395 lines
18 KiB
Python
395 lines
18 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project ：microproduct
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@File ：LandCoverMain.py
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@Function ：实现对数据地表覆盖的训练和分类识别
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@Author ：SHJ
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@Date ：2021/11/15
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@Version ：1.0.0
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"""
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import datetime
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import logging
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import os
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import shutil
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import sys
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import numpy as np
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import cv2
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import pyproj._compat
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# 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
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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 LandCoverAuxData import LandCoverMeasCsv
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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.CreatMetafile import CreateMetafile
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from LandCoverXmlInfo import CreateDict, CreateStadardXmlFile
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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.file.fileHandle import fileHandle
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from tool.algorithm.ml.machineLearning import MachineLeaning as ml
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from tool.algorithm.algtools.ROIAlg import ROIAlg as roi
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import multiprocessing
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csvh = csvHandle()
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if cf.get('debug') == 'True':
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FILE_DEBUG = True
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else:
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FILE_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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file =fileHandle(FILE_DEBUG)
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MAX_TRAN_NUM = int(cf.get('max_tran__num_per_class'))
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env_str = os.path.split(os.path.realpath(__file__))[0]
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os.environ['PROJ_LIB'] = env_str
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DEBUG = False
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class LandCoverMain:
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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 = None
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self.__out_para = None
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self.__input_paras, self.__processing_paras, self.__preprocessed_paras = {}, {}, {}
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# 多时相影像名称
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self.__HHHV_names_list, self.__VVVH_names_list, self.__HH_names_list, self.__VV_names_list = [], [],[],[]
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# 保存特征数据的名称
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self.__feature_name_list = []
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# xml参数字典
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self.__parameters_dic = {}
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# 参考影像路径
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self.__ref_img_path = ''
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# 宽/列数,高/行数
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self.__cols, self.__rows = 0, 0
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# 坐标系
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self.__proj = ''
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# 影像投影变换矩阵
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self.__geo = [0, 0, 0, 0, 0, 0]
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self.__polar = 1
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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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checkFlag, self.__parameters_dic = self.__check_handler.check_input_paras(self.__input_paras)
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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(InitPara(DEBUG).get_mult_tar_gz_infs(self.__processing_paras, self.__workspace_preprocessing_path))
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self.__polar = self.__processing_paras['Polarization']
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self.image_meta_xml = self.__processing_paras['META']
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for name in self.__processing_paras['name_list']:
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flag_list = self.__processing_paras[name + '_pola']
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if self.__polar == 1:
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if flag_list[0] == 1:
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self.__HH_names_list.append(name)
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elif flag_list[3] == 1:
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self.__VV_names_list.append(name)
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elif self.__polar == 2:
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if flag_list[0] == 1 and flag_list[1] == 1:
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self.__HHHV_names_list.append(name)
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elif flag_list[2] == 1 and flag_list[3] == 1:
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self.__VVVH_names_list.append(name)
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self.__feature_name_list = [key for key in self.__processing_paras.keys() if ('FeatureMap' in key)]
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self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', r"LandCoverProduct.tar.gz")
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self.__alg_xml_handler.write_out_para("LandCoverProductDir", self.__out_para) # 写入输出参数
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logger.info('check_source success!')
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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 + r"\Temporary\preprocessing""\\"
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self.__workspace_preprocessed_path = self.__workspace_path + EXE_NAME + r"\Temporary\preprocessed""\\"
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self.__workspace_processing_path = self.__workspace_path + EXE_NAME + r"\Temporary\processing""\\"
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self.__workspace_block_tif_path = self.__workspace_path + EXE_NAME + r"\Temporary\blockTif""\\"
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self.__workspace_block_tif_processed_path = self.__workspace_path + EXE_NAME + r"\Temporary\blockTifProcessed""\\"
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self.__feature_tif_dir = self.__workspace_processing_path + 'feature_tif'
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self.__product_dir = 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.__feature_tif_dir,self.__product_dir]
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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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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 get_name_tif_list(self):
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para_names = []
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for tif_name in self.__HHHV_names_list:
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para_names.append(tif_name + '_HH')
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para_names.append(tif_name + '_HV')
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for tif_name in self.__VVVH_names_list:
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para_names.append(tif_name + '_VH')
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para_names.append(tif_name + '_VV')
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for tif_name in self.__HH_names_list:
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para_names.append(tif_name + '_HH')
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for tif_name in self.__VV_names_list:
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para_names.append(tif_name + '_VV')
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return para_names
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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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# 读取每一张图像,检查图像坐标系
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para_names = self.get_name_tif_list()
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ref_img_name = para_names[0]
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para_names = para_names + self.__feature_name_list
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p = pp()
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self.__preprocessed_paras = p.preprocessing(para_names, ref_img_name,
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self.__processing_paras,
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self.__workspace_preprocessing_path, self.__workspace_preprocessed_path)
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self.__ref_img_path, self.__cols, self.__rows, self.__proj, self.__geo = p.get_ref_inf(self.__preprocessed_paras[ref_img_name])
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logger.info('progress bar: 40%')
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logger.info('preprocess_handle success!')
