# -*- coding: UTF-8 -*- """ @Project ：microproduct @File ：SoilMoistureMain.py @Author ：SHJ @Contact： @Date ：2021/7/26 17:11 @Version ：1.0.0 修改历史： [修改序列] [修改日期] [修改者] [修改内容] 1 2022-6-30 石海军 1.使用cover_roi_id来选取ROI区域; 2.内部处理使用地理坐标系(4326)； """ import glob from osgeo import osr from tool.algorithm.algtools.PreProcess import PreProcess as pp from tool.algorithm.transforml1a.transHandle import TransImgL1A from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource,InitPara # 导入xml文件读取与检查文件 from tool.algorithm.image.ImageHandle import ImageHandler from tool.algorithm.algtools.logHandler import LogHandler from SoilMoistureALg import MoistureAlg as alg from tool.algorithm.block.blockprocess import BlockProcess from tool.algorithm.algtools.MetaDataHandler import MetaDataHandler from tool.config.ConfigeHandle import Config as cf from tool.algorithm.xml.CreatMetafile import CreateMetafile from tool.algorithm.algtools.ROIAlg import ROIAlg as roi from SoilMoistureXmlInfo import CreateDict, CreateStadardXmlFile from tool.algorithm.algtools.filter.lee_Filter import Filter from tool.file.fileHandle import fileHandle from SoilMoistureTool import SoilMoistureTool import logging import os import shutil import datetime import numpy as np import sys import multiprocessing cover_id_list = [] threshold_of_ndvi_min = 0 threshold_of_ndvi_max = 0 multiprocessing_num = int(cf.get('multiprocessing_num')) if cf.get('debug') == 'True': DEBUG = True else: DEBUG = False file =fileHandle(DEBUG) EXE_NAME = cf.get('exe_name') FILTER_SIZE = int(cf.get('filter_size')) soil_moisture_value_min = float(cf.get('product_value_min')) soil_moisture_value_max = float(cf.get('product_value_max')) LogHandler.init_log_handler('run_log\\' + EXE_NAME) logger = logging.getLogger("mylog") env_str = os.path.split(os.path.realpath(__file__))[0] os.environ['PROJ_LIB'] = env_str class MoistureMain: """ 土壤水分处理主函数 """ def __init__(self, alg_xml_path): self.alg_xml_path = alg_xml_path self.imageHandler = ImageHandler() self.__alg_xml_handler = ManageAlgXML(alg_xml_path) self.__check_handler = CheckSource(self.__alg_xml_handler) self.__workspace_path, self.__out_para = None, None self.__input_paras, self.__output_paras, self.__processing_paras, self.__preprocessed_paras = {},{},{},{} # 参考影像路径 self.__ref_img_path = '' # 宽/列数,高/行数 self.__cols, self.__rows = 0, 0 # 坐标系 self.__proj = '' # 影像投影变换矩阵 self.__geo = [0, 0, 0, 0, 0, 0] self.name = '' def check_source(self): """ 检查算法相关的配置文件，图像，辅助文件是否齐全 """ env_str = os.getcwd() logger.info("sysdir: %s", env_str) if self.__check_handler.check_alg_xml() is False: return False if self.__check_handler.check_run_env() is False: return False input_para_names = ['box', 'DualPolarSAR'] if self.__check_handler.check_input_paras(input_para_names) is False: return False self.__workspace_path = self.__alg_xml_handler.get_workspace_path() self.__create_work_space() self.__input_paras = self.__alg_xml_handler.get_input_paras() self.__processing_paras = InitPara.init_processing_paras(self.__input_paras) self.__processing_paras.update(self.get_tar_gz_inf(self.__processing_paras["sar_path0"])) self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', r"SurfaceRoughnessProduct.tar.gz") self.__alg_xml_handler.write_out_para("SurfaceRoughnessProduct", self.