""" @Project ：microproduct @File ：BackScatteringXmlInfo.PY @Function ：主函数 @Author ：LMM @Date ：2021/10/19 14:39 @Version ：1.0.0 """ import os from xml.etree.ElementTree import ElementTree, Element import xml.dom.minidom from lxml import etree import shutil from tool.algorithm.image.ImageHandle import ImageHandler from tool.algorithm.algtools.PreProcess import PreProcess as pp from osgeo import gdal import numpy as np import datetime from PIL import Image class CreateDict: """根据影像/DEM的属性信息添加到字典中""" def __init__(self): self.ImageHandler = ImageHandler() pass def calu_nature(self, image_path, image_pair, out_path1, out_path2): """ 将productinfo节点需要填写的信息存入字典中 image_path:影像路径 image_pair:输入的压缩包中的极化对 例：hh,hv,vh,vv=【1，1，1，1】 out_path1：地理转平面的输出路径 out_path2：平面转地理的输出路径 """ para_dict = {} imageinfo_width = self.ImageHandler.get_img_width(image_path) para_dict.update({"imageinfo_width":imageinfo_width}) imageinfo_height = self.ImageHandler.get_img_height(image_path) para_dict.update({"imageinfo_height":imageinfo_height}) para_dict.update({"imageinfo_EarthModel": "WGS84"}) para_dict.update({"imageinfo_ProjectModel": "UTM"}) proj = self.ImageHandler.get_projection(image_path) # 输出的影像若是投影坐标系则先转成地理坐标系 keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass if keyword == "GEOGCS": pass elif keyword == "PROJCS": pp.trans_projcs2geogcs(out_path2, image_path) image_path = out_path2 elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True: raise Exception('image projection is missing!') pp.trans_geogcs2projcs(out_path1, image_path) # 坐标投影, 地理转平面投影坐标 imageinfo_widthspace = self.ImageHandler.get_geotransform(out_path1)[1] # 投影后的分辨率 imageinfo_heightspace = -self.ImageHandler.get_geotransform(out_path1)[5] # 投影后的分辨率 para_dict.update({"imageinfo_widthspace":imageinfo_widthspace}) para_dict.update({"imageinfo_heightspace":imageinfo_heightspace}) para_dict.update({"NominalResolution":imageinfo_widthspace}) WidthInMeters = imageinfo_width*imageinfo_widthspace # 投影后的分辨率×宽度 para_dict.update({"WidthInMeters":WidthInMeters}) image_array = self.ImageHandler.get_band_array(image_path) a2 = np.where(np.isnan(image_array), 999999, image_array) MinValue = np.min(a2) a3 = np.where(np.isnan(image_array), -999999, image_array) MaxValue = np.max(a3) para_dict.update({"MaxValue":MaxValue}) para_dict.update({"MinValue":MinValue}) get_scope = self.ImageHandler.get_scope(image_path) point_upleft, point_upright, point_downleft, point_downright=get_scope[0], get_scope[1], get_scope[2], get_scope[3] para_dict.update({"imageinfo_corner_topLeft_latitude": point_upleft[1]}) para_dict.update({"imageinfo_corner_topLeft_longitude": point_upleft[0]}) para_dict.update({"imageinfo_corner_topRight_latitude": point_upright[1]}) para_dict.update({"imageinfo_corner_topRight_longitude": point_upright[0]}) para_dict.update({"imageinfo_corner_bottomLeft_latitude": point_downleft[1]}) para_dict.update({"imageinfo_corner_bottomLeft_longitude": point_downleft[0]}) para_dict.update({"imageinfo_corner_bottomRight_latitude": point_downright[1]}) para_dict.update({"imageinfo_corner_bottomRight_longitude": point_downright[0]}) longitude_max=np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).max() longitude_min=np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).min() latitude_max=np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).max() latitude_min=np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).min() imageinfo_center_latitude=(latitude_max+latitude_min)/2 