209 lines
9.8 KiB
Python
209 lines
9.8 KiB
Python
"""
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@Project ：microproduct
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@File ：BackScatteringXmlInfo.PY
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@Function ：主函数
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@Author ：LMM
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@Date ：2021/10/19 14:39
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@Version ：1.0.0
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"""
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import os
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from xml.etree.ElementTree import ElementTree, Element
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import xml.dom.minidom
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from lxml import etree
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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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from PIL import Image
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class CreateDict:
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"""根据影像信息输出属性字典"""
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def __init__(self, image_path, image_pair, out_path1, out_path2):
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self.ImageHandler = ImageHandler()
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self.para_dict = {}
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self.image_path = image_path
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self.out_path = out_path1
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self.out_path2 = out_path2
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self.image_pair = image_pair
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pass
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def calu_nature(self,start):
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"""存入字典"""
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imageinfo_width=self.ImageHandler.get_img_width(self.image_path)
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self.para_dict.update({"imageinfo_width":imageinfo_width})
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imageinfo_height=self.ImageHandler.get_img_height(self.image_path)
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self.para_dict.update({"imageinfo_height":imageinfo_height})
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# imageinfo_EarthModel=self.ImageHandler.get_projection(self.image_path).split("SPHEROID[", 2)[1].split(",", 2)[0]
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# self.para_dict.update({"imageinfo_EarthModel":imageinfo_EarthModel.replace('"','')})
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self.para_dict.update({"imageinfo_EarthModel": "WGS84"})
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# imageinfo_ProjectModel = self.ImageHandler.get_projection(self.image_path).split("DATUM[", 2)[1].split(",", 2)[0]
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# self.para_dict.update({"imageinfo_ProjectModel":imageinfo_ProjectModel.replace('"','')})
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self.para_dict.update({"imageinfo_ProjectModel": "UTM"})
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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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self.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_path, 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_path)[1] # 投影后的分辨率
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imageinfo_heightspace = -self.ImageHandler.get_geotransform(self.out_path)[5] # 投影后的分辨率
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self.para_dict.update({"imageinfo_widthspace":imageinfo_widthspace})
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self.para_dict.update({"imageinfo_heightspace":imageinfo_heightspace})
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self.para_dict.update({"imageinfo_NearincidenceAngle": "0"})
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self.para_dict.update({"imageinfo_FarIncidenceAngle": "30"})
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self.para_dict.update({"imageinfo_Azimuth": "1"}) # todo 结果xml新增字节
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self.para_dict.update({"NominalResolution":imageinfo_widthspace})
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WidthInMeters = imageinfo_width*imageinfo_widthspace # 投影后的分辨率×宽度
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self.para_dict.update({"WidthInMeters":WidthInMeters})
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# image_array = self.ImageHandler.get_band_array(self.image_path)
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# a2 = np.where(np.isnan(image_array), 999999, image_array)
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# MinValue = np.min(a2)
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# a3 = np.where(np.isnan(image_array), -999999, image_array)
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# MaxValue = np.max(a3)
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#
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# self.para_dict.update({"MaxValue":MaxValue})
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# self.para_dict.update({"MinValue":MinValue})
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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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self.para_dict.update({"imageinfo_corner_topLeft_latitude": point_upleft[1]})
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self.para_dict.update({"imageinfo_corner_topLeft_longitude": point_upleft[0]})
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self.para_dict.update({"imageinfo_corner_topRight_latitude": point_upright[1]})
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self.para_dict.update({"imageinfo_corner_topRight_longitude": point_upright[0]})
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self.para_dict.update({"imageinfo_corner_bottomLeft_latitude": point_downleft[1]})
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self.para_dict.update({"imageinfo_corner_bottomLeft_longitude": point_downleft[0]})
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self.para_dict.update({"imageinfo_corner_bottomRight_latitude": point_downright[1]})
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self.para_dict.update({"imageinfo_corner_bottomRight_longitude": 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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self.para_dict.update({"imageinfo_center_latitude": imageinfo_center_latitude})
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self.para_dict.update({"imageinfo_center_longitude": imageinfo_center_longitude})
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self.para_dict.update({"productType": "GTC"}) # 设置产品类型
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self.para_dict.update({"productFormat": "TIF"})
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productGentime = datetime.datetime.now()
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self.para_dict.update({"productGentime": productGentime})
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msg=productGentime-start
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self.para_dict.update({"productConsumeTime": msg})