__out_para) #写入输出参数 logger.info('check_source success!') logger.info('progress bar: 10%') return True def get_tar_gz_inf(self, tar_gz_path): para_dic = {} self.name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz') name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz') file_dir = os.path.join(self.__workspace_preprocessing_path, name + '\\') file.de_targz(tar_gz_path, file_dir) # 元文件字典 # para_dic.update(InitPara.get_meta_dic(InitPara.get_meta_paths(file_dir, name), name)) para_dic.update(InitPara.get_meta_dic_new(InitPara.get_meta_paths(file_dir, name), name)) # tif路径字典 pol_dic = InitPara.get_polarization_mode(InitPara.get_tif_paths(file_dir, name)) flag_list = [0, 0, 0, 0] for key, in_tif_path in pol_dic.items(): # 获取极化类型 if 'HH' == key: para_dic.update({'HH': in_tif_path}) flag_list[0] = 1 elif 'HV' == key: para_dic.update({'HV': in_tif_path}) # 如果没有VV，用HV代替 flag_list[1] = 1 elif 'VV' == key: para_dic.update({'VV': in_tif_path}) flag_list[2] = 1 elif 'VH' == key: para_dic.update({'VH': in_tif_path}) flag_list[3] = 1 elif 'inc_angle' == key: para_dic.update({'inc_angle': in_tif_path}) elif 'ori_sim' == key: para_dic.update({'ori_sim': in_tif_path}) elif 'LocalIncidenceAngle' == key: para_dic.update({'LocalIncidenceAngle': in_tif_path}) elif 'inci_Angle-ortho' == key: para_dic.update({'inci_Angle-ortho': in_tif_path}) elif 'LocalIncidentAngle-ortho' == key: para_dic.update({'LocalIncidentAngle-ortho': in_tif_path}) if flag_list != [1, 1, 1, 1]: raise Exception('There are not tar_gz path: HH、HV(VV)、IncidenceAngle in path:', tar_gz_path) self.processinfo = flag_list return para_dic def __create_work_space(self): """ 删除原有工作区文件夹,创建新工作区文件夹 """ self.__workspace_preprocessing_path = self.__workspace_path + EXE_NAME + r'\Temporary\preprocessing''\\' self.__workspace_preprocessed_path = self.__workspace_path + EXE_NAME + r'\Temporary\preprocessed''\\' self.__workspace_processing_path = self.__workspace_path + EXE_NAME + r'\Temporary\processing''\\' self.__workspace_block_tif_path = self.__workspace_path + EXE_NAME + r'\Temporary\blockTif''\\' self.__workspace_block_tif_processed_path = self.__workspace_path + EXE_NAME + r'\Temporary\blockTifProcessed''\\' self.__product_dic = self.__workspace_processing_path + 'product\\' path_list = [self.__workspace_preprocessing_path, self.__workspace_preprocessed_path, self.__workspace_processing_path, self.__workspace_block_tif_path, self.__workspace_block_tif_processed_path, self.__product_dic] file.creat_dirs(path_list) logger.info('create new workspace success!') def preprocess_handle(self): """ 预处理 """ # para_names = [] # p = pp() # self.__preprocessed_paras, scopes_roi = p.preprocessing_oh2004(para_names, self.__processing_paras, # self.__workspace_preprocessing_path, self.__workspace_preprocessed_path) para_names_geo = ['Covering', 'NDVI'] p = pp() cutted_img_paths, scopes_roi = p.cut_geoimg(self.__workspace_preprocessing_path, para_names_geo, self.__processing_paras) self.__preprocessed_paras.update(cutted_img_paths) para_names_l1a = ["HH", "VV", "HV", "VH", 'inci_Angle-ortho', 'ori_sim'] self._tr = TransImgL1A(self.__processing_paras['ori_sim'], scopes_roi) for name in para_names_l1a: out_path = os.path.join(self.__workspace_preprocessed_path, name + "_preprocessed.tif") self._tr.cut_L1A(self.__processing_paras[name], out_path) self.__preprocessed_paras.update({name: out_path}) logger.info('preprocess_handle success!') self.