imageinfo_center_longitude=(longitude_max+longitude_min)/2 para_dict.update({"imageinfo_center_latitude": imageinfo_center_latitude}) para_dict.update({"imageinfo_center_longitude": imageinfo_center_longitude}) # self.para_dict.update({"productType": "GTC"}) # 设置产品类型 para_dict.update({"productFormat": "TIF"}) productGentime = datetime.datetime.now() para_dict.update({"productGentime": productGentime}) para_dict.update({"unit": "none"}) # 设置单位 para_dict.update({"NoDataValue": "nan"}) para_dict.update({"productLevel": "4"}) # 设置图像位深度 image_array = self.ImageHandler.get_band_array(image_path) try: # 设置图像位深度 gdal_dtypes = { 'int8': gdal.GDT_Byte, 'unit16': gdal.GDT_UInt16, 'int16': gdal.GDT_Int16, 'unit32': gdal.GDT_UInt32, 'int32': gdal.GDT_Int32, 'float32': gdal.GDT_Float32, 'float64': gdal.GDT_Float64, } bit_dtypes = { 'int8': 8, 'unit16': 16, 'int16': 16, 'unit32': 32, 'int32': 32, 'float32': 32, 'float64': 64, } if not gdal_dtypes.get(image_array.dtype.name, None) is None: bit_num = str(bit_dtypes[image_array.dtype.name]) datatype=bit_num+"bit" else: datatype = str(32) + "bit" # datatype = str(gdal.GDT_Float32)+"bit" para_dict.update({"imagebit": datatype}) except Exception: para_dict.update({"imagebit": "None"}) HH, HV, VH ,VV= image_pair[0], image_pair[1], image_pair[2], image_pair[3] if HH == 0: HH = "delete" else: HH = "NULL" para_dict.update({"imageinfo_QualifyValue_HH": HH}) if HV==0: HV = "delete" else: HV = "NULL" para_dict.update({"imageinfo_QualifyValue_HV": HV}) if VH==0: VH = "delete" else: VH = "NULL" para_dict.update({"imageinfo_QualifyValue_VH": VH}) if VV==0: VV = "delete" else: VV = "NULL" para_dict.update({"imageinfo_QualifyValue_VV": VV}) return para_dict def calu_dem_nature(self, dem_path, out_dem_path1, out_dem_path2, sampling_f, para_A_arr): """ 正射需要单独加上dem影像的信息 dem_path：mergedDEM.tif路径 out_dem_path1：将mergedDEM.tif由地理坐标转化为平面坐标的保存路径 out_dem_path2：将mergedDEM.tif由平面坐标转化为地理坐标的保存路径 sampling_f： 采样率 para_A_arr：四次多项式模型的参数数组 """ para_dict2 = {} proj = self.ImageHandler.get_projection(dem_path) # 输出的影像若是投影坐标系则先转成地理坐标系 keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass if keyword == "GEOGCS": pass elif keyword == "PROJCS": pp.trans_projcs2geogcs(out_dem_path2, dem_path) dem_path = out_dem_path2 elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True: raise Exception('image projection is missing!') pp.trans_geogcs2projcs(out_dem_path1, dem_path) # 坐标投影, 地理转平面投影坐标 DEM_widthspace = self.ImageHandler.get_geotransform(out_dem_path1)[1] # 投影后的分辨率 DEM_heightspace = -self.ImageHandler.get_geotransform(out_dem_path1)[5] # 投影后的分辨率 para_dict2.update({"DEM_widthspace":DEM_widthspace}) para_dict2.update({"DEM_heightspace":DEM_heightspace}) # tree = ElementTree() # 