637 lines
30 KiB
Python
637 lines
30 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project ：microproduct
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@File ：PhenologyMain.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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"""
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import csv
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import glob
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import logging
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import os
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import datetime
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import shutil
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import pyproj._compat
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import cv2
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import numpy as np
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from osgeo import gdal, osr, ogr
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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.xml.AlgXmlHandle import ManageAlgXML, CheckSource, InitPara
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from tool.algorithm.algtools.logHandler import LogHandler
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from tool.algorithm.xml.AnalysisXml import xml_extend
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from tool.algorithm.xml.CreatMetafile import CreateMetafile
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from tool.algorithm.algtools.ROIAlg import ROIAlg as alg
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from VegetationPhenologyXmlInfo import CreateDict, CreateStadardXmlFile
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from VegetationPhenologyAuxData import PhenoloyMeasCsv_geo
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from tool.algorithm.polsarpro.AHVToPolsarpro import AHVToPolsarpro
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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 tool.algorithm.algtools.ROIAlg import ROIAlg as roi
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import sys
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from tool.algorithm.transforml1a.transHandle import TransImgL1A
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from tool.csv.csvHandle import csvHandle
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from tool.algorithm.polsarpro.createfeature import CreateFeature
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import multiprocessing
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from tool.algorithm.ml.machineLearning import MachineLeaning as ml
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from tool.config.ConfigeHandle import Config as cf
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csvh = csvHandle()
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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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LogHandler.init_log_handler('run_log\\' + EXE_NAME)
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logger = logging.getLogger("mylog")
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FILTER_SIZE = int(cf.get('filter_size'))
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MAX_TRAN_NUM = int(cf.get('max_tran__num_per_class'))
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tar = r'-' + cf.get('tar')
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productLevel = cf.get('productLevel')
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multiprocessing_num = int(cf.get('multiprocessing_num'))
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file =fileHandle(DEBUG)
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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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class PhenologyMain:
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"""
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土壤粗糙度处理主函数
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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.__processing_paras, self.__preprocessed_paras = {}, {}, {}
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self.processinfo = [1, 1, 1, 1]
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self._name_tr_dic = {} #l1a转换geo的对象
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self.__FeatureMap = {}
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# 特征索引对应的文件名
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self.___FeatureFileNameMap = {}
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# 初始化特征参数
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self.__FeatureParaInit()
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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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self._env_str = os.getcwd()
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logger.info("sysdir: %s", self._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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checkFlag, self.__parameters_dic = self.__check_handler.check_input_paras(self.__input_paras)
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if checkFlag is False:
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return False
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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, self.__workspace_preprocessing_path)
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self.__processing_paras.update(InitPara(DEBUG).get_mult_tar_gz_infs(self.__processing_paras, self.__workspace_preprocessing_path))
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SrcImagePath = self.__input_paras["AHVS"]['ParaValue']
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paths = SrcImagePath.split(';')
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self.SrcImageName = os.path.split(paths[0])[1].split('.tar.gz')[0]
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result_name = self.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("VegetationPhenologyProduct", 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 __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.__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.__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_single_tar(self,name,scopes_roi):
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key_list = [key for key in self.__processing_paras.keys() if((name in key) and ('inc_angle' not in key) and ('LocalIncidenceAngle' not in key)and ('pola' not in key)
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and ('paraMeter' not in key) and ('ori_sim' not in key)and ('Origin_META' not in key)and ('META' not in key))]
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ori_sim_key = [key for key in key_list if ('sim_ori' in key)][0]
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ori_sim_path = self.__processing_paras[ori_sim_key]
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key_list.remove(ori_sim_key)
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hh_key = [key for key in key_list if ('HH' in key)][0]
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l1a_width = ImageHandler.get_img_width(self.__processing_paras[hh_key])
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l1a_height = ImageHandler.get_img_height(self.__processing_paras[hh_key])
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tr = TransImgL1A(ori_sim_path, scopes_roi, l1a_height, l1a_width)
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for k in key_list:
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out_path = os.path.join(self.__workspace_preprocessed_path, k.split('_')[6] + '_' + k.split('_')[10] + "_preprocessed.tif")
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tr.cut_L1A(self.__processing_paras[k], out_path)
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self.__preprocessed_paras.update({k: out_path})
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self._name_tr_dic.update({name: tr})
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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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# scopes_roi = pp().cal_scopes_roi(self.__processing_paras)
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p = pp()
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for name in self.__processing_paras['name_list']:
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para_names_geo = [name + '_sim_ori', 'Covering']
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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 += (xml_extend(self.__processing_paras[name + '_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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cutted_img_paths = p.cut_imgs_VP(self.__workspace_preprocessing_path, para_names_geo, self.__processing_paras,
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intersect_shp_path, name)
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name_d = name.split('_')[6]
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self.__preprocessed_paras.update({name + '_sim_ori': cutted_img_paths.get(name + '_sim_ori')})
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self.__preprocessed_paras.update({name_d + '_Covering': cutted_img_paths.get('Covering')})
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self.preprocess_single_tar(name, scopes_roi)
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# self.__feature_name_list = para_names_geo
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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({name + '_sim_ori': cutted_img_paths.get(name + '_sim_ori')})
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# self.__preprocessed_paras.update({name + '_Covering': cutted_img_paths.get('Covering')})
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logger.info('preprocess_handle success!')
