陆探代码适配环境初始化--未做适配
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# Prerequisites
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*.d
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# Compiled Object files
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*.slo
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*.lo
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*.o
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*.obj
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# Precompiled Headers
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*.gch
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*.pch
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*.toc
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# Compiled Dynamic libraries
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*.so
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*.dylib
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*.dll
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# Fortran module files
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*.mod
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*.smod
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# Compiled Static libraries
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*.lai
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*.la
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*.a
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*.lib
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# Executables
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*.exe
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*.out
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*.app
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*.dll
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x64/
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x64/*
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.vs/
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.vs/*
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/x64/*
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/.vs/*
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./x64/*
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./.vs/*
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./x64/*
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/x64/*
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*.ipch
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*.db
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*.pdb
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*.tlog
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*.log
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*.pdb
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*.db
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*.tiff
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*.tif
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*.jpg
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*.dll
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*.lib
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*.exe
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Temporary*/
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*.pyc
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# 输入文件
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Input/
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Input2/
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Input3/
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Input4/
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Input5/
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# 输出文件
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baseTool/
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Temporary/
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# 忽略工具文件
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baseTool/
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__pycache__/
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dist/
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build/
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Ortho/
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run_log/
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*.tiff
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*.tif
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*.log
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# -*- mode: python ; coding: utf-8 -*-
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block_cipher = None
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a = Analysis(
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['OrthoMain.py'],
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pathex=['.'],
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binaries=[],
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datas=[],
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hiddenimports=[],
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hookspath=[],
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runtime_hooks=[],
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excludes=[],
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win_no_prefer_redirects=False,
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win_private_assemblies=False,
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cipher=block_cipher,
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noarchive=False,
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)
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pyz = PYZ(a.pure, a.zipped_data, cipher=block_cipher)
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exe = EXE(
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pyz,
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a.scripts,
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[],
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exclude_binaries=True,
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name='OrthOne',
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debug=False,
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bootloader_ignore_signals=False,
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strip=False,
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upx=True,
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console=True,
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disable_windowed_traceback=False,
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argv_emulation=False,
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target_arch=None,
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codesign_identity=None,
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entitlements_file=None,
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)
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coll = COLLECT(
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exe,
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a.binaries,
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a.zipfiles,
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a.datas,
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strip=False,
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upx=True,
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upx_exclude=[],
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name='OrthOne',
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)
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<?xml version='1.0' encoding='utf-8'?>
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<Root>
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<TaskID>CSAR_202107275419_0001-0</TaskID>
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<WorkSpace>D:\micro\LWork\</WorkSpace>
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<AlgCompt>
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<DataTransModel>File</DataTransModel>
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<Artificial>ElementAlg</Artificial>
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<AlgorithmName>Ortho_C_SAR_V2.1</AlgorithmName>
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<DllName>Ortho_C_SAR_V2.1.exe</DllName>
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<ChsName>正射校正</ChsName>
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<AlgorithmDesc>微波卫星3-5级产品生产模型</AlgorithmDesc>
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<AlgorithmAlias>Ortho-C-SAR-V2.1-1</AlgorithmAlias>
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<Version>1.0</Version>
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<AlgorithmClass>辐射类产品_正射校正</AlgorithmClass>
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<AlgorithmLevel>4</AlgorithmLevel>
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<AlgoirthmID>Ortho_中科卫星应用德清研究院_2.1</AlgoirthmID>
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<Author>中科卫星应用德清研究院</Author>
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<Type>景-算法</Type>
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<InputTestFilePath>Ortho\\Input6</InputTestFilePath>
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<InputTestFileName>
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2599253_San_Francisco
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</InputTestFileName>
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<OutputTestFilePath>Ortho\\Output</OutputTestFilePath>
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<OutputTestFileName>
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</OutputTestFileName>
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<jdkVersion>1.8</jdkVersion>
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<algDevlanguage>python</algDevlanguage>
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<Environment>
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<IsCluster>0</IsCluster>
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<ClusterNum>0</ClusterNum>
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<OperatingSystem>Windows10</OperatingSystem>
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<CPU>4核</CPU>
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<Memory>8GB</Memory>
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<Storage>25GB</Storage>
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<NetworkCard>无需求</NetworkCard>
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<Bandwidth>无需求</Bandwidth>
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<GPU>无需求</GPU>
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</Environment>
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<Utility Satellite="GF3" Sensor="MSS" Resolution="1" />
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<Inputs ParameterNum="3">
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<Parameter>
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<ParaName>SLC</ParaName>
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<ParaChsName>SLC元文件</ParaChsName>
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<Description>原始SLC各相关文件和参数</Description>
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<ParaType>File</ParaType>
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<DataType>tar.gz</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>E:\MicroWorkspace\LT1B\LT230919\LT1B_MONO_MYC_STRIP4_005860_E130.9_N47.7_20230327_SLC_AHV_L1A_0000086966.tar.gz</ParaValue>
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<EnModification>True</EnModification>
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<EnMultipleChoice>False</EnMultipleChoice>
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<Control>File</Control>
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<InputType>Satellite</InputType>
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<InputNum>1</InputNum>
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<DateFrom>GF3A</DateFrom>
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</Parameter>
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<Parameter>
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<ParaName>DEM</ParaName>
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<ParaChsName>DEM数字高程影像</ParaChsName>
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<Description>30m分辨率DEM数字高程影像tif</Description>
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<ParaType>File</ParaType>
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<DataType>File</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>E:\MicroWorkspace\LT1B\LT230919\dem</ParaValue>
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<EnModification>True</EnModification>
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<EnMultipleChoice>True</EnMultipleChoice>
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<Control>File</Control>
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<InputType>DEM</InputType>
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<InputNum>0</InputNum>
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<DateFrom>DEM</DateFrom>
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</Parameter>
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<Parameter>
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<ParaName>CorrectMethod</ParaName>
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<ParaChsName>选择校正方法</ParaChsName>
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<Description>1.RPC;2.RD</Description>
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<ParaType>int</ParaType>
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<DataType>int</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>2</ParaValue>
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<EnModification>True</EnModification>
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<EnMultipleChoice>True</EnMultipleChoice>
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<Control>UploadInput</Control>
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<InputType>Aux</InputType>
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<InputNum>0</InputNum>
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<DateFrom>Aux</DateFrom>
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</Parameter>
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</Inputs>
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<Outputs ParameterNum="1">
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<Parameter>
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<ParaName>OrthoProduct</ParaName>
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<ParaChsName>产品结果文件</ParaChsName>
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<Description>产品结果文件</Description>
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<ParaType>File</ParaType>
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<DataType>tar.gz</DataType>
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<ParaSource>Cal</ParaSource>
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<ParaValue>D:\micro\LWork\Ortho\Output\LT1B_MONO_MYC_STRIP4_005860_E130.9_N47.7_20230327_SLC_AHV_L1A_0000086966-ortho.tar.gz</ParaValue>
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<MaxValue>DEFAULT</MaxValue>
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<MinValue>DEFAULT</MinValue>
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<OptionValue>DEFAULT</OptionValue>
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<NoDataValue>DEFAULT</NoDataValue>
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</Parameter>
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</Outputs>
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</AlgCompt>
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</Root>
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File diff suppressed because it is too large
Load Diff
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# 一米正射辅助数据处理类
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import time
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import math
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import numpy as np
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from osgeo import gdal
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from xml.etree.ElementTree import ElementTree
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from scipy.optimize import leastsq
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class OrthoAuxData:
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def __init__(self):
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pass
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@staticmethod
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def time_stamp(tm):
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list = tm.split(':')
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sec = math.ceil(float(list[2]))
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tm1 = list[0] + ':' + list[1] + ':' + str(sec)
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tmArr = time.strptime(tm1, "%Y-%m-%d %H:%M:%S")
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# tmArr = time.strptime(tm1, "%Y-%m-%d %H:%M:%S.%f")
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ts = float(time.mktime(tmArr)) # 转换为时间戳
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return ts
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@staticmethod
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def read_meta(meta_file_path):
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tree = ElementTree()
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tree.parse(meta_file_path)
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root = tree.getroot()
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T = []
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Xs = []
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Ys = []
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Zs = []
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Vsx = []
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Vsy = []
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Vsz = []
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GPS_data = root.find('GPS')
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for child in GPS_data:
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Xs.append(float(child.find('xPosition').text))
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Ys.append(float(child.find('yPosition').text))
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Zs.append(float(child.find('zPosition').text))
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Vsx.append(float(child.find('xVelocity').text))
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Vsy.append(float(child.find('yVelocity').text))
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Vsz.append(float(child.find('zVelocity').text))
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tm = child.find('TimeStamp').text
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ts = OrthoAuxData.time_stamp(tm)
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T.append(ts)
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meta_data = [Xs, Ys, Zs, Vsx, Vsy, Vsz]
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return T, meta_data
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@staticmethod
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def read_control_points(meta_file_path):
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tree = ElementTree()
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tree.parse(meta_file_path)
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root = tree.getroot()
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imageinfo = root.find('imageinfo')
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center = imageinfo.find('center')
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corner = imageinfo.find('corner')
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ctrl_pts = [[] for i in range(2)]
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ctrl_pts[0].append(float(center.find('longitude').text))
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ctrl_pts[1].append(float(center.find('latitude').text))
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for child in corner:
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ctrl_pts[0].append(float(child.find('longitude').text))
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ctrl_pts[1].append(float(child.find('latitude').text))
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return ctrl_pts
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@staticmethod
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def read_dem(dem_resampled_path, flag=1):
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in_ds = gdal.Open(dem_resampled_path)
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gt = list(in_ds.GetGeoTransform())
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bands_num = in_ds.RasterCount
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x_size = in_ds.RasterXSize
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y_size = in_ds.RasterYSize
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pstn_arr = np.zeros([y_size, x_size, 3], dtype=np.float)
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for i in range(1, bands_num + 1):
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data = in_ds.GetRasterBand(i).ReadAsArray(0, 0, x_size, y_size)
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for y in range(y_size):
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for x in range(x_size):
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longitude = gt[0] + x * gt[1]
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latitude = gt[3] + y * gt[5]
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altitude = data[y, x]
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if flag == 1:
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pstn = OrthoAuxData.LLA2XYZ(longitude, latitude, altitude)
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else:
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pstn = [longitude, latitude, altitude]
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pstn_arr[y, x, 0] = pstn[0]
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pstn_arr[y, x, 1] = pstn[1]
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pstn_arr[y, x, 2] = pstn[2]
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del in_ds, data
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return pstn_arr
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@staticmethod
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def read_demM(dem_resampled_path, part_cnt, r_cnt, c_cnt, flag=1):
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in_ds = gdal.Open(dem_resampled_path)
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gt = list(in_ds.GetGeoTransform())
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bands_num = in_ds.RasterCount
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x_size = in_ds.RasterXSize // part_cnt
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y_size = in_ds.RasterYSize // part_cnt
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x = [[i] * y_size for i in range(x_size)]
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y = [[i] * x_size for i in range(y_size)]
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x = np.array(x)
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x = x.T
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y = np.array(y)
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x_off = c_cnt * x_size
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y_off = r_cnt * y_size
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gt[0] = gt[0] + c_cnt * x_size * gt[1]
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gt[3] = gt[3] + r_cnt * y_size * gt[5]
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for i in range(1, bands_num + 1):
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data = in_ds.GetRasterBand(i).ReadAsArray(x_off, y_off, x_size, y_size)
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altitude = data / 255 * 1024
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longitude = gt[0] + x * gt[1]
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latitude = gt[3] + y * gt[5]
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if flag == 1:
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pstn = OrthoAuxData.LLA2XYZM(longitude, latitude, altitude)
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else:
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pstn = [longitude, latitude, altitude]
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del in_ds, data
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return pstn
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@staticmethod
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def read_dem_row(dem_resampled_path, p, flag=1):
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in_ds = gdal.Open(dem_resampled_path)
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gt = list(in_ds.GetGeoTransform())
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bands_num = in_ds.RasterCount
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x_size = in_ds.RasterXSize
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y_size = in_ds.RasterYSize
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x = [[i] for i in range(x_size)]
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x = np.array(x)
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x = x.T
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y = np.ones((1, x_size)) * p
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x_off = 0
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y_off = p
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for i in range(1, bands_num + 1):
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data = in_ds.GetRasterBand(i).ReadAsArray(x_off, y_off, x_size, 1)
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altitude = data
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longitude = gt[0] + x * gt[1]
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||||||
|
latitude = gt[3] + y * gt[5]
|
||||||
|
if flag == 1:
|
||||||
|
pstn = OrthoAuxData.LLA2XYZM(longitude, latitude, altitude)
|
||||||
|
else:
|
||||||
|
pstn = [longitude, latitude, altitude]
|
||||||
|
|
||||||
|
del in_ds, data
|
||||||
|
return pstn
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def orbit_fitting(time_array, meta_data):
|
||||||
|
# 最小二乘法求解轨道参数
|
||||||
|
T0 = (time_array[0] + time_array[len(time_array)-1]) / 2
|
||||||
|
t = []
|
||||||
|
for i in range(len(time_array)):
|
||||||
|
t.append(time_array[i]-T0)
|
||||||
|
|
||||||
|
def func(p, x):
|
||||||
|
w3, w2, w1, w0 = p
|
||||||
|
return w3*x**3 + w2*x**2 + w1*x + w0
|
||||||
|
|
||||||
|
def error(p, x, y):
|
||||||
|
return func(p, x) - y
|
||||||
|
|
||||||
|
orbital_paras = []
|
||||||
|
for j in range(len(meta_data)):
|
||||||
|
p0 = [1, 2, 3, 4]
|
||||||
|
x = np.array(t)
|
||||||
|
y = np.array(meta_data[j])
|
||||||
|
Para = leastsq(error, p0, args=(x, y))
|
||||||
|
orbital_paras.append(Para[0])
|
||||||
|
print(Para[0], Para[1])
|
||||||
|
|
||||||
|
return orbital_paras, T0
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_PRF(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
sensor = root.find('sensor')
|
||||||
|
waveParams = sensor.find('waveParams')
|
||||||
|
PRF = float(waveParams.find('wave').find('prf').text)
|
||||||
|
return PRF
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_delta_R(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
sensor = root.find('sensor')
|
||||||
|
pulseWidth = float(sensor.find('waveParams').find('wave').find('pulseWidth').text)
|
||||||
|
bandWidth = float(sensor.find('waveParams').find('wave').find('bandWidth').text)
|
||||||
|
c = 299792458
|
||||||
|
delta_R = c / (1000000 * 2 * bandWidth)
|
||||||
|
return delta_R
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_doppler_rate_coef(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
processinfo = root.find('processinfo')
|
||||||
|
doppler = processinfo.find('DopplerRateValuesCoefficients')
|
||||||
|
t0 = float(processinfo.find('DopplerParametersReferenceTime').text)
|
||||||
|
r0 = float(doppler.find('r0').text)
|
||||||
|
r1 = float(doppler.find('r1').text)
|
||||||
|
r2 = float(doppler.find('r2').text)
|
||||||
|
r3 = float(doppler.find('r3').text)
|
||||||
|
r4 = float(doppler.find('r4').text)
|
||||||
|
|
||||||
|
return t0, np.array([r0, r1, r2, r3, r4]).reshape(5, 1)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_doppler_center_coef(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
processinfo = root.find('processinfo')
|
||||||
|
doppler = processinfo.find('DopplerCentroidCoefficients')
|
||||||
|
b0 = float(doppler.find('d0').text)
|
||||||
|
b1 = float(doppler.find('d1').text)
|
||||||
|
b2 = float(doppler.find('d2').text)
|
||||||
|
return b0, b1, b2
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_lamda(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
sensor = root.find('sensor')
|
||||||
|
λ = float(sensor.find('lamda').text)
|
||||||
|
return λ
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_t0(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
tm = imageinfo.find('imagingTime').find('start').text
|
||||||
|
t0 = OrthoAuxData.time_stamp(tm)
|
||||||
|
return t0
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_start_and_end_time(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
tm0 = imageinfo.find('imagingTime').find('start').text
|
||||||
|
tm1 = imageinfo.find('imagingTime').find('end').text
|
||||||
|
starttime = OrthoAuxData.time_stamp(tm0)
|
||||||
|
endtime = OrthoAuxData.time_stamp(tm1)
|
||||||
|
return starttime, endtime
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_width_and_height(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
width = int(imageinfo.find('width').text)
|
||||||
|
height = int(imageinfo.find('height').text)
|
||||||
|
return width, height
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_R0(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
R0 = float(imageinfo.find('nearRange').text)
|
||||||
|
return R0
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_h():
|
||||||
|
h = 6.6
|
||||||
|
return h
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def LLA2XYZ(longitude, latitude, altitude):
|
||||||
|
'''
|
||||||
|
WGS-84坐标系下：经纬度坐标转空间直角坐标
|
||||||
|
'''
|
||||||
|
# 经纬度余弦值
|
||||||
|
cosLat = math.cos(latitude * math.pi / 180)
|
||||||
|
sinLat = math.sin(latitude * math.pi / 180)
|
||||||
|
cosLon = math.cos(longitude * math.pi / 180)
|
||||||
|
sinLon = math.sin(longitude * math.pi / 180)
|
||||||
|
# WGS84坐标系参数
|
||||||
|
rad = 6378137.0 #地球赤道平均半径
|
||||||
|
f = 1.0/298.257224 #WGS84椭球扁率
|
||||||
|
C = 1.0/math.sqrt(cosLat*cosLat + (1-f)*(1-f)*sinLat*sinLat)
|
||||||
|
S = (1-f)*(1-f)*C
|
||||||
|
h = altitude
|
||||||
|
# 计算XYZ坐标
|
||||||
|
X = (rad * C + h) * cosLat * cosLon
|
||||||
|
Y = (rad * C + h) * cosLat * sinLon
|
||||||
|
Z = (rad * S + h) * sinLat
|
||||||
|
# return np.array([X, Y, Z]).reshape(1,3)
|
||||||
|
return [X, Y, Z]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def LLA2XYZM(longitude, latitude, altitude):
|
||||||
|
# 经纬度余弦值
|
||||||
|
cosLat = np.cos(latitude * math.pi / 180).reshape(-1,1)
|
||||||
|
sinLat = np.sin(latitude * math.pi / 180).reshape(-1,1)
|
||||||
|
cosLon = np.cos(longitude * math.pi / 180).reshape(-1,1)
|
||||||
|
sinLon = np.sin(longitude * math.pi / 180).reshape(-1,1)
|
||||||
|
# WGS84坐标系参数
|
||||||
|
rad = 6378137.0 #地球赤道平均半径
|
||||||
|
f = 1.0/298.257224 #WGS84椭球扁率
|
||||||
|
C = 1.0/(np.sqrt(cosLat*cosLat + (1-f)*(1-f)*sinLat*sinLat)).reshape(-1,1)
|
||||||
|
S = (1-f)*(1-f)*C
|
||||||
|
h = altitude.reshape(-1,1)
|
||||||
|
# 计算XYZ坐标
|
||||||
|
X = (rad * C + h) * cosLat * cosLon
|
||||||
|
Y = (rad * C + h) * cosLat * sinLon
|
||||||
|
Z = (rad * S + h) * sinLat
|
||||||
|
return [X, Y, Z]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def XYZ2LLA(X, Y, Z):
|
||||||
|
''' 大地坐标系转经纬度
|
||||||
|
适用于WGS84坐标系
|
||||||
|
args:
|
||||||
|
x,y,z
|
||||||
|
return:
|
||||||
|
lat,long,altitude
|
||||||
|
'''
|
||||||
|
# WGS84坐标系的参数
|
||||||
|
a = 6378137.0 # 椭球长半轴
|
||||||
|
b = 6356752.314245 # 椭球短半轴
|
||||||
|
ea = np.sqrt((a ** 2 - b ** 2) / a ** 2)
|
||||||
|
eb = np.sqrt((a ** 2 - b ** 2) / b ** 2)
|
||||||
|
p = np.sqrt(X ** 2 + Y ** 2)
|
||||||
|
theta = np.arctan2(Z * a, p * b)
|
||||||
|
|
||||||
|
# 计算经纬度及海拔
|
||||||
|
longitude = np.arctan2(Y, X)
|
||||||
|
latitude = np.arctan2(Z + eb ** 2 * b * np.sin(theta) ** 3, p - ea ** 2 * a * np.cos(theta) ** 3)
|
||||||
|
N = a / np.sqrt(1 - ea ** 2 * np.sin(latitude) ** 2)
|
||||||
|
altitude = p / np.cos(latitude) - N
|
||||||
|
|
||||||
|
# return np.array([np.degrees(latitude), np.degrees(longitude), altitude])
|
||||||
|
return [np.degrees(longitude), np.degrees(latitude), altitude]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def XYZ2LLAM(X, Y, Z):
|
||||||
|
''' 大地坐标系转经纬度
|
||||||
|
适用于WGS84坐标系
|
||||||
|
args:
|
||||||
|
x,y,z
|
||||||
|
return:
|
||||||
|
lat,long,altitude
|
||||||
|
'''
|
||||||
|
# WGS84坐标系的参数
|
||||||
|
a = 6378137.0 # 椭球长半轴
|
||||||
|
b = 6356752.314245 # 椭球短半轴
|
||||||
|
ea = np.sqrt((a ** 2 - b ** 2) / a ** 2)
|
||||||
|
eb = np.sqrt((a ** 2 - b ** 2) / b ** 2)
|
||||||
|
p = np.sqrt(X ** 2 + Y ** 2)
|
||||||
|
theta = np.arctan2(Z * a, p * b)
|
||||||
|
|
||||||
|
# 计算经纬度及海拔
|
||||||
|
longitude = np.arctan2(Y, X)
|
||||||
|
latitude = np.arctan2(Z + eb ** 2 * b * np.sin(theta) ** 3, p - ea ** 2 * a * np.cos(theta) ** 3)
|
||||||
|
N = a / np.sqrt(1 - ea ** 2 * np.sin(latitude) ** 2)
|
||||||
|
altitude = p / np.cos(latitude) - N
|
||||||
|
|
||||||
|
# return np.array([np.degrees(latitude), np.degrees(longitude), altitude])
|
||||||
|
return [np.degrees(longitude), np.degrees(latitude), altitude]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def world2Pixel(geoMatrix, x, y):
|
||||||
|
"""
|
||||||
|
使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置
|
||||||
|
"""
|
||||||
|
ulx = geoMatrix[0]
|
||||||
|
uly = geoMatrix[3]
|
||||||
|
xDist = geoMatrix[1]
|
||||||
|
yDist = geoMatrix[5]
|
||||||
|
rtnX = geoMatrix[2]
|
||||||
|
rtnY = geoMatrix[4]
|
||||||
|
pixel = int((x - ulx) / xDist)
|
||||||
|
line = int((uly - y) / abs(yDist))
|
||||||
|
return pixel, line
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def sar_intensity_synthesis(in_sar_tif, out_sar_tif):
|
||||||
|
# 获取SLC格式SAR影像的相关信息
|
||||||
|
in_ds = gdal.Open(in_sar_tif)
|
||||||
|
bands_num = in_ds.RasterCount
|
||||||
|
rows = in_ds.RasterYSize
|
||||||
|
columns = in_ds.RasterXSize
|
||||||
|
proj = in_ds.GetProjection()
|
||||||
|
geotrans = in_ds.GetGeoTransform()
|
||||||
|
|
||||||
|
# 创建输出的SAR强度图
|
||||||
|
gtiff_driver = gdal.GetDriverByName('GTiff')
|
||||||
|
out_ds = gtiff_driver.Create(out_sar_tif, columns, rows, 1)
|
||||||
|
out_ds.SetProjection(proj)
|
||||||
|
out_ds.SetGeoTransform(geotrans)
|
||||||
|
|
||||||
|
# 输出SAR强度图
|
||||||
|
in_data1 = in_ds.GetRasterBand(1).ReadAsArray(0, 0, columns, rows)
|
||||||
|
in_data1 = in_data1/10
|
||||||
|
in_data1 = np.power(10, in_data1)
|
||||||
|
in_data2 = in_ds.GetRasterBand(2).ReadAsArray(0, 0, columns, rows)
|
||||||
|
in_data2 = in_data2 / 10
|
||||||
|
in_data2 = np.power(10, in_data2)
|
||||||
|
out_data = np.sqrt(in_data1**2 + in_data2**2)
|
||||||
|
out_ds.GetRasterBand(1).WriteArray(out_data)
|
||||||
|
|
||||||
|
del in_ds, out_ds
|
||||||
|
|
@ -0,0 +1,749 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：OneOrthoMain.py
|
||||||
|
@Function ：正射校正
|
||||||
|
@Author ：KHZ
|
||||||
|
@Contact：
|
||||||
|
@Date ：2021/8/14
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml
|
||||||
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
||||||
|
import tarfile
|
||||||
|
from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource # 导入xml文件读取与检查文件
|
||||||
|
from OrthoAlg import IndirectOrthorectification, DEMProcess,rpc_correction,getRCImageRC,get_RPC_lon_lat,getRCImageRC2
|
||||||
|
from OrthoAlg import ScatteringAlg as alg
|
||||||
|
from tool.algorithm.algtools.logHandler import LogHandler
|
||||||
|
from tool.algorithm.xml.CreatMetafile import CreateMetafile
|
||||||
|
from OrthoXmlInfo import CreateDict, CreateStadardXmlFile
|
||||||
|
from osgeo import gdal, osr
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
# import gc
|
||||||
|
import datetime
|
||||||
|
import shutil
|
||||||
|
import sys
|
||||||
|
import scipy #解决打包错误
|
||||||
|
import scipy.spatial.transform # 用于解决打包错误
|
||||||
|
import scipy.spatial.transform.rotation
|
||||||
|
import scipy.spatial.transform._rotation_groups # 用于解决打包错误
|
||||||
|
|
||||||
|
DEBUG = True
|
||||||
|
EXE_NAME = 'Ortho'
|
||||||
|
#env_str = os.getcwd()
|
||||||
|
env_str =os.path.dirname(os.path.abspath(sys.argv[0])) #os.path.split(os.path.realpath(__file__))[0]
|
||||||
|
os.environ['PROJ_LIB'] = env_str
|
||||||
|
LogHandler.init_log_handler(os.path.join("run_log", EXE_NAME)) # r"run_log\Ortho"
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
logger.info(env_str)
|
||||||
|
|
||||||
|
|
||||||
|
class LogHandler2:
|
||||||
|
"""日志记录工具，用于输出程序运行状况。
|
||||||
|
这里因为是单程序执行，没有必要调用logging 类。
|
||||||
|
具体日志策略：
|
||||||
|
1. 最外层使用try,catch 捕捉异常
|
||||||
|
2. 最后程序执行终止判断。
|
||||||
|
日志记录格式：
|
||||||
|
第一行：
|
||||||
|
TaskID，时间，输入参数文件地址。
|
||||||
|
中间：
|
||||||
|
时间，状态，执行步骤，消息
|
||||||
|
|
||||||
|
最后一行：
|
||||||
|
finished 表示程序执行完成
|
||||||
|
failed 程序执行出现错误
|
||||||
|
|
||||||
|
"""
|
||||||
|
def __init__(self, sLogPath) -> None:
|
||||||
|
'''
|
||||||
|
初始化日志文件
|
||||||
|
args:
|
||||||
|
sLogPath:str 日志文件的路径
|
||||||
|
raise:
|
||||||
|
IOError:Exception 错误
|
||||||
|
'''
|
||||||
|
self.__sLogPath = sLogPath
|
||||||
|
|
||||||
|
def loggingStart(self,sTaskID,sParamPath):
|
||||||
|
'''输出日志开头
|
||||||
|
TaskID，时间，输入参数文件地址。
|
||||||
|
args:
|
||||||
|
sTaskID:str 任务ID
|
||||||
|
sParamPath:str 任务参数文件地址
|
||||||
|
return:
|
||||||
|
None
|
||||||
|
'''
|
||||||
|
sDateTime=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f") # 执行时间
|
||||||
|
sOutput="[{}],[{}],[{}]".format(sTaskID, sDateTime, sParamPath)
|
||||||
|
self.__outputText(sOutput)
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
def logging(self,sStates,sExecuteStep,sException):
|
||||||
|
"""输出中间计算信息"""
|
||||||
|
sDateTime=datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f") # 执行时间
|
||||||
|
sOutput="[{}],[{}],[{}],[{}]".format(sDateTime,sStates,sExecuteStep,sExecuteStep)
|
||||||
|
self.__outputText(sOutput)
|
||||||
|
pass
|
||||||
|
|
||||||
|
def __outputText(self,sMessage):
|
||||||
|
'''将消息输出到最终的日志文件中
|
||||||
|
'''
|
||||||
|
with open(self.__sLogPath, 'a', encoding='utf-8') as fp:
|
||||||
|
fp.write("{}".format(sMessage))
|
||||||
|
|
||||||
|
def logggingEnd(self, bSuccessful):
|
||||||
|
'''
|
||||||
|
最后一行输出，判断输出的结果是否是正确的
|
||||||
|
'''
|
||||||
|
if bSuccessful:
|
||||||
|
sEndText="\n{}\nfinished".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"))
|
||||||
|
else:
|
||||||
|
sEndText="\n{}\nError".format(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"))
|
||||||
|
self.__outputText(sEndText)
|
||||||
|
|
||||||
|
|
||||||
|
class OrthoMain:
|
||||||
|
"""
|
||||||
|
间接定位法正射校正 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.__task_id = None
|
||||||
|
self.__input_paras = {}
|
||||||
|
# self.__output_paras = {}
|
||||||
|
self.__in_processing_paras = {}
|
||||||
|
# self.__out_processing_paras = {}
|
||||||
|
self.__preprocessed_paras = {}
|
||||||
|
self.__out_para = None
|
||||||
|
|
||||||
|
def check_source(self):
|
||||||
|
"""
|
||||||
|
检查算法相关的配置文件，图
|
||||||
|
像，辅助文件是否齐全
|
||||||
|
"""
|
||||||
|
if self.__check_handler.check_alg_xml() is False:
|
||||||
|
return False
|
||||||
|
|
||||||
|
if self.__check_handler.check_run_env() is False:
|
||||||
|
return False
|
||||||
|
|
||||||
|
input_para_names = ["SLC", "DEM", "CorrectMethod"] # //todo 增加检查校正方法
|
||||||
|
if self.__check_handler.check_input_paras(input_para_names) is False:
|
||||||
|
return False
|
||||||
|
|
||||||
|
self.__workspace_path = self.__alg_xml_handler.get_workspace_path()
|
||||||
|
self.__task_id = self.__alg_xml_handler.get_task_id()
|
||||||
|
self.__input_paras = self.__alg_xml_handler.get_input_paras()
|
||||||
|
# self.__output_paras = self.__alg_xml_handler.get_output_paras()
|
||||||
|
self.__create_work_space()
|
||||||
|
self.__in_processing_paras = self.__init_processing_paras(self.__input_paras) # 输入{paraname：paravalue}
|
||||||
|
# self.__out_processing_paras = self.__init_processing_paras(self.__output_paras) # 输入{paraname：paravalue}
|
||||||
|
|
||||||
|
self.__out_name = os.path.splitext(os.path.splitext(os.path.basename(self.__input_paras['SLC']['ParaValue']))[0])[0]
|
||||||
|
# AlgorithmName = self.__alg_xml_handler.get_algorithm_name()
|
||||||
|
# TaskId = self.__alg_xml_handler.get_task_id()
|
||||||
|
result_name = self.__out_name + ".tar.gz"
|
||||||
|
self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', result_name)
|
||||||
|
|
||||||
|
isError, CorrectMethod = self.__check_handler.check_input_paras(['CorrectMethod']) # //todo 获取xml中校正方法 根据不同方法进行结果处理
|
||||||
|
|
||||||
|
if CorrectMethod.get('CorrectMethod') == '1' or CorrectMethod.get('CorrectMethod') == 1:
|
||||||
|
logger.info("CorrectMethod is RPC!")
|
||||||
|
# self.__out_para=self.__out_para.replace(".tar.gz","_RPC.tar.gz")
|
||||||
|
self.__out_para=self.__out_para.replace(".tar.gz","-ortho.tar.gz")
|
||||||
|
|
||||||
|
elif CorrectMethod.get('CorrectMethod') == '2' or CorrectMethod.get('CorrectMethod') == 2:
|
||||||
|
logger.info("CorrectMethod is RD!")
|
||||||
|
# self.__out_para=self.__out_para.replace(".tar.gz","_RD.tar.gz")
|
||||||
|
self.__out_para=self.__out_para.replace(".tar.gz","-ortho.tar.gz")
|
||||||
|
else:
|
||||||
|
raise Exception('No CorrectMethod')
|
||||||
|
|
||||||
|
self.__alg_xml_handler.write_out_para("OrthoProduct", self.__out_para) #写入输出参数
|
||||||
|
|
||||||
|
logger.info('check_source finished!')
|
||||||
|
logger.info('progress bar :5％')
|
||||||
|
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_Output_path = os.path.join(self.__workspace_path, EXE_NAME, "Output")
|
||||||
|
self.__workspace_Temporary_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary")
|
||||||
|
self.__workspace_unpack_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "unpack")
|
||||||
|
self.__workspace_ResampledDEM_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestDEM')
|
||||||
|
self.__workspace_LutImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestLut')
|
||||||
|
self.__workspace_IncidenceImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestInc')
|
||||||
|
self.__workspace_SimImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestSim')
|
||||||
|
self.__workspace_SARIntensity_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestSAR')
|
||||||
|
self.__workspace_package_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'package')
|
||||||
|
self.__workspace_origin_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "origin")
|
||||||
|
|
||||||
|
path_list = [self.__workspace_Output_path, self.__workspace_Temporary_path,
|
||||||
|
self.__workspace_unpack_path, self.__workspace_ResampledDEM_path,
|
||||||
|
self.__workspace_LutImg_path, self.__workspace_IncidenceImg_path,
|
||||||
|
self.__workspace_SimImg_path, self.__workspace_SARIntensity_path,
|
||||||
|
self.__workspace_package_path, self.__workspace_origin_path]
|
||||||
|
|
||||||
|
for path in path_list:
|
||||||
|
if os.path.exists(path):
|
||||||
|
if DEBUG is True:
|
||||||
|
continue
|
||||||
|
self.del_floder(path)
|
||||||
|
os.makedirs(path)
|
||||||
|
else:
|
||||||
|
os.makedirs(path)
|
||||||
|
logger.info('create new workspace success!')
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def force_del_file(file_path):
|
||||||
|
"""
|
||||||
|
强制删除文件
|
||||||
|
"""
|
||||||
|
if os.path.isdir(file_path):
|
||||||
|
for main_dir, subdir, file_name_list in os.walk(file_path):
|
||||||
|
for filename in file_name_list:
|
||||||
|
apath = main_dir + filename
|
||||||
|
# noinspection PyBroadException
|
||||||
|
try:
|
||||||
|
os.remove(apath)
|
||||||
|
except Exception as error: # 使用windows命令行强制删除
|
||||||
|
os.system("del /f /q %s" % apath)
|
||||||
|
elif os.path.isfile(file_path) is True:
|
||||||
|
# noinspection PyBroadException
|
||||||
|
try:
|
||||||
|
os.remove(file_path)
|
||||||
|
except Exception as error: # 使用windows命令行强制删除
|
||||||
|
os.system("del /f /q %s" % file_path)
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def make_targz(output_filename, source_dir):
|
||||||
|
"""
|
||||||
|
一次性打包整个根目录。空子目录会被打包。
|
||||||
|
如果只打包不压缩，将"w:gz"参数改为"w:"或"w"即可。
|
||||||
|
:param output_filename:输出压缩包的完整路径，eg:'E:\test.tar.gz'
|
||||||
|
:param source_dir:需要打包的跟目录，eg: 'E:\testFfile\'打包文件夹里面的所有文件,'E:\testFfile'打包文件夹
|
||||||
|
"""
|
||||||
|
dir = os.path.split(output_filename)[0]
|
||||||
|
if os.path.exists(dir) is False:
|
||||||
|
os.makedirs(dir)
|
||||||
|
with tarfile.open(output_filename, "w:gz") as tar:
|
||||||
|
tar.add(source_dir, arcname=os.path.basename(source_dir))
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def del_floder(path_data):
|
||||||
|
"""
|
||||||
|
删除整个文件夹
|
||||||
|
"""
|
||||||
|
if os.path.isdir(path_data):
|
||||||
|
shutil.rmtree(path_data)
|
||||||
|
|
||||||
|
def del_temp_workspace(self):
|
||||||
|
"""
|
||||||
|
临时工作区
|
||||||
|
"""
|
||||||
|
if DEBUG is True:
|
||||||
|
return
|
||||||
|
path = self.__workspace_path + EXE_NAME + r'\Temporary'
|
||||||
|
if os.path.exists(path):
|
||||||
|
self.del_floder(path)
|
||||||
|
|
||||||
|
def __init_processing_paras(self, names):
|
||||||
|
"""
|
||||||
|
:param names:字典列表，每个字典为一个输入产品的配置信息
|
||||||
|
"""
|
||||||
|
processing_paras = {}
|
||||||
|
for name in names:
|
||||||
|
para = names[name]
|
||||||
|
if para is None:
|
||||||
|
logger.error(name + "is None!")
|
||||||
|
return False
|
||||||
|
|
||||||
|
if para['ParaType'] == 'File':
|
||||||
|
if para['DataType'] == 'File':
|
||||||
|
para_path = os.path.join(self.__workspace_path, para['ParaValue'])
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
if para['DataType'] == 'xml':
|
||||||
|
para_path = os.path.join(self.__workspace_path, para['ParaValue'])
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
if para['DataType']=="ymal":
|
||||||
|
para_path = os.path.join(self.__workspace_path, para['ParaValue'])
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
if para['DataType'] == 'tar.gz':
|
||||||
|
para_path = os.path.join(self.__workspace_path, para['ParaValue'])
|
||||||
|
tar_gz_dic = self.__dec_tar_gz(name, para_path, self.__workspace_unpack_path)
|
||||||
|
processing_paras.update(tar_gz_dic)
|
||||||
|
if para['DataType'] == 'tif' or para['DataType'] == 'tiff': # 新增修改dem数据为文件绝对路径
|
||||||
|
if para['ParaValue'] != 'empty' and para['ParaValue'] != 'Empty' and para['ParaValue'] != '':
|
||||||
|
para_path_list = para['ParaValue'].split(";")
|
||||||
|
if len(para_path_list) != 0:
|
||||||
|
dem_path = os.path.join(self.__workspace_origin_path, para['ParaName'])
|
||||||
|
if os.path.exists(dem_path) is False:
|
||||||
|
os.mkdir(dem_path)
|
||||||
|
for file_path in para_path_list:
|
||||||
|
tif_name = os.path.basename(file_path)
|
||||||
|
shutil.copy(file_path, os.path.join(dem_path, tif_name))
|
||||||
|
para_path = os.path.join(self.__workspace_origin_path,para['ParaName'])
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
elif para['ParaType'] == 'Value':
|
||||||
|
if para['DataType'] == 'float':
|
||||||
|
value = float(para['ParaValue'])
|
||||||
|
processing_paras.update({name: value})
|
||||||
|
return processing_paras
|
||||||
|
|
||||||
|
def __dec_tar_gz(self, name1, tar_gz_path, out_dir):
|
||||||
|
"""
|
||||||
|
解压.tar_gz格式景影像文件
|
||||||
|
:param tar_gz_path:.tar_gz文件路径
|
||||||
|
:param out_dir:输出文件夹
|
||||||
|
:return para_dic:全极化影像路径
|
||||||
|
"""
|
||||||
|
# 创建文件夹
|
||||||
|
name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz')
|
||||||
|
file_dir = os.path.join(out_dir, name + '\\')
|
||||||
|
if os.path.exists(file_dir) is False:
|
||||||
|
os.makedirs(file_dir)
|
||||||
|
# 解压
|
||||||
|
t = tarfile.open(tar_gz_path)
|
||||||
|
t.extractall(path=file_dir)
|
||||||
|
|
||||||
|
# 获取文件夹内的文件
|
||||||
|
para_dic = {}
|
||||||
|
# if os.path.exists(file_dir + name + '\\'):
|
||||||
|
# meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.meta.xml')))
|
||||||
|
# para_dic.update({'SLC': file_dir + name})
|
||||||
|
# else:
|
||||||
|
# meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.meta.xml')))
|
||||||
|
# para_dic.update({'SLC': file_dir})
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.xml')))
|
||||||
|
para_dic.update({'SLC': file_dir + name})
|
||||||
|
else:
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.xml')))
|
||||||
|
para_dic.update({'SLC': file_dir})
|
||||||
|
|
||||||
|
if meta_xml_paths == []:
|
||||||
|
raise Exception('there is not .meta.xml in path: ', file_dir + '\\')
|
||||||
|
para_dic.update({'META': meta_xml_paths[0]})
|
||||||
|
self.image_meta_xml = meta_xml_paths
|
||||||
|
# para_dic.update({name1: file_dir}) # {SLC: file_path}
|
||||||
|
|
||||||
|
# 获取文件夹内的文件
|
||||||
|
hh_flag, hv_flag, vh_flag, vv_flag, dh_flag = 0, 0, 0, 0, 0 #
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir + name + '\\', '*.tif')))
|
||||||
|
if in_tif_paths == []:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir + name + '\\', '*.tiff')))
|
||||||
|
else:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir, '*.tif')))
|
||||||
|
if in_tif_paths == []:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir, '*.tiff')))
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
# 获取极化类型
|
||||||
|
if 'hh' in os.path.basename(in_tif_path) or 'HH' in os.path.basename(in_tif_path):
|
||||||
|
hh_flag = 1
|
||||||
|
elif 'hv' in os.path.basename(in_tif_path) or 'HV' in os.path.basename(in_tif_path):
|
||||||
|
hv_flag = 1
|
||||||
|
elif 'vh' in os.path.basename(in_tif_path) or 'VH' in os.path.basename(in_tif_path):
|
||||||
|
vh_flag = 1
|
||||||
|
elif 'vv' in os.path.basename(in_tif_path) or 'VV' in os.path.basename(in_tif_path):
|
||||||
|
vv_flag = 1
|
||||||
|
elif "DH" in os.path.basename(in_tif_path):
|
||||||
|
dh_flag = 1
|
||||||
|
if hh_flag == 0 and hv_flag == 0 and vh_flag == 0 and vv_flag == 0 and dh_flag == 0:
|
||||||
|
raise Exception('can not found files: HH、HV、VH、VV、DH in path:', tar_gz_path)
|
||||||
|
self.processinfo = [hh_flag, hv_flag, vh_flag, vv_flag,dh_flag]
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
def process_handle(self):
|
||||||
|
isError, CorrectMethod = self.__check_handler.check_input_paras(['CorrectMethod']) # //todo 获取xml中校正方法 根据不同方法进行结果处理
|
||||||
|
|
||||||
|
if CorrectMethod.get('CorrectMethod') == '1' or CorrectMethod.get('CorrectMethod') == 1:
|
||||||
|
logger.info("CorrectMethod is RPC!")
|
||||||
|
return self.RPC_process_handle()
|
||||||
|
|
||||||
|
elif CorrectMethod.get('CorrectMethod') == '2' or CorrectMethod.get('CorrectMethod') == 2:
|
||||||
|
logger.info("CorrectMethod is RD!")
|
||||||
|
|
||||||
|
return self.RD_process_handle()
|
||||||
|
else:
|
||||||
|
raise Exception('No CorrectMethod')
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def RPC_process_handle(self):
|
||||||
|
logger.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
|
||||||
|
# print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
|
||||||
|
# 1、DEM拼接、裁剪、重采样
|
||||||
|
Orth_Slc=[]
|
||||||
|
in_dem_path = self.__in_processing_paras['DEM']
|
||||||
|
meta_file_path = self.__in_processing_paras['META'] # .meta文件路径
|
||||||
|
out_dem_path = self.__workspace_ResampledDEM_path
|
||||||
|
dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
|
||||||
|
|
||||||
|
# 2、间接定位法求解行列坐标
|
||||||
|
slc_paths = self.__in_processing_paras["SLC"]
|
||||||
|
rpc_slc_path=None
|
||||||
|
for slc_path in os.listdir(slc_paths):
|
||||||
|
if slc_path.find(".tiff")>0:
|
||||||
|
slc_path_temp=os.path.join(slc_paths,slc_path)
|
||||||
|
# out_power_path=os.path.join(self.__workspace_Temporary_path,slc_path.replace(".tiff","_db.tif").replace("L1A","L1B"))
|
||||||
|
out_power_path=os.path.join(self.__workspace_Temporary_path,slc_path.replace(".tiff","_db.tif").replace("L1A","L1B").replace("HH","h_h").replace("HV","h_v").replace("VH","v_h").replace("VV","v_v"))
|
||||||
|
alg.sar_backscattering_coef(slc_path_temp,self.__in_processing_paras['META'],out_power_path)
|
||||||
|
rpc_slc_path=slc_path_temp.replace(".tiff",".rpc")
|
||||||
|
if not os.path.exists(rpc_slc_path):
|
||||||
|
rpc_slc_path=slc_path_temp.replace(".tiff",".rpb")
|
||||||
|
if not os.path.exists(rpc_slc_path):
|
||||||
|
logger.error("rpc file Not Found!")
|
||||||
|
# out_slc_power_path=os.path.join(self.__workspace_package_path,os.path.basename(out_power_path.replace("_db.tif",".tif").replace("L1B","L4")))
|
||||||
|
out_slc_power_path=os.path.join(self.__workspace_package_path,os.path.basename(out_power_path.replace("_db.tif","-ortho.tif")))
|
||||||
|
rpc_correction(out_power_path,rpc_slc_path,out_slc_power_path)
|
||||||
|
break
|
||||||
|
logger.info('progress bar: 30%')
|
||||||
|
# 2.1 生成映射表
|
||||||
|
slc_path=os.path.join(slc_paths,os.listdir(slc_paths)[0])
|
||||||
|
out_rpc_rc_path = os.path.join(self.__workspace_package_path,"ori_sim-ortho.tif")
|
||||||
|
get_RPC_lon_lat(out_power_path,out_rpc_rc_path)
|
||||||
|
#getRCImageRC(slc_path_temp,out_rpc_rc_path,rpc_slc_path)
|
||||||
|
logger.info('progress bar: 70%')
|
||||||
|
# 2.2 生成局地入射角
|
||||||
|
Orthorectification = IndirectOrthorectification(os.path.join(os.path.dirname(__file__),"config.yaml"))
|
||||||
|
Orthorectification.IndirectOrthorectification(self.__in_processing_paras["SLC"],self.__workspace_package_path) # 改动1
|
||||||
|
out_incangle_path=os.path.join(self.__workspace_package_path,"inci_Angle-ortho.tif")
|
||||||
|
out_localincangle_path=os.path.join(self.__workspace_package_path,"LocalincidentAngle-ortho.tif")
|
||||||
|
out_incangle_geo_path=os.path.join(self.__workspace_package_path,"inc_angle.tif")
|
||||||
|
out_localincangle_geo_path=os.path.join(self.__workspace_package_path,"LocalincidenceAngle.tif") # 决定入射角名字
|
||||||
|
Orthorectification.getRPC_incidenceAngle_lon_lat(dem_merged_path,out_rpc_rc_path,self.__workspace_Temporary_path,self.__workspace_package_path,out_incangle_path,out_localincangle_path,out_incangle_geo_path,out_localincangle_geo_path)
|
||||||
|
# 2.3 输出结果
|
||||||
|
|
||||||
|
# self.del_floder(self.__workspace_processing_path)
|
||||||
|
logger.info('process_handle finished!')
|
||||||
|
logger.info('progress bar :90％')
|
||||||
|
# 7、打包生成快视图
|
||||||
|
for tiff_name in os.listdir(self.__workspace_package_path):
|
||||||
|
if tiff_name.find(".tiff")>0 or tiff_name.find(".tif")>0:
|
||||||
|
self.imageHandler.write_quick_view(os.path.join(self.__workspace_package_path,tiff_name))
|
||||||
|
|
||||||
|
# 1/5、移动原始数据 ------------------------- 这里限制 原始数据是否进入 最终产品列表中
|
||||||
|
for maindir, subdir, file_name_list in os.walk(slc_paths):
|
||||||
|
for filename in file_name_list:
|
||||||
|
apath = os.path.join(maindir, filename)
|
||||||
|
file_type = apath.split('.')[-1]
|
||||||
|
if file_type in ["xml"]:
|
||||||
|
output = os.path.join(self.__workspace_package_path, filename)
|
||||||
|
shutil.copy(apath, output)
|
||||||
|
else:
|
||||||
|
output=os.path.join(self.__workspace_package_path, filename)
|
||||||
|
shutil.copy(apath, output)
|
||||||
|
|
||||||
|
# 生成元文件案例
|
||||||
|
# xml_path = "./model_meta.xml"
|
||||||
|
tem_folder=self.__workspace_path + EXE_NAME + r"\Temporary""\\"
|
||||||
|
image_path=out_slc_power_path# os.path.join(self.__workspace_package_path, "OrthoMapTable.tif")
|
||||||
|
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().calu_nature(image_path, self.processinfo, out_path1, out_path2)
|
||||||
|
#
|
||||||
|
# dem_path=os.path.join(self.__workspace_ResampledDEM_path, 'mergedDEM.tif')
|
||||||
|
# out_dem_path1 = os.path.join(tem_folder, "trans_dem_geo_projcs.tif")
|
||||||
|
# out_dem_path2 = os.path.join(tem_folder, "trans_dem_projcs_geo.tif")
|
||||||
|
# # par_dict2 = CreateDict().calu_dem_nature(dem_path, dem_meta, out_dem_path1, out_dem_path2, sampling_f, para_A_arr)
|
||||||
|
# # par_dict2 = CreateDict().calu_dem_nature(dem_path, dem_meta, out_dem_path1, out_dem_path2, sampling_f, para_A_arr)
|
||||||
|
# par_dict2 = CreateDict().calu_dem_nature(dem_path, out_dem_path1, out_dem_path2, None,Orthorectification.SatelliteOrbitModel.A_arr)
|
||||||
|
# model_xml_path = os.path.join(self.__workspace_Temporary_path, "creat_standard.meta.xml") # 输出xml路径
|
||||||
|
# CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, par_dict2, model_xml_path).create_standard_xml()
|
||||||
|
#
|
||||||
|
# sar_image_meta_xml = list(glob.glob(os.path.join(self.__workspace_package_path, '*.meta.xml')))
|
||||||
|
# meta_xml_path = os.path.join(self.__workspace_package_path, os.path.basename(self.__out_para).replace(".tar.gz",".meta.xml"))
|
||||||
|
# CreateMetafile(sar_image_meta_xml[0], self.alg_xml_path, model_xml_path, meta_xml_path).process(os.path.basename(self.__in_processing_paras["SLC"]))
|
||||||
|
|
||||||
|
model_path = "./product.xml"
|
||||||
|
meta_xml_path = os.path.join(self.__workspace_package_path,
|
||||||
|
os.path.basename(self.__out_para).replace(".tar.gz", ".meta.xml"))
|
||||||
|
|
||||||
|
para_dict = CreateMetaDict(image_path, self.__in_processing_paras['META'], self.__workspace_package_path,
|
||||||
|
out_path1, out_path2).calu_nature()
|
||||||
|
para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
|
||||||
|
para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "DEM"})
|
||||||
|
CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
|
||||||
|
# 生成压缩包
|
||||||
|
logger.info('progress bar :94％')
|
||||||
|
logger.info('start make targz..')
|
||||||
|
self.del_floder(self.__workspace_unpack_path)
|
||||||
|
self.del_floder(self.__workspace_ResampledDEM_path)
|
||||||
|
self.del_floder(self.__workspace_LutImg_path)
|
||||||
|
self.del_floder(self.__workspace_IncidenceImg_path)
|
||||||
|
self.del_floder(self.__workspace_SimImg_path)
|
||||||
|
self.del_floder(self.__workspace_SARIntensity_path)
|
||||||
|
self.make_targz(self.__out_para, self.__workspace_package_path+"\\")
|
||||||
|
logger.info('make targz finish')
|
||||||
|
logger.info('progress bar :100％')
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
def cut_dem(self, dem_merged_path, meta_file_path):
|
||||||
|
left_up_lon = 0
|
||||||
|
left_up_lat = 0
|
||||||
|
|
||||||
|
|
||||||
|
def process_sim_ori(self, ori_sim, sim_ori):
|
||||||
|
|
||||||
|
scopes = ()
|
||||||
|
scopes += (ImageHandler.get_scope_ori_sim(ori_sim),)
|
||||||
|
|
||||||
|
intersect_polygon = pp().intersect_polygon(scopes)
|
||||||
|
if intersect_polygon is None:
|
||||||
|
raise Exception('create intersect shp fail!')
|
||||||
|
shp_path = os.path.join(self.__workspace_Temporary_path, 'IntersectPolygon.shp')
|
||||||
|
if pp().write_polygon_shp(shp_path, intersect_polygon, 4326) is False:
|
||||||
|
raise Exception('create intersect shp fail!')
|
||||||
|
sim_ori_process = os.path.join(self.__workspace_Temporary_path, 'sim_ori_process.tif')
|
||||||
|
pp().cut_img(sim_ori_process, sim_ori, shp_path)
|
||||||
|
return sim_ori_process
|
||||||
|
|
||||||
|
|
||||||
|
def RD_process_handle(self):
|
||||||
|
# RPC
|
||||||
|
logger.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
|
||||||
|
# print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
|
||||||
|
# 1、DEM拼接、裁剪、重采样
|
||||||
|
Orth_Slc=[]
|
||||||
|
in_dem_path = self.__in_processing_paras['DEM']
|
||||||
|
meta_file_path = self.__in_processing_paras['META'] # .meta文件路径
|
||||||
|
out_dem_path = self.__workspace_ResampledDEM_path
|
||||||
|
dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
|
||||||
|
|
||||||
|
# self.cut_dem(dem_merged_path, meta_file_path)
|
||||||
|
# 2、间接定位法求解行列坐标
|
||||||
|
slc_paths = self.__in_processing_paras["SLC"]
|
||||||
|
# 2.1 生成映射表
|
||||||
|
slc_path=os.path.join(slc_paths,os.listdir(slc_paths)[0])
|
||||||
|
|
||||||
|
# 2.2 生成局地入射角
|
||||||
|
path2 = env_str
|
||||||
|
Orthorectification = IndirectOrthorectification(os.path.join(path2,"config.yaml"))
|
||||||
|
Orthorectification.IndirectOrthorectification(self.__in_processing_paras["SLC"], self.__workspace_package_path) # 改动1
|
||||||
|
# 2.3 输出结果
|
||||||
|
|
||||||
|
# 3 处理RD
|
||||||
|
in_slc_path=None
|
||||||
|
for slc_path in os.listdir(slc_paths):
|
||||||
|
if slc_path.find(".tiff")>0 and (slc_path.find("_HH_")>0 or slc_path.find("_VV_")>0 or slc_path.find("_DH_")>0):
|
||||||
|
in_slc_path=os.path.join(slc_paths,slc_path)
|
||||||
|
break
|
||||||
|
# 获取校正模型后
|
||||||
|
Orthorectification.preCaldem_sar_rc(dem_merged_path,in_slc_path,self.__workspace_Temporary_path,self.__workspace_package_path.replace("\\","\\\\")) # 初步筛选坐标范围
|
||||||
|
|
||||||
|
logger.info('progress bar: 40%')
|
||||||
|
# clip_dem_reample_path=os.path.join(self.__workspace_Temporary_path, "SAR_dem.tiff")
|
||||||
|
# infooption=gdal.InfoOptions("-json")
|
||||||
|
# clip_dem_tif_info=gdal.Info(clip_dem_reample_path,options=infooption)
|
||||||
|
# dem_merged_info=gdal.Info(dem_merged_path,options=infooption)
|
||||||
|
# sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0]
|
||||||
|
|
||||||
|
out_dir_path=self.__workspace_package_path.replace("\\","\\\\")
|
||||||
|
this_outSpace_path = out_dir_path
|
||||||
|
this_out_dem_slantRange_path = out_dir_path + "\\" + "dem_slantRange.tiff"#// 地形斜距
|
||||||
|
this_out_plant_slantRange_path = out_dir_path + "\\" + "flat_slantRange.tiff"#// 平地斜距
|
||||||
|
# 保留结果
|
||||||
|
if(os.path.exists(this_out_dem_slantRange_path)):
|
||||||
|
os.remove(this_out_dem_slantRange_path)
|
||||||
|
|
||||||
|
if(os.path.exists(this_out_plant_slantRange_path)):
|
||||||
|
os.remove(this_out_plant_slantRange_path)
|
||||||
|
|
||||||
|
|
||||||
|
this_out_dem_rc_path = out_dir_path + "\\" + "WGS_SAR_map.tiff"#// 经纬度与行列号映射
|
||||||
|
if(os.path.exists(this_out_dem_rc_path)):
|
||||||
|
os.remove(this_out_dem_rc_path)
|
||||||
|
|
||||||
|
this_out_sar_sim_path = out_dir_path + "\\" + "sar_sim.tiff"
|
||||||
|
if (os.path.exists(this_out_sar_sim_path)):
|
||||||
|
os.remove(this_out_sar_sim_path)
|
||||||
|
|
||||||
|
this_out_sar_sim_wgs_path = out_dir_path + "\\" + "sar_sim_wgs.tiff" # // 经纬度与行列号映射
|
||||||
|
if (os.path.exists(this_out_sar_sim_wgs_path)):
|
||||||
|
os.remove(this_out_sar_sim_wgs_path)
|
||||||
|
|
||||||
|
|
||||||
|
this_out_incidence_path = out_dir_path + "\\" + "incidentAngle.tiff"#// 入射角
|
||||||
|
this_out_localIncidenct_path = out_dir_path + "\\" + "localincidentAngle.tiff"#// 局地入射角
|
||||||
|
this_out_inc_angle_rpc_path = out_dir_path + "\\" + "RD_incidentAngle.tiff"#// 局地入射角
|
||||||
|
this_out_local_inc_angle_rpc_path = out_dir_path + "\\" + "RD_localincidentAngle.tiff"#// 局地入射角
|
||||||
|
|
||||||
|
if (os.path.exists(this_out_inc_angle_rpc_path)):
|
||||||
|
shutil.move(this_out_inc_angle_rpc_path, out_dir_path + "\\" + "inci_Angle-ortho.tif")
|
||||||
|
|
||||||
|
if (os.path.exists(this_out_local_inc_angle_rpc_path)):
|
||||||
|
shutil.move(this_out_local_inc_angle_rpc_path, out_dir_path + "\\" + "LocalIncidentAngle-ortho.tif")
|
||||||
|
|
||||||
|
if(os.path.exists(this_out_incidence_path)):
|
||||||
|
shutil.move(this_out_incidence_path,out_dir_path + "\\" + "inc_angle.tif")
|
||||||
|
|
||||||
|
if(os.path.exists(this_out_localIncidenct_path)):
|
||||||
|
shutil.move(this_out_localIncidenct_path,out_dir_path + "\\" + "LocalIncidenceAngle.tif")
|
||||||
|
|
||||||
|
this_out_ori_sim_tiff = out_dir_path + "\\" + "RD_ori_sim.tif"#// 局地入射角
|
||||||
|
if (os.path.exists(this_out_ori_sim_tiff)):
|
||||||
|
shutil.move(this_out_ori_sim_tiff, out_dir_path + "\\" + "ori_sim-ortho.tif")
|
||||||
|
|
||||||
|
this_out_sim_ori_tiff = out_dir_path + "\\" + "RD_sim_ori.tif" # // 局地入射角
|
||||||
|
if (os.path.exists(this_out_sim_ori_tiff)):
|
||||||
|
shutil.move(this_out_sim_ori_tiff, out_dir_path + "\\" + "sim_ori-ortho.tif")
|
||||||
|
|
||||||
|
# GTC 入射角
|
||||||
|
GTC_rc_path=os.path.join(self.__workspace_package_path,"ori_sim-ortho.tif")
|
||||||
|
GTC_out_path=self.__workspace_package_path
|
||||||
|
|
||||||
|
parameter_path = os.path.join(self.__workspace_package_path, "orth_para.txt")
|
||||||
|
this_in_rpc_lon_lat_path = os.path.join(self.__workspace_package_path, "ori_sim-ortho.tif")
|
||||||
|
dem_rc = os.path.join(self.__workspace_package_path, "sim_ori-ortho.tif")
|
||||||
|
dem_rc_pro = self.process_sim_ori(this_in_rpc_lon_lat_path, dem_rc)
|
||||||
|
shutil.move(dem_rc_pro, dem_rc)
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(slc_paths, '*.tiff')))
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
out_sar_path = os.path.join(GTC_out_path, os.path.split(in_tif_path)[1])
|
||||||
|
slc_path_temp=os.path.join(slc_paths,in_tif_path)
|
||||||
|
out_power_path = os.path.join(self.__workspace_Temporary_path,
|
||||||
|
slc_path_temp.replace(".tiff", "-lin.tif").replace("L1A", "L1B")).replace(
|
||||||
|
"HH", "h_h").replace("HV", "h_v").replace("VH", "v_h").replace("VV", "v_v").replace("DH", "d_h")
|
||||||
|
# out_power_path=os.path.join(self.__workspace_Temporary_path,slc_path_temp.replace(".tiff","_db.tif"))
|
||||||
|
alg.sar_backscattering_coef(slc_path_temp, self.__in_processing_paras['META'], out_power_path)
|
||||||
|
|
||||||
|
lin_tif_path = os.path.join(self.__workspace_Temporary_path,
|
||||||
|
os.path.basename(out_power_path).split('-')[0] + "-lin_geo.tif")
|
||||||
|
# Orthorectification.calInterpolation_cubic_Wgs84_rc_sar_sigma(parameter_path, dem_rc,
|
||||||
|
# out_power_path,
|
||||||
|
# lin_tif_path)
|
||||||
|
|
||||||
|
Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, dem_rc,
|
||||||
|
out_power_path,
|
||||||
|
lin_tif_path)
|
||||||
|
tempout_tif_path = os.path.join(self.__workspace_package_path,
|
||||||
|
os.path.basename(lin_tif_path).split('-')[0] + "-ortho.tif")
|
||||||
|
alg.lin_to_db(lin_tif_path, tempout_tif_path) # 线性值转回DB值
|
||||||
|
|
||||||
|
# temp_slc_path=os.path.join(self.__workspace_package_path, os.path.basename(out_power_path))
|
||||||
|
# temp_slc_path=temp_slc_path.replace("_db.tif","-ortho.tif")
|
||||||
|
#inter_Range2Geo(self,lon_lat_path , data_tiff , grid_path , space)
|
||||||
|
|
||||||
|
# Orthorectification.inter_Range2Geo(GTC_rc_path,out_power_path,temp_slc_path,Orthorectification.heightspace)
|
||||||
|
# Orthorectification.calInterpolation_cubic_Wgs84_rc_sar_sigma(parameter_path, dem_rc, out_power_path, temp_slc_path) #
|
||||||
|
break
|
||||||
|
#Orth_Slc.append(temp_slc_path)
|
||||||
|
# power_list.append(out_power_path)
|
||||||
|
|
||||||
|
for tiff_name in os.listdir(self.__workspace_package_path):
|
||||||
|
if tiff_name.find(".tiff")>0 or tiff_name.find(".tif")>0:
|
||||||
|
self.imageHandler.write_quick_view(os.path.join(self.__workspace_package_path,tiff_name))
|
||||||
|
|
||||||
|
# 1/5、原始数据中的.incidence.xml、.meta.xml输出，影像不输出
|
||||||
|
for maindir, subdir, file_name_list in os.walk(slc_paths):
|
||||||
|
for filename in file_name_list:
|
||||||
|
apath = os.path.join(maindir, filename)
|
||||||
|
file_type = apath.split('.')[-1]
|
||||||
|
if file_type in ["xml"]:
|
||||||
|
output = os.path.join(self.__workspace_package_path, filename)
|
||||||
|
shutil.copy(apath, output)
|
||||||
|
else:
|
||||||
|
output=os.path.join(self.__workspace_package_path, filename)
|
||||||
|
shutil.copy(apath, output)
|
||||||
|
|
||||||
|
# 2/5、正射的成果图
|
||||||
|
# for maindir, subdir, file_name_list in os.walk(self.__workspace_package_path):
|
||||||
|
# for filename in file_name_list:
|
||||||
|
# apath = os.path.join(maindir, filename)
|
||||||
|
# file_type = filename.split('.')[-1]
|
||||||
|
# image_name = os.path.splitext(filename)[0]
|
||||||
|
#self.imageHandler.write_quick_view(output_OrthoResult) # 快视图
|
||||||
|
# 生成元文件案例
|
||||||
|
# xml_path = "./model_meta.xml"
|
||||||
|
tem_folder=self.__workspace_path + EXE_NAME + r"\Temporary""\\"
|
||||||
|
image_path=tempout_tif_path# os.path.join(self.__workspace_package_path, "OrthoMapTable.tif")
|
||||||
|
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().calu_nature(image_path, self.processinfo, out_path1, out_path2)
|
||||||
|
#
|
||||||
|
# dem_path=os.path.join(self.__workspace_ResampledDEM_path, 'mergedDEM.tif')
|
||||||
|
# out_dem_path1 = os.path.join(tem_folder, "trans_dem_geo_projcs.tif")
|
||||||
|
# out_dem_path2 = os.path.join(tem_folder, "trans_dem_projcs_geo.tif")
|
||||||
|
# # par_dict2 = CreateDict().calu_dem_nature(dem_path, dem_meta, out_dem_path1, out_dem_path2, sampling_f, para_A_arr)
|
||||||
|
# par_dict2 = CreateDict().calu_dem_nature(dem_path, out_dem_path1, out_dem_path2, sampling_f,Orthorectification.SatelliteOrbitModel.A_arr)
|
||||||
|
# model_xml_path = os.path.join(self.__workspace_Temporary_path, "creat_standard.meta.xml") # 输出xml路径
|
||||||
|
# CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, par_dict2, model_xml_path).create_standard_xml()
|
||||||
|
#
|
||||||
|
# sar_image_meta_xml = list(glob.glob(os.path.join(self.__workspace_package_path, '*.meta.xml')))
|
||||||
|
# meta_xml_path = os.path.join(self.__workspace_package_path, os.path.basename(self.__out_para).replace(".tar.gz",".meta.xml"))
|
||||||
|
# CreateMetafile(sar_image_meta_xml[0], self.alg_xml_path, model_xml_path, meta_xml_path).process(os.path.basename(self.__in_processing_paras["SLC"]))
|
||||||
|
model_path = "./product.xml"
|
||||||
|
meta_xml_path = os.path.join(self.__workspace_package_path, os.path.basename(self.__out_para).replace(".tar.gz",".meta.xml"))
|
||||||
|
|
||||||
|
para_dict = CreateMetaDict(image_path, self.__in_processing_paras['META'], self.__workspace_package_path, out_path1, out_path2).calu_nature()
|
||||||
|
para_dict.update({"imageinfo_ProductName": '正射校正'})
|
||||||
|
para_dict.update({"imageinfo_ProductIdentifier": 'Ortho'})
|
||||||
|
para_dict.update({"imageinfo_ProductLevel": '3A'})
|
||||||
|
para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
|
||||||
|
para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "DEM"})
|
||||||
|
CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
# 生成压缩包
|
||||||
|
logger.info('progress bar :94％')
|
||||||
|
logger.info('start make targz..')
|
||||||
|
self.del_floder(self.__workspace_unpack_path)
|
||||||
|
self.del_floder(self.__workspace_ResampledDEM_path)
|
||||||
|
self.del_floder(self.__workspace_LutImg_path)
|
||||||
|
self.del_floder(self.__workspace_IncidenceImg_path)
|
||||||
|
self.del_floder(self.__workspace_SimImg_path)
|
||||||
|
self.del_floder(self.__workspace_SARIntensity_path)
|
||||||
|
self.make_targz(self.__out_para, self.__workspace_package_path+"\\")
|
||||||
|
logger.info('make targz finish')
|
||||||
|
logger.info('progress bar :100％')
|
||||||
|
return True
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
DEBUG=False
|
||||||
|
if '-DEBUG' in sys.argv:
|
||||||
|
DEBUG=True
|
||||||
|
start = datetime.datetime.now()
|
||||||
|
try:
|
||||||
|
if len(sys.argv) < 2:
|
||||||
|
xml_path = 'Ortho.xml'
|
||||||
|
else:
|
||||||
|
xml_path = sys.argv[1]
|
||||||
|
OrthoMain = OrthoMain(xml_path)
|
||||||
|
if OrthoMain.check_source() is False:
|
||||||
|
raise Exception('check_source() failed!')
|
||||||
|
if OrthoMain.process_handle() is False:
|
||||||
|
raise Exception('check_source() failed!')
|
||||||
|
logger.info('successful production of ortho products!')
|
||||||
|
except Exception:
|
||||||
|
logger.exception("run-time error!")
|
||||||
|
finally:
|
||||||
|
OrthoMain.del_temp_workspace()
|
||||||
|
pass
|
||||||
|
end = datetime.datetime.now()
|
||||||
|
logger.info('running use time: %s ' % (end - start))
|
||||||
|
|
@ -0,0 +1,40 @@
|
||||||
|
# -*- mode: python ; coding: utf-8 -*-
|
||||||
|
|
||||||
|
|
||||||
|
block_cipher = None
|
||||||
|
|
||||||
|
|
||||||
|
a = Analysis(['OrthoMain.py'],
|
||||||
|
pathex=[],
|
||||||
|
binaries=[],
|
||||||
|
datas=[],
|
||||||
|
hiddenimports=[],
|
||||||
|
hookspath=[],
|
||||||
|
hooksconfig={},
|
||||||
|
runtime_hooks=[],
|
||||||
|
excludes=[],
|
||||||
|
win_no_prefer_redirects=False,
|
||||||
|
win_private_assemblies=False,
|
||||||
|
cipher=block_cipher,
|
||||||
|
noarchive=False)
|
||||||
|
pyz = PYZ(a.pure, a.zipped_data,
|
||||||
|
cipher=block_cipher)
|
||||||
|
|
||||||
|
exe = EXE(pyz,
|
||||||
|
a.scripts,
|
||||||
|
a.binaries,
|
||||||
|
a.zipfiles,
|
||||||
|
a.datas,
|
||||||
|
[],
|
||||||
|
name='OrthoMain',
|
||||||
|
debug=False,
|
||||||
|
bootloader_ignore_signals=False,
|
||||||
|
strip=False,
|
||||||
|
upx=True,
|
||||||
|
upx_exclude=[],
|
||||||
|
runtime_tmpdir=None,
|
||||||
|
console=True,
|
||||||
|
disable_windowed_traceback=False,
|
||||||
|
target_arch=None,
|
||||||
|
codesign_identity=None,
|
||||||
|
entitlements_file=None )
|
||||||
|
|
@ -0,0 +1,310 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：BackScatteringXmlInfo.PY
|
||||||
|
@Function ：主函数
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
from xml.etree.ElementTree import ElementTree, Element
|
||||||
|
import xml.dom.minidom
|
||||||
|
from lxml import etree
|
||||||
|
import shutil
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
||||||
|
from osgeo import gdal
|
||||||
|
import numpy as np
|
||||||
|
import datetime
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
|
||||||
|
class CreateDict:
|
||||||
|
"""根据影像/DEM的属性信息添加到字典中"""
|
||||||
|
def __init__(self):
|
||||||
|
self.ImageHandler = ImageHandler()
|
||||||
|
pass
|
||||||
|
|
||||||
|
def calu_nature(self, image_path, image_pair, out_path1, out_path2):
|
||||||
|
"""
|
||||||
|
将productinfo节点需要填写的信息存入字典中
|
||||||
|
image_path:影像路径
|
||||||
|
image_pair:输入的压缩包中的极化对 例：hh,hv,vh,vv=【1，1，1，1】
|
||||||
|
out_path1：地理转平面的输出路径
|
||||||
|
out_path2：平面转地理的输出路径
|
||||||
|
"""
|
||||||
|
|
||||||
|
para_dict = {}
|
||||||
|
imageinfo_width = self.ImageHandler.get_img_width(image_path)
|
||||||
|
para_dict.update({"imageinfo_width":imageinfo_width})
|
||||||
|
imageinfo_height = self.ImageHandler.get_img_height(image_path)
|
||||||
|
para_dict.update({"imageinfo_height":imageinfo_height})
|
||||||
|
para_dict.update({"imageinfo_EarthModel": "WGS84"})
|
||||||
|
para_dict.update({"imageinfo_ProjectModel": "UTM"})
|
||||||
|
proj = self.ImageHandler.get_projection(image_path) # 输出的影像若是投影坐标系则先转成地理坐标系
|
||||||
|
keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass
|
||||||
|
if keyword == "GEOGCS":
|
||||||
|
pass
|
||||||
|
elif keyword == "PROJCS":
|
||||||
|
pp.trans_projcs2geogcs(out_path2, image_path)
|
||||||
|
image_path = out_path2
|
||||||
|
elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
|
||||||
|
raise Exception('image projection is missing!')
|
||||||
|
|
||||||
|
pp.trans_geogcs2projcs(out_path1, image_path) # 坐标投影, 地理转平面投影坐标
|
||||||
|
imageinfo_widthspace = self.ImageHandler.get_geotransform(out_path1)[1] # 投影后的分辨率
|
||||||
|
imageinfo_heightspace = -self.ImageHandler.get_geotransform(out_path1)[5] # 投影后的分辨率
|
||||||
|
para_dict.update({"imageinfo_widthspace":imageinfo_widthspace})
|
||||||
|
para_dict.update({"imageinfo_heightspace":imageinfo_heightspace})
|
||||||
|
para_dict.update({"NominalResolution":imageinfo_widthspace})
|
||||||
|
|
||||||
|
WidthInMeters = imageinfo_width*imageinfo_widthspace # 投影后的分辨率×宽度
|
||||||
|
para_dict.update({"WidthInMeters":WidthInMeters})
|
||||||
|
|
||||||
|
image_array = self.ImageHandler.get_band_array(image_path)
|
||||||
|
a2 = np.where(np.isnan(image_array), 999999, image_array)
|
||||||
|
MinValue = np.min(a2)
|
||||||
|
a3 = np.where(np.isnan(image_array), -999999, image_array)
|
||||||
|
MaxValue = np.max(a3)
|
||||||
|
|
||||||
|
para_dict.update({"MaxValue":MaxValue})
|
||||||
|
para_dict.update({"MinValue":MinValue})
|
||||||
|
|
||||||
|
get_scope = self.ImageHandler.get_scope(image_path)
|
||||||
|
point_upleft, point_upright, point_downleft, point_downright=get_scope[0], get_scope[1], get_scope[2], get_scope[3]
|
||||||
|
para_dict.update({"imageinfo_corner_topLeft_latitude": point_upleft[1]})
|
||||||
|
para_dict.update({"imageinfo_corner_topLeft_longitude": point_upleft[0]})
|
||||||
|
para_dict.update({"imageinfo_corner_topRight_latitude": point_upright[1]})
|
||||||
|
para_dict.update({"imageinfo_corner_topRight_longitude": point_upright[0]})
|
||||||
|
para_dict.update({"imageinfo_corner_bottomLeft_latitude": point_downleft[1]})
|
||||||
|
para_dict.update({"imageinfo_corner_bottomLeft_longitude": point_downleft[0]})
|
||||||
|
para_dict.update({"imageinfo_corner_bottomRight_latitude": point_downright[1]})
|
||||||
|
para_dict.update({"imageinfo_corner_bottomRight_longitude": point_downright[0]})
|
||||||
|
|
||||||
|
longitude_max=np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).max()
|
||||||
|
longitude_min=np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).min()
|
||||||
|
latitude_max=np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).max()
|
||||||
|
latitude_min=np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).min()
|
||||||
|
imageinfo_center_latitude=(latitude_max+latitude_min)/2
|
||||||
|
imageinfo_center_longitude=(longitude_max+longitude_min)/2
|
||||||
|
para_dict.update({"imageinfo_center_latitude": imageinfo_center_latitude})
|
||||||
|
para_dict.update({"imageinfo_center_longitude": imageinfo_center_longitude})
|
||||||
|
|
||||||
|
# self.para_dict.update({"productType": "GTC"}) # 设置产品类型
|
||||||
|
para_dict.update({"productFormat": "TIF"})
|
||||||
|
productGentime = datetime.datetime.now()
|
||||||
|
para_dict.update({"productGentime": productGentime})
|
||||||
|
para_dict.update({"unit": "none"}) # 设置单位
|
||||||
|
para_dict.update({"NoDataValue": "nan"})
|
||||||
|
para_dict.update({"productLevel": "4"}) # 设置图像位深度
|
||||||
|
|
||||||
|
image_array = self.ImageHandler.get_band_array(image_path)
|
||||||
|
try: # 设置图像位深度
|
||||||
|
gdal_dtypes = {
|
||||||
|
'int8': gdal.GDT_Byte,
|
||||||
|
'unit16': gdal.GDT_UInt16,
|
||||||
|
'int16': gdal.GDT_Int16,
|
||||||
|
'unit32': gdal.GDT_UInt32,
|
||||||
|
'int32': gdal.GDT_Int32,
|
||||||
|
'float32': gdal.GDT_Float32,
|
||||||
|
'float64': gdal.GDT_Float64,
|
||||||
|
}
|
||||||
|
bit_dtypes = {
|
||||||
|
'int8': 8,
|
||||||
|
'unit16': 16,
|
||||||
|
'int16': 16,
|
||||||
|
'unit32': 32,
|
||||||
|
'int32': 32,
|
||||||
|
'float32': 32,
|
||||||
|
'float64': 64,
|
||||||
|
}
|
||||||
|
if not gdal_dtypes.get(image_array.dtype.name, None) is None:
|
||||||
|
bit_num = str(bit_dtypes[image_array.dtype.name])
|
||||||
|
datatype=bit_num+"bit"
|
||||||
|
else:
|
||||||
|
datatype = str(32) + "bit"
|
||||||
|
# datatype = str(gdal.GDT_Float32)+"bit"
|
||||||
|
para_dict.update({"imagebit": datatype})
|
||||||
|
except Exception:
|
||||||
|
para_dict.update({"imagebit": "None"})
|
||||||
|
|
||||||
|
HH, HV, VH ,VV= image_pair[0], image_pair[1], image_pair[2], image_pair[3]
|
||||||
|
if HH == 0:
|
||||||
|
HH = "delete"
|
||||||
|
else:
|
||||||
|
HH = "NULL"
|
||||||
|
para_dict.update({"imageinfo_QualifyValue_HH": HH})
|
||||||
|
if HV==0:
|
||||||
|
HV = "delete"
|
||||||
|
else:
|
||||||
|
HV = "NULL"
|
||||||
|
para_dict.update({"imageinfo_QualifyValue_HV": HV})
|
||||||
|
if VH==0:
|
||||||
|
VH = "delete"
|
||||||
|
else:
|
||||||
|
VH = "NULL"
|
||||||
|
para_dict.update({"imageinfo_QualifyValue_VH": VH})
|
||||||
|
if VV==0:
|
||||||
|
VV = "delete"
|
||||||
|
else:
|
||||||
|
VV = "NULL"
|
||||||
|
para_dict.update({"imageinfo_QualifyValue_VV": VV})
|
||||||
|
|
||||||
|
return para_dict
|
||||||
|
|
||||||
|
def calu_dem_nature(self, dem_path, out_dem_path1, out_dem_path2, sampling_f, para_A_arr):
|
||||||
|
"""
|
||||||
|
正射需要单独加上dem影像的信息
|
||||||
|
dem_path：mergedDEM.tif路径
|
||||||
|
out_dem_path1：将mergedDEM.tif由地理坐标转化为平面坐标的保存路径
|
||||||
|
out_dem_path2：将mergedDEM.tif由平面坐标转化为地理坐标的保存路径
|
||||||
|
sampling_f： 采样率
|
||||||
|
para_A_arr：四次多项式模型的参数数组
|
||||||
|
"""
|
||||||
|
para_dict2 = {}
|
||||||
|
proj = self.ImageHandler.get_projection(dem_path) # 输出的影像若是投影坐标系则先转成地理坐标系
|
||||||
|
keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass
|
||||||
|
if keyword == "GEOGCS":
|
||||||
|
pass
|
||||||
|
elif keyword == "PROJCS":
|
||||||
|
pp.trans_projcs2geogcs(out_dem_path2, dem_path)
|
||||||
|
dem_path = out_dem_path2
|
||||||
|
elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
|
||||||
|
raise Exception('image projection is missing!')