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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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para_names = self.get_name_tif_list()
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tif_mask_path = roi().roi_process(para_names, self.__workspace_processing_path + "/roi/", self.__processing_paras, self.__preprocessed_paras)
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logger.info('create ROI image success!')
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return tif_mask_path
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def create_features(self):
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"""
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使用每一景全极化影像生成极化特征影像
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:para name_list: 每一景影像的路径
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"""
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for fea_name in self.__feature_name_list:
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path = self.__preprocessed_paras[fea_name]
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feature_path = os.path.join(self.__feature_tif_dir, os.path.basename(path))
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shutil.copyfile(path, feature_path)
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feature_array = ImageHandler.get_band_array(feature_path)
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feature_array = ml.standardization(feature_array)
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self.imageHandler.write_img(feature_path, self.__proj, self.__geo, feature_array)
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if self.__polar == 2:
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self.create_double_polar_features()
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elif self.__polar == 1:
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self.create_single_polar_features()
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def create_single_polar_features(self):
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name_list = [] + self.__HH_names_list + self.__VV_names_list
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for name in name_list:
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if name in self.__HH_names_list:
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tif_name = name + '_HH'
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else:
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tif_name = name + '_VV'
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path = self.__preprocessed_paras[tif_name]
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array_single_pol = ImageHandler.get_band_array(path)
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# 后向散射无效值设置为nan
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array_single_pol[np.where(array_single_pol < -100.0)] = np.nan
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array_single_pol = ml.standardization(array_single_pol)
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feature_path = os.path.join(self.__feature_tif_dir, os.path.basename(path))
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self.imageHandler.write_img(feature_path, self.__proj, self.__geo, array_single_pol)
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def create_double_polar_features(self):
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name_list = [] + self.__HHHV_names_list + self.__VVVH_names_list
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# # 生成极化组合特征
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for name in name_list:
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if name in self.__HHHV_names_list:
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array_single_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name+'_HH'])
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array_cross_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name+'_HV'])
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else:
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array_single_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name + '_VV'])
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array_cross_pol = ImageHandler.get_band_array(self.__preprocessed_paras[name + '_VH'])
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# 后向散射无效值设置为nan
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array_single_pol[np.where(array_single_pol < -100.0)] = np.nan
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array_cross_pol[np.where(array_cross_pol < -100.0)] = np.nan
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decomposeList = self.__parameters_dic["FeatureCombination"].split(',')
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if len(decomposeList) == 1:
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decomposeList = self.__parameters_dic["FeatureCombination"].split(';')
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for n in range(8):
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if str(n) in decomposeList:
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feature_path = os.path.join(self.__feature_tif_dir, name + '_feature'+str(n)+'.tif')
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if n == 0:
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array = array_single_pol
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elif n == 1:
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array = array_cross_pol
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elif n == 2:
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array = array_single_pol + array_cross_pol
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elif n == 3:
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array = array_single_pol - array_cross_pol
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elif n == 4:
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array = (array_single_pol - array_cross_pol)/(array_single_pol + array_cross_pol)
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elif n == 5:
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array = array_single_pol/array_cross_pol
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elif n == 6:
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array = array_cross_pol + array_single_pol/array_cross_pol
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elif n == 7:
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array = (array_single_pol**2 + array_cross_pol**2)/(array_single_pol**2 - array_cross_pol**2)
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else:
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continue
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array = ml.standardization(array)
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self.imageHandler.write_img(feature_path, self.__proj, self.__geo, array)
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logging.info("feature0 = array_single_pol, feature1= array_cross_pol")
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logging.info("feature1 = array_cross_pol")
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logging.info("feature2 = array_single_pol + array_cross_pol")
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logging.info("feature3 = array_single_pol - array_cross_pol")
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logging.info("feature4 = (array_single_pol - array_cross_pol)/(array_single_pol + array_cross_pol)")
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logging.info("feature5 = array_single_pol/array_cross_pol")
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logging.info("feature6 = array_cross_pol + array_single_pol/array_cross_pol")
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logging.info("feature7 = (array_single_pol**2 + array_cross_pol**2)/(array_single_pol**2 - array_cross_pol**2)")
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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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"""