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logger.info('progress bar: 10%')
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def gene_test_set(self, test_data, features_path_dic, name):
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# 生成测试集
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X_test_dic = {} # 同时测试多张影像
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for tif_name in features_path_dic:
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feature_array = self.imageHandler.get_data(features_path_dic[tif_name])
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dim = feature_array.shape[0]
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row_list = []
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col_list = []
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x_test = np.empty(shape=(0, dim))
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for data in test_data:
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if data[0] == tif_name:
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row, col = zip(*data[2])
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row_list = row_list + list(row)
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col_list = col_list + list(col)
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x_test = np.vstack((x_test, feature_array[:, row, col].T))
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if row_list != []:
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X_test_dic.update({tif_name: [x_test, row_list, col_list]})
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return X_test_dic
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def get_name_rows_cols(self, name):
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key_list = [key for key in self.__preprocessed_paras.keys() if
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((name in key) and ('inc_angle' not in key) and ('LocalIncidenceAngle' not in key))]
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for key in key_list:
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if "_HH" in key:
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cols = self.imageHandler.get_img_width(self.__preprocessed_paras[key])
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rows = self.imageHandler.get_img_height(self.__preprocessed_paras[key])
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return rows, cols
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def __getInputFeatures(self):
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"""
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获取输入特征组合
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1.Freeman：表面散射p_s、偶次散射p_d、体散射p_v 对应序号：[0,1,2]
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2.Yamaguchi：表面散射f_s、二次散射f_d、体散射f_v、螺旋体散射f_h 对应序号：[3,4,5,6]
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3.Cloude - Pottier：分解散射熵H、反熵A、平均散射角α 对应序号：[7,8,9,10]
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"""
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decomposeList = self.__parameters_dic["FeatureCombination"].split(',')
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stepSet = set()
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featureInput = set()
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for item in decomposeList:
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stepSet.add(int(item))
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for i in stepSet:
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if 0 <= i < 3:
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featureInput.add('Freeman')
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elif 3 <= i < 7:
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featureInput.add('Yamaguchi')
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elif 7 <= i < 10:
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featureInput.add('Cloude')
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else:
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logger.warning("__decomposeSteps Invalid data:%s!", i)
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return featureInput
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def __FeatureParaInit(self):
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self.__FeatureMap["Freeman"] = [0, 1, 2]
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self.__FeatureMap["Yamaguchi"] = [3, 4, 5, 6]
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self.__FeatureMap["Cloude"] = [7, 8, 9]
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self.___FeatureFileNameMap[0] = ['Freeman', "Freeman_Odd.bin"]
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self.___FeatureFileNameMap[1] = ['Freeman', "Freeman_Dbl.bin"]
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self.___FeatureFileNameMap[2] = ['Freeman', "Freeman_Vol.bin"]
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self.___FeatureFileNameMap[3] = ['Yamaguchi', "Yamaguchi4_Odd.bin"]
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self.___FeatureFileNameMap[4] = ['Yamaguchi', "Yamaguchi4_Dbl.bin"]
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self.___FeatureFileNameMap[5] = ['Yamaguchi', "Yamaguchi4_Vol.bin"]
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self.___FeatureFileNameMap[6] = ['Yamaguchi', "Yamaguchi4_Hlx.bin"]
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self.___FeatureFileNameMap[7] = ['Cloude', "anisotropy.bin"]
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self.___FeatureFileNameMap[8] = ['Cloude', "entropy.bin"]
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self.___FeatureFileNameMap[9] = ['Cloude', "alpha.bin"]
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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 create_feature_single_tar(self, name):
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key_list = [key for key in self.__preprocessed_paras.keys() if((name in key) and ('inc_angle' not in key) and ('LocalIncidenceAngle' not in key))]
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# ori_sim_key = [key for key in key_list if ('ori_sim' in key)][0]
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sim_ori_key = [key for key in key_list if ('sim_ori' in key)][0]
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# ori_sim_path = self.__preprocessed_paras[ori_sim_key]
