456 lines
22 KiB
Python
456 lines
22 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project ：microproduct
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@File ：SoilSalinityMain.py
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@Author ：SHJ
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@Contact：土壤盐碱度算法主函数
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@Date ：2021/9/6
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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 2022-6-27 石海军 1.增加配置文件config.ini; 2.内部处理使用地理坐标系(4326)；
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"""
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import logging
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import shutil
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from tool.algorithm.algtools.MetaDataHandler import Calibration
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from tool.algorithm.algtools.PreProcess import PreProcess as pp
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from tool.algorithm.image.ImageHandle import ImageHandler
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from tool.algorithm.polsarpro.pspLeeRefinedFilterT3 import LeeRefinedFilterT3
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from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource, InitPara
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from tool.algorithm.algtools.logHandler import LogHandler
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from tool.algorithm.algtools.ROIAlg import ROIAlg as roi
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from tool.algorithm.block.blockprocess import BlockProcess
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from tool.algorithm.xml.AnalysisXml import xml_extend
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from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml
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from tool.file.fileHandle import fileHandle
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# from AHVToPolsarpro import AHVToPolsarpro
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from tool.algorithm.polsarpro.AHVToPolsarpro import AHVToPolsarpro
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from pspHAAlphaDecomposition import PspHAAlphaDecomposition
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import scipy.spatial.transform
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import scipy.spatial.transform._rotation_groups # 用于解决打包错误
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import scipy.special.cython_special # 用于解决打包错误
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from sklearn.cross_decomposition import PLSRegression
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from tool.algorithm.xml.CreatMetafile import CreateMetafile
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from SoilSalinityXmlInfo import CreateDict, CreateStadardXmlFile
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import os
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import datetime
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import numpy as np
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from PIL import Image
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import sys
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from tool.config.ConfigeHandle import Config as cf
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from tool.csv.csvHandle import csvHandle
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from tool.algorithm.transforml1a.transHandle import TransImgL1A
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from tool.algorithm.ml.machineLearning import MachineLeaning as ml
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import multiprocessing
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csvh = csvHandle()
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soil_salinity_value_min = float(cf.get('product_value_min'))
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soil_salinity_value_max = float(cf.get('product_value_max'))
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pixelspace=float(cf.get('pixelspace'))
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tar = r'-' + cf.get('tar')
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productLevel = cf.get('productLevel')
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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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EXE_NAME = cf.get('exe_name')
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# env_str = os.path.split(os.path.realpath(__file__))[0]
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env_str =os.path.dirname(os.path.abspath(sys.argv[0]))
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os.environ['PROJ_LIB'] = env_str
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LogHandler.init_log_handler('run_log\\'+EXE_NAME)
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logger = logging.getLogger("mylog")
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file = fileHandle(DEBUG)
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class SalinityMain:
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"""
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算法主函数
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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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self.__feature_name_list = []
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# 参考影像路径,坐标系
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self.__ref_img_path, self.__proj = '', ''
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# 宽/列数,高/行数
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self.__cols, self.__rows = 0, 0
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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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self.__check_handler.check_alg_xml()
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self.__check_handler.check_run_env()
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# 检查景影像是否为全极化
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self.__input_paras = self.__alg_xml_handler.get_input_paras()
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if self.__check_handler.check_input_paras(self.__input_paras) 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.__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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SrcImageName = os.path.split(self.__input_paras["AHV"]['ParaValue'])[1].split('.tar.gz')[0]
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result_name = SrcImageName + tar + ".tar.gz"
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self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', result_name)
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self.__alg_xml_handler.write_out_para("SoilSalinityProduct", 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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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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para_dic.update(InitPara.get_polarization_mode(InitPara.get_tif_paths(file_dir, name)))
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parameter_path = os.path.join(file_dir, "orth_para.txt")
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para_dic.update({"paraMeter": parameter_path})