__cols = self.imageHandler.get_img_width(self.__preprocessed_paras['HH']) self.__rows = self.imageHandler.get_img_height(self.__preprocessed_paras['HH']) logger.info('progress bar: 40%') def create_roi(self): """ 计算ROI掩膜 :return: 掩膜路径 """ processing_path = self.__workspace_processing_path # 利用角度为nan生成Mask pp.check_LocalIncidenceAngle(self.__preprocessed_paras['LocalIncidenceAngle'], self.__preprocessed_paras['LocalIncidenceAngle']) angle_nan_mask_path = processing_path + 'angle_nan_mask.tif' roi.trans_tif2mask(angle_nan_mask_path, self.__preprocessed_paras['LocalIncidenceAngle'], np.nan) # 利用影像的有效范围生成MASK tif_mask_path = processing_path + 'tif_mask.tif' roi.trans_tif2mask(tif_mask_path, self.__preprocessed_paras['HH'], 0, 0) # 0，0修改为np.nan tif_zero_mask_path = processing_path + 'tif_mask_zero.tif' roi.trans_tif2mask(tif_zero_mask_path, self.__preprocessed_paras['HH'], np.nan) # 利用cover计算植被覆盖范围 cover_mask_path = processing_path + 'cover_mask.tif' #alg.trans_tif2mask(cover_mask_path, self.__preprocessed_paras['Covering'], threshold_of_cover_min, threshold_of_cover_max) cover_id_list = list(self.__processing_paras['CoveringIDs'].split(';')) cover_id_list = [int(num) for num in cover_id_list] roi.trans_cover2mask(cover_mask_path, self.__preprocessed_paras["Covering"], cover_id_list) # 利用NDVI计算裸土范围该指数的输出值在 -1.0 和 1.0 之间，大部分表示植被量， # 负值主要根据云、水和雪而生成 # 接近零的值则主要根据岩石和裸土而生成。 # 较低的（小于等于 0.1）NDVI 值表示岩石、沙石或雪覆盖的贫瘠区域。 # 中等值（0.2 至 0.3）表示灌木丛和草地 # 较高的值（0.6 至 0.8）表示温带雨林和热带雨林。 ndvi_mask_path = processing_path + 'ndvi_mask.tif' ndvi_scope = list(self.__processing_paras['NDVIScope'].split(';')) threshold_of_ndvi_min = float(ndvi_scope[0]) threshold_of_ndvi_max = float(ndvi_scope[1]) roi.trans_tif2mask(ndvi_mask_path, self.__preprocessed_paras['NDVI'], threshold_of_ndvi_min, threshold_of_ndvi_max) logger.info('create masks success!') # 利用覆盖范围和裸土范围 生成MASK bare_land_mask_path = processing_path + 'bare_land_mask.tif' roi.combine_mask(bare_land_mask_path, cover_mask_path, ndvi_mask_path) roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_mask_path) roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_zero_mask_path) roi.combine_mask(bare_land_mask_path, bare_land_mask_path, angle_nan_mask_path) logger.info('combine_mask success!') # 计算roi区域 roi.cal_roi(self.__preprocessed_paras['LocalIncidenceAngle'], self.__preprocessed_paras['LocalIncidenceAngle'], bare_land_mask_path, background_value=1) shutil.copyfile(bare_land_mask_path, self.__workspace_preprocessed_path + 'mask.tif') logger.info('create ROI image success!') return bare_land_mask_path def resampleImgs(self, refer_img_path): ndvi_rampling_path = self.__workspace_processing_path + "ndvi.tif" pp.resampling_by_scale(self.__preprocessed_paras["NDVI"], ndvi_rampling_path, refer_img_path) self.__preprocessed_paras["NDVI"] = ndvi_rampling_path cover_rampling_path = self.__workspace_processing_path + "cover.tif" pp.resampling_by_scale(self.__preprocessed_paras["Covering"], cover_rampling_path, refer_img_path) self.__preprocessed_paras["Covering"] = cover_rampling_path def process_handle(self,start): """ 算法主处理函数 :return: True or False """ tem_folder = self.__workspace_path + EXE_NAME + r"\Temporary""\\" soilOh2004 = SoilMoistureTool(self.__workspace_preprocessed_path, self.__workspace_processing_path, self.__cols, self.