395 lines
18 KiB
Python
395 lines
18 KiB
Python
|
# -*- coding: UTF-8 -*-
|
|||
|
|
"""
|
|||
|
|
@Project ：microproduct
|
|||
|
|
@File ：LandCoverMain.py
|
|||
|
|
@Function ：实现对数据地表覆盖的训练和分类识别
|
|||
|
|
@Author ：SHJ
|
|||
|
|
@Date ：2021/11/15
|
|||
|
|
@Version ：1.0.0
|
|||
|
|
"""
|
|||
|
|
import datetime
|
|||
|
|
import logging
|
|||
|
|
import os
|
|||
|
|
import shutil
|
|||
|
|
import sys
|
|||
|
|
import numpy as np
|
|||
|
|
import cv2
|
|||
|
|
import pyproj._compat
|
|||
|
|
# 导入PreProcess模块要在其他包含gdal库的模块前面，不然剪切影像会报错，详见https://blog.csdn.net/u014656611/article/details/106450006
|
|||
|
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
|||
|
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
|||
|
|
from LandCoverAuxData import LandCoverMeasCsv
|
|||
|
|
from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource, InitPara
|
|||
|
|
from tool.algorithm.algtools.logHandler import LogHandler
|
|||
|
|
from tool.algorithm.xml.CreatMetafile import CreateMetafile
|
|||
|
|
from LandCoverXmlInfo import CreateDict, CreateStadardXmlFile
|
|||
|
|
from tool.config.ConfigeHandle import Config as cf
|
|||
|
|
from tool.csv.csvHandle import csvHandle
|
|||
|
|
from tool.file.fileHandle import fileHandle
|
|||
|
|
from tool.algorithm.ml.machineLearning import MachineLeaning as ml
|
|||
|
|
from tool.algorithm.algtools.ROIAlg import ROIAlg as roi
|
|||
|
|
import multiprocessing
|
|||
|
|
csvh = csvHandle()
|
|||
|
|
|
|||
|
|
if cf.get('debug') == 'True':
|
|||
|
|
FILE_DEBUG = True
|
|||
|
|
else:
|
|||
|
|
FILE_DEBUG = False
|
|||
|
|
EXE_NAME = cf.get('exe_name')
|
|||
|
|
LogHandler.init_log_handler('run_log\\' + EXE_NAME)
|
|||
|
|
logger = logging.getLogger("mylog")
|
|||
|
|
FILTER_SIZE = int(cf.get('filter_size'))
|
|||
|
|
file =fileHandle(FILE_DEBUG)
|
|||
|
|
MAX_TRAN_NUM = int(cf.get('max_tran__num_per_class'))
|
|||
|
|
env_str = os.path.split(os.path.realpath(__file__))[0]
|
|||
|
|
os.environ['PROJ_LIB'] = env_str
|
|||
|
|
|
|||
|
|
DEBUG = False
|
|||
|
|
|
|||
|
|
class LandCoverMain:
|
|||
|
|
"""
|
|||
|
|
算法主函数
|
|||
|
|
"""
|
|||
|
|
def __init__(self, alg_xml_path):
|
|||
|
|
self.alg_xml_path = alg_xml_path
|
|||
|
|
self.imageHandler = ImageHandler()
|
|||
|
|
self.__alg_xml_handler = ManageAlgXML(alg_xml_path)
|
|||
|
|
self.__check_handler = CheckSource(self.__alg_xml_handler)
|
|||
|
|
self.__workspace_path = None
|
|||
|
|
self.__out_para = None
|
|||
|
|
self.__input_paras, self.__processing_paras, self.__preprocessed_paras = {}, {}, {}
|
|||
|
|
# 多时相影像名称
|
|||
|
|
self.__HHHV_names_list, self.__VVVH_names_list, self.__HH_names_list, self.__VV_names_list = [], [],[],[]
|
|||
|
|
# 保存特征数据的名称
|
|||
|
|
self.__feature_name_list = []
|
|||
|
|
# xml参数字典
|
|||
|
|
self.__parameters_dic = {}
|
|||
|
|
# 参考影像路径
|
|||
|
|
self.__ref_img_path = ''
|
|||
|
|
# 宽/列数,高/行数
|
|||
|
|
self.__cols, self.__rows = 0, 0
|
|||
|
|
# 坐标系
|
|||
|
|
self.__proj = ''
|
|||
|
|
# 影像投影变换矩阵
|