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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 +'\\Temporary\\preprocessing\\'
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self.__workspace_preprocessed_path = self.__workspace_path + EXE_NAME + '\\Temporary\\preprocessed\\'
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self.__workspace_processing_path = self.__workspace_path + EXE_NAME + '\\Temporary\\processing\\'
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self.__workspace_block_tif_path = self.__workspace_path + EXE_NAME + '\\Temporary\\blockTif\\'
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self.__workspace_block_tif_processed_path = self.__workspace_path + EXE_NAME + '\\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 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 True:
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return
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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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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_geo = ["Covering", "NDVI", 'sim_ori']
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p = pp()
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p.check_img_projection(self.__workspace_preprocessing_path, para_names_geo, self.__processing_paras)
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#计算roi
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scopes = ()
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# scopes += (self.imageHandler.get_scope_ori_sim(self.__processing_paras['ori_sim']),)
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scopes += (xml_extend(self.__processing_paras['META']).get_extend(),)
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scopes += p.box2scope(self.__processing_paras['box'])
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# 计算图像的轮廓,并求相交区域
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intersect_shp_path = self.__workspace_preprocessing_path + 'IntersectPolygon.shp'
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scopes_roi = p.cal_intersect_shp(intersect_shp_path, para_names_geo, self.__processing_paras, scopes)
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#裁剪
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# 裁剪图像：裁剪微波图像，裁剪其他图像
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cutted_img_paths = p.cut_imgs(self.__workspace_preprocessing_path, para_names_geo, self.__processing_paras, intersect_shp_path)
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self.__preprocessed_paras.update(cutted_img_paths)
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para_names_l1a = ["HH", "VV", "HV", "VH"]
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self.l1a_width = ImageHandler.get_img_width(self.__processing_paras['HH'])
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self.l1a_height = ImageHandler.get_img_height(self.__processing_paras['HH'])
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self._tr = TransImgL1A(self.__processing_paras['sim_ori'], scopes_roi, self.l1a_height, self.l1a_width) # 裁剪图像
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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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logger.info('progress bar: 15%')
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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 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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names = ['Covering', 'NDVI']
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bare_land_mask_path = roi().roi_process(names, self.__workspace_processing_path + "/roi/", self.__processing_paras, self.__preprocessed_paras)
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logger.info('create masks success!')
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return bare_land_mask_path
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def AHVToPolsarpro(self,out_dir):
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atp = AHVToPolsarpro()
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ahv_path = self.__workspace_preprocessed_path
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t3_path = self.__workspace_processing_path+'psp_t3\\'
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# atp.ahv_to_polsarpro_t3_soil(t3_path, ahv_path)
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polarization = ['HH', 'HV', 'VH', 'VV']
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calibration = Calibration.get_Calibration_coefficient(self.__processing_paras['Origin_META'], polarization)
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tif_path = atp.calibration(calibration, in_ahv_dir=self.__workspace_preprocessed_path)
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atp.ahv_to_polsarpro_t3_soil(t3_path, tif_path)
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logger.info('ahv transform to polsarpro T3 matrix success!')
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logger.info('progress bar: 20%')
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# Lee滤波
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leeFilter = LeeRefinedFilterT3()
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lee_filter_path = os.path.join(self.__workspace_processing_path,
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'lee_filter\\')
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leeFilter.api_lee_refined_filter_T3('', t3_path, lee_filter_path, 0, 0, atp.rows(), atp.cols())
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logger.info('Refined_lee process success!')
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haa = PspHAAlphaDecomposition(normalization=True)
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haa.api_creat_h_a_alpha_features(h_a_alpha_out_dir=out_dir,
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h_a_alpha_decomposition_T3_path='h_a_alpha_decomposition_T3.exe' ,
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h_a_alpha_eigenvalue_set_T3_path='h_a_alpha_eigenvalue_set_T3.exe' ,
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h_a_alpha_eigenvector_set_T3_path='h_a_alpha_eigenvector_set_T3.exe',
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polsarpro_in_dir=lee_filter_path)
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def create_meta_file(self, product_path):
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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, [1, 1, 1, 1], 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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# CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, model_xml_path).create_standard_xml()
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# 文件夹打包
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SrcImagePath = self.__input_paras["AHV"]['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(
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# SrcImageName)
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model_path = "./product.xml"
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meta_xml_path = os.path.join(self.__product_dic, SrcImageName + tar + ".meta.xml")
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para_dict = CreateMetaDict(image_path, self.__processing_paras['Origin_META'], self.__workspace_processing_path,
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out_path1, out_path2).calu_nature()