|
||||||
|
pp.trans_geogcs2projcs(out_dem_path1, dem_path) # 坐标投影, 地理转平面投影坐标
|
||||||
|
DEM_widthspace = self.ImageHandler.get_geotransform(out_dem_path1)[1] # 投影后的分辨率
|
||||||
|
DEM_heightspace = -self.ImageHandler.get_geotransform(out_dem_path1)[5] # 投影后的分辨率
|
||||||
|
para_dict2.update({"DEM_widthspace":DEM_widthspace})
|
||||||
|
para_dict2.update({"DEM_heightspace":DEM_heightspace})
|
||||||
|
# tree = ElementTree() # 获取DEMProduct：dem产品来源、DEMDate：dem对应的日期
|
||||||
|
# tree.parse(dem_meta) # 影像头文件
|
||||||
|
# root = tree.getroot()
|
||||||
|
# productinfo = root.find("metadata")
|
||||||
|
# DEMProduct = list(productinfo)[0].tag
|
||||||
|
# para_dict2.update({"DEM_DEMProduct":DEMProduct})
|
||||||
|
#
|
||||||
|
# DEMDate = root.find("metadata").find(DEMProduct).text
|
||||||
|
# para_dict2.update({"DEM_DEMDate": DEMDate})
|
||||||
|
get_scope = self.ImageHandler.get_scope(dem_path) # 获取mergedDEM.tif数据的四个角的经纬度信息
|
||||||
|
point_upleft, point_upright, point_downleft, point_downright=get_scope[0], get_scope[1], get_scope[2], get_scope[3]
|
||||||
|
para_dict2.update({"DEM_corner_topLeft_latitude": point_upleft[1]})
|
||||||
|
para_dict2.update({"DEM_corner_topLeft_longitude": point_upleft[0]})
|
||||||
|
para_dict2.update({"DEM_corner_topRight_latitude": point_upright[1]})
|
||||||
|
para_dict2.update({"DEM_corner_topRight_longitude": point_upright[0]})
|
||||||
|
para_dict2.update({"DEM_corner_bottomLeft_latitude": point_downleft[1]})
|
||||||
|
para_dict2.update({"DEM_corner_bottomLeft_longitude": point_downleft[0]})
|
||||||
|
para_dict2.update({"DEM_corner_bottomRight_latitude": point_downright[1]})
|
||||||
|
para_dict2.update({"DEM_corner_bottomRight_longitude": point_downright[0]})
|
||||||
|
#para_dict2.update({"orthoModel_samplingrate": sampling_f})
|
||||||
|
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_X_a0": para_A_arr[0, 0]}) # 获取四次多项式模型6个参数的数值
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_X_a1": para_A_arr[1, 0]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_X_a2": para_A_arr[2, 0]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_X_a3": para_A_arr[3, 0]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_X_a4": para_A_arr[4, 0]})
|
||||||
|
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Y_b0": para_A_arr[0, 1]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Y_b1": para_A_arr[1, 1]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Y_b2": para_A_arr[2, 1]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Y_b3": para_A_arr[3, 1]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Y_b4": para_A_arr[4, 1]})
|
||||||
|
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Z_c0": para_A_arr[0, 2]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Z_c1": para_A_arr[1, 2]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Z_c2": para_A_arr[2, 2]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Z_c3": para_A_arr[3, 2]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Z_c4": para_A_arr[4, 2]})
|
||||||
|
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vx_d0": para_A_arr[0, 3]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vx_d1": para_A_arr[1, 3]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vx_d2": para_A_arr[2, 3]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vx_d3": para_A_arr[3, 3]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vx_d4": para_A_arr[4, 3]})
|
||||||
|
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vy_e0": para_A_arr[0, 4]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vy_e1": para_A_arr[1, 4]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vy_e2": para_A_arr[2, 4]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vy_e3": para_A_arr[3, 4]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vy_e4": para_A_arr[4, 4]})
|
||||||
|
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vz_f0": para_A_arr[0, 5]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vz_f1": para_A_arr[1, 5]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vz_f2": para_A_arr[2, 5]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vz_f3": para_A_arr[3, 5]})
|
||||||
|
para_dict2.update({"satalliteOrbitModel_parameter_Vz_f4": para_A_arr[4, 5]})
|
||||||
|
return para_dict2
|
||||||
|
|
||||||
|
|
||||||
|
class CreateStadardXmlFile:
|
||||||
|
"""读取字典中的属性值，生成一个标准的xml文件"""
|
||||||
|
def __init__(self, xml_path, para_xml_path, par_dict, par_dict2, path):
|
||||||
|
"""
|
||||||
|
xml_path:模板路径
|
||||||
|
para_xml_path:算法配置文件的路径
|
||||||
|
par_dict:字典
|
||||||
|
path:xml模板输出路径
|
||||||
|
"""
|
||||||
|
self.par_dict = par_dict
|
||||||
|
self.par_dict2 = par_dict2
|
||||||
|
self.path = path
|
||||||
|
shutil.copy(xml_path, path)
|
||||||
|
pass
|
||||||
|
|
||||||
|
def create_standard_xml(self):
|
||||||
|
"""将字典中的信息写入到copy的xml文件中"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path) # 影像头文件
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
productinfo = root.find("productinfo")
|
||||||
|
for key, value in self.par_dict.items():
|
||||||
|
if key.split("_")[0] != "imageinfo":
|
||||||
|
productinfo.find(key).text = str(value)
|
||||||
|
elif key.split("_")[0] == "imageinfo":
|
||||||
|
imageinfo = productinfo.find("imageinfo")
|
||||||
|
if key.split("_")[1] in ["EarthModel", "ProjectModel", "width", "height", "widthspace", "heightspace"]:
|
||||||
|
imageinfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "center":
|
||||||
|
center = imageinfo.find("center")
|
||||||
|
center.find(key.split("_")[2]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "corner":
|
||||||
|
corner = imageinfo.find("corner")
|
||||||
|
corner.find(key.split("_")[2]).find(key.split("_")[3]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "QualifyValue":
|
||||||
|
QualifyValue = imageinfo.find("QualifyValue")
|
||||||
|
if value =="delete":
|
||||||
|
element_QualifyValue = list(QualifyValue)
|
||||||
|
for i in element_QualifyValue:
|
||||||
|
if i.tag == key.split("_")[2]:
|
||||||
|
QualifyValue.remove(i)
|
||||||
|
else:
|
||||||
|
QualifyValue.find(key.split("_")[2]).text = str(value)
|
||||||
|
pass
|
||||||
|
orthoModel = root.find("processinfo").find("orthoModel") # 写入四次多项式模型
|
||||||
|
for key, value in self.par_dict2.items():
|
||||||
|
if key.split("_")[0] == "satalliteOrbitModel":
|
||||||
|
satalliteOrbitModel = orthoModel.find("satalliteOrbitModel")
|
||||||
|
satalliteOrbitModel.find(key.split("_")[1]).find(key.split("_")[2]).find(key.split("_")[3]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "DEM": # 写入dem四个角坐标
|
||||||
|
DEM= orthoModel.find("DEM")
|
||||||
|
if key.split("_")[1] == "corner":
|
||||||
|
corner = DEM.find("corner")
|
||||||
|
corner.find(key.split("_")[2]).find(key.split("_")[3]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "widthspace" or key.split("_")[1] == "heightspace":
|
||||||
|
DEM.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "samplingrate":
|
||||||
|
orthoModel.find(key.split("_")[1]).text = str(value)
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
#
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
#
|
||||||
|
# xml_path = "./model_meta.xml"
|
||||||
|
# tem_folder= r"E:\microproduct\测试"
|
||||||
|
# image_path = r"E:\microproduct\测试\GF3_MYN_QPSI_011437_E98_HH_AtmosphericDelay.tif" # 输入影像
|
||||||
|
# out_path = os.path.join(tem_folder, "trans_geo_projcs.tif")
|
||||||
|
# image_pair=[1, 1, 1, 0]
|
||||||
|
# par_dict = CreateDict(image_path, image_pair, out_path).calu_nature() # 计算属性字典
|
||||||
|
#
|
||||||
|
# out_xml_path = os.path.join(tem_folder, "creat_standard.meta.xml") # 输出xml路径
|
||||||
|
# CreateStadardXmlFile(xml_path, par_dict, out_xml_path).create_standard_xml()
|
||||||
|
|
@ -0,0 +1,105 @@
|
||||||
|
SatelliteOribit: # 轨道起算时间
|
||||||
|
StartTime:
|
||||||
|
Value:
|
||||||
|
'2017-09-15 01:55:21.0000'
|
||||||
|
format: # 时间格式
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
ReferenceSpheroid:
|
||||||
|
Re: # 长半轴
|
||||||
|
6378137
|
||||||
|
Rp: # 短半轴
|
||||||
|
6356752.3142451795
|
||||||
|
we:
|
||||||
|
0.000072292115
|
||||||
|
GPS: # GPS 轨道节点 LT
|
||||||
|
GPSNode_Path:
|
||||||
|
['level1Product','platform','orbit', 'stateVec']
|
||||||
|
NodeInfomation_Name: # [时间，x坐标，y坐标，z坐标，x速度，y速度，z速度]
|
||||||
|
['timeUTC', 'posX', 'posY', 'posZ', 'velX', 'velY', 'velZ']
|
||||||
|
Time_format:
|
||||||
|
"%Y-%m-%dT%H:%M:%S.%f"
|
||||||
|
|
||||||
|
imageinfo: # 影像信息
|
||||||
|
ImageBox:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','orbit', 'stateVec']
|
||||||
|
NodeName:
|
||||||
|
['topLeft','topRight','bottomLeft','bottomRight']
|
||||||
|
latLon:
|
||||||
|
["latitude","longitude"]
|
||||||
|
ImageWidthSpace:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','imageDataInfo', 'imageRaster','columnSpacing']
|
||||||
|
ImageHeightSpace:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','imageDataInfo', 'imageRaster','rowSpacing']
|
||||||
|
ImageWidth: # 影像宽
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','imageDataInfo', 'imageRaster', 'numberOfColumns']
|
||||||
|
ImageHeight: # 影像高
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','imageDataInfo', 'imageRaster', 'numberOfRows']
|
||||||
|
Groundspace:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','imageDataInfo', 'imageRaster', 'groundRangeResolution' ]
|
||||||
|
Azspace:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','imageDataInfo', 'imageRaster', 'azimuthResolution' ]
|
||||||
|
StartImageTime: # 影像开始时间
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','sceneInfo', 'start', 'timeUTC']
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%dT%H:%M:%S.%f"
|
||||||
|
EndImageTime: # 影像中止时间
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','sceneInfo', 'stop', 'timeUTC']
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%dT%H:%M:%S.%f"
|
||||||
|
CenterImageTime: # 中心像元时间
|
||||||
|
NodePath:
|
||||||
|
['product','platform','CenterTime']
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S"
|
||||||
|
CenterFrequency: # 中心频率
|
||||||
|
NodePath:
|
||||||
|
['level1Product','instrument','radarParameters', 'centerFrequency']
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S"
|
||||||
|
CenterImagePositon: # 中心像元，可以作为迭代起算点
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','center']
|
||||||
|
Value:
|
||||||
|
['latitude','longitude']
|
||||||
|
NearRange: # 近斜距
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','sceneInfo','rangeTime', 'firstPixel']
|
||||||
|
FarRange:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','productInfo','sceneInfo','rangeTime', 'lastPixel']
|
||||||
|
DopplerCentroidCoefficients: # 多普勒质心常数
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','DopplerCentroidCoefficients']
|
||||||
|
DopplerCentroidCoefficients_Name:
|
||||||
|
['d0','d1','d2','d3','d4']
|
||||||
|
DopplerParametersReferenceTime:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','processing',"geometry", 'dopplerRate', 'dopplerRatePolynomial', 'referencePoint']
|
||||||
|
DopplerRate:
|
||||||
|
NodePath:
|
||||||
|
['level1Product','processing',"geometry", 'dopplerRate', 'dopplerRatePolynomial', 'coefficient']
|
||||||
|
|
||||||
|
sensor:
|
||||||
|
PRF: # 脉冲重复频率
|
||||||
|
NodePath:
|
||||||
|
['level1Product','instrument','settings', 'settingRecord', 'PRF']
|
||||||
|
bandWidth: # 信号带宽，计算距离向分辨率 用于距离向分辨率
|
||||||
|
NodePath:
|
||||||
|
['product','sensor','waveParams','wave','bandWidth']
|
||||||
|
lambda: # 波长
|
||||||
|
NodePath:
|
||||||
|
['product','sensor','lamda']
|
||||||
|
Fs: # 等效采样频率 eqvFs
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','eqvFs']
|
||||||
|
LightSpeed:
|
||||||
|
299792458
|
||||||
|
|
@ -0,0 +1,98 @@
|
||||||
|
SatelliteOribit: # 轨道起算时间
|
||||||
|
StartTime:
|
||||||
|
Value:
|
||||||
|
'2017-09-15 01:55:21.0000'
|
||||||
|
format: # 时间格式
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
ReferenceSpheroid:
|
||||||
|
Re: # 长半轴
|
||||||
|
6378137
|
||||||
|
Rp: # 短半轴
|
||||||
|
6356752.3142451795
|
||||||
|
we:
|
||||||
|
0.000072292115
|
||||||
|
GPS: # GPS 轨道节点
|
||||||
|
GPSNode_Path:
|
||||||
|
['product','GPS','GPSParam']
|
||||||
|
NodeInfomation_Name: # [时间，x坐标，y坐标，z坐标，x速度，y速度，z速度]
|
||||||
|
['TimeStamp', 'xPosition', 'yPosition', 'zPosition', 'xVelocity', 'yVelocity', 'zVelocity']
|
||||||
|
Time_format:
|
||||||
|
"%Y-%m-%d %H:%M:%S"
|
||||||
|
|
||||||
|
imageinfo: # 影像信息
|
||||||
|
ImageBox:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','corner']
|
||||||
|
NodeName:
|
||||||
|
['topLeft','topRight','bottomLeft','bottomRight']
|
||||||
|
latLon:
|
||||||
|
["latitude","longitude"]
|
||||||
|
ImageWidthSpace:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','widthspace']
|
||||||
|
ImageHeightSpace:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','heightspace']
|
||||||
|
ImageWidth: # 影像宽
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','width']
|
||||||
|
ImageHeight: # 影像高
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','height']
|
||||||
|
StartImageTime: # 影像开始时间
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','imagingTime',start]
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
EndImageTime: # 影像中止时间
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','imagingTime',end]
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
CenterImageTime: # 中心像元时间
|
||||||
|
NodePath:
|
||||||
|
['product','platform','CenterTime']
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S"
|
||||||
|
CenterImagePositon: # 中心像元，可以作为迭代起算点
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','center']
|
||||||
|
Value:
|
||||||
|
['latitude','longitude']
|
||||||
|
NearRange: # 近斜距
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','nearRange']
|
||||||
|
DopplerCentroidCoefficients: # 多普勒质心常数
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','DopplerCentroidCoefficients']
|
||||||
|
DopplerCentroidCoefficients_Name:
|
||||||
|
['d0','d1','d2','d3','d4']
|
||||||
|
DopplerParametersReferenceTime:
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo',"DopplerParametersReferenceTime"]
|
||||||
|
ReferenceRange:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','refRange']
|
||||||
|
incidenceAngle: # 入射角
|
||||||
|
NearRange: # 近入射角
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','incidenceAngleNearRange']
|
||||||
|
FarRange: # 远入射角
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','incidenceAngleFarRange']
|
||||||
|
|
||||||
|
sensor:
|
||||||
|
PRF: # 脉冲重复频率
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','eqvPRF']
|
||||||
|
bandWidth: # 信号带宽，计算距离向分辨率 用于距离向分辨率
|
||||||
|
NodePath:
|
||||||
|
['product','sensor','waveParams','wave','bandWidth']
|
||||||
|
lambda: # 波长
|
||||||
|
NodePath:
|
||||||
|
['product','sensor','lamda']
|
||||||
|
Fs: # 等效采样频率 eqvFs
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','eqvFs']
|
||||||
|
LightSpeed:
|
||||||
|
299792458
|
||||||
|
|
@ -0,0 +1,330 @@
|
||||||
|
#命名为：geo_rpc.py
|
||||||
|
"""
|
||||||
|
RPC model parsers, localization, and projection
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
from osgeo import gdal
|
||||||
|
|
||||||
|
#最大迭代次数超过则报错
|
||||||
|
class MaxLocalizationIterationsError(Exception):
|
||||||
|
"""
|
||||||
|
Custom rpcm Exception.
|
||||||
|
"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
def apply_poly(poly, x, y, z):
|
||||||
|
"""
|
||||||
|
Evaluates a 3-variables polynom of degree 3 on a triplet of numbers.
|
||||||
|
将三次多项式的统一模式构建为一个单独的函数
|
||||||
|
Args:
|
||||||
|
poly: list of the 20 coefficients of the 3-variate degree 3 polynom,
|
||||||
|
ordered following the RPC convention.
|
||||||
|
x, y, z: triplet of floats. They may be numpy arrays of same length.
|
||||||
|
Returns:
|
||||||
|
the value(s) of the polynom on the input point(s).
|
||||||
|
"""
|
||||||
|
out = 0
|
||||||
|
out += poly[0]
|
||||||
|
out += poly[1]*y + poly[2]*x + poly[3]*z
|
||||||
|
out += poly[4]*y*x + poly[5]*y*z +poly[6]*x*z
|
||||||
|
out += poly[7]*y*y + poly[8]*x*x + poly[9]*z*z
|
||||||
|
out += poly[10]*x*y*z
|
||||||
|
out += poly[11]*y*y*y
|
||||||
|
out += poly[12]*y*x*x + poly[13]*y*z*z + poly[14]*y*y*x
|
||||||
|
out += poly[15]*x*x*x
|
||||||
|
out += poly[16]*x*z*z + poly[17]*y*y*z + poly[18]*x*x*z
|
||||||
|
out += poly[19]*z*z*z
|
||||||
|
return out
|
||||||
|
|
||||||
|
def apply_rfm(num, den, x, y, z):
|
||||||
|
"""
|
||||||
|
Evaluates a Rational Function Model (rfm), on a triplet of numbers.
|
||||||
|
执行20个参数的分子和20个参数的除法
|
||||||
|
Args:
|
||||||
|
num: list of the 20 coefficients of the numerator
|
||||||
|
den: list of the 20 coefficients of the denominator
|
||||||
|
All these coefficients are ordered following the RPC convention.
|
||||||
|
x, y, z: triplet of floats. They may be numpy arrays of same length.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
the value(s) of the rfm on the input point(s).
|
||||||
|
"""
|
||||||
|
return apply_poly(num, x, y, z) / apply_poly(den, x, y, z)
|
||||||
|
|
||||||
|
def rpc_from_geotiff(geotiff_path):
|
||||||
|
"""
|
||||||
|
Read the RPC coefficients from a GeoTIFF file and return an RPCModel object.
|
||||||
|
该函数返回影像的Gdal格式的影像和RPCmodel
|
||||||
|
Args:
|
||||||
|
geotiff_path (str): path or url to a GeoTIFF file
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
instance of the rpc_model.RPCModel class
|
||||||
|
"""
|
||||||
|
# with rasterio.open(geotiff_path, 'r') as src:
|
||||||
|
#
|
||||||
|
dataset = gdal.Open(geotiff_path, gdal.GA_ReadOnly)
|
||||||
|
rpc_dict = dataset.GetMetadata("RPC")
|
||||||
|
# 同时返回影像与rpc
|
||||||
|
return dataset, RPCModel(rpc_dict,'geotiff')
|
||||||
|
|
||||||
|
|
||||||
|
def parse_rpc_file(rpc_file):
|
||||||
|
# rpc_file:.rpc文件的绝对路径
|
||||||
|
# rpc_dict：符号RPC域下的16个关键字的字典
|
||||||
|
# 参考网址：http://geotiff.maptools.org/rpc_prop.html；
|
||||||
|
# https://www.osgeo.cn/gdal/development/rfc/rfc22_rpc.html
|
||||||
|
|
||||||
|
rpc_dict = {}
|
||||||
|
with open(rpc_file) as f:
|
||||||
|
text = f.read()
|
||||||
|
|
||||||
|
# .rpc文件中的RPC关键词
|
||||||
|
words = ['errBias', 'errRand', 'lineOffset', 'sampOffset', 'latOffset',
|
||||||
|
'longOffset', 'heightOffset', 'lineScale', 'sampScale', 'latScale',
|
||||||
|
'longScale', 'heightScale', 'lineNumCoef', 'lineDenCoef','sampNumCoef', 'sampDenCoef',]
|
||||||
|
|
||||||
|
# GDAL库对应的RPC关键词
|
||||||
|
keys = ['ERR_BIAS', 'ERR_RAND', 'LINE_OFF', 'SAMP_OFF', 'LAT_OFF', 'LONG_OFF',
|
||||||
|
'HEIGHT_OFF', 'LINE_SCALE', 'SAMP_SCALE', 'LAT_SCALE',
|
||||||
|
'LONG_SCALE', 'HEIGHT_SCALE', 'LINE_NUM_COEFF', 'LINE_DEN_COEFF',
|
||||||
|
'SAMP_NUM_COEFF', 'SAMP_DEN_COEFF']
|
||||||
|
|
||||||
|
for old, new in zip(words, keys):
|
||||||
|
text = text.replace(old, new)
|
||||||
|
# 以‘;\n’作为分隔符
|
||||||
|
text_list = text.split(';\n')
|
||||||
|
# 删掉无用的行
|
||||||
|
text_list = text_list[3:-2]
|
||||||
|
#
|
||||||
|
text_list[0] = text_list[0].split('\n')[1]
|
||||||
|
# 去掉制表符、换行符、空格
|
||||||
|
text_list = [item.strip('\t').replace('\n', '').replace(' ', '') for item in text_list]
|
||||||
|
|
||||||
|
for item in text_list:
|
||||||
|
# 去掉‘=’
|
||||||
|
key, value = item.split('=')
|
||||||
|
# 去掉多余的括号‘(’，‘)’
|
||||||
|
if '(' in value:
|
||||||
|
value = value.replace('(', '').replace(')', '')
|
||||||
|
rpc_dict[key] = value
|
||||||
|
|
||||||
|
for key in keys[:12]:
|
||||||
|
# 为正数添加符号‘+’
|
||||||
|
if not rpc_dict[key].startswith('-'):
|
||||||
|
rpc_dict[key] = '+' + rpc_dict[key]
|
||||||
|
# 为归一化项和误差标志添加单位
|
||||||
|
if key in ['LAT_OFF', 'LONG_OFF', 'LAT_SCALE', 'LONG_SCALE']:
|
||||||
|
rpc_dict[key] = rpc_dict[key] + ' degrees'
|
||||||
|
if key in ['LINE_OFF', 'SAMP_OFF', 'LINE_SCALE', 'SAMP_SCALE']:
|
||||||
|
rpc_dict[key] = rpc_dict[key] + ' pixels'
|
||||||
|
if key in ['ERR_BIAS', 'ERR_RAND', 'HEIGHT_OFF', 'HEIGHT_SCALE']:
|
||||||
|
rpc_dict[key] = rpc_dict[key] + ' meters'
|
||||||
|
|
||||||
|
# 处理有理函数项
|
||||||
|
for key in keys[-4:]:
|
||||||
|
values = []
|
||||||
|
for item in rpc_dict[key].split(','):
|
||||||
|
#print(item)
|
||||||
|
if not item.startswith('-'):
|
||||||
|
values.append('+'+item)
|
||||||
|
else:
|
||||||
|
values.append(item)
|
||||||
|
rpc_dict[key] = ' '.join(values)
|
||||||
|
return rpc_dict
|
||||||
|
|
||||||
|
|
||||||
|
def read_rpc_file(rpc_file):
|
||||||
|
"""
|
||||||
|
Read RPC from a RPC_txt file and return a RPCmodel
|
||||||
|
从TXT中直接单独读取RPC模型
|
||||||
|
Args:
|
||||||
|
rpc_file: RPC sidecar file path
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
dictionary read from the RPC file, or an empty dict if fail
|
||||||
|
|
||||||
|
"""
|
||||||
|
rpc = parse_rpc_file(rpc_file)
|
||||||
|
return RPCModel(rpc)
|
||||||
|
|
||||||
|
class RPCModel:
|
||||||
|
def __init__(self, d, dict_format="geotiff"):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
d (dict): dictionary read from a geotiff file with
|
||||||
|
rasterio.open('/path/to/file.tiff', 'r').tags(ns='RPC'),
|
||||||
|
or from the .__dict__ of an RPCModel object.
|
||||||
|
dict_format (str): format of the dictionary passed in `d`.
|
||||||
|
Either "geotiff" if read from the tags of a geotiff file,
|
||||||
|
or "rpcm" if read from the .__dict__ of an RPCModel object.
|
||||||
|
"""
|
||||||
|
if dict_format == "geotiff":
|
||||||
|
self.row_offset = float(d['LINE_OFF'][0:d['LINE_OFF'].rfind(' ')])
|
||||||
|
self.col_offset = float(d['SAMP_OFF'][0:d['SAMP_OFF'].rfind(' ')])
|
||||||
|
self.lat_offset = float(d['LAT_OFF'][0:d['LAT_OFF'].rfind(' ')])
|
||||||
|
self.lon_offset = float(d['LONG_OFF'][0:d['LONG_OFF'].rfind(' ')])
|
||||||
|
self.alt_offset = float(d['HEIGHT_OFF'][0:d['HEIGHT_OFF'].rfind(' ')])
|
||||||
|
|
||||||
|
self.row_scale = float(d['LINE_SCALE'][0:d['LINE_SCALE'].rfind(' ')])
|
||||||
|
self.col_scale = float(d['SAMP_SCALE'][0:d['SAMP_SCALE'].rfind(' ')])
|
||||||
|
self.lat_scale = float(d['LAT_SCALE'][0:d['LAT_SCALE'].rfind(' ')])
|
||||||
|
self.lon_scale = float(d['LONG_SCALE'][0:d['LONG_SCALE'].rfind(' ')])
|
||||||
|
self.alt_scale = float(d['HEIGHT_SCALE'][0:d['HEIGHT_SCALE'].rfind(' ')])
|
||||||
|
|
||||||
|
self.row_num = list(map(float, d['LINE_NUM_COEFF'].split()))
|
||||||
|
self.row_den = list(map(float, d['LINE_DEN_COEFF'].split()))
|
||||||
|
self.col_num = list(map(float, d['SAMP_NUM_COEFF'].split()))
|
||||||
|
self.col_den = list(map(float, d['SAMP_DEN_COEFF'].split()))
|
||||||
|
|
||||||
|
if 'LON_NUM_COEFF' in d:
|
||||||
|
self.lon_num = list(map(float, d['LON_NUM_COEFF'].split()))
|
||||||
|
self.lon_den = list(map(float, d['LON_DEN_COEFF'].split()))
|
||||||
|
self.lat_num = list(map(float, d['LAT_NUM_COEFF'].split()))
|
||||||
|
self.lat_den = list(map(float, d['LAT_DEN_COEFF'].split()))
|
||||||
|
|
||||||
|
elif dict_format == "rpcm":
|
||||||
|
self.__dict__ = d
|
||||||
|
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"dict_format '{}' not supported. "
|
||||||
|
"Should be {{'geotiff','rpcm'}}".format(dict_format)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def projection(self, lon, lat, alt):
|
||||||
|
"""
|
||||||
|
Convert geographic coordinates of 3D points into image coordinates.
|
||||||
|
正投影：从地理坐标到图像坐标
|
||||||
|
Args:
|
||||||
|
lon (float or list): longitude(s) of the input 3D point(s)
|
||||||
|
lat (float or list): latitude(s) of the input 3D point(s)
|
||||||
|
alt (float or list): altitude(s) of the input 3D point(s)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
float or list: horizontal image coordinate(s) (column index, ie x)
|
||||||
|
float or list: vertical image coordinate(s) (row index, ie y)
|
||||||
|
"""
|
||||||
|
nlon = (np.asarray(lon) - self.lon_offset) / self.lon_scale
|
||||||
|
nlat = (np.asarray(lat) - self.lat_offset) / self.lat_scale
|
||||||
|
nalt = (np.asarray(alt) - self.alt_offset) / self.alt_scale
|
||||||
|
|
||||||
|
col = apply_rfm(self.col_num, self.col_den, nlat, nlon, nalt)
|
||||||
|
row = apply_rfm(self.row_num, self.row_den, nlat, nlon, nalt)
|
||||||
|
|
||||||
|
col = col * self.col_scale + self.col_offset
|
||||||
|
row = row * self.row_scale + self.row_offset
|
||||||
|
|
||||||
|
return col, row
|
||||||
|
|
||||||
|
|
||||||
|
def localization(self, col, row, alt, return_normalized=False):
|
||||||
|
"""
|
||||||
|
Convert image coordinates plus altitude into geographic coordinates.
|
||||||
|
反投影：从图像坐标到地理坐标
|
||||||
|
Args:
|
||||||
|
col (float or list): x image coordinate(s) of the input point(s)
|
||||||
|
row (float or list): y image coordinate(s) of the input point(s)
|
||||||
|
alt (float or list): altitude(s) of the input point(s)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
float or list: longitude(s)
|
||||||
|
float or list: latitude(s)
|
||||||
|
"""
|
||||||
|
ncol = (np.asarray(col) - self.col_offset) / self.col_scale
|
||||||
|
nrow = (np.asarray(row) - self.row_offset) / self.row_scale
|
||||||
|
nalt = (np.asarray(alt) - self.alt_offset) / self.alt_scale
|
||||||
|
|
||||||
|
if not hasattr(self, 'lat_num'):
|
||||||
|
lon, lat = self.localization_iterative(ncol, nrow, nalt)
|
||||||
|
else:
|
||||||
|
lon = apply_rfm(self.lon_num, self.lon_den, nrow, ncol, nalt)
|
||||||
|
lat = apply_rfm(self.lat_num, self.lat_den, nrow, ncol, nalt)
|
||||||
|
|
||||||
|
if not return_normalized:
|
||||||
|
lon = lon * self.lon_scale + self.lon_offset
|
||||||
|
lat = lat * self.lat_scale + self.lat_offset
|
||||||
|
|
||||||
|
return lon, lat
|
||||||
|
|
||||||
|
|
||||||
|
def localization_iterative(self, col, row, alt):
|
||||||
|
"""
|
||||||
|
Iterative estimation of the localization function (image to ground),
|
||||||
|
for a list of image points expressed in image coordinates.
|
||||||
|
逆投影时的迭代函数
|
||||||
|
Args:
|
||||||
|
col, row: normalized image coordinates (between -1 and 1)
|
||||||
|
alt: normalized altitude (between -1 and 1) of the corresponding 3D
|
||||||
|
point
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
lon, lat: normalized longitude and latitude
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
MaxLocalizationIterationsError: if the while loop exceeds the max
|
||||||
|
number of iterations, which is set to 100.
|
||||||
|
"""
|
||||||
|
# target point: Xf (f for final)
|
||||||
|
Xf = np.vstack([col, row]).T
|
||||||
|
|
||||||
|
# use 3 corners of the lon, lat domain and project them into the image
|
||||||
|
# to get the first estimation of (lon, lat)
|
||||||
|
# EPS is 2 for the first iteration, then 0.1.
|
||||||
|
lon = -col ** 0 # vector of ones
|
||||||
|
lat = -col ** 0
|
||||||
|
EPS = 2
|
||||||
|
x0 = apply_rfm(self.col_num, self.col_den, lat, lon, alt)
|
||||||
|
y0 = apply_rfm(self.row_num, self.row_den, lat, lon, alt)
|
||||||
|
x1 = apply_rfm(self.col_num, self.col_den, lat, lon + EPS, alt)
|
||||||
|
y1 = apply_rfm(self.row_num, self.row_den, lat, lon + EPS, alt)
|
||||||
|
x2 = apply_rfm(self.col_num, self.col_den, lat + EPS, lon, alt)
|
||||||
|
y2 = apply_rfm(self.row_num, self.row_den, lat + EPS, lon, alt)
|
||||||
|
|
||||||
|
n = 0
|
||||||
|
while not np.all((x0 - col) ** 2 + (y0 - row) ** 2 < 1e-18):
|
||||||
|
|
||||||
|
if n > 100:
|
||||||
|
raise MaxLocalizationIterationsError("Max localization iterations (100) exceeded")
|
||||||
|
|
||||||
|
X0 = np.vstack([x0, y0]).T
|
||||||
|
X1 = np.vstack([x1, y1]).T
|
||||||
|
X2 = np.vstack([x2, y2]).T
|
||||||
|
e1 = X1 - X0
|
||||||
|
e2 = X2 - X0
|
||||||
|
u = Xf - X0
|
||||||
|
|
||||||
|
# project u on the base (e1, e2): u = a1*e1 + a2*e2
|
||||||
|
# the exact computation is given by:
|
||||||
|
# M = np.vstack((e1, e2)).T
|
||||||
|
# a = np.dot(np.linalg.inv(M), u)
|
||||||
|
# but I don't know how to vectorize this.
|
||||||
|
# Assuming that e1 and e2 are orthogonal, a1 is given by
|
||||||
|
# <u, e1> / <e1, e1>
|
||||||
|
num = np.sum(np.multiply(u, e1), axis=1)
|
||||||
|
den = np.sum(np.multiply(e1, e1), axis=1)
|
||||||
|
a1 = np.divide(num, den).squeeze()
|
||||||
|
|
||||||
|
num = np.sum(np.multiply(u, e2), axis=1)
|
||||||
|
den = np.sum(np.multiply(e2, e2), axis=1)
|
||||||
|
a2 = np.divide(num, den).squeeze()
|
||||||
|
|
||||||
|
# use the coefficients a1, a2 to compute an approximation of the
|
||||||
|
# point on the gound which in turn will give us the new X0
|
||||||
|
lon += a1 * EPS
|
||||||
|
lat += a2 * EPS
|
||||||
|
|
||||||
|
# update X0, X1 and X2
|
||||||
|
EPS = .1
|
||||||
|
x0 = apply_rfm(self.col_num, self.col_den, lat, lon, alt)
|
||||||
|
y0 = apply_rfm(self.row_num, self.row_den, lat, lon, alt)
|
||||||
|
x1 = apply_rfm(self.col_num, self.col_den, lat, lon + EPS, alt)
|
||||||
|
y1 = apply_rfm(self.row_num, self.row_den, lat, lon + EPS, alt)
|
||||||
|
x2 = apply_rfm(self.col_num, self.col_den, lat + EPS, lon, alt)
|
||||||
|
y2 = apply_rfm(self.row_num, self.row_den, lat + EPS, lon, alt)
|
||||||
|
|
||||||
|
n += 1
|
||||||
|
|
||||||
|
return lon, lat
|
||||||
|
|
||||||
|
|
@ -0,0 +1,133 @@
|
||||||
|
|
||||||
|
<product>
|
||||||
|
<productinfo>
|
||||||
|
<NominalResolution desc="分辨率"> </NominalResolution>
|
||||||
|
<WidthInMeters> </WidthInMeters>
|
||||||
|
<productLevel desc="产品等级"> </productLevel>
|
||||||
|
<productType>GEC</productType>
|
||||||
|
<productFormat>TIF</productFormat>
|
||||||
|
<productGentime> </productGentime>
|
||||||
|
<unit desc="单位"> </unit>
|
||||||
|
<MinValue desc="最小值"> </MinValue>
|
||||||
|
<MaxValue desc="最大值"> </MaxValue>
|
||||||
|
<NoDataValue desc="无数据值">nan</NoDataValue>
|
||||||
|
<datastructure desc="数据组织方式">BSQ</datastructure>
|
||||||
|
<imagebit> </imagebit>
|
||||||
|
<imageinfo desc="产品影像信息">
|
||||||
|
<EarthModel> </EarthModel>
|
||||||
|
<ProjectModel> </ProjectModel>
|
||||||
|
<center>
|
||||||
|
<latitude unit="degree"> </latitude>
|
||||||
|
<longitude unit="degree"> </longitude>
|
||||||
|
</center>
|
||||||
|
<corner>
|
||||||
|
<topLeft>
|
||||||
|
<latitude unit="degree"> </latitude>
|
||||||
|
<longitude unit="degree"> </longitude>
|
||||||
|
</topLeft>
|
||||||
|
<topRight>
|
||||||
|
<latitude unit="degree"> </latitude>
|
||||||
|
<longitude unit="degree"> </longitude>
|
||||||
|
</topRight>
|
||||||
|
<bottomLeft>
|
||||||
|
<latitude unit="degree"> </latitude>
|
||||||
|
<longitude unit="degree"> </longitude>
|
||||||
|
</bottomLeft>
|
||||||
|
<bottomRight>
|
||||||
|
<latitude unit="degree"> </latitude>
|
||||||
|
<longitude unit="degree"> </longitude>
|
||||||
|
</bottomRight>
|
||||||
|
</corner>
|
||||||
|
<width> </width>
|
||||||
|
<height> </height>
|
||||||
|
<widthspace desc="宽度分辨率"> </widthspace>
|
||||||
|
<heightspace desc="高度分辨率"> </heightspace>
|
||||||
|
<QualifyValue desc="质量标识">
|
||||||
|
<HH> </HH>
|
||||||
|
<HV> </HV>
|
||||||
|
<VH> </VH>
|
||||||
|
<VV> </VV>
|
||||||
|
</QualifyValue>
|
||||||
|
</imageinfo>
|
||||||
|
</productinfo>
|
||||||
|
<processinfo desc="算法处理信息">
|
||||||
|
<orthoModel desc="正射校正方法">
|
||||||
|
<orthoModelName>模拟影像法</orthoModelName>
|
||||||
|
<simulationModel>常数累加法</simulationModel>
|
||||||
|
<satalliteOrbitModel desc="卫星轨道模型">
|
||||||
|
<ModelName>四次多项式模型</ModelName>
|
||||||
|
<EarthModel>WGS84</EarthModel>
|
||||||
|
<parameter desc="四次多项式模型">
|
||||||
|
<X>
|
||||||
|
<a0> </a0>
|
||||||
|
<a1> </a1>
|
||||||
|
<a2> </a2>
|
||||||
|
<a3> </a3>
|
||||||
|
<a4> </a4>
|
||||||
|
</X>
|
||||||
|
<Y>
|
||||||
|
<b0> </b0>
|
||||||
|
<b1> </b1>
|
||||||
|
<b2> </b2>
|
||||||
|
<b3> </b3>
|
||||||
|
<b4> </b4>
|
||||||
|
</Y>
|
||||||
|
<Z>
|
||||||
|
<c0> </c0>
|
||||||
|
<c1> </c1>
|
||||||
|
<c2> </c2>
|
||||||
|
<c3> </c3>
|
||||||
|
<c4> </c4>
|
||||||
|
</Z>
|
||||||
|
<Vx>
|
||||||
|
<d0> </d0>
|
||||||
|
<d1> </d1>
|
||||||
|
<d2> </d2>
|
||||||
|
<d3> </d3>
|
||||||
|
<d4> </d4>
|
||||||
|
</Vx>
|
||||||
|
<Vy>
|
||||||
|
<e0> </e0>
|
||||||
|
<e1> </e1>
|
||||||
|
<e2> </e2>
|
||||||
|
<e3> </e3>
|
||||||
|
<e4> </e4>
|
||||||
|
</Vy>
|
||||||
|
<Vz>
|
||||||
|
<f0> </f0>
|
||||||
|
<f1> </f1>
|
||||||
|
<f2> </f2>
|
||||||
|
<f3> </f3>
|
||||||
|
<f4> </f4>
|
||||||
|
</Vz>
|
||||||
|
</parameter>
|
||||||
|
</satalliteOrbitModel>
|
||||||
|
<DEM desc="DEM的参数">
|
||||||
|
<DEMProduct desc="DEM产品的来源">NULL</DEMProduct>
|
||||||
|
<DEMDate desc="DEM对应的时间">NULL</DEMDate>
|
||||||
|
<heightspace desc="行分辨率"> </heightspace>
|
||||||
|
<widthspace desc="列分辨率"> </widthspace>
|
||||||
|
<corner desc="DEM的范围">
|
||||||
|
<topLeft>
|
||||||
|
<latitude> </latitude>
|
||||||
|
<longitude> </longitude>
|
||||||
|
</topLeft>
|
||||||
|
<topRight>
|
||||||
|
<latitude> </latitude>
|
||||||
|
<longitude> </longitude>
|
||||||
|
</topRight>
|
||||||
|
<bottomLeft>
|
||||||
|
<latitude> </latitude>
|
||||||
|
<longitude> </longitude>
|
||||||
|
</bottomLeft>
|
||||||
|
<bottomRight>
|
||||||
|
<latitude> </latitude>
|
||||||
|
<longitude> </longitude>
|
||||||
|
</bottomRight>
|
||||||
|
</corner>
|
||||||
|
</DEM>
|
||||||
|
<samplingrate desc="DEM的重采样率"> </samplingrate>
|
||||||
|
<MatchModel desc="匹配方法">标准相关匹配</MatchModel>
|
||||||
|
</orthoModel>
|
||||||
|
</processinfo>
|
||||||
|
</product>
|
||||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,89 @@
|
||||||
|
# -*- mode: python ; coding: utf-8 -*-
|
||||||
|
import sys
|
||||||
|
import shutil
|
||||||
|
import os
|
||||||
|
import tarfile
|
||||||
|
|
||||||
|
#文件夹遍历深度
|
||||||
|
sys.setrecursionlimit(5000)
|
||||||
|
block_cipher = None
|
||||||
|
|
||||||
|
#######begin-打包前处理##############
|
||||||
|
#将上一级的tool文件夹替换到当前路径下的tool
|
||||||
|
# 测试代码
|
||||||
|
cwdpath = os.getcwd()
|
||||||
|
tool_path = ''
|
||||||
|
src = '../tool'
|
||||||
|
des = os.path.join(cwdpath, "tool")
|
||||||
|
targz_path = os.path.join(cwdpath, "tool.tar.gz")
|
||||||
|
#创建文件夹
|
||||||
|
if os.path.exists(des):
|
||||||
|
if os.path.isdir(des):
|
||||||
|
shutil.rmtree(des)
|
||||||
|
os.makedirs(des)
|
||||||
|
#压缩
|
||||||
|
dir = os.path.split(targz_path )[0]
|
||||||
|
if os.path.exists(dir) is False:
|
||||||
|
os.makedirs(dir)
|
||||||
|
with tarfile.open(targz_path, "w:gz") as tar:
|
||||||
|
tar.add(src, arcname=os.path.basename(src))
|
||||||
|
#解压
|
||||||
|
t = tarfile.open(targz_path)
|
||||||
|
t.extractall(path=cwdpath)
|
||||||
|
#删除临时压缩包
|
||||||
|
#os.remove(targz_path)
|
||||||
|
|
||||||
|
#生成名称
|
||||||
|
main_name = ''
|
||||||
|
for name in os.listdir(cwdpath):
|
||||||
|
if 'Main.py' in name:
|
||||||
|
main_name = name
|
||||||
|
exe_name = exe_name = main_name .split('.')[0][:-4] + '-C-SAR-V2.0'
|
||||||
|
#######end-打包前处理##############
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#######beging-pyinstaller打包参数##############
|
||||||
|
|
||||||
|
a = Analysis(['OrthoMain.py',
|
||||||
|
'./tool/algorithm/algtools/ScatteringAuxData.py',
|
||||||
|
'./tool/algorithm/algtools/CoordinateTransformation.py',
|
||||||
|
'./tool/algorithm/algtools/DEMJoint.py',
|
||||||
|
'./tool/algorithm/algtools/logHandler.py',
|
||||||
|
'./tool/algorithm/algtools/PreProcess.py',
|
||||||
|
'./tool/algorithm/algtools/RieveFilter.py',
|
||||||
|
'./tool/algorithm/algtools/ROIAlg.py',
|
||||||
|
'./tool/algorithm/block/blockprocess.py',
|
||||||
|
'./tool/algorithm/image/ImageHandle.py',
|
||||||
|
'./tool/algorithm/xml/AlgXmlHandle.py',
|
||||||
|
'./tool/algorithm/xml/CreatMetafile.py',
|
||||||
|
'./tool/config/ConfigeHandle.py',
|
||||||
|
'./tool/logs/logHandler.py',],
|
||||||
|
pathex=[cwdpath],
|
||||||
|
binaries=[],
|
||||||
|
datas=[],
|
||||||
|
hiddenimports=[],
|
||||||
|
hookspath=[],
|
||||||
|
runtime_hooks=[],
|
||||||
|
excludes=[],
|
||||||
|
win_no_prefer_redirects=False,
|
||||||
|
win_private_assemblies=False,
|
||||||
|
cipher=block_cipher,
|
||||||
|
noarchive=False)
|
||||||
|
pyz = PYZ(a.pure, a.zipped_data,
|
||||||
|
cipher=block_cipher)
|
||||||
|
exe = EXE(pyz,
|
||||||
|
a.scripts,
|
||||||
|
a.binaries,
|
||||||
|
a.zipfiles,
|
||||||
|
a.datas,
|
||||||
|
[],
|
||||||
|
name= 'Ortho-C-SAR-V2.0',
|
||||||
|
debug=False,
|
||||||
|
bootloader_ignore_signals=False,
|
||||||
|
strip=False,
|
||||||
|
upx=True,
|
||||||
|
upx_exclude=[],
|
||||||
|
runtime_tmpdir=None,
|
||||||
|
console=True )
|
||||||
|
######beging-pyinstaller打包参数#################
|
||||||
|
|
@ -0,0 +1,60 @@
|
||||||
|
<Root>
|
||||||
|
<ProductBasicInfo>
|
||||||
|
<ProductName>正射校正</ProductName>
|
||||||
|
<ProductIdentifier>Ortho</ProductIdentifier>
|
||||||
|
<ProductLevel>LEVEL3</ProductLevel>
|
||||||
|
<ProductResolution> </ProductResolution>
|
||||||
|
<ProductDate> </ProductDate>
|
||||||
|
<ProductFormat> </ProductFormat>
|
||||||
|
<CompressionMethod> </CompressionMethod>
|
||||||
|
<ProductSize> </ProductSize>
|
||||||
|
<SpatialCoverageInformation>
|
||||||
|
<TopLeftLatitude> </TopLeftLatitude>
|
||||||
|
<TopLeftLongitude> </TopLeftLongitude>
|
||||||
|
<TopRightLatitude> </TopRightLatitude>
|
||||||
|
<TopRightLongitude> </TopRightLongitude>
|
||||||
|
<BottomRightLatitude> </BottomRightLatitude>
|
||||||
|
<BottomRightLongitude> </BottomRightLongitude>
|
||||||
|
<BottomLeftLatitude> </BottomLeftLatitude>
|
||||||
|
<BottomLeftLongitude> </BottomLeftLongitude>
|
||||||
|
<CenterLatitude> </CenterLatitude>
|
||||||
|
<CenterLongitude> </CenterLongitude>
|
||||||
|
</SpatialCoverageInformation>
|
||||||
|
<TimeCoverageInformation>
|
||||||
|
<StartTime> </StartTime>
|
||||||
|
<EndTime> </EndTime>
|
||||||
|
<CenterTime> </CenterTime>
|
||||||
|
</TimeCoverageInformation>
|
||||||
|
<CoordinateReferenceSystemInformation>
|
||||||
|
<MapProjection> </MapProjection>
|
||||||
|
<EarthEllipsoid> </EarthEllipsoid>
|
||||||
|
<ZoneNo> </ZoneNo>
|
||||||
|
</CoordinateReferenceSystemInformation>
|
||||||
|
<MetaInfo>
|
||||||
|
<Unit> </Unit>
|
||||||
|
<UnitDes> </UnitDes>
|
||||||
|
</MetaInfo>
|
||||||
|
</ProductBasicInfo>
|
||||||
|
<ProductProductionInfo>
|
||||||
|
<DataSources number="1">
|
||||||
|
<DataSource>
|
||||||
|
<Satellite> </Satellite>
|
||||||
|
<Sensor> </Sensor>
|
||||||
|
</DataSource>
|
||||||
|
</DataSources>
|
||||||
|
<ObservationGeometry>
|
||||||
|
<SatelliteAzimuth> </SatelliteAzimuth>
|
||||||
|
<SatelliteRange> </SatelliteRange>
|
||||||
|
</ObservationGeometry>
|
||||||
|
<BandSelection>1</BandSelection>
|
||||||
|
<DataSourceDescription>None</DataSourceDescription>
|
||||||
|
<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
|
||||||
|
<ProductionDate> </ProductionDate>
|
||||||
|
<AuxiliaryDataDescription> </AuxiliaryDataDescription>
|
||||||
|
</ProductProductionInfo>
|
||||||
|
<ProductPublishInfo>
|
||||||
|
<Processor>德清</Processor>
|
||||||
|
<DistributionUnit> </DistributionUnit>
|
||||||
|
<ContactInformation> </ContactInformation>
|
||||||
|
</ProductPublishInfo>
|
||||||
|
</Root>
|
||||||
|
|
@ -0,0 +1,47 @@
|
||||||
|
''' 去地平效应功能代码
|
||||||
|
因为去地平效应是需要像元的成像时间作为的输入参数量，因此需要原始影像的SLC作为输出项，或者原始的SLC中，保留了影像的成像时刻。
|
||||||
|
作为模型约定：
|
||||||
|
1.模型的输入需要已经经过了正射校正，否则没有办法与已有的DEM进行匹配。
|
||||||
|
2.开发示例使用的高分三号，注意高分三号的正射模型为rpc，因此需要需要计算的对应的时间。
|
||||||
|
'''
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
# import gdal
|
||||||
|
import math
|
||||||
|
|
||||||
|
class DeLevelingEffect:
|
||||||
|
''' 去地平效应
|
||||||
|
计算公式：
|
||||||
|
Phi=(4*pi/lambda_)*(r1-r2)
|
||||||
|
lambda_ 波长
|
||||||
|
pi=3.14159265358979323846264338327950288
|
||||||
|
r1:主
|
||||||
|
r2:辅
|
||||||
|
'''
|
||||||
|
def LevelingEffect(lamda_,r1,r2,pi=3.14159265358979323846264338327950288):
|
||||||
|
'''计算地平效应的相位
|
||||||
|
agrs:
|
||||||
|
lamda_: double, 波长
|
||||||
|
r1:shape nx1 ,主影像的斜距
|
||||||
|
r2:shape nx1 ,辅影像的斜距
|
||||||
|
pi:double,圆周率
|
||||||
|
return：
|
||||||
|
Phi:shape nx1
|
||||||
|
'''
|
||||||
|
Phi=(4*pi/lamda_)*(r1-r2)
|
||||||
|
return Phi
|
||||||
|
|
||||||
|
def CalSlantDistance(Rs_Salatellite,Rs_SeaLevel):
|
||||||
|
'''计算斜率
|
||||||
|
agrs:
|
||||||
|
Rs_Salatellite:nx3 x,y,z 地面对应时刻的 卫星位置和速度
|
||||||
|
Rs_SeaLevel:nx3 x,y,z 地面的坐标
|
||||||
|
return:
|
||||||
|
SlantDistance:nx1 斜距
|
||||||
|
'''
|
||||||
|
SlantDistance=np.sqrt(np.sum((Rs_Salatellite-Rs_SeaLevel)**2,axis=1)).reshape(-1,1)
|
||||||
|
return SlantDistance
|
||||||
|
|
||||||
|
# 根据根据卫星轨道数据，构建合适的影像信息
|
||||||
|
|
||||||
|
pass
|
||||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,24 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：OnePlantHeight.PY
|
||||||
|
@Function ：主函数
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
from scipy.special import lambertw
|
||||||
|
H_arr=np.array([1,2])
|
||||||
|
array_angle=np.array([1,0.5])
|
||||||
|
a = 0.5 * H_arr * H_arr
|
||||||
|
b = -0.5 * np.sin(array_angle)
|
||||||
|
y = 2
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
a1 = 2 * lambertw(-b * np.sqrt(y / a) / 2) / b
|
||||||
|
print(a1)
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,19 @@
|
||||||
|
import os
|
||||||
|
import sys
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
img1 = cv2.imread('D:\MicroSAR\C-SAR\Ortho\Ortho\Temporary\dem_rcs.jpg', 0)
|
||||||
|
img2 = cv2.imread('D:\MicroSAR\C-SAR\Ortho\Ortho\Temporary\sar_rcs.jpg', 0)
|
||||||
|
def cv_show(name,img):
|
||||||
|
cv2.imshow(name, img)
|
||||||
|
cv2.waitKey(0)
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
sift = cv2.xfeatures2d.SIFT_create()
|
||||||
|
kp1, des1 = sift.detectAndCompute(img1, None)
|
||||||
|
kp2, des2 = sift.detectAndCompute(img2, None)
|
||||||
|
bf = cv2.BFMatcher(crossCheck=True)
|
||||||
|
matches = bf.match(des1, des2)
|
||||||
|
matches = sorted(matches, key=lambda x: x.distance)
|
||||||
|
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None,flags=2)
|
||||||
|
cv_show('img3',img3)
|
||||||
|
|
@ -0,0 +1,30 @@
|
||||||
|
|
||||||
|
def cal(base_block,i,j):
|
||||||
|
|
||||||
|
std=np.std(base_block)
|
||||||
|
dist=np.max(base_block)-np.min(base_block)
|
||||||
|
return [i,j,std,dist]
|
||||||
|
|
||||||
|
|
||||||
|
plist=[]
|
||||||
|
with Pool() as pool:
|
||||||
|
for i in range(height):
|
||||||
|
print("\r",i,"/",height," ",end="")
|
||||||
|
if i<50 or i>height-51:
|
||||||
|
continue
|
||||||
|
for j in range(width):
|
||||||
|
if j<50 or j>width-51:
|
||||||
|
continue
|
||||||
|
base_block=data[i-50:i+50,j-50:j+50]
|
||||||
|
plist.append(pool.apply_async(cal,args=(base,i,j,)))
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
for p in plist:
|
||||||
|
result=p.get()
|
||||||
|
[i,j,std,dist]=result
|
||||||
|
base_std[i,j]=std
|
||||||
|
base_dist[i,j]=dist
|
||||||
|
|
||||||
|
base_std.astype(np.float32).tofile(r"D:\MicroSAR\C-SAR\Ortho\Ortho\Temporary\HH_std.bin")
|
||||||
|
base_dist.astype(np.float32).tofile(r"D:\MicroSAR\C-SAR\Ortho\Ortho\Temporary\HH_dist.bin")
|
||||||
|
print(base_dist.shape,base_dist.dtype)
|
||||||
|
|
@ -0,0 +1,113 @@
|
||||||
|
import logging
|
||||||
|
# from re import S
|
||||||
|
# from oneOrthoAuxData import OrthoAuxData
|
||||||
|
# from OrthoImage import ImageHandler
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
import tarfile
|
||||||
|
# from OrthoDB import ManageAlgXML, CheckSource
|
||||||
|
from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource # 导入xml文件读取与检查文件
|
||||||
|
from OrthoAlg import IndirectOrthorectification, DEMProcess,ImageMatchClass
|
||||||
|
# from logHandler import LogHandler
|
||||||
|
from tool.algorithm.algtools.logHandler import LogHandler
|
||||||
|
from tool.algorithm.xml.CreatMetafile import CreateMetafile
|
||||||
|
from OrthoXmlInfo import CreateDict, CreateStadardXmlFile
|
||||||
|
from osgeo import gdal, osr
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
import gc
|
||||||
|
import datetime
|
||||||
|
import shutil
|
||||||
|
import sys
|
||||||
|
import cv2
|
||||||
|
|
||||||
|
ori_sar_path="D:\MicroWorkspace\C-SAR\Ortho\Temporary\TestSAR\GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_HH_L10003923848.jpg"
|
||||||
|
sim_sar_path="D:\MicroWorkspace\C-SAR\Ortho\Temporary\TestSim\sim_img_sum.jpg"
|
||||||
|
work_sapce_path="D:\MicroWorkspace\C-SAR\Ortho\Temporary"
|
||||||
|
|
||||||
|
'''
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
img1 = cv2.imread(ori_sar_path, 0)
|
||||||
|
img2 = cv2.imread(sim_sar_path, 0)
|
||||||
|
def cv_show(name,img):
|
||||||
|
cv2.imshow(name, img)
|
||||||
|
cv2.waitKey(0)
|
||||||
|
cv2.destroyAllWindows()
|
||||||
|
sift = cv2.SIFT_create()
|
||||||
|
kp1, des1 = sift.detectAndCompute(img1, None)
|
||||||
|
kp2, des2 = sift.detectAndCompute(img2, None)
|
||||||
|
bf = cv2.BFMatcher(crossCheck=True)
|
||||||
|
matches = bf.match(des1, des2)
|
||||||
|
matches = sorted(matches, key=lambda x: x.distance)
|
||||||
|
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None,flags=2)
|
||||||
|
cv2.imwrite(work_sapce_path,img3)
|
||||||
|
'''
|
||||||
|
|
||||||
|
# 匹配影像
|
||||||
|
def ImageMatch(ori_sar_path,sim_sar_path,work_sapce_path):
|
||||||
|
ori_sar=gdal.Open(ori_sar_path)
|
||||||
|
sim_sar=gdal.Open(sim_sar_path)
|
||||||
|
# 影像尺寸
|
||||||
|
ori_height=ori_sar.RasterYSize
|
||||||
|
ori_width=ori_sar.RasterXSize
|
||||||
|
sim_height=sim_sar.RasterYSize
|
||||||
|
sim_width=sim_sar.RasterXSize
|
||||||
|
# 分块匹配
|
||||||
|
ori_sar_arr=ori_sar.GetRasterBand(1).ReadAsArray(0,0,ori_width,ori_height) # 原始影像
|
||||||
|
ori_img=(255*ori_sar_arr/np.max(ori_sar_arr)).astype(np.uint8)
|
||||||
|
|
||||||
|
sim_sar_arr=np.log(sim_sar.GetRasterBand(1).ReadAsArray(0,0,sim_width,sim_height)+1) # 模拟影像
|
||||||
|
sim_img=(1+253*sim_sar_arr/np.max(sim_sar_arr)).astype(np.uint8)
|
||||||
|
|
||||||
|
res = cv.matchTemplate(img,template,method)
|
||||||
|
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
|
||||||
|
top_left = max_loc
|
||||||
|
# 范围
|
||||||
|
min_w=top_left[0] if top_left[0]>0 else 0
|
||||||
|
min_h=top_left[1] if top_left[1]>0 else 0
|
||||||
|
|
||||||
|
max_w=top_left[0]+ori_width if top_left[0]+ori_width<sim_width else sim_width
|
||||||
|
max_h=top_left[1]+ori_height if top_left[0]+ori_height<sim_height else sim_height
|
||||||
|
# 裁剪
|
||||||
|
sim_clip=sim_img[min_h:max_h,min_w:max_w]
|
||||||
|
|
||||||
|
for i in range(0,ori_img.shape[0],10):
|
||||||
|
for j in range(0,ori_img.shape[1],10):
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
import cv2 as cv
|
||||||
|
import numpy as np
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
|
img = cv.imread(sim_sar_path,0)
|
||||||
|
img2 = img.copy()
|
||||||
|
template = cv.imread(ori_sar_path,0)
|
||||||
|
w, h = template.shape[::-1]
|
||||||
|
# 列表中所有的6种比较方法
|
||||||
|
methods = ['cv.TM_CCOEFF', 'cv.TM_CCOEFF_NORMED', 'cv.TM_CCORR',
|
||||||
|
'cv.TM_CCORR_NORMED', 'cv.TM_SQDIFF', 'cv.TM_SQDIFF_NORMED']
|
||||||
|
i=0
|
||||||
|
for meth in methods:
|
||||||
|
img = img2.copy()
|
||||||
|
method = eval(meth)
|
||||||
|
# 应用模板匹配
|
||||||
|
res = cv.matchTemplate(img,template,method)
|
||||||
|
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(res)
|
||||||
|
# 如果方法是TM_SQDIFF或TM_SQDIFF_NORMED，则取最小值
|
||||||
|
if method in [cv.TM_SQDIFF, cv.TM_SQDIFF_NORMED]:
|
||||||
|
top_left = min_loc
|
||||||
|
else:
|
||||||
|
top_left = max_loc
|
||||||
|
bottom_right = (top_left[0] + w, top_left[1] + h)
|
||||||
|
cv.rectangle(img,top_left, bottom_right, 255, 2)
|
||||||
|
cv.imwrite(os.path.join(work_sapce_path,"matchresult_{}.jpg".format(i)),img)
|
||||||
|
i=i+1
|
||||||
|
|
@ -0,0 +1,8 @@
|
||||||
|
import geo_rpc
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
from osgeo import gdal
|
||||||
|
rpc = geo_rpc.read_rpc_file(r'D:\MicroSAR\C-SAR\Ortho\Temporary\unpack\GF3B_MYC_FSI_002233_E120.8_N30.2_20220426_L1A_VHVV_L10000030053\GF3B_MYC_FSI_002233_E120.8_N30.2_20220426_L1A_VV_L10000030053.rpb')
|
||||||
|
rows ,cols = rpc.localization(19820,14284,0)
|
||||||
|
print(rows,cols)
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,166 @@
|
||||||
|
#
|
||||||
|
# 模型计算的库
|
||||||
|
#
|
||||||
|
import cython
|
||||||
|
cimport cython # 必须导入
|
||||||
|
import numpy as np
|
||||||
|
cimport numpy as np
|
||||||
|
from libc.math cimport pi
|
||||||
|
from scipy.optimize import leastsq
|
||||||
|
import random
|
||||||
|
import logging
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
def WMCModel(param_arr,sample_lai,sample_soil,sample_inc,sample_sigma):
|
||||||
|
""" WMC模型 增加 归一化植被指数
|
||||||
|
|
||||||
|
Args:
|
||||||
|
param_arr (np.ndarray): 参数数组
|
||||||
|
sample_lai (double): 叶面积指数
|
||||||
|
sample_soil (double): 土壤含水量
|
||||||
|
sample_inc (double): 入射角（弧度值）
|
||||||
|
sample_sigma (double): 后向散射系数（线性值）
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
double: 方程值
|
||||||
|
"""
|
||||||
|
# 映射参数，方便修改模型
|
||||||
|
A,B,C,D,M,N=param_arr # 在这里修改模型
|
||||||
|
V_lai=sample_lai
|
||||||
|
#V_lai=E*sample_lai+F
|
||||||
|
exp_gamma=np.exp(-2*B*((V_lai*D+C))*(1/np.cos(sample_inc)))
|
||||||
|
sigma_soil=M*sample_soil+N
|
||||||
|
sigma_veg=A*((V_lai))*np.cos(sample_inc)
|
||||||
|
f_veg=1
|
||||||
|
result=sigma_veg*(1-exp_gamma)+sigma_soil*exp_gamma-sample_sigma
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def train_WMCmodel(lai_water_inc_sigma_list,params_X0,train_err_image_path,draw_flag=True):
|
||||||
|
""" 训练模型参数
|
||||||
|
|
||||||
|
Args:
|
||||||
|
lai_waiter_inc_sigma_list (list): 训练模型使用的样本呢
|
||||||
|
"""
|
||||||
|
def f(X):
|
||||||
|
eqs=[]
|
||||||
|
for lai_water_inc_sigma_item in lai_water_inc_sigma_list:
|
||||||
|
sample_lai=lai_water_inc_sigma_item[4]
|
||||||
|
sample_sigma=lai_water_inc_sigma_item[5] # 5: csv_sigma, 8:tiff_sigma
|
||||||
|
sample_soil=lai_water_inc_sigma_item[6]
|
||||||
|
sample_inc=lai_water_inc_sigma_item[7]
|
||||||
|
FVC=lai_water_inc_sigma_item[8]
|
||||||
|
eqs.append(WMCModel(X,sample_lai,sample_soil,sample_inc,sample_sigma))
|
||||||
|
return eqs
|
||||||
|
|
||||||
|
X0 = params_X0 # 初始值
|
||||||
|
logger.info(str(X0))
|
||||||
|
h = leastsq(f, X0)
|
||||||
|
logger.info(h[0],h[1])
|
||||||
|
err_f=f(h[0])
|
||||||
|
x_arr=[lai_waiter_inc_sigma_item[4] for lai_waiter_inc_sigma_item in lai_water_inc_sigma_list]
|
||||||
|
# 根据误差大小进行排序
|
||||||
|
logger.info("训练集：\n根据误差输出点序\n数量：{}\n点序\t误差值\t 样点信息".format(str(np.array(err_f).shape)))
|
||||||
|
for i in np.argsort(np.array(err_f)):
|
||||||
|
logger.info('{}\t{}\t{}'.format(i,err_f[i],str(lai_water_inc_sigma_list[i])))
|
||||||
|
logger.info("\n误差点序输出结束\n")
|
||||||
|
|
||||||
|
if draw_flag:
|
||||||
|
logger.info(err_f)
|
||||||
|
logger.info(np.where(np.abs(err_f)<10))
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
|
plt.scatter(x_arr,err_f)
|
||||||
|
plt.title("equation-err")
|
||||||
|
plt.savefig(train_err_image_path,dpi=600)
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
return h[0]
|
||||||
|
|
||||||
|
def test_WMCModel(lai_waiter_inc_sigma_list,param_arr,lai_X0,test_err_image_path,draw_flag=True):
|
||||||
|
""" 测试模型训练结果
|
||||||
|
|
||||||
|
Args:
|
||||||
|
lai_waiter_inc_sigma_list (list): 测试使用的样本集
|
||||||
|
A (_type_): 参数A
|
||||||
|
B (_type_): 参数B
|
||||||
|
C (_type_): 参数C
|
||||||
|
D (_type_): 参数D
|
||||||
|
M (_type_): 参数M
|
||||||
|
N (_type_): 参数N
|
||||||
|
lai_X0 (_type_): 初始值
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
list: 误差列表 [sample_lai,err,predict]
|
||||||
|
"""
|
||||||
|
err=[]
|
||||||
|
err_f=[]
|
||||||
|
x_arr=[]
|
||||||
|
err_lai=[]
|
||||||
|
for lai_waiter_inc_sigma_item in lai_waiter_inc_sigma_list:
|
||||||
|
sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma=lai_waiter_inc_sigma_item
|
||||||
|
def f(X):
|
||||||
|
lai=X[0]
|
||||||
|
eqs=[WMCModel(param_arr,lai,sample_soil,sample_inc,csv_sigma)]
|
||||||
|
return eqs
|
||||||
|
X0=lai_X0
|
||||||
|
h = leastsq(f, X0)
|
||||||
|
temp_err=h[0]-sample_lai
|
||||||
|
err_lai.append(temp_err[0]) # lai预测的插值
|
||||||
|
err.append([sample_lai,temp_err[0],h[0][0],sample_code])
|
||||||
|
err_f.append(f(h[0])[0]) # 方程差
|
||||||
|
x_arr.append(sample_lai)
|
||||||
|
|
||||||
|
# 根据误差大小进行排序
|
||||||
|
logger.info("测试集：\n根据误差输出点序\n数量：{}\n点序\t误差值\t 方程差\t样点信息".format(str(np.array(err_lai).shape)))
|
||||||
|
for i in np.argsort(np.array(err_lai)):
|
||||||
|
logger.info('{}\t{}\t{}\t{}'.format(i,err_lai[i],err_f[i],str(lai_waiter_inc_sigma_list[i])))
|
||||||
|
logger.info("\n误差点序输出结束\n")
|
||||||
|
|
||||||
|
if draw_flag:
|
||||||
|
from matplotlib import pyplot as plt
|
||||||
|
plt.scatter(x_arr,err_lai)
|
||||||
|
plt.title("equation-err")
|
||||||
|
plt.savefig(test_err_image_path,dpi=600)
|
||||||
|
plt.show()
|
||||||
|
return err
|
||||||
|
|
||||||
|
def processs_WMCModel(param_arr,lai_X0,sigma,inc_angle,soil_water):
|
||||||
|
|
||||||
|
if(sigma<0 ):
|
||||||
|
return np.nan
|
||||||
|
def f(X):
|
||||||
|
lai=X[0]
|
||||||
|
eqs=[WMCModel(param_arr,lai,soil_water,inc_angle,sigma )]
|
||||||
|
return eqs
|
||||||
|
h = leastsq(f, [lai_X0])
|
||||||
|
|
||||||
|
return h[0][0]
|
||||||
|
|
||||||
|
# Cython 的扩展地址
|
||||||
|
cpdef np.ndarray[double,ndim=2] process_tiff(np.ndarray[double,ndim=2] sigma_tiff,
|
||||||
|
np.ndarray[double,ndim=2] inc_tiff,
|
||||||
|
np.ndarray[double,ndim=2] soil_water_tiff,
|
||||||
|
np.ndarray[double,ndim=1] param_arr,
|
||||||
|
double lai_X0):
|
||||||
|
|
||||||
|
cdef np.ndarray[double,ndim=2] result=sigma_tiff
|
||||||
|
cdef int param_arr_length=param_arr.shape[0]
|
||||||
|
cdef int height=sigma_tiff.shape[0]
|
||||||
|
cdef int width=sigma_tiff.shape[1]
|
||||||
|
cdef int i=0
|
||||||
|
cdef int j=0
|
||||||
|
cdef double temp=0
|
||||||
|
|
||||||
|
while i<height:
|
||||||
|
j=0
|
||||||
|
while j<width:
|
||||||
|
temp = processs_WMCModel(param_arr,lai_X0,sigma_tiff[i,j],inc_tiff[i,j],soil_water_tiff[i,j])
|
||||||
|
temp=temp if temp<10 and temp>=0 else np.nan
|
||||||
|
result[i,j]=temp
|
||||||
|
j=j+1
|
||||||
|
i=i+1
|
||||||
|
return result
|
||||||
|
|
||||||
|
|
@ -0,0 +1,45 @@
|
||||||
|
from setuptools import setup
|
||||||
|
from setuptools.extension import Extension
|
||||||
|
from Cython.Distutils import build_ext
|
||||||
|
from Cython.Build import cythonize
|
||||||
|
import numpy
|
||||||
|
from pathlib import Path
|
||||||
|
import shutil
|
||||||
|
|
||||||
|
|
||||||
|
class MyBuildExt(build_ext):
|
||||||
|
def run(self):
|
||||||
|
build_ext.run(self)
|
||||||
|
|
||||||
|
build_dir = Path(self.build_lib)
|
||||||
|
root_dir = Path(__file__).parent
|
||||||
|
target_dir = build_dir if not self.inplace else root_dir
|
||||||
|
|
||||||
|
self.copy_file(Path('./LAIProcess') / '__init__.py', root_dir, target_dir)
|
||||||
|
#self.copy_file(Path('./pkg2') / '__init__.py', root_dir, target_dir)
|
||||||
|
self.copy_file(Path('.') / '__init__.py', root_dir, target_dir)
|
||||||
|
def copy_file(self, path, source_dir, destination_dir):
|
||||||
|
if not (source_dir / path).exists():
|
||||||
|
return
|
||||||
|
shutil.copyfile(str(source_dir / path), str(destination_dir / path))
|
||||||
|
|
||||||
|
setup(
|
||||||
|
name="MyModule",
|
||||||
|
ext_modules=cythonize(
|
||||||
|
[
|
||||||
|
#Extension("pkg1.*", ["root/pkg1/*.py"]),
|
||||||
|
Extension("pkg2.*", ["./LAIProcess.pyx"]),
|
||||||
|
#Extension("1.*", ["root/*.py"])
|
||||||
|
],
|
||||||
|
build_dir="build",
|
||||||
|
compiler_directives=dict(
|
||||||
|
always_allow_keywords=True
|
||||||
|
)),
|
||||||
|
cmdclass=dict(
|
||||||
|
build_ext=MyBuildExt
|
||||||
|
),
|
||||||
|
packages=[],
|
||||||
|
include_dirs=[numpy.get_include()],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 指令： python setup.py build_ext --inplace
|
||||||
|
|
@ -0,0 +1,117 @@
|
||||||
|
# -*- encoding: utf-8 -*-
|
||||||
|
# code from https://blog.csdn.net/theonegis/article/details/54427906
|
||||||
|
from osgeo import gdal
|
||||||
|
from osgeo import osr
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
def getSRSPair(dataset):
|
||||||
|
"""
|
||||||
|
获得给定数据的投影参考系和地理参考系
|
||||||
|
:param dataset: GDAL地理数据
|
||||||
|
:return: 投影参考系和地理参考系
|
||||||
|
"""
|
||||||
|
prosrs = osr.SpatialReference()
|
||||||
|
prosrs.ImportFromWkt(dataset.GetProjection())
|
||||||
|
geosrs = prosrs.CloneGeogCS()
|
||||||
|
return prosrs, geosrs
|
||||||
|
|
||||||
|
|
||||||
|
def geo2lonlat(dataset, x, y):
|
||||||
|
"""
|
||||||
|
将投影坐标转为经纬度坐标（具体的投影坐标系由给定数据确定）
|
||||||
|
:param dataset: GDAL地理数据
|
||||||
|
:param x: 投影坐标x
|
||||||
|
:param y: 投影坐标y
|
||||||
|
:return: 投影坐标(x, y)对应的经纬度坐标(lon, lat)
|
||||||
|
"""
|
||||||
|
prosrs, geosrs = getSRSPair(dataset)
|
||||||
|
ct = osr.CoordinateTransformation(prosrs, geosrs)
|
||||||
|
coords = ct.TransformPoint(x, y)
|
||||||
|
return coords[:2]
|
||||||
|
|
||||||
|
|
||||||
|
def lonlat2geo(dataset, lon, lat):
|
||||||
|
"""
|
||||||
|
将经纬度坐标转为投影坐标（具体的投影坐标系由给定数据确定）
|
||||||
|
:param dataset: GDAL地理数据
|
||||||
|
:param lon: 地理坐标lon经度
|
||||||
|
:param lat: 地理坐标lat纬度
|
||||||
|
:return: 经纬度坐标(lon, lat)对应的投影坐标
|
||||||
|
"""
|
||||||
|
prosrs, geosrs = getSRSPair(dataset)
|
||||||
|
ct = osr.CoordinateTransformation(geosrs, prosrs)
|
||||||
|
coords = ct.TransformPoint(lat, lon)
|
||||||
|
return coords[:2]
|
||||||
|
|
||||||
|
|
||||||
|
def imagexy2geo(dataset, row, col):
|
||||||
|
"""
|
||||||
|
根据GDAL的六参数模型将影像图上坐标（行列号）转为投影坐标或地理坐标（根据具体数据的坐标系统转换）
|
||||||
|
:param dataset: GDAL地理数据
|
||||||
|
:param row: 像素的行号
|
||||||
|
:param col: 像素的列号
|
||||||
|
:return: 行列号(row, col)对应的投影坐标或地理坐标(x, y)
|
||||||
|
"""
|
||||||
|
trans = dataset.GetGeoTransform()
|
||||||
|
px = trans[0] + col * trans[1] + row * trans[2]
|
||||||
|
py = trans[3] + col * trans[4] + row * trans[5]
|
||||||
|
return px, py
|
||||||
|
|
||||||
|
|
||||||
|
def geo2imagexy(dataset, x, y):
|
||||||
|
"""
|
||||||
|
根据GDAL的六 参数模型将给定的投影或地理坐标转为影像图上坐标（行列号）
|
||||||
|
:param dataset: GDAL地理数据
|
||||||
|
:param x: 投影或地理坐标x
|
||||||
|
:param y: 投影或地理坐标y
|
||||||
|
:return: 影坐标或地理坐标(x, y)对应的影像图上行列号(col, row)
|
||||||
|
"""
|
||||||
|
trans = dataset.GetGeoTransform()
|
||||||
|
a = np.array([[trans[1], trans[2]], [trans[4], trans[5]]])
|
||||||
|
b = np.array([x - trans[0], y - trans[3]])
|
||||||
|
return np.linalg.solve(a, b) # 使用numpy的linalg.solve进行二元一次方程的求解
|
||||||
|
|
||||||
|
|
||||||
|
def test1():
|
||||||
|
gdal.AllRegister()
|
||||||
|
tif = 'D:/DATA/testdata/GLCFCS30_E110N25.tif'
|
||||||
|
# dataset = gdal.Open(r"D:\\DATA\\雷达测试\\GaoFen3_20200528_HH_DB.tif")
|
||||||
|
dataset = gdal.Open(tif)
|
||||||
|
|
||||||
|
print('数据投影：')
|
||||||
|
print(dataset.GetProjection())
|
||||||
|
print('数据的大小（行，列）：')
|
||||||
|
print('(%s %s)' % (dataset.RasterYSize, dataset.RasterXSize))
|
||||||
|
|
||||||
|
x = 793214.118
|
||||||
|
y = 2485865.527
|
||||||
|
lon = 113.84897082317516
|
||||||
|
lat = 22.453998686022448
|
||||||
|
row = 24576
|
||||||
|
col = 22540
|
||||||
|
|
||||||
|
print('图上坐标 -> 投影坐标：')
|
||||||
|
coords = imagexy2geo(dataset, row, col)
|
||||||
|
print('(%s, %s)->(%s, %s)' % (row, col, coords[0], coords[1]))
|
||||||
|
print('投影坐标 -> 图上坐标：')
|
||||||
|
coords = geo2imagexy(dataset, x, y)
|
||||||
|
col = coords[0]
|
||||||
|
row = coords[1]
|
||||||
|
print('(%s, %s)->(%s, %s)' % (x, y, coords[0], coords[1]))
|
||||||
|
|
||||||
|
print('投影坐标 -> 经纬度：')
|
||||||
|
coords = geo2lonlat(dataset, x, y)
|
||||||
|
print('(%s, %s)->(%s, %s)' % (x, y, coords[0], coords[1]))
|
||||||
|
print('经纬度 -> 投影坐标：')
|
||||||
|
coords = lonlat2geo(dataset, lon, lat)
|
||||||
|
print('(%s, %s)->(%s, %s)' % (lon, lat, coords[0], coords[1]))
|
||||||
|
|
||||||
|
coords1 = geo2lonlat(dataset, 657974.118, 2633321.527)
|
||||||
|
print(coords1)
|
||||||
|
coords2 = geo2lonlat(dataset, 793214.118, 2485865.527)
|
||||||
|
print(coords2)
|
||||||
|
pass
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
#
|
||||||
|
# print('done')
|
||||||
|
|
@ -0,0 +1,156 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：DEMJoint
|
||||||
|
@Function ：主函数
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
from osgeo import gdal, osr
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
class DEMProcess:
|
||||||
|
"""
|
||||||
|
DEM拼接、重采样
|
||||||
|
"""
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_extent(fn):
|
||||||
|
'''
|
||||||
|
原文链接：https://blog.csdn.net/XBR_2014/article/details/85255412
|
||||||
|
'''
|
||||||
|
ds = gdal.Open(fn)
|
||||||
|
rows = ds.RasterYSize
|
||||||
|
cols = ds.RasterXSize
|
||||||
|
# 获取图像角点坐标
|
||||||
|
gt = ds.GetGeoTransform()
|
||||||
|
minx = gt[0]
|
||||||
|
maxy = gt[3]
|
||||||
|
maxx = gt[0] + gt[1] * rows
|
||||||
|
miny = gt[3] + gt[5] * cols
|
||||||
|
return (minx, maxy, maxx, miny)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def img_mosaic(in_files, out_dem_path):
|
||||||
|
# 通过两两比较大小,将最终符合条件的四个角点坐标保存
|
||||||
|
# 即为拼接图像的四个角点坐标
|
||||||
|
minX, maxY, maxX, minY = DEMProcess.get_extent(in_files[0])
|
||||||
|
for fn in in_files[1:]:
|
||||||
|
minx, maxy, maxx, miny = DEMProcess.get_extent(fn)
|
||||||
|
minX = min(minX, minx)
|
||||||
|
maxY = max(maxY, maxy)
|
||||||
|
maxX = max(maxX, maxx)
|
||||||
|
minY = min(minY, miny)
|
||||||
|
|
||||||
|
# 获取输出图像的行列数
|
||||||
|
in_ds = gdal.Open(in_files[0])
|
||||||
|
bands_num = in_ds.RasterCount
|
||||||
|
gt = in_ds.GetGeoTransform()
|
||||||
|
rows = int((maxX - minX) / abs(gt[5]))
|
||||||
|
cols = int((maxY - minY) / gt[1])
|
||||||
|
|
||||||
|
# 判断栅格数据的数据类型
|
||||||
|
datatype = gdal.GDT_UInt16
|
||||||
|
|
||||||
|
# 创建输出图像
|
||||||
|
driver = gdal.GetDriverByName('GTiff')
|
||||||
|
out_dem = os.path.join(out_dem_path, 'mosaic0.tif')
|
||||||
|
out_ds = driver.Create(out_dem, cols, rows, bands_num, datatype)
|
||||||
|
out_ds.SetProjection(in_ds.GetProjection())
|
||||||
|
|
||||||
|
gt = list(in_ds.GetGeoTransform())
|
||||||
|
gt[0], gt[3] = minX, maxY
|
||||||
|
out_ds.SetGeoTransform(gt)
|
||||||
|
|
||||||
|
for fn in in_files:
|
||||||
|
in_ds = gdal.Open(fn)
|
||||||
|
x_size = in_ds.RasterXSize
|
||||||
|
y_size = in_ds.RasterYSize
|
||||||
|
trans = gdal.Transformer(in_ds, out_ds, [])
|
||||||
|
success, xyz = trans.TransformPoint(False, 0, 0)
|
||||||
|
x, y, z = map(int, xyz)
|
||||||
|
for i in range(1, bands_num + 1):
|
||||||
|
data = in_ds.GetRasterBand(i).ReadAsArray()
|
||||||
|
out_band = out_ds.GetRasterBand(i)
|
||||||
|
out_data = out_band.ReadAsArray(x, y, x_size, y_size)
|
||||||
|
data = np.maximum(data, out_data)
|
||||||
|
out_band.WriteArray(data, x, y)
|
||||||
|
|
||||||
|
del in_ds, out_band, out_ds
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def dem_clip(OutFilePath, DEMFilePath, SelectArea):
|
||||||
|
'''
|
||||||
|
根据选择范围裁剪DEM,并输出
|
||||||
|
agrs:
|
||||||
|
outFilePath:裁剪DEM输出地址
|
||||||
|
DEMFilePath:被裁减DEM地址
|
||||||
|
SelectArea:list [(xmin,ymax),(xmax,ymin)] 框选范围 左上角，右下角
|
||||||
|
'''
|
||||||
|
DEM_ptr = gdal.Open(DEMFilePath)
|
||||||
|
DEM_GeoTransform = DEM_ptr.GetGeoTransform() # 读取影像的投影变换
|
||||||
|
DEM_InvGeoTransform = gdal.InvGeoTransform(DEM_GeoTransform)
|
||||||
|
SelectAreaArrayPoints = [gdal.ApplyGeoTransform(DEM_InvGeoTransform, p[0], p[1]) for p in SelectArea]
|
||||||
|
SelectAreaArrayPoints = list(map(lambda p: (int(p[0]), int(p[1])), SelectAreaArrayPoints)) # 确定坐标
|
||||||
|
|
||||||
|
[(ulx, uly), (brx, bry)] = SelectAreaArrayPoints
|
||||||
|
rowCount, colCount = bry - uly, brx - ulx
|
||||||
|
|
||||||
|
# 输出DEM的桌面坐标转换
|
||||||
|
Out_Transfrom = list(DEM_GeoTransform)
|
||||||
|
Out_Transfrom[0] = SelectArea[0][0]
|
||||||
|
Out_Transfrom[3] = SelectArea[0][1]
|
||||||
|
|
||||||
|
# 构建输出DEM
|
||||||
|
Bands_num = DEM_ptr.RasterCount
|
||||||
|
gtiff_driver = gdal.GetDriverByName('GTiff')
|
||||||
|
datatype = gdal.GDT_UInt16
|
||||||
|
out_dem = gtiff_driver.Create(OutFilePath, colCount, rowCount, Bands_num, datatype)
|
||||||
|
out_dem.SetProjection(DEM_ptr.GetProjection())
|
||||||
|
out_dem.SetGeoTransform(Out_Transfrom)
|
||||||
|
|
||||||
|
for i in range(1, Bands_num + 1):
|
||||||
|
data_band = DEM_ptr.GetRasterBand(i)
|
||||||
|
out_band = out_dem.GetRasterBand(i)
|
||||||
|
data = data_band.ReadAsArray(ulx, uly, colCount, rowCount)
|
||||||
|
out_band.WriteArray(data)
|
||||||
|
del out_dem
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def dem_resample(in_dem_path, out_dem_path):
|
||||||
|
'''
|
||||||
|
DEM重采样函数，默认坐标系为WGS84
|
||||||
|
agrs:
|
||||||
|
in_dem_path: 输入的DEM文件夹路径
|
||||||
|
meta_file_path: 输入的xml元文件路径
|
||||||
|
out_dem_path: 输出的DEM文件夹路径
|
||||||
|
'''
|
||||||
|
# 读取文件夹中所有的DEM
|
||||||
|
dem_file_paths=[os.path.join(in_dem_path,dem_name) for dem_name in os.listdir(in_dem_path) if dem_name.find(".tif")>=0 and dem_name.find(".tif.")==-1]
|
||||||
|
spatialreference=osr.SpatialReference()
|
||||||
|
spatialreference.SetWellKnownGeogCS("WGS84") # 设置地理坐标，单位为度 degree # 设置投影坐标，单位为度 degree
|
||||||
|
spatialproj=spatialreference.ExportToWkt() # 导出投影结果
|
||||||
|
# 将DEM拼接成一张大图
|
||||||
|
mergeFile =gdal.BuildVRT(os.path.join(out_dem_path,"mergeDEM.tif"), dem_file_paths)
|
||||||
|
out_DEM=os.path.join(out_dem_path,"mosaic.tif")
|
||||||
|
gdal.Warp(out_DEM,
|
||||||
|
mergeFile,
|
||||||
|
format="GTiff",
|
||||||
|
dstSRS=spatialproj,
|
||||||
|
dstNodata=-9999,
|
||||||
|
outputType=gdal.GDT_Float32)
|
||||||
|
return out_DEM
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == "__main__":
|
||||||
|
# DEMProcess = DEMProcess()
|
||||||
|
# in_dem_path = r'F:\大气延迟\out_dem'
|
||||||
|
# out_dem_path = r'F:\大气延迟\out_dem'
|
||||||
|
# DEMProcess.dem_resample(in_dem_path, out_dem_path)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,154 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：ScatteringAuxData.py
|
||||||
|
@Function ：后向散射
|
||||||
|
@Author ：SHJ
|
||||||
|
@Contact：
|
||||||
|
@Date ：2022/6/29
|
||||||
|
@Version ：1.0.0
|
||||||
|
修改历史：
|
||||||
|
[修改序列] [修改日期] [修改者] [修改内容]
|
||||||
|
1 2022-6-29 石海军 1.兼容GF3元文件和正射校正元文件提取信息
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from xml.etree.ElementTree import ElementTree
|
||||||
|
import math
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
class GF3L1AMetaData:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
@staticmethod
|
||||||
|
def get_QualifyValue(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
QualifyValue = float(root.find('imageinfo').find('QualifyValue').find(polarization).text)
|
||||||
|
return QualifyValue
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Kdb(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
Kdb = float(root.find('processinfo').find('CalibrationConst').find(polarization).text) if root.find('processinfo').find('CalibrationConst').find(polarization).text!="NULL" else 0
|
||||||
|
return Kdb
|
||||||
|
|
||||||
|
class OrthoMetaData:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
@staticmethod
|
||||||
|
def get_QualifyValue(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
QualifyValue = float(root.find('l1aInfo').find('imageinfo').find('QualifyValue').find(polarization).text)
|
||||||
|
return QualifyValue
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Kdb(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
Kdb = float(root.find('l1aInfo').find('processinfo').find('CalibrationConst').find(polarization).text)
|
||||||
|
return Kdb
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_RadarCenterFrequency(meta_file_path):
|
||||||
|
# 获取微波中心频率
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
RadarCenterFrequency = float(root.find('sensor').find('RadarCenterFrequency').text)
|
||||||
|
return RadarCenterFrequency
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_lamda(meta_file_path):
|
||||||
|
# 获取微波波长，单位：m
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
lamda = float(root.find('sensor').find('lamda').text)
|
||||||
|
return lamda
|
||||||
|
|
||||||
|
class MetaDataHandler:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_QualifyValue(meta_file_path, polarization):
|
||||||
|
try:
|
||||||
|
QualifyValue = OrthoMetaData.get_QualifyValue(meta_file_path, polarization)
|
||||||
|
except Exception:
|
||||||
|
logger.warning('OrthoMetaData.get_QualifyValue() error!')
|
||||||
|
QualifyValue = GF3L1AMetaData.get_QualifyValue(meta_file_path, polarization)
|
||||||
|
logger.info('GF3L1AMetaData.get_QualifyValue() success!')
|
||||||
|
return QualifyValue
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Kdb(meta_file_path, polarization):
|
||||||
|
try:
|
||||||
|
Kdb = OrthoMetaData.get_Kdb(meta_file_path, polarization)
|
||||||
|
except Exception:
|
||||||
|
logger.warning('OrthoMetaData.get_Kdb() error!')
|
||||||
|
Kdb = GF3L1AMetaData.get_Kdb(meta_file_path, polarization)
|
||||||
|
logger.info('GF3L1AMetaData.get_Kdb() success!')
|
||||||
|
return Kdb
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_RadarCenterFrequency(meta_file_path):
|
||||||
|
# 获取微波中心频率,单位GHz
|
||||||
|
RadarCenterFrequency = OrthoMetaData.get_RadarCenterFrequency(meta_file_path)
|
||||||
|
return RadarCenterFrequency
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_lamda(meta_file_path):
|
||||||
|
# 获取微波波长，单位：m
|
||||||
|
lamda = OrthoMetaData.get_lamda(meta_file_path)
|
||||||
|
return lamda
|
||||||
|
|
||||||
|
class Calibration:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Calibration_coefficient(meta_file_path, polarization):
|
||||||
|
calibration = [0, 0, 0, 0]
|
||||||
|
for i in polarization:
|
||||||
|
if i == 'HH':
|
||||||
|
quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
|
||||||
|
kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
|
||||||
|
data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
|
||||||
|
calibration[0] = math.sqrt(data_value)
|
||||||
|
if i == 'HV':
|
||||||
|
quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
|
||||||
|
kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
|
||||||
|
data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
|
||||||
|
calibration[1] = math.sqrt(data_value)
|
||||||
|
if i == 'VH':
|
||||||
|
quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
|
||||||
|
kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
|
||||||
|
data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
|
||||||
|
calibration[2] = math.sqrt(data_value)
|
||||||
|
if i == 'VV':
|
||||||
|
quality = MetaDataHandler.get_QualifyValue(meta_file_path, i)
|
||||||
|
kdb = MetaDataHandler.get_Kdb(meta_file_path, i)
|
||||||
|
data_value = ((quality/32767)**2) * (10**((kdb/10)*-1))
|
||||||
|
calibration[3] = math.sqrt(data_value)
|
||||||
|
return calibration
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# A = ScatteringAuxData()
|
||||||
|
# dir = 'G:\MicroWorkspace\C-SAR\AuxSAR\GF3_KAS_FSII_020008_E113.2_N23.1_20200528_L1A_HHHV_L10004829485_geo/'
|
||||||
|
# path = dir + 'GF3_KAS_FSII_020008_E113.2_N23.1_20200528_L1A_HHHV_L10004829485.meta.xml'
|
||||||
|
# path1 = dir + 'OrthoProduct.meta.xml'
|
||||||
|
# t1 = A.get_QualifyValue(path, 'HH')
|
||||||
|
# t2 = A.get_Kdb(path, 'HH')
|
||||||
|
# t3 = A.get_RadarCenterFrequency(path)
|
||||||
|
# t4 = A.get_lamda(path)
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,527 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：PreProcess.py
|
||||||
|
@Function ：@Function: 坐标转换,坐标系转换，图像裁剪，重投影，重采样
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/8/25 14:17
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
from shapely.geometry import Polygon # 导入 gdal库要放到这一句的后面，不然会引起错误
|
||||||
|
|
||||||
|
from osgeo import gdal
|
||||||
|
from osgeo import gdalconst
|
||||||
|
from osgeo import osr
|
||||||
|
from osgeo import ogr
|
||||||
|
import os
|
||||||
|
import cv2
|
||||||
|
import numpy as np
|
||||||
|
import shutil
|
||||||
|
import scipy.spatial.transform
|
||||||
|
import scipy.spatial.transform._rotation_groups # 用于解决打包错误
|
||||||
|
import scipy.special.cython_special # 用于解决打包错误
|
||||||
|
import scipy.spatial.transform._rotation_groups # 解决打包的问题
|
||||||
|
import shapefile
|
||||||
|
from shapely.errors import TopologicalError
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
import logging
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
os.environ['PROJ_LIB'] = os.getcwd()
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
class PreProcess:
|
||||||
|
"""
|
||||||
|
预处理，所有的影像配准
|
||||||
|
"""
|
||||||
|
def __init__(self):
|
||||||
|
self._ImageHandler = ImageHandler()
|
||||||
|
pass
|
||||||
|
|
||||||
|
def cal_scopes(self, processing_paras):
|
||||||
|
# 计算roi
|
||||||
|
scopes = ()
|
||||||
|
for key, value in processing_paras.items():
|
||||||
|
if 'ori_sim' in key:
|
||||||
|
scopes += (ImageHandler.get_scope_ori_sim(value),)
|
||||||
|
if(processing_paras['box'] != "" or processing_paras['box'] != "empty"):
|
||||||
|
scopes += self.box2scope(processing_paras['box'])
|
||||||
|
return scopes
|
||||||
|
|
||||||
|
def cal_scopes_roi(self, processing_paras):
|
||||||
|
return self.intersect_polygon(self.cal_scopes(processing_paras))
|
||||||
|
|
||||||
|
def cut_geoimg(self,workspace_preprocessing_path, para_names_geo, processing_paras):
|
||||||
|
self.check_img_projection(workspace_preprocessing_path, para_names_geo, processing_paras)
|
||||||
|
# 计算roi
|
||||||
|
|
||||||
|
scopes = self.cal_scopes(processing_paras)
|
||||||
|
# 计算图像的轮廓,并求相交区域
|
||||||
|
intersect_shp_path = os.path.join(workspace_preprocessing_path, 'IntersectPolygon.shp')
|
||||||
|
scopes_roi = self.cal_intersect_shp(intersect_shp_path, para_names_geo, processing_paras, scopes)
|
||||||
|
# 裁剪
|
||||||
|
# 裁剪图像：裁剪微波图像，裁剪其他图像
|
||||||
|
cutted_img_paths = self.cut_imgs(workspace_preprocessing_path, para_names_geo, processing_paras, intersect_shp_path)
|
||||||
|
return cutted_img_paths, scopes_roi
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def preprocessing(self, para_names, ref_img_name, processing_paras, workspace_preprocessing_path, workspace_preprocessed_path):
|
||||||
|
# 读取每一张图像,检查图像坐标系
|
||||||
|
|
||||||
|
self.check_img_projection(workspace_preprocessing_path, para_names, processing_paras)
|
||||||
|
|
||||||
|
# 计算图像的轮廓,并求相交区域
|
||||||
|
intersect_shp_path = os.path.join(workspace_preprocessing_path, 'IntersectPolygon.shp')
|
||||||
|
self.cal_intersect_shp(intersect_shp_path, para_names, processing_paras,
|
||||||
|
self.box2scope(processing_paras['box']))
|
||||||
|
logger.info('create intersect shp success!')