获取DEMProduct：dem产品来源、DEMDate：dem对应的日期 # tree.parse(dem_meta) # 影像头文件 # root = tree.getroot() # productinfo = root.find("metadata") # DEMProduct = list(productinfo)[0].tag # para_dict2.update({"DEM_DEMProduct":DEMProduct}) # # DEMDate = root.find("metadata").find(DEMProduct).text # para_dict2.update({"DEM_DEMDate": DEMDate}) get_scope = self.ImageHandler.get_scope(dem_path) # 获取mergedDEM.tif数据的四个角的经纬度信息 point_upleft, point_upright, point_downleft, point_downright=get_scope[0], get_scope[1], get_scope[2], get_scope[3] para_dict2.update({"DEM_corner_topLeft_latitude": point_upleft[1]}) para_dict2.update({"DEM_corner_topLeft_longitude": point_upleft[0]}) para_dict2.update({"DEM_corner_topRight_latitude": point_upright[1]}) para_dict2.update({"DEM_corner_topRight_longitude": point_upright[0]}) para_dict2.update({"DEM_corner_bottomLeft_latitude": point_downleft[1]}) para_dict2.update({"DEM_corner_bottomLeft_longitude": point_downleft[0]}) para_dict2.update({"DEM_corner_bottomRight_latitude": point_downright[1]}) para_dict2.update({"DEM_corner_bottomRight_longitude": point_downright[0]}) #para_dict2.update({"orthoModel_samplingrate": sampling_f}) para_dict2.update({"satalliteOrbitModel_parameter_X_a0": para_A_arr[0, 0]}) # 获取四次多项式模型6个参数的数值 para_dict2.update({"satalliteOrbitModel_parameter_X_a1": para_A_arr[1, 0]}) para_dict2.update({"satalliteOrbitModel_parameter_X_a2": para_A_arr[2, 0]}) para_dict2.update({"satalliteOrbitModel_parameter_X_a3": para_A_arr[3, 0]}) para_dict2.update({"satalliteOrbitModel_parameter_X_a4": para_A_arr[4, 0]}) para_dict2.update({"satalliteOrbitModel_parameter_Y_b0": para_A_arr[0, 1]}) para_dict2.update({"satalliteOrbitModel_parameter_Y_b1": para_A_arr[1, 1]}) para_dict2.update({"satalliteOrbitModel_parameter_Y_b2": para_A_arr[2, 1]}) para_dict2.update({"satalliteOrbitModel_parameter_Y_b3": para_A_arr[3, 1]}) para_dict2.update({"satalliteOrbitModel_parameter_Y_b4": para_A_arr[4, 1]}) para_dict2.update({"satalliteOrbitModel_parameter_Z_c0": para_A_arr[0, 2]}) para_dict2.update({"satalliteOrbitModel_parameter_Z_c1": para_A_arr[1, 2]}) para_dict2.update({"satalliteOrbitModel_parameter_Z_c2": para_A_arr[2, 2]}) para_dict2.update({"satalliteOrbitModel_parameter_Z_c3": para_A_arr[3, 2]}) para_dict2.update({"satalliteOrbitModel_parameter_Z_c4": para_A_arr[4, 2]}) para_dict2.update({"satalliteOrbitModel_parameter_Vx_d0": para_A_arr[0, 3]}) para_dict2.update({"satalliteOrbitModel_parameter_Vx_d1": para_A_arr[1, 3]}) para_dict2.update({"satalliteOrbitModel_parameter_Vx_d2": para_A_arr[2, 3]}) para_dict2.update({"satalliteOrbitModel_parameter_Vx_d3": para_A_arr[3, 3]}) para_dict2.update({"satalliteOrbitModel_parameter_Vx_d4": para_A_arr[4, 3]}) para_dict2.update({"satalliteOrbitModel_parameter_Vy_e0": para_A_arr[0, 4]}) para_dict2.update({"satalliteOrbitModel_parameter_Vy_e1": para_A_arr[1, 4]}) para_dict2.update({"satalliteOrbitModel_parameter_Vy_e2": para_A_arr[2, 4]}) para_dict2.update({"satalliteOrbitModel_parameter_Vy_e3": para_A_arr[3, 4]}) para_dict2.update({"satalliteOrbitModel_parameter_Vy_e4": para_A_arr[4, 