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sim_ori_path = self.__preprocessed_paras[sim_ori_key]
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hh_path = self.__preprocessed_paras[name + "_HH"]
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hh_geo_path = os.path.join(self.__workspace_processing_path, os.path.split(os.path.basename(hh_path))[0] + '_geo.tif')
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paramter = self.__processing_paras[name + "paraMeter"]
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self.calInterpolation_bil_Wgs84_rc_sar_sigma(paramter, sim_ori_path, hh_path, hh_geo_path)
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# 读取实测值，获取多边形区域内所有的点，分为训练集数据和测试集数据
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pm = PhenoloyMeasCsv_geo(self.__processing_paras['MeasuredData'], hh_geo_path, MAX_TRAN_NUM)
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train_data_list = pm.api_read_measure_by_name(name.split('_')[6])
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train_data_dic = csvh.trans_landCover_list2dic(train_data_list)
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# train_data, test_data, type_map = csvh.trans_VegePhenology_measdata_dic(csvh.readcsv(self.__processing_paras['MeasuredData']), ori_sim_path)
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logger.info("read phenology Measure.csv success!")
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# 添加四极化后向散射系数到特征图中
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feature_tif_dir = os.path.join(self.__workspace_processing_path, name.split('_')[6], 'features')
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if not os.path.exists(feature_tif_dir):
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os.makedirs(feature_tif_dir)
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origin_xml = self.__processing_paras[name + "_Origin_META"]
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# 特征分解
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hh_hv_vh_vv_list = ["","","",""]
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for key in key_list:
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if "_HH" in key :
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hh_hv_vh_vv_list[0] = self.__preprocessed_paras[key]
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outPath = os.path.join(feature_tif_dir, 'cal_HH.tif')
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AHVToPolsarpro.sar_backscattering_sigma(hh_hv_vh_vv_list[0], origin_xml, outPath)
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if "_HV" in key:
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hh_hv_vh_vv_list[1] = self.__preprocessed_paras[key]
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outPath = os.path.join(feature_tif_dir, 'cal_HV.tif')
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AHVToPolsarpro.sar_backscattering_sigma(hh_hv_vh_vv_list[1], origin_xml, outPath)
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if "_VH" in key:
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hh_hv_vh_vv_list[2] = self.__preprocessed_paras[key]
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outPath = os.path.join(feature_tif_dir, 'cal_VH.tif')
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AHVToPolsarpro.sar_backscattering_sigma(hh_hv_vh_vv_list[2], origin_xml, outPath)
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if "_VV" in key:
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hh_hv_vh_vv_list[3] = self.__preprocessed_paras[key]
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outPath = os.path.join(feature_tif_dir, 'cal_VV.tif')
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AHVToPolsarpro.sar_backscattering_sigma(hh_hv_vh_vv_list[3], origin_xml, outPath)
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cols = self.imageHandler.get_img_width(hh_hv_vh_vv_list[0])
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rows = self.imageHandler.get_img_height(hh_hv_vh_vv_list[0])
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featureInput = self.__getInputFeatures()
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feature_dir = CreateFeature.decompose_single_tar(hh_hv_vh_vv_list, self.__workspace_processing_path, self.__workspace_preprocessing_path, name, self._env_str, rows, cols, FILTER_SIZE=7, debug=DEBUG, FeatureInput=featureInput)
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feature_geo_dir = self.features_geo(feature_dir, paramter, sim_ori_path, name)
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# # 获取训练集提取特征的信息
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# ids = []
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# class_ids = []
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# ch_names = []
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# positions = []
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# for data in train_data:
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# if data[0] == name:
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# class_ids.append(data[1])
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# positions.append(data[2])
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# ch_names.append(data[3])
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# class_id = [map for map in type_map if (data[1] == map[1])][0]
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# ids.append(class_id[0])
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# train_data_dic = {}
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# train_data_dic.update({"ids": ids})
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# train_data_dic.update({"class_ids": class_ids})
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# train_data_dic.update({"ch_names": ch_names})
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# train_data_dic.update({"positions": positions})
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# name_test_data =[]
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# for dt in test_data:
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# if dt[0] == name:
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# name_test_data.append(dt)
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logger.info("create_features success!")