|
||||||
|
|
||||||
|
# 裁剪图像：裁剪微波图像，裁剪其他图像
|
||||||
|
cutted_img_paths = self.cut_imgs(workspace_preprocessing_path, para_names, processing_paras,
|
||||||
|
intersect_shp_path)
|
||||||
|
logger.info('cut images success!')
|
||||||
|
|
||||||
|
# 重采样：重采样到跟微波图像一致的分辨率，然后保存到临时目录
|
||||||
|
|
||||||
|
preprocessed_paras = self.resampling_img(workspace_preprocessed_path, para_names, cutted_img_paths,cutted_img_paths[ref_img_name])
|
||||||
|
# 清除预处理缓存文件
|
||||||
|
logger.info('preprocess_handle success!')
|
||||||
|
return preprocessed_paras # cutted_img_paths
|
||||||
|
|
||||||
|
def get_ref_inf(self, ref_img_path):
|
||||||
|
"""获取参考影像的图像信息"""
|
||||||
|
ref_img_path = ref_img_path
|
||||||
|
cols = ImageHandler.get_img_width(ref_img_path)
|
||||||
|
rows = ImageHandler.get_img_height(ref_img_path)
|
||||||
|
proj = ImageHandler.get_projection(ref_img_path)
|
||||||
|
geo = ImageHandler.get_geotransform(ref_img_path)
|
||||||
|
return ref_img_path, cols, rows, proj, geo
|
||||||
|
|
||||||
|
def check_img_projection(self, out_dir, para_names, processing_paras):
|
||||||
|
"""
|
||||||
|
读取每一张图像,检查图像坐标系;
|
||||||
|
将投影坐标系影像转换为地理坐标系影像(EPSG:4326)
|
||||||
|
:param para_names:需要检查的参数名称
|
||||||
|
"""
|
||||||
|
if len(para_names) == 0:
|
||||||
|
return False
|
||||||
|
for name in para_names:
|
||||||
|
proj = ImageHandler.get_projection(processing_paras[name])
|
||||||
|
keyword = proj.split("[", 2)[0]
|
||||||
|
|
||||||
|
if keyword == "PROJCS":
|
||||||
|
# 投影坐标系 转 地理坐标系
|
||||||
|
para_dir = os.path.split(processing_paras[name])
|
||||||
|
out_para = os.path.join(out_dir, para_dir[1].split(".", 1)[0] + "_EPSG4326.tif")
|
||||||
|
self.trans_epsg4326(out_para, processing_paras[name])
|
||||||
|
processing_paras[name] = out_para
|
||||||
|
elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
|
||||||
|
raise Exception('coordinate is missing!')
|
||||||
|
|
||||||
|
|
||||||
|
def preprocessing_oh2004(self, para_names, processing_paras, workspace_preprocessing_path, workspace_preprocessed_path):
|
||||||
|
# 读取每一张图像,检查图像坐标系
|
||||||
|
|
||||||
|
self.check_img_projection(workspace_preprocessing_path, para_names, processing_paras)
|
||||||
|
|
||||||
|
# 计算图像的轮廓,并求相交区域
|
||||||
|
intersect_shp_path = os.path.join(workspace_preprocessing_path, 'IntersectPolygon.shp')
|
||||||
|
scopes = self.cal_intersect_shp(intersect_shp_path, para_names, processing_paras,
|
||||||
|
self.box2scope(processing_paras['box']))
|
||||||
|
logger.info('create intersect shp success!')
|
||||||
|
|
||||||
|
# 裁剪图像：裁剪微波图像，裁剪其他图像
|
||||||
|
cutted_img_paths = self.cut_imgs(workspace_preprocessed_path, para_names, processing_paras,
|
||||||
|
intersect_shp_path)
|
||||||
|
logger.info('cut images success!')
|
||||||
|
|
||||||
|
# 重采样：重采样到跟微波图像一致的分辨率，然后保存到临时目录
|
||||||
|
|
||||||
|
return cutted_img_paths, scopes
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def lonlat2geo(lat, lon):
|
||||||
|
"""
|
||||||
|
WGS84转平面坐标
|
||||||
|
Param: lat 为WGS_1984的纬度
|
||||||
|
Param: lon 为WGS_1984的经度
|
||||||
|
输出转换后的坐标x,y
|
||||||
|
"""
|
||||||
|
|
||||||
|
dstsrs1 = osr.SpatialReference()
|
||||||
|
dstsrs1.ImportFromEPSG(32649)
|
||||||
|
|
||||||
|
dstsrs2 = osr.SpatialReference()
|
||||||
|
dstsrs2.ImportFromEPSG(4326)
|
||||||
|
|
||||||
|
ct = osr.CoordinateTransformation(dstsrs2, dstsrs1)
|
||||||
|
coords = ct.TransformPoint(lat, lon)
|
||||||
|
# print("输出转换后的坐标x,y:",coords[:2])
|
||||||
|
return coords[:2]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_geogcs2projcs(out_path, in_path):
|
||||||
|
"""
|
||||||
|
:param out_path:wgs84投影坐标影像保存路径
|
||||||
|
:param in_path:地理坐标影像输入路径
|
||||||
|
"""
|
||||||
|
# 创建文件
|
||||||
|
if os.path.exists(os.path.split(out_path)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(out_path)[0])
|
||||||
|
options = gdal.WarpOptions(format='GTiff', srcSRS='EPSG:4326', dstSRS='EPSG:32649')
|
||||||
|
gdal.Warp(out_path, in_path, options=options)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_projcs2geogcs(out_path, in_path):
|
||||||
|
"""
|
||||||
|
:param out_path:wgs84地理坐标影像输入路径
|
||||||
|
:param in_path:wgs84投影坐标影像保存路径
|
||||||
|
"""
|
||||||
|
# 创建文件
|
||||||
|
if os.path.exists(os.path.split(out_path)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(out_path)[0])
|
||||||
|
options = gdal.WarpOptions(format='GTiff', srcSRS='EPSG:32649', dstSRS='EPSG:4326')
|
||||||
|
gdal.Warp(out_path, in_path, options=options)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_projcs2geogcs(out_path, in_path ,EPSG_src=32649,EPSG_dst=4326):
|
||||||
|
"""
|
||||||
|
:param out_path:wgs84地理坐标影像输入路径
|
||||||
|
:param in_path:wgs84投影坐标影像保存路径
|
||||||
|
:param EPSG_src:原始投影系
|
||||||
|
:param EPSG_dst:目标坐标系
|
||||||
|
"""
|
||||||
|
str_EPSG_src = 'EPSG:'+ str(EPSG_src)
|
||||||
|
str_EPSG_dst = 'EPSG:'+ str(EPSG_dst)
|
||||||
|
|
||||||
|
# 创建文件
|
||||||
|
if os.path.exists(os.path.split(out_path)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(out_path)[0])
|
||||||
|
options = gdal.WarpOptions(format='GTiff', srcSRS=str_EPSG_src, dstSRS=str_EPSG_dst)
|
||||||
|
gdal.Warp(out_path, in_path, options=options)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_epsg4326(out_path, in_path):
|
||||||
|
OutTile = gdal.Warp(out_path, in_path,
|
||||||
|
dstSRS='EPSG:4326',
|
||||||
|
resampleAlg=gdalconst.GRA_Bilinear
|
||||||
|
)
|
||||||
|
OutTile = None
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def box2scope(str_box):
|
||||||
|
roi_box = ()
|
||||||
|
if str_box == '' or str_box == 'empty':
|
||||||
|
return roi_box
|
||||||
|
box_list = [float(num) for num in list(str_box.split(';'))]
|
||||||
|
if len(box_list) == 4:
|
||||||
|
roi_box = ([[box_list[2], box_list[1]], [box_list[3], box_list[1]], [box_list[2], box_list[0]],
|
||||||
|
[box_list[3], box_list[0]]],)
|
||||||
|
return roi_box
|
||||||
|
|
||||||
|
def cal_intersect_shp(self, shp_path, para_names,processing_paras, add_scope =()):
|
||||||
|
"""
|
||||||
|
:param shp_path:相交区域矢量文件保存区域
|
||||||
|
:param para_names:判断相交影像的名称
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
scopes = ()
|
||||||
|
|
||||||
|
if len(add_scope) != 0:
|
||||||
|
scopes += add_scope
|
||||||
|
for name in para_names:
|
||||||
|
scope_tuple = (self._ImageHandler.get_scope(processing_paras[name]),)
|
||||||
|
scopes += scope_tuple
|
||||||
|
for n, scope in zip( range(len(scopes)), scopes):
|
||||||
|
logging.info("scope" + str(n) + ":%s", scope)
|
||||||
|
|
||||||
|
intersect_polygon = self.intersect_polygon(scopes)
|
||||||
|
if intersect_polygon is None:
|
||||||
|
logger.error('image range does not overlap!')
|
||||||
|
raise Exception('create intersect shp fail!')
|
||||||
|
logging.info("scope roi :%s", intersect_polygon)
|
||||||
|
if self.write_polygon_shp(shp_path, intersect_polygon, 4326) is False:
|
||||||
|
raise Exception('create intersect shp fail!')
|
||||||
|
return intersect_polygon
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def intersect_polygon(scopes_tuple):
|
||||||
|
"""
|
||||||
|
功能说明：计算多边形相交的区域坐标;注意：多边形区域会转变成凸区域再求交
|
||||||
|
:param scopes_tuple: 输入多个区域坐标的tuple
|
||||||
|
:return: 多边形相交的区域坐标((x0,y0),(x1,y1),..., (xn,yn))
|
||||||
|
"""
|
||||||
|
if len(scopes_tuple) < 2:
|
||||||
|
logger.error('len(scopes_tuple) < 2')
|
||||||
|
# return # todo 修改只有单景会出现无法判断相交区域问题
|
||||||
|
|
||||||
|
try:
|
||||||
|
# python四边形对象，会自动计算四个点，最后四个点顺序为：左上 左下 右下 右上 左上
|
||||||
|
tmp = tuple(scopes_tuple[0])
|
||||||
|
poly_intersect = Polygon(tmp).convex_hull
|
||||||
|
for i in range(len(scopes_tuple)-1):
|
||||||
|
polygon_next = Polygon(tuple(scopes_tuple[i+1])).convex_hull
|
||||||
|
if poly_intersect.intersects(polygon_next):
|
||||||
|
poly_intersect = poly_intersect.intersection(polygon_next)
|
||||||
|
else:
|
||||||
|
msg = 'Image:' + str(i) + 'range does not overlap!'
|
||||||
|
logger.error(msg)
|
||||||
|
return
|
||||||
|
return list(poly_intersect.boundary.coords)[:-1]
|
||||||
|
# except shapely.geos.TopologicalError:
|
||||||
|
except TopologicalError:
|
||||||
|
logger.error('shapely.geos.TopologicalError occurred!')
|
||||||
|
return
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def write_polygon_shp(out_shp_path, point_list, EPSG =32649):
|
||||||
|
"""
|
||||||
|
功能说明：创建闭环的矢量文件。
|
||||||
|
:param out_shp_path :矢量文件保存路径
|
||||||
|
:param point_list :装有闭环点的列表[[x0,y0],[x1,y1]...[xn,yn]]
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
# 为了支持中文路径，请添加下面这句代码
|
||||||
|
gdal.SetConfigOption("GDAL_FILENAME_IS_UTF8", "NO")
|
||||||
|
# 为了使属性表字段支持中文，请添加下面这句
|
||||||
|
gdal.SetConfigOption("SHAPE_ENCODING", "")
|
||||||
|
# 注册所有的驱动
|
||||||
|
ogr.RegisterAll()
|
||||||
|
|
||||||
|
# 创建数据，这里以创建ESRI的shp文件为例
|
||||||
|
str_driver_name = "ESRI Shapefile"
|
||||||
|
o_driver = ogr.GetDriverByName(str_driver_name)
|
||||||
|
if o_driver is None:
|
||||||
|
msg = 'driver('+str_driver_name+')is invalid value'
|
||||||
|
logger.error(msg)
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 创建数据源
|
||||||
|
if os.path.exists(out_shp_path) and os.path.isfile(out_shp_path): # 如果已存在同名文件
|
||||||
|
os.remove(out_shp_path) # 则删除之
|
||||||
|
o_ds = o_driver.CreateDataSource(out_shp_path)
|
||||||
|
if o_ds is None:
|
||||||
|
msg = 'create file failed!' + out_shp_path
|
||||||
|
logger.error(msg)
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 创建图层，创建一个多边形图层
|
||||||
|
srs = osr.SpatialReference()
|
||||||
|
#srs.ImportFromEPSG(32649) # 投影坐标系，空间参考：WGS84
|
||||||
|
|
||||||
|
srs.ImportFromEPSG(EPSG) # 地理坐标系EPSG
|
||||||
|
o_layer = o_ds.CreateLayer("TestPolygon", srs, ogr.wkbPolygon)
|
||||||
|
if o_layer is None:
|
||||||
|
msg = 'create coverage failed!'
|
||||||
|
logger.error(msg)
|
||||||
|
return False
|
||||||
|
|
||||||
|
# 下面创建属性表
|
||||||
|
# 先创建一个叫FieldID的整型属性
|
||||||
|
o_field_id = ogr.FieldDefn("FieldID", ogr.OFTInteger)
|
||||||
|
o_layer.CreateField(o_field_id, 1)
|
||||||
|
|
||||||
|
# 再创建一个叫FeatureName的字符型属性，字符长度为50
|
||||||
|
o_field_name = ogr.FieldDefn("FieldName", ogr.OFTString)
|
||||||
|
o_field_name.SetWidth(100)
|
||||||
|
o_layer.CreateField(o_field_name, 1)
|
||||||
|
|
||||||
|
o_defn = o_layer.GetLayerDefn()
|
||||||
|
|
||||||
|
# 创建矩形要素
|
||||||
|
o_feature_rectangle = ogr.Feature(o_defn)
|
||||||
|
o_feature_rectangle.SetField(0, 1)
|
||||||
|
o_feature_rectangle.SetField(1, "IntersectRegion")
|
||||||
|
|
||||||
|
# 创建环对象ring
|
||||||
|
ring = ogr.Geometry(ogr.wkbLinearRing)
|
||||||
|
|
||||||
|
for i in range(len(point_list)):
|
||||||
|
ring.AddPoint(point_list[i][0], point_list[i][1])
|
||||||
|
ring.CloseRings()
|
||||||
|
|
||||||
|
# 创建环对象polygon
|
||||||
|
geom_rect_polygon = ogr.Geometry(ogr.wkbPolygon)
|
||||||
|
geom_rect_polygon.AddGeometry(ring)
|
||||||
|
|
||||||
|
o_feature_rectangle.SetGeometry(geom_rect_polygon)
|
||||||
|
o_layer.CreateFeature(o_feature_rectangle)
|
||||||
|
|
||||||
|
o_ds.Destroy()
|
||||||
|
return True
|
||||||
|
|
||||||
|
def cut_imgs(self, out_dir, para_names, processing_paras, shp_path):
|
||||||
|
"""
|
||||||
|
使用矢量数据裁剪影像
|
||||||
|
:param para_names:需要检查的参数名称
|
||||||
|
:param shp_path：裁剪的shp文件
|
||||||
|
"""
|
||||||
|
if len(para_names) == 0:
|
||||||
|
return {}
|
||||||
|
cutted_img_paths = {}
|
||||||
|
try:
|
||||||
|
for name in para_names:
|
||||||
|
input_path = processing_paras[name]
|
||||||
|
output_path = os.path.join(out_dir, name + '_cut.tif')
|
||||||
|
self.cut_img(output_path, input_path, shp_path)
|
||||||
|
cutted_img_paths.update({name: output_path})
|
||||||
|
logger.info('cut %s success!', name)
|
||||||
|
except BaseException:
|
||||||
|
logger.error('cut_img failed!')
|
||||||
|
return {}
|
||||||
|
return cutted_img_paths
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def cut_img(output_path, input_path, shp_path):
|
||||||
|
"""
|
||||||
|
:param output_path:剪切后的影像
|
||||||
|
:param input_path:待剪切的影像
|
||||||
|
:param shp_path:矢量数据
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
r = shapefile.Reader(shp_path)
|
||||||
|
box = r.bbox
|
||||||
|
|
||||||
|
input_dataset = gdal.Open(input_path)
|
||||||
|
|
||||||
|
gdal.Warp(output_path, input_dataset, format='GTiff', outputBounds=box, cutlineDSName=shp_path, dstNodata=-9999)
|
||||||
|
# cutlineWhere="FIELD = ‘whatever’",
|
||||||
|
# optionally you can filter your cutline (shapefile) based on attribute values
|
||||||
|
# select the no data value you like
|
||||||
|
# ds = None
|
||||||
|
# do other stuff with ds object, it is your cropped dataset. in this case we only close the dataset.
|
||||||
|
del input_dataset
|
||||||
|
return True
|
||||||
|
|
||||||
|
def resampling_img(self, out_dir, para_names, img_paths, refer_img_path):
|
||||||
|
"""
|
||||||
|
以主影像为参考，对影像重采样
|
||||||
|
:param para_names:需要检查的参数名称
|
||||||
|
:param img_paths：待重采样影像路径
|
||||||
|
:param refer_img_path：参考影像路径
|
||||||
|
"""
|
||||||
|
if len(para_names) == 0 or len(img_paths) == 0:
|
||||||
|
return
|
||||||
|
prepro_imgs_path = {}
|
||||||
|
for name in para_names:
|
||||||
|
img_path = img_paths[name]
|
||||||
|
output_para = os.path.join(out_dir, name + '_preprocessed.tif') # + name + '_preprocessed.tif'
|
||||||
|
self.resampling_by_scale(img_path, output_para, refer_img_path)
|
||||||
|
prepro_imgs_path.update({name: output_para})
|
||||||
|
logger.info('resampling %s success!', name)
|
||||||
|
return prepro_imgs_path
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def resampling_by_scale(input_path, target_file, refer_img_path):
|
||||||
|
"""
|
||||||
|
按照缩放比例对影像重采样
|
||||||
|
:param input_path: GDAL地理数据路径
|
||||||
|
:param target_file: 输出影像
|
||||||
|
:param refer_img_path:参考影像
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
ref_dataset = gdal.Open(refer_img_path)
|
||||||
|
ref_cols = ref_dataset.RasterXSize # 列数
|
||||||
|
ref_rows = ref_dataset.RasterYSize # 行数
|
||||||
|
|
||||||
|
target_dataset = gdal.Open(input_path)
|
||||||
|
target_cols = target_dataset.RasterXSize # 列数
|
||||||
|
target_rows = target_dataset.RasterYSize # 行数
|
||||||
|
|
||||||
|
if(ref_cols == target_cols) and (ref_rows == target_rows):
|
||||||
|
shutil.copyfile(input_path, target_file)
|
||||||
|
return True
|
||||||
|
|
||||||
|
dataset = gdal.Open(input_path)
|
||||||
|
if dataset is None:
|
||||||
|
logger.error('resampling_by_scale:dataset is None!')
|
||||||
|
return False
|
||||||
|
|
||||||
|
band_count = dataset.RasterCount # 波段数
|
||||||
|
if (band_count == 0) or (target_file == ""):
|
||||||
|
logger.error("resampling_by_scale:Parameters of the abnormal!")
|
||||||
|
return False
|
||||||
|
|
||||||
|
cols = dataset.RasterXSize # 列数
|
||||||
|
rows = dataset.RasterYSize # 行数
|
||||||
|
scale_x = ref_cols/cols
|
||||||
|
scale_y = ref_rows/rows
|
||||||
|
|
||||||
|
# rows = dataset.RasterYSize # 行数
|
||||||
|
# cols = int(cols * scale) # 计算新的行列数
|
||||||
|
# rows = int(rows * scale)
|
||||||
|
cols = ref_cols
|
||||||
|
rows = ref_rows
|
||||||
|
|
||||||
|
geotrans = list(dataset.GetGeoTransform())
|
||||||
|
geotrans[1] = geotrans[1] / scale_x # 像元宽度变为原来的scale倍
|
||||||
|
geotrans[5] = geotrans[5] / scale_y # 像元高度变为原来的scale倍
|
||||||
|
|
||||||
|
if os.path.exists(target_file) and os.path.isfile(target_file): # 如果已存在同名影像
|
||||||
|
os.remove(target_file) # 则删除之
|
||||||
|
if not os.path.exists(os.path.split(target_file)[0]):
|
||||||
|
os.makedirs(os.path.split(target_file)[0])
|
||||||
|
|
||||||
|
band1 = dataset.GetRasterBand(1)
|
||||||
|
data_type = band1.DataType
|
||||||
|
target = dataset.GetDriver().Create(target_file, xsize=cols, ysize=rows, bands=band_count,
|
||||||
|
eType=data_type)
|
||||||
|
target.SetProjection(dataset.GetProjection()) # 设置投影坐标
|
||||||
|
target.SetGeoTransform(geotrans) # 设置地理变换参数
|
||||||
|
total = band_count + 1
|
||||||
|
for index in range(1, total):
|
||||||
|
# 读取波段数据
|
||||||
|
data = dataset.GetRasterBand(index).ReadAsArray(buf_xsize=cols, buf_ysize=rows)
|
||||||
|
out_band = target.GetRasterBand(index)
|
||||||
|
|
||||||
|
no_data_value = dataset.GetRasterBand(index).GetNoDataValue() # 获取没有数据的点
|
||||||
|
if not (no_data_value is None):
|
||||||
|
out_band.SetNoDataValue(no_data_value)
|
||||||
|
|
||||||
|
out_band.WriteArray(data) # 写入数据到新影像中
|
||||||
|
out_band.FlushCache()
|
||||||
|
out_band.ComputeBandStats(False) # 计算统计信息
|
||||||
|
del dataset
|
||||||
|
del target
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def cv_mean_filter(out_path, in_path, filter_size):
|
||||||
|
"""
|
||||||
|
:param out_path:滤波后的影像
|
||||||
|
:param in_path:滤波前的影像
|
||||||
|
:param filter_size:滤波尺寸
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
proj = ImageHandler.get_projection(in_path)
|
||||||
|
geotrans = ImageHandler.get_geotransform(in_path)
|
||||||
|
array = ImageHandler.get_band_array(in_path, 1)
|
||||||
|
array = cv2.blur(array, (filter_size, filter_size)) # 均值滤波
|
||||||
|
ImageHandler.write_img(out_path, proj, geotrans, array)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def check_LocalIncidenceAngle(out_tif_path, in_tif_path):
|
||||||
|
"""
|
||||||
|
将角度的无效值设置为nan，把角度值转为弧度值
|
||||||
|
:param out_tif_path:处理后影像路径
|
||||||
|
:param in_tif_path:处理前影像路径
|
||||||
|
"""
|
||||||
|
proj, geo, angle = ImageHandler.read_img(in_tif_path)
|
||||||
|
angle = angle.astype(np.float32, order='C')
|
||||||
|
angle[angle == -9999] = np.nan
|
||||||
|
|
||||||
|
mean = np.nanmean(angle)
|
||||||
|
if mean > np.pi:
|
||||||
|
angle = np.deg2rad(angle)# 角度转弧度
|
||||||
|
|
||||||
|
angle[np.where(angle >= 0.5 * np.pi)] = np.nan
|
||||||
|
angle[np.where(angle < 0)] = np.nan
|
||||||
|
|
||||||
|
ImageHandler.write_img(out_tif_path, proj, geo, angle)
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,180 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:microproduct
|
||||||
|
@File:ROIAlg.py
|
||||||
|
@Function:
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/11/17
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class ROIAlg:
|
||||||
|
def __init__(self,):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def roi_process(names, processing_path, processing_paras, preprocessed_paras):
|
||||||
|
roi_paths = []
|
||||||
|
roi = ROIAlg()
|
||||||
|
for name in names:
|
||||||
|
if 'LocalIncidenceAngle' in name:
|
||||||
|
# 利用角度为nan生成Mask
|
||||||
|
pp.check_LocalIncidenceAngle(preprocessed_paras[name],preprocessed_paras[name])
|
||||||
|
angle_nan_mask_path = processing_path + 'angle_nan_mask.tif'
|
||||||
|
roi.trans_tif2mask(angle_nan_mask_path, preprocessed_paras[name], np.nan)
|
||||||
|
roi_paths.append(angle_nan_mask_path)
|
||||||
|
elif ("HH" in name) or ("HV" in name) or ("VH" in name) or ("VV" in name):
|
||||||
|
# 利用影像的有效范围生成MASK
|
||||||
|
tif_mask_path = processing_path + name + "_tif_mask.tif"
|
||||||
|
roi.trans_tif2mask(tif_mask_path, preprocessed_paras[name], np.nan)
|
||||||
|
roi_paths.append(tif_mask_path)
|
||||||
|
elif name == 'Covering':
|
||||||
|
# 利用cover计算植被覆盖范围
|
||||||
|
cover_mask_path = processing_path + "cover_mask.tif"
|
||||||
|
cover_id_list = list(processing_paras['CoveringIDs'].split(';'))
|
||||||
|
cover_id_list = [int(num) for num in cover_id_list]
|
||||||
|
roi.trans_cover2mask(cover_mask_path, preprocessed_paras[name], cover_id_list)
|
||||||
|
roi_paths.append(cover_mask_path)
|
||||||
|
elif name == "NDVI":
|
||||||
|
# 利用NDVI计算裸土范围该指数的输出值在 -1.0 和 1.0 之间，大部分表示植被量，
|
||||||
|
# 负值主要根据云、水和雪而生成
|
||||||
|
# 接近零的值则主要根据岩石和裸土而生成。
|
||||||
|
# 较低的（小于等于 0.1）NDVI 值表示岩石、沙石或雪覆盖的贫瘠区域。
|
||||||
|
# 中等值（0.2 至 0.3）表示灌木丛和草地
|
||||||
|
# 较高的值（0.6 至 0.8）表示温带雨林和热带雨林。
|
||||||
|
ndvi_mask_path = processing_path + "ndvi_mask.tif"
|
||||||
|
ndvi_scope = list(processing_paras['NDVIScope'].split(';'))
|
||||||
|
threshold_of_ndvi_min = float(ndvi_scope[0])
|
||||||
|
threshold_of_ndvi_max = float(ndvi_scope[1])
|
||||||
|
roi.trans_tif2mask(ndvi_mask_path, preprocessed_paras[name], threshold_of_ndvi_min, threshold_of_ndvi_max)
|
||||||
|
roi_paths.append(ndvi_mask_path)
|
||||||
|
# else:
|
||||||
|
# # 其他特征影像
|
||||||
|
# tif_mask_path = processing_path + name + "_mask.tif"
|
||||||
|
# roi.trans_tif2mask(tif_mask_path, preprocessed_paras[name], np.nan)
|
||||||
|
# roi_paths.append(tif_mask_path)
|
||||||
|
|
||||||
|
|
||||||
|
bare_land_mask_path = processing_path + "bare_land_mask.tif"
|
||||||
|
for roi_path in roi_paths:
|
||||||
|
roi.combine_mask(bare_land_mask_path, roi_path, bare_land_mask_path)
|
||||||
|
return bare_land_mask_path
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_tif2mask(out_mask_path, in_tif_path, threshold_min, threshold_max = None):
|
||||||
|
"""
|
||||||
|
:param out_mask_path:mask输出路径
|
||||||
|
:param in_tif_path:输入路径
|
||||||
|
:param threshold_min:最小阈值
|
||||||
|
:param threshold_max:最大阈值
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
image_handler = ImageHandler()
|
||||||
|
proj = image_handler.get_projection(in_tif_path)
|
||||||
|
geotrans = image_handler.get_geotransform(in_tif_path)
|
||||||
|
array = image_handler.get_band_array(in_tif_path, 1)
|
||||||
|
if threshold_max == None and np.isnan(threshold_min)==True:
|
||||||
|
nan = np.isnan(array)
|
||||||
|
mask = (nan.astype(int) == 0).astype(int)
|
||||||
|
mask1 = ((array == -9999).astype(int) == 0).astype(int)
|
||||||
|
mask *= mask1
|
||||||
|
image_handler.write_img(out_mask_path, proj, geotrans, mask)
|
||||||
|
else:
|
||||||
|
if threshold_min < threshold_max:
|
||||||
|
mask = ((array > threshold_min) & (array < threshold_max)).astype(int)
|
||||||
|
image_handler.write_img(out_mask_path, proj, geotrans, mask)
|
||||||
|
elif threshold_min > threshold_max:
|
||||||
|
mask = ((array < threshold_min) & (array > threshold_max)).astype(int)
|
||||||
|
image_handler.write_img(out_mask_path, proj, geotrans, mask)
|
||||||
|
elif threshold_max == threshold_min:
|
||||||
|
mask = ((array == threshold_min).astype(int) == 0).astype(int)
|
||||||
|
image_handler.write_img(out_mask_path, proj, geotrans, mask)
|
||||||
|
|
||||||
|
logger.info("trans_tif2mask success, path: %s", out_mask_path)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_cover2mask(out_mask_path, in_tif_path, cover_id_list):
|
||||||
|
"""
|
||||||
|
:param out_mask_path:mask输出路径
|
||||||
|
:param in_tif_path:输入路径
|
||||||
|
:param cover_id_list 地表覆盖类型数据的id
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
image_handler = ImageHandler()
|
||||||
|
proj = image_handler.get_projection(in_tif_path)
|
||||||
|
geotrans = image_handler.get_geotransform(in_tif_path)
|
||||||
|
array = image_handler.get_band_array(in_tif_path, 1)
|
||||||
|
|
||||||
|
mask = np.zeros(array.shape, dtype=bool)
|
||||||
|
for id in cover_id_list:
|
||||||
|
mask_tmp = (array == id)
|
||||||
|
mask = mask | mask_tmp
|
||||||
|
|
||||||
|
mask = mask.astype(int)
|
||||||
|
image_handler.write_img(out_mask_path, proj, geotrans, mask)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def combine_mask(out_mask_path, in_main_mask_path, in_sub_mask_path):
|
||||||
|
"""
|
||||||
|
:param out_mask_path:输出路径
|
||||||
|
:param in_main_mask_path:主mask路径，输出影像采用主mask的地理信息
|
||||||
|
:param in_sub_mask_path:副mask路径
|
||||||
|
"""
|
||||||
|
image_handler = ImageHandler()
|
||||||
|
proj = image_handler.get_projection(in_main_mask_path)
|
||||||
|
geotrans = image_handler.get_geotransform(in_main_mask_path)
|
||||||
|
main_array = image_handler.get_band_array(in_main_mask_path, 1)
|
||||||
|
if image_handler.get_dataset(in_sub_mask_path) != None:
|
||||||
|
sub_array = image_handler.get_band_array(in_sub_mask_path, 1)
|
||||||
|
main_array = main_array * sub_array
|
||||||
|
image_handler.write_img(out_mask_path, proj, geotrans, main_array)
|
||||||
|
logger.info("combine_mask success, path: %s", out_mask_path)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def cal_roi(out_tif_path, in_tif_path, mask_path, background_value=1):
|
||||||
|
"""
|
||||||
|
:param out_tif_path:ROI的影像
|
||||||
|
:param in_tif_path:计算ROI的影像
|
||||||
|
:param mask_path:掩模
|
||||||
|
:param background_value:无效区域设置的背景值
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
image_handler = ImageHandler()
|
||||||
|
proj = image_handler.get_projection(in_tif_path)
|
||||||
|
geotrans = image_handler.get_geotransform(in_tif_path)
|
||||||
|
tif_array = image_handler.get_data(in_tif_path) # 读取所有波段的像元值存为数组
|
||||||
|
mask_array = image_handler.get_band_array(mask_path, 1)
|
||||||
|
if len(tif_array.shape) == 3:
|
||||||
|
im_bands, im_height, im_width = tif_array.shape
|
||||||
|
else:
|
||||||
|
im_bands, (im_height, im_width) = 1, tif_array.shape
|
||||||
|
if im_bands == 1:
|
||||||
|
tif_array[np.isnan(mask_array)] = background_value
|
||||||
|
tif_array[mask_array == 0] = background_value
|
||||||
|
elif im_bands>1:
|
||||||
|
for i in range(0, im_bands):
|
||||||
|
tif_array[i, :, :][np.isnan(mask_array)] = background_value
|
||||||
|
tif_array[i, :, :][mask_array == 0] = background_value
|
||||||
|
image_handler.write_img(out_tif_path, proj, geotrans, tif_array, '0')
|
||||||
|
logger.info("cal_roi success, path: %s", out_tif_path)
|
||||||
|
return True
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# dir = r'G:\MicroWorkspace\C-SAR\SoilMoisture\Temporary\processing/'
|
||||||
|
# out_tif_path = dir + 'soil_moisture_roi.tif'
|
||||||
|
# in_tif_path = dir + 'soil_moisture.tif'
|
||||||
|
# mask_path = dir + 'bare_land_mask.tif'
|
||||||
|
# background_value = np.nan
|
||||||
|
# ROIAlg.cal_roi(out_tif_path, in_tif_path, mask_path, background_value)
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,57 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:sieve_filter.py
|
||||||
|
@Function:gdal斑点滤波功能
|
||||||
|
@Contact: 'https://www.osgeo.cn/gdal/api/gdal_alg.html?highlight=gdalsievefilter#'
|
||||||
|
'_CPPv415GDALSieveFilter15GDALRasterBandH15GDALRasterBandH15GDALRasterBandHiiPPc16GDALProgressFuncPv'
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/8/30 8:42
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from osgeo import gdal
|
||||||
|
import numpy as np
|
||||||
|
# from onestar.soilMoisture.OneMoistureImage import ImageHandler
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
def gdal_sieve_filter(dst_filename, src_filename, threshold=100, connectedness=4):
|
||||||
|
"""
|
||||||
|
基于python GDAL栅格滤波
|
||||||
|
:param dst_filename: 输出滤波后的影像
|
||||||
|
:param src_filename: 输入需要处理的文件
|
||||||
|
:param threshold: 滤波的值大小
|
||||||
|
:param connectedness: 连通域, 范围：4或者8
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
# 4表示对角像素不被视为直接相邻用于多边形成员资格，8表示对角像素不相邻
|
||||||
|
# connectedness = 4
|
||||||
|
gdal.AllRegister()
|
||||||
|
# print('需要处理滤波的栅格文件:{},阈值(分辨率):{}'.format(src_filename, threshold))
|
||||||
|
dataset = gdal.Open(src_filename, gdal.GA_Update)
|
||||||
|
if dataset is None:
|
||||||
|
logger.error('{}open tif fail!'.format(src_filename))
|
||||||
|
return False
|
||||||
|
# 获取需要处理的源栅格波段
|
||||||
|
src_band = dataset.GetRasterBand(1)
|
||||||
|
mask_band = src_band.GetMaskBand()
|
||||||
|
dst_band = src_band
|
||||||
|
prog_func = gdal.TermProgress_nocb
|
||||||
|
# 调用gdal滤波函数
|
||||||
|
result = gdal.SieveFilter(src_band, mask_band, dst_band, threshold, connectedness, callback=prog_func)
|
||||||
|
if result != 0:
|
||||||
|
return False
|
||||||
|
proj = dataset.GetProjection()
|
||||||
|
geotransform = dataset.GetGeoTransform()
|
||||||
|
dst_array = dst_band.ReadAsArray(0, 0, dst_band.XSize, dst_band.YSize)
|
||||||
|
ImageHandler.write_img(dst_filename, proj, geotransform, dst_array)
|
||||||
|
del dataset
|
||||||
|
return True
|
||||||
|
|
||||||
|
#
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# inputfile = r'D:\DATA\testdata\srcimg\GLCFCS30_E110N25.tif'
|
||||||
|
# outputfile = r'D:\DATA\testdata\srcimg\GLCFCS30_E110N25_sieve_filter.tif'
|
||||||
|
# flag = gdal_sieve_filter(outputfile, inputfile, threshold=100, connectedness=4)
|
||||||
|
|
@ -0,0 +1,122 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：ScatteringAuxData.py
|
||||||
|
@Function ：后向散射
|
||||||
|
@Author ：SHJ
|
||||||
|
@Contact：
|
||||||
|
@Date ：2022/6/29
|
||||||
|
@Version ：1.0.0
|
||||||
|
修改历史：
|
||||||
|
[修改序列] [修改日期] [修改者] [修改内容]
|
||||||
|
1 2022-6-29 石海军 1.兼容GF3元文件和正射校正元文件提取信息
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from xml.etree.ElementTree import ElementTree
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
class GF3L1AMetaData:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
@staticmethod
|
||||||
|
def get_QualifyValue(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
QualifyValue = float(root.find('imageinfo').find('QualifyValue').find(polarization).text)
|
||||||
|
return QualifyValue
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Kdb(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
Kdb = float(root.find('processinfo').find('CalibrationConst').find(polarization).text)
|
||||||
|
return Kdb
|
||||||
|
|
||||||
|
class OrthoMetaData:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
@staticmethod
|
||||||
|
def get_QualifyValue(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
QualifyValue = float(root.find('l1aInfo').find('imageinfo').find('QualifyValue').find(polarization).text)
|
||||||
|
return QualifyValue
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Kdb(meta_file_path, polarization):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
Kdb = float(root.find('l1aInfo').find('processinfo').find('CalibrationConst').find(polarization).text)
|
||||||
|
return Kdb
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_RadarCenterFrequency(meta_file_path):
|
||||||
|
# 获取微波中心频率
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
RadarCenterFrequency = float(root.find('sensor').find('RadarCenterFrequency').text)
|
||||||
|
return RadarCenterFrequency
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_lamda(meta_file_path):
|
||||||
|
# 获取微波波长，单位：m
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
lamda = float(root.find('sensor').find('lamda').text)
|
||||||
|
return lamda
|
||||||
|
|
||||||
|
class ScatteringAuxData:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_QualifyValue(meta_file_path, polarization):
|
||||||
|
try:
|
||||||
|
QualifyValue = OrthoMetaData.get_QualifyValue(meta_file_path, polarization)
|
||||||
|
except Exception:
|
||||||
|
logger.warning('OrthoMetaData.get_QualifyValue() error!')
|
||||||
|
QualifyValue = GF3L1AMetaData.get_QualifyValue(meta_file_path, polarization)
|
||||||
|
logger.info('GF3L1AMetaData.get_QualifyValue() success!')
|
||||||
|
return QualifyValue
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_Kdb(meta_file_path, polarization):
|
||||||
|
try:
|
||||||
|
Kdb = OrthoMetaData.get_Kdb(meta_file_path, polarization)
|
||||||
|
except Exception:
|
||||||
|
logger.warning('OrthoMetaData.get_Kdb() error!')
|
||||||
|
Kdb = GF3L1AMetaData.get_Kdb(meta_file_path, polarization)
|
||||||
|
logger.info('GF3L1AMetaData.get_Kdb() success!')
|
||||||
|
return Kdb
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_RadarCenterFrequency(meta_file_path):
|
||||||
|
# 获取微波中心频率,单位GHz
|
||||||
|
RadarCenterFrequency = OrthoMetaData.get_RadarCenterFrequency(meta_file_path)
|
||||||
|
return RadarCenterFrequency
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_lamda(meta_file_path):
|
||||||
|
# 获取微波波长，单位：m
|
||||||
|
lamda = OrthoMetaData.get_lamda(meta_file_path)
|
||||||
|
return lamda
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# A = ScatteringAuxData()
|
||||||
|
# dir = 'G:\MicroWorkspace\C-SAR\AuxSAR\GF3_KAS_FSII_020008_E113.2_N23.1_20200528_L1A_HHHV_L10004829485_geo/'
|
||||||
|
# path = dir + 'GF3_KAS_FSII_020008_E113.2_N23.1_20200528_L1A_HHHV_L10004829485.meta.xml'
|
||||||
|
# path1 = dir + 'OrthoProduct.meta.xml'
|
||||||
|
# t1 = A.get_QualifyValue(path, 'HH')
|
||||||
|
# t2 = A.get_Kdb(path, 'HH')
|
||||||
|
# t3 = A.get_RadarCenterFrequency(path)
|
||||||
|
# t4 = A.get_lamda(path)
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,414 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：CalculateIncident.py
|
||||||
|
@Function ：计算、局部入射角计算
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/8/25 14:17
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
from osgeo import gdal
|
||||||
|
from osgeo import gdalconst
|
||||||
|
import gc
|
||||||
|
import math
|
||||||
|
from xml.dom import minidom # 不需要安装，默认环境里就有
|
||||||
|
|
||||||
|
|
||||||
|
class CalculateIncident:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def add_round(npgrid):
|
||||||
|
"""
|
||||||
|
边缘填充一圈,然后输出填充得到的矩阵
|
||||||
|
param：npgrid dem数组
|
||||||
|
"""
|
||||||
|
ny, nx = npgrid.shape # ny:行数，nx:列数
|
||||||
|
zbc = np.zeros((ny + 2, nx + 2))
|
||||||
|
zbc[1:-1, 1:-1] = npgrid
|
||||||
|
# 四边
|
||||||
|
zbc[0, 1:-1] = npgrid[0, :]
|
||||||
|
zbc[-1, 1:-1] = npgrid[-1, :]
|
||||||
|
zbc[1:-1, 0] = npgrid[:, 0]
|
||||||
|
zbc[1:-1, -1] = npgrid[:, -1]
|
||||||
|
# 角点
|
||||||
|
zbc[0, 0] = npgrid[0, 0]
|
||||||
|
zbc[0, -1] = npgrid[0, -1]
|
||||||
|
zbc[-1, 0] = npgrid[-1, 0]
|
||||||
|
zbc[-1, -1] = npgrid[-1, -1]
|
||||||
|
print("输出填充后的数组的形状", zbc.shape)
|
||||||
|
return zbc
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def cal_dxdy(zbc, dx):
|
||||||
|
"""
|
||||||
|
计算dx,dy
|
||||||
|
param：zbc填充后的数组
|
||||||
|
param：dx dem数据像元大小
|
||||||
|
|
||||||
|
"""
|
||||||
|
we_x = ((zbc[1:-1, :-2]) - (zbc[1:-1, 2:])) / dx / 2 # WE方向
|
||||||
|
ns_y = ((zbc[2:, 1:-1]) - (zbc[:-2, 1:-1])) / dx / 2 # NS方向
|
||||||
|
print("输出Sx的数组的形状", we_x.shape, "输出Sy的数组的形状", ns_y.shape)
|
||||||
|
sx = we_x[1:-1, 1:-1]
|
||||||
|
sy = ns_y[1:-1, 1:-1]
|
||||||
|
# np.savetxt("dxdy.csv",dx,delimiter=",")
|
||||||
|
print("输出Sx2的数组的形状", sx.shape, "输出Sy2的数组的形状", sy.shape)
|
||||||
|
return sx, sy
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def cal_slopasp(dx, dy):
|
||||||
|
# 计算坡度\坡向
|
||||||
|
# 坡度计算 slope
|
||||||
|
slope = (np.arctan(np.sqrt(dx * dx + dy * dy))) * 57.29578 # 转换成°,57.29578=180/math.pi
|
||||||
|
slope = slope[1:-1, 1:-1]
|
||||||
|
# 坡向计算 aspect
|
||||||
|
aspect = np.ones([dx.shape[0], dx.shape[1]]).astype(np.float32) # 生成一个全是0的数组
|
||||||
|
|
||||||
|
# dx = dx.astype(np.float32)
|
||||||
|
# dy = dy.astype(np.float32)
|
||||||
|
# a1=(np.where(dx==0) and np.where(dy ==0))
|
||||||
|
# print(a1)
|
||||||
|
# aspect[a1]=-1
|
||||||
|
# a2 = (np.where(dx == 0) and np.where(dy > 0))
|
||||||
|
# aspect[a2] =0.0
|
||||||
|
# a3 = (np.where(dx == 0) and np.where(dy <0))
|
||||||
|
# aspect[a3] =180.0
|
||||||
|
# a4 = (np.where(dx > 0) and np.where(dy ==0))
|
||||||
|
# aspect[a4] =90.0
|
||||||
|
# a5 = (np.where(dx < 0) and np.where(dy ==0))
|
||||||
|
# aspect[a5] =270.0
|
||||||
|
# a6 = (np.where(dx != 0) or np.where(dy !=0))
|
||||||
|
# b=dy[a6]
|
||||||
|
# print(":", 1)
|
||||||
|
# aspect[a6] =float(math.atan2(dy[i, j], dx[i, j])) * 57.29578
|
||||||
|
# a7=np.where(aspect[a6]< 0.0)
|
||||||
|
# aspect[a7] = 90.0 - aspect[a7]
|
||||||
|
# a8=np.where(aspect[a6]> 90.0)
|
||||||
|
# aspect[a8] = 450.0- aspect[a8]
|
||||||
|
# a9 =np.where(aspect[a6] >= 0 or aspect[a6] <= 90)
|
||||||
|
# aspect[a9] =90.0 - aspect[a9]
|
||||||
|
|
||||||
|
for i in range(dx.shape[0]):
|
||||||
|
for j in range(dx.shape[1]):
|
||||||
|
x = float(dx[i, j])
|
||||||
|
y = float(dy[i, j])
|
||||||
|
if (x == 0.0) & (y == 0.0):
|
||||||
|
aspect[i, j] = -1
|
||||||
|
elif x == 0.0:
|
||||||
|
if y > 0.0:
|
||||||
|
aspect[i, j] = 0.0
|
||||||
|
else:
|
||||||
|
aspect[i, j] = 180.0
|
||||||
|
elif y == 0.0:
|
||||||
|
if x > 0.0:
|
||||||
|
aspect[i, j] = 90.0
|
||||||
|
else:
|
||||||
|
aspect[i, j] = 270.0
|
||||||
|
else:
|
||||||
|
aspect[i, j] = float(math.atan2(y, x)) * 57.29578 # 范围（-Π/2，Π/2）
|
||||||
|
if aspect[i, j] < 0.0:
|
||||||
|
aspect[i, j] = 90.0 - aspect[i, j]
|
||||||
|
elif aspect[i, j] > 90.0:
|
||||||
|
aspect[i, j] = 450.0 - aspect[i, j]
|
||||||
|
else:
|
||||||
|
aspect[i, j] = 90.0 - aspect[i, j]
|
||||||
|
print("输出aspect形状:", aspect.shape) # 3599, 3599
|
||||||
|
print("输出aspect:", aspect)
|
||||||
|
return slope, aspect
|
||||||
|
|
||||||
|
def creat_twofile(self, dem_file_path, slope_out_path, aspect_out_path):
|
||||||
|
"""
|
||||||
|
生成坡度图、坡向图
|
||||||
|
param: path_file1 为输入文件tif数据的文件路径
|
||||||
|
|
||||||
|
"""
|
||||||
|
if os.path.isfile(dem_file_path):
|
||||||
|
print("高程数据文件存在")
|
||||||
|
else:
|
||||||
|
print("高程数据文件不存在")
|
||||||
|
|
||||||
|
dataset_caijian = gdal.Open(dem_file_path)
|
||||||
|
x_size = dataset_caijian.RasterXSize
|
||||||
|
y_size = dataset_caijian.RasterYSize
|
||||||
|
geo = dataset_caijian.GetGeoTransform()
|
||||||
|
pro = dataset_caijian.GetProjection()
|
||||||
|
array0 = dataset_caijian.ReadAsArray(0, 0, x_size, y_size)
|
||||||
|
print("输出dem数据的数组", array0)
|
||||||
|
zbc = self.add_round(array0)
|
||||||
|
sx, sy = self.cal_dxdy(zbc, 30)
|
||||||
|
slope, aspect = self.cal_slopasp(sx, sy)
|
||||||
|
|
||||||
|
driver = gdal.GetDriverByName("GTiff") # 创建一个数据格式
|
||||||
|
driver.Register()
|
||||||
|
newfile = driver.Create(slope_out_path, x_size, y_size, 1, gdal.GDT_Float32) # 存放路径文件名，长，宽，波段，数据类型
|
||||||
|
newfile.SetProjection(pro)
|
||||||
|
geo = [geo[0], geo[1], 0, geo[3], 0, -geo[1]]
|
||||||
|
newfile.SetGeoTransform(geo)
|
||||||
|
newfile.GetRasterBand(1).WriteArray(slope)
|
||||||
|
|
||||||
|
driver2 = gdal.GetDriverByName("GTiff") # 创建一个数据格式
|
||||||
|
driver2.Register()
|
||||||
|
newfile2 = driver2.Create(aspect_out_path, x_size, y_size, 1, gdal.GDT_Float32) # 存放路径文件名，长，宽，波段，数据类型
|
||||||
|
geo = [geo[0], geo[1], 0, geo[3], 0, -geo[1]]
|
||||||
|
newfile2.SetGeoTransform(geo)
|
||||||
|
newfile2.GetRasterBand(1).WriteArray(aspect)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def resampling(input_file1, input_file2, ref_file, output_file, output_file2):
|
||||||
|
"""
|
||||||
|
采用gdal.Warp()方法进行重采样，差值法为双线性插值
|
||||||
|
:param input_file1 slope path
|
||||||
|
:param input_file2 aspect path
|
||||||
|
:param ref_file: 参考图像路径
|
||||||
|
:param output_file: slope path
|
||||||
|
:param output_file2 aspect path
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
in_ds1 = gdal.Open(input_file1)
|
||||||
|
in_ds2 = gdal.Open(input_file2)
|
||||||
|
ref_ds = gdal.Open(ref_file, gdal.GA_ReadOnly)
|
||||||
|
|
||||||
|
# 获取输入影像信息
|
||||||
|
input_file_proj = in_ds1.GetProjection()
|
||||||
|
# inputefileTrans = in_ds1.GetGeoTransform()
|
||||||
|
reference_file_proj = ref_ds.GetProjection()
|
||||||
|
reference_file_trans = ref_ds.GetGeoTransform()
|
||||||
|
|
||||||
|
nbands = in_ds1.RasterCount
|
||||||
|
bandinputfile1 = in_ds1.GetRasterBand(1)
|
||||||
|
bandinputfile2 = in_ds2.GetRasterBand(1)
|
||||||
|
x = ref_ds.RasterXSize
|
||||||
|
y = ref_ds.RasterYSize
|
||||||
|
|
||||||
|
# 创建重采样输出文件（设置投影及六参数）
|
||||||
|
driver1 = gdal.GetDriverByName('GTiff')
|
||||||
|
output1 = driver1.Create(output_file, x, y, nbands, bandinputfile1.DataType)
|
||||||
|
output1.SetGeoTransform(reference_file_trans)
|
||||||
|
output1.SetProjection(reference_file_proj)
|
||||||
|
# options = gdal.WarpOptions(srcSRS=inputProj, dstSRS=referencefileProj, resampleAlg=gdalconst.GRA_Bilinear)
|
||||||
|
# resampleAlg = gdalconst.GRA_NearestNeighbour
|
||||||
|
gdal.ReprojectImage(in_ds1, output1, input_file_proj, reference_file_proj, gdalconst.GRA_Bilinear)
|
||||||
|
|
||||||
|
driver2 = gdal.GetDriverByName('GTiff')
|
||||||
|
output2 = driver2.Create(output_file2, x, y, nbands, bandinputfile2.DataType)
|
||||||
|
output2.SetGeoTransform(reference_file_trans)
|
||||||
|
output2.SetProjection(reference_file_proj)
|
||||||
|
# options = gdal.WarpOptions(srcSRS=inputProj, dstSRS=referencefileProj, resampleAlg=gdalconst.GRA_Bilinear)
|
||||||
|
# resampleAlg = gdalconst.GRA_NearestNeighbour
|
||||||
|
gdal.ReprojectImage(in_ds2, output2, input_file_proj, reference_file_proj, gdalconst.GRA_Bilinear)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def getorbitparameter(xml_path):
|
||||||
|
"""
|
||||||
|
从轨道参数文件xml中获取升降轨信息、影像四个角的经纬度坐标
|
||||||
|
|
||||||
|
"""
|
||||||
|
# 打开xml文档,根据路径初始化DOM
|
||||||
|
doc = minidom.parse(xml_path)
|
||||||
|
# 得到xml文档元素对象,初始化root对象
|
||||||
|
root = doc.documentElement
|
||||||
|
|
||||||
|
# 输出升降轨信息，DEC降轨，ASC升轨
|
||||||
|
direction = root.getElementsByTagName("Direction")[0]
|
||||||
|
# print("输出Direction的子节点列表",Direction.firstChild.data)
|
||||||
|
pd = direction.firstChild.data
|
||||||
|
|
||||||
|
imageinfo = root.getElementsByTagName("imageinfo")[0]
|
||||||
|
# 输出topLeft的纬度和经度
|
||||||
|
top_left = imageinfo.getElementsByTagName("topLeft")[0]
|
||||||
|
latitude = top_left.getElementsByTagName("latitude")[0]
|
||||||
|
longitude = top_left.getElementsByTagName("longitude")[0]
|
||||||
|
# print("输出topLeft的纬度lat和经度lon：", latitude.firstChild.data,longitude.firstChild.data)
|
||||||
|
tl_lat, tl_lon = latitude.firstChild.data, longitude.firstChild.data
|
||||||
|
|
||||||
|
# 输出topRight的纬度和经度
|
||||||
|
top_right = imageinfo.getElementsByTagName("topRight")[0]
|
||||||
|
latitude = top_right.getElementsByTagName("latitude")[0]
|
||||||
|
longitude = top_right.getElementsByTagName("longitude")[0]
|
||||||
|
# print("输出topRight的纬度lat和经度lon：", latitude.firstChild.data,longitude.firstChild.data)
|
||||||
|
tr_lat, tr_lon = latitude.firstChild.data, longitude.firstChild.data
|
||||||
|
|
||||||
|
# 输出 bottomLeft的纬度和经度
|
||||||
|
bottom_left = imageinfo.getElementsByTagName("bottomLeft")[0]
|
||||||
|
latitude = bottom_left.getElementsByTagName("latitude")[0]
|
||||||
|
longitude = bottom_left.getElementsByTagName("longitude")[0]
|
||||||
|
# print("输出bottomLeft的纬度lat和经度lon：", latitude.firstChild.data,longitude.firstChild.data)
|
||||||
|
bl_lat, bl_lon = latitude.firstChild.data, longitude.firstChild.data
|
||||||
|
|
||||||
|
# 输出 bottomRight的纬度和经度
|
||||||
|
bottom_right = imageinfo.getElementsByTagName("bottomRight")[0]
|
||||||
|
latitude = bottom_right.getElementsByTagName("latitude")[0]
|
||||||
|
longitude = bottom_right.getElementsByTagName("longitude")[0]
|
||||||
|
# print("输出bottomRight的纬度lat和经度lon：", latitude.firstChild.data,longitude.firstChild.data)
|
||||||
|
br_lat, br_lon = latitude.firstChild.data, longitude.firstChild.data
|
||||||
|
print("pd,tl_lat,tl_lon,tr_lat,tr_lon,bl_lat,bl_lon,br_lat,br_lon", pd, tl_lat, tl_lon, tr_lat, tr_lon, bl_lat,
|
||||||
|
bl_lon, br_lat, br_lon)
|
||||||
|
return pd, tl_lat, tl_lon, tr_lat, tr_lon, bl_lat, bl_lon, br_lat, br_lon
|
||||||
|
|
||||||
|
def get_rparademeter(self, xml_path):
|
||||||
|
"""
|
||||||
|
计算雷达视线向方向角R
|
||||||
|
"""
|
||||||
|
pd, tl_lat, tl_lon, tr_lat, tr_lon, bl_lat, bl_lon, br_lat, br_lon = self.getorbitparameter(xml_path)
|
||||||
|
|
||||||
|
tl_lat = float(tl_lat) # 原来的数是带有小数点的字符串，int会报错，使用float
|
||||||
|
tl_lon = float(tl_lon)
|
||||||
|
# tr_lat = float(tr_lat)
|
||||||
|
# tr_lon = float(tr_lon)
|
||||||
|
bl_lat = float(bl_lat)
|
||||||
|
bl_lon = float(bl_lon)
|
||||||
|
# br_lat = float(br_lat)
|
||||||
|
# br_lon = float(br_lon)
|
||||||
|
|
||||||
|
if pd == "DEC":
|
||||||
|
# 降轨
|
||||||
|
b = np.arctan((tl_lat - bl_lat) / (tl_lon - bl_lon)) * 57.29578
|
||||||
|
r = 270 + b
|
||||||
|
return r
|
||||||
|
# tl_lat, tl_lon = lonlat2geo(tl_lat, tl_lon)
|
||||||
|
# tr_lat, tr_lon = lonlat2geo(tr_lat, tr_lon)
|
||||||
|
# bl_lat, bl_lon = lonlat2geo(bl_lat, bl_lon)
|
||||||
|
# br_lat, br_lon = lonlat2geo(br_lat, br_lon)
|
||||||
|
# B2 = np.arctan((tl_lat - bl_lat) / (tl_lon - bl_lon)) * 57.29578
|
||||||
|
# R2 = 270 + B2
|
||||||
|
# print(("输出R2：", R2))
|
||||||
|
if pd == "ASC":
|
||||||
|
# 升轨
|
||||||
|
b = np.arctan((tl_lat - bl_lat) / (tl_lon - bl_lon)) * 57.29578
|
||||||
|
return b
|
||||||
|
|
||||||
|
def clau(self, pathfile1, pathfile2, pathfile3, xml_path, save_localangle_path):
|
||||||
|
"""
|
||||||
|
计算局部入射角
|
||||||
|
param: pathfile1是slope的坡度图路径
|
||||||
|
param: pathfile2是aspect的坡向图路径
|
||||||
|
param: pathfile3是入射角文件的路径
|
||||||
|
param: xml_path是轨道参数文件
|
||||||
|
r是雷达视线向方位角
|
||||||
|
"""
|
||||||
|
r = self.get_rparademeter(xml_path)
|
||||||
|
pd, tl_lat, tl_lon, tr_lat, tr_lon, bl_lat, bl_lon, br_lat, br_lon = self.getorbitparameter(xml_path)
|
||||||
|
print("输出升降轨：", pd)
|
||||||
|
dataset = gdal.Open(pathfile1)
|
||||||
|
x = dataset.RasterXSize
|
||||||
|
y = dataset.RasterYSize
|
||||||
|
print("输出slope的行、列：", x, y)
|
||||||
|
slope_array = dataset.ReadAsArray(0, 0, x, y)
|
||||||
|
|
||||||
|
dataset2 = gdal.Open(pathfile2)
|
||||||
|
x2 = dataset2.RasterXSize
|
||||||
|
y2 = dataset2.RasterYSize
|
||||||
|
print("输出aspect的行、列：", x2, y2)
|
||||||
|
aspect_array = dataset2.ReadAsArray(0, 0, x2, y2)
|
||||||
|
|
||||||
|
dataset3 = gdal.Open(pathfile3)
|
||||||
|
x3 = dataset3.RasterXSize
|
||||||
|
y3 = dataset3.RasterYSize
|
||||||
|
geo3 = dataset3.GetGeoTransform()
|
||||||
|
pro3 = dataset3.GetProjection()
|
||||||
|
print("输出入射角文件的行、列：", x3, y3)
|
||||||
|
|
||||||
|
rushe_array = dataset3.ReadAsArray(0, 0, x3, y3)
|
||||||
|
# b0 = np.where(rushe_array > 0.00001, 0, 1)
|
||||||
|
radina_value = 0
|
||||||
|
if pd == "DEC":
|
||||||
|
# 降轨数据
|
||||||
|
# 雷达视线角-坡度角在90度到270度之间
|
||||||
|
where_0 = np.where(rushe_array == 0)
|
||||||
|
|
||||||
|
bb1 = (r-aspect_array).all() and (r-aspect_array).all()
|
||||||
|
bb2 = np.where(90 < bb1 < 270, 1, 0)
|
||||||
|
b1 = (bb1 and bb2)
|
||||||
|
# b1 = np.where(90 < ((r-aspect_array).all()) and ((r-aspect_array).all()) < 270, 1, 0)
|
||||||
|
c1 = np.cos(rushe_array*(math.pi/180)) * np.cos(slope_array*(math.pi/180)) - np.sin(slope_array*(math.pi/180)) * np.sin(
|
||||||
|
rushe_array*(math.pi/180)) * np.cos((r - aspect_array)*(math.pi/180))
|
||||||
|
d1 = b1 * c1
|
||||||
|
# 雷达视线角-坡度角=90度或=270度时
|
||||||
|
b2 = np.where((r-aspect_array == 90) | (r-aspect_array == 270), 1, 0)
|
||||||
|
d2 = b2*c1
|
||||||
|
# 雷达视线角-坡度角在90度到270度之间
|
||||||
|
b3 = 1-b1-b2
|
||||||
|
c3 = np.cos(rushe_array*(math.pi/180)) * np.cos(slope_array*(math.pi/180)) + np.sin(
|
||||||
|
slope_array*(math.pi/180)) * np.sin(rushe_array*(math.pi/180)) * np.cos((r - aspect_array)*(math.pi/180))
|
||||||
|
d3 = b3 * c3
|
||||||
|
del b1, b2, b3, c3, c1
|
||||||
|
gc.collect()
|
||||||
|
radina_value = d1 + d2 + d3
|
||||||
|
radina_value[where_0] = 0
|
||||||
|
del d1, d2, d3
|
||||||
|
gc.collect()
|
||||||
|
if pd == "ASC":
|
||||||
|
# 升轨数据
|
||||||
|
# 坡度-雷达视线角在90度到270度之间
|
||||||
|
where_0 = np.where(rushe_array == 0)
|
||||||
|
|
||||||
|
bb1 = (r-aspect_array).all() and (r-aspect_array).all()
|
||||||
|
bb2 = np.where(90 < bb1 < 270, 1, 0)
|
||||||
|
b1 = (bb1 and bb2)
|
||||||
|
# b1 = np.where(90 < ((r-aspect_array).all()) and ((r-aspect_array).all()) < 270, 1, 0)
|
||||||
|
c1 = np.cos(rushe_array*(math.pi/180)) * np.cos(slope_array*(math.pi/180)) + np.sin(
|
||||||
|
slope_array*(math.pi/180)) * np.sin(rushe_array*(math.pi/180)) * np.cos((r - aspect_array)*(math.pi/180))
|
||||||
|
d1 = b1 * c1
|
||||||
|
# 坡度-雷达视线角=90或=270时
|
||||||
|
b2 = np.where((aspect_array-r == 90) | (aspect_array-r == 270), 1, 0)
|
||||||
|
d2 = b2 * c1
|
||||||
|
# 坡度-雷达视线角在0-90度或270-360度之间
|
||||||
|
b3 = 1 - b1-b2
|
||||||
|
c3 = np.cos(rushe_array*(math.pi/180)) * np.cos(slope_array*(math.pi/180)) - np.sin(slope_array*(math.pi/180)) *\
|
||||||
|
np.sin(rushe_array*(math.pi/180)) * np.cos((r - aspect_array)*(math.pi/180))
|
||||||
|
d3 = b3 * c3
|
||||||
|
radina_value = d1 + d2 + d3
|
||||||
|
radina_value[where_0] = 0
|
||||||
|
del b1, b2, b3, c3, c1, d1, d2, d3
|
||||||
|
gc.collect()
|
||||||
|
jubu_o = 57.29578 * np.arccos(radina_value)
|
||||||
|
print("输出局部入射角", jubu_o)
|
||||||
|
driver = gdal.GetDriverByName("GTiff") # 创建一个数据格式
|
||||||
|
driver.Register()
|
||||||
|
newfile = driver.Create(save_localangle_path, x3, y3, 1, gdal.GDT_Float32) # 存放路径文件名，长，宽，波段，数据类型
|
||||||
|
newfile.SetProjection(pro3)
|
||||||
|
newfile.SetGeoTransform(geo3)
|
||||||
|
newfile.GetRasterBand(1).WriteArray(jubu_o)
|
||||||
|
|
||||||
|
def localangle(self, dem_path, incidence_angle_path, orbital_parameters_path):
|
||||||
|
"""
|
||||||
|
获取输入文件的路径
|
||||||
|
计算坡度图、坡向图
|
||||||
|
计算局部入射角
|
||||||
|
"""
|
||||||
|
para_names = ["Dem", "IncidenceAngle", "OrbitalParameters", "经验A"]
|
||||||
|
if len(para_names) == 0:
|
||||||
|
return False
|
||||||
|
# 获取三个文件的路径
|
||||||
|
|
||||||
|
# print("输出三个文件路径",Dem_path,IncidenceAngle_path,OrbitalParameters_path)
|
||||||
|
# 确定坡度、坡向的输出路径，输出坡度、坡向图
|
||||||
|
slope_out_path = r"D:\MicroWorkspace\LeafAreaIndex\Temporary\UnClipslope.tif"
|
||||||
|
aspect_out_path = r"D:\MicroWorkspace\LeafAreaIndex\Temporary\UnClipaspect.tif"
|
||||||
|
print("slope_out_path的路径是", slope_out_path)
|
||||||
|
print("aspect_out_path的路径是", aspect_out_path)
|
||||||
|
self.creat_twofile(dem_path, slope_out_path, aspect_out_path)
|
||||||
|
# 根据入射角文件对坡度坡向图进行裁剪与重采样
|
||||||
|
slope_out_path2 = r"D:\MicroWorkspace\LocaLangle\Temporary\Clipslope.tif"
|
||||||
|
aspect_out_path2 = r"D:\MicroWorkspace\LocaLangle\Temporary\Clipaspect.tif"
|
||||||
|
self.resampling(slope_out_path, aspect_out_path, incidence_angle_path, slope_out_path2, aspect_out_path2)
|
||||||
|
|
||||||
|
# 输出局部入射角文件
|
||||||
|
save_localangle_path = r"D:\\MicroWorkspace\\LocaLangle\\Temporary\\\localangle.tif"
|
||||||
|
self.clau(slope_out_path2, aspect_out_path2, incidence_angle_path,
|
||||||
|
orbital_parameters_path, save_localangle_path)
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# calu_incident = CalculateIncident()
|
||||||
|
# Dem_path = "D:\\MicroWorkspace\\LocaLangle\\Input\\dem.tif"
|
||||||
|
# IncidenceAngle_path = "D:\\MicroWorkspace\\LocaLangle\\Input\\RSJ.tif"
|
||||||
|
# OrbitalParameters_path = "D:\\MicroWorkspace\\LocaLangle\\Input\\" \
|
||||||
|
# "GF3_KAS_FSII_020008_E113.2_N23.1_20200528_L1A_HHHV_L10004829485.meta.xml"
|
||||||
|
# calu_incident.localangle(Dem_path, IncidenceAngle_path, OrbitalParameters_path)
|
||||||
|
# print('done')
|
||||||
|
|
@ -0,0 +1,302 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:lee_filter.py
|
||||||
|
@Function:lee_filter
|
||||||
|
@Contact: https://github.com/PyRadar/pyradar
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/8/30 8:42
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import math
|
||||||
|
from PIL import Image
|
||||||
|
import multiprocessing
|
||||||
|
import multiprocessing
|
||||||
|
from tool.algorithm.block.blockprocess import BlockProcess
|
||||||
|
import logging
|
||||||
|
import shutil
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.file.fileHandle import fileHandle
|
||||||
|
from tool.algorithm.algtools.filter import lee_Filter_c as lee_Filter_c
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
file =fileHandle(False)
|
||||||
|
COEF_VAR_DEFAULT = 0.01
|
||||||
|
CU_DEFAULT = 0.25
|
||||||
|
import os
|
||||||
|
|
||||||
|
|
||||||
|
class Filter:
|
||||||
|
def __int__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def assert_window_size(win_size):
|
||||||
|
"""
|
||||||
|
Asserts invalid window size.
|
||||||
|
Window size must be odd and bigger than 3.
|
||||||
|
"""
|
||||||
|
assert win_size >= 3, 'ERROR: win size must be at least 3'
|
||||||
|
|
||||||
|
if win_size % 2 == 0:
|
||||||
|
print('It is highly recommended to user odd window sizes.'
|
||||||
|
'You provided %s, an even number.' % (win_size, ))
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def assert_indices_in_range(width, height, xleft, xright, yup, ydown):
|
||||||
|
"""
|
||||||
|
Asserts index out of image range.
|
||||||
|
"""
|
||||||
|
|
||||||
|
# assert xleft >= 0 and xleft <= width, \
|
||||||
|
assert 0 <= xleft <= width, \
|
||||||
|
"index xleft:%s out of range (%s<= xleft < %s)" % (xleft, 0, width)
|
||||||
|
|
||||||
|
# assert xright >= 0 and xright <= width, \
|
||||||
|
assert 0 <= xright <= width, "index xright:%s out of range (%s<= xright < %s)" % (xright, 0, width)
|
||||||
|
|
||||||
|
# assert yup >= 0 and yup <= height, \
|
||||||
|
assert 0 <= yup <= height, "index yup:%s out of range. (%s<= yup < %s)" % (yup, 0, height)
|
||||||
|
|
||||||
|
# assert ydown >= 0 and ydown <= height, \
|
||||||
|
assert 0 <= ydown <= height, "index ydown:%s out of range. (%s<= ydown < %s)" % (ydown, 0, height)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def weighting(window, cu=CU_DEFAULT):
|
||||||
|
"""
|
||||||
|
Computes the weighthing function for Lee filter using cu as the noise
|
||||||
|
coefficient.
|
||||||
|
"""
|
||||||
|
# cu is the noise variation coefficient
|
||||||
|
two_cu = cu * cu
|
||||||
|
|
||||||
|
# ci is the variation coefficient in the window
|
||||||
|
window_mean = window.mean()
|
||||||
|
window_std = window.std()
|
||||||
|
ci = window_std / window_mean
|
||||||
|
|
||||||
|
two_ci = ci * ci
|
||||||
|
|
||||||
|
if not two_ci: # dirty patch to avoid zero division
|
||||||
|
two_ci = COEF_VAR_DEFAULT
|
||||||
|
|
||||||
|
if cu > ci:
|
||||||
|
w_t = 0.0
|
||||||
|
else:
|
||||||
|
w_t = 1.0 - (two_cu / two_ci)
|
||||||
|
|
||||||
|
return w_t
|
||||||
|
|
||||||
|
def lee_filter(self, in_path, out_path, win_size):
|
||||||
|
"""
|
||||||
|
Apply lee to a numpy matrix containing the image, with a window of
|
||||||
|
win_size x win_size.
|
||||||
|
"""
|
||||||
|
cu = CU_DEFAULT
|
||||||
|
self.assert_window_size(win_size)
|
||||||
|
# img = self.ImageHandler.get_band_array(img, 1)
|
||||||
|
array1 = Image.open(in_path)
|
||||||
|
img = np.array(array1)
|
||||||
|
# we process the entire img as float64 to avoid type overflow error
|
||||||
|
img = np.float64(img)
|
||||||
|
img_filtered = np.zeros_like(img)
|
||||||
|
# n, m = img.shape
|
||||||
|
# win_offset = win_size / 2
|
||||||
|
#
|
||||||
|
# for i in range(0, n):
|
||||||
|
# xleft = i - win_offset
|
||||||
|
# xright = i + win_offset
|
||||||
|
#
|
||||||
|
# if xleft < 0:
|
||||||
|
# xleft = 0
|
||||||
|
# if xright >= n:
|
||||||
|
# xright = n
|
||||||
|
#
|
||||||
|
# for j in range(0, m):
|
||||||
|
# yup = j - win_offset
|
||||||
|
# ydown = j + win_offset
|
||||||
|
#
|
||||||
|
# if yup < 0:
|
||||||
|
# yup = 0
|
||||||
|
# if ydown >= m:
|
||||||
|
# ydown = m
|
||||||
|
#
|
||||||
|
# self.assert_indices_in_range(n, m, xleft, xright, yup, ydown)
|
||||||
|
#
|
||||||
|
# pix_value = img[i, j]
|
||||||
|
#
|
||||||
|
# window = img[math.ceil(xleft):int(xright)+1, math.ceil(yup):int(ydown)+1]
|
||||||
|
# w_t = self.weighting(window, cu)
|
||||||
|
# window_mean = window.mean()
|
||||||
|
# new_pix_value = (pix_value * w_t) + (window_mean * (1.0 - w_t))
|
||||||
|
#
|
||||||
|
# if not new_pix_value > 0:
|
||||||
|
# new_pix_value = 0
|
||||||
|
# img_filtered[i, j] = round(new_pix_value)
|
||||||
|
# # return img_filtered
|
||||||
|
self.lee_filter_array(img, img_filtered, win_size)
|
||||||
|
out_image = Image.fromarray(img_filtered)
|
||||||
|
out_image.save(out_path)
|
||||||
|
print("lee_filter finish! path:" + out_path)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def lee_filter_array(in_arry, out_arry, win_size):
|
||||||
|
"""
|
||||||
|
Apply lee to a numpy matrix containing the image, with a window of
|
||||||
|
win_size x win_size.
|
||||||
|
"""
|
||||||
|
f = Filter()
|
||||||
|
#cu = CU_DEFAULT
|
||||||
|
f.assert_window_size(win_size)
|
||||||
|
img = in_arry
|
||||||
|
# we process the entire img as float64 to avoid type overflow error
|
||||||
|
img = np.float64(img)
|
||||||
|
img = img + 100
|
||||||
|
|
||||||
|
# lee_filter_array(np.ndarray[double,ndim=2] img,np.ndarray[double,ndim=2] out_arryint win_offset,int win_size):
|
||||||
|
newOUt=lee_Filter_c.lee_filter_array(img,out_arry,win_size)
|
||||||
|
newOUt=newOUt-100
|
||||||
|
out_arry[:,:]=newOUt[:,:]
|
||||||
|
|
||||||
|
# def lee_filter_array(self, in_arry, out_arry, win_size):
|
||||||
|
# """
|
||||||
|
# Apply lee to a numpy matrix containing the image, with a window of
|
||||||
|
# win_size x win_size.
|
||||||
|
# """
|
||||||
|
# cu = CU_DEFAULT
|
||||||
|
# self.assert_window_size(win_size)
|
||||||
|
# img = in_arry
|
||||||
|
# # we process the entire img as float64 to avoid type overflow error
|
||||||
|
# img = np.float64(img)
|
||||||
|
# img = img + 100
|
||||||
|
# img_filtered = np.zeros_like(img)
|
||||||
|
# n, m = img.shape
|
||||||
|
# win_offset = win_size / 2
|
||||||
|
#
|
||||||
|
# for i in range(0, n):
|
||||||
|
# xleft = i - win_offset
|
||||||
|
# xright = i + win_offset
|
||||||
|
#
|
||||||
|
# if xleft < 0:
|
||||||
|
# xleft = 0
|
||||||
|
# if xright >= n:
|
||||||
|
# xright = n
|
||||||
|
#
|
||||||
|
# for j in range(0, m):
|
||||||
|
# yup = j - win_offset
|
||||||
|
# ydown = j + win_offset
|
||||||
|
#
|
||||||
|
# if yup < 0:
|
||||||
|
# yup = 0
|
||||||
|
# if ydown >= m:
|
||||||
|
# ydown = m
|
||||||
|
#
|
||||||
|
# self.assert_indices_in_range(n, m, xleft, xright, yup, ydown)
|
||||||
|
#
|
||||||
|
# pix_value = img[i, j]
|
||||||
|
#
|
||||||
|
# window = img[math.ceil(xleft):int(xright)+1, math.ceil(yup):int(ydown)+1]
|
||||||
|
# w_t = self.weighting(window, cu)
|
||||||
|
# window_mean = window.mean()
|
||||||
|
# new_pix_value = (pix_value * w_t) + (window_mean * (1.0 - w_t))
|
||||||
|
#
|
||||||
|
# if not new_pix_value > 0:
|
||||||
|
# new_pix_value = 0
|
||||||
|
# out_arry[i, j] = round(new_pix_value)
|
||||||
|
# out_arry = out_arry - 100
|
||||||
|
#
|
||||||
|
|
||||||
|
def lee_filter_multiprocess(self, in_paths, out_paths, win_size =3,processes_num=10):
|
||||||
|
if len(in_paths) != len(out_paths):
|
||||||
|
return False
|
||||||
|
# 开启多进程处理
|
||||||
|
pool = multiprocessing.Pool(processes=processes_num)
|
||||||
|
pl = []
|
||||||
|
for i in range(len(in_paths)):
|
||||||
|
#self.lee_filter(in_paths[i], out_paths[i], win_size)
|
||||||
|
pl.append(pool.apply_async(self.lee_filter,(in_paths[i], out_paths[i], win_size)))
|
||||||
|
print("lee_filter runing! path:" + in_paths[i])
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
def lee_filter_block_multiprocess(self, in_path, out_path, win_size =3):
|
||||||
|
in_name = os.path.basename(in_path)
|
||||||
|
out_name = os.path.basename(out_path)
|
||||||
|
outDir= os.path.split(out_path)[0]
|
||||||
|
#创建工作文件夹
|
||||||
|
src_path = os.path.join(outDir, "src_img")
|
||||||
|
block_path = os.path.join(outDir, "block")
|
||||||
|
block_filtered = os.path.join(outDir, "block_filtered")
|
||||||
|
file.creat_dirs([src_path, block_path, block_filtered])
|
||||||
|
|
||||||
|
shutil.copyfile(in_path, os.path.join(src_path, in_name))
|
||||||
|
cols = ImageHandler.get_img_width(in_path)
|
||||||
|
rows = ImageHandler.get_img_height(in_path)
|
||||||
|
# 分块
|
||||||
|
bp = BlockProcess()
|
||||||
|
block_size = bp.get_block_size(rows, cols)
|
||||||
|
bp.cut(src_path, block_path, ['tif', 'tiff'], 'tif', block_size)
|
||||||
|
logger.info('blocking tifs success!')
|
||||||
|
|
||||||
|
img_dir, img_name = bp.get_file_names(block_path, ['tif'])
|
||||||
|
dir_dict = bp.get_same_img(img_dir, img_name)
|
||||||
|
|
||||||
|
img_path_list = [value for value in dir_dict.values()][0]
|
||||||
|
|
||||||
|
processes_num = min([len(img_path_list), multiprocessing.cpu_count() - 1])
|
||||||
|
|
||||||
|
out_img_path_list =[]
|
||||||
|
for in_path in img_path_list:
|
||||||
|
suffix = bp.get_suffix(os.path.basename(in_path))
|
||||||
|
out_path = os.path.join(block_filtered, out_name.replace('.tif', suffix))
|
||||||
|
out_img_path_list.append(out_path)
|
||||||
|
|
||||||
|
self.lee_filter_multiprocess(img_path_list, out_img_path_list, win_size = win_size, processes_num=processes_num)
|
||||||
|
# 开启多进程处理
|
||||||
|
# pool = multiprocessing.Pool(processes=processes_num)
|
||||||
|
#
|
||||||
|
# for i in range(len(hh_list)):
|
||||||
|
# block_img_path = hh_list[i]
|
||||||
|
# suffix = bp.get_suffix(os.path.basename(hh_list[i]))
|
||||||
|
# filed_block_img_path = os.path.join(block_filtered,out_name.replace('.tif',suffix))
|
||||||
|
# pool.apply_async(self.lee_filter, (block_img_path, filed_block_img_path, win_size))
|
||||||
|
# print("lee_filter runing! path:" + block_img_path)
|
||||||
|
# logger.info('total:%s, block:%s lee_filter!', len(hh_list), i)
|
||||||
|
#
|
||||||
|
# pool.close()
|
||||||
|
# pool.join()
|
||||||
|
# # 合并处理后的影像
|
||||||
|
bp.combine(block_filtered, cols, rows, outDir, file_type=['tif'], datetype='float32')
|
||||||
|
|
||||||
|
file.del_folder(src_path)
|
||||||
|
file.del_folder(block_path)
|
||||||
|
file.del_folder(block_filtered)
|
||||||
|
pass
|
||||||
|
|
||||||
|
def lee_process_sar(self, in_sar, out_sar, win_size, noise_var):
|
||||||
|
'''
|
||||||
|
# std::cout << "mode 12"
|
||||||
|
# std::cout << "SIMOrthoProgram.exe 12 in_sar_path out_sar_path win_size noise_var"
|
||||||
|
'''
|
||||||
|
exe_path = r".\baseTool\x64\Release\SIMOrthoProgram.exe"
|
||||||
|
exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3} {4} {5}".format(exe_path, 12, in_sar,
|
||||||
|
out_sar, win_size, noise_var)
|
||||||
|
print(exe_cmd)
|
||||||
|
print(os.system(exe_cmd))
|
||||||
|
print("==========================================================================")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
# 示例1：
|
||||||
|
# path = r"I:\MicroWorkspace\product\C-SAR\LeafAreaIndex\Temporary\cai_sartif\HV_0_512_0_512.tif"
|
||||||
|
# f = Filter()
|
||||||
|
# f.lee_filter(path,path,3)
|
||||||
|
#示例2：
|
||||||
|
|
||||||
|
f = Filter()
|
||||||
|
f.lee_filter_block_multiprocess('I:\preprocessed\HH.tif','I:\preprocessed\HHf.tif')
|
||||||
|
pass
|
||||||
|
|
@ -0,0 +1,124 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:lee_filter.py
|
||||||
|
@Function:lee_filter
|
||||||
|
@Contact: https://github.com/PyRadar/pyradar
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/8/30 8:42
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
cimport cython # 必须导入
|
||||||
|
import numpy as np##必须为c类型和python类型的数据都申明一个np
|
||||||
|
cimport numpy as np # 必须为c类型和python类型的数据都申明一个np
|
||||||
|
from libc.math cimport pi
|
||||||
|
from libc.math cimport atan as math_atan
|
||||||
|
from libc.math cimport log10 as math_log10
|
||||||
|
from libc.math cimport log as math_log
|
||||||
|
from libc.math cimport floor as math_floor
|
||||||
|
from libc.math cimport sqrt as math_sqrt
|
||||||
|
from libc.math cimport exp as math_exp
|
||||||
|
from libc.math cimport sin as math_sin
|
||||||
|
from libc.math cimport cos as math_cos
|
||||||
|
from libc.math cimport tan as math_tan
|
||||||
|
from libc.math cimport asin as math_asin
|
||||||
|
from libc.math cimport acos as math_acos
|
||||||
|
from libc.math cimport tan as math_atan
|
||||||
|
from libc.math cimport sinh as math_sinh
|
||||||
|
from libc.math cimport cosh as math_cosh
|
||||||
|
from libc.math cimport tanh as math_tanh
|
||||||
|
from libc.math cimport floor as math_floor
|
||||||
|
from libc.math cimport ceil as math_ceil
|
||||||
|
from libc.math cimport lround as math_round
|
||||||
|
|
||||||
|
cdef double COEF_VAR_DEFAULT = 0.01
|
||||||
|
cdef double CU_DEFAULT = 0.25
|
||||||
|
|
||||||
|
|
||||||
|
cdef int ceil_usr(double v):
|
||||||
|
return int(math_ceil(v))
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
cdef double weighting(np.ndarray[double,ndim=2] window,double cu):
|
||||||
|
"""
|
||||||
|
Computes the weighthing function for Lee filter using cu as the noise
|
||||||
|
coefficient.
|
||||||
|
"""
|
||||||
|
# cu is the noise variation coefficient
|
||||||
|
cdef double two_cu = cu * cu
|
||||||
|
|
||||||
|
# ci is the variation coefficient in the window
|
||||||
|
cdef double window_mean = window.mean()
|
||||||
|
cdef double window_std = window.std()
|
||||||
|
cdef double ci = window_std / window_mean
|
||||||
|
|
||||||
|
cdef double two_ci = ci * ci
|
||||||
|
cdef double w_t=0;
|
||||||
|
if not (two_ci==0): # dirty patch to avoid zero division
|
||||||
|
two_ci = COEF_VAR_DEFAULT
|
||||||
|
|
||||||
|
if cu > ci:
|
||||||
|
w_t = 0.0
|
||||||
|
else:
|
||||||
|
w_t = 1.0 - (two_cu / two_ci)
|
||||||
|
|
||||||
|
return w_t
|
||||||
|
|
||||||
|
cpdef np.ndarray[double,ndim=2] lee_filter_array(np.ndarray[double,ndim=2] img,np.ndarray[double,ndim=2] out_arry,int win_size):
|
||||||
|
"""
|
||||||
|
Apply lee to a numpy matrix containing the image, with a window of
|
||||||
|
win_size x win_size.
