401 lines
19 KiB
Python
401 lines
19 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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from tool.algorithm.algtools.PreProcess import PreProcess as pp
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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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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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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', 'CoveringIDs', 'DualPolarSAR', 'Covering', 'NDVI', 'NDWI', 'e1', 'e2', 'Chh', 'Dhh', 'Cvv', 'Dvv', 'T', 'Pb', 'vsand', 'vclay']
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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"SoilMoistureProduct.tar.gz")
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self.__alg_xml_handler.write_out_para("SoilMoistureProduct", 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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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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# 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, 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 'LocalIncidenceAngle' == key:
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para_dic.update({'LocalIncidenceAngle': in_tif_path})
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flag_list[4] = 1
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if flag_list != [1, 1, 1, 1, 1]:
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raise Exception('There are not tar_gz path: HH、HV(VV)、LocalIncidenceAngle or meta.xml 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 = ['HH', 'VV', 'VH', 'HV', 'Covering', 'NDVI', 'NDWI', 'LocalIncidenceAngle']
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ref_img_name = 'HH'
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p = pp()
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self.__preprocessed_paras = p.preprocessing(para_names, ref_img_name,
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self.__processing_paras,
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self.__workspace_preprocessing_path, self.__workspace_preprocessed_path)
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self.__ref_img_path, self.__cols, self.__rows, self.__proj, self.__geo = p.get_ref_inf(self.__preprocessed_paras[ref_img_name])
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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 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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radar_center_frequency = MetaDataHandler.get_RadarCenterFrequency(self.__processing_paras['META'])
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lamda = MetaDataHandler.get_lamda(self.__processing_paras['META']) * 100 #单位转为cm
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k=2*3.1415926/lamda
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# 计算ROI区域
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bare_land_mask_path = self.create_roi()
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logger.info('progress bar: 50%')
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# 分块
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bp = BlockProcess()
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block_size = bp.get_block_size(self.__rows, self.__cols)
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bp.cut(self.__workspace_preprocessed_path, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
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logger.info('blocking tifs success!')
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img_dir, img_name = bp.get_file_names(self.__workspace_block_tif_path, ['tif'])
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dir_dict = bp.get_same_img(img_dir, img_name)
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angle_list, hh_list, vv_list,vh_list, hv_list, ndwi_list, mask_list = None, None, None, None, None, None, None
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for key in dir_dict.keys():
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tmp = key.split('_', 2)[0]
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if tmp == 'LocalIncidenceAngle':
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angle_list = dir_dict[key]
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elif tmp == 'HH':
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hh_list = dir_dict[key]
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elif tmp == 'VV':
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vv_list = dir_dict[key]
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elif tmp == 'VH':
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vh_list = dir_dict[key]
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elif tmp == 'HV':
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hv_list = dir_dict[key]
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elif tmp == 'NDWI':
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ndwi_list = dir_dict[key]
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elif tmp == 'mask':
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mask_list = dir_dict[key]
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if angle_list is None or hh_list is None or vv_list is None or vh_list is None or hv_list is None or ndwi_list is None:
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logger.info('angle_list is None or hh_list is None or vv_list is None or vh_list is None or hv_list is None or ndwi_list is None')
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return False
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if not len(angle_list) == len(hh_list) == len(vv_list) == len(vh_list) == len(hv_list) == len(ndwi_list):
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logger.info('[len(angle_list) == len(hh_list) == len(vv_list) == len(vh_list) == len(hv_list) == len(ndwi_list)] is False!')