__rows, self.__preprocessed_paras['inci_Angle-ortho'], self.__processing_paras['Origin_META']) result = soilOh2004.soil_oh2004() logger.info('progress bar: 80%') product_temp_path = os.path.join(tem_folder, 'SurfaceRoughnessProduct_temp.tif') tif_files = list(glob.glob(os.path.join(result, '*.tif'))) for tif_file in tif_files: if 'oh2004_s' in os.path.basename(tif_file): shutil.copy(tif_file, product_temp_path) product_geo_path = os.path.join(tem_folder, 'SurfaceRoughnessProduct_geo.tif') self._tr.l1a_2_geo_int(self.__preprocessed_paras['ori_sim'], product_temp_path, product_geo_path, 'linear') hh_geo_path = self.__workspace_processing_path + "hh_geo.tif" self._tr.l1a_2_geo_int(self.__preprocessed_paras['ori_sim'], self.__preprocessed_paras['HH'], hh_geo_path, 'linear') self.resampleImgs(product_geo_path) para_names = ['Covering', 'NDVI'] bare_land_mask_path = roi().roi_process(para_names, self.__workspace_processing_path + "/roi/", self.__processing_paras, self.__preprocessed_paras) product_path = os.path.join(self.__product_dic, 'SurfaceRoughnessProduct.tif') # 获取影像roi区域 roi.cal_roi(product_path, product_geo_path, bare_land_mask_path, background_value=np.nan) # 生成快视图 self.imageHandler.write_quick_view(product_path) # 生成元文件案例 xml_path = "./model_meta.xml" image_path = product_path out_path1 = os.path.join(tem_folder, "trans_geo_projcs.tif") out_path2 = os.path.join(tem_folder, "trans_projcs_geo.tif") par_dict = CreateDict(image_path, self.processinfo, out_path1, out_path2).calu_nature(start) model_xml_path = os.path.join(tem_folder, "creat_standard.meta.xml") # 输出xml路径 id_min = 0 id_max = 1000 threshold_of_ndvi_min = 0 threshold_of_ndvi_max = 1 set_threshold = [id_max, id_min, threshold_of_ndvi_min, threshold_of_ndvi_max] CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, set_threshold, model_xml_path).create_standard_xml() SrcImagePath = self.__input_paras["DualPolarSAR"]['ParaValue'] paths = SrcImagePath.split(';') SrcImageName=os.path.split(paths[0])[1].split('.tar.gz')[0] if len(paths) >= 2: for i in range(1, len(paths)): SrcImageName=SrcImageName+";"+os.path.split(paths[i])[1].split('.tar.gz')[0] meta_xml_path = self.__product_dic + EXE_NAME + "Product.meta.xml" CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process(SrcImageName) # 文件夹打包 file.make_targz(self.__out_para, self.__product_dic) logger.info('process_handle success!') logger.info('progress bar: 100%') def del_temp_workspace(self): """ 临时工作区 """ if DEBUG is False: path = self.__workspace_path + EXE_NAME + r'\Temporary' if os.path.exists(path): file.del_folder(path) if __name__ == '__main__': multiprocessing.freeze_support() start = datetime.datetime.now() try: if len(sys.argv) < 2: xml_path = 'SurfaceRoughness.xml' else: xml_path = sys.argv[1] main_handler = MoistureMain(xml_path) if main_handler.check_source() is False: raise Exception('check_source() failed!') if main_handler.preprocess_handle() is False: raise Exception('preprocess_handle() failed!') if main_handler.process_handle(start) is False: raise Exception('process_handle() failed!') logger.info('successful production of ' + EXE_NAME + ' products!') except Exception: logger.exception('run-time error!') finally: # main_handler.del_temp_workspace() pass end = datetime.datetime.now() msg = 'running use time: %s ' % (end - start) logger.info(msg)