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