microproduct/leafAreaIndex/LeafIndexMain.py

# -*- coding: UTF-8 -*-
"""
@Project ：MicroProduct
@File ：LeafIndexMain.PY
@Function ：主函数
@Author ：SHJ
@Date ：2022/11/07
@Version ：2.0.0
修改历史：
[修改序列]   [修改日期]    [修改者]     [修改内容]
   1       2022-6-27     李明明      1.增加配置文件config.ini;  2.修复快速图全黑的问题; 3.内部处理使用地理坐标系(4326)；
"""
from tool.algorithm.algtools.PreProcess import PreProcess as pp   # 此行放在下面会报错，最好放在上面
from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource, InitPara  # 导入xml文件读取与检查文件
from tool.algorithm.algtools.logHandler import LogHandler
from tool.algorithm.image.ImageHandle import ImageHandler
from tool.algorithm.block.blockprocess import BlockProcess
from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml
from tool.config.ConfigeHandle import Config as cf
from tool.algorithm.xml.CreatMetafile import CreateMetafile
from tool.algorithm.algtools.ROIAlg import ROIAlg as roi
from tool.algorithm.algtools.filter.lee_Filter import Filter
from LeafIndexXmlInfo import CreateDict, CreateStadardXmlFile
from tool.csv.csvHandle import csvHandle
import os
import shutil
import logging
import datetime
import glob
import numpy as np
import scipy.spatial.transform  # 用于解决打包错误
import scipy.spatial.transform._rotation_groups  # 用于解决打包错误
import scipy.special.cython_special  # 用于解决打包错误
from scipy.interpolate import griddata
import sys
import multiprocessing
from tool.file.fileHandle import fileHandle

cover_id_list = []
threshold_of_ndvi_min = 0
threshold_of_ndvi_max = 0
multiprocessing_num = int(cf.get('multiprocessing_num'))
leaf_index_value_min = float(cf.get('product_value_min'))
leaf_index_value_max = float(cf.get('product_value_max'))

FILTER_SISE = int(cf.get('filter_sise'))
if cf.get('debug') == 'True':
    DEBUG = True
else:
    DEBUG = False
file = fileHandle(DEBUG)
EXE_NAME = cf.get('exe_name')
LogHandler.init_log_handler('run_log\\' + EXE_NAME)
logger = logging.getLogger("mylog")
csvh = csvHandle()
env_str = os.path.split(os.path.realpath(__file__))[0]
os.environ['PROJ_LIB'] = env_str
class LeafIndexMain:
    """
    叶面积指数主函数
    """
    def __init__(self, alg_xml_path):
        self.alg_xml_path = alg_xml_path
        self.imageHandler = ImageHandler()
        self.BlockProcess = BlockProcess()
        self.__alg_xml_handler = ManageAlgXML(alg_xml_path)
        self.__check_handler = CheckSource(self.__alg_xml_handler)

        self.__workspace_path, self.__workspace_soimois_path,self.__workspace_soimois_path = None, None, None
        self.__input_paras, self.__processing_paras, self.__processing_paras = {}, {}, {}
        self.__out_para = None
        # 参考影像路径,坐标系
        self.__ref_img_path, self.__proj = '', ''
        # 宽/列数,高/行数
        self.__cols, self.__rows = 0, 0
        # 影像投影变换矩阵
        self.__geo = [0, 0, 0, 0, 0, 0]
        # 极化影像的名称
        self.__sar_tif_name = None

    def check_source(self):
        """
        检查算法相关的配置文件，图像，辅助文件是否齐全
        """
        env_str = os.getcwd()
        logger.info("sysdir: %s", env_str)
        if self.__check_handler.check_alg_xml() is False:
            return False
        if self.__check_handler.check_run_env() is False:
            return False
        self.__workspace_path = self.__alg_xml_handler.get_workspace_path()
        self.__create_work_space()
        self.__task_id = self.__alg_xml_handler.get_task_id()
        self.__input_paras = self.__alg_xml_handler.get_input_paras()  # 获取输入文件夹中的数据名、类型、地址
        # self.__out_para = os.path.join(self.__workspace_path,EXE_NAME, 'Output', r"LeafAreaIndexProduct.tar.gz")
        SrcImageName = os.path.split(self.__input_paras["SAR"]['ParaValue'])[1].split('.tar.gz')[0]
        result_name = SrcImageName + "-lef.tar.gz"
        # out_name = os.path.splitext(os.path.splitext(os.path.basename(self.__input_paras['SLC']['ParaValue']))[0])[0]
        # self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', "BackScatteringProduct.tar.gz")
        self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', result_name)
        self.__alg_xml_handler.write_out_para("LeafAreaIndexProduct", self.__out_para)  # 写入输出参数