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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 = LandCoverMeasCsv(self.__processing_paras['LabelData'], self.__ref_img_path,MAX_TRAN_NUM)
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train_data_list = pm.api_read_measure()
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train_data_dic = csvh.trans_landCover_list2dic(train_data_list)
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csvh_roi = csvHandle(self.__rows, self.__cols)
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label_img = csvh_roi.get_roi_img()
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if (len(label_img) != 0):
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self.imageHandler.write_img(self.__workspace_processing_path + "label_img.tif", "", [0, 0, 0, 0, 0, 0],
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label_img)
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# csvh_roi = csvHandle(self.__rows, self.__cols)
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# train_data_dic = csvh_roi.trans_landCover_measuredata_dic(
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# csvh_roi.readcsv(self.__processing_paras['LabelData']), self.__preprocessed_paras['ori_sim'], MAX_TRAN_NUM)
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# label_img = csvh_roi.get_roi_img()
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# if (len(label_img) != 0):
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# self.imageHandler.write_img(self.__workspace_processing_path + "label_img.tif", "", [0, 0, 0, 0, 0, 0],
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# label_img)
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type_id_name, type_id_parent = pm.class_list()
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self.create_meta_file(self.__ref_img_path, type_id_name, type_id_parent)
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logger.info("read csv data success!")
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logger.info('progress bar: 45%')
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roi_img = self.imageHandler.get_band_array(self.create_roi())
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# out_tif_path = self.ref_lee_filter(name_list)
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self.create_features()
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logger.info("create_features success!")
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logger.info('progress bar: 50%')
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# 生成最优特征子集训练集
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X_train, Y_train, optimal_feature = ml.gene_optimal_train_set(train_data_dic, self.__feature_tif_dir)
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# RF
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clf = ml.trainRF(X_train, Y_train)
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logger.info('progress bar: 60%')
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# 生成测试集
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X_test_path_list = ml.gene_test_set(self.__feature_tif_dir, optimal_feature)
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# 预测
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logger.info('testing')
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cover_path = ml.predict(clf, X_test_path_list, EXE_NAME, self.__workspace_processing_path, self.__rows, self.__cols)
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logger.info('test success!')
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logger.info('progress bar: 95%')
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# 转换数据
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proj, geo, cover_data = self.imageHandler.read_img(cover_path)
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# 形态学（闭运算）去roi区域噪点
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cover_data = np.uint8(cover_data)
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kernel = np.ones((5, 5), np.uint8)
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cover_data = cv2.erode(cv2.dilate(cover_data, kernel), kernel)
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for id, class_id in zip(train_data_dic['ids'], train_data_dic['class_ids']):
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cover_data[np.where(cover_data == id)] = class_id
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cover_data = np.int16(cover_data)
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# 获取影像roi区域
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cover_data = cover_data * roi_img
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product_dir = self.__workspace_processing_path + 'product\\'
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product_path = os.path.join(product_dir, 'LandCoverProduct.tif')
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self.imageHandler.write_img(product_path, self.__proj, self.__geo, cover_data)
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# 生成快视图
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self.imageHandler.write_quick_view(product_path, color_img=True)
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# 文件夹打包
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file.make_targz(self.__out_para, product_dir)
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logger.info('process_handle success!')
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def create_meta_file(self, product_path, type_id_name, type_id_parent):
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product_dir = self.__workspace_processing_path + 'product\\'
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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, 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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SrcImagePath = self.__input_paras["MultiTempSAR"]['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 = product_dir+EXE_NAME+"Product.meta.xml"
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CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process2(type_id_name, type_id_parent, SrcImageName)
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if __name__ == '__main__':
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multiprocessing.freeze_support() #解决打包与运行错误
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start = datetime.datetime.now()
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try:
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if len(sys.argv) < 2:
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xml_path = 'LandCover_geo.xml'
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else:
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xml_path = sys.argv[1]
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main_handler = LandCoverMain(xml_path)
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if main_handler.check_source() is False:
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raise Exception('check_source() failed!')
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if main_handler.preprocess_handle() is False:
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raise Exception('preprocess_handle() failed!')
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if main_handler.process_handle(start) is False:
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raise Exception('process_handle() failed!')
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logger.info('successful production of ' + EXE_NAME + ' products!')
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except Exception:
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logger.exception("run-time error!")
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finally:
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main_handler.del_temp_workspace()
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pass
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end = datetime.datetime.now()
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msg = 'running use time: %s ' % (end - start)
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logger.info(msg)
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