|||
|
|
self.__geo = [0, 0, 0, 0, 0, 0]
|
|||
|
|
self.__polar = 1
|
|||
|
|
|
|||
|
|
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)
|
|||
|
|
|
|||
|
|
self.__check_handler.check_alg_xml()
|
|||
|
|
self.__check_handler.check_run_env()
|
|||
|
|
|
|||
|
|
# 检查景影像是否为全极化
|
|||
|
|
self.__input_paras = self.__alg_xml_handler.get_input_paras()
|
|||
|
|
|
|||
|
|
checkFlag, self.__parameters_dic = self.__check_handler.check_input_paras(self.__input_paras)
|
|||
|
|
|
|||
|
|
self.__workspace_path = self.__alg_xml_handler.get_workspace_path()
|
|||
|
|
self.__create_work_space()
|
|||
|
|
self.__processing_paras = InitPara.init_processing_paras(self.__input_paras)
|
|||
|
|
self.__processing_paras.update(InitPara(DEBUG).get_mult_tar_gz_infs(self.__processing_paras, self.__workspace_preprocessing_path))
|
|||
|
|
self.__polar = self.__processing_paras['Polarization']
|
|||
|
|
self.image_meta_xml = self.__processing_paras['META']
|
|||
|
|
for name in self.__processing_paras['name_list']:
|
|||
|
|
flag_list = self.__processing_paras[name + '_pola']
|
|||
|
|
if self.__polar == 1:
|
|||
|
|
if flag_list[0] == 1:
|
|||
|
|
self.__HH_names_list.append(name)
|
|||
|
|
elif flag_list[3] == 1:
|
|||
|
|
self.__VV_names_list.append(name)
|
|||
|
|
elif self.__polar == 2:
|
|||
|
|
if flag_list[0] == 1 and flag_list[1] == 1:
|
|||
|
|
self.__HHHV_names_list.append(name)
|
|||
|
|
elif flag_list[2] == 1 and flag_list[3] == 1:
|
|||
|
|
self.__VVVH_names_list.append(name)
|
|||
|
|
self.__feature_name_list = [key for key in self.__processing_paras.keys() if ('FeatureMap' in key)]
|
|||
|
|
self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', r"LandCoverProduct.tar.gz")
|
|||
|
|
self.__alg_xml_handler.write_out_para("LandCoverProductDir", self.__out_para) # 写入输出参数
|
|||
|
|
|
|||
|
|
logger.info('check_source success!')
|
|||
|
|
return True
|
|||
|
|
|
|||
|
|
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 + r"\Temporary\preprocessing""\\"
|
|||
|
|
self.__workspace_preprocessed_path = self.__workspace_path + EXE_NAME + r"\Temporary\preprocessed""\\"
|
|||
|
|
self.__workspace_processing_path = self.__workspace_path + EXE_NAME + r"\Temporary\processing""\\"
|
|||
|
|
self.__workspace_block_tif_path = self.__workspace_path + EXE_NAME + r"\Temporary\blockTif""\\"
|
|||
|
|
self.__workspace_block_tif_processed_path = self.__workspace_path + EXE_NAME + r"\Temporary\blockTifProcessed""\\"
|
|||
|
|
self.__feature_tif_dir = self.__workspace_processing_path + 'feature_tif'
|
|||
|
|
self.__product_dir = self.__workspace_processing_path + 'product\\'
|
|||
|
|
path_list = [self.__workspace_preprocessing_path, self.__workspace_preprocessed_path,
|
|||
|
|
self.__workspace_processing_path, self.__workspace_block_tif_path,
|
|||
|
|
self.__workspace_block_tif_processed_path, self.__feature_tif_dir,self.__product_dir]
|
|||
|
|
file.creat_dirs(path_list)
|
|||
|
|
logger.info('create new workspace success!')