        Polarization = self.__input_paras['Polarization']['ParaValue']
        if Polarization == "HV":
            self.__sar_tif_name = "HV"
        elif Polarization == "HH":
            self.__sar_tif_name = "HH"
        elif Polarization == "VV":
            self.__sar_tif_name = "VV"
        elif Polarization == 'empty' or Polarization == 'Empty' or Polarization == 'EMPTY':
            self.__sar_tif_name = 'empty'
        else:
            raise ValueError("input Para 'Polarization' is not 'HV'、'HH'、'VV'or 'empty'!")
        self.__processing_paras = InitPara.init_processing_paras(self.__input_paras)
        self.__processing_paras.update(self.get_tar_gz_inf(self.__processing_paras["sar_path0"]))
        logger.info('check_source success!')
        return True

    def get_tar_gz_inf(self, tar_gz_path):
        para_dic = {}
        name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz')
        file_dir = os.path.join(self.__workspace_preprocessing_path, name + '\\')
        file.de_targz(tar_gz_path, file_dir)
        # 元文件字典
        para_dic.update(InitPara.get_meta_dic(InitPara.get_meta_paths(file_dir, name), name))
        # tif路径字典
        pol_dic = InitPara.get_polarization_mode(InitPara.get_tif_paths(file_dir, name))
        flag_list = [0, 0]
        if self.__sar_tif_name == 'empty':
            for key, in_tif_path in pol_dic.items():
                # 获取极化类型
                if 'HV' == key and flag_list[0] == 0:
                    para_dic.update({'HV': in_tif_path})
                    flag_list[0] = 1
                elif 'HH' == key and flag_list[0] == 0:
                    para_dic.update({'HH': in_tif_path})
                    flag_list[0] = 1
                elif 'VV' == key and flag_list[0] == 0:
                    para_dic.update({'VV': in_tif_path})
                    flag_list[0] = 1
                elif 'LocalIncidenceAngle' == key:
                    para_dic.update({'LocalIncidenceAngle': in_tif_path})
                    flag_list[1] = 1
                else:
                    continue
        else:
            for key, in_tif_path in pol_dic.items():
                # 获取极化类型
                if self.__sar_tif_name == key and flag_list[0] == 0:
                    para_dic.update({self.__sar_tif_name: in_tif_path})
                    flag_list[0] = 1
                elif 'LocalIncidenceAngle' == key:
                    para_dic.update({'LocalIncidenceAngle': in_tif_path})
                    flag_list[1] = 1
        if flag_list != [1, 1]:
            raise Exception('There are not ' + self.__sar_tif_name + '、LocalIncidenceAngle or meta.xml in path:', tar_gz_path)
        return para_dic

    def __create_work_space(self):
        """
        删除原有工作区文件夹,创建新工作区文件夹
        """
        self.__workspace_preprocessing_path = self.__workspace_path + EXE_NAME + "\\Temporary\\preprocessing""\\"
        self.__workspace_preprocessed_path = self.__workspace_path + EXE_NAME + "\\Temporary\\preprocessed""\\"
        self.__workspace_processing_path = self.__workspace_path + EXE_NAME + "\\Temporary\\processing""\\"

        self.__workspace_maskcai_image_path = self.__workspace_path + EXE_NAME + "\\Temporary\\maskcai_image""\\"
        self.__workspace_maskcai_SoilMoi_path = self.__workspace_path + EXE_NAME + "\\Temporary\\maskcai_SoilMoi""\\"
        self.__workspace_maskcai_localang_path = self.__workspace_path + EXE_NAME + "\\Temporary\\maskcai_localang\\"

        self.__workspace_cai_sartif_path = self.__workspace_path + EXE_NAME + "\\Temporary\\cai_sartif""\\"
        self.__workspace_cai_SoilMoi_path = self.__workspace_path + EXE_NAME + "\\Temporary\\cai_SoilMoi""\\"
        self.__workspace_cai_localang_path = self.__workspace_path + EXE_NAME + "\\Temporary\\cai_localang""\\"
        self.__workspace_Leafindex_path = self.__workspace_path + EXE_NAME + "\\Temporary\\Leafindex""\\"
        self.__product_dic = self.__workspace_processing_path + "product\\"