|
||||||
|
"""
|
||||||
|
# we process the entire img as float64 to avoid type overflow error
|
||||||
|
#n, m = img.shape
|
||||||
|
cdef double cu = CU_DEFAULT
|
||||||
|
cdef int i=0
|
||||||
|
cdef int j=0
|
||||||
|
cdef int xleft=0
|
||||||
|
cdef int xright=0
|
||||||
|
cdef int yup=0
|
||||||
|
cdef int ydown=0
|
||||||
|
cdef np.ndarray[double,ndim=2] window;
|
||||||
|
cdef double w_t=0;
|
||||||
|
cdef double window_mean=0;
|
||||||
|
cdef double new_pix_valu=0;
|
||||||
|
cdef int n = img.shape[0]
|
||||||
|
cdef int m=img.shape[1]
|
||||||
|
cdef int win_offset=int(win_size/2)
|
||||||
|
|
||||||
|
while i<n:
|
||||||
|
xleft=ceil_usr(i-win_offset)
|
||||||
|
xright=int(i+win_offset)
|
||||||
|
if xleft < 0:
|
||||||
|
xleft = 0
|
||||||
|
if xright >= n:
|
||||||
|
xright = n
|
||||||
|
j=0
|
||||||
|
while j<m:
|
||||||
|
yup = ceil_usr(j - win_offset)
|
||||||
|
yup=0 if yup<0 else yup
|
||||||
|
ydown = int(j + win_offset)
|
||||||
|
if yup < 0:
|
||||||
|
yup = 0
|
||||||
|
if ydown >= m:
|
||||||
|
ydown = m
|
||||||
|
|
||||||
|
pix_value = img[i, j]
|
||||||
|
|
||||||
|
window = img[xleft:xright+1, yup:ydown+1]
|
||||||
|
|
||||||
|
w_t = weighting(window, cu)
|
||||||
|
|
||||||
|
window_mean = np.mean(window)
|
||||||
|
new_pix_value = (pix_value * w_t) + (window_mean * (1.0 - w_t))
|
||||||
|
|
||||||
|
if not new_pix_value > 0:
|
||||||
|
new_pix_value = 0
|
||||||
|
out_arry[i, j] = round(new_pix_value*100000.0)/100000.0
|
||||||
|
j=j+1
|
||||||
|
i=i+1
|
||||||
|
return out_arry
|
||||||
|
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,45 @@
|
||||||
|
from setuptools import setup
|
||||||
|
from setuptools.extension import Extension
|
||||||
|
from Cython.Distutils import build_ext
|
||||||
|
from Cython.Build import cythonize
|
||||||
|
import numpy
|
||||||
|
from pathlib import Path
|
||||||
|
import shutil
|
||||||
|
|
||||||
|
|
||||||
|
class MyBuildExt(build_ext):
|
||||||
|
def run(self):
|
||||||
|
build_ext.run(self)
|
||||||
|
|
||||||
|
build_dir = Path(self.build_lib)
|
||||||
|
root_dir = Path(__file__).parent
|
||||||
|
target_dir = build_dir if not self.inplace else root_dir
|
||||||
|
|
||||||
|
self.copy_file(Path('./lee_Filter') / '__init__.py', root_dir, target_dir)
|
||||||
|
#self.copy_file(Path('./pkg2') / '__init__.py', root_dir, target_dir)
|
||||||
|
self.copy_file(Path('.') / '__init__.py', root_dir, target_dir)
|
||||||
|
def copy_file(self, path, source_dir, destination_dir):
|
||||||
|
if not (source_dir / path).exists():
|
||||||
|
return
|
||||||
|
shutil.copyfile(str(source_dir / path), str(destination_dir / path))
|
||||||
|
|
||||||
|
setup(
|
||||||
|
name="MyModule",
|
||||||
|
ext_modules=cythonize(
|
||||||
|
[
|
||||||
|
#Extension("pkg1.*", ["root/pkg1/*.py"]),
|
||||||
|
Extension("pkg2.*", ["./lee_Filter/lee_Filter_c.pyx"]),
|
||||||
|
#Extension("1.*", ["root/*.py"])
|
||||||
|
],
|
||||||
|
build_dir="build",
|
||||||
|
compiler_directives=dict(
|
||||||
|
always_allow_keywords=True
|
||||||
|
)),
|
||||||
|
cmdclass=dict(
|
||||||
|
build_ext=MyBuildExt
|
||||||
|
),
|
||||||
|
packages=[],
|
||||||
|
include_dirs=[numpy.get_include()],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 指令： python setup.py build_ext --inplace
|
||||||
|
|
@ -0,0 +1,90 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：logHandler.py
|
||||||
|
@Function ：日志检查、生成
|
||||||
|
@Author ：SHJ
|
||||||
|
@Date ：2021/12/1
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import time
|
||||||
|
import datetime
|
||||||
|
|
||||||
|
|
||||||
|
class LogHandler:
|
||||||
|
"""
|
||||||
|
生成日志
|
||||||
|
"""
|
||||||
|
__logger = logging.getLogger("mylog")
|
||||||
|
__format_str = logging.Formatter("[%(asctime)s] [%(process)d] [%(levelname)s] - %(module)s.%(funcName)s "
|
||||||
|
"(%(filename)s:%(lineno)d) - %(message)s")
|
||||||
|
__log_path = None
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def init_log_handler(log_name):
|
||||||
|
"""
|
||||||
|
初始化日志
|
||||||
|
:param log_name: 日志保存的路径和名称
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
path = os.getcwd()
|
||||||
|
current_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime(time.time()))
|
||||||
|
LogHandler.__log_path = os.path.join(path, log_name + current_time + ".log")
|
||||||
|
para_dir = os.path.split(LogHandler.__log_path)
|
||||||
|
if not os.path.exists(para_dir[0]):
|
||||||
|
os.makedirs(para_dir[0])
|
||||||
|
# 删除七天以前的文件
|
||||||
|
LogHandler.delete_outdate_files(para_dir[0])
|
||||||
|
|
||||||
|
# 方法1：普通日志
|
||||||
|
log_format = "[%(asctime)s] [%(process)d] [%(levelname)s]- %(message)s ---from: %(module)s.%(funcName)s" \
|
||||||
|
" (%(filename)s:Line%(lineno)d) "
|
||||||
|
date_format = "%m/%d/%Y %H:%M:%S"
|
||||||
|
fp = logging.FileHandler(LogHandler.__log_path, encoding='utf-8')
|
||||||
|
fs = logging.StreamHandler()
|
||||||
|
logging.basicConfig(level=logging.INFO, format=log_format, datefmt=date_format, handlers=[fp, fs]) # 调用
|
||||||
|
|
||||||
|
# 方法2：回滚日志
|
||||||
|
# LogHandler.__logger.setLevel(logging.DEBUG)
|
||||||
|
# th = handlers.TimedRotatingFileHandler(filename=LogHandler.__log_path, when='S', interval=1,
|
||||||
|
# backupCount=2, encoding='utf-8')
|
||||||
|
# th.suffix = "%Y-%m-%d-%H-%M-%S.log"
|
||||||
|
# th.setFormatter(LogHandler.__format_str)
|
||||||
|
# th.setLevel(level=logging.DEBUG)
|
||||||
|
|
||||||
|
# console = logging.StreamHandler()
|
||||||
|
# console.setLevel(logging.INFO)
|
||||||
|
# LogHandler.__logger.addHandler(console)
|
||||||
|
# LogHandler.__logger.addHandler(th)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def delete_outdate_files(path, date_interval=7):
|
||||||
|
"""
|
||||||
|
删除目录下七天前创建的文件
|
||||||
|
"""
|
||||||
|
current_time = time.strftime("%Y-%m-%d", time.localtime(time.time()))
|
||||||
|
current_time_list = current_time.split("-")
|
||||||
|
current_time_day = datetime.datetime(int(current_time_list[0]), int(current_time_list[1]),
|
||||||
|
int(current_time_list[2]))
|
||||||
|
for root, dirs, files in os.walk(path):
|
||||||
|
for item in files:
|
||||||
|
item_format = item.split(".", 2)
|
||||||
|
if item_format[1] == "log":
|
||||||
|
file_path = os.path.join(root, item)
|
||||||
|
create_time = time.strftime("%Y-%m-%d", time.localtime((os.stat(file_path)).st_mtime))
|
||||||
|
create_time_list = create_time.split("-")
|
||||||
|
create_time_day = datetime.datetime(int(create_time_list[0]), int(create_time_list[1]),
|
||||||
|
int(create_time_list[2]))
|
||||||
|
time_difference = (current_time_day - create_time_day).days
|
||||||
|
if time_difference > date_interval:
|
||||||
|
os.remove(file_path)
|
||||||
|
|
||||||
|
#
|
||||||
|
# if __name__ == "__main__":
|
||||||
|
# # eg2:
|
||||||
|
# log_handler = LogHandler()
|
||||||
|
# log_handler.init_log_handler(r"run_log\myrun1")
|
||||||
|
# logging.warning("1")
|
||||||
|
# print("done")
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,90 @@
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Sun Mar 14 18:53:14 2021
|
||||||
|
|
||||||
|
@author: Dipankar
|
||||||
|
References
|
||||||
|
----------
|
||||||
|
Oh (2004): Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surface. IEEE TGRS 42(3). 596-601.
|
||||||
|
"""
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------------------
|
||||||
|
# Copyright (C) 2021 by Microwave Remote Sensing Lab, IITBombay http://www.mrslab.in
|
||||||
|
|
||||||
|
# This program is free software; you can redistribute it and/or modify it
|
||||||
|
# under the terms of the GNU General Public License as published by the Free
|
||||||
|
# Software Foundation; either version 3 of the License, or (at your option)
|
||||||
|
# any later version.
|
||||||
|
# This program is distributed in the hope that it will be useful, but WITHOUT
|
||||||
|
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
|
||||||
|
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
|
||||||
|
# more details.
|
||||||
|
|
||||||
|
# You should have received a copy of the GNU General Public License along
|
||||||
|
# with this program; if not, see http://www.gnu.org/licenses/
|
||||||
|
# ---------------------------------------------------------------------------------------
|
||||||
|
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
#import matplotlib.pyplot as plt
|
||||||
|
|
||||||
|
|
||||||
|
## Description: Given sigma_0_vv, sigma_0_hh, and sigma_0_hv, the inverse
|
||||||
|
## model computes s, and mv
|
||||||
|
|
||||||
|
sigma0vvdB = -14.1
|
||||||
|
sigma0hhdB = -16.0
|
||||||
|
sigma0hvdB = -26.5
|
||||||
|
theta = 35. ##Incidence angle
|
||||||
|
f = 5.0 ##GHz
|
||||||
|
|
||||||
|
k = 2*np.pi*f/0.3 #calculate the wave number
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
theta_rad = theta*np.pi/180 #represent angle in radians
|
||||||
|
|
||||||
|
sigma_0_vv = np.power(10,(sigma0vvdB/10)) #%represent data in linear scale
|
||||||
|
sigma_0_hh = np.power(10,(sigma0hhdB/10))
|
||||||
|
sigma_0_hv = np.power(10,(sigma0hvdB/10))
|
||||||
|
|
||||||
|
|
||||||
|
p = sigma_0_hh / sigma_0_vv #calculate the p-ratio
|
||||||
|
q = sigma_0_hv / sigma_0_vv #calculate the q-ratio
|
||||||
|
|
||||||
|
mv0 = np.arange(0.05,0.5,0.01) # set Gamma0 range of values (fine increments)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
## First estimates s1 and mv1
|
||||||
|
ks = ((-1)*3.125*np.log(1 - sigma_0_hv/(0.11 * mv0**0.7 * (np.cos(theta_rad))**2.2)))**0.556
|
||||||
|
err = (1 - (2.*theta_rad/np.pi)**(0.35*mv0**(-0.65)) * np.exp(-0.4 * ks**1.4))-p
|
||||||
|
abs_err = np.abs(err)
|
||||||
|
min_err = np.min(abs_err) #find the value of minimum error
|
||||||
|
mv1 = mv0[np.where(abs_err == min_err)]
|
||||||
|
ks1 = ((-1)*3.125*np.log(1 - sigma_0_hv/(0.11 * mv1**0.7 * (np.cos(theta_rad))**2.2)))**0.556
|
||||||
|
s1 = ks1/k
|
||||||
|
|
||||||
|
|
||||||
|
## Second estimate s2 and mv2
|
||||||
|
ks2 = (np.log(1-(q/(0.095 * (0.13 + np.sin(1.5*theta_rad))**1.4))) /(-1.3))**(10./9.)
|
||||||
|
s2 = ks2/k
|
||||||
|
|
||||||
|
xx = (1-p)/np.exp(-0.4 * ks2**1.4)
|
||||||
|
if xx<=0:
|
||||||
|
mv2 =0
|
||||||
|
else:
|
||||||
|
yy = np.log(xx)/(0.35*np.log(2*theta_rad/np.pi))
|
||||||
|
mv2 = yy**(-100/65)
|
||||||
|
print(mv2,yy,np.power(yy,-100/65))
|
||||||
|
## Third estimate mv3
|
||||||
|
mv3 = ((sigma_0_hv/(1 - np.exp(-0.32 * ks2**1.8)))/(0.11 * np.cos(theta_rad)**2.2))**(1/0.7)
|
||||||
|
|
||||||
|
## weighted average s and mv-------------------------------------
|
||||||
|
sf = (s1 + 0.25*s2)/(1+0.25)
|
||||||
|
mvf = (mv1+mv2+mv3)/3
|
||||||
|
print(mv1,mv2,mv3,s1,s2)
|
||||||
|
|
||||||
|
print('Estimated rms height s (cm): ', sf*100)
|
||||||
|
print('Estimated volumetric soil moisture: ', mvf)
|
||||||
|
|
@ -0,0 +1,128 @@
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Tue Jun 4 14:59:54 2013
|
||||||
|
|
||||||
|
@author: Sat Kumar Tomer
|
||||||
|
@email: satkumartomer@gmail.com
|
||||||
|
@website: www.ambhas.com
|
||||||
|
|
||||||
|
"""
|
||||||
|
cimport cython # 必须导入
|
||||||
|
import numpy as np##必须为c类型和python类型的数据都申明一个np
|
||||||
|
cimport numpy as np # 必须为c类型和python类型的数据都申明一个np
|
||||||
|
from libc.math cimport pi
|
||||||
|
from scipy.optimize import fmin
|
||||||
|
|
||||||
|
cpdef np.ndarray[double,ndim=1] inverse_oh2004(double sigma0vvdB,double sigma0hhdB,double sigma0hvdB,double theta,double f):
|
||||||
|
"""
|
||||||
|
sigma0vvdB = -14.1 dB
|
||||||
|
sigma0hhdB = -16.0
|
||||||
|
sigma0hvdB = -26.5
|
||||||
|
theta = 35. 角度值 ##Incidence angle
|
||||||
|
f = 5.0 ##GHz
|
||||||
|
"""
|
||||||
|
#print("--------------------------------------------------------\n")
|
||||||
|
cdef np.ndarray[double,ndim=1] result=np.ones((2))
|
||||||
|
result[0]=np.nan
|
||||||
|
result[1]=np.nan
|
||||||
|
#print("*************设置为nan****************")
|
||||||
|
#print(sigma0vvdB,sigma0hhdB,sigma0hvdB,theta,f)
|
||||||
|
cdef double k = 2*3.1415926*f/0.299792458; #calculate the wave number
|
||||||
|
cdef double theta_rad = theta*3.1415926/180; #represent angle in radians
|
||||||
|
|
||||||
|
cdef double sigma_0_vv = np.power(10.,(sigma0vvdB/10.)) #%represent data in linear scale
|
||||||
|
cdef double sigma_0_hh = np.power(10.,(sigma0hhdB/10.))
|
||||||
|
cdef double sigma_0_hv = np.power(10.,(sigma0hvdB/10.))
|
||||||
|
|
||||||
|
if sigma_0_vv==0:
|
||||||
|
#print("***********sigma_0_vv==0*************")
|
||||||
|
return result
|
||||||
|
cdef double p = sigma_0_hh / sigma_0_vv; #calculate the p-ratio
|
||||||
|
cdef double q = sigma_0_hv / sigma_0_vv; #calculate the q-ratio
|
||||||
|
|
||||||
|
|
||||||
|
cdef np.ndarray[double,ndim=1] mv0 = np.arange(0.05,0.9,0.01) # set Gamma0 range of values (fine increments)
|
||||||
|
|
||||||
|
## First estimates s1 and mv1
|
||||||
|
cdef np.ndarray[double,ndim=1] ks = ((-1.)*3.125*np.log(1 - sigma_0_hv/(0.11 * mv0**0.7 * (np.cos(theta_rad))**2.2)))**0.556
|
||||||
|
cdef np.ndarray[double,ndim=1] err = (1. - (2.*theta_rad/np.pi)**(0.35*mv0**(-0.65)) * np.exp(-0.4 * ks**1.4))-p
|
||||||
|
cdef np.ndarray[double,ndim=1] abs_err = np.abs(err);
|
||||||
|
cdef double min_err = np.nanmin(abs_err); #find the value of minimum error
|
||||||
|
#print(np.where(abs_err == min_err)[0].shape)
|
||||||
|
if min_err==np.nan or np.max(np.where(abs_err == min_err)[0].shape)==0 :
|
||||||
|
#print("***************min_err==np.nan or np.max(np.where(abs_err == min_err)[0].shape)==0")
|
||||||
|
return result
|
||||||
|
cdef double mv1 = mv0[np.where(abs_err == min_err)[0][0]]
|
||||||
|
cdef double temp_ks1=1. - sigma_0_hv/(0.11 * mv1**0.7 * (np.cos(theta_rad))**2.2)
|
||||||
|
if temp_ks1<0:
|
||||||
|
#print("*********************temp_ks1<0")
|
||||||
|
return result
|
||||||
|
cdef double ks1 = ((-1)*3.125*np.log(temp_ks1))**0.556
|
||||||
|
cdef double s1 = ks1/k
|
||||||
|
|
||||||
|
## Second estimate s2 and mv2
|
||||||
|
cdef double ks2 = (np.log(1-(q/(0.095 * (0.13 + np.sin(1.5*theta_rad))**1.4))) /(-1.3))**(10./9.)
|
||||||
|
cdef double s2 = ks2/k
|
||||||
|
cdef double mv2 =0.
|
||||||
|
cdef double yy =0.
|
||||||
|
cdef double xx = (1-p)/np.exp(-0.4 * ks2**1.4)
|
||||||
|
if xx<=0:
|
||||||
|
mv2 =0.
|
||||||
|
else:
|
||||||
|
yy = np.log(xx)/(0.35*np.log(2*theta_rad/np.pi))
|
||||||
|
mv2=np.power(yy,-100.0/65)
|
||||||
|
|
||||||
|
## Third estimate mv3
|
||||||
|
cdef double mv3 = ((sigma_0_hv/(1 - np.exp(-0.32 * ks2**1.8)))/(0.11 * np.cos(theta_rad)**2.2))**(1/0.7)
|
||||||
|
## weighted average s and mv-------------------------------------
|
||||||
|
#print("q:\t",q)
|
||||||
|
#print("k:\t",k)
|
||||||
|
#print("ks1:\t",ks1)
|
||||||
|
#print("ks2:\t",ks2)
|
||||||
|
#print("theta_rad:\t",theta_rad)
|
||||||
|
|
||||||
|
cdef double sf = (s1 + 0.25*s2)/(1+0.25)
|
||||||
|
cdef double mvf = (mv1+mv2+mv3)/3
|
||||||
|
|
||||||
|
result[0]=mvf*1.0
|
||||||
|
result[1]=sf*1.0
|
||||||
|
#print("mv1:\t",mv1)
|
||||||
|
#print("mv2:\t",mv2)
|
||||||
|
#print("mv3:\t",mv3)
|
||||||
|
#print("s1:\t",s1)
|
||||||
|
#print("s2:\t",s2)
|
||||||
|
#print("Estimated volumetric soil moisture: ",result[0])
|
||||||
|
#print("Estimated rms height s (m): ",result[1])
|
||||||
|
#print("\nend\n")
|
||||||
|
return result
|
||||||
|
|
||||||
|
cpdef double lamda2freq(double lamda):
|
||||||
|
return 299792458.0/lamda
|
||||||
|
|
||||||
|
cpdef double freq2lamda(double freq):
|
||||||
|
return 299792458.0/freq
|
||||||
|
|
||||||
|
# double sigma0vvdB,double sigma0hhdB,double sigma0hvdB,double theta,double f
|
||||||
|
cpdef int retrieve_oh2004_main(int n,np.ndarray[double,ndim=1] mv,np.ndarray[double,ndim=1] h,np.ndarray[int,ndim=1] mask,np.ndarray[double,ndim=1] sigma0vvdB,np.ndarray[double,ndim=1] sigma0hhdB,np.ndarray[double,ndim=1] sigma0hvdB, np.ndarray[double,ndim=1] vh, np.ndarray[double,ndim=1] theta,double f):
|
||||||
|
cdef int i=0;
|
||||||
|
cdef np.ndarray[double,ndim=1] result;
|
||||||
|
while i<n:
|
||||||
|
if mask[i]<0.5:
|
||||||
|
mv[i]=np.nan
|
||||||
|
h[i] =np.nan
|
||||||
|
else:
|
||||||
|
#print(i)
|
||||||
|
##print(sigma0vvdB[i], sigma0hhdB[i],sigma0hvdB[i], theta[i], f)
|
||||||
|
result= inverse_oh2004(sigma0vvdB[i], sigma0hhdB[i],sigma0hvdB[i], theta[i], f)
|
||||||
|
##print(result)
|
||||||
|
mv[i]=result[0]
|
||||||
|
h[i] =result[1]
|
||||||
|
##print(mv[i],h[i])
|
||||||
|
##print(result[0],result[1])
|
||||||
|
i=i+1
|
||||||
|
return 1
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,45 @@
|
||||||
|
from setuptools import setup
|
||||||
|
from setuptools.extension import Extension
|
||||||
|
from Cython.Distutils import build_ext
|
||||||
|
from Cython.Build import cythonize
|
||||||
|
import numpy
|
||||||
|
from pathlib import Path
|
||||||
|
import shutil
|
||||||
|
|
||||||
|
|
||||||
|
class MyBuildExt(build_ext):
|
||||||
|
def run(self):
|
||||||
|
build_ext.run(self)
|
||||||
|
|
||||||
|
build_dir = Path(self.build_lib)
|
||||||
|
root_dir = Path(__file__).parent
|
||||||
|
target_dir = build_dir if not self.inplace else root_dir
|
||||||
|
|
||||||
|
self.copy_file(Path('./oh2004') / '__init__.py', root_dir, target_dir)
|
||||||
|
#self.copy_file(Path('./pkg2') / '__init__.py', root_dir, target_dir)
|
||||||
|
self.copy_file(Path('.') / '__init__.py', root_dir, target_dir)
|
||||||
|
def copy_file(self, path, source_dir, destination_dir):
|
||||||
|
if not (source_dir / path).exists():
|
||||||
|
return
|
||||||
|
shutil.copyfile(str(source_dir / path), str(destination_dir / path))
|
||||||
|
|
||||||
|
setup(
|
||||||
|
name="MyModule",
|
||||||
|
ext_modules=cythonize(
|
||||||
|
[
|
||||||
|
#Extension("pkg1.*", ["root/pkg1/*.py"]),
|
||||||
|
Extension("pkg2.*", ["./oh2004/oh2004.pyx"]),
|
||||||
|
#Extension("1.*", ["root/*.py"])
|
||||||
|
],
|
||||||
|
build_dir="build",
|
||||||
|
compiler_directives=dict(
|
||||||
|
always_allow_keywords=True
|
||||||
|
)),
|
||||||
|
cmdclass=dict(
|
||||||
|
build_ext=MyBuildExt
|
||||||
|
),
|
||||||
|
packages=[],
|
||||||
|
include_dirs=[numpy.get_include()],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 指令： python setup.py build_ext --inplace
|
||||||
|
|
@ -0,0 +1,26 @@
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import oh2004
|
||||||
|
|
||||||
|
sigma0vvdB = -14.1
|
||||||
|
sigma0hhdB = -16.0
|
||||||
|
sigma0hvdB = -26.5
|
||||||
|
theta = 35. ##Incidence angle
|
||||||
|
f = 5.0 ##GHz
|
||||||
|
#print(sigma0vvdB,sigma0hhdB,sigma0hvdB,theta,f)
|
||||||
|
#print(oh2004.inverse_oh2004(sigma0vvdB,sigma0hhdB,sigma0hvdB,theta,f))
|
||||||
|
|
||||||
|
n=3
|
||||||
|
mask=np.ones((3))
|
||||||
|
mask[1]=0
|
||||||
|
mask=mask.astype(np.int32)
|
||||||
|
sigma0hhdB=np.ones((3))*sigma0hhdB
|
||||||
|
sigma0vvdB=np.ones((3))*sigma0vvdB
|
||||||
|
sigma0hvdB=np.ones((3))*sigma0hvdB
|
||||||
|
theta=np.ones((3))*theta
|
||||||
|
mv=np.zeros(3)*1.0
|
||||||
|
h=np.zeros(3)*1.0
|
||||||
|
oh2004.retrieve_oh2004_main(n,mv,h, mask,sigma0vvdB,sigma0hhdB,sigma0hvdB,sigma0hvdB, theta,f)
|
||||||
|
print(mask)
|
||||||
|
print(mv)
|
||||||
|
print(h)
|
||||||
|
|
@ -0,0 +1,92 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:sieve_filter.py
|
||||||
|
@Function:gdal斑点滤波功能
|
||||||
|
@Contact: 'https://www.osgeo.cn/gdal/api/gdal_alg.html?highlight=gdalsievefilter#'
|
||||||
|
'_CPPv415GDALSieveFilter15GDALRasterBandH15GDALRasterBandH15GDALRasterBandHiiPPc16GDALProgressFuncPv'
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/8/30 8:42
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from osgeo import gdal
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
def gdal_sieve_filter_test(dst_filename, src_filename, threshold=100, connectedness=8):
|
||||||
|
"""
|
||||||
|
基于python GDAL栅格滤波
|
||||||
|
:param dst_filename: 输出滤波后的影像
|
||||||
|
:param src_filename: 输入需要处理的文件
|
||||||
|
:param threshold: 滤波的值大小
|
||||||
|
:param connectedness: 连通域, 范围：4或者8
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
# 4表示对角像素不被视为直接相邻用于多边形成员资格，8表示对角像素不相邻
|
||||||
|
# connectedness = 4
|
||||||
|
gdal.AllRegister()
|
||||||
|
# print('需要处理滤波的栅格文件:{},阈值(分辨率):{}'.format(src_filename, threshold))
|
||||||
|
dataset = gdal.Open(src_filename, gdal.GA_Update)
|
||||||
|
if dataset is None:
|
||||||
|
logger.error('{}open tif fail!'.format(src_filename))
|
||||||
|
return False
|
||||||
|
# 获取需要处理的源栅格波段
|
||||||
|
src_band = dataset.GetRasterBand(1)
|
||||||
|
mask_band = src_band.GetMaskBand()
|
||||||
|
dst_band = src_band
|
||||||
|
prog_func = gdal.TermProgress_nocb
|
||||||
|
# 调用gdal滤波函数
|
||||||
|
result = gdal.SieveFilter(src_band, mask_band, dst_band, threshold, connectedness, callback=prog_func)
|
||||||
|
if result != 0:
|
||||||
|
return False
|
||||||
|
proj = dataset.GetProjection()
|
||||||
|
geotransform = dataset.GetGeoTransform()
|
||||||
|
dst_array = dst_band.ReadAsArray(0, 0, dst_band.XSize, dst_band.YSize)
|
||||||
|
ImageHandler.write_img(dst_filename, proj, geotransform, dst_array)
|
||||||
|
del dataset
|
||||||
|
return True
|
||||||
|
|
||||||
|
def gdal_sieve_filter(dst_filename, src_filename, threshold=2, connectedness=4):
|
||||||
|
"""
|
||||||
|
基于python GDAL栅格滤波
|
||||||
|
:param dst_filename: 输出滤波后的影像
|
||||||
|
:param src_filename: 输入需要处理的文件
|
||||||
|
:param threshold: 滤波的值大小
|
||||||
|
:param connectedness: 连通域, 范围：4或者8
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
# 4表示对角像素不被视为直接相邻用于多边形成员资格，8表示对角像素不相邻
|
||||||
|
# connectedness = 4
|
||||||
|
gdal.AllRegister()
|
||||||
|
# print('需要处理滤波的栅格文件:{},阈值(分辨率):{}'.format(src_filename, threshold))
|
||||||
|
dataset = gdal.Open(src_filename, gdal.GA_Update)
|
||||||
|
if dataset is None:
|
||||||
|
logger.error('{}open tif fail!'.format(src_filename))
|
||||||
|
return False
|
||||||
|
# 获取需要处理的源栅格波段
|
||||||
|
src_band = dataset.GetRasterBand(1)
|
||||||
|
#只能处理整数
|
||||||
|
src_array = src_band.ReadAsArray(0, 0, src_band.XSize, src_band.YSize)
|
||||||
|
src_array = src_array * 1000
|
||||||
|
src_band.WriteArray(src_array)
|
||||||
|
mask_band = None
|
||||||
|
dst_band = src_band
|
||||||
|
prog_func = gdal.TermProgress_nocb
|
||||||
|
# 调用gdal滤波函数
|
||||||
|
result = gdal.SieveFilter(src_band, mask_band, dst_band, threshold, connectedness, callback=prog_func)
|
||||||
|
if result != 0:
|
||||||
|
return False
|
||||||
|
proj = dataset.GetProjection()
|
||||||
|
geotransform = dataset.GetGeoTransform()
|
||||||
|
dst_array = dst_band.ReadAsArray(0, 0, dst_band.XSize, dst_band.YSize)
|
||||||
|
dst_array = dst_array / 1000
|
||||||
|
ImageHandler.write_img(dst_filename, proj, geotransform, dst_array)
|
||||||
|
del dataset
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
inputfile = r'D:\DATA\testdata\srcimg\GLCFCS30_E110N25.tif'
|
||||||
|
outputfile = r'D:\DATA\testdata\srcimg\GLCFCS30_E110N25_sieve_filter.tif'
|
||||||
|
flag = gdal_sieve_filter(outputfile, inputfile, threshold=100, connectedness=4)
|
||||||
|
|
@ -0,0 +1,449 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project : microproduct
|
||||||
|
@File : blockprocess.py
|
||||||
|
@Function : tif、tiff图像分块处理拼接功能
|
||||||
|
@Contact : https://blog.csdn.net/qq_38308388/article/details/102978755
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/9/6
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
from osgeo import osr, gdal
|
||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
from PIL import Image
|
||||||
|
# import time
|
||||||
|
# from skimage import io
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
|
||||||
|
class BlockProcess:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_block_size(rows, cols):
|
||||||
|
block_size = 512
|
||||||
|
if rows > 2048 and cols > 2048:
|
||||||
|
block_size = 1024
|
||||||
|
return block_size
|
||||||
|
|
||||||
|
# def get_block_size(rows, cols, block_size_config):
|
||||||
|
# block_size = 512 if block_size_config < 512 else block_size_config
|
||||||
|
# if rows > 2048 and cols > 2048:
|
||||||
|
# block_size = block_size_config
|
||||||
|
# return block_size
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_suffix(path_name):
|
||||||
|
name = path_name
|
||||||
|
suffix = '_' + name.split('_')[-4] + '_' + name.split('_')[-3] + '_' + name.split('_')[-2] + '_' + \
|
||||||
|
name.split('_')[-1]
|
||||||
|
return suffix
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_file_names(data_dir, file_type=['tif', 'tiff']):
|
||||||
|
"""
|
||||||
|
获取data_dir文件夹下file_type类型的文件路径
|
||||||
|
"""
|
||||||
|
result_dir = []
|
||||||
|
result_name = []
|
||||||
|
for maindir, subdir, file_name_list in os.walk(data_dir):
|
||||||
|
for filename in file_name_list:
|
||||||
|
apath = os.path.join(maindir, filename)
|
||||||
|
ext = apath.split('.')[-1]
|
||||||
|
if ext in file_type:
|
||||||
|
result_dir.append(apath)
|
||||||
|
result_name.append(filename)
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
return result_dir, result_name
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_same_img(img_dir, img_name):
|
||||||
|
"""
|
||||||
|
在img_dir路径下，用img_name的子图像路径集合，将集合以字典输出
|
||||||
|
"""
|
||||||
|
result = {}
|
||||||
|
for idx, name in enumerate(img_name):
|
||||||
|
temp_name = ''
|
||||||
|
for idx2, item in enumerate(name.split('_')[:-4]):
|
||||||
|
if idx2 == 0:
|
||||||
|
temp_name = temp_name + item
|
||||||
|
else:
|
||||||
|
temp_name = temp_name + '_' + item
|
||||||
|
|
||||||
|
if temp_name in result:
|
||||||
|
result[temp_name].append(img_dir[idx])
|
||||||
|
else:
|
||||||
|
result[temp_name] = []
|
||||||
|
result[temp_name].append(img_dir[idx])
|
||||||
|
return result
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def assign_spatial_reference_byfile(src_path, dst_path):
|
||||||
|
"""
|
||||||
|
将src_path的地理信息，输入到dst_path图像中
|
||||||
|
"""
|
||||||
|
src_ds = gdal.Open(src_path, gdal.GA_ReadOnly)
|
||||||
|
if src_ds is None:
|
||||||
|
return False
|
||||||
|
sr = osr.SpatialReference()
|
||||||
|
sr.ImportFromWkt(src_ds.GetProjectionRef())
|
||||||
|
geo_transform = src_ds.GetGeoTransform()
|
||||||
|
|
||||||
|
dst_ds = gdal.Open(dst_path, gdal.GA_Update)
|
||||||
|
if dst_ds is None:
|
||||||
|
return False
|
||||||
|
dst_ds.SetProjection(sr.ExportToWkt())
|
||||||
|
dst_ds.SetGeoTransform(geo_transform)
|
||||||
|
del dst_ds
|
||||||
|
del src_ds
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def assign_spatial_reference_bypoint(row_begin, col_begin, src_proj, src_geo, img_path):
|
||||||
|
"""
|
||||||
|
将src_path的地理信息，输入到dst_path图像中
|
||||||
|
"""
|
||||||
|
|
||||||
|
sr = osr.SpatialReference()
|
||||||
|
sr.ImportFromWkt(src_proj)
|
||||||
|
geo_transform = src_geo
|
||||||
|
geo_transform[0] = src_geo[0] + col_begin * src_geo[1] + row_begin * src_geo[2]
|
||||||
|
geo_transform[3] = src_geo[3] + col_begin * src_geo[4] + row_begin * src_geo[5]
|
||||||
|
dst_ds = gdal.Open(img_path, gdal.GA_Update)
|
||||||
|
if dst_ds is None:
|
||||||
|
return False
|
||||||
|
dst_ds.SetProjection(sr.ExportToWkt())
|
||||||
|
dst_ds.SetGeoTransform(geo_transform)
|
||||||
|
del dst_ds
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __get_band_array(filename, num):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:param num: 波段序号
|
||||||
|
:return: 对应波段的矩阵数据
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
bands = dataset.GetRasterBand(num)
|
||||||
|
array = bands.ReadAsArray(0, 0, bands.XSize, bands.YSize)
|
||||||
|
del dataset
|
||||||
|
return array
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_data(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 获取所有波段的数据
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
im_width = dataset.RasterXSize
|
||||||
|
im_height = dataset.RasterYSize
|
||||||
|
im_data = dataset.ReadAsArray(0, 0, im_width, im_height)
|
||||||
|
del dataset
|
||||||
|
return im_data
|
||||||
|
|
||||||
|
def get_tif_dtype(self, filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: tif数据类型
|
||||||
|
"""
|
||||||
|
image = self.__get_band_array(filename, 1)
|
||||||
|
return image.dtype.name
|
||||||
|
|
||||||
|
def cut(self, in_dir, out_dir, file_type=['tif', 'tiff'], out_type='tif', out_size=2048):
|
||||||
|
"""
|
||||||
|
:param in_dir:存放待裁剪的影像文件夹，不用指定到tif文件
|
||||||
|
:param out_dir:存放裁剪结果的影像文件夹
|
||||||
|
:param file_type:待裁剪的影像文件类型tif、tiff、bmp、jpg、png等等
|
||||||
|
:param out_type:裁剪结果影像文件类型
|
||||||
|
:param out_size:裁剪尺寸，裁剪为n*n的方形
|
||||||
|
:return: True or Flase
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
data_dir_list, _ = self.get_file_names(in_dir, file_type)
|
||||||
|
count = 0
|
||||||
|
|
||||||
|
for each_dir in data_dir_list:
|
||||||
|
|
||||||
|
name_suffix = os.path.basename(each_dir)
|
||||||
|
img_name = os.path.splitext(name_suffix)[0]
|
||||||
|
|
||||||
|
# gdal读取方法
|
||||||
|
image = self.__get_band_array(each_dir, 1)
|
||||||
|
|
||||||
|
cut_factor_row = int(np.ceil(image.shape[0] / out_size))
|
||||||
|
cut_factor_clo = int(np.ceil(image.shape[1] / out_size))
|
||||||
|
for i in range(cut_factor_row):
|
||||||
|
for j in range(cut_factor_clo):
|
||||||
|
|
||||||
|
if i == cut_factor_row - 1:
|
||||||
|
i = image.shape[0] / out_size - 1
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
if j == cut_factor_clo - 1:
|
||||||
|
j = image.shape[1] / out_size - 1
|
||||||
|
else:
|
||||||
|
pass
|
||||||
|
|
||||||
|
start_x = int(np.rint(i * out_size))
|
||||||
|
start_y = int(np.rint(j * out_size))
|
||||||
|
end_x = int(np.rint((i + 1) * out_size))
|
||||||
|
end_y = int(np.rint((j + 1) * out_size))
|
||||||
|
out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
|
||||||
|
end_y) + '.' + out_type)
|
||||||
|
# + '/' + img_name \
|
||||||
|
# + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
|
||||||
|
# end_y) + '.' + out_type
|
||||||
|
|
||||||
|
# temp_image = image[start_x:end_x, start_y:end_y]
|
||||||
|
# out_image = Image.fromarray(temp_data)
|
||||||
|
# out_image = Image.fromarray(temp_image)
|
||||||
|
# out_image.save(out_dir_images)
|
||||||
|
|
||||||
|
data = ImageHandler.get_data(each_dir)
|
||||||
|
if ImageHandler.get_bands(each_dir) > 1:
|
||||||
|
temp_data = data[:,start_x:end_x, start_y:end_y]
|
||||||
|
else:
|
||||||
|
temp_data = data[start_x:end_x, start_y:end_y]
|
||||||
|
ImageHandler.write_img(out_dir_images, '', [0, 0, 0, 0, 0, 0], temp_data)
|
||||||
|
count += 1
|
||||||
|
return True
|
||||||
|
|
||||||
|
def cut_new(self, in_dir, out_dir, file_type=['tif', 'tiff'], out_type='tif', out_size=2048):
|
||||||
|
"""
|
||||||
|
:param in_dir:存放待裁剪的影像文件夹，不用指定到tif文件
|
||||||
|
:param out_dir:存放裁剪结果的影像文件夹
|
||||||
|
:param file_type:待裁剪的影像文件类型tif、tiff、bmp、jpg、png等等
|
||||||
|
:param out_type:裁剪结果影像文件类型
|
||||||
|
:param out_size:裁剪尺寸，裁剪为n*n的方形
|
||||||
|
:return: True or Flase
|
||||||
|
20230831修改 ----tjx
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
data_dir_list, _ = self.get_file_names(in_dir, file_type)
|
||||||
|
count = 0
|
||||||
|
|
||||||
|
for each_dir in data_dir_list:
|
||||||
|
|
||||||
|
name_suffix = os.path.basename(each_dir)
|
||||||
|
img_name = os.path.splitext(name_suffix)[0]
|
||||||
|
|
||||||
|
# gdal读取方法
|
||||||
|
image = self.__get_band_array(each_dir, 1)
|
||||||
|
|
||||||
|
block_x = int(np.ceil(image.shape[1] / out_size))
|
||||||
|
block_y = int(np.ceil(image.shape[0] / out_size)) # todo 修改分块
|
||||||
|
for i in range(block_y):
|
||||||
|
for j in range(block_x):
|
||||||
|
start_x = j * out_size
|
||||||
|
start_y = i * out_size
|
||||||
|
end_x = image.shape[1] if (j + 1) * out_size > image.shape[1] else (j + 1) * out_size
|
||||||
|
end_y = image.shape[0] if (i + 1) * out_size > image.shape[0] else (i + 1) * out_size
|
||||||
|
|
||||||
|
out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
|
||||||
|
end_y) + '.' + out_type)
|
||||||
|
# print(out_dir_images)
|
||||||
|
|
||||||
|
data = ImageHandler.get_data(each_dir)
|
||||||
|
if ImageHandler.get_bands(each_dir) > 1:
|
||||||
|
# temp_data = data[:,start_x:end_x, start_y:end_y]
|
||||||
|
temp_data = data[:,start_y:end_y, start_x:end_x]
|
||||||
|
else:
|
||||||
|
# temp_data = data[start_x:end_x, start_y:end_y]
|
||||||
|
temp_data = data[start_y:end_y, start_x:end_x]
|
||||||
|
ImageHandler.write_img(out_dir_images, '', [0, 0, 0, 0, 0, 0], temp_data)
|
||||||
|
count += 1
|
||||||
|
return True
|
||||||
|
|
||||||
|
def combine(self, data_dir, w, h, out_dir, out_type='tif', file_type=['tif', 'tiff'], datetype='float16'):
|
||||||
|
"""
|
||||||
|
:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
|
||||||
|
:param w 拼接影像的宽度，
|
||||||
|
:param h 拼接影像的高度
|
||||||
|
:param out_dir: 存放裁剪结果的影像文件夹
|
||||||
|
:param out_type: 裁剪结果影像文件类型
|
||||||
|
:param file_type: 待裁剪的影像文件类型
|
||||||
|
:param datetype:数据类型 int8，int16，float16，float32 等
|
||||||
|
:return: True or Flase
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
img_dir, img_name = self.get_file_names(data_dir, file_type)
|
||||||
|
|
||||||
|
dir_dict = self.get_same_img(img_dir, img_name)
|
||||||
|
count = 0
|
||||||
|
for key in dir_dict.keys():
|
||||||
|
temp_label = np.zeros(shape=(h, w), dtype=datetype)
|
||||||
|
dir_list = dir_dict[key]
|
||||||
|
for item in dir_list:
|
||||||
|
name_split = item.split('_')
|
||||||
|
x_start = int(name_split[-4])
|
||||||
|
x_end = int(name_split[-3])
|
||||||
|
y_start = int(name_split[-2])
|
||||||
|
y_end = int(name_split[-1].split('.')[0])
|
||||||
|
# img = Image.open(item)
|
||||||
|
img = ImageHandler.get_band_array(item, 1)
|
||||||
|
img = np.array(img)
|
||||||
|
|
||||||
|
temp_label[x_start:x_end, y_start:y_end] = img
|
||||||
|
|
||||||
|
img_name = key + '.' + out_type
|
||||||
|
new_out_dir = os.path.join(out_dir, img_name)
|
||||||
|
ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
|
||||||
|
# label = Image.fromarray(temp_label)
|
||||||
|
# label.save(new_out_dir)
|
||||||
|
|
||||||
|
count += 1
|
||||||
|
return True
|
||||||
|
|
||||||
|
# todo 20230901 修改分块同步修改合并代码
|
||||||
|
def combine_new(self, data_dir, w, h, out_dir, out_type='tif', file_type=['tif', 'tiff'], datetype='float16'):
|
||||||
|
"""
|
||||||
|
:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
|
||||||
|
:param w 拼接影像的宽度，
|
||||||
|
:param h 拼接影像的高度
|
||||||
|
:param out_dir: 存放裁剪结果的影像文件夹
|
||||||
|
:param out_type: 裁剪结果影像文件类型
|
||||||
|
:param file_type: 待裁剪的影像文件类型
|
||||||
|
:param datetype:数据类型 int8，int16，float16，float32 等
|
||||||
|
:return: True or Flase
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
img_dir, img_name = self.get_file_names(data_dir, file_type)
|
||||||
|
|
||||||
|
dir_dict = self.get_same_img(img_dir, img_name)
|
||||||
|
count = 0
|
||||||
|
for key in dir_dict.keys():
|
||||||
|
dir_list = dir_dict[key]
|
||||||
|
bands = ImageHandler.get_bands(dir_list[0])
|
||||||
|
if bands > 1:
|
||||||
|
temp_label = np.zeros(shape=(bands, h, w), dtype=datetype)
|
||||||
|
for item in dir_list:
|
||||||
|
name_split = item.split('_')
|
||||||
|
x_start = int(name_split[-4])
|
||||||
|
x_end = int(name_split[-3])
|
||||||
|
y_start = int(name_split[-2])
|
||||||
|
y_end = int(name_split[-1].split('.')[0])
|
||||||
|
# img = Image.open(item)
|
||||||
|
img = ImageHandler.get_band_array(item, 1)
|
||||||
|
img = np.array(img)
|
||||||
|
|
||||||
|
temp_label[:, y_start:y_end, x_start:x_end] = img
|
||||||
|
|
||||||
|
img_name = key + '.' + out_type
|
||||||
|
new_out_dir = os.path.join(out_dir, img_name)
|
||||||
|
ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
|
||||||
|
# label = Image.fromarray(temp_label)
|
||||||
|
# label.save(new_out_dir)
|
||||||
|
count += 1
|
||||||
|
else:
|
||||||
|
temp_label = np.zeros(shape=(h, w), dtype=datetype)
|
||||||
|
for item in dir_list:
|
||||||
|
name_split = item.split('_')
|
||||||
|
x_start = int(name_split[-4])
|
||||||
|
x_end = int(name_split[-3])
|
||||||
|
y_start = int(name_split[-2])
|
||||||
|
y_end = int(name_split[-1].split('.')[0])
|
||||||
|
# img = Image.open(item)
|
||||||
|
img = ImageHandler.get_band_array(item, 1)
|
||||||
|
img = np.array(img)
|
||||||
|
|
||||||
|
temp_label[y_start:y_end, x_start:x_end] = img
|
||||||
|
|
||||||
|
img_name = key + '.' + out_type
|
||||||
|
new_out_dir = os.path.join(out_dir, img_name)
|
||||||
|
ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
|
||||||
|
# label = Image.fromarray(temp_label)
|
||||||
|
# label.save(new_out_dir)
|
||||||
|
|
||||||
|
count += 1
|
||||||
|
return True
|
||||||
|
|
||||||
|
def combine_Tif(self, data_dir, w, h, out_dir, proj, geo, out_type='tif', file_type=['tif', 'tiff'],
|
||||||
|
datetype='float16'):
|
||||||
|
"""
|
||||||
|
将文件夹下的tif拼接成一个大的tif
|
||||||
|
:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
|
||||||
|
:param w 拼接影像的宽度，
|
||||||
|
:param h 拼接影像的高度
|
||||||
|
:param out_dir: 存放裁剪结果的影像文件夹
|
||||||
|
:param proj: 指定投影系
|
||||||
|
:param geo: 指定变换参数
|
||||||
|
:param out_type: 裁剪结果影像文件类型
|
||||||
|
:param file_type: 待裁剪的影像文件类型
|
||||||
|
:param datetype:数据类型 int8，int16，float16，float32 等
|
||||||
|
:return: True or Flase
|
||||||
|
"""
|
||||||
|
image_handler = ImageHandler()
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
img_dir, img_name = self.get_file_names(data_dir, file_type)
|
||||||
|
|
||||||
|
dir_dict = self.get_same_img(img_dir, img_name)
|
||||||
|
count = 0
|
||||||
|
for key in dir_dict.keys():
|
||||||
|
temp_label = np.zeros(shape=(h, w), dtype=datetype)
|
||||||
|
dir_list = dir_dict[key]
|
||||||
|
for item in dir_list:
|
||||||
|
name_split = item.split('_')
|
||||||
|
x_start = int(name_split[-4])
|
||||||
|
x_end = int(name_split[-3])
|
||||||
|
y_start = int(name_split[-2])
|
||||||
|
y_end = int(name_split[-1].split('.')[0])
|
||||||
|
img = image_handler.get_data(item)
|
||||||
|
temp_label[x_start:x_end, y_start:y_end] = img
|
||||||
|
|
||||||
|
img_name = key + '.' + out_type
|
||||||
|
new_out_dir = os.path.join(out_dir,img_name)
|
||||||
|
image_handler.write_img(new_out_dir, proj, geo, temp_label)
|
||||||
|
count += 1
|
||||||
|
return True
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# bp = BlockProcess()
|
||||||
|
# # # cut
|
||||||
|
# data_dir = r"D:\micro\WorkSpace\LandCover\Temporary\processing\feature_tif\cut"
|
||||||
|
# out_dir = r"D:\micro\WorkSpace\LandCover\Temporary\processing\feature_tif\combine"
|
||||||
|
# file_type = ['tif']
|
||||||
|
# out_type = 'tif'
|
||||||
|
# cut_size = 1024
|
||||||
|
# #
|
||||||
|
# bp.cut_new(data_dir, out_dir, file_type, out_type, cut_size)
|
||||||
|
# # # combine
|
||||||
|
# # data_dir=r"D:\Workspace\SoilMoisture\Temporary\test"
|
||||||
|
# w= 5043
|
||||||
|
# h= 1239
|
||||||
|
# out_dirs=r"D:\BaiduNetdiskDownload\HF\cut_outs"
|
||||||
|
# # out_type='tif'
|
||||||
|
# # file_type=['tif']
|
||||||
|
# datetype = 'float'
|
||||||
|
# # src_path = r"D:\Workspace\SoilMoisture\Temporary\preprocessed\HH_preprocessed.tif"
|
||||||
|
# # datetype = bp.get_tif_dtype(src_path)
|
||||||
|
# bp.combine_new(out_dir, w, h, out_dirs, out_type, file_type, datetype)
|
||||||
|
|
||||||
|
#
|
||||||
|
# # 添加地理信息
|
||||||
|
# new_out_dir =r"D:\DATA\testdata1\combine\TEST_20200429_NDVI.tif"
|
||||||
|
# bp.assign_spatial_reference_byfile(src_path, new_out_dir)
|
||||||
|
|
||||||
|
# fn = r'D:\Workspace\SoilMoisture\Temporary\combine\soil_moisture.tif'
|
||||||
|
# product_path = r'D:\Workspace\SoilMoisture\Temporary\combine\soil_moisture_1.tif'
|
||||||
|
#
|
||||||
|
# proj, geos, img = ImageHandler.read_img(fn)
|
||||||
|
# img[img>1] = 1
|
||||||
|
# img[img<0] = 0
|
||||||
|
# ImageHandler.write_img(product_path, proj, geos, img)
|
||||||
|
|
||||||
|
|
@ -0,0 +1,660 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：ImageHandle.py
|
||||||
|
@Function ：实现对待处理SAR数据的读取、格式标准化和处理完后保存文件功能
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
from PIL import Image
|
||||||
|
from osgeo import gdal
|
||||||
|
from osgeo import osr
|
||||||
|
import numpy as np
|
||||||
|
from PIL import Image
|
||||||
|
import cv2
|
||||||
|
import logging
|
||||||
|
|
||||||
|
import math
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class ImageHandler:
|
||||||
|
"""
|
||||||
|
影像读取、编辑、保存
|
||||||
|
"""
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
@staticmethod
|
||||||
|
def get_dataset(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 图像句柄
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
return dataset
|
||||||
|
|
||||||
|
def get_scope(self, filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 图像范围
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
im_scope = self.cal_img_scope(dataset)
|
||||||
|
del dataset
|
||||||
|
return im_scope
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_projection(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 地图投影信息
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
im_proj = dataset.GetProjection()
|
||||||
|
del dataset
|
||||||
|
return im_proj
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_geotransform(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 从图像坐标空间（行、列），也称为（像素、线）到地理参考坐标空间（投影或地理坐标）的仿射变换
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
geotransform = dataset.GetGeoTransform()
|
||||||
|
del dataset
|
||||||
|
return geotransform
|
||||||
|
|
||||||
|
def get_invgeotransform(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 从地理参考坐标空间（投影或地理坐标）的到图像坐标空间（行、列
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
geotransform = dataset.GetGeoTransform()
|
||||||
|
geotransform=gdal.InvGeoTransform(geotransform)
|
||||||
|
del dataset
|
||||||
|
return geotransform
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_bands(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 影像的波段数
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
bands = dataset.RasterCount
|
||||||
|
del dataset
|
||||||
|
return bands
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def geo2lonlat(dataset, x, y):
|
||||||
|
"""
|
||||||
|
将投影坐标转为经纬度坐标（具体的投影坐标系由给定数据确定）
|
||||||
|
:param dataset: GDAL地理数据
|
||||||
|
:param x: 投影坐标x
|
||||||
|
:param y: 投影坐标y
|
||||||
|
:return: 投影坐标(x, y)对应的经纬度坐标(lon, lat)
|
||||||
|
"""
|
||||||
|
prosrs = osr.SpatialReference()
|
||||||
|
prosrs.ImportFromWkt(dataset.GetProjection())
|
||||||
|
geosrs = prosrs.CloneGeogCS()
|
||||||
|
ct = osr.CoordinateTransformation(prosrs, geosrs)
|
||||||
|
coords = ct.TransformPoint(x, y)
|
||||||
|
return coords[:2]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_band_array(filename, num=1):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:param num: 波段序号
|
||||||
|
:return: 对应波段的矩阵数据
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
bands = dataset.GetRasterBand(num)
|
||||||
|
array = bands.ReadAsArray(0, 0, bands.XSize, bands.YSize)
|
||||||
|
|
||||||
|
# if 'int' in str(array.dtype):
|
||||||
|
# array[np.where(array == -9999)] = np.inf
|
||||||
|
# else:
|
||||||
|
# array[np.where(array < -9000.0)] = np.nan
|
||||||
|
|
||||||
|
del dataset
|
||||||
|
return array
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_data(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 获取所有波段的数据
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
im_width = dataset.RasterXSize
|
||||||
|
im_height = dataset.RasterYSize
|
||||||
|
im_data = dataset.ReadAsArray(0, 0, im_width, im_height)
|
||||||
|
del dataset
|
||||||
|
return im_data
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_all_band_array(filename):
|
||||||
|
"""
|
||||||
|
（大气延迟算法）
|
||||||
|
将ERA-5影像所有波段存为一个数组, 波段数在第三维度 get_data（）->（37，8，8）
|
||||||
|
:param filename： 影像路径 get_all_band_array ->（8，8，37）
|
||||||
|
:return: 影像数组
|
||||||
|
"""
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
x_size = dataset.RasterXSize
|
||||||
|
y_size = dataset.RasterYSize
|
||||||
|
nums = dataset.RasterCount
|
||||||
|
array = np.zeros((y_size, x_size, nums), dtype=float)
|
||||||
|
if nums == 1:
|
||||||
|
bands_0 = dataset.GetRasterBand(1)
|
||||||
|
array = bands_0.ReadAsArray(0, 0, x_size, y_size)
|
||||||
|
else:
|
||||||
|
for i in range(0, nums):
|
||||||
|
bands = dataset.GetRasterBand(i+1)
|
||||||
|
arr = bands.ReadAsArray(0, 0, x_size, y_size)
|
||||||
|
array[:, :, i] = arr
|
||||||
|
return array
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_img_width(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 影像宽度
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
width = dataset.RasterXSize
|
||||||
|
|
||||||
|
del dataset
|
||||||
|
return width
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_img_height(filename):
|
||||||
|
"""
|
||||||
|
:param filename: tif路径
|
||||||
|
:return: 影像高度
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
height = dataset.RasterYSize
|
||||||
|
del dataset
|
||||||
|
return height
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_img(filename):
|
||||||
|
"""
|
||||||
|
影像读取
|
||||||
|
:param filename:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
img_dataset = gdal.Open(filename) # 打开文件
|
||||||
|
|
||||||
|
if img_dataset is None:
|
||||||
|
msg = 'Could not open ' + filename
|
||||||
|
logger.error(msg)
|
||||||
|
return None, None, None
|
||||||
|
|
||||||
|
im_proj = img_dataset.GetProjection() # 地图投影信息
|
||||||
|
if im_proj is None:
|
||||||
|
return None, None, None
|
||||||
|
im_geotrans = img_dataset.GetGeoTransform() # 仿射矩阵
|
||||||
|
|
||||||
|
im_width = img_dataset.RasterXSize # 栅格矩阵的行数
|
||||||
|
im_height = img_dataset.RasterYSize # 栅格矩阵的行数
|
||||||
|
im_arr = img_dataset.ReadAsArray(0, 0, im_width, im_height)
|
||||||
|
del img_dataset
|
||||||
|
return im_proj, im_geotrans, im_arr
|
||||||
|
|
||||||
|
def cal_img_scope(self, dataset):
|
||||||
|
"""
|
||||||
|
计算影像的地理坐标范围
|
||||||
|
根据GDAL的六参数模型将影像图上坐标（行列号）转为投影坐标或地理坐标（根据具体数据的坐标系统转换）
|
||||||
|
:param dataset :GDAL地理数据
|
||||||
|
:return: list[point_upleft, point_upright, point_downleft, point_downright]
|
||||||
|
"""
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
|
||||||
|
img_geotrans = dataset.GetGeoTransform()
|
||||||
|
if img_geotrans is None:
|
||||||
|
return None
|
||||||
|
|
||||||
|
width = dataset.RasterXSize # 栅格矩阵的列数
|
||||||
|
height = dataset.RasterYSize # 栅格矩阵的行数
|
||||||
|
|
||||||
|
point_upleft = self.trans_rowcol2geo(img_geotrans, 0, 0)
|
||||||
|
point_upright = self.trans_rowcol2geo(img_geotrans, width, 0)
|
||||||
|
point_downleft = self.trans_rowcol2geo(img_geotrans, 0, height)
|
||||||
|
point_downright = self.trans_rowcol2geo(img_geotrans, width, height)
|
||||||
|
|
||||||
|
return [point_upleft, point_upright, point_downleft, point_downright]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_scope_ori_sim(filename):
|
||||||
|
"""
|
||||||
|
计算影像的地理坐标范围
|
||||||
|
根据GDAL的六参数模型将影像图上坐标（行列号）转为投影坐标或地理坐标（根据具体数据的坐标系统转换）
|
||||||
|
:param dataset :GDAL地理数据
|
||||||
|
:return: list[point_upleft, point_upright, point_downleft, point_downright]
|
||||||
|
"""
|
||||||
|
gdal.AllRegister()
|
||||||
|
dataset = gdal.Open(filename)
|
||||||
|
if dataset is None:
|
||||||
|
return None
|
||||||
|
|
||||||
|
width = dataset.RasterXSize # 栅格矩阵的列数
|
||||||
|
height = dataset.RasterYSize # 栅格矩阵的行数
|
||||||
|
|
||||||
|
band1 = dataset.GetRasterBand(1)
|
||||||
|
array1 = band1.ReadAsArray(0, 0, band1.XSize, band1.YSize)
|
||||||
|
|
||||||
|
band2 = dataset.GetRasterBand(2)
|
||||||
|
array2 = band2.ReadAsArray(0, 0, band2.XSize, band2.YSize)
|
||||||
|
|
||||||
|
if array1[0, 0] < array1[0, width-1]:
|
||||||
|
point_upleft = [array1[0, 0], array2[0, 0]]
|
||||||
|
point_upright = [array1[0, width-1], array2[0, width-1]]
|
||||||
|
else:
|
||||||
|
point_upright = [array1[0, 0], array2[0, 0]]
|
||||||
|
point_upleft = [array1[0, width-1], array2[0, width-1]]
|
||||||
|
|
||||||
|
|
||||||
|
if array1[height-1, 0] < array1[height-1, width-1]:
|
||||||
|
point_downleft = [array1[height - 1, 0], array2[height - 1, 0]]
|
||||||
|
point_downright = [array1[height - 1, width - 1], array2[height - 1, width - 1]]
|
||||||
|
else:
|
||||||
|
point_downright = [array1[height - 1, 0], array2[height - 1, 0]]
|
||||||
|
point_downleft = [array1[height - 1, width - 1], array2[height - 1, width - 1]]
|
||||||
|
|
||||||
|
|
||||||
|
if(array2[0, 0] < array2[height - 1, 0]):
|
||||||
|
#上下调换顺序
|
||||||
|
tmp1 = point_upleft
|
||||||
|
point_upleft = point_downleft
|
||||||
|
point_downleft = tmp1
|
||||||
|
|
||||||
|
tmp2 = point_upright
|
||||||
|
point_upright = point_downright
|
||||||
|
point_downright = tmp2
|
||||||
|
|
||||||
|
return [point_upleft, point_upright, point_downleft, point_downright]
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_rowcol2geo(img_geotrans,img_col, img_row):
|
||||||
|
"""
|
||||||
|
据GDAL的六参数模型仿射矩阵将影像图上坐标（行列号）转为投影坐标或地理坐标（根据具体数据的坐标系统转换）
|
||||||
|
:param img_geotrans: 仿射矩阵
|
||||||
|
:param img_col:图像纵坐标
|
||||||
|
:param img_row:图像横坐标
|
||||||
|
:return: [geo_x,geo_y]
|
||||||
|
"""
|
||||||
|
geo_x = img_geotrans[0] + img_geotrans[1] * img_col + img_geotrans[2] * img_row
|
||||||
|
geo_y = img_geotrans[3] + img_geotrans[4] * img_col + img_geotrans[5] * img_row
|
||||||
|
return [geo_x, geo_y]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def write_era_into_img(filename, im_proj, im_geotrans, im_data):
|
||||||
|
"""
|
||||||
|
影像保存
|
||||||
|
:param filename:
|
||||||
|
:param im_proj:
|
||||||
|
:param im_geotrans:
|
||||||
|
:param im_data:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
gdal_dtypes = {
|
||||||
|
'int8': gdal.GDT_Byte,
|
||||||
|
'unit16': gdal.GDT_UInt16,
|
||||||
|
'int16': gdal.GDT_Int16,
|
||||||
|
'unit32': gdal.GDT_UInt32,
|
||||||
|
'int32': gdal.GDT_Int32,
|
||||||
|
'float32': gdal.GDT_Float32,
|
||||||
|
'float64': gdal.GDT_Float64,
|
||||||
|
}
|
||||||
|
if not gdal_dtypes.get(im_data.dtype.name, None) is None:
|
||||||
|
datatype = gdal_dtypes[im_data.dtype.name]
|
||||||
|
else:
|
||||||
|
datatype = gdal.GDT_Float32
|
||||||
|
|
||||||
|
# 判读数组维数
|
||||||
|
if len(im_data.shape) == 3:
|
||||||
|
im_height, im_width, im_bands = im_data.shape # shape[0] 行数
|
||||||
|
else:
|
||||||
|
im_bands, (im_height, im_width) = 1, im_data.shape
|
||||||
|
|
||||||
|
# 创建文件
|
||||||
|
if os.path.exists(os.path.split(filename)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(filename)[0])
|
||||||
|
|
||||||
|
driver = gdal.GetDriverByName("GTiff") # 数据类型必须有，因为要计算需要多大内存空间
|
||||||
|
dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
|
||||||
|
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
|
||||||
|
dataset.SetProjection(im_proj) # 写入投影
|
||||||
|
|
||||||
|
if im_bands == 1:
|
||||||
|
dataset.GetRasterBand(1).WriteArray(im_data) # 写入数组数据
|
||||||
|
else:
|
||||||
|
for i in range(im_bands):
|
||||||
|
dataset.GetRasterBand(i + 1).WriteArray(im_data[:, :, i])
|
||||||
|
# dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
|
||||||
|
del dataset
|
||||||
|
|
||||||
|
# 写GeoTiff文件
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def write_img(filename, im_proj, im_geotrans, im_data, no_data='0'):
|
||||||
|
"""
|
||||||
|
影像保存
|
||||||
|
:param filename: 保存的路径
|
||||||
|
:param im_proj:
|
||||||
|
:param im_geotrans:
|
||||||
|
:param im_data:
|
||||||
|
:param no_data: 把无效值设置为 nodata
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
|
||||||
|
gdal_dtypes = {
|
||||||
|
'int8': gdal.GDT_Byte,
|
||||||
|
'unit16': gdal.GDT_UInt16,
|
||||||
|
'int16': gdal.GDT_Int16,
|
||||||
|
'unit32': gdal.GDT_UInt32,
|
||||||
|
'int32': gdal.GDT_Int32,
|
||||||
|
'float32': gdal.GDT_Float32,
|
||||||
|
'float64': gdal.GDT_Float64,
|
||||||
|
}
|
||||||
|
if not gdal_dtypes.get(im_data.dtype.name, None) is None:
|
||||||
|
datatype = gdal_dtypes[im_data.dtype.name]
|
||||||
|
else:
|
||||||
|
datatype = gdal.GDT_Float32
|
||||||
|
flag = False
|
||||||
|
# 判读数组维数
|
||||||
|
if len(im_data.shape) == 3:
|
||||||
|
im_bands, im_height, im_width = im_data.shape
|
||||||
|
flag = True
|
||||||
|
else:
|
||||||
|
im_bands, (im_height, im_width) = 1, im_data.shape
|
||||||
|
|
||||||
|
# 创建文件
|
||||||
|
if os.path.exists(os.path.split(filename)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(filename)[0])
|
||||||
|
|
||||||
|
driver = gdal.GetDriverByName("GTiff") # 数据类型必须有，因为要计算需要多大内存空间
|
||||||
|
dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
|
||||||
|
|
||||||
|
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
|
||||||
|
|
||||||
|
dataset.SetProjection(im_proj) # 写入投影
|
||||||
|
|
||||||
|
if im_bands == 1:
|
||||||
|
# outRaster.GetRasterBand(1).WriteArray(array) # 写入数组数据
|
||||||
|
if flag:
|
||||||
|
outband = dataset.GetRasterBand(1)
|
||||||
|
outband.WriteArray(im_data[0])
|
||||||
|
if no_data != 'null':
|
||||||
|
outband.SetNoDataValue(np.double(no_data))
|
||||||
|
outband.FlushCache()
|
||||||
|
else:
|
||||||
|
outband = dataset.GetRasterBand(1)
|
||||||
|
outband.WriteArray(im_data)
|
||||||
|
if no_data != 'null':
|
||||||
|
outband.SetNoDataValue(np.double(no_data))
|
||||||
|
outband.FlushCache()
|
||||||
|
else:
|
||||||
|
for i in range(im_bands):
|
||||||
|
outband = dataset.GetRasterBand(1 + i)
|
||||||
|
outband.WriteArray(im_data[i])
|
||||||
|
if no_data != 'null':
|
||||||
|
outband.SetNoDataValue(np.double(no_data))
|
||||||
|
outband.FlushCache()
|
||||||
|
# outRaster.GetRasterBand(i + 1).WriteArray(array[i])
|
||||||
|
del dataset
|
||||||
|
|
||||||
|
# 写GeoTiff文件
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def write_img_envi(filename, im_proj, im_geotrans, im_data, no_data='null'):
|
||||||
|
"""
|
||||||
|
影像保存
|
||||||
|
:param filename: 保存的路径
|
||||||
|
:param im_proj:
|
||||||
|
:param im_geotrans:
|
||||||
|
:param im_data:
|
||||||
|
:param no_data: 把无效值设置为 nodata
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
|
||||||
|
gdal_dtypes = {
|
||||||
|
'int8': gdal.GDT_Byte,
|
||||||
|
'unit16': gdal.GDT_UInt16,
|
||||||
|
'int16': gdal.GDT_Int16,
|
||||||
|
'unit32': gdal.GDT_UInt32,
|
||||||
|
'int32': gdal.GDT_Int32,
|
||||||
|
'float32': gdal.GDT_Float32,
|
||||||
|
'float64': gdal.GDT_Float64,
|
||||||
|
}
|
||||||
|
if not gdal_dtypes.get(im_data.dtype.name, None) is None:
|
||||||
|
datatype = gdal_dtypes[im_data.dtype.name]
|
||||||
|
else:
|
||||||
|
datatype = gdal.GDT_Float32
|
||||||
|
|
||||||
|
# 判读数组维数
|
||||||
|
if len(im_data.shape) == 3:
|
||||||
|
im_bands, im_height, im_width = im_data.shape
|
||||||
|
else:
|
||||||
|
im_bands, (im_height, im_width) = 1, im_data.shape
|
||||||
|
|
||||||
|
# 创建文件
|
||||||
|
if os.path.exists(os.path.split(filename)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(filename)[0])
|
||||||
|
|
||||||
|
driver = gdal.GetDriverByName("ENVI") # 数据类型必须有，因为要计算需要多大内存空间
|
||||||
|
dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
|
||||||
|
|
||||||
|
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
|
||||||
|
|
||||||
|
dataset.SetProjection(im_proj) # 写入投影
|
||||||
|
|
||||||
|
if im_bands == 1:
|
||||||
|
# outRaster.GetRasterBand(1).WriteArray(array) # 写入数组数据
|
||||||
|
outband = dataset.GetRasterBand(1)
|
||||||
|
outband.WriteArray(im_data)
|
||||||
|
if no_data != 'null':
|
||||||
|
outband.SetNoDataValue(no_data)
|
||||||
|
outband.FlushCache()
|
||||||
|
else:
|
||||||
|
for i in range(im_bands):
|
||||||
|
outband = dataset.GetRasterBand(1 + i)
|
||||||
|
outband.WriteArray(im_data[i])
|
||||||
|
outband.FlushCache()
|
||||||
|
# outRaster.GetRasterBand(i + 1).WriteArray(array[i])
|
||||||
|
del dataset
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def write_img_rpc(filename, im_proj, im_geotrans, im_data, rpc_dict):
|
||||||
|
"""
|
||||||
|
图像中写入rpc信息
|
||||||
|
"""
|
||||||
|
# 判断栅格数据的数据类型
|
||||||
|
if 'int8' in im_data.dtype.name:
|
||||||
|
datatype = gdal.GDT_Byte
|
||||||
|
elif 'int16' in im_data.dtype.name:
|
||||||
|
datatype = gdal.GDT_Int16
|
||||||
|
else:
|
||||||
|
datatype = gdal.GDT_Float32
|
||||||
|
|
||||||
|
# 判读数组维数
|
||||||
|
if len(im_data.shape) == 3:
|
||||||
|
im_bands, im_height, im_width = im_data.shape
|
||||||
|
else:
|
||||||
|
im_bands, (im_height, im_width) = 1, im_data.shape
|
||||||
|
|
||||||
|
# 创建文件
|
||||||
|
driver = gdal.GetDriverByName("GTiff")
|
||||||
|
dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
|
||||||
|
|
||||||
|
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
|
||||||
|
dataset.SetProjection(im_proj) # 写入投影
|
||||||
|
|
||||||
|
# 写入RPC参数
|
||||||
|
for k in rpc_dict.keys():
|
||||||
|
dataset.SetMetadataItem(k, rpc_dict[k], 'RPC')
|
||||||
|
|
||||||
|
if im_bands == 1:
|
||||||
|
dataset.GetRasterBand(1).WriteArray(im_data) # 写入数组数据
|
||||||
|
else:
|
||||||
|
for i in range(im_bands):
|
||||||
|
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
|
||||||
|
|
||||||
|
del dataset
|
||||||
|
|
||||||
|
|
||||||
|
def transtif2mask(self,out_tif_path, in_tif_path, threshold):
|
||||||
|
"""
|
||||||
|
:param out_tif_path:输出路径
|
||||||
|
:param in_tif_path:输入的路径
|
||||||
|
:param threshold:阈值
|
||||||
|
"""
|
||||||
|
im_proj, im_geotrans, im_arr, im_scope = self.read_img(in_tif_path)
|
||||||
|
im_arr_mask = (im_arr < threshold).astype(int)
|
||||||
|
self.write_img(out_tif_path, im_proj, im_geotrans, im_arr_mask)
|
||||||
|
|
||||||
|
def write_quick_view(self, tif_path, color_img=False, quick_view_path=None):
|
||||||
|
"""
|
||||||
|
生成快视图,默认快视图和影像同路径且同名
|
||||||
|
:param tif_path:影像路径
|
||||||
|
:param color_img:是否生成随机伪彩色图
|
||||||
|
:param quick_view_path:快视图路径
|
||||||
|
"""
|
||||||
|
if quick_view_path is None:
|
||||||
|
quick_view_path = os.path.splitext(tif_path)[0]+'.jpg'
|
||||||
|
|
||||||
|
n = self.get_bands(tif_path)
|
||||||
|
if n == 1: # 单波段
|
||||||
|
t_data = self.get_data(tif_path)
|
||||||
|
else: # 多波段，转为强度数据
|
||||||
|
t_data = self.get_data(tif_path)
|
||||||
|
t_data = t_data.astype(float)
|
||||||
|
t_data = np.sqrt(t_data[0] ** 2 + t_data[1] ** 2)
|
||||||
|
|
||||||
|
t_r = self.get_img_height(tif_path)
|
||||||
|
t_c = self.get_img_width(tif_path)
|
||||||
|
if t_r > 10000 or t_c > 10000:
|
||||||
|
q_r = int(t_r / 10)
|
||||||
|
q_c = int(t_c / 10)
|
||||||
|
elif 1024 < t_r < 10000 or 1024 < t_c < 10000:
|
||||||
|
if t_r > t_c:
|
||||||
|
q_r = 1024
|
||||||
|
q_c = int(t_c/t_r * 1024)
|
||||||
|
else:
|
||||||
|
q_c = 1024
|
||||||
|
q_r = int(t_r/t_c * 1024)
|
||||||
|
else:
|
||||||
|
q_r = t_r
|
||||||
|
q_c = t_c
|
||||||
|
|
||||||
|
if color_img is True:
|
||||||
|
# 生成伪彩色图
|
||||||
|
img = np.zeros((t_r, t_c, 3), dtype=np.uint8) # (高，宽，维度)
|
||||||
|
u = np.unique(t_data)
|
||||||
|
for i in u:
|
||||||
|
if i != 0:
|
||||||
|
w = np.where(t_data == i)
|
||||||
|
img[w[0], w[1], 0] = np.random.randint(0, 255) # 随机生成一个0到255之间的整数 可以通过挑参数设定不同的颜色范围
|
||||||
|
img[w[0], w[1], 1] = np.random.randint(0, 255)
|
||||||
|
img[w[0], w[1], 2] = np.random.randint(0, 255)
|
||||||
|
|
||||||
|
img = cv2.resize(img, (q_c, q_r)) # (宽，高)
|
||||||
|
cv2.imwrite(quick_view_path, img)
|
||||||
|
# cv2.imshow("result4", img)
|
||||||
|
# cv2.waitKey(0)
|
||||||
|
else:
|
||||||
|
# 灰度图
|
||||||
|
min = np.percentile(t_data, 2) # np.nanmin(t_data)
|
||||||
|
max = np.percentile(t_data, 98) # np.nanmax(t_data)
|
||||||
|
t_data[np.isnan(t_data)] = max
|
||||||
|
if (max - min) < 256:
|
||||||
|
t_data = (t_data - min) / (max - min) * 255
|
||||||
|
out_img = Image.fromarray(t_data)
|
||||||
|
out_img = out_img.resize((q_c, q_r)) # 重采样
|
||||||
|
out_img = out_img.convert("L") # 转换成灰度图
|
||||||
|
out_img.save(quick_view_path)
|
||||||
|
|
||||||
|
def limit_field(self, out_path, in_path, min_value, max_value):
|
||||||
|
"""
|
||||||
|
:param out_path:输出路径
|
||||||
|
:param in_path:主mask路径，输出影像采用主mask的地理信息
|
||||||
|
:param min_value
|
||||||
|
:param max_value
|
||||||
|
"""
|
||||||
|
proj = self.get_projection(in_path)
|
||||||
|
geotrans = self.get_geotransform(in_path)
|
||||||
|
array = self.get_band_array(in_path, 1)
|
||||||
|
array[array < min_value] = min_value
|
||||||
|
array[array > max_value] = max_value
|
||||||
|
self.write_img(out_path, proj, geotrans, array)
|
||||||
|
return True
|
||||||
|
|
||||||
|
def band_merge(self, lon, lat, ori_sim):
|
||||||
|
lon_arr = self.get_data(lon)
|
||||||
|
lat_arr = self.get_data(lat)
|
||||||
|
temp = np.zeros((2, lon_arr.shape[0], lon_arr.shape[1]), dtype=float)
|
||||||
|
temp[0, :, :] = lon_arr[:, :]
|
||||||
|
temp[1, :, :] = lat_arr[:, :]
|
||||||
|
self.write_img(ori_sim, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], temp, '0')
|
||||||
|
|
||||||
|
|
||||||
|
def get_scopes(self, ori_sim):
|
||||||
|
ori_sim_data = self.get_data(ori_sim)
|
||||||
|
lon = ori_sim_data[0, :, :]
|
||||||
|
lat = ori_sim_data[1, :, :]
|
||||||
|
|
||||||
|
min_lon = np.nanmin(np.where((lon != 0) & ~np.isnan(lon), lon, np.inf))
|
||||||
|
max_lon = np.nanmax(np.where((lon != 0) & ~np.isnan(lon), lon, -np.inf))
|
||||||
|
min_lat = np.nanmin(np.where((lat != 0) & ~np.isnan(lat), lat, np.inf))
|
||||||
|
max_lat = np.nanmax(np.where((lat != 0) & ~np.isnan(lat), lat, -np.inf))
|
||||||
|
|
||||||
|
scopes = [[min_lon, max_lat], [max_lon, max_lat], [min_lon, min_lat], [max_lon, min_lat]]
|
||||||
|
return scopes
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# path = r'D:\BaiduNetdiskDownload\GZ\lon.rdr'
|
||||||
|
# path2 = r'D:\BaiduNetdiskDownload\GZ\lat.rdr'
|
||||||
|
# path3 = r'D:\BaiduNetdiskDownload\GZ\lon_lat.tif'
|
||||||
|
# s = ImageHandler().band_merge(path, path2, path3)
|
||||||
|
# print(s)
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,185 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:SalinityMain.py
|
||||||
|
@File:MonteCarloSampling.py
|
||||||
|
@Function:基于蒙特卡洛随机抽样的最优特征选择算法
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/10/19 11:30
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
from numpy import random
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import seaborn as sns
|
||||||
|
import logging
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
def api_sel_feature(x_list, iter=100, alpha=0.5, ts=-0.5, iter_ratio=0.2):
|
||||||
|
"""
|
||||||
|
:para x_list: k类别的单个特征的训练样本 [X1,X2,X3,...,Xi,...,Xk],
|
||||||
|
Xi = np.array([x1,x2,x3...xn]), 第i类别的训练样本数为n
|
||||||
|
:para iter: 迭代次数
|
||||||
|
:para alpha: 调节因子
|
||||||
|
:para ts: com_sep_coef的阈值
|
||||||
|
:para iter_ratio : 迭代次数阈值
|
||||||
|
:return : True-特征与类别相关度高，False-特征与类别相关度低
|
||||||
|
"""
|
||||||
|
com_sep_coef_old = cal_com_sep_coef(x_list, alpha)
|
||||||
|
# print('com_sep_coef_old:', com_sep_coef_old)
|
||||||
|
if com_sep_coef_old < ts:
|
||||||
|
return False, com_sep_coef_old
|
||||||
|
|
||||||
|
X = np.zeros(1) # x_list组合为行向量X
|
||||||
|
x_len_list = [] # 记录每个类别x的位置
|
||||||
|
num_sampler = 0 # 样本总数
|
||||||
|
t = 0
|
||||||
|
flag = 0
|
||||||
|
for x in x_list:
|
||||||
|
len_x = len(x)
|
||||||
|
if t == 0:
|
||||||
|
X = x
|
||||||
|
x_len_list.append(len_x)
|
||||||
|
else:
|
||||||
|
X = np.hstack([X, x])
|
||||||
|
x_len_list.append(x_len_list[t - 1] + len_x)
|
||||||
|
num_sampler += len_x
|
||||||
|
t += 1
|
||||||
|
x_len_list.pop()
|
||||||
|
num = int(np.ceil(num_sampler / 3))
|
||||||
|
|
||||||
|
for i in range(iter):
|
||||||
|
# 生成随机数组
|
||||||
|
randmtx = np.random.rand(1, num)
|
||||||
|
randmtx_ceil = np.ceil(randmtx * num_sampler).astype(int)
|
||||||
|
randmtx_ceil = np.sort(randmtx_ceil[0, :]) - 1
|
||||||
|
|
||||||
|
# 随机取值，重排后，替换原来的数据,组成新数组
|
||||||
|
X_new_sel = X.copy()
|
||||||
|
X_new_sel[randmtx_ceil] = np.random.permutation(X[randmtx_ceil])
|
||||||
|
|
||||||
|
X_new_list = np.split(X_new_sel, x_len_list)
|
||||||
|
com_sep_coef_new = cal_com_sep_coef(X_new_list, alpha)
|
||||||
|
if com_sep_coef_new <= com_sep_coef_old:
|
||||||
|
flag += 1
|
||||||
|
# print('com_sep_coef_new:', com_sep_coef_new)
|
||||||
|
logger.info('flag:' + str(flag) +', iter:' + str(iter) + ', falg/iter:' + str(int(flag)/int(iter)))
|
||||||
|
if flag > (iter * iter_ratio):
|
||||||
|
return False, com_sep_coef_old
|
||||||
|
return True, com_sep_coef_old
|
||||||
|
|
||||||
|
def cal_com_coef(x_list):
|
||||||
|
"""
|
||||||
|
:para x_list: k类别的单个特征的训练样本 [X1,X2,X3,...,Xi,...,Xk],Xi = np.array([x1,x2,x3...xn]), 第i类别的训练样本数为n
|
||||||
|
:return com_coef : 类内聚合因子（Compactness Coefficient）
|
||||||
|
"""
|
||||||
|
class_num = len(x_list)
|
||||||
|
coef_array = np.full((1, class_num), 0.0)
|
||||||
|
for m in range(class_num):
|
||||||
|
sample_num = len(x_list[m])
|
||||||
|
c = np.full((1, sample_num), 0.0)
|
||||||
|
for u in range(sample_num):
|
||||||
|
l = np.full((1, sample_num), x_list[m][u])
|
||||||
|
c[0, u] = np.sum(np.abs(l - x_list[m]))
|
||||||
|
coef_array[0, m] = np.sum(c) / (sample_num * (sample_num - 1))
|
||||||
|
com_coef = np.sum(coef_array) / class_num
|
||||||
|
return com_coef
|
||||||
|
|
||||||
|
def cal_sep_coef(x_list):
|
||||||
|
"""
|
||||||
|
:para x_list : k类别的单个特征的训练样本 [X1,X2,X3,...,Xi,...,Xk],Xi = np.array([x1,x2,x3...xn]), 第i类别的训练样本数为n
|
||||||
|
:return sep_coef : 类间离散度（Separation Coefficient）
|
||||||
|
"""
|
||||||
|
class_num = len(x_list)
|
||||||
|
coef_list = []
|
||||||
|
coef_sum = 0
|
||||||
|
for m in range(class_num):
|
||||||
|
xm = x_list[m]
|
||||||
|
l_xm = len(xm)
|
||||||
|
for n in range(class_num):
|
||||||
|
if not n == m:
|
||||||
|
xn = x_list[n]
|
||||||
|
l_xn = len(xn)
|
||||||
|
xm = np.expand_dims(xm, 1)
|
||||||
|
coef_list.append(np.sum(np.abs(xm - xn)) / (l_xm * l_xn))
|
||||||
|
for coef in coef_list:
|
||||||
|
coef_sum = coef_sum + coef
|
||||||
|
|
||||||
|
if class_num == 1 or class_num == 0:
|
||||||
|
sep_coef = coef_sum
|
||||||
|
else:
|
||||||
|
sep_coef = coef_sum / (class_num * (class_num - 1))
|
||||||
|
return sep_coef
|
||||||
|
|
||||||
|
def cal_com_sep_coef(x_list, alpha = 0.5):
|
||||||
|
"""
|
||||||
|
:para x_list: k类别的单个特征的训练样本 [X1,X2,X3,...,Xi,...,Xk],Xi = np.array([x1,x2,x3...xn]), 第i类别的训练样本数为n
|
||||||
|
:para alpha : 调节因子
|
||||||
|
:return com_sep_coef: 类内聚合度和类间离散度的因子（Compactness- Separation Coeffcient）
|
||||||
|
"""
|
||||||
|
if not alpha >= 0 and alpha <= 1:
|
||||||
|
raise ('input_para_alpha beyond (0,1)!')