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self.para_dict.update({"unit": "none"}) # 设置单位
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self.para_dict.update({"NoDataValue": "nan"})
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self.para_dict.update({"productLevel": "1"}) # 设置图像位深度
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image_array = self.ImageHandler.get_band_array(self.image_path)
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try:
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gdal_dtypes = {
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'int8': gdal.GDT_Byte,
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'unit16': gdal.GDT_UInt16,
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'int16': gdal.GDT_Int16,
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'unit32': gdal.GDT_UInt32,
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'int32': gdal.GDT_Int32,
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'float32': gdal.GDT_Float32,
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'float64': gdal.GDT_Float64,
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}
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bit_dtypes = {
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'int8': 8,
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'unit16': 16,
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'int16': 16,
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'unit32': 32,
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'int32': 32,
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'float32': 32,
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'float64': 64,
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}
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if not gdal_dtypes.get(image_array.dtype.name, None) is None:
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bit_num = str(bit_dtypes[image_array.dtype.name])
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datatype=bit_num+"bit"
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else:
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datatype = str(32) + "bit"
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# datatype = str(gdal.GDT_Float32)+"bit"
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self.para_dict.update({"imagebit": datatype})
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except Exception:
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self.para_dict.update({"imagebit": "None"})
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HH, HV, VH ,VV= self.image_pair[0],self.image_pair[1],self.image_pair[2],self.image_pair[3]
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if HH == 0:
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HH = "delete"
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else:
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HH = "NULL"
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self.para_dict.update({"imageinfo_QualifyValue_HH": HH})
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if HV==0:
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HV = "delete"
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else:
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HV = "NULL"
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self.para_dict.update({"imageinfo_QualifyValue_HV": HV})
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if VH==0:
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VH = "delete"
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else:
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VH = "NULL"
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self.para_dict.update({"imageinfo_QualifyValue_VH": VH})
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if VV==0:
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VV = "delete"
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else:
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VV = "NULL"
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self.para_dict.update({"imageinfo_QualifyValue_VV": VV})
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return self.para_dict
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class CreateStadardXmlFile:
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"""读取字典中的属性值，生成一个标准的xml文件"""
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def __init__(self, xml_path, par_dict, path):
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"""
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par_dict:字典
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path:xml模板输出路径
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"""
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self.par_dict = par_dict
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self.path = path
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shutil.copy(xml_path, 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.path) # 影像头文件
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root = tree.getroot()
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productinfo = root.find("productinfo")
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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).text = str(value)
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elif key.split("_")[0] == "imageinfo":
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imageinfo = productinfo.find("imageinfo")
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if key.split("_")[1] in ["EarthModel", "ProjectModel", "width", "height", "widthspace", "heightspace",
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"NearincidenceAngle", "FarIncidenceAngle", "Azimuth"]: # todo 新增xml字节
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imageinfo.find(key.split("_")[1]).text = str(value)
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elif key.split("_")[1] == "center":
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center = imageinfo.find("center")
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center.find(key.split("_")[2]).text = str(value)
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elif key.split("_")[1] == "corner":
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corner = imageinfo.find("corner")
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corner.find(key.split("_")[2]).find(key.split("_")[3]).text = str(value)
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elif key.split("_")[1] == "QualifyValue":
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QualifyValue = imageinfo.find("QualifyValue")
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if value =="delete":
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element_QualifyValue = list(QualifyValue)
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for i in element_QualifyValue:
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if i.tag == key.split("_")[2]:
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QualifyValue.remove(i)
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else:
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QualifyValue.find(key.split("_")[2]).text = str(value)
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pass
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processinfo = root.find("processinfo")
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tree.write(self.path, encoding="utf-8", xml_declaration=True)
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