        path_list = [self.__workspace_preprocessing_path, self.__workspace_preprocessed_path,
                     self.__workspace_processing_path, self.__workspace_maskcai_image_path,
                     self.__workspace_maskcai_SoilMoi_path, self.__workspace_maskcai_localang_path,
                     self.__workspace_cai_sartif_path,
                     self.__workspace_cai_SoilMoi_path, self.__workspace_cai_localang_path,
                     self.__workspace_Leafindex_path]
        file.creat_dirs(path_list)
        logger.info('create new workspace success!')

    def del_temp_workspace(self):
        """
        临时工作区
        """
        if DEBUG is True:
            return
        path = self.__workspace_path + EXE_NAME + r'\Temporary'
        if os.path.exists(path):
           file.del_folder(path)

    def preprocess_handle(self):
        """
        预处理
        """
        para_names = [self.__sar_tif_name, 'LocalIncidenceAngle', "NDVI", "surface_coverage"]
        ref_img_name = self.__sar_tif_name
        p = pp()
        self.__preprocessed_paras = p.preprocessing(para_names, ref_img_name,
                                                    self.__processing_paras,
                                                    self.__workspace_preprocessing_path,
                                                    self.__workspace_preprocessed_path)
        self.__ref_img_path, self.__cols, self.__rows, self.__proj, self.__geo = p.get_ref_inf(
            self.__preprocessed_paras[ref_img_name])

        logger.info('preprocess_handle success!')
        logger.info('progress bar: 30%')


    def cal_roi(self):
        """
        创建掩膜，并裁剪成统一范围
        """
        # 1、利用叠掩区域生成局部入射角的Mask
        processing_path = self.__workspace_processing_path
        # 利用角度为nan生成Mask
        pp.check_LocalIncidenceAngle(self.__preprocessed_paras['LocalIncidenceAngle'], self.__preprocessed_paras['LocalIncidenceAngle'])
        angle_nan_mask_path = processing_path + 'angle_nan_mask.tif'
        roi.trans_tif2mask(angle_nan_mask_path, self.__preprocessed_paras['LocalIncidenceAngle'], np.nan)
        # 利用影像的有效范围生成MASK
        tif_nan_mask_path = processing_path + 'tif_mask_nan.tif'
        roi.trans_tif2mask(tif_nan_mask_path, self.__preprocessed_paras[self.__sar_tif_name], np.nan)
        # tif_zero_mask_path = processing_path + 'tif_mask_zero.tif'
        # roi.trans_tif2mask(tif_zero_mask_path, self.__preprocessed_paras[self.__sar_tif_name], 0,0)

        # 2、利用cover计算植被覆盖范围
        cover_mask_path = self.__workspace_processing_path + "SurfaceCover_mask.tif"
        cover_id_list = list(self.__processing_paras['CoveringIDs'].split(';'))
        cover_id_list = [int(num) for num in cover_id_list]
        roi.trans_cover2mask(cover_mask_path, self.__preprocessed_paras["surface_coverage"], cover_id_list)
        # 3、利用NDVI计算裸土范围该指数的输出值在 -1.0 和 1.0 之间，大部分表示植被量，
        # 负值主要根据云、水和雪而生成
        # 接近零的值则主要根据岩石和裸土而生成。
        # 较低的（小于等于 0.1）NDVI 值表示岩石、沙石或雪覆盖的贫瘠区域。
        # 中等值（0.2 至 0.3）表示灌木丛和草地
        # 较高的值（0.6 至 0.8）表示温带雨林和热带雨林。
        ndvi_mask_path = self.__workspace_processing_path + "Ndvi_mask.tif"
        ndvi_scope = list(self.__processing_paras['NDVIScope'].split(';'))
        threshold_of_ndvi_min = float(ndvi_scope[0])
        threshold_of_ndvi_max = float(ndvi_scope[1])
        roi.trans_tif2mask(ndvi_mask_path, self.__preprocessed_paras["NDVI"], threshold_of_ndvi_min, threshold_of_ndvi_max)
        logger.info('create masks success!')
        # 4、利用覆盖范围和裸土范围 生成总的MASK
        bare_land_mask_path = self.__workspace_processing_path + "bare_land_mask.tif"
        roi.combine_mask(bare_land_mask_path, ndvi_mask_path, cover_mask_path)
        roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_nan_mask_path)
        # roi.combine_mask(bare_land_mask_path, bare_land_mask_path, tif_zero_mask_path)
        roi.combine_mask(bare_land_mask_path, bare_land_mask_path, angle_nan_mask_path)
        logger.info('combine_mask success!')