|
||||||
|
com_coef = cal_com_coef(x_list)
|
||||||
|
sep_coef = cal_sep_coef(x_list)
|
||||||
|
com_sep_coef = alpha * com_coef - (1-alpha) * sep_coef
|
||||||
|
return com_sep_coef
|
||||||
|
|
||||||
|
def get_logistic_rand_number(num, u=0.4): #弃用
|
||||||
|
randmtx = np.full((1, num), 0.0)
|
||||||
|
# randmtx[0,0] = np.random.rand(1, 1) #随机初始值
|
||||||
|
randmtx[0, 0] = 0.5 #初始值
|
||||||
|
|
||||||
|
for i in range(1, num):
|
||||||
|
randmtx[0, i] = u * randmtx[0, i-1]*(1-randmtx[0, i-1])
|
||||||
|
randmtx = randmtx * 3 * num
|
||||||
|
randmtx_ceil = np.ceil(randmtx)
|
||||||
|
|
||||||
|
# 绘制随机数分布图
|
||||||
|
# randmty = np.arange(0,num,1)
|
||||||
|
# randmty = np.expand_dims( randmty, 1)
|
||||||
|
# fig, axes = plt.subplots(1, 1, figsize=(5, 5))
|
||||||
|
# axes.scatter(randmty, randmtx_ceil, alpha=.3, label='ground truth')
|
||||||
|
# axes.legend()
|
||||||
|
# plt.tight_layout()
|
||||||
|
# plt.show()
|
||||||
|
return randmtx_ceil
|
||||||
|
|
||||||
|
def test():
|
||||||
|
'''测试生成随机数'''
|
||||||
|
# 插入
|
||||||
|
# a = np.array([3.4, 2.5, 1.8, 4.7, 5.6, 2.1])
|
||||||
|
# b = np.array([2.5, 4.7, 5.6])
|
||||||
|
# c = a[[0,1]]
|
||||||
|
# a[[0,1]] = np.array([1, 1])
|
||||||
|
|
||||||
|
# 随机排列
|
||||||
|
random.shuffle()
|
||||||
|
|
||||||
|
# logist随机数
|
||||||
|
sns.distplot(random.normal(scale=2, size=1000), hist=False, label='normal')
|
||||||
|
sns.distplot(random.logistic(loc=2, scale=0.5, size=1000), hist=False, label='logistic')
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
# 绘制随机数
|
||||||
|
randmtx = random.logistic(loc=0.5, scale=0.5, size=100)
|
||||||
|
randmtx.sort(axis=0)
|
||||||
|
randmty = np.arange(0,100,1)
|
||||||
|
randmty = np.expand_dims(randmty, 1)
|
||||||
|
fig, axes = plt.subplots(1, 1, figsize=(5, 5))
|
||||||
|
axes.scatter(randmty, randmtx, alpha=.3, label='ground truth')
|
||||||
|
axes.legend()
|
||||||
|
plt.tight_layout()
|
||||||
|
plt.show()
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# 例子
|
||||||
|
# x1 = np.array([1, 1.1])
|
||||||
|
# x2 = np.array([2, 2.1, 2.2])
|
||||||
|
# x3 = np.array([3, 3.4, 3.1])
|
||||||
|
# x_list = [x1, x2, x3]
|
||||||
|
# com_sep_coef = cal_com_sep_coef(x_list, 0.5)
|
||||||
|
# flag = api_sel_feature(x_list)
|
||||||
|
# print('done')
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,416 @@
|
||||||
|
import sklearn # 用于解决打包错误
|
||||||
|
import sklearn.utils # 用于解决打包错误
|
||||||
|
import sklearn.utils._cython_blas # 用于解决打包错误
|
||||||
|
import sklearn.utils._weight_vector # 用于解决打包错误
|
||||||
|
import sklearn.neighbors # 用于解决打包错误
|
||||||
|
import sklearn.neighbors._typedefs # 用于解决打包错误
|
||||||
|
import sklearn.neighbors._partition_nodes # 用于解决打包错误
|
||||||
|
import sklearn.neighbors._quad_tree # 用于解决打包错误
|
||||||
|
import sklearn.tree._utils # 用于解决打包错误
|
||||||
|
from sklearn.cross_decomposition import PLSRegression
|
||||||
|
from sklearn.ensemble import ExtraTreesClassifier
|
||||||
|
from sklearn.ensemble import RandomForestClassifier
|
||||||
|
from sklearn.ensemble import ExtraTreesClassifier
|
||||||
|
from sklearn.svm import SVC
|
||||||
|
import numpy as np
|
||||||
|
from scipy.stats import pearsonr
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.block.blockprocess import BlockProcess
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
from PIL import Image
|
||||||
|
from tool.file.fileHandle import fileHandle
|
||||||
|
import multiprocessing
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
file = fileHandle()
|
||||||
|
|
||||||
|
class MachineLeaning:
|
||||||
|
"""
|
||||||
|
机器学习库
|
||||||
|
"""
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def gene_optimal_train_set(train_data_dic, feature_tif_dir, important_threshold=0.3, correlation_threshold=0.7): # todo 修改特征重要性
|
||||||
|
ml = MachineLeaning()
|
||||||
|
name_list = ml.get_name_list(feature_tif_dir)
|
||||||
|
X_train, Y_train = ml.gene_train_set(train_data_dic, feature_tif_dir)
|
||||||
|
optimal_feature = ml.sel_optimal_feature_set(X_train, Y_train, threshold=important_threshold)
|
||||||
|
optimal_feature = ml.remove_correlation_feature(X_train, optimal_feature, threshold=correlation_threshold)
|
||||||
|
X_train = X_train[:, optimal_feature]
|
||||||
|
logger.info('train_feature:%s', np.array(name_list)[optimal_feature])
|
||||||
|
return X_train, Y_train, optimal_feature
|
||||||
|
|
||||||
|
@ staticmethod
|
||||||
|
def sel_optimal_feature(X_train, Y_train, name_list,important_threshold=0.3, correlation_threshold=0.7):
|
||||||
|
ml = MachineLeaning()
|
||||||
|
optimal_feature = ml.sel_optimal_feature_set(X_train, Y_train, threshold=important_threshold)
|
||||||
|
optimal_feature = ml.remove_correlation_feature(X_train, optimal_feature, threshold=correlation_threshold)
|
||||||
|
X_train = X_train[:, optimal_feature]
|
||||||
|
logger.info('train_feature:%s', np.array(name_list)[optimal_feature])
|
||||||
|
return X_train, Y_train, optimal_feature
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def gene_test_set(feature_tif_dir, optimal_feature):
|
||||||
|
"""
|
||||||
|
生成测试集
|
||||||
|
:param feature_tif_dir : 特征影像路径字典
|
||||||
|
:param optimal_feature : 最优特征子集
|
||||||
|
:return X_test_list : 分块测试集影像路径
|
||||||
|
"""
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(feature_tif_dir, '*.tif')))
|
||||||
|
cols = ImageHandler.get_img_width(in_tif_paths[0])
|
||||||
|
rows = ImageHandler.get_img_height(in_tif_paths[0])
|
||||||
|
workspace_block_tif_path = os.path.join(feature_tif_dir, 'block')
|
||||||
|
workspace_block_feature_path = os.path.join(feature_tif_dir, 'feature')
|
||||||
|
file.creat_dirs([workspace_block_tif_path, workspace_block_feature_path])
|
||||||
|
|
||||||
|
# 特征分块
|
||||||
|
bp = BlockProcess()
|
||||||
|
block_size = bp.get_block_size(rows, cols)
|
||||||
|
|
||||||
|
bp.cut(feature_tif_dir, workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
|
||||||
|
img_dir, img_name = bp.get_file_names(workspace_block_tif_path, ['tif'])
|
||||||
|
dir_dict_all = bp.get_same_img(img_dir, img_name)
|
||||||
|
|
||||||
|
# 选择最优特征子集特征影像
|
||||||
|
dir_dict = {}
|
||||||
|
for n, key in zip(range(len(dir_dict_all)), dir_dict_all):
|
||||||
|
if n in optimal_feature:
|
||||||
|
dir_dict.update({key: dir_dict_all[key]})
|
||||||
|
logger.info('test_feature:%s', dir_dict.keys())
|
||||||
|
logger.info('blocking tifs success!')
|
||||||
|
X_test_list = []
|
||||||
|
# 特征维度合并
|
||||||
|
for key in dir_dict:
|
||||||
|
key_name = key
|
||||||
|
block_num = len(dir_dict[key])
|
||||||
|
break
|
||||||
|
for n in range(block_num):
|
||||||
|
name = os.path.basename(dir_dict[key_name][n])
|
||||||
|
suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + name.split('_')[-1]
|
||||||
|
features_path = os.path.join(workspace_block_feature_path, "features" + suffix) # + "\\features" + suffix
|
||||||
|
X_test_list.append(features_path)
|
||||||
|
features_array = np.zeros((len(dir_dict), block_size, block_size), dtype='float32')
|
||||||
|
for m, value in zip(range(len(dir_dict)), dir_dict.values()):
|
||||||
|
features_array[m, :, :] = ImageHandler.get_band_array(value[n])
|
||||||
|
features_array[np.isnan(features_array)] = 0.0 # 异常值转为0
|
||||||
|
ImageHandler.write_img(features_path, '', [0, 0, 0, 0, 0, 0], features_array)
|
||||||
|
logger.info('create features matrix success!')
|
||||||
|
# file.del_folder(workspace_block_tif_path)
|
||||||
|
# file.del_folder(workspace_block_feature_path)
|
||||||
|
return X_test_list
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def predict_blok(clf, X_test, rows, cols, img_path, row_begin, col_begin, block_sum, n):
|
||||||
|
logger.info('total:%s,block:%s testing data !path:%s', block_sum, n, img_path)
|
||||||
|
|
||||||
|
Y_test = clf.predict(X_test)
|
||||||
|
img = Y_test.reshape(rows, cols)
|
||||||
|
out_image = Image.fromarray(img)
|
||||||
|
out_image.save(img_path)
|
||||||
|
# bp = BlockProcess()
|
||||||
|
# bp.assign_spatial_reference_bypoint(row_begin, col_begin, self.__proj, self.__geo, img_path)
|
||||||
|
# sr = osr.SpatialReference()
|
||||||
|
# sr.ImportFromWkt(self.__proj)
|
||||||
|
# geo_transform = (self.__geo[0] + col_begin * self.__geo[1] + row_begin * self.__geo[2],
|
||||||
|
# self.__geo[1],
|
||||||
|
# self.__geo[2],
|
||||||
|
# self.__geo[3] + col_begin * self.__geo[4] + row_begin * self.__geo[5],
|
||||||
|
# self.__geo[4],
|
||||||
|
# self.__geo[5]
|
||||||
|
# )
|
||||||
|
# dst_ds = gdal.Open(img_path, gdal.GA_Update)
|
||||||
|
# if dst_ds is None:
|
||||||
|
# return False
|
||||||
|
# dst_ds.SetProjection(sr.ExportToWkt())
|
||||||
|
# dst_ds.SetGeoTransform(geo_transform)
|
||||||
|
# del dst_ds
|
||||||
|
logger.info('total:%s,block:%s test data finished !path:%s', block_sum, n, img_path)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def predict(clf, X_test_list, out_tif_name, workspace_processing_path,rows, cols):
|
||||||
|
"""
|
||||||
|
预测数据
|
||||||
|
:param clf : svm模型
|
||||||
|
:return X_test_list: 分块测试集影像路径
|
||||||
|
"""
|
||||||
|
ml = MachineLeaning()
|
||||||
|
# 开启多进程处理
|
||||||
|
bp = BlockProcess()
|
||||||
|
block_size = bp.get_block_size(rows, cols)
|
||||||
|
|
||||||
|
block_features_dir = X_test_list
|
||||||
|
bp_cover_dir = os.path.join(workspace_processing_path, out_tif_name + '\\') # workspace_processing_path + out_tif_name + '\\'
|
||||||
|
file.creat_dirs([bp_cover_dir])
|
||||||
|
|
||||||
|
processes_num = min([len(block_features_dir), multiprocessing.cpu_count() - 1])
|
||||||
|
pool = multiprocessing.Pool(processes=processes_num)
|
||||||
|
|
||||||
|
for path, n in zip(block_features_dir, range(len(block_features_dir))):
|
||||||
|
name = os.path.split(path)[1]
|
||||||
|
features_array = ImageHandler.get_data(path)
|
||||||
|
|
||||||
|
X_test = np.reshape(features_array, (features_array.shape[0], features_array[0].size)).T
|
||||||
|
|
||||||
|
suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + name.split('_')[-1]
|
||||||
|
img_path = os.path.join(bp_cover_dir, out_tif_name + suffix) # bp_cover_dir + out_tif_name + suffix
|
||||||
|
row_begin = int(name.split('_')[-4])
|
||||||
|
col_begin = int(name.split('_')[-2])
|
||||||
|
pool.apply_async(ml.predict_blok, (clf, X_test, block_size, block_size, img_path, row_begin, col_begin, len(block_features_dir), n))
|
||||||
|
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
|
||||||
|
# 合并影像
|
||||||
|
data_dir = bp_cover_dir
|
||||||
|
out_path = workspace_processing_path[0:-1]
|
||||||
|
bp.combine(data_dir, cols, rows, out_path, file_type=['tif'], datetype='float32')
|
||||||
|
|
||||||
|
# 添加地理信息
|
||||||
|
cover_path = os.path.join(workspace_processing_path, out_tif_name + ".tif") # workspace_processing_path + out_tif_name + ".tif"
|
||||||
|
# bp.assign_spatial_reference_byfile(self.__ref_img_path, cover_path)
|
||||||
|
return cover_path
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def predict_VP(clf, X_test_list, out_tif_name, workspace_processing_path, rows, cols):
|
||||||
|
"""
|
||||||
|
预测数据
|
||||||
|
:param clf : svm模型
|
||||||
|
:return X_test_list: 分块测试集影像路径
|
||||||
|
"""
|
||||||
|
ml = MachineLeaning()
|
||||||
|
# 开启多进程处理
|
||||||
|
bp = BlockProcess()
|
||||||
|
block_size = bp.get_block_size(rows, cols)
|
||||||
|
|
||||||
|
block_features_dir = X_test_list
|
||||||
|
bp_cover_dir = os.path.join(workspace_processing_path, out_tif_name,
|
||||||
|
'pre_result\\') # workspace_processing_path + out_tif_name + '\\'
|
||||||
|
file.creat_dirs([bp_cover_dir])
|
||||||
|
|
||||||
|
processes_num = min([len(block_features_dir), multiprocessing.cpu_count() - 1])
|
||||||
|
pool = multiprocessing.Pool(processes=processes_num)
|
||||||
|
|
||||||
|
for path, n in zip(block_features_dir, range(len(block_features_dir))):
|
||||||
|
name = os.path.split(path)[1]
|
||||||
|
features_array = ImageHandler.get_data(path)
|
||||||
|
|
||||||
|
X_test = np.reshape(features_array, (features_array.shape[0], features_array[0].size)).T
|
||||||
|
|
||||||
|
suffix = '_' + name.split('_')[-4] + "_" + name.split('_')[-3] + "_" + name.split('_')[-2] + "_" + \
|
||||||
|
name.split('_')[-1]
|
||||||
|
img_path = os.path.join(bp_cover_dir, out_tif_name + suffix) # bp_cover_dir + out_tif_name + suffix
|
||||||
|
row_begin = int(name.split('_')[-4])
|
||||||
|
col_begin = int(name.split('_')[-2])
|
||||||
|
pool.apply_async(ml.predict_blok, (
|
||||||
|
clf, X_test, block_size, block_size, img_path, row_begin, col_begin, len(block_features_dir), n))
|
||||||
|
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
|
||||||
|
# 合并影像
|
||||||
|
data_dir = bp_cover_dir
|
||||||
|
out_path = workspace_processing_path[0:-1]
|
||||||
|
bp.combine(data_dir, cols, rows, out_path, file_type=['tif'], datetype='float32')
|
||||||
|
|
||||||
|
# 添加地理信息
|
||||||
|
cover_path = os.path.join(workspace_processing_path,
|
||||||
|
out_tif_name + ".tif") # workspace_processing_path + out_tif_name + ".tif"
|
||||||
|
# bp.assign_spatial_reference_byfile(self.__ref_img_path, cover_path)
|
||||||
|
return cover_path
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_name_list(feature_tif_dir):
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(feature_tif_dir, '*.tif')))
|
||||||
|
name_list = []
|
||||||
|
dim = len(in_tif_paths)
|
||||||
|
for n, path in zip(range(dim), in_tif_paths):
|
||||||
|
name_list.append(str(n)+': '+os.path.split(path)[1])
|
||||||
|
logger.info('feature_list:%s', name_list)
|
||||||
|
return name_list
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def gene_train_set(train_data_dic, feature_tif_dir):
|
||||||
|
"""
|
||||||
|
生成训练集
|
||||||
|
:param train_data_dic : 从csv读取的训练数据
|
||||||
|
:param feature_tif_dir : 特征影像路径路径
|
||||||
|
:return X_train, Y_train : 训练数据
|
||||||
|
"""
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(feature_tif_dir, '*.tif')))
|
||||||
|
dim = len(in_tif_paths)
|
||||||
|
X_train = np.empty(shape=(0, dim))
|
||||||
|
Y_train = np.empty(shape=(0, 1))
|
||||||
|
|
||||||
|
ids = train_data_dic['ids']
|
||||||
|
positions = train_data_dic['positions']
|
||||||
|
for id, points in zip(ids, positions):
|
||||||
|
# for data in train_data_list:
|
||||||
|
if points == []:
|
||||||
|
raise Exception('data is empty!')
|
||||||
|
row, col = zip(*points)
|
||||||
|
l = len(points)
|
||||||
|
X = np.empty(shape=(l, dim))
|
||||||
|
|
||||||
|
for n, tif_path in zip(range(dim), in_tif_paths):
|
||||||
|
feature_array = ImageHandler.get_data(tif_path)
|
||||||
|
feature_array[np.isnan(feature_array)] = 0 # 异常值填充为0
|
||||||
|
x = feature_array[row, col].T
|
||||||
|
X[:, n] = x
|
||||||
|
|
||||||
|
Y = np.full((l, 1), id)
|
||||||
|
X_train = np.vstack((X_train, X))
|
||||||
|
Y_train = np.vstack((Y_train, Y))
|
||||||
|
Y_train = Y_train.T[0, :]
|
||||||
|
|
||||||
|
logger.info("gene_train_set success!")
|
||||||
|
return X_train, Y_train
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def standardization(data, num=1):
|
||||||
|
# 矩阵标准化到[0,1]
|
||||||
|
min = np.nanmin(data)
|
||||||
|
max = np.nanmax(data)
|
||||||
|
data[np.isnan(data)] = min # 异常值填充为0
|
||||||
|
_range = max - min
|
||||||
|
return (data - min) / _range * num
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def sel_optimal_feature_set(X_train, Y_train, threshold=0.01):
|
||||||
|
"""
|
||||||
|
筛选最优特征组合(极度随机树）
|
||||||
|
"""
|
||||||
|
model = ExtraTreesClassifier()
|
||||||
|
max = np.max(Y_train)
|
||||||
|
if max < 0.1:
|
||||||
|
Y_train = (Y_train*10000).astype('int')
|
||||||
|
model.fit(X_train, Y_train.astype('int'))
|
||||||
|
# select the relative importance of each attribute
|
||||||
|
importances = model.feature_importances_
|
||||||
|
logger.info('importances:%s,threshold=%s', importances, threshold)
|
||||||
|
|
||||||
|
importances_resort = -np.sort(-importances) # 从大到小排序
|
||||||
|
imp_argsort = np.argsort(-importances) # 输出从大到小的序号
|
||||||
|
|
||||||
|
optimal_feature = list(imp_argsort[np.where(importances_resort > threshold)]) # 过滤重要性低的特征
|
||||||
|
logger.info('optimal_feature:%s', optimal_feature)
|
||||||
|
|
||||||
|
if len(optimal_feature)==0:
|
||||||
|
logger.error('optimal_feature is empty')
|
||||||
|
optimal_feature = list(imp_argsort)
|
||||||
|
return optimal_feature
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def correlation_map(x, y):
|
||||||
|
# https://blog.csdn.net/weixin_39836726/article/details/110783640
|
||||||
|
# cc matrix based on scipy pearsonr
|
||||||
|
n_row_x = x.shape[0]
|
||||||
|
n_row_y = x.shape[0]
|
||||||
|
ccmtx_xy = np.empty((n_row_x, n_row_y))
|
||||||
|
for n in range(n_row_x):
|
||||||
|
for m in range(n_row_y):
|
||||||
|
ccmtx_xy[n, m] = pearsonr(x[n, :], y[m, :])[0]
|
||||||
|
return ccmtx_xy
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def remove_correlation_feature(X_train,validity_list, threshold=0.85):
|
||||||
|
"""
|
||||||
|
相关性抑制,去除相关性
|
||||||
|
:param X_train : 训练集
|
||||||
|
:param validity_list : 最优特征子集
|
||||||
|
:param threshold: 相关性阈值
|
||||||
|
:return validity_list : 最优特征子集
|
||||||
|
"""
|
||||||
|
ccmtx = MachineLeaning().correlation_map(X_train[:, validity_list].T, X_train[:, validity_list].T)
|
||||||
|
ccmtx = np.abs(ccmtx)
|
||||||
|
for r in range(len(validity_list)):
|
||||||
|
for c in range(len(validity_list)):
|
||||||
|
if c <= r:
|
||||||
|
ccmtx[r, c] = 0
|
||||||
|
logger.info('correlation_map:\n %s', ccmtx)
|
||||||
|
# 相关性大于0.85的特征，删除com_sep_coef较大的特征
|
||||||
|
high_corr = np.unique(np.where(ccmtx > threshold)[1]) # 删除的特征序号
|
||||||
|
validity_list = np.delete(validity_list, high_corr)
|
||||||
|
logger.info('validity_list_corr:%s', validity_list)
|
||||||
|
logger.info(validity_list)
|
||||||
|
return validity_list
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def gene_train_data(block_features_dir,rows,cols,block_size,measured_data_img):
|
||||||
|
# 生成训练集
|
||||||
|
X_train = []
|
||||||
|
Y_train = []
|
||||||
|
|
||||||
|
block_rows = int(np.ceil(rows/block_size))
|
||||||
|
block_cols = int(np.ceil(cols/block_size))
|
||||||
|
|
||||||
|
for data, n in zip(measured_data_img, range(len(measured_data_img))):
|
||||||
|
row = data[0]
|
||||||
|
col = data[1]
|
||||||
|
block_row = row//block_size
|
||||||
|
block_col = col//block_size
|
||||||
|
|
||||||
|
if block_row == block_rows-1:
|
||||||
|
part_img_row = row - (rows - block_size)
|
||||||
|
else:
|
||||||
|
part_img_row = row % block_size
|
||||||
|
|
||||||
|
if block_col == block_cols-1:
|
||||||
|
part_img_col = col - (cols-block_size)
|
||||||
|
else:
|
||||||
|
part_img_col = col % block_size
|
||||||
|
|
||||||
|
features_path = block_features_dir[block_row*block_rows + block_col]
|
||||||
|
features_array = ImageHandler().get_data(features_path)
|
||||||
|
|
||||||
|
feature = features_array[:, part_img_row, part_img_col]
|
||||||
|
if not np.isnan(feature).any() or np.isinf(feature).any():
|
||||||
|
X_train.append(list(feature))
|
||||||
|
Y_train.append([data[2]])
|
||||||
|
logger.info('total:%s,num:%s create train set success!', len(measured_data_img), n)
|
||||||
|
return np.array(X_train), np.array(Y_train)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trainRF(X_train, Y_train):
|
||||||
|
#随机森林
|
||||||
|
logger.info('RF trainning')
|
||||||
|
clf = RandomForestClassifier()
|
||||||
|
clf.fit(X_train, Y_train)
|
||||||
|
return clf
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trainSVM(X_train, Y_train, cost=1, kernel='rbf'):
|
||||||
|
logger.info('svm trainning')
|
||||||
|
clf = SVC(decision_function_shape='ovo')
|
||||||
|
clf.fit(X_train, Y_train)
|
||||||
|
SVC(C=cost, cache_size=1000, class_weight='balanced', coef0=0.0, decision_function_shape='ovr',
|
||||||
|
degree=3, gamma='auto', kernel=kernel, max_iter=-1, probability=False, random_state=None,
|
||||||
|
shrinking=True, tol=0.001, verbose=True)
|
||||||
|
return clf
|
||||||
|
@staticmethod
|
||||||
|
def vegetationPhenology_combine_feature(feature_dir,workspace_processing_path, name, rows, cols, debug =False):
|
||||||
|
ml = MachineLeaning()
|
||||||
|
path_list = list(glob.glob(os.path.join(feature_dir, '*.tif')))
|
||||||
|
#多维矩阵合并为一个
|
||||||
|
name_featuresPath_dic = {}
|
||||||
|
dim = len(path_list)
|
||||||
|
features_path = workspace_processing_path + name + "/"+ name +'_features.tif'
|
||||||
|
if debug== False:
|
||||||
|
features_array = np.zeros((dim, rows, cols), dtype='float16')
|
||||||
|
for m, path in zip(range(dim), path_list):
|
||||||
|
data = ImageHandler.get_data(path)
|
||||||
|
data = ml.standardization(data)
|
||||||
|
features_array[m, :, :] = data
|
||||||
|
# 异常值转为0
|
||||||
|
features_array[np.isnan(features_array)] = 0.0
|
||||||
|
features_array[np.isinf(features_array)] = 0.0
|
||||||
|
ImageHandler.write_img(features_path, '', [0, 0, 0, 0, 0, 0], features_array)
|
||||||
|
name_featuresPath_dic.update({name: features_path})
|
||||||
|
return name_featuresPath_dic
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,491 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:AHVToPolsarpro.py
|
||||||
|
@Function:全极化影像转成polsarpro格式T3数据
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/9/18 16:44
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import glob
|
||||||
|
import struct
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
|
||||||
|
|
||||||
|
class AHVToPolsarpro:
|
||||||
|
"""
|
||||||
|
全极化影像转换为bin格式T3矩阵，支持polsarpro处理
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, hh_hv_vh_vv_path_list=[]):
|
||||||
|
self._hh_hv_vh_vv_path_list = hh_hv_vh_vv_path_list
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __ahv_to_s2_veg(ahv_dir):
|
||||||
|
"""
|
||||||
|
全极化影像转S2矩阵
|
||||||
|
:param ahv_dir: 全极化影像文件夹路径
|
||||||
|
:return: 极化散射矩阵S2
|
||||||
|
"""
|
||||||
|
global s11
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(ahv_dir, '*.tiff')))
|
||||||
|
in_tif_paths += in_tif_paths1
|
||||||
|
s11, s12, s21, s22 = None, None, None, None
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
|
||||||
|
# 获取极化类型
|
||||||
|
if '_HH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s11 = data_real + 1j * data_imag
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif '_HV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s12 = data_real + 1j * data_imag
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif '_VH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s21 = data_real + 1j * data_imag
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif '_VV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s22 = data_real + 1j * data_imag
|
||||||
|
flag_list[3] = 1
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('HH or HV or VH or VV is not in path :%s', ahv_dir)
|
||||||
|
return s11, s12, s21, s22
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __ahv_to_s2_soil(ahv_dir):
|
||||||
|
"""
|
||||||
|
全极化影像转S2矩阵
|
||||||
|
:param ahv_dir: 全极化影像文件夹路径
|
||||||
|
:return: 极化散射矩阵S2
|
||||||
|
"""
|
||||||
|
global s11
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(ahv_dir, '*.tiff')))
|
||||||
|
in_tif_paths += in_tif_paths1
|
||||||
|
s11, s12, s21, s22 = None, None, None, None
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
|
||||||
|
# 获取极化类型
|
||||||
|
if 'HH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s11 = data_real + 1j * data_imag
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif 'HV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s12 = data_real + 1j * data_imag
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif 'VH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s21 = data_real + 1j * data_imag
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif 'VV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s22 = data_real + 1j * data_imag
|
||||||
|
flag_list[3] = 1
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('HH or HV or VH or VV is not in path :%s', ahv_dir)
|
||||||
|
return s11, s12, s21, s22
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __ahv_to_s2_list(ahv_path_list):
|
||||||
|
"""
|
||||||
|
全极化影像转S2矩阵
|
||||||
|
:param ahv_dir: 全极化影像文件夹路径
|
||||||
|
:return: 极化散射矩阵S2
|
||||||
|
"""
|
||||||
|
global s11
|
||||||
|
in_tif_paths = ahv_path_list
|
||||||
|
s11, s12, s21, s22 = None, None, None, None
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
|
||||||
|
# 获取极化类型
|
||||||
|
if 'HH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s11 = data_real + 1j * data_imag
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif 'HV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s12 = data_real + 1j * data_imag
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif 'VH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s21 = data_real + 1j * data_imag
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif 'VV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s22 = data_real + 1j * data_imag
|
||||||
|
flag_list[3] = 1
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('HH or HV or VH or VV is not in path')
|
||||||
|
return s11, s12, s21, s22
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __ahv_to_s2_list_2(hh_hv_vh_vv_path_list):
|
||||||
|
"""
|
||||||
|
全极化影像转S2矩阵
|
||||||
|
:param ahv_dir: 全极化影像文件夹路径
|
||||||
|
:return: 极化散射矩阵S2
|
||||||
|
"""
|
||||||
|
global s11
|
||||||
|
in_tif_paths = hh_hv_vh_vv_path_list
|
||||||
|
s11, s12, s21, s22 = None, None, None, None
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
for in_tif_path, n in zip(in_tif_paths, range(len(in_tif_paths))):
|
||||||
|
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
|
||||||
|
# 获取极化类型
|
||||||
|
if n == 0:
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s11 = data_real + 1j * data_imag
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif n == 1:
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s12 = data_real + 1j * data_imag
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif n == 2:
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s21 = data_real + 1j * data_imag
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif n == 3:
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s22 = data_real + 1j * data_imag
|
||||||
|
flag_list[3] = 1
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('HH or HV or VH or VV is not in path')
|
||||||
|
return s11, s12, s21, s22
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __s2_to_t3(s11, s12, s21, s22):
|
||||||
|
"""
|
||||||
|
S2矩阵转T3矩阵
|
||||||
|
:param s11: HH极化数据
|
||||||
|
:param s12: HV极化数据
|
||||||
|
:param s21: VH极化数据
|
||||||
|
:param s22: VV极化数据
|
||||||
|
:return: 极化相干矩阵T3
|
||||||
|
"""
|
||||||
|
HH = s11
|
||||||
|
HV = s12
|
||||||
|
VH = s21
|
||||||
|
VV = s22
|
||||||
|
|
||||||
|
t11 = (np.abs(HH + VV)) ** 2 / 2
|
||||||
|
t12 = (HH + VV) * np.conj(HH - VV) / 2
|
||||||
|
t13 = (HH + VV) * np.conj(HV + VH)
|
||||||
|
|
||||||
|
t21 = (HH - VV) * np.conj(HH + VV) / 2
|
||||||
|
t22 = np.abs(HH - VV) ** 2 / 2
|
||||||
|
t23 = (HH - VV) * np.conj(HV + VH)
|
||||||
|
|
||||||
|
t31 = (HV + VH) * np.conj(HH + VV)
|
||||||
|
t32 = (HV + VH) * np.conj(HH - VV)
|
||||||
|
t33 = 2 * np.abs(HV + VH) ** 2
|
||||||
|
return t11, t12, t13, t21, t22, t23, t31, t32, t33
|
||||||
|
|
||||||
|
def __t3_to_polsarpro_t3(self, out_dir, t11, t12, t13, t22, t23, t33):
|
||||||
|
"""
|
||||||
|
T3矩阵转bin格式，支持 polsarpro处理
|
||||||
|
:param out_dir: 输出的文件夹路径
|
||||||
|
:param t11:
|
||||||
|
:param t12:
|
||||||
|
:param t13:
|
||||||
|
:param t22:
|
||||||
|
:param t23:
|
||||||
|
:param t33:
|
||||||
|
:return: bin格式矩阵T3和头文件
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
|
||||||
|
rows = t11.shape[0]
|
||||||
|
cols = t11.shape[1]
|
||||||
|
bins_dict = {
|
||||||
|
'T11.bin': t11,
|
||||||
|
'T12_real.bin': t12.real,
|
||||||
|
'T12_imag.bin': t12.imag,
|
||||||
|
'T13_real.bin': t13.real,
|
||||||
|
'T13_imag.bin': t13.imag,
|
||||||
|
'T22.bin': t22,
|
||||||
|
'T23_real.bin': t23.real,
|
||||||
|
'T23_imag.bin': t23.imag,
|
||||||
|
'T33.bin': t33}
|
||||||
|
|
||||||
|
for name, data in bins_dict.items():
|
||||||
|
bin_path = os.path.join(out_dir, name)
|
||||||
|
self.__write_img_bin(data, bin_path) # todo 修改T3阵保存方式
|
||||||
|
# data.tofile(bin_path)
|
||||||
|
out_hdr_path = bin_path + '.hdr'
|
||||||
|
self.__write_bin_hdr(out_hdr_path, bin_path, rows, cols)
|
||||||
|
|
||||||
|
self.__write_config_file(out_dir, rows, cols)
|
||||||
|
|
||||||
|
def rows(self):
|
||||||
|
"""获取影像行数"""
|
||||||
|
return self._rows
|
||||||
|
|
||||||
|
def cols(self):
|
||||||
|
"""获取影像列数"""
|
||||||
|
return self._cols
|
||||||
|
|
||||||
|
def __write_img_bin(self, im, file_path):
|
||||||
|
"""
|
||||||
|
写入影像到bin文件中，保存为float32类型
|
||||||
|
:param im : 影像矩阵数据，暂支持单通道影像数据
|
||||||
|
:param file_path: bin文件的完整路径
|
||||||
|
"""
|
||||||
|
with open(file_path, 'wb') as f:
|
||||||
|
self._rows = im.shape[0]
|
||||||
|
self._cols = im.shape[1]
|
||||||
|
for row in range(self._rows):
|
||||||
|
im_bin = struct.pack("f" * self._cols, *np.reshape(im[row, :], (self._cols, 1), order='F'))
|
||||||
|
f.write(im_bin)
|
||||||
|
f.close()
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_bin_hdr(out_hdr_path, bin_path, rows, cols):
|
||||||
|
"""
|
||||||
|
写入影像的头文件
|
||||||
|
:param out_hdr_path : 头文件的路径
|
||||||
|
:param bin_path: bin文件的路径
|
||||||
|
:param rows: 影像的行数
|
||||||
|
:param cols: 影像的列数
|
||||||
|
"""
|
||||||
|
h1 = 'ENVI'
|
||||||
|
h2 = 'description = {'
|
||||||
|
h3 = 'File Imported into ENVI. }'
|
||||||
|
h4 = 'samples = ' + str(cols) # 列
|
||||||
|
h5 = 'lines = ' + str(rows) # 行
|
||||||
|
h6 = 'bands = 1 ' # 波段数
|
||||||
|
h7 = 'header offset = 0'
|
||||||
|
h8 = 'file type = ENVI Standard'
|
||||||
|
h9 = 'data type = 4' # 数据格式
|
||||||
|
h10 = 'interleave = bsq' # 存储格式
|
||||||
|
h11 = 'sensor type = Unknown'
|
||||||
|
h12 = 'byte order = 0'
|
||||||
|
h13 = 'band names = {'
|
||||||
|
h14 = bin_path + '}'
|
||||||
|
# h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11, h12, h13, h14]
|
||||||
|
# doc = open(out_hdr_path, 'w')
|
||||||
|
# for i in range(0, 14):
|
||||||
|
# print(h[i], end='', file=doc)
|
||||||
|
# print('\n', end='', file=doc)
|
||||||
|
h = [h1, h4, h5, h6, h7, h8, h9, h10, h12]
|
||||||
|
doc = open(out_hdr_path, 'w')
|
||||||
|
for i in range(0, 9):
|
||||||
|
print(h[i], end='', file=doc)
|
||||||
|
print('\n', end='', file=doc)
|
||||||
|
doc.close()
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_config_file(out_config_dir, rows, cols):
|
||||||
|
"""
|
||||||
|
写入polsarpro配置文件
|
||||||
|
:param out_config_dir : 配置文件路径
|
||||||
|
:param rows: 影像的行数
|
||||||
|
:param cols: 影像的列数
|
||||||
|
"""
|
||||||
|
h1 = 'Nrow'
|
||||||
|
h2 = str(rows)
|
||||||
|
h3 = '---------'
|
||||||
|
h4 = 'Ncol'
|
||||||
|
h5 = str(cols)
|
||||||
|
h6 = '---------'
|
||||||
|
h7 = 'PolarCase'
|
||||||
|
h8 = 'monostatic'
|
||||||
|
h9 = '---------'
|
||||||
|
h10 = 'PolarType'
|
||||||
|
h11 = 'full'
|
||||||
|
h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11]
|
||||||
|
|
||||||
|
out_config_path = os.path.join(out_config_dir, 'config.txt')
|
||||||
|
doc = open(out_config_path, 'w')
|
||||||
|
for i in range(0, 11):
|
||||||
|
print(h[i], end='', file=doc)
|
||||||
|
print('\n', end='', file=doc)
|
||||||
|
doc.close()
|
||||||
|
|
||||||
|
def incidence_tif2bin(self, incidence_file, out_path):
|
||||||
|
if not os.path.exists(out_path):
|
||||||
|
os.mkdir(out_path)
|
||||||
|
incidence_bin = os.path.join(out_path, 'incidence.bin')
|
||||||
|
data = ImageHandler().get_data(incidence_file)
|
||||||
|
rows = data.shape[0]
|
||||||
|
cols = data.shape[1]
|
||||||
|
self.__write_img_bin(data, incidence_bin)
|
||||||
|
if not os.path.exists(incidence_bin):
|
||||||
|
raise Exception('incidence to bin failed')
|
||||||
|
out_hdr_path = incidence_bin + '.hdr'
|
||||||
|
self.__write_bin_hdr(out_hdr_path, incidence_bin, rows, cols)
|
||||||
|
return incidence_bin
|
||||||
|
|
||||||
|
def ahv_to_polsarpro_t3_veg(self, out_file_dir, in_ahv_dir=''):
|
||||||
|
|
||||||
|
if self._hh_hv_vh_vv_path_list == [] :
|
||||||
|
s11, s12, s21, s22 = self.__ahv_to_s2_veg(in_ahv_dir)
|
||||||
|
else:
|
||||||
|
s11, s12, s21, s22 = self.__ahv_to_s2_list_2(self._hh_hv_vh_vv_path_list)
|
||||||
|
|
||||||
|
t11, t12, t13, t21, t22, t23, t31, t32, t33 = self.__s2_to_t3(
|
||||||
|
s11, s12, s21, s22)
|
||||||
|
|
||||||
|
self.__t3_to_polsarpro_t3(out_file_dir, t11, t12, t13, t22, t23, t33)
|
||||||
|
|
||||||
|
|
||||||
|
def ahv_to_polsarpro_t3_soil(self, out_file_dir, in_ahv_dir=''):
|
||||||
|
|
||||||
|
if self._hh_hv_vh_vv_path_list == [] :
|
||||||
|
s11, s12, s21, s22 = self.__ahv_to_s2_soil(in_ahv_dir)
|
||||||
|
else:
|
||||||
|
s11, s12, s21, s22 = self.__ahv_to_s2_list_2(self._hh_hv_vh_vv_path_list)
|
||||||
|
|
||||||
|
t11, t12, t13, t21, t22, t23, t31, t32, t33 = self.__s2_to_t3(
|
||||||
|
s11, s12, s21, s22)
|
||||||
|
|
||||||
|
self.__t3_to_polsarpro_t3(out_file_dir, t11, t12, t13, t22, t23, t33)
|
||||||
|
|
||||||
|
def calibration(self, calibration_value, in_ahv_dir='', name=''):
|
||||||
|
if name == '':
|
||||||
|
out_dir = os.path.join(in_ahv_dir, 'calibration')
|
||||||
|
else:
|
||||||
|
out_dir = os.path.join(in_ahv_dir, name, 'calibration')
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
if self._hh_hv_vh_vv_path_list == []: # 地表覆盖、土壤盐碱度
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(in_ahv_dir, '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(in_ahv_dir, '*.tiff')))
|
||||||
|
in_tif_paths += in_tif_paths1
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
name = os.path.basename(in_tif_path)
|
||||||
|
data_new = np.zeros(data.shape)
|
||||||
|
# 获取极化类型
|
||||||
|
if 'HH' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[0]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[0]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif 'HV' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[1]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[1]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif 'VH' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[2]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[2]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif 'VV' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[3]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[3]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[3] = 1
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('calibration error! ')
|
||||||
|
else:
|
||||||
|
for in_tif_path in self._hh_hv_vh_vv_path_list: # 植被物候
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
name = os.path.basename(in_tif_path)
|
||||||
|
data_new = np.zeros(data.shape)
|
||||||
|
|
||||||
|
# 获取极化类型
|
||||||
|
if '_HH' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[0]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[0]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif '_HV' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[1]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[1]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif '_VH' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[2]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[2]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif '_VV' in os.path.basename(in_tif_path):
|
||||||
|
data_new[0, :, :] = data[0, :, :] * calibration_value[3]
|
||||||
|
data_new[1, :, :] = data[1, :, :] * calibration_value[3]
|
||||||
|
ImageHandler.write_img(os.path.join(out_dir, name), proj, geotrans, data_new)
|
||||||
|
flag_list[3] = 1
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('calibration error! ')
|
||||||
|
self._hh_hv_vh_vv_path_list = []
|
||||||
|
return out_dir
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
#实例1：
|
||||||
|
# atp = AHVToPolsarpro()
|
||||||
|
# ahv_path = 'D:\\DATA\\GAOFEN3\\2-GF3_MYN_WAV_020086_E107.2_N27.6_20200603_L1A_AHV_L10004843087\\'
|
||||||
|
# # ahv_path = 'D:\\DATA\\GAOFEN3\\2598957_Paris\\'
|
||||||
|
# out_file_path = 'D:\\bintest0923\\'
|
||||||
|
# atp.ahv_to_polsarpro_t3(out_file_path, ahv_path)
|
||||||
|
|
||||||
|
# # 极化分解得到T3矩阵
|
||||||
|
# atp = AHVToPolsarpro()
|
||||||
|
# ahv_path = r"I:\MicroWorkspace\product\C-SAR\SoilSalinity\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1A_AHV_L10000073024_RPC"
|
||||||
|
# t3_path = ahv_path + 'psp_t3\\'
|
||||||
|
# atp.ahv_to_polsarpro_t3(t3_path, ahv_path)
|
||||||
|
|
||||||
|
#实例2：
|
||||||
|
# dir = r'D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\preprocessed/'
|
||||||
|
# path_list = [dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HH_preprocessed.tif',
|
||||||
|
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HV_preprocessed.tif',
|
||||||
|
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VH_preprocessed.tif',
|
||||||
|
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VV_preprocessed.tif']
|
||||||
|
#
|
||||||
|
#
|
||||||
|
# atp = AHVToPolsarpro(path_list)
|
||||||
|
# atp.ahv_to_polsarpro_t3(r'D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\processing\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC/t3')
|
||||||
|
|
||||||
|
print("done")
|
||||||
|
|
@ -0,0 +1,228 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：AHVToPolsarpro.PY
|
||||||
|
@Function ：将四个极化数据转成S2矩阵文件
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import glob
|
||||||
|
import struct
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
|
||||||
|
|
||||||
|
class AHVToPolsarproS2:
|
||||||
|
"""
|
||||||
|
全极化影像转换为bin格式S2矩阵，支持polsarpro处理
|
||||||
|
"""
|
||||||
|
def __init__(self):
|
||||||
|
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __ahv_to_s2(ahv_dir):
|
||||||
|
"""
|
||||||
|
全极化影像转S2矩阵
|
||||||
|
:param ahv_dir: 全极化影像文件夹路径
|
||||||
|
:return: 极化散射矩阵S2
|
||||||
|
"""
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tif')))
|
||||||
|
|
||||||
|
if in_tif_paths == []:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(ahv_dir, '*.tiff')))
|
||||||
|
s11, s12, s21, s22 = None,None,None,None
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
|
||||||
|
# 获取极化类型
|
||||||
|
if 'HH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :] # 获取第一个波段 (实部)
|
||||||
|
data_imag = data[1, :, :] # 获取第二个波段 (虚部)
|
||||||
|
s11 = data_real + 1j * data_imag
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif 'HV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s12 = data_real + 1j * data_imag
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif 'VH' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s21 = data_real + 1j * data_imag
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif 'VV' in os.path.basename(in_tif_path):
|
||||||
|
data_real = data[0, :, :]
|
||||||
|
data_imag = data[1, :, :]
|
||||||
|
s22 = data_real + 1j * data_imag
|
||||||
|
flag_list[3] = 1
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
if not flag_list == [1, 1, 1, 1]:
|
||||||
|
raise Exception('tif of HH or HV or VH or VV is not in path :%s', ahv_dir)
|
||||||
|
return s11, s12, s21, s22
|
||||||
|
|
||||||
|
def __s2_to_bin(self, out_dir, s11, s12, s21, s22):
|
||||||
|
"""
|
||||||
|
S2矩阵转bin格式，支持 polsarpro处理
|
||||||
|
:param out_dir: 输出的文件夹路径
|
||||||
|
:param s11:
|
||||||
|
:param s12:
|
||||||
|
:param s21
|
||||||
|
:param s22:
|
||||||
|
:return: bin格式矩阵S2和头文件
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
|
||||||
|
rows = s11.shape[0]
|
||||||
|
cols = s11.shape[1]
|
||||||
|
bins_dict = {'s11.bin': s11,
|
||||||
|
's12.bin': s12,
|
||||||
|
's21.bin': s21,
|
||||||
|
's22.bin': s22}
|
||||||
|
|
||||||
|
|
||||||
|
for name, data in bins_dict.items():
|
||||||
|
|
||||||
|
bin_path = os.path.join(out_dir, name)
|
||||||
|
self.__write_slc_img_bin(data, bin_path,name)
|
||||||
|
out_hdr_path = bin_path+'.hdr'
|
||||||
|
self.__write_bin_hdr(out_hdr_path, bin_path, rows, cols)
|
||||||
|
|
||||||
|
self.__write_config_file(out_dir, rows, cols)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_slc_img_bin(im, file_path,name):
|
||||||
|
"""
|
||||||
|
写入影像到bin文件中，保存为float32类型
|
||||||
|
:param im : 影像矩阵数据，暂支持单通道影像数据
|
||||||
|
:param file_path: bin文件的完整路径
|
||||||
|
"""
|
||||||
|
with open(file_path, 'wb') as f:
|
||||||
|
rows = im.shape[0]
|
||||||
|
cols = im.shape[1]
|
||||||
|
cre_im = np.zeros((rows, 2*cols), dtype=float)
|
||||||
|
cre_im[:, ::2] = im.real #存 real
|
||||||
|
cre_im[:, 1::2] = im.imag #存 imag
|
||||||
|
for row in range(rows):
|
||||||
|
cre_im_bin = struct.pack("f" * 2*cols, *np.reshape(cre_im[row, :], (2*cols, 1), order='F'))
|
||||||
|
f.write(cre_im_bin)
|
||||||
|
f.close()
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_slc_bin_to_img(bin_path):
|
||||||
|
"""
|
||||||
|
读取bin格式二进制数据，输出为矩阵
|
||||||
|
:param bin_path : bin文件的路径，包含.bin,.config
|
||||||
|
:return : 矩阵信息
|
||||||
|
"""
|
||||||
|
(bin_dir, bin_name) = os.path.split(bin_path)
|
||||||
|
config_path = os.path.join(bin_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
rows = int(config[1])
|
||||||
|
cols = int(config[4])
|
||||||
|
|
||||||
|
bin_file = open(bin_path, 'rb') # 打开二进制文件
|
||||||
|
size = os.path.getsize(bin_path) # 获得文件大小
|
||||||
|
if size < rows * cols * 4 * 2:
|
||||||
|
raise Exception(
|
||||||
|
'bin size less than rows*cols*4! size:',
|
||||||
|
size,
|
||||||
|
'byte, rows:',
|
||||||
|
rows,
|
||||||
|
'cols:',
|
||||||
|
cols)
|
||||||
|
|
||||||
|
bin_data = np.zeros([rows, cols*2], dtype=np.float32)
|
||||||
|
img_array = np.zeros([2,rows, cols], dtype=np.float32)
|
||||||
|
for row in range(rows):
|
||||||
|
data = bin_file.read(4 * cols * 2) # 每次读取一行的二进制数据
|
||||||
|
row_data = struct.unpack('f' * cols*2, data) # 转为一行float数据
|
||||||
|
bin_data[row, :] = row_data
|
||||||
|
bin_file.close()
|
||||||
|
img_array[0] = bin_data[:, ::2] # real
|
||||||
|
img_array[1] = bin_data[:, 1::2] # imag
|
||||||
|
return img_array
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_bin_hdr(out_hdr_path, bin_path, rows, cols):
|
||||||
|
"""
|
||||||
|
写入影像的头文件
|
||||||
|
:param out_hdr_path : 头文件的路径
|
||||||
|
:param bin_path: bin文件的路径
|
||||||
|
:param rows: 影像的行数
|
||||||
|
:param cols: 影像的列数
|
||||||
|
"""
|
||||||
|
h1 = 'ENVI'
|
||||||
|
h2 = 'description = {'
|
||||||
|
h3 = 'ENVI File, Created [] }'
|
||||||
|
h4 = 'samples = ' + str(cols) # 列
|
||||||
|
h5 = 'lines = ' + str(rows) # 行
|
||||||
|
h6 = 'bands = 1 ' # 波段数
|
||||||
|
h7 = 'header offset = 0'
|
||||||
|
h8 = 'file type = ENVI Standard'
|
||||||
|
h9 = 'data type = 6' # 数据格式,6代表复数
|
||||||
|
h10 = 'interleave = bsq' # 存储格式
|
||||||
|
h11 = 'sensor type = Unknown'
|
||||||
|
h12 = 'byte order = 0'
|
||||||
|
h13 = 'wavelength units = Unknown'
|
||||||
|
h14 = 'complex function = Power'
|
||||||
|
h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11, h12, h13, h14]
|
||||||
|
doc = open(out_hdr_path, 'w')
|
||||||
|
for i in range(0, 14):
|
||||||
|
print(h[i], end='', file=doc)
|
||||||
|
print('\n', end='', file=doc)
|
||||||
|
doc.close()
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_config_file(out_config_dir, rows, cols):
|
||||||
|
"""
|
||||||
|
写入polsarpro配置文件
|
||||||
|
:param out_config_dir : 配置文件路径
|
||||||
|
:param rows: 影像的行数
|
||||||
|
:param cols: 影像的列数
|
||||||
|
"""
|
||||||
|
h1 = 'Nrow'
|
||||||
|
h2 = str(rows)
|
||||||
|
h3 = '---------'
|
||||||
|
h4 = 'Ncol'
|
||||||
|
h5 = str(cols)
|
||||||
|
h6 = '---------'
|
||||||
|
h7 = 'PolarCase'
|
||||||
|
# h8 = 'monostatic'
|
||||||
|
h8 = 'bistatic'
|
||||||
|
h9 = '---------'
|
||||||
|
h10 = 'PolarType'
|
||||||
|
h11 = 'full'
|
||||||
|
h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11]
|
||||||
|
|
||||||
|
out_config_path = os.path.join(out_config_dir, 'config.txt')
|
||||||
|
doc = open(out_config_path, 'w')
|
||||||
|
for i in range(0, 11):
|
||||||
|
print(h[i], end='', file=doc)
|
||||||
|
print('\n', end='', file=doc)
|
||||||
|
doc.close()
|
||||||
|
|
||||||
|
def api_ahv_to_polsarpro_s2(self, out_file_dir, in_ahv_dir):
|
||||||
|
|
||||||
|
s11, s12, s21, s22 = self.__ahv_to_s2(in_ahv_dir)
|
||||||
|
|
||||||
|
self.__s2_to_bin(out_file_dir, s11, s12, s21, s22)
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# # test()
|
||||||
|
# atp = AHVToPolsarproS2()
|
||||||
|
# ahv_path = r'D:\DATA\GAOFEN3\2-GF3_MYN_WAV_020086_E107.2_N27.6_20200603_L1A_AHV_L10004843087'
|
||||||
|
# # ahv_path = 'D:\\DATA\\GAOFEN3\\2598957_Paris\\'
|
||||||
|
# out_file_path = r'D:\DATA\GAOFEN3\2-GF3_MYN_WAV_020086_E107.2_N27.6_20200603_L1A_AHV_L10004843087\SLC_SHJ_2'
|
||||||
|
# atp.api_ahv_to_polsarpro_s2(out_file_path, ahv_path)
|
||||||
|
# bin_path = r'D:\DATA\GAOFEN3\2-GF3_MYN_WAV_020086_E107.2_N27.6_20200603_L1A_AHV_L10004843087\SLC_SHJ\s11.bin'
|
||||||
|
# # data = atp.read_slc_bin_to_img(bin_path)
|
||||||
|
# print("done")
|
||||||
|
|
@ -0,0 +1,196 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:DualPolarToPolsarproC2.py
|
||||||
|
@Function:双极化影像转成polsarpro格式C2数据
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/11/5
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import glob
|
||||||
|
import struct
|
||||||
|
import gc
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
|
||||||
|
|
||||||
|
class DualPolarToPolsarproC2:
|
||||||
|
"""
|
||||||
|
双极化影像转换为bin格式C2矩阵，支持polsarpro处理
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __dual_polar_to_c2(dual_polar_dir):
|
||||||
|
"""
|
||||||
|
双影像转S2矩阵
|
||||||
|
:param dual_polar_dir: 双极化影像文件夹路径
|
||||||
|
:return: C2矩阵
|
||||||
|
"""
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(dual_polar_dir, '*.tif')))
|
||||||
|
|
||||||
|
if in_tif_paths == []:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(dual_polar_dir, '*.tiff')))
|
||||||
|
s11, s22 = None, None
|
||||||
|
flag_list = [0, 0, 0, 0]
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
# 读取原始SAR影像
|
||||||
|
proj, geotrans, data = ImageHandler.read_img(in_tif_path)
|
||||||
|
# 获取极化类型
|
||||||
|
if 'HH' in os.path.basename(in_tif_path):
|
||||||
|
s11 = data[0, :, :] + 1j * data[1, :, :]
|
||||||
|
flag_list[0] = 1
|
||||||
|
elif 'HV' in os.path.basename(in_tif_path):
|
||||||
|
s22 = data[0, :, :] + 1j * data[1, :, :]
|
||||||
|
flag_list[1] = 1
|
||||||
|
elif 'VH' in os.path.basename(in_tif_path):
|
||||||
|
s22 = data[0, :, :] + 1j * data[1, :, :]
|
||||||
|
flag_list[2] = 1
|
||||||
|
elif 'VV' in os.path.basename(in_tif_path):
|
||||||
|
s11 = data[0, :, :] + 1j * data[1, :, :]
|
||||||
|
flag_list[3] = 1
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
del data
|
||||||
|
gc.collect()
|
||||||
|
|
||||||
|
if flag_list != [1, 1, 0, 0] and flag_list != [0, 0, 1, 1] :
|
||||||
|
raise Exception('Dual-Polarization SAR is not in path :%s',in_tif_path)
|
||||||
|
|
||||||
|
c11,c12,c22 = None, None, None
|
||||||
|
c11 = np.abs(s11)** 2
|
||||||
|
c12 = s11 * np.conj(s22)
|
||||||
|
del s11
|
||||||
|
gc.collect()
|
||||||
|
c22 = np.abs(s22)**2
|
||||||
|
return c11, c12, c22
|
||||||
|
|
||||||
|
def __c2_to_polsarpro_c2(self, out_dir, c11, c12, c22):
|
||||||
|
"""
|
||||||
|
C2矩阵转bin格式，支持 polsarpro处理
|
||||||
|
:param out_dir: 输出的文件夹路径
|
||||||
|
:param c11:
|
||||||
|
:param c12:
|
||||||
|
:param c21:
|
||||||
|
:param c22:
|
||||||
|
:return: bin格式矩阵C3和头文件
|
||||||
|
"""
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
os.makedirs(out_dir)
|
||||||
|
|
||||||
|
rows = c11.shape[0]
|
||||||
|
cols = c11.shape[1]
|
||||||
|
bins_dict = {
|
||||||
|
'C11.bin': c11,
|
||||||
|
'C12_real.bin': c12.real,
|
||||||
|
'C12_imag.bin': c12.imag,
|
||||||
|
'C22.bin': c22}
|
||||||
|
|
||||||
|
for name, data in bins_dict.items():
|
||||||
|
bin_path = os.path.join(out_dir, name)
|
||||||
|
self.__write_img_bin(data, bin_path)
|
||||||
|
out_hdr_path = bin_path + '.hdr'
|
||||||
|
self.__write_bin_hdr(out_hdr_path, bin_path, rows, cols)
|
||||||
|
|
||||||
|
self.__write_config_file(out_dir, rows, cols)
|
||||||
|
|
||||||
|
def rows(self):
|
||||||
|
"""获取影像行数"""
|
||||||
|
return self._rows
|
||||||
|
|
||||||
|
def cols(self):
|
||||||
|
"""获取影像列数"""
|
||||||
|
return self._cols
|
||||||
|
|
||||||
|
def __write_img_bin(self, im, file_path):
|
||||||
|
"""
|
||||||
|
写入影像到bin文件中，保存为float32类型
|
||||||
|
:param im : 影像矩阵数据，暂支持单通道影像数据
|
||||||
|
:param file_path: bin文件的完整路径
|
||||||
|
"""
|
||||||
|
with open(file_path, 'wb') as f:
|
||||||
|
self._rows = im.shape[0]
|
||||||
|
self._cols = im.shape[1]
|
||||||
|
for row in range(self._rows):
|
||||||
|
im_bin = struct.pack("f" * self._cols, *np.reshape(im[row, :], (self._cols, 1), order='F'))
|
||||||
|
f.write(im_bin)
|
||||||
|
f.close()
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_bin_hdr(out_hdr_path, bin_path, rows, cols):
|
||||||
|
"""
|
||||||
|
写入影像的头文件
|
||||||
|
:param out_hdr_path : 头文件的路径
|
||||||
|
:param bin_path: bin文件的路径
|
||||||
|
:param rows: 影像的行数
|
||||||
|
:param cols: 影像的列数
|
||||||
|
"""
|
||||||
|
name = os.path.split(bin_path)[1]
|
||||||
|
h1 = 'ENVI'
|
||||||
|
h2 = 'description = {'
|
||||||
|
h3 = 'File Imported into ENVI. }'
|
||||||
|
h4 = 'samples = ' + str(cols) # 列
|
||||||
|
h5 = 'lines = ' + str(rows) # 行
|
||||||
|
h6 = 'bands = 1 ' # 波段数
|
||||||
|
h7 = 'header offset = 0'
|
||||||
|
h8 = 'file type = ENVI Standard'
|
||||||
|
h9 = 'data type = 4' # 数据格式 浮点型
|
||||||
|
h10 = 'interleave = bsq' # 存储格式
|
||||||
|
h11 = 'sensor type = Unknown'
|
||||||
|
h12 = 'byte order = 0'
|
||||||
|
h13 = 'band names = {'
|
||||||
|
h14 = name + '}'
|
||||||
|
h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11, h12, h13, h14]
|
||||||
|
doc = open(out_hdr_path, 'w')
|
||||||
|
for i in range(0, 14):
|
||||||
|
print(h[i], end='', file=doc)
|
||||||
|
print('\n', end='', file=doc)
|
||||||
|
doc.close()
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __write_config_file(out_config_dir, rows, cols):
|
||||||
|
"""
|
||||||
|
写入polsarpro配置文件
|
||||||
|
:param out_config_dir : 配置文件路径
|
||||||
|
:param rows: 影像的行数
|
||||||
|
:param cols: 影像的列数
|
||||||
|
"""
|
||||||
|
h1 = 'Nrow'
|
||||||
|
h2 = str(rows)
|
||||||
|
h3 = '---------'
|
||||||
|
h4 = 'Ncol'
|
||||||
|
h5 = str(cols)
|
||||||
|
h6 = '---------'
|
||||||
|
h7 = 'PolarCase'
|
||||||
|
h8 = 'monostatic'
|
||||||
|
h9 = '---------'
|
||||||
|
h10 = 'PolarType'
|
||||||
|
h11 = 'pp1'
|
||||||
|
h = [h1, h2, h3, h4, h5, h6, h7, h8, h9, h10, h11]
|
||||||
|
|
||||||
|
out_config_path = os.path.join(out_config_dir, 'config.txt')
|
||||||
|
doc = open(out_config_path, 'w')
|
||||||
|
for i in range(0, 11):
|
||||||
|
print(h[i], end='', file=doc)
|
||||||
|
print('\n', end='', file=doc)
|
||||||
|
doc.close()
|
||||||
|
|
||||||
|
|
||||||
|
def api_dual_polar__to_polsarpro_c2(self, out_file_dir, dual_polar_dir):
|
||||||
|
c11, c12, c22 = self.__dual_polar_to_c2(dual_polar_dir)
|
||||||
|
self.__c2_to_polsarpro_c2(out_file_dir,c11, c12, c22)
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# tp = DualPolarToPolsarproC2()
|
||||||
|
# out_dic = 'E:\\3-GF3_KAS_FSI_020253_E110.8_N25.5_20200614_L1A_HHHV_L10004871459\\SLC_SHJ1'
|
||||||
|
# in_dic = 'E:\\3-GF3_KAS_FSI_020253_E110.8_N25.5_20200614_L1A_HHHV_L10004871459\\'
|
||||||
|
# # out_file_path = 'D:\\bintest0923\\'
|
||||||
|
# tp.api_dual_polar__to_polsarpro_c2(out_dic,in_dic)
|
||||||
|
# # atp.ahv_to_polsarpro_t3(out_file_path, ahv_path)
|
||||||
|
#
|
||||||
|
# print("done")
|
||||||
|
|
@ -0,0 +1,97 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：onestar
|
||||||
|
@File ：GLDM.py
|
||||||
|
@Contact：
|
||||||
|
scikit-image feature计算图像特征：https://blog.csdn.net/lyxleft/article/details/102904909
|
||||||
|
python如何在二维图像上进行卷积：https://www.xz577.com/j/281686.html
|
||||||
|
利用python的skimage计算灰度共生矩阵：https://zhuanlan.zhihu.com/p/147066037
|
||||||
|
@function ：计算图像灰度共生矩阵
|
||||||
|
@Author ：SHJ
|
||||||
|
@Date ：2021/11/10 14:42
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
import os
|
||||||
|
from skimage.feature import greycomatrix, greycoprops
|
||||||
|
import datetime
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
|
||||||
|
class GLDM:
|
||||||
|
def __init__(self,win_size = 15, step=2,levels=16,angles=[0,45,90,135],
|
||||||
|
prop=['contrast', 'dissimilarity', 'homogeneity', 'energy', 'correlation', 'ASM']):
|
||||||
|
self._win_size = win_size # 计算灰度共生矩阵窗口尺寸，为奇数
|
||||||
|
self._step = step # 步长
|
||||||
|
self._levels = levels # 灰度等级：例如16，256
|
||||||
|
self._angles = list(np.deg2rad(np.array(angles))) #角度，使用弧度制
|
||||||
|
"""
|
||||||
|
'contrast':对比度：反映了图像的清晰度和纹理沟纹深浅的程度
|
||||||
|
'dissimilarity':差异性
|
||||||
|
'homogeneity':同质性/逆差距：度量图像纹理局部变化的多少。其值大则说明图像纹理的不同区域间缺少变化，局部非常均匀。
|
||||||
|
'energy':能量：是灰度共生矩阵元素值的平方和，所以也称能量，反映了图像灰度分布均匀程度和纹理粗细度
|
||||||
|
'correlation':相关性：它度量空间灰度共生矩阵元素在行或列方向上的相似程度
|
||||||
|
'ASM':二阶距
|
||||||
|
"""
|
||||||
|
self._prop = prop #纹理特征名称
|
||||||
|
|
||||||
|
def get_glcm_value(self,input):
|
||||||
|
values_temp = []
|
||||||
|
# 统计得到glcm
|
||||||
|
# 得到共生矩阵，参数：图像矩阵，距离，方向，灰度级别，是否对称，是否标准化
|
||||||
|
# para2: [0, np.pi / 4, np.pi / 2, np.pi * 3 / 4] 一共计算了四个方向，你也可以选择一个方向
|
||||||
|
glcm = greycomatrix(input, [self._step], self._angles, self._levels, symmetric=False, normed=True)
|
||||||
|
# print(glcm.shape)
|
||||||
|
# 循环计算表征纹理的参数
|
||||||
|
for prop in self._prop:
|
||||||
|
temp = greycoprops(glcm, prop)
|
||||||
|
# print(temp)
|
||||||
|
values_temp.append(np.mean(temp))
|
||||||
|
return values_temp
|
||||||
|
|
||||||
|
|
||||||
|
def get_glcm_array(self,inputs: np.ndarray, win_size):
|
||||||
|
h, w = inputs.shape
|
||||||
|
pad = (win_size - 1) // 2
|
||||||
|
inputs = np.pad(inputs, pad_width=[(pad, pad), (pad, pad)], mode="constant", constant_values=0)
|
||||||
|
glcm_array ={}
|
||||||
|
for name in self._prop:
|
||||||
|
glcm_array.update({name:np.zeros(shape=(h, w),dtype=np.float32)})
|
||||||
|
|
||||||
|
for i in range(h): # 行号
|
||||||
|
for j in range(w): # 列号
|
||||||
|
window = inputs[i: i + win_size, j: j + win_size]
|
||||||
|
value = self.get_glcm_value(window)
|
||||||
|
print('i:%s,j:%s',i,j)
|
||||||
|
# print(value)
|
||||||
|
for n,array in zip(range(len(glcm_array)),glcm_array.values()):
|
||||||
|
array[i,j] = value[n]
|
||||||
|
return glcm_array
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def standardization(data, num=1):
|
||||||
|
# 矩阵标准化到[0,1]
|
||||||
|
data[np.isnan(data)] = np.min(data) # 异常值填充为0
|
||||||
|
_range = np.max(data) - np.min(data)
|
||||||
|
return (data - np.min(data)) / _range * num
|
||||||
|
|
||||||
|
def api_get_glcm_array(self,out_dir,in_tif_path,name=''):
|
||||||
|
|
||||||
|
ih = ImageHandler()
|
||||||
|
proj, geotrans, array = ih.read_img(in_tif_path)
|
||||||
|
array[np.where(array > 500000)]=500000 #去除过大的值，避免标准化时，大部分的值都接近0
|
||||||
|
array = self.standardization(array,self._levels-1) #标准化到0~（self._levels-1）
|
||||||
|
array = np.uint8(array)
|
||||||
|
glcm_array = self.get_glcm_array(array, self._win_size)
|
||||||
|
for key,value in glcm_array.items():
|
||||||
|
out_path = os.path.join(out_dir,name+'_'+key+'.tif')
|
||||||
|
ih.write_img(out_path, proj, geotrans,value)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
start = datetime.datetime.now()
|
||||||
|
gldm = GLDM(win_size=9,levels=16,step=3,angles=[0,45,90,135])
|
||||||
|
gldm.api_get_glcm_array('D:\glcm','D:\glcm\src_img.tif',)
|
||||||
|
end = datetime.datetime.now()
|
||||||
|
msg = 'running use time: %s ' % (end - start)
|
||||||
|
print(msg)
|
||||||
|
|
||||||
|
# 666*720尺寸影像消耗的running use time: 0:04:23.155424
|
||||||
|
|
@ -0,0 +1,85 @@
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
import numpy as np
|
||||||
|
import struct
|
||||||
|
from PIL import Image
|
||||||
|
from tool.algorithm.ml.machineLearning import MachineLeaning as ml
|
||||||
|
|
||||||
|
|
||||||
|
def read_bin_to_img(bin_path):
|
||||||
|
"""
|
||||||
|
读取bin格式二进制数据，输出为矩阵
|
||||||
|
:param bin_path : bin文件的路径，包含.bin,.config
|
||||||
|
:return : 矩阵信息
|
||||||
|
"""
|
||||||
|
(bin_dir, bin_name) = os.path.split(bin_path)
|
||||||
|
config_path = os.path.join(bin_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
rows = int(config[1])
|
||||||
|
cols = int(config[4])
|
||||||
|
|
||||||
|
bin_file = open(bin_path, 'rb') # 打开二进制文件
|
||||||
|
size = os.path.getsize(bin_path) # 获得文件大小
|
||||||
|
if size < rows * cols * 4:
|
||||||
|
raise Exception(
|
||||||
|
'bin size less than rows*cols*4! size:',
|
||||||
|
size,
|
||||||
|
'byte, rows:',
|
||||||
|
rows,
|
||||||
|
'cols:',
|
||||||
|
cols)
|
||||||
|
|
||||||
|
img = np.zeros([rows, cols], dtype=np.float32)
|
||||||
|
for row in range(rows):
|
||||||
|
data = bin_file.read(4 * cols) # 每次读取一行的二进制数据
|
||||||
|
row_data = struct.unpack('f' * cols, data) # 转为一行float数据
|
||||||
|
img[row, :] = row_data
|
||||||
|
bin_file.close()
|
||||||
|
return img
|
||||||
|
|
||||||
|
def write_bin_to_tif(out_tif_dir, bin_dir):
|
||||||
|
"""
|
||||||
|
读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
|
||||||
|
:param out_tif_dir : tif的输出路径
|
||||||
|
:param bin_dir : 二进制数据的目录,包含.bin,.config
|
||||||
|
:return out_tif_path: 生成tif的路径字典
|
||||||
|
"""
|
||||||
|
bin_paths = list(glob.glob(os.path.join(bin_dir, '*.bin')))
|
||||||
|
out_tif_path = {}
|
||||||
|
for in_path in bin_paths:
|
||||||
|
name = os.path.split(in_path)[1].split('.')[0]
|
||||||
|
out_path = os.path.join(out_tif_dir, name + '.tif')
|
||||||
|
out_tif_path.update({name: out_path})
|
||||||
|
if os.path.exists(os.path.split(out_path)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(out_path)[0])
|
||||||
|
img_array = read_bin_to_img(in_path)
|
||||||
|
img_array[np.isnan(img_array)] = 0 # 异常值填充为0
|
||||||
|
img_array = ml.standardization(img_array) # 数据标准化到[0,1]
|
||||||
|
out_image = Image.fromarray(img_array)
|
||||||
|
out_image.save(out_path)
|
||||||
|
return out_tif_path
|
||||||
|
|
||||||
|
def write_bin_to_tif_soil(out_tif_dir, bin_dir):
|
||||||
|
"""
|
||||||
|
读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
|
||||||
|
:param out_tif_dir : tif的输出路径
|
||||||
|
:param bin_dir : 二进制数据的目录,包含.bin,.config
|
||||||
|
:return out_tif_path: 生成tif的路径字典
|
||||||
|
"""
|
||||||
|
bin_paths = list(glob.glob(os.path.join(bin_dir, '*.bin')))
|
||||||
|
out_tif_path = {}
|
||||||
|
for in_path in bin_paths:
|
||||||
|
name = os.path.split(in_path)[1].split('.')[0]
|
||||||
|
out_path = os.path.join(out_tif_dir, name + '.tif')
|
||||||
|
out_tif_path.update({name: out_path})
|
||||||
|
if os.path.exists(os.path.split(out_path)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(out_path)[0])
|
||||||
|
img_array = read_bin_to_img(in_path)
|
||||||
|
img_array[np.isnan(img_array)] = 0 # 异常值填充为0
|
||||||
|
# img_array = ml.standardization(img_array) # 数据标准化到[0,1]
|
||||||
|
out_image = Image.fromarray(img_array)
|
||||||
|
out_image.save(out_path)
|
||||||
|
return out_tif_path
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,190 @@
|
||||||
|
from tool.algorithm.algtools.MetaDataHandler import Calibration
|
||||||
|
from tool.algorithm.polsarpro.AHVToPolsarpro import AHVToPolsarpro
|
||||||
|
from tool.algorithm.polsarpro.pspLeeRefinedFilterT3 import LeeRefinedFilterT3
|
||||||
|
from tool.algorithm.polsarpro.pspCloudePottierDecomposition import PspCloudePottierDecomposition
|
||||||
|
from tool.algorithm.polsarpro.pspFreemanDecomposition import PspFreemanDecomposition
|
||||||
|
from tool.algorithm.polsarpro.pspYamaguchiDecomposition import PspYamaguchiDecomposition
|
||||||
|
from tool.algorithm.polsarpro.pspTouziDecomposition import PspTouziDecomposition
|
||||||
|
from tool.algorithm.polsarpro.bin2tif import write_bin_to_tif
|
||||||
|
from tool.algorithm.polsarpro.pspHAAlphaDecomposition import PspHAAlphaDecomposition
|
||||||
|
from tool.algorithm.xml.AlgXmlHandle import InitPara
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import glob
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
class CreateFeature:
|
||||||
|
"""
|
||||||
|
生产特征
|
||||||
|
"""
|
||||||
|
def __init__(self, debug = False, exe_dir = ''):
|
||||||
|
self._debug = debug
|
||||||
|
self._exe_dir = exe_dir
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
def ahv_to_t3(self, workspace_processing_path, workspace_preprocessing_path, hh_hv_vh_vv_list, name='',FILTER_SIZE=3):
|
||||||
|
# 全极化tif转bin格式T3数据
|
||||||
|
atp = AHVToPolsarpro()
|
||||||
|
|
||||||
|
atp = AHVToPolsarpro(hh_hv_vh_vv_list)
|
||||||
|
lee_filter_path = os.path.join(workspace_processing_path, name, 'lee_filter\\') # workspace_processing_path + name + '\\lee_filter\\'
|
||||||
|
if self._debug == False:
|
||||||
|
|
||||||
|
t3_path = os.path.join(workspace_processing_path, name, 'psp_t3\\') # workspace_processing_path + name + '\\psp_t3\\'
|
||||||
|
# atp.ahv_to_polsarpro_t3(t3_path, tif_path)
|
||||||
|
|
||||||
|
polarization = ['HH', 'HV', 'VH', 'VV']
|
||||||
|
if os.path.exists(workspace_preprocessing_path + name + '\\'):
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(workspace_preprocessing_path + name, '*.meta.xml')))
|
||||||
|
meta_dic = InitPara.get_meta_dic_new(meta_xml_paths, name)
|
||||||
|
calibration = Calibration.get_Calibration_coefficient(meta_dic['Origin_META'], polarization)
|
||||||
|
tif_path = atp.calibration(calibration, workspace_preprocessing_path, name)
|
||||||
|
atp.ahv_to_polsarpro_t3_veg(t3_path, tif_path)
|
||||||
|
|
||||||
|
# Lee滤波
|
||||||
|
leeFilter = LeeRefinedFilterT3()
|
||||||
|
leeFilter.api_lee_refined_filter_T3('', t3_path, lee_filter_path, 0, 0, atp.rows(), atp.cols(), FILTER_SIZE)
|
||||||
|
logger.info("refine_lee filter success!")
|
||||||
|
return lee_filter_path
|
||||||
|
|
||||||
|
def decompose(self,workspace_processing_path, name, t3_path, rows, cols, hh_hv_vh_vv_dic={},FeatureInput=['Freeman', 'Yamaguchi', 'Cloude']): # , 'Touzi'
|
||||||
|
"""
|
||||||
|
极化分解：Freeman、Touzi、Yamaguchi、Cloude
|
||||||
|
:param t3_path: t3文件路径
|
||||||
|
:param rows: 影像行数
|
||||||
|
:return cols:影像列数
|
||||||
|
"""
|
||||||
|
# 计算特征组合
|
||||||
|
exeDir = self._exe_dir
|
||||||
|
outFolderDic = {}
|
||||||
|
if 'Freeman' in FeatureInput:
|
||||||
|
# freeman分解
|
||||||
|
freemanOutDir = os.path.join(workspace_processing_path, name + '\\freeman\\')
|
||||||
|
if self._debug == False:
|
||||||
|
freemDecom = PspFreemanDecomposition(exeDir, t3_path, freemanOutDir)
|
||||||
|
flag = freemDecom.api_freeman_decomposition_T3(0, 0, rows, cols)
|
||||||
|
if not flag:
|
||||||
|
logger.error('FreemanDecomposition err')
|
||||||
|
return False, None
|
||||||
|
outFolderDic['Freeman'] = freemanOutDir
|
||||||
|
|
||||||
|
# Touzi分解
|
||||||
|
if 'Touzi' in FeatureInput:
|
||||||
|
|
||||||
|
touziOutDir = os.path.join(workspace_processing_path, name + '\\touzi\\')
|
||||||
|
if not os.path.exists(touziOutDir):
|
||||||
|
os.makedirs(touziOutDir)
|
||||||
|
if self._debug == False:
|
||||||
|
# touzi分解耗时较长，且对特征表达效果较差
|
||||||
|
p = PspTouziDecomposition(hh_hv_vh_vv_dic, touziOutDir)
|
||||||
|
p.Touzi_decomposition_multiprocessing()
|
||||||
|
outFolderDic['Touzi'] = touziOutDir
|
||||||
|
|
||||||
|
if 'Yamaguchi' in FeatureInput:
|
||||||
|
# Yamaguchi分解
|
||||||
|
yamaguchiOutDir = os.path.join(workspace_processing_path, name + '\\yamaguchi\\')
|
||||||
|
if self._debug == False:
|
||||||
|
yamaguchiDecom = PspYamaguchiDecomposition(exeDir, t3_path, yamaguchiOutDir)
|
||||||
|
flag = yamaguchiDecom.api_yamaguchi_4components_decomposition_T3(0, 0, rows, cols)
|
||||||
|
if not flag:
|
||||||
|
logger.error('CloudePottierDecomposition err')
|
||||||
|
return False, None
|
||||||
|
outFolderDic['Yamaguchi'] = yamaguchiOutDir
|
||||||
|
|
||||||
|
if 'Cloude' in FeatureInput:
|
||||||
|
# CloudePottier分解
|
||||||
|
cloudeOutDir = os.path.join(workspace_processing_path, name + '\\cloude\\')
|
||||||
|
if self._debug == False:
|
||||||
|
cloudeDecom = PspCloudePottierDecomposition(
|
||||||
|
exeDir, t3_path, cloudeOutDir)
|
||||||
|
flag = cloudeDecom.api_h_a_alpha_decomposition_T3(
|
||||||
|
0, 0, rows, cols)
|
||||||
|
if not flag:
|
||||||
|
logger.error('CloudePottierDecomposition err')
|
||||||
|
return False, None
|
||||||
|
outFolderDic['Cloude'] = cloudeOutDir
|
||||||
|
return True, outFolderDic
|
||||||
|
|
||||||
|
def creat_h_a_alpha_features(self, t3_path, out_dir):
|
||||||
|
logger.info('ahv transform to polsarpro T3 matrix success!')
|
||||||
|
logger.info('progress bar: 20%')
|
||||||
|
h_a_alpha_decomposition_T3_path = os.path.join(self._exe_dir, 'h_a_alpha_decomposition_T3.exe')
|
||||||
|
h_a_alpha_eigenvalue_set_T3_path = os.path.join(self._exe_dir, 'h_a_alpha_eigenvalue_set_T3.exe')
|
||||||
|
h_a_alpha_eigenvector_set_T3_path = os.path.join(self._exe_dir, 'h_a_alpha_eigenvector_set_T3.exe')
|
||||||
|
|
||||||
|
if self._debug == False:
|
||||||
|
haa = PspHAAlphaDecomposition(normalization=True)
|
||||||
|
haa.api_creat_h_a_alpha_features(h_a_alpha_out_dir=out_dir,
|
||||||
|
h_a_alpha_decomposition_T3_path=h_a_alpha_decomposition_T3_path ,
|
||||||
|
h_a_alpha_eigenvalue_set_T3_path=h_a_alpha_eigenvalue_set_T3_path ,
|
||||||
|
h_a_alpha_eigenvector_set_T3_path=h_a_alpha_eigenvector_set_T3_path,
|
||||||
|
polsarpro_in_dir=t3_path)
|
||||||
|
|
||||||
|
|
||||||
|
def cereat_features_dic(self,outFolderDic, feature_tif_dir):
|
||||||
|
|
||||||
|
if not os.path.exists(feature_tif_dir):
|
||||||
|
os.makedirs(feature_tif_dir)
|
||||||
|
|
||||||
|
feature_tif_paths = {}
|
||||||
|
for key in outFolderDic:
|
||||||
|
feature_bin_dic = outFolderDic[key]
|
||||||
|
if key == 'Touzi':
|
||||||
|
for path in list(glob.glob(os.path.join(feature_bin_dic, '*.tif'))):
|
||||||
|
name = os.path.split(path)[1].split('.')[0]
|
||||||
|
if self._debug == False:
|
||||||
|
shutil.copyfile(path, os.path.join(feature_tif_dir, name + '.tif')) # feature_tif_dir + '\\' + name + '.tif')
|
||||||
|
feature_tif_paths.update({name: os.path.join(feature_tif_dir, name + '.tif')}) # feature_tif_dir + '\\' + name + '.tif'
|
||||||
|
else:
|
||||||
|
feature_tif_paths.update(write_bin_to_tif(feature_tif_dir, feature_bin_dic))
|
||||||
|
return feature_tif_paths
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def decompose_single_tar(hh_hv_vh_vv_list, workspace_processing_path, workspace_preprocessing_path, name, exe_dir, rows, cols, FILTER_SIZE = 3, debug =False, FeatureInput=['Freeman', 'Yamaguchi', 'Cloude']):
|
||||||
|
hh_hv_vh_vv_dic = {}
|
||||||
|
hh_hv_vh_vv_dic.update({'HH': hh_hv_vh_vv_list[0]})
|
||||||
|
hh_hv_vh_vv_dic.update({'HV': hh_hv_vh_vv_list[1]})
|
||||||
|
hh_hv_vh_vv_dic.update({'VH': hh_hv_vh_vv_list[2]})
|
||||||
|
hh_hv_vh_vv_dic.update({'VV': hh_hv_vh_vv_list[3]})
|
||||||
|
t3_path = os.path.join(workspace_processing_path, name, "lee_filter") # workspace_processing_path + name + "\\lee_filter"
|
||||||
|
feature_tif_dir = os.path.join(workspace_processing_path, name, 'features') # workspace_processing_path + name + "\\features"
|
||||||
|
|
||||||
|
cfeature = CreateFeature(debug, exe_dir)
|
||||||
|
|
||||||
|
cfeature.creat_h_a_alpha_features(t3_path, feature_tif_dir)
|
||||||
|
|
||||||
|
t3_path = cfeature.ahv_to_t3(workspace_processing_path, workspace_preprocessing_path, hh_hv_vh_vv_list, name, FILTER_SIZE)
|
||||||
|
flag, outFolderDic = cfeature.decompose(workspace_processing_path, name, t3_path, rows, cols, hh_hv_vh_vv_dic, FeatureInput) # , 'Touzi'
|
||||||
|
cfeature.cereat_features_dic(outFolderDic, feature_tif_dir)
|
||||||
|
return feature_tif_dir
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
# # 实例1：
|
||||||
|
# exe_dir = os.getcwd()
|
||||||
|
# dir = r'D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\preprocessed/'
|
||||||
|
# hh_hv_vh_vv_list = [dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HH_preprocessed.tif',
|
||||||
|
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HV_preprocessed.tif',
|
||||||
|
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VH_preprocessed.tif',
|
||||||
|
# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VV_preprocessed.tif']
|
||||||
|
#
|
||||||
|
# workspace_processing_path= r"D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\processing/"
|
||||||
|
# name= 'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC'
|
||||||
|
# hh_hv_vh_vv_dic = {}
|
||||||
|
# hh_hv_vh_vv_dic.update({'HH': hh_hv_vh_vv_list[0]})
|
||||||
|
# hh_hv_vh_vv_dic.update({'HV': hh_hv_vh_vv_list[1]})
|
||||||
|
# hh_hv_vh_vv_dic.update({'VH': hh_hv_vh_vv_list[2]})
|
||||||
|
# hh_hv_vh_vv_dic.update({'VV': hh_hv_vh_vv_list[3]})
|
||||||
|
# t3_path = workspace_processing_path + name + "\\lee_filter"
|
||||||
|
# feature_tif_dir = workspace_processing_path + name + "\\features"
|
||||||
|
#
|
||||||
|
# cfeature = CreateFeature(False, exe_dir)
|
||||||
|
#
|
||||||
|
# cfeature.creat_h_a_alpha_features(t3_path, feature_tif_dir)
|
||||||
|
#
|
||||||
|
# t3_path = cfeature.ahv_to_t3(workspace_processing_path, hh_hv_vh_vv_list, name, 3)
|
||||||
|
# flag, outFolderDic = cfeature.decompose(workspace_processing_path, name, t3_path, 997, 1227, hh_hv_vh_vv_dic, FeatureInput=['Freeman', 'Touzi', 'Yamaguchi', 'Cloude'])
|
||||||
|
#
|
||||||
|
# feature_tifs_dic = cfeature.cereat_features_dic(outFolderDic, feature_tif_dir)
|
||||||
|
pass
|
||||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,132 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspHAAlphaDecomposition.py Cloude-Pottier分解
|
||||||
|
@Function: Cloude-Pottier eigenvector/eigenvalue based decomposition of a 3x3 coherency matrix [T3]
|
||||||
|
(Averaging using a sliding window)
|
||||||
|
V1.0.1:（1）可选分解特征；（2）bin转tif格式
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/9/24 9:06
|
||||||
|
@Version:1.0.1
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
import logging
|
||||||
|
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class PspCloudePottierDecomposition:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的Cloude-Pottier极化分解 h_a_alpha_decomposition_T3.exe
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
exeDecomposeName='h_a_alpha_decomposition_T3.exe'):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
"""
|
||||||
|
self.__exeName = exeDecomposeName
|
||||||
|
self.__exeDir = exeDir
|
||||||
|
self.__inT3Dir = inT3Dir
|
||||||
|
self.__outDir = outDir
|
||||||
|
self.__DecompostFlag = False
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_h_a_alpha_decomposition_T3(
|
||||||
|
self,
|
||||||
|
rectX,
|
||||||
|
rectY,
|
||||||
|
rectWidth,
|
||||||
|
rectHeight,
|
||||||
|
Nwin=1):
|
||||||
|
"""
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin :Size of the (Nwin, Nwin) sliding window used to compute local estimates. (int)
|
||||||
|
"""
|
||||||
|
if self.__DecompostFlag:
|
||||||
|
return True
|
||||||
|
if len(self.__exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
logger.error(self.__exeName + ' not exists.')
|
||||||
|
return False
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(self.__exeDir + '\\' + self.__exeName):
|
||||||
|
logger.error(self.__exeName + ' not exists.')
|
||||||
|
return False
|
||||||
|
exePath = self.__exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(self.__inT3Dir):
|
||||||
|
logger.error('T3 Matrix check failed.')
|
||||||
|
return False
|
||||||
|
if not os.path.exists(self.__outDir):
|
||||||
|
os.makedirs(self.__outDir)
|
||||||
|
|
||||||
|
alpbetdelgam = 1
|
||||||
|
Lambda = 1
|
||||||
|
alpha = 1
|
||||||
|
entropy = 1
|
||||||
|
anisotropy = 1
|
||||||
|
|
||||||
|
CombHA = 1
|
||||||
|
CombH1mA = 1
|
||||||
|
Comb1mHA = 1
|
||||||
|
Comb1mH1mA = 1
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = rectWidth
|
||||||
|
Sub_Ncol = rectHeight
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
self.__inT3Dir,
|
||||||
|
self.__outDir,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol,
|
||||||
|
alpbetdelgam,
|
||||||
|
Lambda,
|
||||||
|
alpha,
|
||||||
|
entropy,
|
||||||
|
anisotropy,
|
||||||
|
CombHA,
|
||||||
|
CombH1mA,
|
||||||
|
Comb1mHA,
|
||||||
|
Comb1mH1mA]
|
||||||
|
cmd = " ".join(str(i) for i in para_list)
|
||||||
|
config_path = os.path.join(self.__inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(self.__outDir, 'config.txt'))
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
self.__DecompostFlag = True
|
||||||
|
return True
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
@ -0,0 +1,109 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspFreemanDecomposition.py
|
||||||
|
@Function:
|
||||||
|
@Contact:
|
||||||
|
@Author:LVY
|
||||||
|
@Date:2021/10/12 18:45
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
import logging
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class PspFreemanDecomposition:
|
||||||
|
"""
|
||||||
|
Freeman分解
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
exeDecomposeName='freeman_decomposition_T3.exe'):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
"""
|
||||||
|
self.__exeName = exeDecomposeName
|
||||||
|
self.__exeDir = exeDir
|
||||||
|
self.__inT3Dir = inT3Dir
|
||||||
|
self.__outDir = outDir
|
||||||
|
self.__DecompostFlag = False
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_freeman_decomposition_T3(
|
||||||
|
self,
|
||||||
|
rectX,
|
||||||
|
rectY,
|
||||||
|
rectWidth,
|
||||||
|
rectHeight,
|
||||||
|
Nwin=1):
|
||||||
|
"""
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin :Size of the (Nwin, Nwin) sliding window used to compute local estimates. (int)
|
||||||
|
"""
|
||||||
|
if self.__DecompostFlag:
|
||||||
|
return True
|
||||||
|
if len(self.__exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
logger.error(self.__exeName + ' not exists.')