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logger.info('progress bar: 20%')
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# return feature_dir, train_data_dic, name_test_data, type_map
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return feature_geo_dir, train_data_dic
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def features_geo(self, features_path, paraMeter, sim_ori, sar_name):
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dir = os.path.join(self.__workspace_processing_path, sar_name.split('_')[6], 'features_geo')
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if not os.path.exists(dir):
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os.makedirs(dir)
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in_tif_paths = list(glob.glob(os.path.join(features_path, '*.tif')))
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processes_num = min([len(in_tif_paths), multiprocessing_num, multiprocessing.cpu_count() - 1])
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pool = multiprocessing.Pool(processes=processes_num)
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pl = []
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for file in in_tif_paths:
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name = os.path.basename(file).split('.')[0] + '_geo.tif'
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out_path = os.path.join(dir, name)
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pl.append(pool.apply_async(self.calInterpolation_bil_Wgs84_rc_sar_sigma,
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(paraMeter, sim_ori, file, out_path))) # topp
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||
pool.close()
|
||
pool.join()
|
||
return dir
|
||
|
||
def process_handle(self, start):
|
||
"""
|
||
算法主处理函数
|
||
:return: True or False
|
||
"""
|
||
# 生成每个时相的特征， 并提取训练集和测试集
|
||
# 每个时相的影像生成特征图
|
||
|
||
X_train = []
|
||
Y_train = []
|
||
flag = True
|
||
total_name_list = []
|
||
X_test_dic = {}
|
||
train_data_dict = {}
|
||
for name in self.__processing_paras['name_list']:
|
||
|
||
feature_dir, train_data_dic = self.create_feature_single_tar(name)
|
||
# 生成训练集
|
||
# X_train_part, Y_train_part, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_dir, 0.08, 0.7)
|
||
|
||
X_train_part, Y_train_part = ml.gene_train_set_deLandcover(train_data_dic, feature_dir, self.__preprocessed_paras[name.split('_')[6] + '_Covering'], self.__processing_paras['CoveringIDs'])
|
||
name_list = ml.get_name_list(feature_dir)
|
||
|
||
# 生成测试集合
|
||
# X_test_path_list = ml.gene_test_set(feature_dir, optimal_feature)
|
||
|
||
# X_test_dic.update({name: X_test_path_list})
|
||
X_test_dic.update({name + '_features': feature_dir})
|
||
|
||
if X_train == []:
|
||
X_train = X_train_part
|
||
Y_train = Y_train_part
|
||
total_name_list = name_list
|
||
else:
|
||
X_train = np.vstack((X_train, X_train_part))
|
||
Y_train = np.hstack((Y_train, Y_train_part))
|
||
total_name_list = total_name_list + name_list
|
||
|
||
train_data_dict.update({name: train_data_dic})
|
||
|
||
logger.info("generate train and test set success!")
|
||
logger.info('progress bar: 30%')
|
||
|
||
optimal_X_train, optimal_Y_train, optimal_feature = ml.sel_optimal_feature(X_train, Y_train, total_name_list,important_threshold=0.07, correlation_threshold=0.85)
|
||
|
||
# RF
|
||
clf = ml.trainRF(optimal_X_train, optimal_Y_train)
|
||
logger.info('RF train success!')