        # 5、使用ROI区域进行掩膜
        roi.cal_roi(self.__workspace_maskcai_localang_path + "LocalIncidenceAngle_preproed.tif",
                    self.__preprocessed_paras["LocalIncidenceAngle"], bare_land_mask_path, background_value=1)
        logger.info('create ROI three image success!')
        return bare_land_mask_path

    def cal_leaf_index(self, vm_file, image_file, angle_file, v2_path, num):
        """
        叶面积指数计算公式
        V1:植被层特征参数，V1设定为1
        v2：植被层特征参数，V2为叶面积指数
        A,B,C,D 经验系数
        vm 土壤体积含水量
        """
        # 滤波
        # image_filter_path = v2_path.replace("LeafAreaIndexProduct","lee_filer_img")
        # f.lee_filter(image_file, image_filter_path, FILTER_SISE)

        a = self.__processing_paras["A"]
        b = self.__processing_paras["B"]
        c = self.__processing_paras["C"]
        d = self.__processing_paras["D"]
        # k = self.__processing_paras["K"]
        # m = self.__processing_paras["M"]
        v1 = 1
        vm_value = self.imageHandler.get_band_array(vm_file, 1)
        image_value = self.imageHandler.get_band_array(image_file, 1)

        # Filter.lee_filter_array(image_value, image_value, FILTER_SISE)

        angle = self.imageHandler.get_band_array(angle_file, 1)
        cos_angle_value = np.cos(angle)
        # 开始计算叶面积指数
        back_soil = c * vm_value - d
        t = (image_value - a * v1 * cos_angle_value) / (back_soil - a * v1 * cos_angle_value)
        where_0 = np.where(t <= 0)
        t[where_0] = 1
        array_v2 = -1 * (np.log(t)) * cos_angle_value / (2 * b)
        array_v2[where_0] = 0
        self.imageHandler.write_img(v2_path, self.__proj, self.__geo, array_v2)
        logger.info('block:%s cal LeafIndex success!', num)

    def create_soil_moisture_tif(self):
        """
        创建土壤介电常数影像
        """
        # 读取实测值，从经纬度坐标系转为图像坐标系
        measured_data_img = csvh.trans_measuredata(csvh.readcsv(self.__processing_paras['MeasuredData']), self.__ref_img_path)
        # 插值法生成土壤水分二维影像
        grid_x, grid_y = np.mgrid[0:self.__rows:1, 0:self.__cols:1]
        points = np.array(measured_data_img)[:, 0:2]
        values = np.array(measured_data_img)[:, 2].T
        grid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest')
        self.__processing_paras['SoilMoisture'] = os.path.join(self.__workspace_maskcai_SoilMoi_path, 'soil_moisture.tif')
        self.imageHandler.write_img(self.__processing_paras['SoilMoisture'], self.__proj, self.__geo, grid_z0)
        logger.info('create soil_moisture image success!')
        logger.info('progress bar: 40%')

    def block_process(self,start):
        """
        生成叶面积指数产品
        """
        # 生成土壤水分影像
        self.create_soil_moisture_tif()
        shutil.copyfile(self.__preprocessed_paras[self.__sar_tif_name], self.__workspace_maskcai_image_path + self.__sar_tif_name+'.tif')
        logger.info('progress bar: 50%')

        block_size = self.BlockProcess.get_block_size(self.__rows, self.__cols)

        self.BlockProcess.cut_new(self.__workspace_maskcai_image_path, self.__workspace_cai_sartif_path, ['tif', 'tiff'], 'tif',  block_size)
        self.BlockProcess.cut_new(self.__workspace_maskcai_localang_path, self.__workspace_cai_localang_path, ['tif', 'tiff'], 'tif',  block_size)
        self.BlockProcess.cut_new(self.__workspace_maskcai_SoilMoi_path, self.__workspace_cai_SoilMoi_path, ['tif', 'tiff'], 'tif',  block_size)
        logger.info('mask data image success!')