|
||||||
|
return False
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(self.__exeDir + '\\' + self.__exeName):
|
||||||
|
logger.error(self.__exeName + ' not exists.')
|
||||||
|
return False
|
||||||
|
exePath = self.__exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(self.__inT3Dir):
|
||||||
|
logger.error('T3 Matrix check failed.')
|
||||||
|
return False
|
||||||
|
if not os.path.exists(self.__outDir):
|
||||||
|
os.makedirs(self.__outDir)
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = rectWidth
|
||||||
|
Sub_Ncol = rectHeight
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
self.__inT3Dir,
|
||||||
|
self.__outDir,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol]
|
||||||
|
cmd = " ".join(str(i) for i in para_list)
|
||||||
|
config_path = os.path.join(self.__inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(self.__outDir, 'config.txt'))
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
self.__DecompostFlag = True
|
||||||
|
return True
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
@ -0,0 +1,435 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspHAAlphaDecomposition.py
|
||||||
|
@Function: Cloude-Pottier eigenvector/eigenvalue based decomposition of a 3x3 coherency matrix [T3]
|
||||||
|
(Averaging using a sliding window)
|
||||||
|
V1.0.1:（1）可选分解特征；（2）bin转tif格式
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/9/24 9:06
|
||||||
|
@Version:1.0.1
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
import struct
|
||||||
|
import numpy as np
|
||||||
|
import glob
|
||||||
|
from PIL import Image
|
||||||
|
import logging
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
import multiprocessing
|
||||||
|
|
||||||
|
class PspHAAlphaDecomposition:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的Cloude-Pottier极化分解
|
||||||
|
"""
|
||||||
|
def __init__(self,normalization = False):
|
||||||
|
self.__normalization = normalization #是否做归一化
|
||||||
|
self.__res_h_a_alpha_decomposition_T3 = {}
|
||||||
|
self.__res_h_a_alpha_eigenvalue_set_T3 = {}
|
||||||
|
self.__res_h_a_alpha_eigenvector_set_T3 = {}
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_creat_h_a_alpha_features_single_process(self, h_a_alpha_out_dir,
|
||||||
|
h_a_alpha_decomposition_T3_path, h_a_alpha_eigenvalue_set_T3_path,
|
||||||
|
h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir,is_trans_to_tif=True, is_read_to_dic=False):
|
||||||
|
"""
|
||||||
|
对porsarpro格式T3矩阵做Cloude-Pottier分解(h_a_alpha_decomposition、h_a_alpha_eigenvalue_set 和 h_a_alpha_eigenvector_set)
|
||||||
|
:param h_a_alpha_out_dir : 输出h_a_alpha二进制数据的目录
|
||||||
|
:param h_a_alpha_decomposition_T3_path: haalphadecompositionT3.exe路径
|
||||||
|
:param h_a_alpha_eigenvalue_set_T3_path: h_a_alpha_eigenvalue_set_T3.exe路径
|
||||||
|
:param h_a_alpha_eigenvector_set_T3_path: h_a_alpha_eigenvector_set_T3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
"""
|
||||||
|
h_a_alpha_features ={}
|
||||||
|
h_a_alpha_features.update(self.api_h_a_alpha_decomposition_T3(h_a_alpha_out_dir, h_a_alpha_decomposition_T3_path, polsarpro_in_dir, is_trans_to_tif,is_read_to_dic, *(1, 1, 1, 1, 1, 1, 1, 1, 1)))
|
||||||
|
logger.info("run h_a_alpha_decomposition_T3 success!")
|
||||||
|
logger.info('progress bar: 40%')
|
||||||
|
h_a_alpha_features.update(self.api_h_a_alpha_eigenvalue_set_T3(h_a_alpha_out_dir, h_a_alpha_eigenvalue_set_T3_path, polsarpro_in_dir, is_trans_to_tif, is_read_to_dic, *(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)))
|
||||||
|
logger.info("run h_a_alpha_eigenvalue_set_T3 success!")
|
||||||
|
logger.info('progress bar: 60%')
|
||||||
|
h_a_alpha_features.update(self.api_h_a_alpha_eigenvector_set_T3(h_a_alpha_out_dir, h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir, is_trans_to_tif,is_read_to_dic, *(1, 1, 1, 1, 1)))
|
||||||
|
logger.info("run h_a_alpha_eigenvector_set_T3 success!")
|
||||||
|
logger.info('progress bar: 80%')
|
||||||
|
if is_trans_to_tif:
|
||||||
|
self.api_trans_T3_to_tif(h_a_alpha_out_dir, polsarpro_in_dir)
|
||||||
|
if is_read_to_dic:
|
||||||
|
h_a_alpha_features.update(self.api_read_T3_matrix(polsarpro_in_dir))
|
||||||
|
return h_a_alpha_features
|
||||||
|
|
||||||
|
def api_creat_h_a_alpha_features(self, h_a_alpha_out_dir,
|
||||||
|
h_a_alpha_decomposition_T3_path, h_a_alpha_eigenvalue_set_T3_path,
|
||||||
|
h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir,is_trans_to_tif=True, is_read_to_dic=False):
|
||||||
|
"""
|
||||||
|
对porsarpro格式T3矩阵做Cloude-Pottier分解(h_a_alpha_decomposition、h_a_alpha_eigenvalue_set 和 h_a_alpha_eigenvector_set)
|
||||||
|
:param h_a_alpha_out_dir : 输出h_a_alpha二进制数据的目录
|
||||||
|
:param h_a_alpha_decomposition_T3_path: haalphadecompositionT3.exe路径
|
||||||
|
:param h_a_alpha_eigenvalue_set_T3_path: h_a_alpha_eigenvalue_set_T3.exe路径
|
||||||
|
:param h_a_alpha_eigenvector_set_T3_path: h_a_alpha_eigenvector_set_T3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
"""
|
||||||
|
|
||||||
|
pool = multiprocessing.Pool(processes=3)
|
||||||
|
pl = []
|
||||||
|
|
||||||
|
logger.info("run h_a_alpha_decomposition_T3!")
|
||||||
|
pl.append(pool.apply_async(self.api_h_a_alpha_decomposition_T3, (h_a_alpha_out_dir, h_a_alpha_decomposition_T3_path, polsarpro_in_dir, is_trans_to_tif, is_read_to_dic, *(1, 1, 1, 1, 1, 1, 1, 1, 1))))
|
||||||
|
logger.info("run h_a_alpha_eigenvalue_set_T3!")
|
||||||
|
pl.append(pool.apply_async(self.api_h_a_alpha_eigenvalue_set_T3, (h_a_alpha_out_dir, h_a_alpha_eigenvalue_set_T3_path, polsarpro_in_dir, is_trans_to_tif, is_read_to_dic, *(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))))
|
||||||
|
logger.info("run h_a_alpha_eigenvector_set_T3!")
|
||||||
|
pl.append(pool.apply_async(self.api_h_a_alpha_eigenvector_set_T3, (h_a_alpha_out_dir, h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir, is_trans_to_tif,is_read_to_dic, *(1, 1, 1, 1, 1))))
|
||||||
|
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
logger.info(pl)
|
||||||
|
logger.info('progress bar: 60%')
|
||||||
|
|
||||||
|
h_a_alpha_features = {}
|
||||||
|
h_a_alpha_features.update(self.__res_h_a_alpha_decomposition_T3)
|
||||||
|
logger.info("run h_a_alpha_decomposition_T3 success!")
|
||||||
|
h_a_alpha_features.update(self.__res_h_a_alpha_eigenvalue_set_T3)
|
||||||
|
logger.info("run h_a_alpha_eigenvalue_set_T3 success!")
|
||||||
|
h_a_alpha_features.update(self.__res_h_a_alpha_eigenvector_set_T3)
|
||||||
|
logger.info("run h_a_alpha_eigenvector_set_T3 success!")
|
||||||
|
if is_trans_to_tif:
|
||||||
|
self.api_trans_T3_to_tif(h_a_alpha_out_dir, polsarpro_in_dir)
|
||||||
|
if is_read_to_dic:
|
||||||
|
h_a_alpha_features.update(self.api_read_T3_matrix(polsarpro_in_dir))
|
||||||
|
return h_a_alpha_features
|
||||||
|
|
||||||
|
def api_h_a_alpha_decomposition_T3(self, h_a_alpha_out_dir, h_a_alpha_decomposition_T3_path, polsarpro_in_dir, is_trans_to_tif=True, is_read_to_dic=False, *args):
|
||||||
|
"""
|
||||||
|
对porsarpro格式T3矩阵做Cloude-Pottier分解（H-A-Alpha分解）
|
||||||
|
:param h_a_alpha_out_dir : 输出h_a_alpha二进制数据的目录
|
||||||
|
:param h_a_alpha_decomposition_T3_path: haalphadecompositionT3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:param is_trans_to_tif:分解特征是否转换为tif
|
||||||
|
:param is_read_to_dic:分解特征是否以字典输出
|
||||||
|
:param *args:9个可选分解特征(alpbetdelgam,Lambda,alpha,entropy,anisotropy,
|
||||||
|
CombHA,CombH1mA,Comb1mHA,Comb1mH1mA),不输出:0，输出:1
|
||||||
|
:return : 包含分解特征的字典
|
||||||
|
"""
|
||||||
|
if not os.path.exists(h_a_alpha_out_dir):
|
||||||
|
os.makedirs(h_a_alpha_out_dir)
|
||||||
|
self.__h_a_alpha_decomposition_T3(h_a_alpha_out_dir, h_a_alpha_decomposition_T3_path, polsarpro_in_dir, *args)
|
||||||
|
name_list = ['entropy', 'anisotropy', 'alpha', 'beta', 'delta', 'gamma', 'lambda',
|
||||||
|
'combination_1mH1mA', 'combination_1mHA', 'combination_H1mA', 'combination_HA']
|
||||||
|
if is_trans_to_tif:
|
||||||
|
self.__write_haalpha_to_tif(h_a_alpha_out_dir, h_a_alpha_out_dir, name_list)
|
||||||
|
|
||||||
|
if is_read_to_dic:
|
||||||
|
self.__res_h_a_alpha_decomposition_T3 = self.__read_haalpha(h_a_alpha_out_dir, name_list)
|
||||||
|
return self.__res_h_a_alpha_decomposition_T3
|
||||||
|
else:
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def api_h_a_alpha_eigenvalue_set_T3(self, h_a_alpha_out_dir, h_a_alpha_eigenvalue_set_T3_path, polsarpro_in_dir, is_trans_to_tif=True, is_read_to_dic=False, *args):
|
||||||
|
|
||||||
|
"""
|
||||||
|
Cloude-Pottier eigenvalue based decomposition of a coherency matrix
|
||||||
|
:param h_a_alpha_out_dir : Cloude-Pottier eigenvalue
|
||||||
|
:param h_a_alpha_eigenvalue_set_T3_path: h_a_alpha_eigenvalue_set_T3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:param is_trans_to_tif:分解特征是否转换为tif
|
||||||
|
:param is_read_to_dic:分解特征是否以字典输出
|
||||||
|
:param *args:9个可选分解特征(alpbetdelgam,Lambda,alpha,entropy,anisotropy,
|
||||||
|
CombHA,CombH1mA,Comb1mHA,Comb1mH1mA),不输出:0，输出:1
|
||||||
|
:return : 包含分解特征的字典
|
||||||
|
"""
|
||||||
|
if not os.path.exists(h_a_alpha_out_dir):
|
||||||
|
os.makedirs(h_a_alpha_out_dir)
|
||||||
|
self.__h_a_alpha_eigenvalue_set_T3(h_a_alpha_out_dir, h_a_alpha_eigenvalue_set_T3_path, polsarpro_in_dir, *args)
|
||||||
|
name_list = ['anisotropy', 'anisotropy_lueneburg', 'anisotropy12', 'asymetry', 'derd', 'derd_norm', 'entropy_shannon',
|
||||||
|
'entropy_shannon_I', 'entropy_shannon_I_norm', 'entropy_shannon_norm', 'entropy_shannon_P',
|
||||||
|
'entropy_shannon_P_norm', 'l1', 'l2', 'l3', 'p1', 'p2', 'p3', 'pedestal', 'polarisation_fraction',
|
||||||
|
'rvi', 'serd', 'serd_norm']
|
||||||
|
if is_trans_to_tif:
|
||||||
|
self.__write_haalpha_to_tif(h_a_alpha_out_dir, h_a_alpha_out_dir, name_list)
|
||||||
|
|
||||||
|
if is_read_to_dic:
|
||||||
|
self.__res_h_a_alpha_eigenvalue_set_T3 = self.__read_haalpha(h_a_alpha_out_dir, name_list)
|
||||||
|
return self.__res_h_a_alpha_eigenvalue_set_T3
|
||||||
|
else:
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def api_h_a_alpha_eigenvector_set_T3(self, h_a_alpha_out_dir, h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir, is_trans_to_tif=True, is_read_to_dic=False, *args):
|
||||||
|
|
||||||
|
"""
|
||||||
|
Cloude-Pottier eigenvector based decomposition of a coherency matrix
|
||||||
|
:param h_a_alpha_out_dir : Cloude-Pottier eigenvector
|
||||||
|
:param h_a_alpha_eigenvector_set_T3_path: h_a_alpha_eigenvector_set_T3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:param is_trans_to_tif:分解特征是否转换为tif
|
||||||
|
:param is_read_to_dic:分解特征是否以字典输出
|
||||||
|
:param *args:9个可选分解特征(alpbetdelgam,Lambda,alpha,entropy,anisotropy,
|
||||||
|
CombHA,CombH1mA,Comb1mHA,Comb1mH1mA),不输出:0，输出:1
|
||||||
|
:return : 包含分解特征的字典
|
||||||
|
"""
|
||||||
|
if not os.path.exists(h_a_alpha_out_dir):
|
||||||
|
os.makedirs(h_a_alpha_out_dir)
|
||||||
|
self.__h_a_alpha_eigenvector_set_T3(h_a_alpha_out_dir, h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir, *args)
|
||||||
|
name_list = ['alpha', 'alpha1', 'alpha2', 'alpha3',
|
||||||
|
'beta', 'beta1', 'beta2', 'beta3',
|
||||||
|
'delta', 'delta1', 'delta2', 'delta3',
|
||||||
|
'gamma', 'gamma1', 'gamma2', 'gamma3']
|
||||||
|
if is_trans_to_tif:
|
||||||
|
self.__write_haalpha_to_tif(h_a_alpha_out_dir, h_a_alpha_out_dir, name_list)
|
||||||
|
|
||||||
|
if is_read_to_dic:
|
||||||
|
self.__res_h_a_alpha_eigenvector_set_T3 = self.__read_haalpha(h_a_alpha_out_dir, name_list)
|
||||||
|
return self.__res_h_a_alpha_eigenvector_set_T3
|
||||||
|
else:
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def api_read_T3_matrix(self,polsarpro_T3_dir):
|
||||||
|
"""
|
||||||
|
读取T3矩阵，转换字典
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:return : 包含T3矩阵的字典
|
||||||
|
"""
|
||||||
|
name_list = ['T11', 'T12_imag', 'T12_real',
|
||||||
|
'T22', 'T13_imag', 'T13_real',
|
||||||
|
'T33', 'T23_imag', 'T23_real']
|
||||||
|
return self.__read_haalpha(polsarpro_T3_dir, name_list)
|
||||||
|
|
||||||
|
def api_trans_T3_to_tif(self, out_tif_dir, polsarpro_T3_dir):
|
||||||
|
"""
|
||||||
|
将T3矩阵从bin格式转换为tif格式
|
||||||
|
:param out_tif_dir:保存路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
"""
|
||||||
|
name_list = ['T11', 'T12_imag', 'T12_real',
|
||||||
|
'T22', 'T13_imag', 'T13_real',
|
||||||
|
'T33', 'T23_imag', 'T23_real']
|
||||||
|
self.__write_haalpha_to_tif(out_tif_dir, polsarpro_T3_dir, name_list)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __h_a_alpha_decomposition_T3(h_a_alpha_out_dir, h_a_alpha_decomposition_T3_path, polsarpro_in_dir, *args):
|
||||||
|
"""
|
||||||
|
对porsarpro格式T3矩阵做Cloude-Pottier分解（H-A-Alpha分解）
|
||||||
|
:param h_a_alpha_out_dir : 输出h_a_alpha二进制数据的目录
|
||||||
|
:param h_a_alpha_decomposition_T3_path: haalphadecompositionT3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:param *args:9个可选输出变量(alpbetdelgam,Lambda,alpha,entropy,anisotropy,
|
||||||
|
CombHA,CombH1mA,Comb1mHA,Comb1mH1mA),不输出:0，输出:1
|
||||||
|
"""
|
||||||
|
if not os.path.exists(h_a_alpha_decomposition_T3_path):
|
||||||
|
raise Exception(h_a_alpha_decomposition_T3_path +' is not exists!')
|
||||||
|
|
||||||
|
NwinFilter = 1
|
||||||
|
offsetRow = 0
|
||||||
|
offsetCol = 0
|
||||||
|
|
||||||
|
config_path = os.path.join(polsarpro_in_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
|
||||||
|
numRow = int(config[1])
|
||||||
|
numCol = int(config[4])
|
||||||
|
|
||||||
|
alpbetdelgam = int(args[0])
|
||||||
|
Lambda = int(args[1])
|
||||||
|
alpha = int(args[2])
|
||||||
|
entropy = int(args[3])
|
||||||
|
anisotropy = int(args[4])
|
||||||
|
|
||||||
|
CombHA = int(args[5])
|
||||||
|
CombH1mA = int(args[6])
|
||||||
|
Comb1mHA = int(args[7])
|
||||||
|
Comb1mH1mA = int(args[8])
|
||||||
|
|
||||||
|
para_list = [h_a_alpha_decomposition_T3_path, polsarpro_in_dir, h_a_alpha_out_dir,
|
||||||
|
str(NwinFilter), str(offsetRow), str(offsetCol), str(numRow), str(numCol),
|
||||||
|
str(alpbetdelgam), str(Lambda), str(alpha), str(entropy), str(anisotropy),
|
||||||
|
str(CombHA), str(CombH1mA), str(Comb1mHA), str(Comb1mH1mA)]
|
||||||
|
cmd = ' '.join(para_list)
|
||||||
|
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1 or result_tuple[1].find('Could not open') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
shutil.copyfile(config_path, os.path.join(h_a_alpha_out_dir, 'config.txt'))
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __h_a_alpha_eigenvalue_set_T3(h_a_alpha_out_dir, h_a_alpha_eigenvalue_set_T3_path, polsarpro_in_dir, *args):
|
||||||
|
|
||||||
|
"""
|
||||||
|
Cloude-Pottier eigenvalue based decomposition of a coherency matrix
|
||||||
|
:param h_a_alpha_out_dir : Cloude-Pottier eigenvalue
|
||||||
|
:param h_a_alpha_eigenvalue_set_T3_path: h_a_alpha_eigenvalue_set_T3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:param *args:11个可选输出变量(eigen123,proba123,anisotropy,anisotropy12,asymetry,
|
||||||
|
polarisation_fraction,erd,rvi,pedestal,shannon,lueneburg),不输出：0，输出：1
|
||||||
|
"""
|
||||||
|
if not os.path.exists(h_a_alpha_eigenvalue_set_T3_path):
|
||||||
|
raise Exception(h_a_alpha_eigenvalue_set_T3_path +' is not exists!')
|
||||||
|
NwinFilter = 1
|
||||||
|
offsetRow = 0
|
||||||
|
offsetCol = 0
|
||||||
|
|
||||||
|
config_path = os.path.join(polsarpro_in_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
|
||||||
|
numRow = int(config[1])
|
||||||
|
numCol = int(config[4])
|
||||||
|
|
||||||
|
eigen123 = int(args[0])
|
||||||
|
proba123 = int(args[1])
|
||||||
|
anisotropy = int(args[2])
|
||||||
|
anisotropy12 = int(args[3])
|
||||||
|
asymetry = int(args[4])
|
||||||
|
polarisation_fraction = int(args[5])
|
||||||
|
erd = int(args[6])
|
||||||
|
rvi = int(args[7])
|
||||||
|
pedestal = int(args[8])
|
||||||
|
shannon = int(args[9])
|
||||||
|
lueneburg = int(args[10])
|
||||||
|
|
||||||
|
para_list = [h_a_alpha_eigenvalue_set_T3_path, polsarpro_in_dir, h_a_alpha_out_dir,
|
||||||
|
str(NwinFilter), str(offsetRow), str(offsetCol), str(numRow), str(numCol),
|
||||||
|
str(eigen123), str(proba123), str(anisotropy), str(anisotropy12), str(asymetry),
|
||||||
|
str(polarisation_fraction), str(erd), str(rvi), str(pedestal),
|
||||||
|
str(shannon), str(lueneburg)]
|
||||||
|
cmd = ' '.join(para_list)
|
||||||
|
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1 or result_tuple[1].find('Could not open') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
shutil.copyfile(config_path, os.path.join(h_a_alpha_out_dir, 'config.txt'))
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __h_a_alpha_eigenvector_set_T3(h_a_alpha_out_dir, h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir, *args):
|
||||||
|
|
||||||
|
"""
|
||||||
|
Cloude-Pottier eigenvector based decomposition of a coherency matrix
|
||||||
|
:param h_a_alpha_out_dir : Cloude-Pottier eigenvector
|
||||||
|
:param h_a_alpha_eigenvector_set_T3_set_T3_path: h_a_alpha_eigenvector_set_T3.exe路径
|
||||||
|
:param polsarpro_in_dir:输入porsarpro格式T3矩阵目录，包含.bin,.config
|
||||||
|
:param *args:5个可选输出变量(alpha123,beta123,delta123,gamma123,alpbetdelgam),不输出：0，输出：1
|
||||||
|
"""
|
||||||
|
if not os.path.exists(h_a_alpha_eigenvector_set_T3_path):
|
||||||
|
raise Exception(h_a_alpha_eigenvector_set_T3_path +' is not exists!')
|
||||||
|
NwinFilter = 1
|
||||||
|
offsetRow = 0
|
||||||
|
offsetCol = 0
|
||||||
|
|
||||||
|
config_path = os.path.join(polsarpro_in_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
|
||||||
|
numRow = int(config[1])
|
||||||
|
numCol = int(config[4])
|
||||||
|
|
||||||
|
alpha123 = int(args[0])
|
||||||
|
beta123 = int(args[1])
|
||||||
|
delta123 = int(args[2])
|
||||||
|
gamma123 = int(args[3])
|
||||||
|
alpbetdelgam = int(args[4])
|
||||||
|
|
||||||
|
para_list = [h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir, h_a_alpha_out_dir,
|
||||||
|
str(NwinFilter), str(offsetRow), str(offsetCol), str(numRow), str(numCol),
|
||||||
|
str(alpha123), str(beta123), str(delta123), str(gamma123), str(alpbetdelgam)]
|
||||||
|
cmd = ' '.join(para_list)
|
||||||
|
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1 or result_tuple[1].find('Could not open') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
shutil.copyfile(config_path, os.path.join(h_a_alpha_out_dir, 'config.txt'))
|
||||||
|
|
||||||
|
def __read_haalpha(self, h_a_alpha_dir, name_list):
|
||||||
|
"""
|
||||||
|
读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
|
||||||
|
:param h_a_alpha_dir : h_a_alpha二进制数据的目录,包含.bin,.config
|
||||||
|
:name_list : 需要组合的名称集合['entropy', 'anisotropy', 'alpha', 'beta', 'delta', 'gamma', 'lambda',
|
||||||
|
'combination_1mH1mA', 'combination_1mHA', 'combination_H1mA', 'combination_HA']
|
||||||
|
:return : 包含H-A-Alpha矩阵信息的字典
|
||||||
|
"""
|
||||||
|
dir = os.path.join(h_a_alpha_dir, '*.bin')
|
||||||
|
bin_paths = list(glob.glob(dir))
|
||||||
|
haalpha_dic ={}
|
||||||
|
for name in name_list:
|
||||||
|
path = os.path.join(h_a_alpha_dir, name + '.bin')
|
||||||
|
if path in bin_paths:
|
||||||
|
img = self.__read_bin_to_img(path)
|
||||||
|
haalpha_dic.update({name: img})
|
||||||
|
return haalpha_dic
|
||||||
|
|
||||||
|
def standardization(self, data, num=1):
|
||||||
|
# 矩阵标准化到[0,1]
|
||||||
|
data[np.isnan(data)] = np.min(data) # 异常值填充为0
|
||||||
|
_range = np.max(data) - np.min(data)
|
||||||
|
return (data - np.min(data)) / _range * num
|
||||||
|
|
||||||
|
def __write_haalpha_to_tif(self, out_tif_dir, h_a_alpha_dir, name_list):
|
||||||
|
"""
|
||||||
|
读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
|
||||||
|
:param out_tif_dir : tif的输出路径
|
||||||
|
:param h_a_alpha_dir : h_a_alpha二进制数据的目录,包含.bin,.config
|
||||||
|
:name_list : 需要组合的名称集合['entropy', 'anisotropy', 'alpha', 'beta', 'delta', 'gamma', 'lambda',
|
||||||
|
'combination_1mH1mA', 'combination_1mHA', 'combination_H1mA', 'combination_HA']
|
||||||
|
|
||||||
|
"""
|
||||||
|
dir = os.path.join(h_a_alpha_dir, '*.bin')
|
||||||
|
bin_paths = list(glob.glob(dir))
|
||||||
|
|
||||||
|
for name in name_list:
|
||||||
|
in_path = os.path.join(h_a_alpha_dir, name + '.bin')
|
||||||
|
out_path = os.path.join(out_tif_dir, name + '.tif')
|
||||||
|
if in_path in bin_paths:
|
||||||
|
img_array = self.__read_bin_to_img(in_path)
|
||||||
|
if self.__normalization is True:
|
||||||
|
img_array = self.standardization(img_array, num=1)
|
||||||
|
out_image = Image.fromarray(img_array)
|
||||||
|
out_image.save(out_path)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __read_bin_to_img(bin_path):
|
||||||
|
"""
|
||||||
|
读取bin格式二进制数据，输出为矩阵
|
||||||
|
:param bin_path : bin文件的路径，包含.bin,.config
|
||||||
|
:return : 矩阵信息
|
||||||
|
"""
|
||||||
|
(bin_dir, bin_name) = os.path.split(bin_path)
|
||||||
|
config_path = os.path.join(bin_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
rows = int(config[1])
|
||||||
|
cols = int(config[4])
|
||||||
|
|
||||||
|
bin_file = open(bin_path, 'rb') # 打开二进制文件
|
||||||
|
size = os.path.getsize(bin_path) # 获得文件大小
|
||||||
|
if size < rows*cols*4:
|
||||||
|
raise Exception('bin size less than rows*cols*4! size:', size, 'byte, rows:', rows, 'cols:', cols)
|
||||||
|
|
||||||
|
img = np.zeros([rows, cols], dtype=np.float32)
|
||||||
|
for row in range(rows):
|
||||||
|
data = bin_file.read(4 * cols) # 每次读取一行的二进制数据
|
||||||
|
row_data = struct.unpack('f' * cols, data) # 转为一行float数据
|
||||||
|
img[row, :] = row_data
|
||||||
|
bin_file.close()
|
||||||
|
return img
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# h_a_alpha_decomposition_T3_path = 'D:\\PolSARpro_v4.2.0\\Soft\data_process_sngl\\h_a_alpha_decomposition_T3.exe'
|
||||||
|
# h_a_alpha_eigenvalue_set_T3_path = 'D:\\PolSARpro_v4.2.0\\Soft\data_process_sngl\\h_a_alpha_eigenvalue_set_T3.exe'
|
||||||
|
# h_a_alpha_eigenvector_set_T3_path = 'D:\\PolSARpro_v4.2.0\\Soft\data_process_sngl\\h_a_alpha_eigenvector_set_T3.exe'
|
||||||
|
# polsarpro_in_dir = 'D:\\PolSARpro_v4.2.0\\in'
|
||||||
|
# haalpha_out_dir = 'D:\\PolSARpro_v4.2.0\\out'
|
||||||
|
# h_a_alpha_eigenvalue_set_T3_out = 'D:\\PolSARpro_v4.2.0\\out\\h_a_alpha_eigenvalue_set_T3'
|
||||||
|
# h_a_alpha_eigenvector_set_T3_out = 'D:\\PolSARpro_v4.2.0\\out\\h_a_alpha_eigenvector_set_T3'
|
||||||
|
#
|
||||||
|
# haa = PspHAAlphaDecomposition()
|
||||||
|
# h_a_alpha_features = haa.api_creat_h_a_alpha_features(haalpha_out_dir, h_a_alpha_decomposition_T3_path, h_a_alpha_eigenvalue_set_T3_path, h_a_alpha_eigenvector_set_T3_path, polsarpro_in_dir)
|
||||||
|
|
||||||
|
|
||||||
|
# haa = PspHAAlphaDecomposition(normalization=True)
|
||||||
|
# psp_path = r"I:\MicroWorkspace\product\C-SAR\SoilSalinity\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1A_AHV_L10000073024_RPCpsp_t3"
|
||||||
|
# t3_path = r"I:\MicroWorkspace\product\C-SAR\SoilSalinity\t3"
|
||||||
|
# exe_dir = r"I:\microproduct\soilSalinity/"
|
||||||
|
# haa.api_creat_h_a_alpha_features(h_a_alpha_out_dir=t3_path,
|
||||||
|
# h_a_alpha_decomposition_T3_path= exe_dir + 'h_a_alpha_decomposition_T3.exe',
|
||||||
|
# h_a_alpha_eigenvalue_set_T3_path= exe_dir + 'h_a_alpha_eigenvalue_set_T3.exe',
|
||||||
|
# h_a_alpha_eigenvector_set_T3_path=exe_dir +'h_a_alpha_eigenvector_set_T3.exe',
|
||||||
|
# polsarpro_in_dir=psp_path)
|
||||||
|
|
||||||
|
# print('done')
|
||||||
|
|
@ -0,0 +1,170 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspLeeRefinedFilterC2.py
|
||||||
|
@Function:
|
||||||
|
@Contact:
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2021/11/5
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
import glob
|
||||||
|
import numpy as np
|
||||||
|
import struct
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class LeeRefinedFilterC2:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的lee_refined_filter_C2.exe做精致Lee滤波
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, exeFilterName='lee_refined_filter_C2.exe'):
|
||||||
|
self.__exeName = exeFilterName
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_lee_refined_filter_C2(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inC2Dir,
|
||||||
|
outDir,
|
||||||
|
off_row,
|
||||||
|
off_col,
|
||||||
|
Nrow,
|
||||||
|
Ncol,
|
||||||
|
Nwin=7,
|
||||||
|
Nlook=1):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inC2Dir:C2矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
:param off_row:行偏移，行启始位置
|
||||||
|
:param off_col:列偏移，列启始位置
|
||||||
|
:param Nrow:终止行
|
||||||
|
:param Ncol:终止列
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
:param Nlook:一般是1
|
||||||
|
"""
|
||||||
|
if len(exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
raise Exception(self.__exeName + ' not exists.')
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(exeDir + '\\' + self.__exeName):
|
||||||
|
raise Exception(
|
||||||
|
exeDir +
|
||||||
|
'\\' +
|
||||||
|
self.__exeName +
|
||||||
|
' not exists.')
|
||||||
|
exePath = exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
# if not self._checkT3Matrix(inT3Dir):
|
||||||
|
# raise Exception('T3 Matrix check failed.')
|
||||||
|
if not os.path.exists(outDir):
|
||||||
|
os.makedirs(outDir)
|
||||||
|
if (Nwin % 2) == 0 or Nwin < 0: # 如果为偶数或小于0，则使用默认值
|
||||||
|
Nwin = 7
|
||||||
|
|
||||||
|
Off_lig = off_row
|
||||||
|
Off_col = off_col
|
||||||
|
Sub_Nlig = Nrow
|
||||||
|
Sub_Ncol = Ncol
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
inC2Dir,
|
||||||
|
outDir,
|
||||||
|
Nlook,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol]
|
||||||
|
cmd = ' '.join(str(i) for i in para_list)
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
config_path = os.path.join(inC2Dir, 'config.txt')
|
||||||
|
if config_path != os.path.join(outDir, 'config.txt'):
|
||||||
|
shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
@staticmethod
|
||||||
|
def _checkC2Matrix(C2Dir):
|
||||||
|
# 检测C2矩阵
|
||||||
|
if not os.path.exists(C2Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['C11.bin', 'C12_real.bin', 'C12_imag.bin', 'C22.bin','config.txt']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(C2Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
def write_bin_to_tif(self, out_tif_dir, bin_dir):
|
||||||
|
"""
|
||||||
|
读取H-A-Alpha分解二进制数据，输出为矩阵格式的字典
|
||||||
|
:param out_tif_dir : tif的输出路径
|
||||||
|
:param bin_dir : 二进制数据的目录,包含.bin,.config
|
||||||
|
:return out_tif_path: 生成tif的路径字典
|
||||||
|
"""
|
||||||
|
bin_paths = list(glob.glob(os.path.join(bin_dir, '*.bin')))
|
||||||
|
out_tif_path = {}
|
||||||
|
for in_path in bin_paths:
|
||||||
|
name = os.path.split(in_path)[1].split('.')[0]
|
||||||
|
out_path = os.path.join(out_tif_dir, name + '.tif')
|
||||||
|
out_tif_path.update({name: out_path})
|
||||||
|
if os.path.exists(os.path.split(out_path)[0]) is False:
|
||||||
|
os.makedirs(os.path.split(out_path)[0])
|
||||||
|
img_array = self.__read_bin_to_img(in_path)
|
||||||
|
img_array[np.isnan(img_array)] = 0 # 异常值填充为0
|
||||||
|
# img_array = self.standardization(img_array) # 数据标准化到[0,1]
|
||||||
|
out_image = Image.fromarray(img_array)
|
||||||
|
out_image.save(out_path)
|
||||||
|
return out_tif_path
|
||||||
|
@staticmethod
|
||||||
|
|
||||||
|
def __read_bin_to_img(bin_path):
|
||||||
|
"""
|
||||||
|
读取bin格式二进制数据，输出为矩阵
|
||||||
|
:param bin_path : bin文件的路径，包含.bin,.config
|
||||||
|
:return : 矩阵信息
|
||||||
|
"""
|
||||||
|
(bin_dir, bin_name) = os.path.split(bin_path)
|
||||||
|
config_path = os.path.join(bin_dir, 'config.txt')
|
||||||
|
config = open(config_path, 'r').read().split('\n', -1)
|
||||||
|
rows = int(config[1])
|
||||||
|
cols = int(config[4])
|
||||||
|
|
||||||
|
bin_file = open(bin_path, 'rb') # 打开二进制文件
|
||||||
|
size = os.path.getsize(bin_path) # 获得文件大小
|
||||||
|
if size < rows * cols * 4:
|
||||||
|
raise Exception(
|
||||||
|
'bin size less than rows*cols*4! size:',
|
||||||
|
size,
|
||||||
|
'byte, rows:',
|
||||||
|
rows,
|
||||||
|
'cols:',
|
||||||
|
cols)
|
||||||
|
|
||||||
|
img = np.zeros([rows, cols], dtype=np.float32)
|
||||||
|
for row in range(rows):
|
||||||
|
data = bin_file.read(4 * cols) # 每次读取一行的二进制数据
|
||||||
|
row_data = struct.unpack('f' * cols, data) # 转为一行float数据
|
||||||
|
img[row, :] = row_data
|
||||||
|
bin_file.close()
|
||||||
|
return img
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
tp =LeeRefinedFilterC2()
|
||||||
|
inC2Dir=r'E:\MicroWorkspace\LandCover\HHHV1'
|
||||||
|
outDir =r'E:\MicroWorkspace\LandCover\HHHV1_f'
|
||||||
|
off_row = 0
|
||||||
|
off_col = 0
|
||||||
|
Nrow = 666
|
||||||
|
Ncol = 746
|
||||||
|
tp.api_lee_refined_filter_C2( '',inC2Dir,outDir,off_row,off_col,Nrow,Ncol)
|
||||||
|
tp.write_bin_to_tif(outDir,outDir)
|
||||||
|
print('done')
|
||||||
|
|
@ -0,0 +1,104 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspLeeRefinedFilterT3.py
|
||||||
|
@Function: Cloude-Pottier eigenvector/eigenvalue based decomposition of a 3x3 coherency matrix [T3]
|
||||||
|
(Averaging using a sliding window)
|
||||||
|
@Contact:
|
||||||
|
@Author:LVY
|
||||||
|
@Date:2021/10/12 9:06
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class LeeRefinedFilterT3:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的lee_refined_filter_T3.exe做精致Lee滤波
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, exeFilterName='lee_refined_filter_T3.exe'):
|
||||||
|
self.__exeName = exeFilterName
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_lee_refined_filter_T3(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
rectX,
|
||||||
|
rectY,
|
||||||
|
rectWidth,
|
||||||
|
rectHeight,
|
||||||
|
Nwin=7,
|
||||||
|
Nlook=1):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
:param Nlook:一般是1
|
||||||
|
"""
|
||||||
|
if len(exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
raise Exception(self.__exeName + ' not exists.')
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(exeDir + '\\' + self.__exeName):
|
||||||
|
raise Exception(
|
||||||
|
exeDir +
|
||||||
|
'\\' +
|
||||||
|
self.__exeName +
|
||||||
|
' not exists.')
|
||||||
|
exePath = exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(inT3Dir):
|
||||||
|
raise Exception('T3 Matrix check failed.')
|
||||||
|
if not os.path.exists(outDir):
|
||||||
|
os.makedirs(outDir)
|
||||||
|
if (Nwin % 2) == 0 or Nwin < 0: # 如果为偶数或小于0，则使用默认值
|
||||||
|
Nwin = 7
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = rectWidth
|
||||||
|
Sub_Ncol = rectHeight
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
Nlook,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol]
|
||||||
|
cmd = ' '.join(str(i) for i in para_list)
|
||||||
|
config_path = os.path.join(inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
@ -0,0 +1,393 @@
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class SurfaceInversionDubois:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的surface_inversion_dubois.exe做土壤水分反演
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, exeFilterName='surface_inversion_dubois.exe'):
|
||||||
|
self.__exeName = exeFilterName
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_surface_inversion_dubois(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
incidence,
|
||||||
|
rectX,
|
||||||
|
rectY,
|
||||||
|
row,
|
||||||
|
col,
|
||||||
|
frequency, # GHZ
|
||||||
|
angleFlag, # 0:deg, 1:rad
|
||||||
|
):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
:param Nlook:一般是1
|
||||||
|
"""
|
||||||
|
if len(exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
raise Exception(self.__exeName + ' not exists.')
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(exeDir + '\\' + self.__exeName):
|
||||||
|
raise Exception(
|
||||||
|
exeDir +
|
||||||
|
'\\' +
|
||||||
|
self.__exeName +
|
||||||
|
' not exists.')
|
||||||
|
exePath = exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(inT3Dir):
|
||||||
|
raise Exception('T3 Matrix check failed.')
|
||||||
|
if not os.path.exists(outDir):
|
||||||
|
os.makedirs(outDir)
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = row
|
||||||
|
Sub_Ncol = col
|
||||||
|
dataFormat = 'T3'
|
||||||
|
calibration_flag = 1
|
||||||
|
calibration_coefficient = 0.0
|
||||||
|
threshold_HHHH_VVVV = 0.0
|
||||||
|
threshold_HVHV_VVVV = 0.0
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
dataFormat,
|
||||||
|
incidence,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol,
|
||||||
|
frequency, # GHZ
|
||||||
|
angleFlag,
|
||||||
|
]
|
||||||
|
|
||||||
|
cmd = "surface_inversion_dubois.exe -id {} -od {} -iodf {} -ang {} -ofr {} -ofc {} -fnr {} -fnc {} -fr {} -un {} -caf {} -cac {} -th1 {} -th2 {}".format(
|
||||||
|
inT3Dir, outDir, dataFormat, incidence, Off_lig, Off_col, Sub_Nlig, Sub_Ncol, frequency, angleFlag,
|
||||||
|
calibration_flag, calibration_coefficient, threshold_HHHH_VVVV, threshold_HVHV_VVVV)
|
||||||
|
|
||||||
|
logger.info('surface_inversion_dubois:{}'.format(cmd))
|
||||||
|
result = os.system(cmd)
|
||||||
|
logger.info('cmd_result:{}'.format(result))
|
||||||
|
logger.info('surface_inversion_dubois finish!')
|
||||||
|
|
||||||
|
config_path = os.path.join(inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
|
||||||
|
# cmd = ' '.join(str(i) for i in para_list)
|
||||||
|
# config_path = os.path.join(inT3Dir, 'config.txt')
|
||||||
|
# shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
# result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
#
|
||||||
|
# if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
# raise Exception(result_tuple[1])
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
class SurfaceInversionHisto:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的surface_inversion_histo.exe做土壤水分反演
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, exeFilterName='surface_inversion_histo.exe'):
|
||||||
|
self.__exeName = exeFilterName
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_surface_inversion_histo(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
rectX,
|
||||||
|
rectY,
|
||||||
|
rectWidth,
|
||||||
|
rectHeight,
|
||||||
|
Nwin=7,
|
||||||
|
Nlook=1):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
:param Nlook:一般是1
|
||||||
|
"""
|
||||||
|
if len(exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
raise Exception(self.__exeName + ' not exists.')
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(exeDir + '\\' + self.__exeName):
|
||||||
|
raise Exception(
|
||||||
|
exeDir +
|
||||||
|
'\\' +
|
||||||
|
self.__exeName +
|
||||||
|
' not exists.')
|
||||||
|
exePath = exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(inT3Dir):
|
||||||
|
raise Exception('T3 Matrix check failed.')
|
||||||
|
if not os.path.exists(outDir):
|
||||||
|
os.makedirs(outDir)
|
||||||
|
if (Nwin % 2) == 0 or Nwin < 0: # 如果为偶数或小于0，则使用默认值
|
||||||
|
Nwin = 7
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = rectWidth
|
||||||
|
Sub_Ncol = rectHeight
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
Nlook,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol]
|
||||||
|
cmd = ' '.join(str(i) for i in para_list)
|
||||||
|
config_path = os.path.join(inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
class SurfaceInversionOh:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的surface_inversion_oh.exe做土壤水分反演
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, exeFilterName='surface_inversion_oh.exe'):
|
||||||
|
self.__exeName = exeFilterName
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_surface_inversion_oh(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
rectX,
|
||||||
|
rectY,
|
||||||
|
rectWidth,
|
||||||
|
rectHeight,
|
||||||
|
Nwin=7,
|
||||||
|
Nlook=1):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
:param Nlook:一般是1
|
||||||
|
"""
|
||||||
|
if len(exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
raise Exception(self.__exeName + ' not exists.')
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(exeDir + '\\' + self.__exeName):
|
||||||
|
raise Exception(
|
||||||
|
exeDir +
|
||||||
|
'\\' +
|
||||||
|
self.__exeName +
|
||||||
|
' not exists.')
|
||||||
|
exePath = exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(inT3Dir):
|
||||||
|
raise Exception('T3 Matrix check failed.')
|
||||||
|
if not os.path.exists(outDir):
|
||||||
|
os.makedirs(outDir)
|
||||||
|
if (Nwin % 2) == 0 or Nwin < 0: # 如果为偶数或小于0，则使用默认值
|
||||||
|
Nwin = 7
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = rectWidth
|
||||||
|
Sub_Ncol = rectHeight
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
Nlook,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol]
|
||||||
|
cmd = ' '.join(str(i) for i in para_list)
|
||||||
|
config_path = os.path.join(inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
class SurfaceInversionOh2004:
|
||||||
|
"""
|
||||||
|
调用polsarpro4.2.0的surface_inversion_oh2004.exe做土壤水分反演
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, exeFilterName='surface_inversion_oh2004.exe'):
|
||||||
|
self.__exeName = exeFilterName
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_surface_inversion_oh2004(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
incidence,
|
||||||
|
rectY,
|
||||||
|
rectX,
|
||||||
|
row,
|
||||||
|
col,
|
||||||
|
frequency, # GHZ
|
||||||
|
angleFlag):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
:param Nlook:一般是1
|
||||||
|
"""
|
||||||
|
if len(exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
raise Exception(self.__exeName + ' not exists.')
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(exeDir + '\\' + self.__exeName):
|
||||||
|
raise Exception(
|
||||||
|
exeDir +
|
||||||
|
'\\' +
|
||||||
|
self.__exeName +
|
||||||
|
' not exists.')
|
||||||
|
exePath = exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(inT3Dir):
|
||||||
|
raise Exception('T3 Matrix check failed.')
|
||||||
|
if not os.path.exists(outDir):
|
||||||
|
os.makedirs(outDir)
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = row
|
||||||
|
Sub_Ncol = col
|
||||||
|
dataFormat = 'T3'
|
||||||
|
threshold_mv = 1.0
|
||||||
|
threshold_s = 7.0
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
dataFormat,
|
||||||
|
incidence,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol,
|
||||||
|
frequency, # GHZ
|
||||||
|
angleFlag,
|
||||||
|
threshold_mv,
|
||||||
|
threshold_s]
|
||||||
|
cmd = "surface_inversion_oh2004.exe -id {} -od {} -iodf {} -ang {} -ofr {} -ofc {} -fnr {} -fnc {} -fr {} -un {} -th1 {} -th2 {}".format(
|
||||||
|
inT3Dir, outDir, dataFormat, incidence, Off_lig, Off_col, Sub_Nlig, Sub_Ncol, frequency, angleFlag, threshold_mv, threshold_s)
|
||||||
|
|
||||||
|
logger.info('surface_inversion_oh2004:{}'.format(cmd))
|
||||||
|
result = os.system(cmd)
|
||||||
|
logger.info('cmd_result:{}'.format(result))
|
||||||
|
logger.info('surface_inversion_oh2004 finish!')
|
||||||
|
|
||||||
|
|
||||||
|
config_path = os.path.join(inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(outDir, 'config.txt'))
|
||||||
|
# cmd = ' '.join(str(i) for i in para_list)
|
||||||
|
# result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
# #
|
||||||
|
# if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
# raise Exception(result_tuple[1])
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
# file_name_in_out = ['T11.img', 'T12_real.img', 'T12_imag.img',
|
||||||
|
# 'T13_real.img', 'T13_imag.img', 'T22.img',
|
||||||
|
# 'T23_real.img', 'T23_imag.img', 'T33.img']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
@ -0,0 +1,146 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspTouziDecomposition.py
|
||||||
|
@Function:
|
||||||
|
@Contact:
|
||||||
|
@Author:LVY
|
||||||
|
@Date:2021/10/14 10:11
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import logging
|
||||||
|
from tool.algorithm.polsarpro.polarizationDecomposition import ModTouzi as TouziDecomp
|
||||||
|
from osgeo import gdal
|
||||||
|
import multiprocessing
|
||||||
|
from tool.algorithm.block.blockprocess import BlockProcess
|
||||||
|
import shutil
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.file.fileHandle import fileHandle
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
file =fileHandle(False)
|
||||||
|
|
||||||
|
class PspTouziDecomposition:
|
||||||
|
"""
|
||||||
|
Touzi分解
|
||||||
|
"""
|
||||||
|
def __init__(self, inDic, outDir):
|
||||||
|
"""
|
||||||
|
:param inDic:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
"""
|
||||||
|
self.__inDic = inDic
|
||||||
|
self.__outDir = outDir
|
||||||
|
self.__DecompostFlag = False
|
||||||
|
if self._checkTifFileDic(self.__inDic) is False:
|
||||||
|
return False
|
||||||
|
if not os.path.exists(self.__outDir):
|
||||||
|
os.makedirs(self.__outDir)
|
||||||
|
|
||||||
|
def api_Touzi_decomposition_TIF(self, Nwin = 5):
|
||||||
|
"""
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
"""
|
||||||
|
bandHH = gdal.Open(self.__inDic["HH"])
|
||||||
|
bandHV = gdal.Open(self.__inDic["HV"])
|
||||||
|
bandVH = gdal.Open(self.__inDic["VH"])
|
||||||
|
bandVV = gdal.Open(self.__inDic["VV"])
|
||||||
|
bandAll = [bandHH, bandHV, bandVH, bandVV]
|
||||||
|
decomposition = TouziDecomp(bandAll, Nwin)
|
||||||
|
decomposition.get_result(self.__outDir)
|
||||||
|
return True
|
||||||
|
|
||||||
|
def Touzi_decomposition_TIF(self,hh_path,hv_path,vh_path,vv_path,out_dir,suffix,Nwin = 5):
|
||||||
|
"""
|
||||||
|
:param Nwin:滤波窗口大小 3 5 7 9 11
|
||||||
|
"""
|
||||||
|
bandHH = gdal.Open(hh_path)
|
||||||
|
bandHV = gdal.Open(hv_path)
|
||||||
|
bandVH = gdal.Open(vh_path)
|
||||||
|
bandVV = gdal.Open(vv_path)
|
||||||
|
bandAll = [bandHH, bandHV, bandVH, bandVV]
|
||||||
|
decomposition = TouziDecomp(bandAll, Nwin)
|
||||||
|
decomposition.get_result_block(out_dir, suffix)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _checkTifFileDic(inDic):
|
||||||
|
file_name_in_out = ['HH', 'VV', 'HV', 'VH']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if item in inDic:
|
||||||
|
print(inDic[item])
|
||||||
|
if not os.path.exists(os.path.join(inDic[item])):
|
||||||
|
return False
|
||||||
|
else:
|
||||||
|
return False
|
||||||
|
return True
|
||||||
|
|
||||||
|
def Touzi_decomposition_multiprocessing(self):
|
||||||
|
|
||||||
|
#创建工作文件夹
|
||||||
|
src_path = os.path.join(self.__outDir, "src_img")
|
||||||
|
block_path = os.path.join(self.__outDir, "block")
|
||||||
|
decomposition_path = os.path.join(self.__outDir, "feature")
|
||||||
|
file.creat_dirs([src_path,block_path,decomposition_path])
|
||||||
|
|
||||||
|
shutil.copyfile(self.__inDic["HH"], os.path.join(src_path, "HH.tif"))
|
||||||
|
shutil.copyfile(self.__inDic["HV"], os.path.join(src_path, "HV.tif"))
|
||||||
|
shutil.copyfile(self.__inDic["VH"], os.path.join(src_path, "VH.tif"))
|
||||||
|
shutil.copyfile(self.__inDic["VV"], os.path.join(src_path, "VV.tif"))
|
||||||
|
self.__cols = ImageHandler.get_img_width(self.__inDic["HH"])
|
||||||
|
self.__rows = ImageHandler.get_img_height(self.__inDic["HH"])
|
||||||
|
# 分块
|
||||||
|
bp = BlockProcess()
|
||||||
|
block_size = bp.get_block_size(self.__rows, self.__cols)
|
||||||
|
bp.cut(src_path, block_path, ['tif', 'tiff'], 'tif', block_size)
|
||||||
|
logger.info('blocking tifs success!')
|
||||||
|
|
||||||
|
img_dir, img_name = bp.get_file_names(block_path, ['tif'])
|
||||||
|
dir_dict = bp.get_same_img(img_dir, img_name)
|
||||||
|
|
||||||
|
hh_list, vv_list, hv_list, vh_list = None, None, None, None
|
||||||
|
for key in dir_dict.keys():
|
||||||
|
tmp = key.split('_', 2)[0]
|
||||||
|
if tmp == 'HH':
|
||||||
|
hh_list = dir_dict[key]
|
||||||
|
elif tmp == 'VV':
|
||||||
|
vv_list = dir_dict[key]
|
||||||
|
elif tmp == 'HV':
|
||||||
|
hv_list = dir_dict[key]
|
||||||
|
elif tmp == 'VH':
|
||||||
|
vh_list = dir_dict[key]
|
||||||
|
|
||||||
|
processes_num = min([len(hh_list), multiprocessing.cpu_count() - 1])
|
||||||
|
|
||||||
|
# 开启多进程处理
|
||||||
|
pool = multiprocessing.Pool(processes=processes_num)
|
||||||
|
|
||||||
|
for i in range(len(hh_list)):
|
||||||
|
suffix = bp.get_suffix(os.path.basename(hh_list[i]))
|
||||||
|
# self.Touzi_decomposition_TIF(hh_list[i], hv_list[i], vh_list[i], vv_list[i], block_path, suffix,5)
|
||||||
|
pool.apply_async(self.Touzi_decomposition_TIF, (hh_list[i], hv_list[i], vh_list[i], vv_list[i], decomposition_path, suffix,5))
|
||||||
|
logger.info('total:%s, block:%s touzi!', len(hh_list), i)
|
||||||
|
|
||||||
|
pool.close()
|
||||||
|
pool.join()
|
||||||
|
# 合并处理后的影像
|
||||||
|
bp.combine(decomposition_path, self.__cols, self.__rows, self.__outDir, file_type=['tif'], datetype='float16')
|
||||||
|
|
||||||
|
file.del_folder(src_path)
|
||||||
|
file.del_folder(block_path)
|
||||||
|
file.del_folder(decomposition_path)
|
||||||
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# dir = {}
|
||||||
|
# dir.update({"HH":"I:\preprocessed\HH_preprocessed.tif"})
|
||||||
|
# dir.update({"HV":"I:\preprocessed\HV_preprocessed.tif"})
|
||||||
|
# dir.update({"VH":"I:\preprocessed\VH_preprocessed.tif"})
|
||||||
|
# dir.update({"VV":"I:\preprocessed\VV_preprocessed.tif"})
|
||||||
|
#
|
||||||
|
#
|
||||||
|
# p = PspTouziDecomposition(dir, "I:/preprocessed/")
|
||||||
|
# p.Touzi_decomposition_multiprocessing()
|
||||||
|
# pass
|
||||||
|
|
||||||
|
|
@ -0,0 +1,104 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project:__init__.py
|
||||||
|
@File:pspFreemanDecomposition.py
|
||||||
|
@Function:
|
||||||
|
@Contact:
|
||||||
|
@Author:LVY
|
||||||
|
@Date:2021/10/12 18:45
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
|
||||||
|
import os
|
||||||
|
import shutil
|
||||||
|
import subprocess
|
||||||
|
import logging
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class PspYamaguchiDecomposition:
|
||||||
|
"""
|
||||||
|
Yamaguchi yamaguchi_3components_decomposition_T3.exe yamaguchi_4components_decomposition_T3.exe
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(
|
||||||
|
self,
|
||||||
|
exeDir,
|
||||||
|
inT3Dir,
|
||||||
|
outDir,
|
||||||
|
exeDecomposeName='yamaguchi_4components_decomposition_T3.exe'):
|
||||||
|
"""
|
||||||
|
:param exeDir:exe所在目录
|
||||||
|
:param inT3Dir:T3矩阵目录
|
||||||
|
:param outDir:输出目录
|
||||||
|
"""
|
||||||
|
self.__exeName = exeDecomposeName
|
||||||
|
self.__exeDir = exeDir
|
||||||
|
self.__inT3Dir = inT3Dir
|
||||||
|
self.__outDir = outDir
|
||||||
|
self.__DecompostFlag = False
|
||||||
|
pass
|
||||||
|
|
||||||
|
def api_yamaguchi_4components_decomposition_T3(
|
||||||
|
self, rectX, rectY, rectWidth, rectHeight, Nwin=1):
|
||||||
|
"""
|
||||||
|
:param rectX:有效区域x
|
||||||
|
:param rectY:有效区域y
|
||||||
|
:param rectWidth:有效区域宽
|
||||||
|
:param rectHeight:有效区域高
|
||||||
|
:param Nwin :Size of the (Nwin, Nwin) sliding window used to compute local estimates. (int)
|
||||||
|
"""
|
||||||
|
if self.__DecompostFlag:
|
||||||
|
return True
|
||||||
|
if len(self.__exeDir) == 0:
|
||||||
|
if not os.path.exists(self.__exeName):
|
||||||
|
logger.error(self.__exeName + ' not exists.')
|
||||||
|
return False
|
||||||
|
exePath = self.__exeName
|
||||||
|
else:
|
||||||
|
if not os.path.exists(self.__exeDir + '\\' + self.__exeName):
|
||||||
|
logger.error(self.__exeName + ' not exists.')
|
||||||
|
return False
|
||||||
|
exePath = self.__exeDir + '\\' + self.__exeName
|
||||||
|
|
||||||
|
if not self._checkT3Matrix(self.__inT3Dir):
|
||||||
|
logger.error('T3 Matrix check failed.')
|
||||||
|
return False
|
||||||
|
if not os.path.exists(self.__outDir):
|
||||||
|
os.makedirs(self.__outDir)
|
||||||
|
|
||||||
|
Off_lig = rectX
|
||||||
|
Off_col = rectY
|
||||||
|
Sub_Nlig = rectWidth
|
||||||
|
Sub_Ncol = rectHeight
|
||||||
|
|
||||||
|
para_list = [
|
||||||
|
exePath,
|
||||||
|
self.__inT3Dir,
|
||||||
|
self.__outDir,
|
||||||
|
Nwin,
|
||||||
|
Off_lig,
|
||||||
|
Off_col,
|
||||||
|
Sub_Nlig,
|
||||||
|
Sub_Ncol]
|
||||||
|
cmd = " ".join(str(i) for i in para_list)
|
||||||
|
config_path = os.path.join(self.__inT3Dir, 'config.txt')
|
||||||
|
shutil.copyfile(config_path, os.path.join(self.__outDir, 'config.txt'))
|
||||||
|
result_tuple = subprocess.getstatusoutput(cmd)
|
||||||
|
|
||||||
|
if result_tuple[0] != 1 or result_tuple[1].find('error') != -1:
|
||||||
|
raise Exception(result_tuple[1])
|
||||||
|
self.__DecompostFlag = True
|
||||||
|
return True
|
||||||
|
@staticmethod
|
||||||
|
def _checkT3Matrix(T3Dir):
|
||||||
|
# 检测T3矩阵
|
||||||
|
if not os.path.exists(T3Dir):
|
||||||
|
return False
|
||||||
|
file_name_in_out = ['T11.bin', 'T12_real.bin', 'T12_imag.bin',
|
||||||
|
'T13_real.bin', 'T13_imag.bin', 'T22.bin',
|
||||||
|
'T23_real.bin', 'T23_imag.bin', 'T33.bin']
|
||||||
|
for item in file_name_in_out:
|
||||||
|
if not os.path.exists(T3Dir + "\\" + item):
|
||||||
|
return False
|
||||||
|
return True
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,157 @@
|
||||||
|
import os
|
||||||
|
cimport cython # 必须导入
|
||||||
|
import numpy as np##必须为c类型和python类型的数据都申明一个np
|
||||||
|
cimport numpy as np # 必须为c类型和python类型的数据都申明一个np
|
||||||
|
from libc.math cimport pi
|
||||||
|
from scipy.interpolate import griddata
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#####################
|
||||||
|
# 结构定义区
|
||||||
|
####################
|
||||||
|
cdef struct Point: # 结构
|
||||||
|
double x
|
||||||
|
double y
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
######################
|
||||||
|
# 射线法
|
||||||
|
######################
|
||||||
|
cdef int rayCasting(Point p,np.ndarray[double,ndim=2] poly):
|
||||||
|
cdef double px = p.x,
|
||||||
|
cdef double py = p.y,
|
||||||
|
cdef int flag = 0
|
||||||
|
|
||||||
|
cdef int i=0
|
||||||
|
cdef int l=poly.shape[0]
|
||||||
|
cdef int j=l-1
|
||||||
|
|
||||||
|
cdef double sx
|
||||||
|
cdef double sy
|
||||||
|
cdef double tx
|
||||||
|
cdef double ty
|
||||||
|
cdef x=0
|
||||||
|
while(i<l):
|
||||||
|
sx=poly[i,0]
|
||||||
|
sy=poly[i,1]
|
||||||
|
tx=poly[j,0]
|
||||||
|
ty=poly[j,1]
|
||||||
|
# 点与多边形顶点重合
|
||||||
|
if((sx == px and sy == py) or (tx == px and ty == py)):
|
||||||
|
return 1
|
||||||
|
#// 判断线段两端点是否在射线两侧
|
||||||
|
if((sy < py and ty >= py) or (sy >= py and ty < py)) :
|
||||||
|
#// 线段上与射线 Y 坐标相同的点的 X 坐标
|
||||||
|
x = sx + (py - sy) * (tx - sx) / (ty - sy)
|
||||||
|
#// 点在多边形的边上
|
||||||
|
if(x == px):
|
||||||
|
return 1
|
||||||
|
|
||||||
|
#// 射线穿过多边形的边界
|
||||||
|
if(x > px):
|
||||||
|
flag = 0 if flag==1 else 1
|
||||||
|
# 循环体
|
||||||
|
j=i
|
||||||
|
i=i+1
|
||||||
|
|
||||||
|
#// 射线穿过多边形边界的次数为奇数时点在多边形内
|
||||||
|
return 1 if flag==1 else 0
|
||||||
|
|
||||||
|
cpdef np.ndarray[double,ndim=2] insert_data(np.ndarray[double,ndim=2] ori2geo_img,np.ndarray[int , ndim=1] row_ids,np.ndarray[int,ndim=1] col_ids,np.ndarray[double,ndim=1] data):
|
||||||
|
cdef int i=0
|
||||||
|
cdef int count=row_ids.shape[0]
|
||||||
|
while i<count:
|
||||||
|
ori2geo_img[row_ids[i],col_ids[i]]=data[i]
|
||||||
|
i=i+1
|
||||||
|
return ori2geo_img
|
||||||
|
|
||||||
|
cpdef np.ndarray[double,ndim=2] cut_L1A_img(np.ndarray[double,ndim=3] ori2geo_img,np.ndarray[double,ndim=2] roi_list):
|
||||||
|
""" 根据roi 获取栅格对象
|
||||||
|
"""
|
||||||
|
cdef int height=ori2geo_img.shape[1]
|
||||||
|
cdef int width=ori2geo_img.shape[2]
|
||||||
|
cdef int i=0
|
||||||
|
cdef int j=0
|
||||||
|
cdef Point temp_p
|
||||||
|
cdef np.ndarray[double,ndim=2] mask=np.zeros((height,width),dtype=np.float64)
|
||||||
|
while i<height:
|
||||||
|
j=0
|
||||||
|
while j<width:
|
||||||
|
temp_p.x=ori2geo_img[0,i,j]
|
||||||
|
temp_p.y=ori2geo_img[1,i,j]
|
||||||
|
if rayCasting(temp_p,roi_list)==1:
|
||||||
|
mask[i,j]=1
|
||||||
|
else:
|
||||||
|
mask[i,j]=np.nan
|
||||||
|
j=j+1
|
||||||
|
i=i+1
|
||||||
|
|
||||||
|
return mask
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
cdef double distance_powe(Point p1,Point p2):
|
||||||
|
return (p1.x-p2.x)**2+(p1.y-p2.y)**2
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
cpdef np.ndarray[int,ndim=2] get_r_c(np.ndarray[double,ndim=3] ori2geo,np.ndarray[double,ndim=2] lon_lat):
|
||||||
|
cdef int p_count=lon_lat.shape[0]
|
||||||
|
cdef int height=ori2geo.shape[1]
|
||||||
|
cdef int width=ori2geo.shape[2]
|
||||||
|
cdef int i=0
|
||||||
|
cdef int j=0
|
||||||
|
cdef int c=0
|
||||||
|
cdef double dist=999
|
||||||
|
cdef double temp_dist=0
|
||||||
|
cdef Point p1
|
||||||
|
cdef Point p2
|
||||||
|
cdef int min_i
|
||||||
|
cdef int min_j
|
||||||
|
cdef np.ndarray[double,ndim=2] result=np.ones((p_count,2))*-1
|
||||||
|
|
||||||
|
|
||||||
|
# 范围
|
||||||
|
cdef double min_lon=np.min(ori2geo[0,:,:])
|
||||||
|
cdef double max_lon=np.max(ori2geo[0,:,:])
|
||||||
|
cdef double min_lat=np.min(ori2geo[1,:,:])
|
||||||
|
cdef double max_lat=np.max(ori2geo[1,:,:])
|
||||||
|
while c<p_count:
|
||||||
|
p1.x=lon_lat[c,0]
|
||||||
|
p1.y=lon_lat[c,1]
|
||||||
|
if min_lon>p1.x or max_lon<p1.x or p2.y<min_lat or p2.y>max_lat:
|
||||||
|
continue
|
||||||
|
c=c+1
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# 测试程序
|
||||||
|
cpdef np.ndarray[double,ndim=2] Add(np.ndarray[double,ndim=2] a,double x):
|
||||||
|
cdef double d=0; # 声明 注意 cython没有 bool类型
|
||||||
|
print("调用成功")
|
||||||
|
print(a)
|
||||||
|
print(x)
|
||||||
|
return a+x
|
||||||
|
|
||||||
|
|
@ -0,0 +1,45 @@
|
||||||
|
from setuptools import setup
|
||||||
|
from setuptools.extension import Extension
|
||||||
|
from Cython.Distutils import build_ext
|
||||||
|
from Cython.Build import cythonize
|
||||||
|
import numpy
|
||||||
|
from pathlib import Path
|
||||||
|
import shutil
|
||||||
|
|
||||||
|
|
||||||
|
class MyBuildExt(build_ext):
|
||||||
|
def run(self):
|
||||||
|
build_ext.run(self)
|
||||||
|
|
||||||
|
build_dir = Path(self.build_lib)
|
||||||
|
root_dir = Path(__file__).parent
|
||||||
|
target_dir = build_dir if not self.inplace else root_dir
|
||||||
|
|
||||||
|
self.copy_file(Path('./SAR_geo') / '__init__.py', root_dir, target_dir)
|
||||||
|
#self.copy_file(Path('./pkg2') / '__init__.py', root_dir, target_dir)
|
||||||
|
self.copy_file(Path('.') / '__init__.py', root_dir, target_dir)
|
||||||
|
def copy_file(self, path, source_dir, destination_dir):
|
||||||
|
if not (source_dir / path).exists():
|
||||||
|
return
|
||||||
|
shutil.copyfile(str(source_dir / path), str(destination_dir / path))
|
||||||
|
|
||||||
|
setup(
|
||||||
|
name="MyModule",
|
||||||
|
ext_modules=cythonize(
|
||||||
|
[
|
||||||
|
#Extension("pkg1.*", ["root/pkg1/*.py"]),
|
||||||
|
Extension("pkg2.*", ["./SAR_geo/SAR_GEO.pyx"]),
|
||||||
|
#Extension("1.*", ["root/*.py"])
|
||||||
|
],
|
||||||
|
build_dir="build",
|
||||||
|
compiler_directives=dict(
|
||||||
|
always_allow_keywords=True
|
||||||
|
)),
|
||||||
|
cmdclass=dict(
|
||||||
|
build_ext=MyBuildExt
|
||||||
|
),
|
||||||
|
packages=[],
|
||||||
|
include_dirs=[numpy.get_include()],
|
||||||
|
)
|
||||||
|
|
||||||
|
# 指令： python setup.py build_ext --inplace
|
||||||
|
|
@ -0,0 +1,573 @@
|
||||||
|
from tool.algorithm.transforml1a import SAR_GEO as SAR_GEO
|
||||||
|
from tool.algorithm.image import ImageHandle
|
||||||
|
import numpy as np
|
||||||
|
import scipy
|
||||||
|
from scipy.interpolate import griddata, RegularGridInterpolator
|
||||||
|
import logging
|
||||||
|
import pyresample as pr
|
||||||
|
# 插值模块
|
||||||
|
from pyresample.bilinear import NumpyBilinearResampler
|
||||||
|
from pyresample import geometry
|
||||||
|
from pyresample.geometry import AreaDefinition
|
||||||
|
from osgeo import osr
|
||||||
|
import os
|
||||||
|
|
||||||
|
# os.environ['PROJ_LIB'] = r"D:\Anaconda\envs\micro\Lib\site-packages\osgeo\data\proj"
|
||||||
|
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
##############
|
||||||
|
# 多项式回归组件
|
||||||
|
##############
|
||||||
|
#
|
||||||
|
def griddata_geo(points, data, lon_grid, lat_grid, method, i, end_i):
|
||||||
|
grid_data = griddata(points, data, (lon_grid, lat_grid), method=method, )
|
||||||
|
grid_data = grid_data[:, :, 0]
|
||||||
|
return [i, end_i, grid_data]
|
||||||
|
|
||||||
|
def griddataBlock(start_x, len_x, start_y, len_y, grid_data_input, grid_x, grid_y, method):
|
||||||
|
grid_x = grid_x.reshape(-1)
|
||||||
|
grid_y = grid_y.reshape(-1)
|
||||||
|
grid_data_input = grid_data_input.reshape(-1)
|
||||||
|
x_list = np.array(list(range(len_x))) + start_x
|
||||||
|
y_list = np.array(list(range(len_y))) + start_y
|
||||||
|
|
||||||
|
x_grid, y_grid = np.meshgrid(x_list, y_list)
|
||||||
|
idx = np.argsort(grid_x)
|
||||||
|
grid_x = grid_x[idx].reshape(-1)
|
||||||
|
grid_y = grid_y[idx].reshape(-1)
|
||||||
|
grid_data_input = grid_data_input[idx].reshape(-1)
|
||||||
|
interp_func = RegularGridInterpolator((grid_x.reshape(-1), grid_y.reshape(-1)), grid_data_input.reshape(-1),
|
||||||
|
method='slinear', bounds_error=False, fill_value=np.nan)
|
||||||
|
grid_data = interp_func((x_grid, y_grid))
|
||||||
|
# grid_data = griddata(p, grid_data_input, (x_grid, y_grid), method=method)
|
||||||
|
grid_data = grid_data[:, :, 0]
|
||||||
|
return (x_grid, y_grid, grid_data)
|
||||||
|
|
||||||
|
|
||||||
|
class polyfit2d_U:
|
||||||
|
def __init__(self, x, y, z) -> None:
|
||||||
|
# 定义参数
|
||||||
|
|
||||||
|
X = np.ones((x.shape[0], 10))
|
||||||
|
X[:, 0] = 1
|
||||||
|
X[:, 1] = x
|
||||||
|
X[:, 2] = y
|
||||||
|
X[:, 3] = x * y
|
||||||
|
X[:, 4] = x ** 2
|
||||||
|
X[:, 5] = y ** 2
|
||||||
|
X[:, 6] = x * X[:, 5]
|
||||||
|
X[:, 7] = y * X[:, 4]
|
||||||
|
X[:, 8] = x ** 3
|
||||||
|
X[:, 9] = y ** 3
|
||||||
|
Y = z.reshape(-1, 1)
|
||||||
|
A = np.matmul(np.matmul(np.linalg.inv(np.matmul(X.T, X)), X.T), Y)
|
||||||
|
self.paras_fit = A
|
||||||
|
|
||||||
|
def fit(self, x, y):
|
||||||
|
X = np.ones((x.shape[0], 10))
|
||||||
|
X[:, 0] = 1
|
||||||
|
X[:, 1] = x
|
||||||
|
X[:, 2] = y
|
||||||
|
X[:, 3] = x * y
|
||||||
|
X[:, 4] = x ** 2
|
||||||
|
X[:, 5] = y ** 2
|
||||||
|
X[:, 6] = x * X[:, 5]
|
||||||
|
X[:, 7] = y * X[:, 4]
|
||||||
|
X[:, 8] = x ** 3
|
||||||
|
X[:, 9] = y ** 3
|
||||||
|
z = np.matmul(X, self.paras_fit)
|
||||||
|
return np.sum(z)
|
||||||
|
|
||||||
|
|
||||||
|
class TransImgL1A:
|
||||||
|
def __init__(self, ori_sim_path, roi):
|
||||||
|
self._begin_r, self._begin_c, self._end_r, self._end_c = 0, 0, 0, 0
|
||||||
|
self.ori2geo_img = None
|
||||||
|
self._mask = None
|
||||||
|
self._min_lon, self._max_lon, self._min_lat, self._max_lat = 0, 0, 0, 0
|
||||||
|
self.init_trans_para(ori_sim_path, roi)
|
||||||
|
|
||||||
|
def get_roi_points(self):
|
||||||
|
rowcol = np.where(self._mask == 1)
|
||||||
|
data = [(self._begin_r + row, self._begin_c + col) for (row, col) in zip(rowcol[0], rowcol[1])]
|
||||||
|
return data
|
||||||
|
|
||||||
|
|
||||||
|
def get_lonlat_points(self):
|
||||||
|
lon = self.ori2geo_img[0, :, :][np.where(self._mask == 1)]
|
||||||
|
lat = self.ori2geo_img[1, :, :][np.where(self._mask == 1)]
|
||||||
|
data = [(row, col) for (row, col) in zip(lon, lat)]
|
||||||
|
return data
|
||||||
|
|
||||||
|
######################
|
||||||
|
# 插值方法
|
||||||
|
######################
|
||||||
|
def init_trans_para(self, ori_sim_path, roi):
|
||||||
|
"""裁剪L1a_img
|
||||||
|
Args:
|
||||||
|
src_img_path (_type_): 原始L1A影像
|
||||||
|
cuted_img_path (_type_): 待裁剪对象
|
||||||
|
roi (_type_): 裁剪roi
|
||||||
|
"""
|
||||||
|
ori2geo_img = ImageHandle.ImageHandler.get_data(ori_sim_path)
|
||||||
|
point_list = np.array(roi)
|
||||||
|
min_lon = np.nanmin(point_list[:, 0])
|
||||||
|
max_lon = np.nanmax(point_list[:, 0])
|
||||||
|
min_lat = np.nanmin(point_list[:, 1])
|
||||||
|
max_lat = np.nanmax(point_list[:, 1])
|
||||||
|
self._min_lon, self._max_lon, self._min_lat, self._max_lat = min_lon, max_lon, min_lat, max_lat
|
||||||
|
|
||||||
|
r_c_list = np.where(
|
||||||
|
(ori2geo_img[0, :, :] >= min_lon) & (ori2geo_img[0, :, :] <= max_lon)
|
||||||
|
& (ori2geo_img[1, :, :] >= min_lat) & (ori2geo_img[1, :, :] <= max_lat)) #
|
||||||
|
|
||||||
|
if len(r_c_list) == 0 or r_c_list[0] == [] or r_c_list[1] == [] or np.array(r_c_list).size == 0:
|
||||||
|
msg = 'csv_roi:' + str(roi) + 'not in box,please revise csv data！'
|
||||||
|
print(msg)
|
||||||
|
else:
|
||||||
|
# print("csv_roi:")
|
||||||
|
# print(roi)
|
||||||
|
r_min = np.nanmin(r_c_list[0])
|
||||||
|
r_max = np.nanmax(r_c_list[0])
|
||||||
|
c_min = np.nanmin(r_c_list[1])
|
||||||
|
c_max = np.nanmax(r_c_list[1])
|
||||||
|
self.ori2geo_img = ori2geo_img[:, r_min:r_max + 1, c_min:c_max + 1]
|
||||||
|
# 开始调用组件 计算
|
||||||
|
|
||||||
|
mask = SAR_GEO.cut_L1A_img(self.ori2geo_img.astype(np.float64), point_list)
|
||||||
|
self._begin_r = r_min
|
||||||
|
self._end_r = r_max
|
||||||
|
self._begin_c = c_min
|
||||||
|
self._end_c = c_max
|
||||||
|
self._mask = mask
|
||||||
|
|
||||||
|
def cut_L1A(self, in_path, out_path):
|
||||||
|
img = ImageHandle.ImageHandler.get_data(in_path)
|
||||||
|
if len(img.shape) == 3:
|
||||||
|
cut_img = img[:, self._begin_r:self._end_r + 1, self._begin_c:self._end_c + 1]
|
||||||
|
cut_img[0, :, :] = cut_img[0, :, :] * self._mask
|
||||||
|
cut_img[1, :, :] = cut_img[1, :, :] * self._mask
|
||||||
|
ImageHandle.ImageHandler.write_img(out_path, '', [0, 0, 0, 0, 0, 0], cut_img)
|
||||||
|
else:
|
||||||
|
cut_img = img[self._begin_r:self._end_r + 1, self._begin_c:self._end_c + 1]
|
||||||
|
cut_img[:, :] = cut_img[:, :] * self._mask
|
||||||
|
cut_img[:, :] = cut_img[:, :] * self._mask
|
||||||
|
ImageHandle.ImageHandler.write_img(out_path, '', [0, 0, 0, 0, 0, 0], cut_img)
|
||||||
|
|
||||||
|
def grid_interp_to_station(self, all_data, station_lon, station_lat, method='linear'):
|
||||||
|
'''
|
||||||
|
func: 将等经纬度网格值 插值到 离散站点。使用griddata进行插值
|
||||||
|
inputs:
|
||||||
|
all_data,形式为：[grid_lon,grid_lat,data] 即[经度网格，纬度网格，数值网格]
|
||||||
|
station_lon: 站点经度
|
||||||
|
station_lat: 站点纬度。可以是 单个点，列表或者一维数组
|
||||||
|
method: 插值方法,默认使用 linear
|
||||||
|
'''
|
||||||
|
station_lon = np.array(station_lon).reshape(-1, 1)
|
||||||
|
station_lat = np.array(station_lat).reshape(-1, 1)
|
||||||
|
|
||||||
|
lon = all_data[0].reshape(-1, 1)
|
||||||
|
lat = all_data[1].reshape(-1, 1)
|
||||||
|
data = all_data[2].reshape(-1, 1)
|
||||||
|
|
||||||
|
points = np.concatenate([lon, lat], axis=1)
|
||||||
|
|
||||||
|
station_value = griddata(points, data, (station_lon, station_lat), method=method)
|
||||||
|
|
||||||
|
station_value = station_value[:, :, 0]
|
||||||
|
|
||||||
|
return station_value
|
||||||
|
|
||||||
|
#####################
|
||||||
|
# 当存在 ori2geo.tif
|
||||||
|
#####################
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def cut_L1a_img(src_img_path, cuted_img_path, roi):
|
||||||
|
"""裁剪L1a_img
|
||||||
|
Args:
|
||||||
|
src_img_path (_type_): 原始L1A影像
|
||||||
|
cuted_img_path (_type_): 待裁剪对象
|
||||||
|
roi (_type_): 裁剪roi
|
||||||
|
"""
|
||||||
|
ori2geo_img = ImageHandle.ImageHandler.get_data(src_img_path)
|
||||||
|
point_list = np.array(roi)
|
||||||
|
# 开始调用组件 计算
|
||||||
|
mask = SAR_GEO.cut_L1A_img(ori2geo_img.astype(np.float64), point_list)
|
||||||
|
#
|
||||||
|
ori2geo_img[0, :, :] = ori2geo_img[0, :, :] * mask
|
||||||
|
ori2geo_img[1, :, :] = ori2geo_img[1, :, :] * mask
|
||||||
|
|
||||||
|
ImageHandle.ImageHandler.write_img(cuted_img_path, '', [0, 0, 0, 0, 0, 0], ori2geo_img)
|
||||||
|
return ori2geo_img # 保存成影像
|
||||||
|
|
||||||
|
def tran_geo_to_l1a(self, geo_img_path, out_l1a_img_path, ori_sim_img_path, is_class=False):
|
||||||
|
"""裁剪后的有投影信息的影像(cover、ndvi)转换到L1A裁剪影像的尺寸
|
||||||
|
Args:
|
||||||
|
geo_img_path (_type_): _description_
|
||||||
|
out_l1a_img_path (_type_): _description_
|
||||||
|
ori_sim_img_path (_type_): _description_
|
||||||
|
|
||||||
|
geo_img_path:地理影像路径
|
||||||
|
out_l1a_img_path：转换L1A坐标系图像路径
|
||||||
|
ori_sim_img_path：裁剪后模拟影像路径
|
||||||
|
is_clss: 是否是 定性类产品
|
||||||
|
|
||||||
|
"""
|
||||||
|
inverse_gt = ImageHandle.ImageHandler.get_invgeotransform(geo_img_path)
|
||||||
|
ori2geo_tif = ImageHandle.ImageHandler.get_data(ori_sim_img_path)
|
||||||
|
height = ImageHandle.ImageHandler.get_img_height(geo_img_path)
|
||||||
|
width = ImageHandle.ImageHandler.get_img_width(geo_img_path)
|
||||||
|
# 计算投影
|
||||||
|
x = ori2geo_tif[0, :, :] # lon lat x,y
|
||||||
|
y = ori2geo_tif[1, :, :]
|
||||||
|
ori2geo_tif[0, :, :] = inverse_gt[0] + inverse_gt[1] * x + inverse_gt[2] * y # x
|
||||||
|
ori2geo_tif[1, :, :] = inverse_gt[3] + inverse_gt[4] * x + inverse_gt[5] * y # y
|
||||||
|
|
||||||
|
del x, y
|
||||||
|
geo_tif = ImageHandle.ImageHandler.get_data(geo_img_path) # 获取目标影像
|
||||||
|
ori2geo_tif_shape = ori2geo_tif.shape # height,width
|
||||||
|
|
||||||
|
if is_class:
|
||||||
|
ori2geo_tif = np.round(ori2geo_tif).astype(np.int32)
|
||||||
|
mask = (ori2geo_tif[0, :, :] >= 0) & (ori2geo_tif[0, :, :] < width) & (ori2geo_tif[1, :, :] >= 0) & (
|
||||||
|
ori2geo_tif[1, :, :] < height)
|
||||||
|
ori2geo_tif[0, :, :] = ori2geo_tif[0, :, :] * mask
|
||||||
|
ori2geo_tif[1, :, :] = ori2geo_tif[1, :, :] * mask
|
||||||
|
geo_tif_shape = geo_tif.shape
|
||||||
|
geo_tif_l1a = geo_tif[ori2geo_tif[1, :, :].reshape(-1), ori2geo_tif[0, :, :].reshape(-1)].reshape(
|
||||||
|
ori2geo_tif.shape[1], ori2geo_tif.shape[2]).astype(np.float32)
|
||||||
|
del ori2geo_tif, geo_tif
|
||||||
|
one_ids = np.where(mask == False)
|
||||||
|
geo_tif_l1a[one_ids[0], one_ids[1]] = np.nan
|
||||||
|
|
||||||
|
ImageHandle.ImageHandler.write_img(out_l1a_img_path, '', [0, 0, 0, 0, 0, 0], geo_tif_l1a)
|
||||||
|
# save_temp_L1A(out_l1a_img_path,geo_tif_l1a)
|
||||||
|
return geo_tif_l1a
|
||||||
|
else: # 数值性插值
|
||||||
|
mask = (ori2geo_tif[0, :, :] > 0) & (ori2geo_tif[0, :, :] < width - 1) & (ori2geo_tif[1, :, :] > 0) & (
|
||||||
|
ori2geo_tif[1, :, :] < height - 1)
|
||||||
|
one_ids = np.where(mask == 1)
|
||||||
|
x, y = np.meshgrid(np.arange(0, width), np.arange(0, height))
|
||||||
|
result_data = self.grid_interp_to_station([y.reshape(-1), x.reshape(-1), geo_tif.reshape(-1)],
|
||||||
|
ori2geo_tif[1, one_ids[0], one_ids[1]].reshape(-1),
|
||||||
|
ori2geo_tif[0, one_ids[0], one_ids[1]].reshape(-1),
|
||||||
|
method='linear').reshape(-1)
|
||||||
|
mask = mask.reshape(-1)
|
||||||
|
result_data_result = np.zeros((ori2geo_tif.shape[1], ori2geo_tif.shape[2]))
|
||||||
|
result_data_result[:, :] = np.nan
|
||||||
|
result_data_result = SAR_GEO.insert_data(result_data_result, one_ids[0].astype(np.int32),
|
||||||
|
one_ids[1].astype(np.int32), result_data)
|
||||||
|
ImageHandle.ImageHandler.write_img(out_l1a_img_path, '', [0, 0, 0, 0, 0, 0], result_data_result)
|
||||||
|
# save_temp_L1A(out_l1a_img_path,result_data_result)
|
||||||
|
return result_data_result
|
||||||
|
|
||||||
|
def tran_lonlats_to_rowcols(self, lonlats, ori_sim_img_path):
|
||||||
|
"""
|
||||||
|
功能：输入经纬度坐标，输出图像行列号
|
||||||
|
函数名称：tran_lonlats_to_rowcols(lonlats,out_rowcols,ori_sim_img_path)
|
||||||
|
Lonlats:经纬度坐标，示例[[120.53, 31.5], [120.61, 31.5], [120.53, 31.45], [120.61, 31.45]]
|
||||||
|
out_rowcols:图像行列号，示例[[0, 0], [7000, 0], [7000, 8000], [0, 8000]]
|
||||||
|
ori_sim_img_path：裁剪后模拟影像路径
|
||||||
|
"""
|
||||||
|
ori2geo_tif = ImageHandle.ImageHandler.get_data(ori_sim_img_path)
|
||||||
|
min_lon = np.nanmin(ori2geo_tif[0, :, :])
|
||||||
|
max_lon = np.nanmax(ori2geo_tif[0, :, :])
|
||||||
|
min_lat = np.nanmin(ori2geo_tif[1, :, :])
|
||||||
|
max_lat = np.nanmax(ori2geo_tif[1, :, :])
|
||||||
|
|
||||||
|
result = []
|
||||||
|
for i in range(len(lonlats)):
|
||||||
|
p = lonlats[i]
|
||||||
|
if min_lon > p[0] or max_lon < p[0] or min_lat > p[1] or max_lat < p[1]:
|
||||||
|
result.append([-1, -1])
|
||||||
|
continue
|
||||||
|
temp_x = np.square(ori2geo_tif[0, :, :] - p[0]) + np.square(ori2geo_tif[1, :, :] - p[1])
|
||||||
|
r_c_list = []
|
||||||
|
r_c = np.argmin(temp_x)
|
||||||
|
r_c = [r_c // temp_x.shape[1], r_c % temp_x.shape[1]]
|
||||||
|
r_c_list.append([r_c[0], r_c[1], ori2geo_tif[0, r_c[0], r_c[1]], ori2geo_tif[1, r_c[0], r_c[1]]])
|
||||||
|
# 插值
|
||||||
|
for i in range(r_c[0] - 3, r_c[0] + 3):
|
||||||
|
if i < 0 or i > temp_x.shape[0] - 1:
|
||||||
|
continue
|
||||||
|
for j in range(r_c[1] - 3, r_c[1] + 3):
|
||||||
|
if j < 0 or j > temp_x.shape[1] - 1:
|
||||||
|
continue
|
||||||
|
r_c_list.append([i, j, ori2geo_tif[0, i, j], ori2geo_tif[1, i, j]])
|
||||||
|
r_c_list = np.array(r_c_list)
|
||||||
|
points = r_c_list[:, 2:]
|
||||||
|
f_r = scipy.interpolate.interp2d(r_c_list[:, 2], r_c_list[:, 3], r_c_list[:, 0], kind='linear')
|
||||||
|
f_c = scipy.interpolate.interp2d(r_c_list[:, 2], r_c_list[:, 3], r_c_list[:, 1], kind='linear')
|
||||||
|
tar_get_r = f_r(p[0], p[1])[0]
|
||||||
|
tar_get_c = f_c(p[0], p[1])[0]
|
||||||
|
if tar_get_r < ori2geo_tif.shape[1] and tar_get_c < ori2geo_tif.shape[
|
||||||
|
2] and tar_get_r >= 0 and tar_get_c >= 0:
|
||||||
|
lon_temp = ori2geo_tif[0, int(round(tar_get_r)), int(round(tar_get_c))]
|
||||||
|
lon_lat = ori2geo_tif[1, int(round(tar_get_r)), int(round(tar_get_c))]
|
||||||
|
# 增加条件筛选
|
||||||
|
result.append([tar_get_r, tar_get_c])
|
||||||
|
else:
|
||||||
|
result.append([-1, -1])
|
||||||
|
return result
|
||||||
|
|
||||||
|
def tran_lonlats_to_L1A_rowcols(self, meas_data, ori_sim_path):
|
||||||
|
lonlats = []
|
||||||
|
data_roi = []
|
||||||
|
for data in meas_data:
|
||||||
|
lon = float(data[1])
|
||||||
|
lat = float(data[2])
|
||||||
|
if (lon > self._min_lon and lon < self._max_lon and lat > self._min_lat and lat < self._max_lat):
|
||||||
|
lonlats.append([lon, lat])
|
||||||
|
data_roi.append(data)
|
||||||
|
|
||||||
|
rowcols = self.tran_lonlats_to_rowcols(lonlats, ori_sim_path)
|
||||||
|
measdata_list = []
|
||||||
|
for data, rowcol in zip(data_roi, rowcols):
|
||||||
|
if (rowcol[0] != -1 and rowcol[1] != -1):
|
||||||
|
measdata_list.append(
|
||||||
|
[round(rowcol[0]) - self._begin_r, round(rowcol[1]) - self._begin_c, float(data[3])])
|
||||||
|
return measdata_list
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_radius_of_influence(lalo_step, src_meta='radar2geo', ratio=3):
|
||||||
|
EARTH_RADIUS = 6378122.65 # m
|
||||||
|
"""Get radius of influence based on the lookup table resolution in lat/lon direction"""
|
||||||
|
if src_meta == "geo2radar":
|
||||||
|
# geo2radar
|
||||||
|
radius = 100e3
|
||||||
|
else:
|
||||||
|
# radar2geo
|
||||||
|
step_deg = max(np.abs(lalo_step))
|
||||||
|
step_m = step_deg * np.pi / 180.0 * EARTH_RADIUS
|
||||||
|
radius = step_m * ratio
|
||||||
|
return radius
|
||||||
|
|
||||||
|
def interp2d_station_to_grid(self, lon, lat, data, loc_range=[18, 54, 73, 135],
|
||||||
|
det_grid=1, method='linear', projCode=4326):
|
||||||
|
# 参考链接 https://blog.csdn.net/weixin_43718675/article/details/103497930
|
||||||
|
'''
|
||||||
|
func : 将站点数据插值到等经纬度格点
|
||||||
|
inputs:
|
||||||
|
lon: 站点的经度
|
||||||
|
lat: 站点的纬度
|
||||||
|
data: 对应经纬度站点的 气象要素值
|
||||||
|
loc_range: [lat_min,lat_max,lon_min,lon_max]。站点数据插值到loc_range这个范围
|
||||||
|
det_grid: 插值形成的网格空间分辨率
|
||||||
|
method: 所选插值方法，默认 0.125
|
||||||
|
return:
|
||||||
|
|
||||||
|
[lon_grid,lat_grid,data_grid]
|
||||||
|
'''
|
||||||
|