|
||
|
||
# 测试数据
|
||
|
||
logger.info('mode testing')
|
||
for name in self.__processing_paras['name_list']:
|
||
feature_dir = X_test_dic.get(name + '_features')
|
||
X_test_path_list = ml.gene_test_set(feature_dir, optimal_feature)
|
||
in_tif_paths = list(glob.glob(os.path.join(feature_dir, '*.tif')))
|
||
rows = ImageHandler.get_img_height(in_tif_paths[0])
|
||
cols = ImageHandler.get_img_width(in_tif_paths[0])
|
||
proj_geo, geo_geo, cover_data_geo = self.imageHandler.read_img(in_tif_paths[0])
|
||
product_path = ml.predict_VP(clf, X_test_path_list, name, self.__workspace_processing_path, rows, cols)
|
||
# product_path = ml.predict_VP(clf, X_test_dic.get(name), name, self.__workspace_processing_path, rows, cols)
|
||
|
||
proj, geo, cover_data = self.imageHandler.read_img(product_path)
|
||
# 形态学（闭运算）去roi区域噪点
|
||
cover_data = np.uint8(cover_data)
|
||
kernel = np.ones((5, 5), np.uint8)
|
||
cover_data = cv2.erode(cv2.dilate(cover_data, kernel), kernel)
|
||
cover_data = np.int16(cover_data)
|
||
train_dic = train_data_dict[name]
|
||
for id, class_id in zip(train_dic['ids'], train_dic['class_ids']):
|
||
cover_data[np.where(cover_data == id)] = class_id
|
||
roi_img = self.imageHandler.get_band_array(self.create_roi(in_tif_paths[0]))
|
||
# 获取影像roi区域
|
||
cover_data_pro = cover_data * roi_img
|
||
cover_geo_path = os.path.join(self.__workspace_processing_path, os.path.basename(product_path).split('.tif')[0] + '-VPtemp.tif')
|
||
self.imageHandler.write_img(cover_geo_path, proj_geo, geo_geo, cover_data_pro)
|
||
|
||
self.resampleImgs(name, cover_geo_path)
|
||
para_names = ['Covering']
|
||
mask_dir = os.path.join(self.__workspace_processing_path, name + '\\')
|
||
bare_land_mask_path = roi().roi_process_VP(para_names, mask_dir,
|
||
self.__processing_paras, self.__preprocessed_paras, name.split('_')[6])
|
||
product_path_pro = os.path.join(self.__product_dic, name + tar + '.tif')
|
||
# 获取影像roi区域
|
||
roi.cal_roi(product_path_pro, cover_geo_path, bare_land_mask_path, background_value=0)
|
||
self.imageHandler.write_quick_view(product_path_pro, color_img=True)
|
||
meta_xml_path = self.create_meta_file(product_path_pro)
|
||
|
||
temp_folder = os.path.join(self.__workspace_path, EXE_NAME, 'Output')
|
||
out_xml = os.path.join(temp_folder, os.path.basename(meta_xml_path))
|
||
if os.path.exists(temp_folder) is False:
|
||
os.mkdir(temp_folder)
|
||
# CreateProductXml(para_dict, model_path, out_xml).create_standard_xml()
|
||
shutil.copy(meta_xml_path, out_xml)
|
||
logger.info('mode test success!')
|
||
logger.info('progress bar: 80%')
|
||
# 文件夹打包
|
||
file.make_targz(self.__out_para, self.__product_dic)
|
||
logger.info('progress bar: 100%')
|
||
|
||
|
||
def predict(self, mode, X_test_dic, validity_list, type_map, start):
|
||
# 测试数据
|
||
clf = mode
|
||
for img_name, value in X_test_dic.items():
|
||
X_test = value[0]
|
||
row_list = value[1]
|
||
col_list = value[2]
|
||
|
||
# a = X_test[:, validity_list]
|
||
# Y_test = clf.predict(a)
|
||
Y_test = clf.predict(X_test[:, validity_list])
|
||
|
||
# for type in type_map:
|
||
# train_type = type[0]
|
||
# type_id = type[1]
|
||
# Y_test[np.where(Y_test == train_type)] = type_id
|
||
|
||
# rows, cols = self.get_name_rows_cols(img_name)
|
||
# array = np.zeros((rows, cols), dtype='float16')
|
||
# array[row_list, col_list] = Y_test
|
||
# array[np.where(array == 0)] = np.nan
|
||
# product_path = self.__workspace_processing_path + img_name + '_VegetationPhenologyProduct.tif'
|
||
# product_geo_path = self.__product_dic + img_name + '_VegetationPhenologyProduct.tif'
|
||
# int_16_array = np.array(array, dtype=np.int16)
|
||
|
||
rows, cols = self.get_name_rows_cols(img_name)
|
||
array = np.zeros((rows, cols), dtype='float16')
|
||
array[row_list, col_list] = Y_test
|
||
|
||
array[np.where(array == 0)] = np.nan
|
||
temp_path = self.__workspace_processing_path + img_name + '_temp.tif'
|
||
self.imageHandler.write_img(temp_path, '', [0, 0, 0, 0, 0, 0], array)
|
||
proj, geo, cover_data = self.imageHandler.read_img(temp_path)
|
||
|
||
cover_data = np.uint8(cover_data)
|
||
kernel = np.ones((3, 3), np.uint8)
