""" @Project ：microproduct @File ：BackScatteringXmlInfo.PY @Function ：主函数 @Author ：LMM @Date ：2021/10/19 14:39 @Version ：1.0.0 """ from xml.etree.ElementTree import ElementTree 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 class CreateDict: """根据影像信息输出属性字典""" def __init__(self, image_path, image_pair, out_path1, out_path2): """ 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 """ self.ImageHandler = ImageHandler() self.para_dict = {} self.image_path = image_path self.out_path = out_path1 self.out_path2 = out_path2 self.image_pair = image_pair pass def calu_nature(self, start): """存入字典""" imageinfo_width=self.ImageHandler.get_img_width(self.image_path) self.para_dict.update({"imageinfo_width":imageinfo_width}) imageinfo_height=self.ImageHandler.get_img_height(self.image_path) self.para_dict.update({"imageinfo_height":imageinfo_height}) # imageinfo_EarthModel=self.ImageHandler.get_projection(self.image_path).split("SPHEROID[", 2)[1].split(",", 2)[0] # self.para_dict.update({"imageinfo_EarthModel":imageinfo_EarthModel.replace('"','')}) self.para_dict.update({"imageinfo_EarthModel": "WGS84"}) # imageinfo_ProjectModel = self.ImageHandler.get_projection(self.image_path).split("DATUM[", 2)[1].split(",", 2)[0] # self.para_dict.update({"imageinfo_ProjectModel":imageinfo_ProjectModel.replace('"','')}) self.para_dict.update({"imageinfo_ProjectModel": "UTM"}) 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 keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass if keyword == "GEOGCS": pass elif keyword == "PROJCS": pp.trans_projcs2geogcs(self.out_path2, self.image_path) self.image_path = self.out_path2 elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True: raise Exception('image projection is missing!') pp.trans_geogcs2projcs(self.out_path, self.image_path) # 坐标投影, 地理转平面投影坐标 imageinfo_widthspace = self.ImageHandler.get_geotransform(self.out_path)[1] # 投影后的分辨率 imageinfo_heightspace = -self.ImageHandler.get_geotransform(self.out_path)[5] # 投影后的分辨率 self.para_dict.update({"imageinfo_widthspace":imageinfo_widthspace}) self.para_dict.update({"imageinfo_heightspace":imageinfo_heightspace}) self.para_dict.update({"NominalResolution":imageinfo_widthspace}) WidthInMeters = imageinfo_width*imageinfo_widthspace # 投影后的分辨率×宽度 self.para_dict.update({"WidthInMeters":WidthInMeters}) # image_array = self.ImageHandler.get_band_array(self.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) # # self.para_dict.update({"MaxValue":MaxValue}) # self.para_dict.update({"MinValue":MinValue}) get_scope = self.ImageHandler.get_scope(self.image_path) point_upleft, point_upright, point_downleft, point_downright=get_scope[0], get_scope[1], get_scope[2], get_scope[3] self.para_dict.update({"imageinfo_corner_topLeft_latitude": point_upleft[1]}) self.para_dict.update({"imageinfo_corner_topLeft_longitude": point_upleft[0]}) self.para_dict.update({"imageinfo_corner_topRight_latitude": point_upright[1]}) self.para_dict.update({"imageinfo_corner_topRight_longitude": point_upright[0]}) self.para_dict.update({"imageinfo_corner_bottomLeft_latitude": point_downleft[1]}) self.para_dict.update({"imageinfo_corner_bottomLeft_longitude": point_downleft[0]}) self.para_dict.update({"imageinfo_corner_bottomRight_latitude": point_downright[1]}) self.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 self.para_dict.update({"imageinfo_center_latitude": imageinfo_center_latitude}) self.para_dict.update({"imageinfo_center_longitude": imageinfo_center_longitude}) self.para_dict.update({"productType": "GTC"}) # 设置产品类型 self.para_dict.update({"productFormat": "TIF"}) productGentime = datetime.datetime.now() self.para_dict.update({"productGentime": productGentime}) msg=productGentime-start self.para_dict.update({"productConsumeTime": msg}) self.para_dict.update({"unit": "none"}) # 设置单位 self.para_dict.update({"NoDataValue": "nan"}) self.para_dict.update({"productLevel": "5"}) # noinspection PyBroadException # try: # 设置图像位深度 # imagebit="0bit" # # im=Image.open(self.image_path) # im = Image.open(r"E:\0test\testworkspace\DEM\ASTGTM2_N24E110_dem.tif") # if "1" in im.getbands(): # imagebit="1bit" # elif "L" in im.getbands() or "P" in im.getbands(): # imagebit = "8bit" # elif "R" in im.getbands() and "G" in im.getbands() and "B" in im.getbands(): # imagebit = "24bit" # elif "R" in im.getbands() and "G" in im.getbands() and "B" in im.getbands() and "A" in im.getbands(): # imagebit = "32bit" # elif "C" in im.getbands() and "M" in im.getbands() and "Y" in im.getbands() and "K" in im.getbands(): # imagebit = "32bit" # elif "Y" in im.getbands() and "C" in im.getbands() and "b" in im.getbands() and "C" in im.getbands(): # imagebit = "32bit" # elif "I" in im.getbands() or "F" in im.getbands(): # imagebit = "32bit" image_array = self.ImageHandler.get_band_array(self.image_path) try: 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" self.para_dict.update({"imagebit": datatype}) except Exception: self.para_dict.update({"imagebit": "None"}) self.para_dict.update({"imageinfo_QualifyValue_Product": "NULL"}) return self.para_dict 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, 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.path = path self.para_xml_path= para_xml_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() para_tree = ElementTree() para_tree.parse(self.para_xml_path) # 影像头文件 para_root = para_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") QualifyValue.find(key.split("_")[2]).text = str(value) pass Parameter = root.find("processinfo").find("Parameter") Parameter.find("NdviThresholdMin").text = str(0) Parameter.find("NdviThresholdMax").text = str(0.8) Parameter.find("CoverThresholdMin").text = str(0) Parameter.find("CoverThresholdMax").text = str(185) tree.write(self.path, encoding="utf-8", xml_declaration=True)