# step1: 先将 lon,lat,data转换成 n*1 的array数组
|
||||||
|
lon = np.array(lon).reshape(-1, 1)
|
||||||
|
lat = np.array(lat).reshape(-1, 1)
|
||||||
|
data = np.array(data).reshape(-1, 1)
|
||||||
|
|
||||||
|
# step2:确定插值区域的经纬度网格
|
||||||
|
lat_min = loc_range[0] # y
|
||||||
|
lat_max = loc_range[1] # y
|
||||||
|
lon_min = loc_range[2] # x
|
||||||
|
lon_max = loc_range[3] # x
|
||||||
|
gt = [0, 0, 0, 0, 0, 0]
|
||||||
|
gt[0] = lon_min # x
|
||||||
|
gt[1] = det_grid
|
||||||
|
gt[3] = lat_max # y
|
||||||
|
gt[5] = -det_grid
|
||||||
|
lat_count = int((lat_max - lat_min) / det_grid + 1) # y
|
||||||
|
lon_count = int((lon_max - lon_min) / det_grid + 1) # x
|
||||||
|
# 替换为pyresample 插值方法
|
||||||
|
proj_osr = osr.SpatialReference()
|
||||||
|
proj_osr.ImportFromEPSG(projCode)
|
||||||
|
projection = proj_osr.ExportToPROJJSON()
|
||||||
|
# （lower_left_x、lower_left_y、upper_right_x、upper_right_y）
|
||||||
|
target_def = AreaDefinition("id1", "WGS84", "proj_id", projection,
|
||||||
|
lon_count, lat_count, [lon_min, lat_min, lon_max, lat_max])
|
||||||
|
source_def = geometry.SwathDefinition(lons=lon, lats=lat)
|
||||||
|
lalo_step = [det_grid, -det_grid]
|
||||||
|
radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
|
||||||
|
result = pr.bilinear.resample_bilinear(data, source_def, target_def,
|
||||||
|
radius=radius, neighbours=32,
|
||||||
|
nprocs=8, fill_value=np.nan,
|
||||||
|
epsilon=0)
|
||||||
|
#
|
||||||
|
return result
|
||||||
|
|
||||||
|
def geocoding(self, ori_geo_tif, produc_arr, pixel_delta=1, method='linear'):
|
||||||
|
# 参考链接 https://blog.csdn.net/weixin_43718675/article/details/103497930
|
||||||
|
ori_geo_tif[np.isnan(ori_geo_tif)] = -1
|
||||||
|
lon_data = ori_geo_tif[0, :, :].reshape(-1)
|
||||||
|
lat_data = ori_geo_tif[1, :, :].reshape(-1)
|
||||||
|
idx = np.where(lat_data != -1)
|
||||||
|
lat_data = lat_data[idx]
|
||||||
|
lon_data = lon_data[idx]
|
||||||
|
idx = np.where(lon_data != -1)
|
||||||
|
|
||||||
|
lat_data = lat_data[idx]
|
||||||
|
lon_data = lon_data[idx]
|
||||||
|
# ###########################################
|
||||||
|
result = self.interp2d_station_to_grid(lon_data, lat_data, produc_arr,
|
||||||
|
[self._min_lat, self._max_lat, self._min_lon, self._max_lon],
|
||||||
|
det_grid=pixel_delta, method=method)
|
||||||
|
return result
|
||||||
|
|
||||||
|
# def l1a_2_geo(self, ori_geo_path, l1a_produc_path, geo_produc_path, method='linear'):
|
||||||
|
# ori_geo_tif = ImageHandle.ImageHandler.get_data(ori_geo_path)
|
||||||
|
# # l1a_produc = ImageHandle.ImageHandler.get_data(l1a_produc_path)
|
||||||
|
# l1a_produc = ImageHandle.ImageHandler.get_band_array(l1a_produc_path, 1)
|
||||||
|
# pixel_delta_y = (self._max_lat - self._min_lat) / (self._end_r - self._begin_r) # 0.001
|
||||||
|
# pixel_delta_x = (self._max_lon - self._min_lon) / (self._end_c - self._begin_c)
|
||||||
|
#
|
||||||
|
# lon_data = ori_geo_tif[0, :, :].reshape(-1)
|
||||||
|
# lat_data = ori_geo_tif[1, :, :].reshape(-1)
|
||||||
|
# l1a_produc = l1a_produc.reshape(-1)
|
||||||
|
# idx = np.logical_not(np.isnan(lon_data))
|
||||||
|
# lat_data = lat_data[idx]
|
||||||
|
# lon_data = lon_data[idx]
|
||||||
|
# l1a_produc = l1a_produc[idx]
|
||||||
|
# idx = np.logical_not(np.isnan(lat_data))
|
||||||
|
# lat_data = lat_data[idx]
|
||||||
|
# lon_data = lon_data[idx]
|
||||||
|
# l1a_produc = l1a_produc[idx]
|
||||||
|
#
|
||||||
|
# gt = [self._min_lon, pixel_delta_x, 0.0,
|
||||||
|
# self._max_lat, 0.0, -pixel_delta_y]
|
||||||
|
# [lat_min, lat_max, lon_min, lon_max] = [self._min_lat, self._max_lat, self._min_lon, self._max_lon]
|
||||||
|
# lat_count = int((lat_max - lat_min) / pixel_delta_y + 1) # y
|
||||||
|
# lon_count = int((lon_max - lon_min) / pixel_delta_x + 1) # x
|
||||||
|
#
|
||||||
|
# # 获取地理坐标系统信息，用于选取需要的地理坐标系统
|
||||||
|
# srs = osr.SpatialReference()
|
||||||
|
# srs.ImportFromEPSG(4326) # 定义输出的坐标系为"WGS 84"
|
||||||
|
# proj = srs.ExportToWkt()
|
||||||
|
#
|
||||||
|
# projection = srs.ExportToPROJJSON()
|
||||||
|
# # （lower_left_x、lower_left_y、upper_right_x、upper_right_y）
|
||||||
|
# target_def = AreaDefinition("id1", "WGS84", "proj_id", projection,
|
||||||
|
# lon_count, lat_count, [lon_min, lat_min, lon_max, lat_max])
|
||||||
|
# lon_data = lon_data.reshape(-1, 1)
|
||||||
|
# lat_data = lat_data.reshape(-1, 1)
|
||||||
|
# l1a_produc = l1a_produc.reshape(-1, 1)
|
||||||
|
# source_def = geometry.SwathDefinition(lons=lon_data, lats=lat_data)
|
||||||
|
# lalo_step = [pixel_delta_x, -pixel_delta_y]
|
||||||
|
# radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
|
||||||
|
# geo_produc = pr.bilinear.resample_bilinear(l1a_produc, source_def, target_def,
|
||||||
|
# radius=radius, neighbours=32,
|
||||||
|
# nprocs=8, fill_value=np.nan,
|
||||||
|
# epsilon=0)
|
||||||
|
#
|
||||||
|
# ImageHandle.ImageHandler.write_img(geo_produc_path, proj, gt, geo_produc, np.nan)
|
||||||
|
#
|
||||||
|
# def l1a_2_geo_int(self, ori_geo_path, l1a_produc_path, geo_produc_path, method='nearest'):
|
||||||
|
# ori_geo_tif = ImageHandle.ImageHandler.get_data(ori_geo_path)
|
||||||
|
# # l1a_produc = ImageHandle.ImageHandler.get_data(l1a_produc_path)
|
||||||
|
# l1a_produc = ImageHandle.ImageHandler.get_band_array(l1a_produc_path, 1)
|
||||||
|
# pixel_delta_y = (self._max_lat - self._min_lat) / (self._end_r - self._begin_r) # 0.001
|
||||||
|
# pixel_delta_x = (self._max_lon - self._min_lon) / (self._end_c - self._begin_c)
|
||||||
|
#
|
||||||
|
# lon_data = ori_geo_tif[0, :, :].reshape(-1)
|
||||||
|
# lat_data = ori_geo_tif[1, :, :].reshape(-1)
|
||||||
|
# l1a_produc = l1a_produc.reshape(-1)
|
||||||
|
# idx = np.logical_not(np.isnan(lon_data))
|
||||||
|
# lat_data = lat_data[idx]
|
||||||
|
# lon_data = lon_data[idx]
|
||||||
|
# l1a_produc = l1a_produc[idx]
|
||||||
|
# idx = np.logical_not(np.isnan(lat_data))
|
||||||
|
# lat_data = lat_data[idx]
|
||||||
|
# lon_data = lon_data[idx]
|
||||||
|
# l1a_produc = l1a_produc[idx]
|
||||||
|
#
|
||||||
|
# gt = [self._min_lon, pixel_delta_x, 0.0,
|
||||||
|
# self._max_lat, 0.0, -pixel_delta_y]
|
||||||
|
# [lat_min, lat_max, lon_min, lon_max] = [self._min_lat, self._max_lat, self._min_lon, self._max_lon]
|
||||||
|
# lat_count = int((lat_max - lat_min) / pixel_delta_y + 1) # y
|
||||||
|
# lon_count = int((lon_max - lon_min) / pixel_delta_x + 1) # x
|
||||||
|
#
|
||||||
|
# # 获取地理坐标系统信息，用于选取需要的地理坐标系统
|
||||||
|
# srs = osr.SpatialReference()
|
||||||
|
# srs.ImportFromEPSG(4326) # 定义输出的坐标系为"WGS 84"
|
||||||
|
# proj = srs.ExportToWkt()
|
||||||
|
#
|
||||||
|
# projection = srs.ExportToPROJJSON()
|
||||||
|
# # （lower_left_x、lower_left_y、upper_right_x、upper_right_y）
|
||||||
|
# target_def = AreaDefinition("id1", "WGS84", "proj_id", projection,
|
||||||
|
# lon_count, lat_count, [lon_min, lat_min, lon_max, lat_max])
|
||||||
|
# lon_data = lon_data.reshape(-1, 1)
|
||||||
|
# lat_data = lat_data.reshape(-1, 1)
|
||||||
|
# l1a_produc = l1a_produc.reshape(-1, 1)
|
||||||
|
# source_def = geometry.SwathDefinition(lons=lon_data, lats=lat_data)
|
||||||
|
# lalo_step = [pixel_delta_x, -pixel_delta_y]
|
||||||
|
# radius = TransImgL1A.get_radius_of_influence(lalo_step, src_meta='radar2geo')
|
||||||
|
# if method == 'linear':
|
||||||
|
# geo_produc = pr.bilinear.resample_bilinear(l1a_produc, source_def, target_def,
|
||||||
|
# radius=radius, neighbours=32,
|
||||||
|
# nprocs=8, fill_value=0,
|
||||||
|
# epsilon=0)
|
||||||
|
# elif method == 'nearest':
|
||||||
|
# geo_produc = pr.kd_tree.resample_nearest(source_def, l1a_produc, target_def, epsilon=0,
|
||||||
|
# radius_of_influence=50000,
|
||||||
|
# fill_value=0, nprocs=8
|
||||||
|
# )
|
||||||
|
# geo_produc = geo_produc[:,:,0]
|
||||||
|
# ImageHandle.ImageHandler.write_img(geo_produc_path, proj, gt, geo_produc)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def mask(self):
|
||||||
|
return self._mask
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
# ori_sim_path = r"I:\坐标转换\坐标转换接口\L1A数据（l1a_img_path数据）\RPC_ori_sim.tif"
|
||||||
|
# roi_Extend = [[120.53, 31.5], [120.61, 31.5], [120.61, 31.45], [120.53, 31.45]]
|
||||||
|
# conver_path = r"I:\坐标转换\坐标转换接口\裁剪后辅助数据（geo_img_path数据）\Covering_cut.tif"
|
||||||
|
# ndvi_path = r"I:\坐标转换\坐标转换接口\裁剪后辅助数据（geo_img_path数据）\NDVI_cut.tif"
|
||||||
|
# out_path = r"I:\坐标转换\SAR2GEO\test"
|
||||||
|
#
|
||||||
|
# tr = TransImgL1A(ori_sim_path,roi_Extend)
|
||||||
|
# tr.l1a_2_geo("I:/cut.tif", "I:/salinity.tif", "I:/salinity_geo2.tif")
|
||||||
|
ori_sim = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\preprocessed\ori_sim_preprocessed.tif'
|
||||||
|
product_tif = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\SurfaceRoughnessProduct_temp.tif'
|
||||||
|
result = r'D:\micro\WorkSpace\SurfaceRoughness\Temporary\SurfaceRoughnessProduct.tif'
|
||||||
|
method = 'linear'
|
||||||
|
"""
|
||||||
|
31.14;31.50;120.34;120.75
|
||||||
|
"""
|
||||||
|
# roi_Extend = [[102.12, 33.879], [102.327, 33.879], [102.327, 33.66], [102.12, 31.45]]
|
||||||
|
ori_sim_data = ImageHandle.ImageHandler.get_data(ori_sim)
|
||||||
|
lon = ori_sim_data[0,:,:]
|
||||||
|
lat = ori_sim_data[1,:,:]
|
||||||
|
min_lon = np.nanmin(lon)
|
||||||
|
max_lon = np.nanmax(lon)
|
||||||
|
min_lat = np.nanmin(lat)
|
||||||
|
max_lat = np.nanmax(lat)
|
||||||
|
print(np.nanmin(lon))
|
||||||
|
print(np.nanmax(lon))
|
||||||
|
print(np.nanmin(lat))
|
||||||
|
print(np.nanmax(lat))
|
||||||
|
|
||||||
|
# roi_Extend = [[min_lon, max_lat], [max_lon, max_lat], [min_lon, min_lat], [max_lon, min_lat]]
|
||||||
|
roi_Extend = [[116.17328, 43.727577], [116.652504, 43.727577], [116.652504, 44.119164], [116.17328, 44.119164]]
|
||||||
|
# roi_Extend = [[108.51960117899473, 38.192443138079895], [109.62308480328566, 38.192443138079895], [109.62308480328566, 37.69300142375064], [108.51960117899473, 37.69300142375064]]
|
||||||
|
|
||||||
|
tr = TransImgL1A(ori_sim, roi_Extend)
|
||||||
|
tr.l1a_2_geo_int(ori_sim, product_tif, result, method)
|
||||||
|
|
||||||
|
pass
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
from pyresample import kd_tree, geometry
|
||||||
|
area_def = geometry.AreaDefinition('areaD', 'Europe (3km, HRV, VTC)', 'areaD',
|
||||||
|
{'a': '6378144.0', 'b': '6356759.0',
|
||||||
|
'lat_0': '50.00', 'lat_ts': '50.00',
|
||||||
|
'lon_0': '8.00', 'proj': 'stere'},
|
||||||
|
800, 800,
|
||||||
|
[-1370912.72, -909968.64,
|
||||||
|
1029087.28, 1490031.36])
|
||||||
|
data = np.fromfunction(lambda y, x: y*x, (50, 10))
|
||||||
|
lons = np.fromfunction(lambda y, x: 3 + x, (50, 10))
|
||||||
|
lats = np.fromfunction(lambda y, x: 75 - y, (50, 10))
|
||||||
|
swath_def = geometry.SwathDefinition(lons=lons, lats=lats)
|
||||||
|
result = kd_tree.resample_nearest(swath_def, data,area_def, radius_of_influence=50000, epsilon=0.5)
|
||||||
|
"""
|
||||||
|
|
@ -0,0 +1,730 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：AlgXmlHandle.py
|
||||||
|
@Function ：算法描述文件读写和检查
|
||||||
|
@Contact ：https://www.cnblogs.com/feifeifeisir/p/10893127.html
|
||||||
|
@Author ：SHJ
|
||||||
|
@Date ：2021/9/6
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from xml.etree.ElementTree import ElementTree
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.file.fileHandle import fileHandle
|
||||||
|
import os
|
||||||
|
import re
|
||||||
|
import platform
|
||||||
|
import psutil
|
||||||
|
import multiprocessing
|
||||||
|
import ctypes
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
import glob
|
||||||
|
|
||||||
|
class ManageAlgXML:
|
||||||
|
"""
|
||||||
|
检查和读取XML文件信息
|
||||||
|
"""
|
||||||
|
def __init__(self, xml_path):
|
||||||
|
self.in_path = xml_path
|
||||||
|
self.__tree = ElementTree()
|
||||||
|
self.__root = None
|
||||||
|
self.__alg_compt = None
|
||||||
|
self.__workspace_path = None
|
||||||
|
self.__taskID = None
|
||||||
|
self.__algorithm_name = None
|
||||||
|
self.__envs = {}
|
||||||
|
self.__input_paras = {}
|
||||||
|
self.__output_paras = {}
|
||||||
|
self.__init_flag = False
|
||||||
|
|
||||||
|
|
||||||
|
def init_xml(self):
|
||||||
|
"""
|
||||||
|
初始化XML文件
|
||||||
|
:return: True：初始化成功 False： 初始化失败
|
||||||
|
"""
|
||||||
|
try:
|
||||||
|
self.__tree.parse(self.in_path)
|
||||||
|
except FileNotFoundError as ex:
|
||||||
|
msg = ex + "xml_path = " + self.in_path
|
||||||
|
raise Exception(msg)
|
||||||
|
except BaseException:
|
||||||
|
raise Exception("cannot open algXMl")
|
||||||
|
|
||||||
|
self.__root = self.__tree.getroot()
|
||||||
|
if self.__root is None:
|
||||||
|
raise Exception("get root failed")
|
||||||
|
|
||||||
|
self.__alg_compt = self.__root.find("AlgCompt")
|
||||||
|
if self.__alg_compt is None:
|
||||||
|
raise Exception("get AlgCompt failed")
|
||||||
|
|
||||||
|
self.__workspace_path = self.__check_workspace_path()
|
||||||
|
if self.__workspace_path is None:
|
||||||
|
raise Exception("check workspace_path failed")
|
||||||
|
|
||||||
|
self.__taskID = self.__check_task_id()
|
||||||
|
if self.__taskID is None:
|
||||||
|
raise Exception("check taskID failed")
|
||||||
|
|
||||||
|
self.__algorithm_name = self.__check_algorithm_name()
|
||||||
|
if self.__algorithm_name is None:
|
||||||
|
raise Exception("check AlgorithmName failed")
|
||||||
|
|
||||||
|
self.__envs = self.__check_environment()
|
||||||
|
if self.__envs is None or self.__envs == {}:
|
||||||
|
raise Exception("check environment failed")
|
||||||
|
|
||||||
|
self.__input_paras = self.__check_input_para()
|
||||||
|
if self.__input_paras is None or self.__input_paras == {}:
|
||||||
|
raise Exception("check input para failed")
|
||||||
|
|
||||||
|
self.__output_paras = self.__check_output_para()
|
||||||
|
|
||||||
|
self.__init_flag = True
|
||||||
|
return True
|
||||||
|
|
||||||
|
def get_workspace_path(self):
|
||||||
|
"""
|
||||||
|
获取工作空间路径
|
||||||
|
:return: 工作空间路径， None-异常
|
||||||
|
"""
|
||||||
|
if not self.__init_flag:
|
||||||
|
raise Exception("XML is not initialized")
|
||||||
|
return self.__workspace_path
|
||||||
|
|
||||||
|
def get_task_id(self):
|
||||||
|
"""
|
||||||
|
获取任务ID
|
||||||
|
:return: taskID， None-异常
|
||||||
|
"""
|
||||||
|
if not self.__init_flag:
|
||||||
|
raise Exception("XML is not initialized")
|
||||||
|
return self.__taskID
|
||||||
|
|
||||||
|
def get_algorithm_name(self):
|
||||||
|
"""
|
||||||
|
获取算法名
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
if not self.__init_flag:
|
||||||
|
raise Exception("AlgorithmName is not initialized")
|
||||||
|
return self.__algorithm_name
|
||||||
|
|
||||||
|
def get_envs(self):
|
||||||
|
"""
|
||||||
|
获取运行环境要求
|
||||||
|
:return:运行环境要求， None-异常
|
||||||
|
"""
|
||||||
|
if not self.__init_flag:
|
||||||
|
raise Exception("XML is not initialized")
|
||||||
|
return self.__envs
|
||||||
|
|
||||||
|
def get_input_paras(self):
|
||||||
|
"""
|
||||||
|
获取输入参数
|
||||||
|
:return:输入参数， None-异常
|
||||||
|
"""
|
||||||
|
if not self.__init_flag:
|
||||||
|
raise Exception("XML is not initialized")
|
||||||
|
return self.__input_paras
|
||||||
|
|
||||||
|
def get_output_paras(self):
|
||||||
|
"""
|
||||||
|
获取输出参数
|
||||||
|
:return:输出参数， None-异常
|
||||||
|
"""
|
||||||
|
if not self.__init_flag:
|
||||||
|
raise Exception("XML is not initialized")
|
||||||
|
return self.__output_paras
|
||||||
|
|
||||||
|
def __check_workspace_path(self):
|
||||||
|
"""
|
||||||
|
检查工作空间路径
|
||||||
|
:return: 工作空间路径， None-异常
|
||||||
|
"""
|
||||||
|
workspace_note = self.__root.find("WorkSpace")
|
||||||
|
workspace_path = str(workspace_note.text).replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
if workspace_path is None:
|
||||||
|
raise Exception("'workspace_path' is None")
|
||||||
|
if not os.path.isdir(workspace_path):
|
||||||
|
raise Exception("'workspace_path' is not save:%s",workspace_path)
|
||||||
|
|
||||||
|
if workspace_path[-1] != '\\':
|
||||||
|
workspace_path += "'\'"
|
||||||
|
|
||||||
|
return workspace_path
|
||||||
|
|
||||||
|
def __check_environment(self):
|
||||||
|
"""
|
||||||
|
检查XML文件中运行环境要求
|
||||||
|
:return: dic-运行环境要求， None-异常
|
||||||
|
"""
|
||||||
|
env_note = self.__alg_compt.find("Environment")
|
||||||
|
|
||||||
|
is_cluster = int(env_note.find("IsCluster").text.replace("\n", "").replace(' ', ''))
|
||||||
|
is_legal = is_cluster in [0, 1]
|
||||||
|
if not is_legal:
|
||||||
|
raise Exception("IsCluster is not 0 or 1")
|
||||||
|
|
||||||
|
cluster_num = int(env_note.find("ClusterNum").text)
|
||||||
|
is_legal = cluster_num in [0, 1, 2, 3, 4, 5, 6, 7]
|
||||||
|
if not is_legal:
|
||||||
|
raise Exception("cluster_num is beyond [0,1,2,3,4,5,6,7]")
|
||||||
|
|
||||||
|
operating_system = env_note.find("OperatingSystem").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
# is_legal = operating_system in ["Windows10", "Windows7", "WindowsXP"]
|
||||||
|
# if not is_legal:
|
||||||
|
# raise Exception("OperatingSystem is beyond [Windows10, Windows7, WindowsXP]")
|
||||||
|
|
||||||
|
cpu = env_note.find("CPU").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
is_legal = cpu in ["单核", "双核", "3核", "4核", "6核", "8核"]
|
||||||
|
if not is_legal:
|
||||||
|
raise Exception("OperatingSystem is beyond [单核, 双核, 3核, 4核, 6核, 8核]")
|
||||||
|
|
||||||
|
memory = env_note.find("Memory").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
is_legal = memory in ["1GB", "2GB", "4GB", "6GB", "8GB", "10GB", "12GB", "16GB"]
|
||||||
|
if not is_legal:
|
||||||
|
raise Exception("OperatingSystem is beyond [1GB, 2GB, 4GB, 6GB, 8GB, 10GB, 12GB, 16GB]")
|
||||||
|
|
||||||
|
storage = env_note.find("Storage").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
is_legal = int(storage[:-2]) > 0
|
||||||
|
if not is_legal:
|
||||||
|
raise Exception("Storage < 0GB")
|
||||||
|
|
||||||
|
network_card = env_note.find("NetworkCard").text
|
||||||
|
# is_legal = network_card in ["无需求"]
|
||||||
|
# if not is_legal:
|
||||||
|
# # 输出异常
|
||||||
|
# return
|
||||||
|
|
||||||
|
band_width = env_note.find("Bandwidth").text
|
||||||
|
# is_legal = band_width in ["无需求"]
|
||||||
|
# if not is_legal:
|
||||||
|
# # 输出异常
|
||||||
|
# return
|
||||||
|
|
||||||
|
gpu = env_note.find("GPU").text
|
||||||
|
# is_legal = GPU in ["无需求"]
|
||||||
|
# if not is_legal:
|
||||||
|
# # 输出异常
|
||||||
|
# return
|
||||||
|
envs = {"is_Cluster": is_cluster, "cluster_num": cluster_num, "operating_system": operating_system,
|
||||||
|
"CPU": cpu, "memory": memory}
|
||||||
|
envs.update({"Storage": storage, "network_card": network_card, "band_width": band_width, "GPU": gpu})
|
||||||
|
return envs
|
||||||
|
|
||||||
|
def __check_input_para(self):
|
||||||
|
"""
|
||||||
|
检查XML文件中输入参数
|
||||||
|
:return: dic-输入参数， None-异常
|
||||||
|
"""
|
||||||
|
input_paras_note = self.__alg_compt.find("Inputs")
|
||||||
|
paras_num = int(input_paras_note.attrib.get("ParameterNum"))
|
||||||
|
para_list = input_paras_note.findall("Parameter")
|
||||||
|
|
||||||
|
if paras_num != len(para_list):
|
||||||
|
msg ="'ParameterNum':"+ str(paras_num) + " != number of 'Parameter':" + str(len(para_list))
|
||||||
|
logger.warning(msg)
|
||||||
|
|
||||||
|
input_paras = {}
|
||||||
|
for para in para_list:
|
||||||
|
para_name = para.find("ParaName").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
para_chs_name = para.find("ParaChsName").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
para_type = para.find("ParaType").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
data_type = para.find("DataType").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
para_value = para.find("ParaValue").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
input_para = {"ParaName": para_name, "ParaChsName": para_chs_name, "ParaType": para_type,
|
||||||
|
"DataType": data_type, "ParaValue": para_value}
|
||||||
|
#print(para_name)
|
||||||
|
if para_type == "Value":
|
||||||
|
# max_value = para.find("MaxValue").text
|
||||||
|
# min_value = para.find("MinValue").text
|
||||||
|
# option_value = para.find("OptionValue").text.replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
# input_para.update({"MaxValue": max_value, "MinValue": min_value, "OptionValue": option_value})
|
||||||
|
# input_para.update({"OptionValue": option_value}) todo
|
||||||
|
pass
|
||||||
|
if para_name is None or para_type is None or para_value is None:
|
||||||
|
msg = 'there is None among para_name:' + para_name + ',para_type:' + para_type + 'or para_value:' + para_value + '!'
|
||||||
|
raise Exception(msg)
|
||||||
|
|
||||||
|
input_paras.update({para_name: input_para})
|
||||||
|
return input_paras
|
||||||
|
|
||||||
|
def __check_output_para(self):
|
||||||
|
"""
|
||||||
|
检查XML文件中输出参数
|
||||||
|
:return: dic-输出参数， None-异常
|
||||||
|
"""
|
||||||
|
output_paras_note = self.__alg_compt.find("Outputs")
|
||||||
|
paras_num = int(output_paras_note.attrib.get("ParameterNum"))
|
||||||
|
para_list = output_paras_note.findall("Parameter")
|
||||||
|
|
||||||
|
if paras_num != len(para_list):
|
||||||
|
raise Exception("'ParameterNum' != number of 'Parameter'")
|
||||||
|
output_paras = {}
|
||||||
|
return output_paras
|
||||||
|
|
||||||
|
def write_out_para(self, para_name, para_value):
|
||||||
|
"""
|
||||||
|
写入输出参数
|
||||||
|
"""
|
||||||
|
output_paras_note = self.__alg_compt.find("Outputs")
|
||||||
|
para_list = output_paras_note.findall("Parameter")
|
||||||
|
flag = False
|
||||||
|
for para in para_list:
|
||||||
|
if para.find("ParaName").text == para_name:
|
||||||
|
para.find("ParaValue").text = para_value
|
||||||
|
flag = True
|
||||||
|
if flag == False:
|
||||||
|
raise Exception('Cannot find Output Parameter:'+para_name+'!')
|
||||||
|
self.__tree.write(self.in_path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def __check_task_id(self):
|
||||||
|
"""
|
||||||
|
检查任务ID
|
||||||
|
:return: taskID， None-异常
|
||||||
|
"""
|
||||||
|
task_id_note = self.__root.find("TaskID")
|
||||||
|
task_id = str(task_id_note.text).replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
if task_id is None:
|
||||||
|
raise Exception("'TaskID' is None")
|
||||||
|
return task_id
|
||||||
|
|
||||||
|
def __check_algorithm_name(self):
|
||||||
|
|
||||||
|
algorithm_name_note = self.__alg_compt.find("AlgorithmName")
|
||||||
|
algorithm_name = str(algorithm_name_note.text).replace("\n", "").replace(' ', '') #去除空格和回车
|
||||||
|
if algorithm_name is None:
|
||||||
|
raise Exception("'AlgorithmName' is None")
|
||||||
|
return algorithm_name
|
||||||
|
|
||||||
|
|
||||||
|
class CheckSource:
|
||||||
|
"""
|
||||||
|
检查配置文件中资源的完整性和有效性
|
||||||
|
"""
|
||||||
|
def __init__(self, alg_xml_handle):
|
||||||
|
self.__alg_xml_handle = alg_xml_handle
|
||||||
|
self.imageHandler = ImageHandler()
|
||||||
|
self.__ParameterDic={}
|
||||||
|
|
||||||
|
|
||||||
|
def check_alg_xml(self):
|
||||||
|
"""
|
||||||
|
检查算法配置文件
|
||||||
|
"""
|
||||||
|
if self.__alg_xml_handle.init_xml():
|
||||||
|
logger.info('init algXML succeed')
|
||||||
|
return True
|
||||||
|
else:
|
||||||
|
raise Exception('init algXML failed')
|
||||||
|
|
||||||
|
def check_run_env(self):
|
||||||
|
"""
|
||||||
|
:return: True-正常，False-异常
|
||||||
|
"""
|
||||||
|
envs = self.__alg_xml_handle.get_envs()
|
||||||
|
# 检查操作系统
|
||||||
|
local_plat = platform.platform()
|
||||||
|
local_plat_list = local_plat.split("-")
|
||||||
|
flag = envs['operating_system'] == local_plat_list[0]+local_plat_list[1]
|
||||||
|
if flag is False:
|
||||||
|
msg = 'operating_system:' + local_plat_list[0] + local_plat_list[1] + ' is not ' + envs['operating_system']
|
||||||
|
#raise Exception(msg)
|
||||||
|
|
||||||
|
# 检查电脑显存
|
||||||
|
mem = psutil.virtual_memory()
|
||||||
|
mem_total = int(round(mem.total / 1024 / 1024 / 1024, 0))
|
||||||
|
mem_free = round(mem.free / 1024 / 1024 / 1024, 0)
|
||||||
|
env_memory = envs['memory']
|
||||||
|
env_memory = int(env_memory[:-2])
|
||||||
|
if env_memory > mem_total:
|
||||||
|
msg = 'memory_total ' + str(mem_total) + ' less than'+str(env_memory) + 'GB'
|
||||||
|
# raise Exception(msg)
|
||||||
|
|
||||||
|
if env_memory >= mem_free:
|
||||||
|
msg = 'mem_free ' + str(mem_free) + 'GB less than' + str(env_memory) + 'GB'
|
||||||
|
logger.warning(msg)
|
||||||
|
|
||||||
|
# 检查CPU核数
|
||||||
|
env_cpu = envs['CPU']
|
||||||
|
if env_cpu == "单核":
|
||||||
|
env_cpu_core_num = 1
|
||||||
|
elif env_cpu == "双核":
|
||||||
|
env_cpu_core_num = 2
|
||||||
|
elif env_cpu == "三核":
|
||||||
|
env_cpu_core_num = 3
|
||||||
|
else:
|
||||||
|
env_cpu_core_num = int(env_cpu[:-1])
|
||||||
|
|
||||||
|
local_cpu_core_num = int(multiprocessing.cpu_count()/2)
|
||||||
|
if env_cpu_core_num > local_cpu_core_num:
|
||||||
|
msg = 'CPU_core_num ' + str(local_cpu_core_num) + 'core less than' + str(env_cpu_core_num) + ' core'
|
||||||
|
# raise Exception(msg)
|
||||||
|
|
||||||
|
# 检查磁盘的内存
|
||||||
|
env_storage = envs['Storage']
|
||||||
|
env_storage = int(env_storage[:-2])
|
||||||
|
workspace_path = self.__alg_xml_handle.get_workspace_path()
|
||||||
|
if not os.path.isdir(workspace_path):
|
||||||
|
raise Exception('workspace_path:%s do not exist!', workspace_path)
|
||||||
|
|
||||||
|
local_storage = self.__get_free_space_mb(workspace_path)
|
||||||
|
if env_storage > local_storage:
|
||||||
|
msg = 'workspace storage ' + str(local_storage) + 'GB less than' + envs['Storage'] +"GB"
|
||||||
|
# raise Exception(msg)
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __get_free_space_mb(folder):
|
||||||
|
"""
|
||||||
|
:param folder:检查的路径 eg:'C:\\'
|
||||||
|
:return: folder/drive free space (GB)
|
||||||
|
"""
|
||||||
|
if platform.system() == 'Windows':
|
||||||
|
free_bytes = ctypes.c_ulonglong(0)
|
||||||
|
ctypes.windll.kernel32.GetDiskFreeSpaceExW(ctypes.c_wchar_p(folder), None, None, ctypes.pointer(free_bytes))
|
||||||
|
return free_bytes.value / 1024 / 1024 / 1024
|
||||||
|
else:
|
||||||
|
st = os.statvfs(folder)
|
||||||
|
return st.f_bavail * st.f_frsize / 1024 / 1024
|
||||||
|
|
||||||
|
def check_input_paras(self, input_para_names):
|
||||||
|
"""
|
||||||
|
:param input_para_names :需要检查参数的名称列表[name1,name2,...]
|
||||||
|
:return: 检测是否正常
|
||||||
|
"""
|
||||||
|
workspace_path = self.__alg_xml_handle.get_workspace_path()
|
||||||
|
input_paras = self.__alg_xml_handle.get_input_paras()
|
||||||
|
for name in input_para_names:
|
||||||
|
para = input_paras[name]
|
||||||
|
if para is None:
|
||||||
|
msg = "check para:"+name + " is failed!"+"para is None!"
|
||||||
|
raise Exception(msg)
|
||||||
|
|
||||||
|
if para['ParaType'] == 'File':
|
||||||
|
if para['DataType'] == 'tif':
|
||||||
|
if para['ParaValue'] != 'empty' and para['ParaValue'] != 'Empty'and para['ParaValue'] != '':
|
||||||
|
para_value_list = para['ParaValue'].split(";")
|
||||||
|
for para_value in para_value_list:
|
||||||
|
para_path = para_value
|
||||||
|
if self.__check_tif(para_path) is False:
|
||||||
|
msg = "check para:"+name + " is failed!" + "Path:" + para_path
|
||||||
|
raise Exception(msg)
|
||||||
|
|
||||||
|
if para['DataType'] == 'xml':
|
||||||
|
para_path = para['ParaValue']
|
||||||
|
if not os.path.exists(para_path):
|
||||||
|
raise Exception('para_file:%s is inexistent!', para_path)
|
||||||
|
|
||||||
|
if para['DataType'] == 'File':
|
||||||
|
para_path = para['ParaValue']
|
||||||
|
if os.path.isdir(para_path) is False:
|
||||||
|
msg = "check para:" + name + " is failed!" + "FilePath:" + para_path
|
||||||
|
raise Exception(msg)
|
||||||
|
if para["DataType"]=="ymal":
|
||||||
|
para_path = para['ParaValue']
|
||||||
|
if os.path.isfile(para_path) is False:
|
||||||
|
msg = "check para: " + name + " is failed! " + " FilePath: " + para_path
|
||||||
|
raise Exception(msg)
|
||||||
|
|
||||||
|
elif para['ParaType'] == 'Value':
|
||||||
|
if para['DataType'] == 'float' or para['DataType'] == 'int' or para['DataType'] == 'double':
|
||||||
|
if para['ParaValue'] is None:
|
||||||
|
msg = "check para:"+name + " is failed!"+"'ParaValue' is None"
|
||||||
|
raise Exception(msg)
|
||||||
|
if self.__is_number(para['ParaValue']) is False:
|
||||||
|
raise Exception("para:"+name+" is not number!")
|
||||||
|
# if (para['MaxValue'] is not None) and (self.__is_number(para['MaxValue']) is True):
|
||||||
|
# value = para['ParaValue']
|
||||||
|
# max = para['MaxValue']
|
||||||
|
# if float(value) > float(max):
|
||||||
|
# msg = "para:" + name + " > max, para:" + value + "max:" + max
|
||||||
|
# raise Exception(msg)
|
||||||
|
# if (para['MinValue'] is not None) and (self.__is_number(para['MinValue']) is True):
|
||||||
|
# value = para['ParaValue']
|
||||||
|
# min = para['MinValue']
|
||||||
|
# if float(value) < float(min):
|
||||||
|
# msg = "para:" + name + " < min, para:" + value + "min:" + min
|
||||||
|
# raise Exception(msg)
|
||||||
|
|
||||||
|
self.__ParameterDic[name] = para['ParaValue']
|
||||||
|
__workspace_path = workspace_path
|
||||||
|
__input_paras = input_paras
|
||||||
|
return True, self.__ParameterDic
|
||||||
|
|
||||||
|
def check_output_paras(self, output_para_names):
|
||||||
|
"""
|
||||||
|
:param output_para_names :需要检查参数的名称列表[name1,name2,...]
|
||||||
|
:return: Ture or False
|
||||||
|
"""
|
||||||
|
workspace_path = self.__alg_xml_handle.get_workspace_path()
|
||||||
|
output_paras = self.__alg_xml_handle.get_output_paras()
|
||||||
|
|
||||||
|
for name in output_para_names:
|
||||||
|
para = output_paras[name]
|
||||||
|
#print(para)
|
||||||
|
if para is None:
|
||||||
|
msg = "check para:" + name + " is failed!" + "para is None!"
|
||||||
|
raise Exception(msg)
|
||||||
|
|
||||||
|
if para['ParaType'] == 'File':
|
||||||
|
if para['DataType'] == 'tif':
|
||||||
|
para_path = workspace_path + para['ParaValue']
|
||||||
|
para_dir = os.path.split(para_path)
|
||||||
|
flag_isdir = os.path.isdir(para_dir[0])
|
||||||
|
flag_istif = (para_dir[1].split(".", 1)[1] == "tif")
|
||||||
|
if flag_isdir and flag_istif is False:
|
||||||
|
msg = "check para:" + name + " is failed!" + para_path + "is invalid!"
|
||||||
|
raise Exception(msg)
|
||||||
|
|
||||||
|
if para['DataType'] == 'File':
|
||||||
|
para_path = workspace_path + para['ParaValue']
|
||||||
|
if os.path.isdir(para_path) is False:
|
||||||
|
os.makedirs(para_path)
|
||||||
|
if os.path.isdir(para_path) is False:
|
||||||
|
msg = "check para:" + name + " is failed!" + para_path + "is invalid!"
|
||||||
|
raise Exception(msg)
|
||||||
|
return True
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __is_number(str_num):
|
||||||
|
"""
|
||||||
|
:param str_num :检查str是否为float或者double
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
if str_num[0] == '-':
|
||||||
|
str_num = str_num[1:]
|
||||||
|
pattern = re.compile(r'(.*)\.(.*)\.(.*)')
|
||||||
|
if pattern.match(str_num):
|
||||||
|
return False
|
||||||
|
return str_num.replace(".", "").isdigit()
|
||||||
|
|
||||||
|
def __check_tif(self, filename):
|
||||||
|
"""
|
||||||
|
:filename: 文件的路径
|
||||||
|
:return: True or False
|
||||||
|
"""
|
||||||
|
if self.imageHandler.get_dataset(filename) is None:
|
||||||
|
msg = "read tif error!,finame: " + filename
|
||||||
|
raise Exception(msg)
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
class InitPara:
|
||||||
|
def __init__(self,debug = False):
|
||||||
|
self._debug = debug
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def init_processing_paras(input_paras):
|
||||||
|
"""
|
||||||
|
:param names:字典列表，每个字典为一个输入产品的配置信息
|
||||||
|
"""
|
||||||
|
processing_paras = {}
|
||||||
|
for name in input_paras:
|
||||||
|
para = input_paras[name]
|
||||||
|
if para is None:
|
||||||
|
logger.error(name + "is None!")
|
||||||
|
return False
|
||||||
|
|
||||||
|
if para['ParaType'] == 'File':
|
||||||
|
if para['DataType'] == 'tif' or para['DataType'] == 'csv':
|
||||||
|
para_value_list = para['ParaValue'].split(";")
|
||||||
|
if len(para_value_list) == 1:
|
||||||
|
para_path = para['ParaValue']
|
||||||
|
if para_path != 'empty' and para_path != '':
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
else:
|
||||||
|
for n, para_value in zip(range(len(para_value_list)), para_value_list):
|
||||||
|
processing_paras.update({name+str(n): para_value})
|
||||||
|
elif para['DataType'] == 'tar.gz':
|
||||||
|
paths = para['ParaValue'].split(';')
|
||||||
|
for n, path in zip(range(len(paths)), paths):
|
||||||
|
processing_paras.update({'sar_path' + str(n): path})
|
||||||
|
else:
|
||||||
|
para_path = para['ParaValue']
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
|
||||||
|
elif para['ParaType'] == 'Value':
|
||||||
|
if para['DataType'] == 'float':
|
||||||
|
value = float(para['ParaValue'])
|
||||||
|
elif para['DataType'] == 'int':
|
||||||
|
value = int(para['ParaValue'])
|
||||||
|
else: # 默认string
|
||||||
|
value = para['ParaValue']
|
||||||
|
processing_paras.update({name: value})
|
||||||
|
elif para['ParaType'] == 'String':
|
||||||
|
value = para['ParaValue']
|
||||||
|
if value == 'empty':
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
processing_paras.update({name: value})
|
||||||
|
return processing_paras
|
||||||
|
|
||||||
|
# 获取文件夹内的文件
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_tif_paths(file_dir,name):
|
||||||
|
in_tif_paths = []
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir + name + '\\', '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(file_dir + name + '\\', '*.tiff')))
|
||||||
|
if in_tif_paths1 != []:
|
||||||
|
in_tif_paths = in_tif_paths + in_tif_paths1
|
||||||
|
else:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir, '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(file_dir, '*.tiff')))
|
||||||
|
if in_tif_paths != []:
|
||||||
|
in_tif_paths = in_tif_paths + in_tif_paths1
|
||||||
|
return in_tif_paths
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_tif_paths_new(file_dir, name):
|
||||||
|
in_tif_paths = []
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir + name + '\\', '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(file_dir + name + '\\', '*.tiff')))
|
||||||
|
if in_tif_paths1 != []:
|
||||||
|
in_tif_paths = in_tif_paths + in_tif_paths1
|
||||||
|
else:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(file_dir, '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(file_dir, '*.tiff')))
|
||||||
|
if len(in_tif_paths) == 0:
|
||||||
|
in_tif_paths = in_tif_paths + in_tif_paths1
|
||||||
|
return in_tif_paths
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_polarization_mode(in_tif_paths):
|
||||||
|
pol_dic = {}
|
||||||
|
pola_list = [0,0,0,0]
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
# 获取极化类型
|
||||||
|
if '_HH_' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'HH': in_tif_path})
|
||||||
|
pola_list[0] = 1
|
||||||
|
elif '_HV_' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'HV': in_tif_path})
|
||||||
|
pola_list[1] = 1
|
||||||
|
elif '_VH_' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'VH': in_tif_path})
|
||||||
|
pola_list[2] = 1
|
||||||
|
elif '_VV_' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'VV': in_tif_path})
|
||||||
|
pola_list[3] = 1
|
||||||
|
elif 'LocalIncidenceAngle' in os.path.basename(in_tif_path) or 'ncidenceAngle' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'LocalIncidenceAngle': in_tif_path})
|
||||||
|
elif 'inc_angle' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'inc_angle': in_tif_path})
|
||||||
|
elif 'inci_Angle-ortho' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'inci_Angle-ortho': in_tif_path})
|
||||||
|
elif 'LocalincidentAngle-ortho' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'LocalIncidentAngle-ortho': in_tif_path})
|
||||||
|
elif 'ori_sim' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'ori_sim': in_tif_path})
|
||||||
|
elif 'sim_ori' in os.path.basename(in_tif_path):
|
||||||
|
pol_dic.update({'sim_ori': in_tif_path})
|
||||||
|
pol_dic.update({'pola':pola_list})
|
||||||
|
return pol_dic
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_meta_paths(file_dir, name):
|
||||||
|
meta_xml_paths = []
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.meta.xml')))
|
||||||
|
else:
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.meta.xml')))
|
||||||
|
if meta_xml_paths is None or meta_xml_paths == []:
|
||||||
|
raise Exception('there is not .meta.xml in path: ', file_dir + '\\')
|
||||||
|
return meta_xml_paths
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_incidence_xml_paths(file_dir, name):
|
||||||
|
meta_xml_paths = []
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.incidence.xml')))
|
||||||
|
else:
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.incidence.xml')))
|
||||||
|
if meta_xml_paths is None or meta_xml_paths == []:
|
||||||
|
raise Exception('there is not .incidence.xml in path: ', file_dir + '\\')
|
||||||
|
return meta_xml_paths
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_meta_dic(meta_xml_paths, name):
|
||||||
|
para_dic = {}
|
||||||
|
for mete_path in meta_xml_paths:
|
||||||
|
if name in mete_path:
|
||||||
|
para_dic.update({'META': mete_path})
|
||||||
|
if para_dic is {}:
|
||||||
|
raise Exception('the name of .meta.xml is error!')
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_incidence_dic(meta_xml_paths, name):
|
||||||
|
para_dic = {}
|
||||||
|
for mete_path in meta_xml_paths:
|
||||||
|
if name in mete_path:
|
||||||
|
para_dic.update({'Incidence': mete_path})
|
||||||
|
if para_dic is {}:
|
||||||
|
raise Exception('the name of .incidence.xml is error!')
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_meta_dic_new(meta_xml_paths, name):
|
||||||
|
para_dic = {}
|
||||||
|
for mete_path in meta_xml_paths:
|
||||||
|
if name in os.path.basename(mete_path):
|
||||||
|
para_dic.update({'META': mete_path})
|
||||||
|
else:
|
||||||
|
para_dic.update({'Origin_META': mete_path})
|
||||||
|
if para_dic is {}:
|
||||||
|
raise Exception('the name of .meta.xml is error!')
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_meta_dic_VP(meta_xml_paths, name):
|
||||||
|
para_dic = {}
|
||||||
|
for mete_path in meta_xml_paths:
|
||||||
|
if name in os.path.basename(mete_path):
|
||||||
|
para_dic.update({name + '_META': mete_path})
|
||||||
|
else:
|
||||||
|
para_dic.update({name + '_Origin_META': mete_path})
|
||||||
|
if para_dic is {}:
|
||||||
|
raise Exception('the name of .meta.xml is error!')
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
def get_mult_tar_gz_inf(self,tar_gz_path, workspace_preprocessing_path):
|
||||||
|
para_dic = {}
|
||||||
|
name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz')
|
||||||
|
para_dic.update({'name': name})
|
||||||
|
|
||||||
|
file_dir = os.path.join(workspace_preprocessing_path, name + '\\')
|
||||||
|
if self._debug == False:
|
||||||
|
fileHandle().de_targz(tar_gz_path, file_dir)
|
||||||
|
# 元文件字典
|
||||||
|
para_dic.update(InitPara.get_meta_dic_VP(InitPara.get_meta_paths(file_dir, name), name))
|
||||||
|
# tif路径字典
|
||||||
|
pol_dic = InitPara.get_polarization_mode(InitPara.get_tif_paths(file_dir, name))
|
||||||
|
parameter_path = os.path.join(file_dir, "orth_para.txt")
|
||||||
|
para_dic.update({name + "paraMeter": parameter_path})
|
||||||
|
for key, in_tif_path in pol_dic.items():
|
||||||
|
para_dic.update({name + '_' + key: in_tif_path})
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
|
||||||
|
def get_mult_tar_gz_infs(self,processing_paras, workspace_preprocessing_path):
|
||||||
|
tif_names_list = []
|
||||||
|
tar_inf_dic = {}
|
||||||
|
for key, value in processing_paras.items():
|
||||||
|
if 'sar_path' in key:
|
||||||
|
para_dic = self.get_mult_tar_gz_inf(value, workspace_preprocessing_path)
|
||||||
|
tif_names_list.append(para_dic['name'])
|
||||||
|
para_dic.pop('name')
|
||||||
|
tar_inf_dic.update(para_dic)
|
||||||
|
tar_inf_dic.update({'name_list': tif_names_list})
|
||||||
|
|
||||||
|
return tar_inf_dic
|
||||||
|
|
@ -0,0 +1,65 @@
|
||||||
|
|
||||||
|
from xml.etree.ElementTree import ElementTree
|
||||||
|
import os
|
||||||
|
|
||||||
|
class DictXml:
|
||||||
|
def __init__(self, xml_path):
|
||||||
|
self.xml_path = xml_path
|
||||||
|
self.__tree = ElementTree()
|
||||||
|
self.__root = None
|
||||||
|
self.init_xml()
|
||||||
|
|
||||||
|
def init_xml(self):
|
||||||
|
self.__root = self.__tree.parse(self.xml_path)
|
||||||
|
if self.__root is None:
|
||||||
|
raise Exception("get root failed")
|
||||||
|
|
||||||
|
def get_extend(self):
|
||||||
|
productInfo = self.__root.find("imageinfo")
|
||||||
|
if productInfo is None:
|
||||||
|
raise Exception("get imageInfo failed")
|
||||||
|
|
||||||
|
corner = productInfo.find("corner")
|
||||||
|
if corner is None:
|
||||||
|
raise Exception("get corner failed")
|
||||||
|
|
||||||
|
topLeft = corner.find("topLeft")
|
||||||
|
if topLeft is None:
|
||||||
|
raise Exception("get topLeft failed")
|
||||||
|
|
||||||
|
topRight = corner.find("topRight")
|
||||||
|
if topRight is None:
|
||||||
|
raise Exception("get topRight failed")
|
||||||
|
|
||||||
|
bottomLeft = corner.find("bottomLeft")
|
||||||
|
if bottomLeft is None:
|
||||||
|
raise Exception("get bottomLeft failed")
|
||||||
|
|
||||||
|
bottomRight = corner.find("bottomRight")
|
||||||
|
if bottomRight is None:
|
||||||
|
raise Exception("get bottomRight failed")
|
||||||
|
|
||||||
|
point_upleft = [float(topLeft.find("longitude").text), float(topLeft.find("latitude").text)]
|
||||||
|
point_upright = [float(topRight.find("longitude").text), float(topRight.find("latitude").text)]
|
||||||
|
point_downleft = [float(bottomLeft.find("longitude").text), float(bottomLeft.find("latitude").text)]
|
||||||
|
point_downright = [float(bottomRight.find("longitude").text), float(bottomRight.find("latitude").text)]
|
||||||
|
scopes = [point_upleft, point_upright, point_downleft, point_downright]
|
||||||
|
|
||||||
|
point_upleft_buf = [float(topLeft.find("longitude").text) - 0.5, float(topLeft.find("latitude").text) + 0.5]
|
||||||
|
point_upright_buf = [float(topRight.find("longitude").text) + 0.5, float(topRight.find("latitude").text) + 0.5]
|
||||||
|
point_downleft_buf = [float(bottomLeft.find("longitude").text) - 0.5, float(bottomLeft.find("latitude").text) - 0.5]
|
||||||
|
point_downright_buf = [float(bottomRight.find("longitude").text) + 0.5, float(bottomRight.find("latitude").text) - 0.5]
|
||||||
|
scopes_buf = [point_upleft_buf, point_upright_buf, point_downleft_buf, point_downright_buf]
|
||||||
|
return scopes, scopes_buf
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
xml_path = r'E:\MicroWorkspace\GF3A_nanjing\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422.meta.xml'
|
||||||
|
scopes, scopes_buf = DictXml(xml_path).get_extend()
|
||||||
|
print(scopes)
|
||||||
|
print(scopes_buf)
|
||||||
|
# path = r'D:\BaiduNetdiskDownload\GZ\lon.rdr'
|
||||||
|
# path2 = r'D:\BaiduNetdiskDownload\GZ\lat.rdr'
|
||||||
|
# path3 = r'D:\BaiduNetdiskDownload\GZ\lon_lat.tif'
|
||||||
|
# s = ImageHandler().band_merge(path, path2, path3)
|
||||||
|
# print(s)
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,319 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：OnePlantHeight.PY
|
||||||
|
@Function ：主函数
|
||||||
|
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
from xml.dom import minidom
|
||||||
|
from xml.etree.ElementTree import ElementTree, Element
|
||||||
|
import xml.dom.minidom
|
||||||
|
from lxml import etree
|
||||||
|
import codecs
|
||||||
|
import xml.etree.cElementTree as ET
|
||||||
|
|
||||||
|
|
||||||
|
class CreateMetafile:
|
||||||
|
"""
|
||||||
|
生成元文件案例
|
||||||
|
"""
|
||||||
|
def __init__(self, input_image_path, input_para_file, an_li_path, path):
|
||||||
|
"""
|
||||||
|
input_image_path: 影像头文件
|
||||||
|
input_para_file: 配置文件
|
||||||
|
an_li_path：案例路径
|
||||||
|
path：保存路径
|
||||||
|
"""
|
||||||
|
self.input_image_path = input_image_path
|
||||||
|
self.input_para_file = input_para_file
|
||||||
|
self.an_li_path= an_li_path
|
||||||
|
self.path = path
|
||||||
|
pass
|
||||||
|
|
||||||
|
def create_xml(self):
|
||||||
|
"""
|
||||||
|
读取元文件（只保留从头文件到sensor节点的部分）
|
||||||
|
输出sensor的节点位置
|
||||||
|
"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.input_image_path) # 影像头文件
|
||||||
|
root = tree.getroot()
|
||||||
|
# 1、只保留从头文件到sensor节点的部分
|
||||||
|
element_trees = list(root)
|
||||||
|
count = 0
|
||||||
|
count_01=1
|
||||||
|
for element in element_trees:
|
||||||
|
count = count+1
|
||||||
|
if element.tag == "sensor":
|
||||||
|
element.tail = "\n\n\t"
|
||||||
|
count_01 = count-1
|
||||||
|
for i in range(0, len(element_trees)):
|
||||||
|
if i > count_01:
|
||||||
|
root.remove(element_trees[i])
|
||||||
|
# 2、只保留"satellite", "orbitType", "attiType", "Direction", "ReceiveTime", "sensor"的部分
|
||||||
|
element_trees2 = list(root)
|
||||||
|
for i in element_trees2:
|
||||||
|
if i.tag not in ["satellite", "orbitType", "attiType", "Direction", "ReceiveTime", "sensor"]:
|
||||||
|
root.remove(i)
|
||||||
|
# 3、获取"sensor"节点的位置，并输出
|
||||||
|
count2 = 0
|
||||||
|
count2_01=1
|
||||||
|
element_trees3 = list(root)
|
||||||
|
for element in element_trees3:
|
||||||
|
count2 = count2+1
|
||||||
|
if element.tag == "sensor":
|
||||||
|
element.tail = "\n\n\t"
|
||||||
|
count2_01 = count2-1
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
return count2_01
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def create_node(tag, property_map, content):
|
||||||
|
"""
|
||||||
|
fun: 新造一个节点
|
||||||
|
para: tag:节点标签
|
||||||
|
para: property_map:属性及属性值map
|
||||||
|
para: content: 节点闭合标签里的文本内容
|
||||||
|
para: return 新节点
|
||||||
|
"""
|
||||||
|
element = Element(tag, property_map)
|
||||||
|
element.text = content
|
||||||
|
element.tail = "\n\t"
|
||||||
|
return element
|
||||||
|
|
||||||
|
def add_standard_xml(self, num):
|
||||||
|
"""
|
||||||
|
模板直接写入到元文件中
|
||||||
|
"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path) # 影像头文件
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
tree2 = ElementTree()
|
||||||
|
tree2.parse(self.an_li_path) # 影像头文件
|
||||||
|
root2 = tree2.getroot()
|
||||||
|
|
||||||
|
productinfo = root2.find("productinfo")
|
||||||
|
root.insert(num + 1, productinfo)
|
||||||
|
processinfo = root2.find("processinfo")
|
||||||
|
root.insert(num + 2, processinfo)
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def add_img_xml(self, num,SrcImageName):
|
||||||
|
"""添加影像信息"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path)
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
a = self.create_node("SrcImageName", {"desc": "原始影像名称"}, SrcImageName)
|
||||||
|
root.insert(num+1, a)
|
||||||
|
# root.append(a)
|
||||||
|
b = self.create_node("AlgCompt", {"desc": "算法信息"}, "\n\t\t")
|
||||||
|
b.tail = "\n\n\t"
|
||||||
|
# root.append(b)
|
||||||
|
root.insert(num+2, b)
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def add_info_xml(self):
|
||||||
|
"""
|
||||||
|
向元文件中添加配置文件的部分节（"AlgorithmName", "ChsName", "AlgorithmDesc", "Version",
|
||||||
|
"AlgorithmClass", "AlgorithmLevel", "AlgoirthmID", "Author"）
|
||||||
|
"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.input_para_file) # 配置文件
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
tree2 = ElementTree()
|
||||||
|
tree2.parse(self.path)
|
||||||
|
root2 = tree2.getroot()
|
||||||
|
AlgCompt = root2.find("AlgCompt")
|
||||||
|
|
||||||
|
a = root.find("AlgCompt")
|
||||||
|
|
||||||
|
element_trees = list(a)
|
||||||
|
for element in element_trees:
|
||||||
|
if element.tag in ["AlgorithmName", "ChsName", "AlgorithmDesc", "Version",
|
||||||
|
"AlgorithmClass", "AlgorithmLevel", "AlgoirthmID", "Author"]:
|
||||||
|
element.tail = "\n\t\t"
|
||||||
|
AlgCompt.append(element)
|
||||||
|
if element.tag == "Author":
|
||||||
|
element.tail = "\n\t"
|
||||||
|
|
||||||
|
tree2.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def add_class_info(self, type_id_name, type_id_parent=None):
|
||||||
|
"""
|
||||||
|
向元文件中：
|
||||||
|
1.删除productinfo-productType信息；
|
||||||
|
2.加入地物类别信息；
|
||||||
|
输出：
|
||||||
|
<Class1>
|
||||||
|
<parent_id>1</parent_id>
|
||||||
|
<id>101</id>
|
||||||
|
<covernm>耕地</covernm>
|
||||||
|
</Class1>
|
||||||
|
<Class2>
|
||||||
|
<parent_id>5</parent_id>
|
||||||
|
<id>502</id>
|
||||||
|
<covernm>草地</covernm>
|
||||||
|
</Class2>
|
||||||
|
"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path) # 配置文件
|
||||||
|
root = tree.getroot()
|
||||||
|
productinfo = root.find("productinfo")
|
||||||
|
# element_trees = list(productinfo)
|
||||||
|
# for element in element_trees:
|
||||||
|
# if element.tag == "productType":
|
||||||
|
# productinfo.remove(element) # 移除 "productType"
|
||||||
|
productinfo.find("productConsumeTime").tail = "\n\t\t" # 定位到productConsumeTime，设置好位置
|
||||||
|
b = self.create_node("LandCoverClass", {}, "\n\t\t\t")
|
||||||
|
b.tail = "\n\t\t"
|
||||||
|
productinfo_count=0
|
||||||
|
for i in list(productinfo):
|
||||||
|
productinfo_count=productinfo_count+1
|
||||||
|
if i.tag=="productConsumeTime":
|
||||||
|
break
|
||||||
|
productinfo.insert(productinfo_count, b)
|
||||||
|
|
||||||
|
# productinfo.insert(num, b) # 插入LandCoverClass
|
||||||
|
class_num = 1
|
||||||
|
for key, value in type_id_name.items():
|
||||||
|
|
||||||
|
LandCoverClass = productinfo.find("LandCoverClass")
|
||||||
|
name="Class"+str(class_num)
|
||||||
|
# name = "Class"
|
||||||
|
c = self.create_node(name, {}, "\n\t\t\t\t")
|
||||||
|
if class_num!=(len(type_id_name.keys())):
|
||||||
|
c.tail = "\n\t\t\t"
|
||||||
|
else:
|
||||||
|
c.tail = "\n\t\t"
|
||||||
|
LandCoverClass.append(c) # 插入LandCoverClass
|
||||||
|
|
||||||
|
# LandCoverClass.find("Class")[num].tail = "\n\t\t\t"
|
||||||
|
aaa=LandCoverClass.find(name)
|
||||||
|
|
||||||
|
if type_id_parent is not None:
|
||||||
|
parent_id = self.create_node("parent_id", {}, type_id_parent[key])
|
||||||
|
parent_id.tail="\n\t\t\t\t"
|
||||||
|
LandCoverClass.find(name).append(parent_id)
|
||||||
|
id = self.create_node("id", {}, str(key))
|
||||||
|
id.tail = "\n\t\t\t\t"
|
||||||
|
LandCoverClass.find(name).append(id)
|
||||||
|
covernm = self.create_node("covernm", {}, value)
|
||||||
|
covernm.tail = "\n\t\t\t"
|
||||||
|
LandCoverClass.find(name).append(covernm)
|
||||||
|
class_num=class_num+1
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def rewrite_name(self):
|
||||||
|
"""
|
||||||
|
修改class的名字:
|
||||||
|
class1->class
|
||||||
|
class2->class
|
||||||
|
"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path) # 配置文件
|
||||||
|
root = tree.getroot()
|
||||||
|
productinfo = root.find("productinfo")
|
||||||
|
LandCoverClass=productinfo.find("LandCoverClass")
|
||||||
|
element_trees = list(LandCoverClass)
|
||||||
|
for element in element_trees:
|
||||||
|
element.tag="Class"
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def OrthoInsertNode(self):
|
||||||
|
"""正射算法专用，插入节点<l1aInfo>"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path) # 影像头文件
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
# 插入节点<l1aInfo>
|
||||||
|
count2 = 0
|
||||||
|
count2_01=1
|
||||||
|
element_trees3 = list(root)
|
||||||
|
for element in element_trees3:
|
||||||
|
count2 = count2+1
|
||||||
|
if element.tag == "sensor":
|
||||||
|
element.tail = "\n\n\t"
|
||||||
|
count2_01 = count2-1
|
||||||
|
b = self.create_node("l1aInfo", {}, "\n\t\t")
|
||||||
|
b.tail = "\n\n\t"
|
||||||
|
root.insert(count2_01+1, b)
|
||||||
|
|
||||||
|
# 查询节点位置<l1aInfo>
|
||||||
|
node_l1aInfo=root.find("l1aInfo")
|
||||||
|
|
||||||
|
img_tree = ElementTree()
|
||||||
|
img_tree.parse(self.input_image_path) # 影像头文件
|
||||||
|
img_root = img_tree.getroot()
|
||||||
|
|
||||||
|
node_imageinfo = img_root.find("imageinfo")
|
||||||
|
node_processinfo=img_root.find("processinfo")
|
||||||
|
|
||||||
|
ele_node_imageinfo = list(node_imageinfo)
|
||||||
|
ele_node_processinfo= list(node_processinfo)
|
||||||
|
|
||||||
|
for i in ele_node_imageinfo:
|
||||||
|
if i.tag == "QualifyValue":
|
||||||
|
i.tail = "\n\t\t"
|
||||||
|
node_l1aInfo.append(i)
|
||||||
|
|
||||||
|
for j in ele_node_processinfo:
|
||||||
|
if j.tag == "CalibrationConst":
|
||||||
|
j.tail = "\n\t" #后一个节点的位置
|
||||||
|
node_l1aInfo.append(j)
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
def process(self,SrcImageName):
|
||||||
|
"""
|
||||||
|
不涉及到地表覆盖调用此函数
|
||||||
|
"""
|
||||||
|
if self.input_image_path is None:
|
||||||
|
import xml.etree.cElementTree as ET
|
||||||
|
product = ET.Element("product") # 根节点tag= "product"
|
||||||
|
product.text = "\n\t"
|
||||||
|
tree = ET.ElementTree(product)
|
||||||
|
tree.write(self.path)
|
||||||
|
count = 0
|
||||||
|
count_2 = -1
|
||||||
|
else:
|
||||||
|
count = self.create_xml()
|
||||||
|
count_2 = count
|
||||||
|
self.add_standard_xml(count)
|
||||||
|
self.add_img_xml(count_2, SrcImageName)
|
||||||
|
self.add_info_xml()
|
||||||
|
|
||||||
|
def process2(self, type_id_name, type_id_parent,SrcImageName):
|
||||||
|
"""
|
||||||
|
涉及到地表覆盖的调用此函数
|
||||||
|
type_id_name={"101":"耕地","502":"草地"}
|
||||||
|
type_id_parent={"101":"1"，"502":"5"}
|
||||||
|
"""
|
||||||
|
count = self.create_xml()
|
||||||
|
self.add_standard_xml(count)
|
||||||
|
self.add_img_xml(count,SrcImageName)
|
||||||
|
self.add_info_xml()
|
||||||
|
self.add_class_info(type_id_name, type_id_parent)
|
||||||
|
self.rewrite_name()
|
||||||
|
|
||||||
|
def process3(self,SrcImageName):
|
||||||
|
"""
|
||||||
|
正射调用此函数
|
||||||
|
"""
|
||||||
|
if self.input_image_path is None:
|
||||||
|
import xml.etree.cElementTree as ET
|
||||||
|
product = ET.Element("product") # 根节点tag= "product"
|
||||||
|
product.text = "\n\t"
|
||||||
|
tree = ET.ElementTree(product)
|
||||||
|
tree.write(self.path)
|
||||||
|
count = 0
|
||||||
|
else:
|
||||||
|
count = self.create_xml()
|
||||||
|
self.add_standard_xml(count)
|
||||||
|
self.add_img_xml(count, SrcImageName)
|
||||||
|
self.add_info_xml()
|
||||||
|
self.OrthoInsertNode()
|
||||||
|
|
||||||
|
|
@ -0,0 +1,205 @@
|
||||||
|
from xml.etree.ElementTree import ElementTree, Element
|
||||||
|
import shutil
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
||||||
|
from osgeo import gdal
|
||||||
|
import numpy as np
|
||||||
|
import datetime
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
os.environ['PROJ_LIB'] = r"E:\soft\Anaconda\envs\micro\Lib\site-packages\osgeo\data\proj"
|
||||||
|
|
||||||
|
class CreateMetaDict:
|
||||||
|
def __init__(self, image_path, origin_xml, pack_path, out_path1, out_path2):
|
||||||
|
self.ImageHandler = ImageHandler()
|
||||||
|
self.image_path = image_path
|
||||||
|
self.origin_xml = origin_xml
|
||||||
|
self.pack_path = pack_path
|
||||||
|
self.file_size = self.get_file_size()
|
||||||
|
self.out_path1 = out_path1
|
||||||
|
self.out_path2 = out_path2
|
||||||
|
self.timeDict = self.get_productTime()
|
||||||
|
pass
|
||||||
|
|
||||||
|
def calu_nature(self):
|
||||||
|
"""
|
||||||
|
将productinfo节点需要填写的信息存入字典中
|
||||||
|
image_path:影像路径
|
||||||
|
image_pair:输入的压缩包中的极化对 例：hh,hv,vh,vv=【1，1，1，1】
|
||||||
|
out_path1：地理转平面的输出路径
|
||||||
|
out_path2：平面转地理的输出路径
|
||||||
|
"""
|
||||||
|
|
||||||
|
para_dict = {}
|
||||||
|
|
||||||
|
proj = self.ImageHandler.get_projection(self.image_path) # 输出的影像若是投影坐标系则先转成地理坐标系
|
||||||
|
keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass
|
||||||
|
if keyword == "GEOGCS":
|
||||||
|
pass
|
||||||
|
elif keyword == "PROJCS":
|
||||||
|
pp.trans_projcs2geogcs(self.out_path2, self.image_path)
|
||||||
|
image_path = self.out_path2
|
||||||
|
elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
|
||||||
|
raise Exception('image projection is missing!')
|
||||||
|
|
||||||
|
pp.trans_geogcs2projcs(self.out_path1, self.image_path) # 坐标投影, 地理转平面投影坐标
|
||||||
|
imageinfo_widthspace = self.ImageHandler.get_geotransform(self.out_path1)[1] # 投影后的分辨率
|
||||||
|
# imageinfo_heightspace = -self.ImageHandler.get_geotransform(out_path1)[5] # 投影后的分辨率
|
||||||
|
# para_dict.update({"imageinfo_widthspace": imageinfo_widthspace})
|
||||||
|
# para_dict.update({"imageinfo_heightspace": imageinfo_heightspace})
|
||||||
|
|
||||||
|
para_dict.update({"imageinfo_ProductResolution": imageinfo_widthspace})
|
||||||
|
|
||||||
|
para_dict.update({"imageinfo_ProductFormat": "GEOTIFF"})
|
||||||
|
para_dict.update({"imageinfo_CompressionMethod": "None"})
|
||||||
|
para_dict.update({"imageinfo_ProductSize": str(self.file_size) + "MB"}) #todo 产品总大小
|
||||||
|
|
||||||
|
get_scope = self.ImageHandler.get_scope(self.image_path)
|
||||||
|
point_upleft, point_upright, point_downleft, point_downright = get_scope[0], get_scope[1], get_scope[2], get_scope[3]
|
||||||
|
para_dict.update({"SpatialCoverageInformation_TopLeftLatitude": point_upleft[1]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_TopLeftLongitude": point_upleft[0]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_TopRightLatitude": point_upright[1]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_TopRightLongitude": point_upright[0]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_BottomLeftLatitude": point_downleft[1]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_BottomLeftLongitude": point_downleft[0]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_BottomRightLatitude": point_downright[1]})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_BottomRightLongitude": point_downright[0]})
|
||||||
|
longitude_max = np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).max()
|
||||||
|
longitude_min = np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).min()
|
||||||
|
latitude_max = np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).max()
|
||||||
|
latitude_min = np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).min()
|
||||||
|
imageinfo_center_latitude = (latitude_max + latitude_min) / 2
|
||||||
|
imageinfo_center_longitude = (longitude_max + longitude_min) / 2
|
||||||
|
para_dict.update({"SpatialCoverageInformation_CenterLatitude": imageinfo_center_latitude})
|
||||||
|
para_dict.update({"SpatialCoverageInformation_CenterLongitude": imageinfo_center_longitude})
|
||||||
|
|
||||||
|
para_dict.update({"TimeCoverageInformation_StartTime": self.timeDict.get("startTime")})
|
||||||
|
para_dict.update({"TimeCoverageInformation_CenterTime": self.timeDict.get("centerTime")})
|
||||||
|
para_dict.update({"TimeCoverageInformation_EndTime": self.timeDict.get("endTime")})
|
||||||
|
|
||||||
|
para_dict.update({"CoordinateReferenceSystemInformation_EarthEllipsoid": "WGS84"})
|
||||||
|
para_dict.update({"CoordinateReferenceSystemInformation_MapProjection": "UTM"})
|
||||||
|
para_dict.update({"CoordinateReferenceSystemInformation_ZoneNo": "None"})
|
||||||
|
|
||||||
|
para_dict.update({"MetaInfo_Unit": "none"}) # 设置单位
|
||||||
|
para_dict.update({"MetaInfo_UnitDes": "无量纲"}) # 设置单位
|
||||||
|
|
||||||
|
# 补充ProductProductionInfo节信息
|
||||||
|
data_name = os.path.basename(self.image_path)
|
||||||
|
strs = data_name.split("_")
|
||||||
|
para_dict.update({"DataSources_DataSource_Satellite": strs[0]})
|
||||||
|
para_dict.update({"DataSources_DataSource_Sensor": strs[0]})
|
||||||
|
|
||||||
|
para_dict.update({"ObservationGeometry_SatelliteAzimuth": "None"})
|
||||||
|
para_dict.update({"ObservationGeometry_SatelliteRange": "None"})
|
||||||
|
|
||||||
|
para_dict.update({"ProductProductionInfo_BandSelection": "1"})
|
||||||
|
para_dict.update({"ProductProductionInfo_DataSourceDescription": "None"})
|
||||||
|
para_dict.update({"ProductProductionInfo_DataSourceProcessingDescription": "参考产品介绍PDF"})
|
||||||
|
productGentime = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||||
|
para_dict.update({"ProductProductionInfo_ProductionDate": productGentime})
|
||||||
|
para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": ""})
|
||||||
|
|
||||||
|
# para_dict.update({"ProductPublishInfo_Processor": "德清院"}) # 生产者
|
||||||
|
# para_dict.update({"ProductPublishInfo_DistributionUnit": "none"}) # 分发单位
|
||||||
|
# para_dict.update({"ProductPublishInfo_ContactInformation": "none"}) # 联系信息
|
||||||
|
return para_dict
|
||||||
|
|
||||||
|
def get_productTime(self):
|
||||||
|
time_dict = {}
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.origin_xml)
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
platform = root.find("platform")
|
||||||
|
if platform is None:
|
||||||
|
centerTime = " "
|
||||||
|
else:
|
||||||
|
centerTime = platform.find("CenterTime").text.split(".")[0]
|
||||||
|
|
||||||
|
productInfo = root.find("imageinfo")
|
||||||
|
imagingTime = productInfo.find("imagingTime")
|
||||||
|
if imagingTime is None:
|
||||||
|
startTime = " "
|
||||||
|
endTime = " "
|
||||||
|
else:
|
||||||
|
startTime = imagingTime.find("start").text.split(".")[0]
|
||||||
|
endTime = imagingTime.find("end").text.split(".")[0]
|
||||||
|
|
||||||
|
time_dict.update({"startTime": startTime})
|
||||||
|
time_dict.update({"centerTime": centerTime})
|
||||||
|
time_dict.update({"endTime": endTime})
|
||||||
|
return time_dict
|
||||||
|
|
||||||
|
def get_file_size(self):
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(self.pack_path, '*.tif')))
|
||||||
|
in_tif_paths1 = list(glob.glob(os.path.join(self.pack_path, '*.tiff')))
|
||||||
|
in_tif_paths += in_tif_paths1
|
||||||
|
size = 0
|
||||||
|
for file in in_tif_paths:
|
||||||
|
fsize = os.path.getsize(file) # 返回的是字节大小
|
||||||
|
size += fsize
|
||||||
|
return round(size / float(1024*1024), 2)
|
||||||
|
|
||||||
|
|
||||||
|
class CreateProductXml:
|
||||||
|
def __init__(self, par_dict, model_path, xml_path):
|
||||||
|
self.par_dict = par_dict
|
||||||
|
self.xml_path = xml_path
|
||||||
|
shutil.copy(model_path, xml_path)
|
||||||
|
pass
|
||||||
|
|
||||||
|
def create_standard_xml(self):
|
||||||
|
"""将字典中的信息写入到copy的xml文件中"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.xml_path) # 影像头文件
|
||||||
|
root = tree.getroot()
|
||||||
|
|
||||||
|
productinfo = root.find("ProductBasicInfo")
|
||||||
|
for key, value in self.par_dict.items():
|
||||||
|
if key.split("_")[0] == "imageinfo":
|
||||||
|
productinfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "SpatialCoverageInformation":
|
||||||
|
imageinfo = productinfo.find("SpatialCoverageInformation")
|
||||||
|
imageinfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "TimeCoverageInformation":
|
||||||
|
timeInfo = productinfo.find("TimeCoverageInformation")
|
||||||
|
timeInfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "CoordinateReferenceSystemInformation":
|
||||||
|
geoInfo = productinfo.find("CoordinateReferenceSystemInformation")
|
||||||
|
geoInfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "MetaInfo":
|
||||||
|
metaInfo = productinfo.find("MetaInfo")
|
||||||
|
metaInfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
ProductProductionInfo = root.find("ProductProductionInfo") # 原始数据信息
|
||||||
|
for key, value in self.par_dict.items():
|
||||||
|
if key.split("_")[0] == "DataSources":
|
||||||
|
dataSources = ProductProductionInfo.find("DataSources")
|
||||||
|
dataSource = dataSources.find("DataSource")
|
||||||
|
dataSource.find(key.split("_")[2]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "ObservationGeometry":
|
||||||
|
ObservationGeometry = ProductProductionInfo.find("ObservationGeometry")
|
||||||
|
ObservationGeometry.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[0] == "ProductProductionInfo":
|
||||||
|
ProductProductionInfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
|
||||||
|
# ProductPublishInfo = root.find("ProductPublishInfo") # 发布者信息
|
||||||
|
# for key, value in self.par_dict.items():
|
||||||
|
# if key.split("_")[0] == "ProductPublishInfo":
|
||||||
|
# ProductPublishInfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
|
||||||
|
tree.write(self.xml_path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
|
||||||
|
image_path = r'D:\Micro\WorkSpace\test\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1B_h_h_L10000073024_db_RD_geo.tif'
|
||||||
|
origin_xml = r'D:\Micro\WorkSpace\Ortho\Temporary\package\GF3B_MYC_QPSI_003581_E120.6_N31.3_20220729_L1A_AHV_L10000073024.meta.xml'
|
||||||
|
tem_folder = r'D:\Micro\WorkSpace\test'
|
||||||
|
pack_path = r'D:\Micro\WorkSpace\Ortho\Temporary\package'
|
||||||
|
out_dem_path1 = os.path.join(tem_folder, "trans_dem_geo_projcs.tif")
|
||||||
|
out_dem_path2 = os.path.join(tem_folder, "trans_dem_projcs_geo.tif")
|
||||||
|
para_dict = CreateMetaDict(image_path, origin_xml, pack_path, out_dem_path1, out_dem_path2).calu_nature()
|
||||||
|
|
||||||
|
model_path = r'D:\Project\microproduct\Ortho\product.xml'
|
||||||
|
xml_path = r'D:\Micro\WorkSpace\test\test.xml'
|
||||||
|
CreateProductXml(para_dict, model_path, xml_path).create_standard_xml()
|
||||||
|
|
@ -0,0 +1,48 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：onestar
|
||||||
|
@File ：ConfigeHandle.py
|
||||||
|
@Contact：https://blog.csdn.net/songlh1234/article/details/83316468
|
||||||
|
@Author ：SHJ
|
||||||
|
@Date ：2021/11/23 16:57
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import configparser
|
||||||
|
|
||||||
|
|
||||||
|
class Config:
|
||||||
|
"""读写初始化配置文件"""
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get(para_name, option='config', config_name='config.ini'):
|
||||||
|
config = configparser.ConfigParser()
|
||||||
|
config_path = os.path.join(os.getcwd(), config_name)
|
||||||
|
config.read(config_path, encoding='utf-8')
|
||||||
|
config.sections()
|
||||||
|
exe_name = config.get(option, para_name)
|
||||||
|
return exe_name
|
||||||
|
|
||||||
|
def get_list(self, para_name, option='config', config_name='config.ini'):
|
||||||
|
config = configparser.ConfigParser()
|
||||||
|
config_path = os.path.join(os.getcwd(), config_name)
|
||||||
|
config.read(config_path, encoding='utf-8')
|
||||||
|
config.sections()
|
||||||
|
str_name = config.get(option, para_name)
|
||||||
|
# 去除空格和回车
|
||||||
|
str_name = str(str_name).replace("\n", "").replace(' ', '') # 去除空格和回车
|
||||||
|
# 分割成lists
|
||||||
|
name_list = str_name.split(',')
|
||||||
|
return name_list
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
# c = Config()
|
||||||
|
# a = c.get('exe_name')
|
||||||
|
# b = bool(c.get('debug'))
|
||||||
|
# d = int(c.get('cover_threshold'))
|
||||||
|
# f = float(c.get('ndvi_threshold'))
|
||||||
|
|
||||||
|
print('done')
|
||||||
|
|
@ -0,0 +1,265 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project : microproduct
|
||||||
|
@File : csvHandle.py
|
||||||
|
@Function : 读写csv文件
|
||||||
|
@Contact :
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2022/11/6
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import random
|
||||||
|
import csv
|
||||||
|
import logging
|
||||||
|
import numpy as np
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.algtools.CoordinateTransformation import geo2imagexy
|
||||||
|
from tool.algorithm.transforml1a.transHandle import TransImgL1A
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
|
||||||
|
|
||||||
|
class csvHandle:
|
||||||
|
def __init__(self, row=0, col=0):
|
||||||
|
self.imageHandler = ImageHandler()
|
||||||
|
self.row = row
|
||||||
|
self.col = col
|
||||||
|
self.img_falg = False
|
||||||
|
if row != 0 and col != 0:
|
||||||
|
self.roi_img = np.zeros((row, col), dtype=float)
|
||||||
|
self.img_falg = True
|
||||||
|
|
||||||
|
def get_roi_img(self):
|
||||||
|
if self.img_falg:
|
||||||
|
self.roi_img[self.roi_img == 0] = np.nan
|
||||||
|
return self.roi_img
|
||||||
|
else:
|
||||||
|
return np.array([])
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def readcsv(csv_path):
|
||||||
|
reader = csv.reader(open(csv_path, newline=''))
|
||||||
|
csv_list = []
|
||||||
|
for line_data in reader:
|
||||||
|
csv_list.append(line_data)
|
||||||
|
return csv_list[1:]
|
||||||
|
|
||||||
|
def trans_measuredata(self, meas_data, tif_path):
|
||||||
|
file_name = tif_path
|
||||||
|
dataset = self.imageHandler.get_dataset(file_name)
|
||||||
|
rows = self.imageHandler.get_img_height(file_name)
|
||||||
|
cols = self.imageHandler.get_img_width(file_name)
|
||||||
|
measdata_list = []
|
||||||
|
logger.info('[MEASURE DATA]')
|
||||||
|
for data in meas_data:
|
||||||
|
lon = float(data[1])
|
||||||
|
lat = float(data[2])
|
||||||
|
coord = geo2imagexy(dataset, lon, lat)
|
||||||
|
row = round(coord[1])
|
||||||
|
col = round(coord[0])
|
||||||
|
|
||||||
|
if row >= 0 and row <= rows and col >= 0 and col <= cols:
|
||||||
|
measdata_list.append([row, col, float(data[3])])
|
||||||
|
logger.info([row, col, float(data[3])])
|
||||||
|
else:
|
||||||
|
logger.warning("measure data: %s is beyond tif scope !", data)
|
||||||
|
pass
|
||||||
|
return measdata_list
|
||||||
|
|
||||||
|
def write_roi_img_data(self, points, type_id):
|
||||||
|
if self.img_falg:
|
||||||
|
for p in points:
|
||||||
|
r = p[0]
|
||||||
|
c = p[1]
|
||||||
|
if r < self.row and c < self.col:
|
||||||
|
self.roi_img[r, c] = type_id
|
||||||
|
|
||||||
|
|
||||||
|
def trans_landCover_measuredata(self, meas_data, cuted_ori_sim_path, max_train_num =100000):
|
||||||
|
"""
|
||||||
|
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
|
||||||
|
:para meas_data: csv读取的实测数据
|
||||||
|
"""
|
||||||
|
type_data = {}
|
||||||
|
n = 1
|
||||||
|
train_data_list = []
|
||||||
|
for data in meas_data:
|
||||||
|
for d in data:
|
||||||
|
if d == '':
|
||||||
|
raise Exception('there are empty data!', data)
|
||||||
|
|
||||||
|
type_id = int(data[1])
|
||||||
|
type_name = data[2]
|
||||||
|
if type_id not in type_data.keys():
|
||||||
|
train_data_list.append([n, type_id, type_name, []])
|
||||||
|
type_data.update({type_id: type_name})
|
||||||
|
n += 1
|
||||||
|
|
||||||
|
pointList = self.__roiPolygonAnalysis(data[3])
|
||||||
|
for points in pointList:
|
||||||
|
roi_poly = [(float(lon), float(lat)) for (lon, lat) in points]
|
||||||
|
tr = TransImgL1A(cuted_ori_sim_path, roi_poly)
|
||||||
|
if tr._mask is not None:
|
||||||
|
points = tr.get_roi_points()
|
||||||
|
for train_data in train_data_list:
|
||||||
|
if train_data[1] == type_id:
|
||||||
|
train_data[3] += points
|
||||||
|
self.write_roi_img_data(points, type_id)
|
||||||
|
if train_data[3] == [] :
|
||||||
|
raise Exception('there are empty data!', train_data)
|
||||||
|
if len(train_data_list) <= 1:
|
||||||
|
raise Exception('there is only one label type!', train_data_list)
|
||||||
|
|
||||||
|
for train_data in train_data_list:
|
||||||
|
logger.info(str(train_data[0]) + "," + str(train_data[2]) + "," + "num:" + str(len(train_data[3])))
|
||||||
|
max_num = max_train_num
|
||||||
|
if (len(train_data[3]) > max_num):
|
||||||
|
logger.info("max number =" + str(max_num) + ", random select" + str(max_num) + " point as train data!")