|
||
cover_data = cv2.erode(cv2.dilate(cover_data, kernel), kernel)
|
||
|
||
for type in type_map:
|
||
train_type = type[0]
|
||
type_id = type[1]
|
||
cover_data[np.where(cover_data == train_type)] = type_id
|
||
|
||
product_path = self.__workspace_processing_path + img_name + '_VegetationPhenologyProduct.tif'
|
||
product_geo_path = self.__product_dic + img_name + '_VegetationPhenologyProduct.tif'
|
||
# int_16_array = np.array(array, dtype=np.int16)
|
||
|
||
ImageHandler.write_img(product_path, '', [0, 0, 0, 0, 0, 0], cover_data)
|
||
tr = self._name_tr_dic[img_name]
|
||
# l1a图像坐标转换地理坐标
|
||
tr.l1a_2_geo_int(self.__preprocessed_paras[img_name + '_ori_sim'], product_path, product_geo_path, 'nearest')
|
||
# 生成快视图
|
||
self.imageHandler.write_quick_view(product_geo_path, color_img=True)
|
||
|
||
return product_geo_path
|
||
|
||
def get_train_dic(self, csv_path):
|
||
reader = csv.reader(open(csv_path, newline=''))
|
||
ids = []
|
||
class_ids = []
|
||
csv_list = []
|
||
for line_data in reader:
|
||
csv_list.append(line_data)
|
||
for data in csv_list[1:]:
|
||
ids.append(data[0])
|
||
class_ids.append(data[1])
|
||
|
||
train_data_dic = {}
|
||
train_data_dic.update({"ids": ids})
|
||
train_data_dic.update({"class_ids": class_ids})
|
||
|
||
return train_data_dic
|
||
|
||
def resampleImgs(self, name, refer_img_path):
|
||
|
||
cover_rampling_path = os.path.join(self.__workspace_processing_path, name + "_cover.tif")
|
||
name_d = name.split('_')[6]
|
||
pp.resampling_by_scale(self.__preprocessed_paras[name_d + "_Covering"], cover_rampling_path, refer_img_path)
|
||
self.__preprocessed_paras[name_d + "_Covering"] = cover_rampling_path
|
||
|
||
def create_roi(self, img_path):
|
||
"""
|
||
计算ROI掩膜
|
||
:return:掩膜路径
|
||
"""
|
||
processing_path = self.__workspace_processing_path
|
||
|
||
# 利用影像的有效范围生成MASK
|
||
tif_mask_path = os.path.join(processing_path, "tif_mask.tif") # processing_path + "tif_mask.tif"
|
||
alg.trans_tif2mask(tif_mask_path, img_path, np.nan)
|
||
logger.info('create ROI image success!')
|
||
return tif_mask_path
|
||
|
||
# def test_resamp():
|
||
# # 形态学（闭运算）去roi区域噪点
|
||
# # cover_data = np.uint8(cover_data)
|
||
# # kernel = np.ones((5, 5), np.uint8)
|
||
# # cover_data = cv2.erode(cv2.dilate(cover_data, kernel), kernel)
|
||
# pass
|
||
|
||
|
||
def create_meta_file(self, product_path):
|
||
# 生成元文件案例
|
||
# 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, 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
|
||
# CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, model_xml_path).create_standard_xml()
|
||
#
|
||
# SrcImagePath = self.__input_paras["AHVS"]['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 = os.path.join(self.__product_dic, EXE_NAME + "Product.meta.xml")
|
||
# self.type_id_name = csvh.vegePhenology_class_list(self.__processing_paras['MeasuredData'])
|
||
#
|
||
# CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process2(
|
||
# self.type_id_name, None, SrcImageName)
|
||
|
||
SrcImageName = os.path.basename(product_path).split('.tif')[0]
|
||
model_path = "./product.xml"
|
||
meta_xml_path = os.path.join(self.__product_dic, SrcImageName + ".meta.xml")
|
||
key = self.SrcImageName + '_Origin_META'
|
||
para_dict = CreateMetaDict(image_path, self.__processing_paras[key], self.__workspace_processing_path,
|
||
out_path1, out_path2).calu_nature()
|
||
para_dict.update({"imageinfo_ProductName": "植被物候"})
|
||
para_dict.update({"imageinfo_ProductIdentifier": "VegetationPhenology"})
|
||
para_dict.update({"imageinfo_ProductLevel": productLevel})
|
||
para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
|
||
para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "Label"})
|
||
CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
|
||
|
||
return meta_xml_path
|
||
|
||
|
||
|
||
if __name__ == '__main__':
|
||
multiprocessing.freeze_support()
|
||
start = datetime.datetime.now()
|
||
try:
|
||
if len(sys.argv) < 2:
|
||
xml_path = 'VegetationPhenology.xml'
|
||
else:
|
||
xml_path = sys.argv[1]
|
||
main_handler = PhenologyMain(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)
|