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para_dict.update({"imageinfo_ProductName": "土壤盐碱度"})
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para_dict.update({"imageinfo_ProductIdentifier": "SoilSalinity"})
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para_dict.update({"imageinfo_ProductLevel": productLevel})
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para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
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CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
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temp_folder = os.path.join(self.__workspace_path, EXE_NAME, 'Output')
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out_xml = os.path.join(temp_folder, os.path.basename(meta_xml_path))
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if os.path.exists(temp_folder) is False:
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os.mkdir(temp_folder)
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# CreateProductXml(para_dict, model_path, out_xml).create_standard_xml()
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shutil.copy(meta_xml_path, out_xml)
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def calInterpolation_bil_Wgs84_rc_sar_sigma(self, parameter_path, dem_rc, in_sar, out_sar):
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'''
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# std::cout << "mode 11";
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# std::cout << "SIMOrthoProgram.exe 11 in_parameter_path in_rc_wgs84_path in_ori_sar_path out_orth_sar_path";
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'''
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exe_path = r".\baseTool\x64\Release\SIMOrthoProgram.exe"
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exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3} {4} {5}".format(exe_path, 11, parameter_path,
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dem_rc, in_sar, out_sar)
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print(exe_cmd)
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print(os.system(exe_cmd))
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print("==========================================================================")
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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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# 读取实测值，从经纬度坐标系转为图像坐标系
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# measured_data_img = self._tr.tran_lonlats_to_L1A_rowcols(csvh.readcsv(self.__processing_paras['MeasuredData']), self.__processing_paras['ori_sim'])
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measured_data_img = self._tr.tran_lonlats_to_L1A_rowcols(csvh.readcsv(self.__processing_paras['MeasuredData']),
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self.__preprocessed_paras['sim_ori'], self.l1a_height,
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self.l1a_width)
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# if len(measured_data_img) < 4:
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# raise ('实测数据不足，无法进行模型训练')
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# 极化分解得到T3矩阵
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out_dir = self.__workspace_processing_path+'psp_haalpha\\'
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self.AHVToPolsarpro(out_dir)
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# 分块
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bp = BlockProcess()
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rows = self.imageHandler.get_img_height(self.__preprocessed_paras['HH'])
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cols = self.imageHandler.get_img_width(self.__preprocessed_paras['HH'])
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block_size = bp.get_block_size(rows, cols)
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bp.cut(out_dir, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
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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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logger.info('blocking tifs success!')
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# 54个特征矩阵合并为一个54维矩阵
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for key in dir_dict:
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key_name = key
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block_num = len(dir_dict[key])
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for n in range(block_num):
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name = os.path.basename(dir_dict[key_name][n])
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suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + \
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name.split('_')[-1]
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features_path = self.__workspace_block_tif_processed_path + "features\\features" + suffix
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features_array = np.zeros((len(dir_dict), block_size, block_size), dtype='float32')
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for m, value in zip(range(len(dir_dict)), dir_dict.values()):
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features_array[m, :, :] = self.imageHandler.get_band_array(value[n], 1)
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# 异常值转为0
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features_array[np.isnan(features_array)] = 0.0
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features_array[np.isinf(features_array)] = 0.0
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self.imageHandler.write_img(features_path, "", [0, 0, 1, 0, 0, 1], features_array)
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logger.info('create features matrix success!')
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# for n in range(block_num):
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# name = os.path.basename(dir_dict[key_name][n])
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# suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + \
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# name.split('_')[-1]
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# features_path = self.__workspace_block_tif_processed_path + "features\\features" + suffix
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# row = self.imageHandler.get_img_height(dir_dict[key_name][n])
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# col = self.imageHandler.get_img_width(dir_dict[key_name][n])
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# features_array = np.zeros((len(dir_dict), row, col), dtype='float32')
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# for m, value in zip(range(len(dir_dict)), dir_dict.values()):
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# features_array[m, :, :] = self.imageHandler.get_band_array(value[n], 1)
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# # 异常值转为0
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# features_array[np.isnan(features_array)] = 0.0
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# features_array[np.isinf(features_array)] = 0.0
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# self.imageHandler.write_img(features_path, "", [0, 0, 1, 0, 0, 1], features_array)
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# logger.info('create features matrix success!')
