tree.getroot()
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productinfo = root.find("ProductBasicInfo")
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for key, value in self.par_dict.items():
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if key.split("_")[0] == "imageinfo":
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productinfo.find(key.split("_")[1]).text = str(value)
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elif key.split("_")[0] == "SpatialCoverageInformation":
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imageinfo = productinfo.find("SpatialCoverageInformation")
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imageinfo.find(key.split("_")[1]).text = str(value)
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elif key.split("_")[0] == "TimeCoverageInformation":
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timeInfo = productinfo.find("TimeCoverageInformation")
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timeInfo.find(key.split("_")[1]).text = str(value)
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elif key.split("_")[0] == "CoordinateReferenceSystemInformation":
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geoInfo = productinfo.find("CoordinateReferenceSystemInformation")
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geoInfo.find(key.split("_")[1]).text = str(value)
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elif key.split("_")[0] == "MetaInfo":
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metaInfo = productinfo.find("MetaInfo")
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metaInfo.find(key.split("_")[1]).text = str(value)
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ProductProductionInfo = root.find("ProductProductionInfo") # 原始数据信息
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for key, value in self.par_dict.items():
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if key.split("_")[0] == "DataSources":
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dataSources = ProductProductionInfo.find("DataSources")
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dataSource = dataSources.find("DataSource")
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dataSource.find(key.split("_")[2]).text = str(value)
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elif key.split("_")[0] == "ObservationGeometry":
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ObservationGeometry = ProductProductionInfo.find("ObservationGeometry")
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ObservationGeometry.find(key.split("_")[1]).text = str(value)
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elif key.split("_")[0] == "ProductProductionInfo":
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ProductProductionInfo.find(key.split("_")[1]).text = str(value)
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# ProductPublishInfo = root.find("ProductPublishInfo") # 发布者信息
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# for key, value in self.par_dict.items():
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# if key.split("_")[0] == "ProductPublishInfo":
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# ProductPublishInfo.find(key.split("_")[1]).text = str(value)
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tree.write(self.xml_path, encoding="utf-8", xml_declaration=True)
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if __name__ == '__main__':
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image_path = r'D:\Micro\WorkSpace\test\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1B_h_h_L10000073024_db_RD_geo.tif'
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origin_xml = r'D:\Micro\WorkSpace\Ortho\Temporary\package\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1A_AHV_L10000073024.meta.xml'
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tem_folder = r'D:\Micro\WorkSpace\test'
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pack_path = r'D:\Micro\WorkSpace\Ortho\Temporary\package'
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out_dem_path1 = os.path.join(tem_folder, "trans_dem_geo_projcs.tif")
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out_dem_path2 = os.path.join(tem_folder, "trans_dem_projcs_geo.tif")
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para_dict = CreateMetaDict(image_path, origin_xml, pack_path, out_dem_path1, out_dem_path2).calu_nature()
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model_path = r'D:\Project\microproduct\Ortho\product.xml'
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xml_path = r'D:\Micro\WorkSpace\test\test.xml'
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CreateProductXml(para_dict, model_path, xml_path).create_standard_xml()
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