|
|||
|
|
|
|||
|
|
def del_temp_workspace(self):
|
|||
|
|
"""
|
|||
|
|
临时工作区
|
|||
|
|
"""
|
|||
|
|
path = self.__workspace_path + EXE_NAME + r"\Temporary"
|
|||
|
|
if os.path.exists(path):
|
|||
|
|
file.del_folder(path)
|
|||
|
|
def get_name_tif_list(self):
|
|||
|
|
para_names = []
|
|||
|
|
for tif_name in self.__HHHV_names_list:
|
|||
|
|
para_names.append(tif_name + '_HH')
|
|||
|
|
para_names.append(tif_name + '_HV')
|
|||
|
|
for tif_name in self.__VVVH_names_list:
|
|||
|
|
para_names.append(tif_name + '_VH')
|
|||
|
|
para_names.append(tif_name + '_VV')
|
|||
|
|
for tif_name in self.__HH_names_list:
|
|||
|
|
para_names.append(tif_name + '_HH')
|
|||
|
|
for tif_name in self.__VV_names_list:
|
|||
|
|
para_names.append(tif_name + '_VV')
|
|||
|
|
return para_names
|
|||
|
|
|
|||
|
|
|
|||
|
|
def preprocess_handle(self):
|
|||
|
|
"""
|
|||
|
|
预处理
|
|||
|
|
"""
|
|||
|
|
# 读取每一张图像,检查图像坐标系
|
|||
|
|
para_names = self.get_name_tif_list()
|
|||
|
|
|
|||
|
|
ref_img_name = para_names[0]
|
|||
|
|
para_names = para_names + self.__feature_name_list
|
|||
|
|
|
|||
|
|
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('progress bar: 40%')
|
|||
|
|
logger.info('preprocess_handle success!')
|
|||
|
|
|
|||
|
|
|
|||
|
|
def create_roi(self):
|
|||
|
|
"""
|
|||
|
|
计算ROI掩膜
|
|||
|
|
:return:掩膜路径
|
|||
|
|
"""
|
|||
|
|
para_names = self.get_name_tif_list()
|
|||
|
|
tif_mask_path = roi().roi_process(para_names, self.__workspace_processing_path + "/roi/", self.__processing_paras, self.__preprocessed_paras)
|
|||
|
|
logger.info('create ROI image success!')
|
|||
|
|
return tif_mask_path
|
|||
|
|
|
|||
|
|
|
|||
|
|
def create_features(self):
|
|||
|
|
"""
|
|||
|
|
使用每一景全极化影像生成极化特征影像
|
|||
|
|
:para name_list: 每一景影像的路径
|
|||
|
|
"""
|
|||
|
|
for fea_name in self.__feature_name_list:
|
|||
|
|
path = self.__preprocessed_paras[fea_name]
|
|||
|
|
feature_path = os.path.join(self.__feature_tif_dir, os.path.basename(path))
|
|||
|
|
shutil.copyfile(path, feature_path)
|
|||
|
|
feature_array = ImageHandler.get_band_array(feature_path)
|
|||
|
|
feature_array = ml.standardization(feature_array)
|
|||
|
|
self.imageHandler.write_img(feature_path, self.__proj, self.__geo, feature_array)
|
|||
|
|
|
|||
|
|
if self.__polar == 2:
|
|||
|
|
self.create_double_polar_features()
|
|||
|
|
elif self.__polar == 1:
|
|||
|
|
self.create_single_polar_features()
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
def create_single_polar_features(self):
|
|||
|
|
name_list = [] + self.__HH_names_list + self.__VV_names_list
|
|||
|
|
for name in name_list:
|
|||
|
|
if name in self.__HH_names_list:
|
|||
|
|
tif_name = name + '_HH'
|
|||
|
|
else:
|
|||
|
|
tif_name = name + '_VV'
|
|||
|
|
|
|||
|
|
path = self.__preprocessed_paras[tif_name]
|
|||
|
|
array_single_pol = ImageHandler.get_band_array(path)
|
|||
|
|
|
|||
|
|
# 后向散射无效值设置为nan
|
|||
|
|
array_single_pol[np.where(array_single_pol < -100.0)] = np.nan
|
|||
|
|
array_single_pol = ml.standardization(array_single_pol)
|
|||
|
|
feature_path = os.path.join(self.__feature_tif_dir, os.path.basename(path))
|
|||
|
|
self.imageHandler.write_img(feature_path, self.__proj, self.__geo, array_single_pol)
|
|||
|
|
|
|||
|
|
def create_double_polar_features(self):
|
|||
|
|
name_list = [] + self.__HHHV_names_list + self.__VVVH_names_list
|
|||
|
|
# # 生成极化组合特征
|
|||
|
|
for name in name_list:
|
|||
|
|
if name in self.__HHHV_names_list:
|
|||
|
|
array_single_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name+'_HH'])
|
|||
|
|
array_cross_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name+'_HV'])