4]}) para_dict2.update({"satalliteOrbitModel_parameter_Vz_f0": para_A_arr[0, 5]}) para_dict2.update({"satalliteOrbitModel_parameter_Vz_f1": para_A_arr[1, 5]}) para_dict2.update({"satalliteOrbitModel_parameter_Vz_f2": para_A_arr[2, 5]}) para_dict2.update({"satalliteOrbitModel_parameter_Vz_f3": para_A_arr[3, 5]}) para_dict2.update({"satalliteOrbitModel_parameter_Vz_f4": para_A_arr[4, 5]}) return para_dict2 class CreateStadardXmlFile: """读取字典中的属性值，生成一个标准的xml文件""" def __init__(self, xml_path, para_xml_path, par_dict, par_dict2, path): """ xml_path:模板路径 para_xml_path:算法配置文件的路径 par_dict:字典 path:xml模板输出路径 """ self.par_dict = par_dict self.par_dict2 = par_dict2 self.path = path shutil.copy(xml_path, path) pass def create_standard_xml(self): """将字典中的信息写入到copy的xml文件中""" tree = ElementTree() tree.parse(self.path) # 影像头文件 root = tree.getroot() productinfo = root.find("productinfo") for key, value in self.par_dict.items(): if key.split("_")[0] != "imageinfo": productinfo.find(key).text = str(value) elif key.split("_")[0] == "imageinfo": imageinfo = productinfo.find("imageinfo") if key.split("_")[1] in ["EarthModel", "ProjectModel", "width", "height", "widthspace", "heightspace"]: imageinfo.find(key.split("_")[1]).text = str(value) elif key.split("_")[1] == "center": center = imageinfo.find("center") center.find(key.split("_")[2]).text = str(value) elif key.split("_")[1] == "corner": corner = imageinfo.find("corner") corner.find(key.split("_")[2]).find(key.split("_")[3]).text = str(value) elif key.split("_")[1] == "QualifyValue": QualifyValue = imageinfo.find("QualifyValue") if value =="delete": element_QualifyValue = list(QualifyValue) for i in element_QualifyValue: if i.tag == key.split("_")[2]: QualifyValue.remove(i) else: QualifyValue.find(key.split("_")[2]).text = str(value) pass orthoModel = root.find("processinfo").find("orthoModel") # 写入四次多项式模型 for key, value in self.par_dict2.items(): if key.split("_")[0] == "satalliteOrbitModel": satalliteOrbitModel = orthoModel.find("satalliteOrbitModel") satalliteOrbitModel.find(key.split("_")[1]).find(key.split("_")[2]).find(key.split("_")[3]).text = str(value) elif key.split("_")[0] == "DEM": # 写入dem四个角坐标 DEM= orthoModel.find("DEM") if key.split("_")[1] == "corner": corner = DEM.find("corner") corner.find(key.split("_")[2]).find(key.split("_")[3]).text = str(value) elif key.split("_")[1] == "widthspace" or key.split("_")[1] == "heightspace": DEM.find(key.split("_")[1]).text = str(value) elif key.split("_")[1] == "samplingrate": orthoModel.find(key.split("_")[1]).text = str(value) tree.write(self.path, encoding="utf-8", xml_declaration=True) # # if __name__ == '__main__': # # xml_path = "./model_meta.xml" # tem_folder= r"E:\microproduct\测试" # image_path = r"E:\microproduct\测试\GF3_MYN_QPSI_011437_E98_HH_AtmosphericDelay.tif" # 输入影像 # out_path = os.path.join(tem_folder, "trans_geo_projcs.tif") # image_pair=[1, 1, 1, 0] # par_dict = CreateDict(image_path, image_pair, out_path).calu_nature() # 计算属性字典 # # out_xml_path = os.path.join(tem_folder, "creat_standard.meta.xml") # 输出xml路径 # CreateStadardXmlFile(xml_path, par_dict, out_xml_path).create_standard_xml()