        # 计算每小块的叶面积指数
        in_tif_paths = list(glob.glob(os.path.join(self.__workspace_cai_sartif_path, '*.tif')))
        num = 0

        # 开启多进程处理
        processes_num = min([len(in_tif_paths), multiprocessing_num])


        Filter().lee_filter_multiprocess(in_tif_paths, in_tif_paths, FILTER_SISE, processes_num)

        pool = multiprocessing.Pool(processes=processes_num)
        pl = []
        for name in in_tif_paths:
            suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + \
                     name.split('_')[-1]
            tif_path = name
            vm_path = self.__workspace_cai_SoilMoi_path + "soil_moisture" + suffix
            angle_path = self.__workspace_cai_localang_path + "LocalIncidenceAngle_preproed" + suffix
            v2_save_path = self.__workspace_Leafindex_path + "LeafAreaIndexProduct" + suffix
            pl.append(pool.apply_async(self.cal_leaf_index, (vm_path, tif_path, angle_path, v2_save_path, num)))
            logger.info('total:%s, block:%s calculating leaf_index!', len(in_tif_paths), num)
            num = num + 1

        pool.close()
        pool.join()
        logger.info('all img cal LeafIndex success!')
        logger.info('progress bar: 80%')
        ref_tif_path = self.__ref_img_path
        date_type = 'float32'
        self.BlockProcess.combine_new(self.__workspace_Leafindex_path, self.__cols, self.__rows, self.__workspace_processing_path + "combine", 'tif',  ['tif'], date_type)
        # 添加地理信息
        out_path = self.__workspace_processing_path + "combine\\LeafAreaIndexProduct.tif"
        self.BlockProcess.assign_spatial_reference_byfile(ref_tif_path, out_path)

        # 截取roi区域
        bare_land_mask_path = self.cal_roi()
        SrcImageName = os.path.split(self.__input_paras["SAR"]['ParaValue'])[1].split('.tar.gz')[0] + '-lef.tif'
        # product_path = self.__product_dic + "LeafAreaIndexProduct.tif"
        product_path = os.path.join(self.__product_dic, SrcImageName)
        roi.cal_roi(product_path, out_path, bare_land_mask_path, background_value=np.nan)

        logger.info('block_process finished!')

        proj, geos, data = self.imageHandler.read_img(product_path)
        data[data < leaf_index_value_min] = leaf_index_value_min
        data[data > leaf_index_value_max] = leaf_index_value_max
        self.imageHandler.write_img(product_path, proj, geos, data)
        # 生成快视图
        self.imageHandler.write_quick_view(product_path)


        xml_path = "./model_meta.xml"
        tem_folder = self.__workspace_path + EXE_NAME + r"\Temporary""\\"
        image_path = product_path
        out_path1 = os.path.join(tem_folder, "trans_geo_projcs.tif")
        out_path2 = os.path.join(tem_folder, "trans_projcs_geo.tif")
        # par_dict = CreateDict(image_path, 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 = 0
        # for id in cover_id_list:
        #     if id < id_min:
        #         id_min = id
        #     if id > id_max:
        #         id_max = id
        # 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["SAR"]['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)

        model_path = "./product.xml"
        meta_xml_path = os.path.join(self.__workspace_processing_path, SrcImageName + "-lef.meta.xml")

        para_dict = CreateMetaDict(image_path, self.__processing_paras['META'], self.__workspace_processing_path,
                                   out_path1, out_path2).calu_nature()
        para_dict.update({"imageinfo_ProductName": "叶面积指数"})
        para_dict.update({"imageinfo_ProductIdentifier": "LeafAreaIndex"})
        para_dict.update({"imageinfo_ProductLevel": "LEVEL3"})
        para_dict.update({"ProductProductionInfo_BandSelection": "1"})
        para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "MeasuredData,NDVI,LandCover"})
        para_dict.update({"MetaInfo_Unit": "None"})  # 设置单位
        CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()

        # 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 process_handle(self,start):
        self.cal_roi()
        self.block_process(start)
        return True


if __name__ == '__main__':
    multiprocessing.freeze_support() # 解决多进程打包错误
    start = datetime.datetime.now()
    try:
        if len(sys.argv) < 2:
            xml_path = 'LeafAreaIndex.xml'
        else:
            xml_path = sys.argv[1]
        main_handler = LeafIndexMain(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)