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return False
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processes_num = min([len(angle_list), multiprocessing_num, multiprocessing.cpu_count() - 1])
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f = Filter()
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f.lee_filter_multiprocess(hh_list, hh_list, FILTER_SIZE, processes_num)
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f.lee_filter_multiprocess(vv_list, vv_list, FILTER_SIZE, processes_num)
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f.lee_filter_multiprocess(vh_list, vh_list, FILTER_SIZE, processes_num)
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f.lee_filter_multiprocess(hv_list, hv_list, FILTER_SIZE, processes_num)
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# 开启多进程处理
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pool = multiprocessing.Pool(processes=processes_num)
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pl = []
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for i in range(len(angle_list)):
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suffix = bp.get_suffix(os.path.basename(angle_list[i]))
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# 利用NDWI计算VWCs
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vwc_path = self.__workspace_block_tif_processed_path + 'vwc' + suffix
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alg.cal_vwc(vwc_path, ndwi_list[i], self.__processing_paras['e1'], self.__processing_paras['e2'])
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# 利用VWC、入射角 计算裸土后向散射系数
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hh_bare_soil_bsc_path = self.__workspace_block_tif_processed_path + 'hh_bare_soil_bsc' + suffix
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vv_bare_soil_bsc_path = self.__workspace_block_tif_processed_path + 'vv_bare_soil_bsc' + suffix
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vh_bare_soil_bsc_path = self.__workspace_block_tif_processed_path + 'vh_bare_soil_bsc' + suffix
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hv_bare_soil_bsc_path = self.__workspace_block_tif_processed_path + 'hv_bare_soil_bsc' + suffix
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flag = alg.cal_bare_soil_bsc(hh_bare_soil_bsc_path, hh_list[i], vwc_path, angle_list[i], self.__processing_paras['Chh'], self.__processing_paras['Dhh'])
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if flag is False:
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return False
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flag = alg.cal_bare_soil_bsc(vv_bare_soil_bsc_path, vv_list[i], vwc_path, angle_list[i], self.__processing_paras['Cvv'], self.__processing_paras['Dvv'])
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if flag is False:
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return False
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flag = alg.cal_bare_soil_bsc(vh_bare_soil_bsc_path, vh_list[i], vwc_path, angle_list[i], self.__processing_paras['Cvv'], self.__processing_paras['Dvv'])
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if flag is False:
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return False
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flag = alg.cal_bare_soil_bsc(hv_bare_soil_bsc_path, hv_list[i], vwc_path, angle_list[i], self.__processing_paras['Cvv'], self.__processing_paras['Dvv'])
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if flag is False:
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return False
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# 利用后向散射系数，计算土壤含水量
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soil_moisture_path = self.__workspace_block_tif_processed_path + 'soil_moisture' + suffix
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pl.append(pool.apply_async(alg.cal_soil_moisture, (soil_moisture_path, hh_bare_soil_bsc_path, vv_bare_soil_bsc_path,
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angle_list[i], mask_list[i], lamda, radar_center_frequency, self.__processing_paras['T'],
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self.__processing_paras['Pb'], self.__processing_paras['vsand'], self.__processing_paras['vclay'],i))) # dobsen
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# pl.append((pool.apply_async(alg.cal_soilM, (soil_moisture_path, hh_list[i], vv_list[i], hv_list[i], vh_list[i], angle_list[i], mask_list[i], lamda)))) # oh 2004
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# alg.cal_soilM(soil_moisture_path, hh_list[i], vv_list[i], hv_list[i], vh_list[i], angle_list[i], mask_list[i], lamda)
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logger.info('total:%s, block:%s calculating soil_moisture!', len(angle_list), i)
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pool.close()
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pool.join()
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logger.info(pl)
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logger.info('progress bar: 80%')
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# 合并处理后的影像
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data_dir = self.__workspace_block_tif_processed_path
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w = ImageHandler.get_img_width(self.__preprocessed_paras['HH'])
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h = ImageHandler.get_img_height(self.__preprocessed_paras['HH'])
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out_path = self.__workspace_processing_path[0:-1]
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tif_type = bp.get_tif_dtype(self.__preprocessed_paras['HH'])
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bp.combine(data_dir, w, h, out_path, file_type=['tif'], datetype=tif_type)
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# 添加地理信息
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soil_moisture_path = self.__workspace_processing_path + 'soil_moisture.tif'
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bp.assign_spatial_reference_byfile(self.__preprocessed_paras['HH'], soil_moisture_path)
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# 生成roi区域
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product_path = self.__product_dic + 'SoilMoistureProduct.tif'
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roi.cal_roi(product_path, soil_moisture_path, bare_land_mask_path, background_value=np.nan)
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logger.info('cal soil_moisture success!')
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proj, geos, data = self.imageHandler.read_img(product_path)
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data[data < soil_moisture_value_min] = soil_moisture_value_min
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data[data > soil_moisture_value_max] = soil_moisture_value_max
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self.imageHandler.write_img(product_path, proj, geos, data)
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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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tem_folder = self.__workspace_path + EXE_NAME + r"\Temporary""\\"
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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)
|
||
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 = 'SoilMoisture.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()
|
||
|
||
end = datetime.datetime.now()
|
||
msg = 'running use time: %s ' % (end - start)
|
||
logger.info(msg) |