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# 生成训练集
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block_features_dir, block_features_name = bp.get_file_names(self.__workspace_block_tif_processed_path + 'features\\', ['tif'])
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X_train, Y_train = ml.gene_train_data(block_features_dir, rows, cols, block_size, measured_data_img)
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optimal_feature = ml.sel_optimal_feature_set(X_train, Y_train, threshold=0.01)
|
||
optimal_feature = ml.remove_correlation_feature(X_train, optimal_feature, threshold=0.85)
|
||
X_train = X_train[:, optimal_feature]
|
||
|
||
feature_name_list = []
|
||
for name in dir_dict.keys():
|
||
feature_name_list.append(name)
|
||
logger.info('feature_list:%s', feature_name_list)
|
||
logger.info('train_feature:%s', np.array(feature_name_list)[optimal_feature])
|
||
logger.info('generating training set success!')
|
||
logger.info('progress bar: 80%')
|
||
|
||
# 训练模型
|
||
logger.info('PLS is training!')
|
||
pls = PLSRegression()
|
||
pls.fit(X_train, Y_train)
|
||
PLSRegression(copy=True, max_iter=1000, n_components=5, scale=True, tol=1e-06)
|
||
logger.info('train PLS success!')
|
||
|
||
# 预测
|
||
for path, name, n in zip(block_features_dir, block_features_name, range(len(block_features_dir))):
|
||
features_array = self.imageHandler.get_data(path)
|
||
X_test = np.reshape(features_array, (features_array.shape[0], features_array[0].size)).T
|
||
X_test = X_test[:, optimal_feature]
|
||
Y_test = pls.predict(X_test)
|
||
Y_test[Y_test < soil_salinity_value_min] = soil_salinity_value_min
|
||
Y_test[Y_test > soil_salinity_value_max] = soil_salinity_value_max
|
||
|
||
salinity_img = Y_test.reshape(features_array.shape[1], features_array.shape[2])
|
||
out_image = Image.fromarray(salinity_img)
|
||
suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + \
|
||
name.split('_')[-1]
|
||
out_path = self.__workspace_block_tif_processed_path + 'salinity\\' + 'salinity' + suffix
|
||
if not os.path.exists(self.__workspace_block_tif_processed_path + 'salinity\\'):
|
||
os.makedirs(self.__workspace_block_tif_processed_path + 'salinity\\')
|
||
out_image.save(out_path)
|
||
# logger.info('total:%s,block:%s test data success!', len(block_features_dir), n)
|
||
logger.info('test data success!')
|
||
|
||
# 合并预测后的影像
|
||
data_dir = self.__workspace_block_tif_processed_path + 'salinity\\'
|
||
out_path = self.__workspace_processing_path[0:-1]
|
||
bp.combine(data_dir, cols, rows, out_path, file_type=['tif'], datetype='float32')
|
||
|
||
# l1a图像坐标转换地理坐标
|
||
salinity_path = self.__workspace_processing_path + "salinity.tif"
|
||
SrcImageName = os.path.split(self.__input_paras["AHV"]['ParaValue'])[1].split('.tar.gz')[0] + tar + '.tif'
|
||
salinity_geo_path = os.path.join(self.__workspace_processing_path, SrcImageName)
|
||
|
||
self.calInterpolation_bil_Wgs84_rc_sar_sigma(self.__processing_paras['paraMeter'], self.__preprocessed_paras['sim_ori'], salinity_path, salinity_geo_path)
|
||
|
||
# self.inter_Range2Geo(self.__preprocessed_paras['ori_sim'], salinity_path, salinity_geo_path, pixelspace)
|
||
# self._tr.l1a_2_geo(self.__preprocessed_paras['ori_sim'], salinity_path, salinity_geo_path)
|
||
self.resampleImgs(salinity_geo_path)
|
||
|
||
# 生成roi区域
|
||
product_path = os.path.join(self.__product_dic, SrcImageName)
|
||
roi.cal_roi(product_path, salinity_geo_path, self.create_roi(), background_value=np.nan)
|
||
|
||
# 生成快视图
|
||
self.imageHandler.write_quick_view(product_path)
|
||
self.create_meta_file(product_path)
|
||
|
||
file.make_targz(self.__out_para, self.__product_dic)
|
||
logger.info('process_handle success!')
|
||
logger.info('progress bar: 100%')
|
||
|
||
|
||
if __name__ == '__main__':
|
||
multiprocessing.freeze_support()
|
||
start = datetime.datetime.now()
|
||
try:
|
||
if len(sys.argv) < 2:
|
||
xml_path = EXE_NAME + '.xml'
|
||
else:
|
||
xml_path = sys.argv[1]
|
||
main_handler = SalinityMain(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)
|