|
|||
|
|
else:
|
|||
|
|
array_single_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name + '_VV'])
|
|||
|
|
array_cross_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name + '_VH'])
|
|||
|
|
|
|||
|
|
# 后向散射无效值设置为nan
|
|||
|
|
array_single_pol[np.where(array_single_pol < -100.0)] = np.nan
|
|||
|
|
array_cross_pol[np.where(array_cross_pol < -100.0)] = np.nan
|
|||
|
|
|
|||
|
|
decomposeList = self.__parameters_dic["FeatureCombination"].split(',')
|
|||
|
|
if len(decomposeList) == 1:
|
|||
|
|
decomposeList = self.__parameters_dic["FeatureCombination"].split(';')
|
|||
|
|
|
|||
|
|
for n in range(8):
|
|||
|
|
if str(n) in decomposeList:
|
|||
|
|
feature_path = os.path.join(self.__feature_tif_dir, name + '_feature'+str(n)+'.tif')
|
|||
|
|
if n == 0:
|
|||
|
|
array = array_single_pol
|
|||
|
|
elif n == 1:
|
|||
|
|
array = array_cross_pol
|
|||
|
|
elif n == 2:
|
|||
|
|
array = array_single_pol + array_cross_pol
|
|||
|
|
elif n == 3:
|
|||
|
|
array = array_single_pol - array_cross_pol
|
|||
|
|
elif n == 4:
|
|||
|
|
array = (array_single_pol - array_cross_pol)/(array_single_pol + array_cross_pol)
|
|||
|
|
elif n == 5:
|
|||
|
|
array = array_single_pol/array_cross_pol
|
|||
|
|
elif n == 6:
|
|||
|
|
array = array_cross_pol + array_single_pol/array_cross_pol
|
|||
|
|
elif n == 7:
|
|||
|
|
array = (array_single_pol**2 + array_cross_pol**2)/(array_single_pol**2 - array_cross_pol**2)
|
|||
|
|
else:
|
|||
|
|
continue
|
|||
|
|
array = ml.standardization(array)
|
|||
|
|
self.imageHandler.write_img(feature_path, self.__proj, self.__geo, array)
|
|||
|
|
logging.info("feature0 = array_single_pol, feature1= array_cross_pol")
|
|||
|
|
logging.info("feature1 = array_cross_pol")
|
|||
|
|
logging.info("feature2 = array_single_pol + array_cross_pol")
|
|||
|
|
logging.info("feature3 = array_single_pol - array_cross_pol")
|
|||
|
|
logging.info("feature4 = (array_single_pol - array_cross_pol)/(array_single_pol + array_cross_pol)")
|
|||
|
|
logging.info("feature5 = array_single_pol/array_cross_pol")
|
|||
|
|
logging.info("feature6 = array_cross_pol + array_single_pol/array_cross_pol")
|
|||
|
|
logging.info("feature7 = (array_single_pol**2 + array_cross_pol**2)/(array_single_pol**2 - array_cross_pol**2)")
|
|||
|
|
|
|||
|
|
def process_handle(self, start):
|
|||
|
|
"""
|
|||
|
|
算法主处理函数
|
|||
|
|
"""
|
|||
|
|
# 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
|
|||
|
|
pm = LandCoverMeasCsv(self.__processing_paras['LabelData'], self.__ref_img_path,MAX_TRAN_NUM)
|
|||
|
|
train_data_list = pm.api_read_measure()
|
|||
|
|
train_data_dic = csvh.trans_landCover_list2dic(train_data_list)
|
|||
|
|
|
|||
|
|
csvh_roi = csvHandle(self.__rows, self.__cols)
|
|||
|
|
label_img = csvh_roi.get_roi_img()
|
|||
|
|
if (len(label_img) != 0):
|
|||
|
|
self.imageHandler.write_img(self.__workspace_processing_path + "label_img.tif", "", [0, 0, 0, 0, 0, 0],
|
|||
|
|
label_img)
|
|||
|
|
|
|||
|
|
# csvh_roi = csvHandle(self.__rows, self.__cols)
|
|||
|
|
# train_data_dic = csvh_roi.trans_landCover_measuredata_dic(
|
|||
|
|
# csvh_roi.readcsv(self.__processing_paras['LabelData']), self.__preprocessed_paras['ori_sim'], MAX_TRAN_NUM)
|
|||
|
|
# label_img = csvh_roi.get_roi_img()
|
|||
|
|
# if (len(label_img) != 0):
|
|||
|
|
# self.imageHandler.write_img(self.__workspace_processing_path + "label_img.tif", "", [0, 0, 0, 0, 0, 0],
|
|||
|
|
# label_img)
|
|||
|
|
|
|||
|
|
type_id_name, type_id_parent = pm.class_list()
|
|||
|
|
self.create_meta_file(self.__ref_img_path, type_id_name, type_id_parent)
|
|||
|
|
logger.info("read csv data success!")
|
|||
|
|
logger.info('progress bar: 45%')
|
|||
|
|
roi_img = self.imageHandler.get_band_array(self.create_roi())
|
|||
|
|
|
|||
|
|
|
|||
|
|
# out_tif_path = self.ref_lee_filter(name_list)
|
|||
|
|
self.create_features()
|
|||
|
|
|
|||
|
|
logger.info("create_features success!")
|
|||
|
|
logger.info('progress bar: 50%')
|
|||
|
|
|
|||
|
|
# 生成最优特征子集训练集
|
|||
|
|
X_train, Y_train, optimal_feature = ml.gene_optimal_train_set(train_data_dic, self.__feature_tif_dir)
|
|||
|
|
# RF
|
|||
|
|
clf = ml.trainRF(X_train, Y_train)
|
|||
|
|
logger.info('progress bar: 60%')
|
|||
|
|
|
|||
|
|
# 生成测试集
|
|||
|
|
X_test_path_list = ml.gene_test_set(self.__feature_tif_dir, optimal_feature)
|
|||
|
|
|
|||
|
|
# 预测
|
|||
|
|
logger.info('testing')
|
|||
|
|
cover_path = ml.predict(clf, X_test_path_list, EXE_NAME, self.__workspace_processing_path, self.__rows, self.__cols)
|
|||
|
|
logger.info('test success!')
|
|||
|
|
logger.info('progress bar: 95%')
|
|||
|
|
|
|||
|
|
# 转换数据
|
|||
|
|
proj, geo, cover_data = self.imageHandler.read_img(cover_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)
|
|||
|
|
|
|||
|
|
for id, class_id in zip(train_data_dic['ids'], train_data_dic['class_ids']):
|
|||
|
|
cover_data[np.where(cover_data == id)] = class_id
|
|||
|
|
cover_data = np.int16(cover_data)
|
|||
|
|
# 获取影像roi区域
|
|||
|
|
cover_data = cover_data * roi_img
|
|||
|
|
product_dir = self.__workspace_processing_path + 'product\\'
|
|||
|
|
product_path = os.path.join(product_dir, 'LandCoverProduct.tif')
|
|||
|
|
self.imageHandler.write_img(product_path, self.__proj, self.__geo, cover_data)
|
|||
|
|
|
|||
|
|
# 生成快视图
|
|||
|
|
self.imageHandler.write_quick_view(product_path, color_img=True)
|
|||
|
|
# 文件夹打包
|
|||
|
|
|
|||
|
|
file.make_targz(self.__out_para, product_dir)
|
|||
|
|
logger.info('process_handle success!')
|
|||
|
|
|
|||
|
|
def create_meta_file(self, product_path, type_id_name, type_id_parent):
|
|||
|
|
product_dir = self.__workspace_processing_path + 'product\\'
|
|||
|
|
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
|
|||
|
|
CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, model_xml_path).create_standard_xml()
|
|||
|
|
|
|||
|
|
SrcImagePath = self.__input_paras["MultiTempSAR"]['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 = product_dir+EXE_NAME+"Product.meta.xml"
|
|||
|
|
CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process2(type_id_name, type_id_parent, SrcImageName)
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
multiprocessing.freeze_support() #解决打包与运行错误
|
|||
|
|
start = datetime.datetime.now()
|
|||
|
|
try:
|
|||
|
|
if len(sys.argv) < 2:
|
|||
|
|
xml_path = 'LandCover_geo.xml'
|
|||
|
|
else:
|
|||
|
|
xml_path = sys.argv[1]
|
|||
|
|
|
|||
|
|
main_handler = LandCoverMain(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)
|