|
||||||
|
train_data[3] = random.sample(train_data[3], max_num)
|
||||||
|
|
||||||
|
return train_data_list
|
||||||
|
|
||||||
|
def trans_landCover_measuredata_dic(self, meas_data, cuted_ori_sim_path,max_train_num=100000):
|
||||||
|
train_data_list = self.trans_landCover_measuredata(meas_data, cuted_ori_sim_path,max_train_num)
|
||||||
|
return self.trans_landCover_list2dic(train_data_list)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def trans_landCover_list2dic(train_data_list):
|
||||||
|
ids = []
|
||||||
|
class_ids = []
|
||||||
|
ch_names = []
|
||||||
|
positions = []
|
||||||
|
for data in train_data_list:
|
||||||
|
ids.append(data[0])
|
||||||
|
class_ids.append(data[1])
|
||||||
|
ch_names.append(data[2])
|
||||||
|
positions.append(data[3])
|
||||||
|
|
||||||
|
train_data_dic = {}
|
||||||
|
train_data_dic.update({"ids": ids})
|
||||||
|
train_data_dic.update({"class_ids": class_ids})
|
||||||
|
train_data_dic.update({"ch_names": ch_names})
|
||||||
|
train_data_dic.update({"positions": positions})
|
||||||
|
return train_data_dic
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def __roiPolygonAnalysis(roiStr):
|
||||||
|
"""
|
||||||
|
将csv的POLY数据转为数组
|
||||||
|
:para roiStr: poly数据
|
||||||
|
:return pointList: 保存多边形的list
|
||||||
|
"""
|
||||||
|
pointList = []
|
||||||
|
strContent = roiStr.replace("POLYGON", "")
|
||||||
|
# 解析轮廓字符串为二维数组
|
||||||
|
bracketsList = []
|
||||||
|
strTemp = ''
|
||||||
|
strList = []
|
||||||
|
for c in strContent:
|
||||||
|
if c == '(':
|
||||||
|
bracketsList.append(c)
|
||||||
|
continue
|
||||||
|
elif c == ')':
|
||||||
|
if len(bracketsList) > 0:
|
||||||
|
bracketsList.pop(0)
|
||||||
|
if len(strTemp) > 0:
|
||||||
|
strList.append(strTemp)
|
||||||
|
strTemp = ''
|
||||||
|
else:
|
||||||
|
strTemp += c
|
||||||
|
for item in strList:
|
||||||
|
if len(item) == 0:
|
||||||
|
continue
|
||||||
|
pTempList = item.split(',')
|
||||||
|
pList = []
|
||||||
|
for row in pTempList:
|
||||||
|
cells = row.split(' ')
|
||||||
|
if len(cells) != 2:
|
||||||
|
continue
|
||||||
|
point = [float(cells[0]), float(cells[1])]
|
||||||
|
pList.append(point)
|
||||||
|
pointList.append(pList)
|
||||||
|
return pointList
|
||||||
|
|
||||||
|
def class_landcover_list(self, csv_path):
|
||||||
|
"""
|
||||||
|
输出csv表中的前三列
|
||||||
|
"""
|
||||||
|
reader = csv.reader(open(csv_path, newline=''))
|
||||||
|
class_list=[]
|
||||||
|
type_id_name = {}
|
||||||
|
type_id_parent = {}
|
||||||
|
for line_data in reader:
|
||||||
|
class_list.append(line_data) # class_list含有四列
|
||||||
|
for data in class_list[1:]:
|
||||||
|
type_parent= data[0]
|
||||||
|
type_id = int(data[1])
|
||||||
|
type_name = data[2]
|
||||||
|
|
||||||
|
if type_id not in type_id_name.keys():
|
||||||
|
type_id_name.update({type_id: type_name})
|
||||||
|
type_id_parent.update({type_id: type_parent})
|
||||||
|
return type_id_name, type_id_parent
|
||||||
|
|
||||||
|
def trans_VegePhenology_measdata_dic(self, meas_data, cuted_ori_sim_path):
|
||||||
|
"""
|
||||||
|
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
|
||||||
|
:para meas_data: csv读取的实测数据
|
||||||
|
"""
|
||||||
|
train_data = []
|
||||||
|
test_data = []
|
||||||
|
type_data = {}
|
||||||
|
|
||||||
|
for data in meas_data:
|
||||||
|
data_use_type = data[0]
|
||||||
|
sar_img_name = data[1]
|
||||||
|
name = sar_img_name.rstrip('.tar.gz')
|
||||||
|
|
||||||
|
if data_use_type == 'train':
|
||||||
|
phenology_id = int(data[2])
|
||||||
|
phenology_name = data[3]
|
||||||
|
if phenology_id not in type_data.keys():
|
||||||
|
type_data.update({phenology_id: phenology_name})
|
||||||
|
else:
|
||||||
|
phenology_id = -1
|
||||||
|
|
||||||
|
pointList = self.__roiPolygonAnalysis(data[4])
|
||||||
|
l1a_points = []
|
||||||
|
for points in pointList:
|
||||||
|
roi_poly = [(float(lon), float(lat)) for (lon, lat) in points]
|
||||||
|
tr = TransImgL1A(cuted_ori_sim_path, roi_poly)
|
||||||
|
l1a_points = tr.get_roi_points()
|
||||||
|
# l1a_points = tr.get_lonlat_points()
|
||||||
|
if data_use_type == 'train':
|
||||||
|
train_data.append([name, phenology_id, l1a_points, type_data[phenology_id]])
|
||||||
|
elif data_use_type == 'test':
|
||||||
|
test_data.append([name, phenology_id, l1a_points])
|
||||||
|
type_map = []
|
||||||
|
for n, id in zip(range(len(type_data)), type_data):
|
||||||
|
type_map.append([n + 1, id, type_data[id]])
|
||||||
|
|
||||||
|
return train_data, test_data, type_map
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def vegePhenology_class_list(csv_path):
|
||||||
|
"""
|
||||||
|
输出csv表中的前三列
|
||||||
|
"""
|
||||||
|
reader = csv.reader(open(csv_path, newline=''))
|
||||||
|
class_list=[]
|
||||||
|
type_id_name = {}
|
||||||
|
for line_data in reader:
|
||||||
|
class_list.append(line_data) # class_list含有四列
|
||||||
|
for data in class_list[1:]:
|
||||||
|
type_id = data[2]
|
||||||
|
type_name = data[3]
|
||||||
|
|
||||||
|
if type_id not in type_id_name.keys():
|
||||||
|
if type_id.strip() != "":
|
||||||
|
type_id_name.update({type_id: type_name})
|
||||||
|
return type_id_name
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# csvh = csvHandle()
|
||||||
|
# csv_path = r"I:\preprocessed\VegetationPhenologyMeasureData_E118.9_N31.4.csv"
|
||||||
|
# data = csvh.trans_VegePhenology_measdata_dic(csvh.readcsv(csv_path),r"I:\preprocessed\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_ori_sim_preprocessed.tif")
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,88 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project : microproduct
|
||||||
|
@File : fileHandle.py
|
||||||
|
@Function : 文件创建、删除、解压、打包
|
||||||
|
@Contact :
|
||||||
|
@Author:SHJ
|
||||||
|
@Date:2022/11/6
|
||||||
|
@Version:1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
import tarfile
|
||||||
|
import shutil
|
||||||
|
|
||||||
|
class fileHandle:
|
||||||
|
def __init__(self, debug_mode=False):
|
||||||
|
self.__debug_mode = debug_mode
|
||||||
|
|
||||||
|
def creat_dirs(self, path_list):
|
||||||
|
"""
|
||||||
|
创建文件夹
|
||||||
|
"""
|
||||||
|
for path in path_list:
|
||||||
|
if os.path.exists(path):
|
||||||
|
if self.__debug_mode is True:
|
||||||
|
continue
|
||||||
|
self.del_folder(path)
|
||||||
|
os.makedirs(path)
|
||||||
|
else:
|
||||||
|
os.makedirs(path)
|
||||||
|
|
||||||
|
def del_folder(self, dic):
|
||||||
|
"""
|
||||||
|
删除整个文件夹
|
||||||
|
"""
|
||||||
|
if self.__debug_mode is True:
|
||||||
|
return
|
||||||
|
if os.path.isdir(dic):
|
||||||
|
shutil.rmtree(dic)
|
||||||
|
|
||||||
|
def del_file(self, path_data):
|
||||||
|
"""
|
||||||
|
只删除文件，不删除文件夹
|
||||||
|
"""
|
||||||
|
for i in os.listdir(path_data): # os.listdir(path_data)#返回一个列表，里面是当前目录下面的所有东西的相对路径
|
||||||
|
file_data = path_data + '\\' + i # 当前文件夹的下面的所有东西的绝对路径
|
||||||
|
if os.path.isfile(file_data) is True: # os.path.isfile判断是否为文件,如果是文件,就删除.如果是文件夹.递归给del_file.
|
||||||
|
os.remove(file_data)
|
||||||
|
else:
|
||||||
|
self.del_file(file_data)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def make_targz(output_filename, source_dir):
|
||||||
|
"""
|
||||||
|
一次性打包整个根目录。空子目录会被打包。
|
||||||
|
如果只打包不压缩，将"w:gz"参数改为"w:"或"w"即可。
|
||||||
|
:param output_filename:输出压缩包的完整路径，eg:'E:\test.tar.gz'
|
||||||
|
:param source_dir:需要打包的跟目录，eg: 'E:\testFfile\'打包文件夹里面的所有文件,'E:\testFfile'打包文件夹
|
||||||
|
"""
|
||||||
|
dir = os.path.split(output_filename)[0]
|
||||||
|
if os.path.exists(dir) is False:
|
||||||
|
os.makedirs(dir)
|
||||||
|
with tarfile.open(output_filename, "w:gz") as tar:
|
||||||
|
tar.add(source_dir, arcname=os.path.basename(source_dir))
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def de_targz(tar_gz_path, file_dir):
|
||||||
|
name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz')
|
||||||
|
if os.path.exists(file_dir) is False:
|
||||||
|
os.makedirs(file_dir)
|
||||||
|
# 解压
|
||||||
|
t = tarfile.open(tar_gz_path)
|
||||||
|
t.extractall(path=file_dir)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def copyfile2dir(srcfile, dir): # 复制函数
|
||||||
|
if not os.path.isfile(srcfile):
|
||||||
|
print("%s not exist!" % (srcfile))
|
||||||
|
else:
|
||||||
|
fpath, fname = os.path.split(srcfile) # 分离文件名和路径
|
||||||
|
if not os.path.exists(dir):
|
||||||
|
os.makedirs(dir) # 创建路径
|
||||||
|
shutil.copy(srcfile, dir + fname) # 复制文件
|
||||||
|
|
||||||
|
# if __name__ == '__main__':
|
||||||
|
# file = fileHandle()
|
||||||
|
# file.del_floder("I:\preprocessed")
|
||||||
|
# pass
|
||||||
|
|
@ -0,0 +1,90 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：logHandler.py
|
||||||
|
@Author ：SHJ
|
||||||
|
@Date ：2021/9/6
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
import os
|
||||||
|
# from logging import handlers
|
||||||
|
import time
|
||||||
|
import datetime
|
||||||
|
|
||||||
|
|
||||||
|
class LogHandler:
|
||||||
|
"""
|
||||||
|
生成日志
|
||||||
|
"""
|
||||||
|
__logger = logging.getLogger("mylog")
|
||||||
|
__format_str = logging.Formatter("[%(asctime)s] [%(process)d] [%(levelname)s] - %(module)s.%(funcName)s "
|
||||||
|
"(%(filename)s:%(lineno)d) - %(message)s")
|
||||||
|
__log_path = None
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def init_log_handler(log_name):
|
||||||
|
"""
|
||||||
|
初始化日志
|
||||||
|
:param log_name: 日志保存的路径和名称
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
path = os.getcwd()
|
||||||
|
current_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime(time.time()))
|
||||||
|
LogHandler.__log_path = os.path.join(path, log_name + current_time + ".log")
|
||||||
|
para_dir = os.path.split(LogHandler.__log_path)
|
||||||
|
if not os.path.exists(para_dir[0]):
|
||||||
|
os.makedirs(para_dir[0])
|
||||||
|
# 删除七天以前的文件
|
||||||
|
LogHandler.delete_outdate_files(para_dir[0], 7)
|
||||||
|
|
||||||
|
# 方法1：普通日志
|
||||||
|
LOG_FORMAT = "[%(asctime)s] [%(process)d] [%(levelname)s]- %(message)s ---from: %(module)s.%(funcName)s" \
|
||||||
|
" (%(filename)s:Line%(lineno)d) "
|
||||||
|
DATE_FORMAT = "%m/%d/%Y %H:%M:%S"
|
||||||
|
fp = logging.FileHandler(LogHandler.__log_path, encoding='utf-8')
|
||||||
|
fs = logging.StreamHandler()
|
||||||
|
logging.basicConfig(level=logging.INFO, format=LOG_FORMAT, datefmt=DATE_FORMAT, handlers=[fp, fs]) # 调用
|
||||||
|
|
||||||
|
# 方法2：回滚日志
|
||||||
|
# LogHandler.__logger.setLevel(logging.DEBUG)
|
||||||
|
# th = handlers.TimedRotatingFileHandler(filename=LogHandler.__log_path, when='S', interval=1,
|
||||||
|
# backupCount=2, encoding='utf-8')
|
||||||
|
# th.suffix = "%Y-%m-%d-%H-%M-%S.log"
|
||||||
|
# th.setFormatter(LogHandler.__format_str)
|
||||||
|
# th.setLevel(level=logging.DEBUG)
|
||||||
|
|
||||||
|
# console = logging.StreamHandler()
|
||||||
|
# console.setLevel(logging.INFO)
|
||||||
|
# LogHandler.__logger.addHandler(console)
|
||||||
|
# LogHandler.__logger.addHandler(th)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def delete_outdate_files(path, date_interval=7):
|
||||||
|
"""
|
||||||
|
删除目录下七天前创建的文件
|
||||||
|
"""
|
||||||
|
current_time = time.strftime("%Y-%m-%d", time.localtime(time.time()))
|
||||||
|
current_timeList = current_time.split("-")
|
||||||
|
current_time_day = datetime.datetime(int(current_timeList[0]), int(current_timeList[1]),
|
||||||
|
int(current_timeList[2]))
|
||||||
|
for root, dirs, files in os.walk(path):
|
||||||
|
for item in files:
|
||||||
|
item_format = item.split(".", 2)
|
||||||
|
if item_format[1] == "log":
|
||||||
|
file_path = os.path.join(root, item)
|
||||||
|
create_time = time.strftime("%Y-%m-%d", time.localtime((os.stat(file_path)).st_mtime))
|
||||||
|
create_time_list = create_time.split("-")
|
||||||
|
create_time_day = datetime.datetime(int(create_time_list[0]), int(create_time_list[1]),
|
||||||
|
int(create_time_list[2]))
|
||||||
|
time_difference = (current_time_day - create_time_day).days
|
||||||
|
if time_difference > date_interval:
|
||||||
|
os.remove(file_path)
|
||||||
|
|
||||||
|
#
|
||||||
|
# if __name__ == "__main__":
|
||||||
|
# # eg2:
|
||||||
|
# log_handler = LogHandler()
|
||||||
|
# log_handler.init_log_handler(r"run_log\myrun1")
|
||||||
|
# logging.warning("1")
|
||||||
|
# print("done")
|
||||||
|
|
@ -0,0 +1,47 @@
|
||||||
|
sar_img_name,phenology_id,phenology_name,roi_polygon
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,10,water,"POLYGON((118.91799748 31.46893509,118.91762055 31.46674878,118.9210883 31.46637183,118.9210883 31.46855814,118.91799748 31.46893509))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,10,water,"POLYGON((118.90864966 31.46388396,118.90861196 31.46384627,118.90857427 31.46380857,118.90608654 31.46188613,118.90608654 31.46181074,118.90940351 31.46015216,118.91155201 31.46199921,118.90864966 31.46388396))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,10,water,"POLYGON((118.91898928 31.45778718,118.91893038 31.45478336,118.91893038 31.45472446,118.91898928 31.45472446,118.9246432 31.45472446,118.9247021 31.45472446,118.92499657 31.45872956,118.91898928 31.45778718))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.89543794 31.46174102,118.89452125 31.4583153,118.8948831 31.45807405,118.89599277 31.46171689,118.89543794 31.46174102))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.8940629 31.45653006,118.89280848 31.45312847,118.8932427 31.45308022,118.89326683 31.45308022,118.89442475 31.45636119,118.8940629 31.45653006))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.89132626 31.44844806,118.89046628 31.4453566,118.89079168 31.44540309,118.89081492 31.44542633,118.89083817 31.44544958,118.89086141 31.44547282,118.89172139 31.44840157,118.89132626 31.44844806))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.89028034 31.4441944,118.89016413 31.44256732,118.89044304 31.44256732,118.89046628 31.44259056,118.89048953 31.4426138,118.89051277 31.44266029,118.8907452 31.44472901,118.89028034 31.4441944))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.89000143 31.44193973,118.88997819 31.44193973,118.88997819 31.44189324,118.88923442 31.43399026,118.8894436 31.43403675,118.89018737 31.44191648,118.89000143 31.44193973))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.88897526 31.42114936,118.8888637 31.4205915,118.8888637 31.4205729,118.89468368 31.41977331,118.89470227 31.41977331,118.89472087 31.41977331,118.89499978 31.42042414,118.88897526 31.42114936))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.88025459 31.42230226,118.88010583 31.42153986,118.88594441 31.42088902,118.885963 31.42088902,118.8866138 31.42155845,118.88025459 31.42230226))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.87595933 31.42280433,118.87577339 31.42222788,118.87977114 31.42178159,118.87999427 31.42228367,118.87595933 31.42280433))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.89870564 31.41809418,118.89826403 31.41748983,118.89826403 31.41746659,118.89828727 31.41744335,118.90193638 31.41511894,118.90261042 31.41551409,118.89870564 31.41809418))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,20,build,"POLYGON((118.86530883 31.42443948,118.86630754 31.42325911,118.87384328 31.42257814,118.87388867 31.42257814,118.87397946 31.42253274,118.87411565 31.42253274,118.87493278 31.42307752,118.87488738 31.42312292,118.87479659 31.42312292,118.86530883 31.42443948))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.91830432 31.38444815,118.91828107 31.3844249,118.91828107 31.38440166,118.9183508 31.38433193,118.91958267 31.3826816,118.91960591 31.3826816,118.9223718 31.38421571,118.9223718 31.38423895,118.9223718 31.38426219,118.92146533 31.38628443,118.91830432 31.38444815))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.93304021 31.37921823,118.93396992 31.37812576,118.93399316 31.37812576,118.93401641 31.37812576,118.93536449 31.37919499,118.93464396 31.38033395,118.93304021 31.37921823))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.93629419 31.37882309,118.93627095 31.37882309,118.93627095 31.37879984,118.93701472 31.37756791,118.93752606 31.37780035,118.93752606 31.37782359,118.93666608 31.37905553,118.93629419 31.37882309))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.93680553 31.38465734,118.9372239 31.38396002,118.93817686 31.38454112,118.9382001 31.38454112,118.93822334 31.38456437,118.93810713 31.38533142,118.93808389 31.38535467,118.93680553 31.38465734))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.94384808 31.38312765,118.94420137 31.38271856,118.94421997 31.38271856,118.94423856 31.38271856,118.94505671 31.38325782,118.94453607 31.38387146,118.94384808 31.38312765))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.94066848 31.37855322,118.94096598 31.37806975,118.94096598 31.37805115,118.94096598 31.37803256,118.94096598 31.37801396,118.94096598 31.37799537,118.94223039 31.37881356,118.94163538 31.37946439,118.94161678 31.37946439,118.94066848 31.37855322))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.93942267 31.3813797,118.9396272 31.38108218,118.9396458 31.38108218,118.94048254 31.38154706,118.94022222 31.38204913,118.93942267 31.3813797))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.94446169 31.38115656,118.94479639 31.38069168,118.94576329 31.38136111,118.94546578 31.38193756,118.94446169 31.38115656))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.94968666 31.38693968,118.94968666 31.38690249,118.9498912 31.38660496,118.94990979 31.38660496,118.95063496 31.38701406,118.95033746 31.38740456,118.94968666 31.38693968))"
|
||||||
|
GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422-ortho,30,road,"POLYGON((118.95310799 31.37931563,118.95310799 31.37929703,118.9537216 31.37885075,118.95374019 31.37883215,118.95379598 31.37879496,118.95463271 31.37931563,118.95418645 31.3798363,118.95310799 31.37931563))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,10,water,"POLYGON((118.91799748 31.46893509,118.91762055 31.46674878,118.9210883 31.46637183,118.9210883 31.46855814,118.91799748 31.46893509))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,10,water,"POLYGON((118.90864966 31.46388396,118.90861196 31.46384627,118.90857427 31.46380857,118.90608654 31.46188613,118.90608654 31.46181074,118.90940351 31.46015216,118.91155201 31.46199921,118.90864966 31.46388396))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,10,water,"POLYGON((118.91898928 31.45778718,118.91893038 31.45478336,118.91893038 31.45472446,118.91898928 31.45472446,118.9246432 31.45472446,118.9247021 31.45472446,118.92499657 31.45872956,118.91898928 31.45778718))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.89543794 31.46174102,118.89452125 31.4583153,118.8948831 31.45807405,118.89599277 31.46171689,118.89543794 31.46174102))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.8940629 31.45653006,118.89280848 31.45312847,118.8932427 31.45308022,118.89326683 31.45308022,118.89442475 31.45636119,118.8940629 31.45653006))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.89132626 31.44844806,118.89046628 31.4453566,118.89079168 31.44540309,118.89081492 31.44542633,118.89083817 31.44544958,118.89086141 31.44547282,118.89172139 31.44840157,118.89132626 31.44844806))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.89028034 31.4441944,118.89016413 31.44256732,118.89044304 31.44256732,118.89046628 31.44259056,118.89048953 31.4426138,118.89051277 31.44266029,118.8907452 31.44472901,118.89028034 31.4441944))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.89000143 31.44193973,118.88997819 31.44193973,118.88997819 31.44189324,118.88923442 31.43399026,118.8894436 31.43403675,118.89018737 31.44191648,118.89000143 31.44193973))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.88897526 31.42114936,118.8888637 31.4205915,118.8888637 31.4205729,118.89468368 31.41977331,118.89470227 31.41977331,118.89472087 31.41977331,118.89499978 31.42042414,118.88897526 31.42114936))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.88025459 31.42230226,118.88010583 31.42153986,118.88594441 31.42088902,118.885963 31.42088902,118.8866138 31.42155845,118.88025459 31.42230226))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.87595933 31.42280433,118.87577339 31.42222788,118.87977114 31.42178159,118.87999427 31.42228367,118.87595933 31.42280433))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.89870564 31.41809418,118.89826403 31.41748983,118.89826403 31.41746659,118.89828727 31.41744335,118.90193638 31.41511894,118.90261042 31.41551409,118.89870564 31.41809418))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,20,build,"POLYGON((118.86530883 31.42443948,118.86630754 31.42325911,118.87384328 31.42257814,118.87388867 31.42257814,118.87397946 31.42253274,118.87411565 31.42253274,118.87493278 31.42307752,118.87488738 31.42312292,118.87479659 31.42312292,118.86530883 31.42443948))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.91830432 31.38444815,118.91828107 31.3844249,118.91828107 31.38440166,118.9183508 31.38433193,118.91958267 31.3826816,118.91960591 31.3826816,118.9223718 31.38421571,118.9223718 31.38423895,118.9223718 31.38426219,118.92146533 31.38628443,118.91830432 31.38444815))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.93304021 31.37921823,118.93396992 31.37812576,118.93399316 31.37812576,118.93401641 31.37812576,118.93536449 31.37919499,118.93464396 31.38033395,118.93304021 31.37921823))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.93629419 31.37882309,118.93627095 31.37882309,118.93627095 31.37879984,118.93701472 31.37756791,118.93752606 31.37780035,118.93752606 31.37782359,118.93666608 31.37905553,118.93629419 31.37882309))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.93680553 31.38465734,118.9372239 31.38396002,118.93817686 31.38454112,118.9382001 31.38454112,118.93822334 31.38456437,118.93810713 31.38533142,118.93808389 31.38535467,118.93680553 31.38465734))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.94384808 31.38312765,118.94420137 31.38271856,118.94421997 31.38271856,118.94423856 31.38271856,118.94505671 31.38325782,118.94453607 31.38387146,118.94384808 31.38312765))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.94066848 31.37855322,118.94096598 31.37806975,118.94096598 31.37805115,118.94096598 31.37803256,118.94096598 31.37801396,118.94096598 31.37799537,118.94223039 31.37881356,118.94163538 31.37946439,118.94161678 31.37946439,118.94066848 31.37855322))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.93942267 31.3813797,118.9396272 31.38108218,118.9396458 31.38108218,118.94048254 31.38154706,118.94022222 31.38204913,118.93942267 31.3813797))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.94446169 31.38115656,118.94479639 31.38069168,118.94576329 31.38136111,118.94546578 31.38193756,118.94446169 31.38115656))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.94968666 31.38693968,118.94968666 31.38690249,118.9498912 31.38660496,118.94990979 31.38660496,118.95063496 31.38701406,118.95033746 31.38740456,118.94968666 31.38693968))"
|
||||||
|
GF3_SAY_QPSI_013952_E118.9_N31.5_20190404_L1A_AHV_L10003923848-ortho,30,road,"POLYGON((118.95310799 31.37931563,118.95310799 31.37929703,118.9537216 31.37885075,118.95374019 31.37883215,118.95379598 31.37879496,118.95463271 31.37931563,118.95418645 31.3798363,118.95310799 31.37931563))"
|
||||||
|
|
|
@ -0,0 +1,84 @@
|
||||||
|
#encoding=utf-8
|
||||||
|
import xml.etree.ElementTree as ET
|
||||||
|
import pandas as pd
|
||||||
|
import csv
|
||||||
|
|
||||||
|
def xml2csv(xmlpath):
|
||||||
|
tree_obj = ET.parse(xmlpath)
|
||||||
|
|
||||||
|
# 得到所有匹配Region 标签的Element对象的list集合
|
||||||
|
list_Region = tree_obj.findall("Region")
|
||||||
|
for Region in list_Region:
|
||||||
|
# 几何面对应的类(phenology_name)在标签<Region name="water" color="255,0,0">
|
||||||
|
Region_dict = Region.attrib
|
||||||
|
phenology_name = Region_dict.get("name")
|
||||||
|
print(phenology_name)
|
||||||
|
list_GeometryDef = Region.findall("GeometryDef")
|
||||||
|
list_Polygon = list_GeometryDef[0].findall("Polygon") # 获得该类下的几何面list
|
||||||
|
for polygon in list_Polygon:
|
||||||
|
# 从polygon list中获取得到<Coordinates>标签的数据 注意是空格分隔的和csv中不同
|
||||||
|
Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
|
||||||
|
# POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
|
||||||
|
print("value")
|
||||||
|
|
||||||
|
# 向csv中写
|
||||||
|
def csvfile(csvpath,data):
|
||||||
|
|
||||||
|
with open(csvpath, 'a', newline='') as file:
|
||||||
|
# 2. step
|
||||||
|
writer = csv.writer(file)
|
||||||
|
# data example
|
||||||
|
#data = ["This", "is", "a", "Test"]
|
||||||
|
writer.writerow(data)
|
||||||
|
|
||||||
|
|
||||||
|
# Define the structure of the data
|
||||||
|
#data示例 student_header = ['name', 'age', 'major', 'minor']
|
||||||
|
def csvcreateTitile(csvpath,data):
|
||||||
|
# 1. Open a new CSV file
|
||||||
|
with open(csvpath, 'w') as file:
|
||||||
|
# 2. Create a CSV writer
|
||||||
|
writer = csv.writer(file)
|
||||||
|
# 3. Write data to the file
|
||||||
|
writer.writerow(data)
|
||||||
|
|
||||||
|
# 将列表中的坐标对转换为字符串
|
||||||
|
def createcsv_roi_polygon(coordinates):
|
||||||
|
coord_str = ','.join([f'{coordinates[i]} {coordinates[i + 1]}' for i in range(0, len(coordinates), 2)])
|
||||||
|
# 构建最终的POLYGON字符串
|
||||||
|
polygon_str = f'POLYGON(({coord_str}))'
|
||||||
|
print(polygon_str)
|
||||||
|
return polygon_str
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
xmlpath = r"E:\MicroWorkspace\GF3A_nanjing\input-ortho\Vp_test.xml"
|
||||||
|
|
||||||
|
tree_obj = ET.parse(xmlpath)
|
||||||
|
csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
|
||||||
|
csvpath = r".\newimage.csv"
|
||||||
|
# csvcreateTitile(csvpath,csv_header)
|
||||||
|
csvfile(csvpath,csv_header)
|
||||||
|
# 得到所有匹配Region 标签的Element对象的list集合
|
||||||
|
list_Region = tree_obj.findall("Region")
|
||||||
|
name_list = {}
|
||||||
|
count = 10
|
||||||
|
for Region in list_Region:
|
||||||
|
# 几何面对应的类(phenology_name)在标签<Region name="water" color="255,0,0">
|
||||||
|
Region_dict = Region.attrib
|
||||||
|
phenology_name = Region_dict.get("name")
|
||||||
|
|
||||||
|
if not phenology_name in name_list.keys():
|
||||||
|
name_list.update({phenology_name: count})
|
||||||
|
count += 10
|
||||||
|
print(phenology_name)
|
||||||
|
# list_GeometryDef = Region.findall("GeometryDef")
|
||||||
|
list_Polygon = Region.findall(".//Polygon") # 获得该类下的几何面list
|
||||||
|
|
||||||
|
for polygon in list_Polygon:
|
||||||
|
# 从polygon list中获取得到<Coordinates>标签的数据 注意是空格分隔的和csv中不同
|
||||||
|
Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
|
||||||
|
# 将坐标和ploygon对应的写入到.csv中
|
||||||
|
polygon_str=createcsv_roi_polygon(Coordinates_list)
|
||||||
|
# POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
|
||||||
|
data = ['0', name_list.get(phenology_name), phenology_name, polygon_str]
|
||||||
|
csvfile(csvpath, data)
|
||||||
|
|
@ -0,0 +1,4 @@
|
||||||
|
cd E:\0test\oneOrtho
|
||||||
|
打包成一个文件： pyinstaller -D packing.spec
|
||||||
|
|
||||||
|
pyinstaller -F --add-data "D:/Anaconda/envs/micro/Lib/site-packages/dask/dask.yaml;./dask" --add-data "D:/Anaconda/envs/micro/Lib/site-packages/distributed/distributed.yaml;./distributed" --hidden-import pyproj._compat
|
||||||
|
|
@ -0,0 +1,85 @@
|
||||||
|
<?xml version='1.0' encoding='utf-8'?>
|
||||||
|
<Root>
|
||||||
|
<TaskID>CSAR_202107275419_0001-0</TaskID>
|
||||||
|
<WorkSpace>D:\micro\SWork\</WorkSpace>
|
||||||
|
<AlgCompt>
|
||||||
|
<DataTransModel>File</DataTransModel>
|
||||||
|
<Artificial>ElementAlg</Artificial>
|
||||||
|
<AlgorithmName>BackScattering-C-SAR-V2.1 </AlgorithmName>
|
||||||
|
<DllName>BackScattering-C-SAR-V2.1.exe</DllName>
|
||||||
|
<ChsName>后向散射系数</ChsName>
|
||||||
|
<AlgorithmDesc>微波卫星3-5级产品生产模型</AlgorithmDesc>
|
||||||
|
<AlgorithmAlias>BackScattering-C-SAR-V2.1-1</AlgorithmAlias>
|
||||||
|
<Version>1.0</Version>
|
||||||
|
<AlgorithmClass>辐射类产品_后向散射系数</AlgorithmClass>
|
||||||
|
<AlgorithmLevel>1</AlgorithmLevel>
|
||||||
|
<AlgoirthmID>BackScattering_中科卫星应用德清研究院_2.1</AlgoirthmID>
|
||||||
|
<Author>中科卫星应用德清研究院</Author>
|
||||||
|
<Type>景-算法</Type>
|
||||||
|
<InputTestFilePath />
|
||||||
|
<InputTestFileName />
|
||||||
|
<OutputTestFilePath />
|
||||||
|
<OutputTestFileName />
|
||||||
|
<jdkVersion>1.8</jdkVersion>
|
||||||
|
<algDevlanguage>python</algDevlanguage>
|
||||||
|
<Environment>
|
||||||
|
<IsCluster>0</IsCluster>
|
||||||
|
<ClusterNum>0</ClusterNum>
|
||||||
|
<OperatingSystem>Windows10</OperatingSystem>
|
||||||
|
<CPU>双核</CPU>
|
||||||
|
<Memory>4GB</Memory>
|
||||||
|
<Storage>20GB</Storage>
|
||||||
|
<NetworkCard>无需求</NetworkCard>
|
||||||
|
<Bandwidth>无需求</Bandwidth>
|
||||||
|
<GPU>无需求</GPU>
|
||||||
|
</Environment>
|
||||||
|
<Utility Satellite="GF3" Sensor="MSS" Resolution="1" />
|
||||||
|
<Inputs ParameterNum="2">
|
||||||
|
<Parameter>
|
||||||
|
<ParaName>SLC</ParaName>
|
||||||
|
<ParaChsName>SLC影像文件</ParaChsName>
|
||||||
|
<Description>正射校正后的SLC影像文件路径</Description>
|
||||||
|
<ParaType>File</ParaType>
|
||||||
|
<DataType>tar.gz</DataType>
|
||||||
|
<ParaSource>Cal</ParaSource>
|
||||||
|
<ParaValue>E:\MicroWorkspace\S_SAR\HJ2E_KSC_STRIP_003375_E100.4_N26.4_20230522_SLC_HHHV_L10000057057.tar.gz</ParaValue>
|
||||||
|
<EnModification>True</EnModification>
|
||||||
|
<EnMultipleChoice>False</EnMultipleChoice>
|
||||||
|
<Control>File</Control>
|
||||||
|
<InputType>Satellite</InputType>
|
||||||
|
<InputNum>1</InputNum>
|
||||||
|
<DateFrom>GF3B</DateFrom>
|
||||||
|
</Parameter>
|
||||||
|
<Parameter>
|
||||||
|
<ParaName>DEM</ParaName>
|
||||||
|
<ParaChsName>DEM数字高程影像</ParaChsName>
|
||||||
|
<Description>30m分辨率DEM数字高程影像</Description>
|
||||||
|
<ParaType>File</ParaType>
|
||||||
|
<DataType>File</DataType>
|
||||||
|
<ParaSource>Cal</ParaSource>
|
||||||
|
<ParaValue>E:\MicroWorkspace\S_SAR\dem</ParaValue>
|
||||||
|
<EnModification>True</EnModification>
|
||||||
|
<EnMultipleChoice>True</EnMultipleChoice>
|
||||||
|
<Control>File</Control>
|
||||||
|
<InputType>DEM</InputType>
|
||||||
|
<InputNum>0</InputNum>
|
||||||
|
<DateFrom>DEM</DateFrom>
|
||||||
|
</Parameter>
|
||||||
|
</Inputs>
|
||||||
|
<Outputs ParameterNum="1">
|
||||||
|
<Parameter>
|
||||||
|
<ParaName>BackScatteringProduct</ParaName>
|
||||||
|
<ParaChsName>后向散射系数产品</ParaChsName>
|
||||||
|
<Description>后向散射系数产品</Description>
|
||||||
|
<ParaType>File</ParaType>
|
||||||
|
<DataType>tar.gz</DataType>
|
||||||
|
<ParaSource>Cal</ParaSource>
|
||||||
|
<ParaValue>D:\micro\SWork\BackScattering\Output\HJ2E_KSC_STRIP_003375_E100.4_N26.4_20230522_SLC_HHHV_L10000057057-cal.tar.gz</ParaValue>
|
||||||
|
<MaxValue>DEFAULT</MaxValue>
|
||||||
|
<MinValue>DEFAULT</MinValue>
|
||||||
|
<OptionValue>DEFAULT</OptionValue>
|
||||||
|
<NoDataValue>DEFAULT</NoDataValue>
|
||||||
|
</Parameter>
|
||||||
|
</Outputs>
|
||||||
|
</AlgCompt>
|
||||||
|
</Root>
|
||||||
|
|
@ -0,0 +1,81 @@
|
||||||
|
<?xml version='1.0' encoding='utf-8'?>
|
||||||
|
<Root>
|
||||||
|
<TaskID>CSAR_202107275419_0001-0</TaskID>
|
||||||
|
<WorkSpace>D:\micro\WorkSpace\</WorkSpace>
|
||||||
|
<AlgCompt>
|
||||||
|
<DataTransModel>File</DataTransModel>
|
||||||
|
<Artificial>ElementAlg</Artificial>
|
||||||
|
<AlgorithmName>BackScattering-C-SAR-V2.2 </AlgorithmName>
|
||||||
|
<DllName>BackScattering-C-SAR-V2.2.exe</DllName>
|
||||||
|
<ChsName>后向散射系数</ChsName>
|
||||||
|
<AlgorithmDesc>微波卫星3-5级产品生产模型</AlgorithmDesc>
|
||||||
|
<AlgorithmAlias>BackScattering-C-SAR-V2.2-1</AlgorithmAlias>
|
||||||
|
<Version>2.2</Version>
|
||||||
|
<AlgorithmClass>辐射类产品_后向散射系数</AlgorithmClass>
|
||||||
|
<AlgorithmLevel>3</AlgorithmLevel>
|
||||||
|
<AlgoirthmID>BackScattering_中科卫星应用德清研究院_2.2</AlgoirthmID>
|
||||||
|
<Author>中科卫星应用德清研究院</Author>
|
||||||
|
<Type>景-算法</Type>
|
||||||
|
<InputTestFilePath />
|
||||||
|
<InputTestFileName />
|
||||||
|
<OutputTestFilePath />
|
||||||
|
<OutputTestFileName />
|
||||||
|
<jdkVersion>1.8</jdkVersion>
|
||||||
|
<algDevlanguage>python</algDevlanguage>
|
||||||
|
<Environment>
|
||||||
|
<IsCluster>0</IsCluster>
|
||||||
|
<ClusterNum>0</ClusterNum>
|
||||||
|
<OperatingSystem>Windows10</OperatingSystem>
|
||||||
|
<CPU>双核</CPU>
|
||||||
|
<Memory>4GB</Memory>
|
||||||
|
<Storage>20GB</Storage>
|
||||||
|
<NetworkCard>无需求</NetworkCard>
|
||||||
|
<Bandwidth>无需求</Bandwidth>
|
||||||
|
<GPU>无需求</GPU>
|
||||||
|
</Environment>
|
||||||
|
<Utility Satellite="GF3" Sensor="MSS" Resolution="1" />
|
||||||
|
<Inputs ParameterNum="2">
|
||||||
|
<Parameter>
|
||||||
|
<ParaName>SLC</ParaName>
|
||||||
|
<ParaChsName>SLC影像文件</ParaChsName>
|
||||||
|
<Description>正射校正后的SLC影像文件路径</Description>
|
||||||
|
<ParaType>File</ParaType>
|
||||||
|
<DataType>tar.gz</DataType>
|
||||||
|
<ParaSource>Cal</ParaSource>
|
||||||
|
<ParaValue>E:\MicroWorkspace\Micro\neimenggu\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L1A_AHV_L10006708819.tar.gz</ParaValue>
|
||||||
|
<EnModification>True</EnModification>
|
||||||
|
<EnMultipleChoice>False</EnMultipleChoice>
|
||||||
|
<Control>File</Control>
|
||||||
|
<InputType>Satellite</InputType>
|
||||||
|
<InputNum>1</InputNum>
|
||||||
|
<DateFrom>GF3B</DateFrom>
|
||||||
|
</Parameter>
|
||||||
|
<Parameter>
|
||||||
|
<ParaName>DEM</ParaName>
|
||||||
|
<ParaChsName>DEM数字高程影像</ParaChsName>
|
||||||
|
<Description>30m分辨率DEM数字高程影像</Description>
|
||||||
|
<ParaType>File</ParaType>
|
||||||
|
<DataType>tif</DataType>
|
||||||
|
<ParaSource>Cal</ParaSource>
|
||||||
|
<ParaValue>E:\MicroWorkspace\Micro\neimenggu\dem.tif</ParaValue>
|
||||||
|
<EnModification>True</EnModification>
|
||||||
|
<EnMultipleChoice>True</EnMultipleChoice>
|
||||||
|
<Control>File</Control>
|
||||||
|
<InputType>DEM</InputType>
|
||||||
|
<InputNum>0</InputNum>
|
||||||
|
<DateFrom>DEM</DateFrom>
|
||||||
|
</Parameter>
|
||||||
|
</Inputs>
|
||||||
|
<Outputs ParameterNum="1">
|
||||||
|
<Parameter>
|
||||||
|
<ParaName>BackScatteringProduct</ParaName>
|
||||||
|
<ParaChsName>后向散射系数产品</ParaChsName>
|
||||||
|
<Description>后向散射系数产品</Description>
|
||||||
|
<ParaType>File</ParaType>
|
||||||
|
<DataType>tar.gz</DataType>
|
||||||
|
<ParaSource>Cal</ParaSource>
|
||||||
|
<ParaValue>D:\micro\WorkSpace\BackScattering\Output\GF3_MDJ_QPSI_031847_E116.4_N43.9_20220828_L1A_AHV_L10006708819-cal.tar.gz</ParaValue>
|
||||||
|
</Parameter>
|
||||||
|
</Outputs>
|
||||||
|
</AlgCompt>
|
||||||
|
</Root>
|
||||||
|
|
@ -0,0 +1,10 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
# 定义config分组
|
||||||
|
[config]
|
||||||
|
######1-算法基本参数######
|
||||||
|
|
||||||
|
# 算法名称。修改临时工作区生成临时文件的名称，日志名称；
|
||||||
|
exe_name = BackScattering
|
||||||
|
# 开启调试模式则不删除临时工作区，True:开启调试，False:不开启调试
|
||||||
|
debug = False
|
||||||
|
|
||||||
|
|
@ -0,0 +1,96 @@
|
||||||
|
SatelliteOribit: # 轨道起算时间
|
||||||
|
StartTime:
|
||||||
|
Value:
|
||||||
|
'2017-09-15 01:55:21.0000'
|
||||||
|
format: # 时间格式
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
ReferenceSpheroid:
|
||||||
|
Re: # 长半轴
|
||||||
|
6378137
|
||||||
|
Rp: # 短半轴
|
||||||
|
6356752.3142451795
|
||||||
|
we:
|
||||||
|
0.000072292115
|
||||||
|
GPS: # GPS 轨道节点
|
||||||
|
GPSNode_Path:
|
||||||
|
['product','GPS','GPSParam']
|
||||||
|
NodeInfomation_Name: # [时间，x坐标，y坐标，z坐标，x速度，y速度，z速度]
|
||||||
|
['TimeStamp', 'xPosition', 'yPosition', 'zPosition', 'xVelocity', 'yVelocity', 'zVelocity']
|
||||||
|
Time_format:
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
|
||||||
|
imageinfo: # 影像信息
|
||||||
|
ImageBox:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','corner']
|
||||||
|
NodeName:
|
||||||
|
['topLeft','topRight','bottomLeft','bottomRight']
|
||||||
|
latLon:
|
||||||
|
["latitude","longitude"]
|
||||||
|
ImageWidthSpace:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','widthspace']
|
||||||
|
ImageHeightSpace:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','heightspace']
|
||||||
|
ImageWidth: # 影像宽
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','width']
|
||||||
|
ImageHeight: # 影像高
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','height']
|
||||||
|
StartImageTime: # 影像开始时间
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','imagingTime',start]
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
EndImageTime: # 影像中止时间
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','imagingTime',end]
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
CenterImageTime: # 中心像元时间
|
||||||
|
NodePath:
|
||||||
|
['product','platform','CenterTime']
|
||||||
|
Format:
|
||||||
|
"%Y-%m-%d %H:%M:%S.%f"
|
||||||
|
CenterImagePositon: # 中心像元，可以作为迭代起算点
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','center']
|
||||||
|
Value:
|
||||||
|
['latitude','longitude']
|
||||||
|
NearRange: # 近斜距
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','nearRange']
|
||||||
|
DopplerCentroidCoefficients: # 多普勒质心常数
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','DopplerCentroidCoefficients']
|
||||||
|
DopplerCentroidCoefficients_Name:
|
||||||
|
['d0','d1','d2','d3','d4']
|
||||||
|
DopplerParametersReferenceTime:
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo',"DopplerParametersReferenceTime"]
|
||||||
|
ReferenceRange:
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','refRange']
|
||||||
|
incidenceAngle: # 入射角
|
||||||
|
NearRange: # 近入射角
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','incidenceAngleNearRange']
|
||||||
|
FarRange: # 远入射角
|
||||||
|
NodePath:
|
||||||
|
['product','processinfo','incidenceAngleFarRange']
|
||||||
|
|
||||||
|
sensor:
|
||||||
|
PRF: # 脉冲重复频率
|
||||||
|
NodePath:
|
||||||
|
['product','imageinfo','eqvPRF']
|
||||||
|
bandWidth: # 信号带宽，计算距离向分辨率 用于距离向分辨率
|
||||||
|
NodePath:
|
||||||
|
['product','sensor','waveParams','wave','bandWidth']
|
||||||
|
lambda: # 波长
|
||||||
|
NodePath:
|
||||||
|
['product','sensor','lamda']
|
||||||
|
|
||||||
|
LightSpeed:
|
||||||
|
299792458
|
||||||
|
|
@ -0,0 +1,60 @@
|
||||||
|
<Root>
|
||||||
|
<ProductBasicInfo>
|
||||||
|
<ProductName>后向散射系数</ProductName>
|
||||||
|
<ProductIdentifier>BackScattering</ProductIdentifier>
|
||||||
|
<ProductLevel>LEVEL3</ProductLevel>
|
||||||
|
<ProductResolution> </ProductResolution>
|
||||||
|
<ProductDate> </ProductDate>
|
||||||
|
<ProductFormat> </ProductFormat>
|
||||||
|
<CompressionMethod> </CompressionMethod>
|
||||||
|
<ProductSize> </ProductSize>
|
||||||
|
<SpatialCoverageInformation>
|
||||||
|
<TopLeftLatitude> </TopLeftLatitude>
|
||||||
|
<TopLeftLongitude> </TopLeftLongitude>
|
||||||
|
<TopRightLatitude> </TopRightLatitude>
|
||||||
|
<TopRightLongitude> </TopRightLongitude>
|
||||||
|
<BottomRightLatitude> </BottomRightLatitude>
|
||||||
|
<BottomRightLongitude> </BottomRightLongitude>
|
||||||
|
<BottomLeftLatitude> </BottomLeftLatitude>
|
||||||
|
<BottomLeftLongitude> </BottomLeftLongitude>
|
||||||
|
<CenterLatitude> </CenterLatitude>
|
||||||
|
<CenterLongitude> </CenterLongitude>
|
||||||
|
</SpatialCoverageInformation>
|
||||||
|
<TimeCoverageInformation>
|
||||||
|
<StartTime> </StartTime>
|
||||||
|
<EndTime> </EndTime>
|
||||||
|
<CenterTime> </CenterTime>
|
||||||
|
</TimeCoverageInformation>
|
||||||
|
<CoordinateReferenceSystemInformation>
|
||||||
|
<MapProjection> </MapProjection>
|
||||||
|
<EarthEllipsoid> </EarthEllipsoid>
|
||||||
|
<ZoneNo> </ZoneNo>
|
||||||
|
</CoordinateReferenceSystemInformation>
|
||||||
|
<MetaInfo>
|
||||||
|
<Unit> </Unit>
|
||||||
|
<UnitDes> </UnitDes>
|
||||||
|
</MetaInfo>
|
||||||
|
</ProductBasicInfo>
|
||||||
|
<ProductProductionInfo>
|
||||||
|
<DataSources number="1">
|
||||||
|
<DataSource>
|
||||||
|
<Satellite> </Satellite>
|
||||||
|
<Sensor> </Sensor>
|
||||||
|
</DataSource>
|
||||||
|
</DataSources>
|
||||||
|
<ObservationGeometry>
|
||||||
|
<SatelliteAzimuth> </SatelliteAzimuth>
|
||||||
|
<SatelliteRange> </SatelliteRange>
|
||||||
|
</ObservationGeometry>
|
||||||
|
<BandSelection>1</BandSelection>
|
||||||
|
<DataSourceDescription>None</DataSourceDescription>
|
||||||
|
<DataSourceProcessingDescription>参考产品介绍PDF</DataSourceProcessingDescription>
|
||||||
|
<ProductionDate> </ProductionDate>
|
||||||
|
<AuxiliaryDataDescription> </AuxiliaryDataDescription>
|
||||||
|
</ProductProductionInfo>
|
||||||
|
<ProductPublishInfo>
|
||||||
|
<Processor>德清</Processor>
|
||||||
|
<DistributionUnit> </DistributionUnit>
|
||||||
|
<ContactInformation> </ContactInformation>
|
||||||
|
</ProductPublishInfo>
|
||||||
|
</Root>
|
||||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,482 @@
|
||||||
|
# -*- coding: UTF-8 -*-
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：BackscatteringMain.py
|
||||||
|
@Function ：后向散射
|
||||||
|
@Author ：KHZ
|
||||||
|
@Contact：
|
||||||
|
@Date ：2021/9/1
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import logging
|
||||||
|
from tool.algorithm.algtools.logHandler import LogHandler
|
||||||
|
from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource
|
||||||
|
from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
||||||
|
from BackScatteringAlg import IndirectOrthorectification, DEMProcess,rpc_correction,getRCImageRC,get_RPC_lon_lat,getRCImageRC2
|
||||||
|
from BackScatteringAlg import ScatteringAlg as alg
|
||||||
|
from tool.config.ConfigeHandle import Config as cf
|
||||||
|
import os
|
||||||
|
import glob
|
||||||
|
import datetime
|
||||||
|
import shutil
|
||||||
|
import tarfile
|
||||||
|
import sys
|
||||||
|
|
||||||
|
|
||||||
|
if cf.get('debug') == 'True':
|
||||||
|
DEBUG = True
|
||||||
|
else:
|
||||||
|
DEBUG = False
|
||||||
|
EXE_NAME = 'BackScattering'
|
||||||
|
LogHandler.init_log_handler('run_log\\' + EXE_NAME)
|
||||||
|
logger = logging.getLogger("mylog")
|
||||||
|
env_str = os.path.split(os.path.realpath(__file__))[0]
|
||||||
|
os.environ['PROJ_LIB'] = env_str
|
||||||
|
|
||||||
|
class ScatteringMain:
|
||||||
|
"""
|
||||||
|
后向散射系数主函数
|
||||||
|
"""
|
||||||
|
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.__task_id = None
|
||||||
|
self.__input_paras = {}
|
||||||
|
self.__output_paras = {}
|
||||||
|
self.__in_processing_paras = {}
|
||||||
|
self.__out_para = None
|
||||||
|
self.__preprocessed_paras = {}
|
||||||
|
self.__tif_names_list = []
|
||||||
|
self.__feature_name_list = []
|
||||||
|
|
||||||
|
def check_source(self):
|
||||||
|
"""
|
||||||
|
检查算法相关的配置文件，图像，辅助文件是否齐全
|
||||||
|
"""
|
||||||
|
env_str = os.getcwd()
|
||||||
|
logger.info("sysdir: %s", env_str)
|
||||||
|
|
||||||
|
if self.__check_handler.check_alg_xml() is False:
|
||||||
|
return False
|
||||||
|
|
||||||
|
if self.__check_handler.check_run_env() is False:
|
||||||
|
return False
|
||||||
|
|
||||||
|
input_para_names = ["SLC","DEM"]
|
||||||
|
if self.__check_handler.check_input_paras(input_para_names) is False:
|
||||||
|
return False
|
||||||
|
|
||||||
|
self.__workspace_path = self.__alg_xml_handler.get_workspace_path()
|
||||||
|
self.__create_work_space()
|
||||||
|
self.__task_id = self.__alg_xml_handler.get_task_id()
|
||||||
|
self.__input_paras = self.__alg_xml_handler.get_input_paras()
|
||||||
|
self.__in_processing_paras = self.__init_processing_paras(self.__input_paras)
|
||||||
|
SrcImageName = os.path.split(self.__input_paras["SLC"]['ParaValue'])[1].split('.tar.gz')[0]
|
||||||
|
AlgorithmName = self.__alg_xml_handler.get_algorithm_name()
|
||||||
|
TaskId = self.__alg_xml_handler.get_task_id()
|
||||||
|
result_name = SrcImageName + "-cal.tar.gz"
|
||||||
|
# out_name = os.path.splitext(os.path.splitext(os.path.basename(self.__input_paras['SLC']['ParaValue']))[0])[0]
|
||||||
|
# self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', "BackScatteringProduct.tar.gz")
|
||||||
|
self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', result_name)
|
||||||
|
self.__alg_xml_handler.write_out_para("BackScatteringProduct", self.__out_para) # 写入输出参数
|
||||||
|
|
||||||
|
logger.info('check_source success!')
|
||||||
|
logger.info('progress bar: 30%')
|
||||||
|
return True
|
||||||
|
|
||||||
|
def __init_processing_paras(self, names):
|
||||||
|
"""
|
||||||
|
:param names:字典列表，每个字典为一个输入产品的配置信息
|
||||||
|
"""
|
||||||
|
processing_paras = {}
|
||||||
|
for name in names:
|
||||||
|
para = self.__input_paras[name]
|
||||||
|
if para is None:
|
||||||
|
logger.error(name + "is None!")
|
||||||
|
return False
|
||||||
|
if para['ParaType'] == 'File':
|
||||||
|
if para['DataType'] == 'File':
|
||||||
|
processing_paras.update({name: para['ParaValue']})
|
||||||
|
# if para['DataType'] == 'csv':
|
||||||
|
# para_value_list = para['ParaValue'].split(";")
|
||||||
|
# if len(para_value_list) == 1:
|
||||||
|
# para_path = para['ParaValue']
|
||||||
|
# processing_paras.update({name: para_path})
|
||||||
|
# else:
|
||||||
|
# for n, para_value in zip(range(len(para_value_list)), para_value_list):
|
||||||
|
# processing_paras.update({'feature'+str(n): para_value})
|
||||||
|
# self.__feature_name_list.append('feature'+str(n))
|
||||||
|
elif para['DataType'] == 'tar.gz':
|
||||||
|
paths = para['ParaValue'].split(';')
|
||||||
|
for path in paths:
|
||||||
|
tar_gz_dic = self.__dec_tar_gz(path, self.__workspace_preprocessing_path)
|
||||||
|
processing_paras.update(tar_gz_dic)
|
||||||
|
elif para['DataType'] == 'tif' or para['DataType'] == 'tiff': # 新增修改dem数据为文件绝对路径
|
||||||
|
if para['ParaValue'] != 'empty' and para['ParaValue'] != 'Empty' and para['ParaValue'] != '':
|
||||||
|
para_path_list = para['ParaValue'].split(";")
|
||||||
|
if len(para_path_list) != 0:
|
||||||
|
dem_path = os.path.join(self.__workspace_origin_path, para['ParaName'])
|
||||||
|
if os.path.exists(dem_path) is False:
|
||||||
|
os.mkdir(dem_path)
|
||||||
|
for file_path in para_path_list:
|
||||||
|
tif_name = os.path.basename(file_path)
|
||||||
|
shutil.copy(file_path, os.path.join(dem_path, tif_name))
|
||||||
|
para_path = os.path.join(self.__workspace_origin_path, para['ParaName'])
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
else:
|
||||||
|
para_path = para['ParaValue']
|
||||||
|
processing_paras.update({name: para_path})
|
||||||
|
elif para['ParaType'] == 'Value':
|
||||||
|
if para['DataType'] == 'float':
|
||||||
|
value = float(para['ParaValue'])
|
||||||
|
elif para['DataType'] == 'int':
|
||||||
|
value = int(para['ParaValue'])
|
||||||
|
else: # 默认string
|
||||||
|
value = para['ParaValue']
|
||||||
|
processing_paras.update({name: value})
|
||||||
|
return processing_paras
|
||||||
|
|
||||||
|
def __dec_tar_gz(self, tar_gz_path, out_dir):
|
||||||
|
"""
|
||||||
|
解压.tar_gz格式景影像文件
|
||||||
|
:param tar_gz_path:.tar_gz文件路径
|
||||||
|
:param out_dir:输出文件夹
|
||||||
|
:return para_dic:全极化影像路径
|
||||||
|
"""
|
||||||
|
# 创建文件夹
|
||||||
|
name = os.path.split(tar_gz_path)[1].rstrip('.tar.gz')
|
||||||
|
file_dir = os.path.join(out_dir, name + '\\')
|
||||||
|
if os.path.exists(file_dir) is False:
|
||||||
|
os.makedirs(file_dir)
|
||||||
|
# 解压
|
||||||
|
t = tarfile.open(tar_gz_path)
|
||||||
|
t.extractall(path=file_dir)
|
||||||
|
|
||||||
|
# 获取文件夹内的文件
|
||||||
|
para_dic = {}
|
||||||
|
# if os.path.exists(file_dir + name + '\\'):
|
||||||
|
# meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.meta.xml')))
|
||||||
|
# para_dic.update({'SLC': file_dir + name})
|
||||||
|
# else:
|
||||||
|
# meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.meta.xml')))
|
||||||
|
# para_dic.update({'SLC': file_dir})
|
||||||
|
if os.path.exists(file_dir + name + '\\'):
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir + name, '*.xml')))
|
||||||
|
para_dic.update({'SLC': file_dir + name})
|
||||||
|
else:
|
||||||
|
meta_xml_paths = list(glob.glob(os.path.join(file_dir, '*.xml')))
|
||||||
|
para_dic.update({'SLC': file_dir})
|
||||||
|
|
||||||
|
if meta_xml_paths == []:
|
||||||
|
raise Exception('there is not .meta.xml in path: ', file_dir + '\\')
|
||||||
|
para_dic.update({'META': meta_xml_paths[0]})
|
||||||
|
self.image_meta_xml = meta_xml_paths
|
||||||
|
return para_dic
|
||||||
|
|
||||||
|
def __create_work_space(self):
|
||||||
|
"""
|
||||||
|
删除原有工作区文件夹,创建新工作区文件夹
|
||||||
|
"""
|
||||||
|
self.__workspace_preprocessing_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "preprocessing") # self.__workspace_path + EXE_NAME + r"\Temporary\preprocessing""\\"
|
||||||
|
self.__workspace_preprocessed_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "preprocessed") # self.__workspace_path + EXE_NAME + r"\Temporary\preprocessed""\\"
|
||||||
|
self.__workspace_processing_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary","processing\\") #self.__workspace_path + EXE_NAME + r"\Temporary\processing""\\"
|
||||||
|
self.__workspace_origin_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "origin")
|
||||||
|
path_list = [self.__workspace_preprocessing_path, self.__workspace_preprocessed_path,
|
||||||
|
self.__workspace_processing_path, self.__workspace_origin_path]
|
||||||
|
|
||||||
|
for path in path_list:
|
||||||
|
if os.path.exists(path):
|
||||||
|
if DEBUG is True:
|
||||||
|
continue
|
||||||
|
self.del_floder(path)
|
||||||
|
os.makedirs(path)
|
||||||
|
else:
|
||||||
|
os.makedirs(path)
|
||||||
|
logger.info('create new workspace success!')
|
||||||
|
|
||||||
|
def del_file(self, path_data):
|
||||||
|
"""
|
||||||
|
只删除文件，不删除文件夹
|
||||||
|
"""
|
||||||
|
if DEBUG is True:
|
||||||
|
return
|
||||||
|
for i in os.listdir(path_data): # os.listdir(path_data)#返回一个列表，里面是当前目录下面的所有东西的相对路径
|
||||||
|
file_data = os.path.join(path_data, i) # path_data + "\\" + i # 当前文件夹的下面的所有东西的绝对路径
|
||||||
|
if os.path.isfile(file_data) is True: # os.path.isfile判断是否为文件,如果是文件,就删除.如果是文件夹.递归给del_file.
|
||||||
|
os.remove(file_data)
|
||||||
|
else:
|
||||||
|
self.del_file(file_data)
|
||||||
|
@staticmethod
|
||||||
|
def del_floder(dic):
|
||||||
|
"""
|
||||||
|
删除整个文件夹
|
||||||
|
"""
|
||||||
|
if DEBUG is True:
|
||||||
|
return
|
||||||
|
if os.path.isdir(dic):
|
||||||
|
shutil.rmtree(dic)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def make_targz(output_filename, source_dir):
|
||||||
|
"""
|
||||||
|
一次性打包整个根目录。空子目录会被打包。
|
||||||
|
如果只打包不压缩，将"w:gz"参数改为"w:"或"w"即可。
|
||||||
|
:param output_filename:输出压缩包的完整路径，eg:'E:\test.tar.gz'
|
||||||
|
:param source_dir:需要打包的跟目录，eg: 'E:\testFfile\'打包文件夹里面的所有文件,'E:\testFfile'打包文件夹
|
||||||
|
"""
|
||||||
|
dir = os.path.split(output_filename)[0]
|
||||||
|
if os.path.exists(dir) is False:
|
||||||
|
os.makedirs(dir)
|
||||||
|
with tarfile.open(output_filename, "w:gz") as tar:
|
||||||
|
tar.add(source_dir, arcname=os.path.basename(source_dir))
|
||||||
|
|
||||||
|
def del_temp_workspace(self):
|
||||||
|
"""
|
||||||
|
临时工作区
|
||||||
|
"""
|
||||||
|
if DEBUG is True:
|
||||||
|
return
|
||||||
|
path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary") # self.__workspace_path + EXE_NAME + r"\Temporary"
|
||||||
|
if os.path.exists(path):
|
||||||
|
self.del_floder(path)
|
||||||
|
|
||||||
|
def process_sim_ori(self, ori_sim, sim_ori):
|
||||||
|
|
||||||
|
scopes = ()
|
||||||
|
scopes += (ImageHandler.get_scope_ori_sim(ori_sim),)
|
||||||
|
|
||||||
|
intersect_polygon = pp().intersect_polygon(scopes)
|
||||||
|
if intersect_polygon is None:
|
||||||
|
raise Exception('create intersect shp fail!')
|
||||||
|
shp_path = os.path.join(self.__workspace_preprocessing_path, 'IntersectPolygon.shp')
|
||||||
|
if pp().write_polygon_shp(shp_path, intersect_polygon, 4326) is False:
|
||||||
|
raise Exception('create intersect shp fail!')
|
||||||
|
sim_ori_process = os.path.join(self.__workspace_preprocessing_path, 'sim_ori_process.tif')
|
||||||
|
pp().cut_img(sim_ori_process, sim_ori, shp_path)
|
||||||
|
return sim_ori_process
|
||||||
|
|
||||||
|
def process_handle(self,start):
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(self.__in_processing_paras['SLC'], '*.tif')))
|
||||||
|
if in_tif_paths == []:
|
||||||
|
in_tif_paths = list(glob.glob(os.path.join(self.__in_processing_paras['SLC'], '*.tiff')))
|
||||||
|
|
||||||
|
hh_flag, hv_flag, vh_flag, vv_flag, angle_flag = 0, 0, 0, 0, 0
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
if 'HH' in os.path.basename(in_tif_path):
|
||||||
|
hh_flag = 1
|
||||||
|
elif 'HV' in os.path.basename(in_tif_path):
|
||||||
|
hv_flag = 1
|
||||||
|
elif 'VH' in os.path.basename(in_tif_path):
|
||||||
|
vh_flag = 1
|
||||||
|
elif 'VV' in os.path.basename(in_tif_path):
|
||||||
|
vv_flag = 1
|
||||||
|
self.processinfo=[hh_flag, hv_flag, vh_flag, vv_flag]
|
||||||
|
|
||||||
|
ref_tif_path = ""
|
||||||
|
meta_file_path = self.__in_processing_paras['META']
|
||||||
|
rpc_path=None
|
||||||
|
|
||||||
|
#######################
|
||||||
|
# RD
|
||||||
|
######################
|
||||||
|
# 2.2 生成局地入射角
|
||||||
|
path2 = env_str
|
||||||
|
print("path2:" + path2)
|
||||||
|
Orthorectification = IndirectOrthorectification(os.path.join(path2,"config.yaml"))
|
||||||
|
Orthorectification.IndirectOrthorectification(self.__in_processing_paras["SLC"], self.__workspace_processing_path) # 改动1
|
||||||
|
# 2.3 输出结果
|
||||||
|
|
||||||
|
# 3 处理RD
|
||||||
|
# 合并DEM
|
||||||
|
Orth_Slc=[]
|
||||||
|
in_dem_path = self.__in_processing_paras['DEM']
|
||||||
|
meta_file_path = self.__in_processing_paras['META'] # .meta文件路径
|
||||||
|
out_dem_path = self.__workspace_preprocessing_path
|
||||||
|
dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
|
||||||
|
|
||||||
|
in_slc_path=None
|
||||||
|
for slc_path in in_tif_paths:
|
||||||
|
if slc_path.find(".tiff")>0 and (slc_path.find("_HH_")>0 or slc_path.find("_VV_")>0):
|
||||||
|
in_slc_path=slc_path
|
||||||
|
break
|
||||||
|
|
||||||
|
# 获取校正模型后
|
||||||
|
Orthorectification.preCaldem_sar_rc(dem_merged_path,in_slc_path,self.__workspace_preprocessing_path,self.__workspace_processing_path.replace("\\","\\\\")) # 初步筛选坐标范围
|
||||||
|
logger.info('progress bar: 40%')
|
||||||
|
# clip_dem_reample_path=os.path.join(self.__workspace_preprocessing_path, "SAR_dem.tiff")
|
||||||
|
# infooption=gdal.InfoOptions("-json")
|
||||||
|
# clip_dem_tif_info=gdal.Info(clip_dem_reample_path,options=infooption)
|
||||||
|
# dem_merged_info=gdal.Info(dem_merged_path,options=infooption)
|
||||||
|
# sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0]
|
||||||
|
# 处理RD 的结果
|
||||||
|
out_dir_path=self.__workspace_processing_path.replace("\\","\\\\")
|
||||||
|
this_outSpace_path = out_dir_path
|
||||||
|
this_out_dem_slantRange_path = os.path.join(out_dir_path, "dem_slantRange.tiff") # out_dir_path + "\\" + "dem_slantRange.tiff"#// 地形斜距
|
||||||
|
this_out_plant_slantRange_path = os.path.join(out_dir_path, "flat_slantRange.tiff") # out_dir_path + "\\" + "flat_slantRange.tiff"#// 平地斜距
|
||||||
|
# 保留结果
|
||||||
|
if(os.path.exists(this_out_dem_slantRange_path)):
|
||||||
|
os.remove(this_out_dem_slantRange_path)
|
||||||
|
|
||||||
|
if(os.path.exists(this_out_plant_slantRange_path)):
|
||||||
|
os.remove(this_out_plant_slantRange_path)
|
||||||
|
|
||||||
|
|
||||||
|
this_out_dem_rc_path = os.path.join(out_dir_path, "WGS_SAR_map.tiff") # out_dir_path + "\\" + "WGS_SAR_map.tiff"#// 经纬度与行列号映射
|
||||||
|
if(os.path.exists(this_out_dem_rc_path)):
|
||||||
|
os.remove(this_out_dem_rc_path)
|
||||||
|
|
||||||
|
this_out_sar_sim_path = out_dir_path + "\\" + "sar_sim.tiff"
|
||||||
|
if (os.path.exists(this_out_sar_sim_path)):
|
||||||
|
os.remove(this_out_sar_sim_path)
|
||||||
|
|
||||||
|
this_out_sar_sim_wgs_path = out_dir_path + "\\" + "sar_sim_wgs.tiff" # // 经纬度与行列号映射
|
||||||
|
if (os.path.exists(this_out_sar_sim_wgs_path)):
|
||||||
|
os.remove(this_out_sar_sim_wgs_path)
|
||||||
|
|
||||||
|
this_out_incidence_path = os.path.join(out_dir_path, "incidentAngle.tiff") # out_dir_path + "\\" + "incidentAngle.tiff"#// 入射角
|
||||||
|
this_out_localIncidenct_path = os.path.join(out_dir_path, "localIncidentAngle.tiff") # out_dir_path + "\\" + "localIncidentAngle.tiff"#// 局地入射角
|
||||||
|
if(os.path.exists(this_out_incidence_path)):
|
||||||
|
shutil.move(this_out_incidence_path,os.path.join(out_dir_path, "inc_angle.tif")) # out_dir_path + "\\" + "inc_angle.tif")
|
||||||
|
if(os.path.exists(this_out_localIncidenct_path)):
|
||||||
|
shutil.move(this_out_localIncidenct_path, os.path.join(out_dir_path, "LocalIncidenceAngle.tif")) # out_dir_path + "\\" + "LocalIncidenceAngle.tif")
|
||||||
|
|
||||||
|
this_out_inc_angle_rpc_path = os.path.join(out_dir_path, "RD_incidentAngle.tiff") # out_dir_path + "\\" + "RD_incidentAngle.tiff"#// 局地入射角
|
||||||
|
this_out_local_inc_angle_rpc_path = os.path.join(out_dir_path, "RD_localIncidentAngle.tiff") # out_dir_path + "\\" + "RD_localIncidentAngle.tiff"#// 局地入射角
|
||||||
|
if(os.path.exists(this_out_inc_angle_rpc_path)):
|
||||||
|
os.remove(this_out_inc_angle_rpc_path)
|
||||||
|
if(os.path.exists(this_out_local_inc_angle_rpc_path)):
|
||||||
|
os.remove(this_out_local_inc_angle_rpc_path)
|
||||||
|
|
||||||
|
this_out_ori_sim_tiff = os.path.join(out_dir_path, "RD_ori_sim.tif") # out_dir_path + "\\" + "RD_ori_sim.tif"#// 局地入射角
|
||||||
|
this_in_rpc_lon_lat_path = this_out_ori_sim_tiff
|
||||||
|
|
||||||
|
this_out_sim_ori_tiff = os.path.join(out_dir_path, "RD_sim_ori.tif")
|
||||||
|
this_in_rpc_x_y_path = this_out_sim_ori_tiff
|
||||||
|
|
||||||
|
this_in_rpc_x_y_path_pro = self.process_sim_ori(this_in_rpc_lon_lat_path, this_in_rpc_x_y_path)
|
||||||
|
|
||||||
|
parameter_path = os.path.join(self.__workspace_processing_path, "orth_para.txt")
|
||||||
|
|
||||||
|
|
||||||
|
for in_tif_path in in_tif_paths:
|
||||||
|
# out_tif_path = os.path.join(self.__workspace_preprocessing_path,os.path.splitext(os.path.basename(in_tif_path))[0]) + r"_lin.tif"
|
||||||
|
out_tif_path = os.path.join(self.__workspace_preprocessing_path,os.path.splitext(os.path.basename(in_tif_path))[0]) + r"_lin.tif"
|
||||||
|
if ('HH' in os.path.basename(in_tif_path)) or ('HV' in os.path.basename(in_tif_path)) or ('VH' in os.path.basename(in_tif_path)) or ('VV' in os.path.basename(in_tif_path)):
|
||||||
|
alg.sar_backscattering_coef(in_tif_path, meta_file_path, out_tif_path)
|
||||||
|
# 构建RPC
|
||||||
|
# 查找RPC
|
||||||
|
rpc_path=in_tif_path.replace(".tiff",".rpc") if os.path.exists(in_tif_path.replace(".tiff",".rpc")) else in_tif_path.replace(".tiff",".rpb")
|
||||||
|
if not os.path.exists(rpc_path):
|
||||||
|
logger.error('rpc not found!')
|
||||||
|
|
||||||
|
# db->地理编码
|
||||||
|
# lin_tif_path = os.path.join(self.__workspace_processing_path,
|
||||||
|
# os.path.splitext(os.path.basename(in_tif_path))[0]) + r"-cal.tif"
|
||||||
|
# Orthorectification.calInterpolation_cubic_Wgs84_rc_sar_sigma(parameter_path, this_in_rpc_x_y_path,
|
||||||
|
# out_tif_path,
|
||||||
|
# lin_tif_path)
|
||||||
|
# 线性->地理编码->db
|
||||||
|
lin_tif_path=os.path.join(self.__workspace_preprocessing_path,os.path.splitext(os.path.basename(in_tif_path))[0]) + r"-lin_geo.tif"
|
||||||
|
# Orthorectification.calInterpolation_cubic_Wgs84_rc_sar_sigma(parameter_path, this_in_rpc_x_y_path_pro,
|
||||||
|
# out_tif_path,
|
||||||
|
# lin_tif_path)
|
||||||
|
|
||||||
|
Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, this_in_rpc_x_y_path_pro,
|
||||||
|
out_tif_path,
|
||||||
|
lin_tif_path)
|
||||||
|
tempout_tif_path = os.path.join(self.__workspace_processing_path,
|
||||||
|
os.path.splitext(os.path.basename(in_tif_path))[0]) + r"-cal.tif"
|
||||||
|
alg.lin_to_db(lin_tif_path, tempout_tif_path) #线性值转回DB值
|
||||||
|
|
||||||
|
|
||||||
|
# 移动RPC
|
||||||
|
#rpc_correction(in_tif_path,rpc_path,out_tif_path,dem_tif_file = None)
|
||||||
|
# Orthorectification.inter_Range2Geo(this_in_rpc_lon_lat_path,out_tif_path,tempout_tif_path,Orthorectification.heightspace)
|
||||||
|
|
||||||
|
self.imageHandler.write_quick_view(tempout_tif_path, color_img=False)
|
||||||
|
# self.imageHandler.write_quick_view(lin_tif_path, color_img=False)
|
||||||
|
else:
|
||||||
|
shutil.copy(in_tif_path,self.__workspace_processing_path)
|
||||||
|
ref_tif_path = tempout_tif_path
|
||||||
|
# ref_tif_path = lin_tif_path
|
||||||
|
# 构建行列号映射表
|
||||||
|
#out_rpc_rc_path = os.path.join(self.__workspace_processing_path,"RPC_ori_sim.tif")
|
||||||
|
#getRCImageRC(in_tif_path,out_rpc_rc_path,rpc_path)
|
||||||
|
logger.info('progress bar: 90%')
|
||||||
|
if(os.path.exists(this_in_rpc_lon_lat_path)):
|
||||||
|
os.remove(this_in_rpc_lon_lat_path)
|
||||||
|
if (os.path.exists(this_in_rpc_x_y_path)):
|
||||||
|
os.remove(this_in_rpc_x_y_path)
|
||||||
|
# out_mate_file_path = os.path.join(self.__workspace_processing_path,os.path.split(meta_file_path)[1].rstrip('.meta.xml') + '_DB.meta.xml')
|
||||||
|
out_mate_file_path = os.path.join(self.__workspace_processing_path,os.path.basename(meta_file_path))
|
||||||
|
shutil.copy(meta_file_path, out_mate_file_path)
|
||||||
|
|
||||||
|
if ref_tif_path != "":
|
||||||
|
# xml_path = "./model_meta.xml"
|
||||||
|
tem_folder = os.path.join(self.__workspace_path, EXE_NAME, "Temporary")
|
||||||
|
image_path = ref_tif_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_standa5rd.meta.xml") # 输出xml路径
|
||||||
|
# CreateStadardXmlFile(xml_path, par_dict, model_xml_path).create_standard_xml()
|
||||||
|
#
|
||||||
|
SrcImageName = os.path.split(self.__input_paras["SLC"]['ParaValue'])[1].split('.tar.gz')[0]
|
||||||
|
# meta_xml_path = os.path.join(self.__workspace_processing_path, SrcImageName + "-cal.meta.xml")
|
||||||
|
# CreateMetafile(self.image_meta_xml[0], self.alg_xml_path, model_xml_path,meta_xml_path).process(SrcImageName)
|
||||||
|
|
||||||
|
model_path = "./product.xml"
|
||||||
|
meta_xml_path = os.path.join(self.__workspace_processing_path, SrcImageName + "-cal.meta.xml")
|
||||||
|
|
||||||
|
para_dict = CreateMetaDict(image_path, self.__in_processing_paras['META'], self.__workspace_processing_path,
|
||||||
|
out_path1, out_path2).calu_nature()
|
||||||
|
para_dict.update({"imageinfo_ProductName": "后向散射系数"})
|
||||||
|
para_dict.update({"imageinfo_ProductIdentifier": "BackScattering"})
|
||||||
|
para_dict.update({"imageinfo_ProductLevel": "LEVEL3"})
|
||||||
|
para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
|
||||||
|
para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "DEM"})
|
||||||
|
para_dict.update({"MetaInfo_UnitDes": "DB"}) # 设置单位
|
||||||
|
CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
self.make_targz(self.__out_para, self.__workspace_processing_path)
|
||||||
|
|
||||||
|
logger.info('process_handle finished!')
|
||||||
|
logger.info('progress bar: 100%')
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
start = datetime.datetime.now()
|
||||||
|
try:
|
||||||
|
if len(sys.argv)<2:
|
||||||
|
xml_path = 'BackScattering.xml'
|
||||||
|
else:
|
||||||
|
xml_path = sys.argv[1]
|
||||||
|
ScatteringMain = ScatteringMain(xml_path)
|
||||||
|
if not ScatteringMain.check_source():
|
||||||
|
raise Exception('check_source() failed!')
|
||||||
|
if not ScatteringMain.process_handle(start):
|
||||||
|
raise Exception('process_handle() failed!')
|
||||||
|
logger.info('successful production of backscattering products!')
|
||||||
|
except Exception:
|
||||||
|
logger.exception("run-time error!")
|
||||||
|
finally:
|
||||||
|
ScatteringMain.del_temp_workspace()
|
||||||
|
# pass
|
||||||
|
|
||||||
|
end = datetime.datetime.now()
|
||||||
|
msg = 'running use time: %s ' % (end - start)
|
||||||
|
logger.info(msg)
|
||||||
|
|
@ -0,0 +1,33 @@
|
||||||
|
# -*- mode: python ; coding: utf-8 -*-
|
||||||
|
|
||||||
|
block_cipher = None
|
||||||
|
|
||||||
|
|
||||||
|
a = Analysis(['BackScatteringMain.py'],
|
||||||
|
pathex=['D:\\estar-proj\\microproduct\\backScattering'],
|
||||||
|
binaries=[],
|
||||||
|
datas=[],
|
||||||
|
hiddenimports=[],
|
||||||
|
hookspath=[],
|
||||||
|
runtime_hooks=[],
|
||||||
|
excludes=[],
|
||||||
|
win_no_prefer_redirects=False,
|
||||||
|
win_private_assemblies=False,
|
||||||
|
cipher=block_cipher,
|
||||||
|
noarchive=False)
|
||||||
|
pyz = PYZ(a.pure, a.zipped_data,
|
||||||
|
cipher=block_cipher)
|
||||||
|
exe = EXE(pyz,
|
||||||
|
a.scripts,
|
||||||
|
a.binaries,
|
||||||
|
a.zipfiles,
|
||||||
|
a.datas,
|
||||||
|
[],
|
||||||
|
name='BackScatteringMain',
|
||||||
|
debug=False,
|
||||||
|
bootloader_ignore_signals=False,
|
||||||
|
strip=False,
|
||||||
|
upx=True,
|
||||||
|
upx_exclude=[],
|
||||||
|
runtime_tmpdir=None,
|
||||||
|
console=True )
|
||||||
|
|
@ -0,0 +1,208 @@
|
||||||
|
"""
|
||||||
|
@Project ：microproduct
|
||||||
|
@File ：BackScatteringXmlInfo.PY
|
||||||
|
@Function ：主函数
|
||||||
|
@Author ：LMM
|
||||||
|
@Date ：2021/10/19 14:39
|
||||||
|
@Version ：1.0.0
|
||||||
|
"""
|
||||||
|
import os
|
||||||
|
from xml.etree.ElementTree import ElementTree, Element
|
||||||
|
import xml.dom.minidom
|
||||||
|
from lxml import etree
|
||||||
|
import shutil
|
||||||
|
from tool.algorithm.image.ImageHandle import ImageHandler
|
||||||
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
||||||
|
from osgeo import gdal
|
||||||
|
import numpy as np
|
||||||
|
import datetime
|
||||||
|
from PIL import Image
|
||||||
|
|
||||||
|
|
||||||
|
class CreateDict:
|
||||||
|
"""根据影像信息输出属性字典"""
|
||||||
|
def __init__(self, image_path, image_pair, out_path1, out_path2):
|
||||||
|
self.ImageHandler = ImageHandler()
|
||||||
|
self.para_dict = {}
|
||||||
|
self.image_path = image_path
|
||||||
|
self.out_path = out_path1
|
||||||
|
self.out_path2 = out_path2
|
||||||
|
self.image_pair = image_pair
|
||||||
|
pass
|
||||||
|
|
||||||
|
def calu_nature(self,start):
|
||||||
|
"""存入字典"""
|
||||||
|
|
||||||
|
imageinfo_width=self.ImageHandler.get_img_width(self.image_path)
|
||||||
|
self.para_dict.update({"imageinfo_width":imageinfo_width})
|
||||||
|
imageinfo_height=self.ImageHandler.get_img_height(self.image_path)
|
||||||
|
self.para_dict.update({"imageinfo_height":imageinfo_height})
|
||||||
|
# imageinfo_EarthModel=self.ImageHandler.get_projection(self.image_path).split("SPHEROID[", 2)[1].split(",", 2)[0]
|
||||||
|
# self.para_dict.update({"imageinfo_EarthModel":imageinfo_EarthModel.replace('"','')})
|
||||||
|
self.para_dict.update({"imageinfo_EarthModel": "WGS84"})
|
||||||
|
# imageinfo_ProjectModel = self.ImageHandler.get_projection(self.image_path).split("DATUM[", 2)[1].split(",", 2)[0]
|
||||||
|
# self.para_dict.update({"imageinfo_ProjectModel":imageinfo_ProjectModel.replace('"','')})
|
||||||
|
self.para_dict.update({"imageinfo_ProjectModel": "UTM"})
|
||||||
|
proj = self.ImageHandler.get_projection(self.image_path) # 输出的影像若是投影坐标系则先转成地理坐标系
|
||||||
|
keyword = proj.split("[", 2)[0] # 若是地理坐标系则pass
|
||||||
|
if keyword == "GEOGCS":
|
||||||
|
pass
|
||||||
|
elif keyword == "PROJCS":
|
||||||
|
pp.trans_projcs2geogcs(self.out_path2, self.image_path)
|
||||||
|
self.image_path = self.out_path2
|
||||||
|
elif len(keyword) == 0 or keyword.strip() == "" or keyword.isspace() is True:
|
||||||
|
raise Exception('image projection is missing!')
|
||||||
|
|
||||||
|
pp.trans_geogcs2projcs(self.out_path, self.image_path) # 坐标投影, 地理转平面投影坐标
|
||||||
|
imageinfo_widthspace = self.ImageHandler.get_geotransform(self.out_path)[1] # 投影后的分辨率
|
||||||
|
imageinfo_heightspace = -self.ImageHandler.get_geotransform(self.out_path)[5] # 投影后的分辨率
|
||||||
|
self.para_dict.update({"imageinfo_widthspace":imageinfo_widthspace})
|
||||||
|
self.para_dict.update({"imageinfo_heightspace":imageinfo_heightspace})
|
||||||
|
|
||||||
|
self.para_dict.update({"imageinfo_NearincidenceAngle": "0"})
|
||||||
|
self.para_dict.update({"imageinfo_FarIncidenceAngle": "30"})
|
||||||
|
self.para_dict.update({"imageinfo_Azimuth": "1"}) # todo 结果xml新增字节
|
||||||
|
|
||||||
|
self.para_dict.update({"NominalResolution":imageinfo_widthspace})
|
||||||
|
|
||||||
|
WidthInMeters = imageinfo_width*imageinfo_widthspace # 投影后的分辨率×宽度
|
||||||
|
self.para_dict.update({"WidthInMeters":WidthInMeters})
|
||||||
|
|
||||||
|
# image_array = self.ImageHandler.get_band_array(self.image_path)
|
||||||
|
# a2 = np.where(np.isnan(image_array), 999999, image_array)
|
||||||
|
# MinValue = np.min(a2)
|
||||||
|
# a3 = np.where(np.isnan(image_array), -999999, image_array)
|
||||||
|
# MaxValue = np.max(a3)
|
||||||
|
#
|
||||||
|
# self.para_dict.update({"MaxValue":MaxValue})
|
||||||
|
# self.para_dict.update({"MinValue":MinValue})
|
||||||
|
|
||||||
|
get_scope = self.ImageHandler.get_scope(self.image_path)
|
||||||
|
point_upleft, point_upright, point_downleft, point_downright=get_scope[0], get_scope[1], get_scope[2], get_scope[3]
|
||||||
|
self.para_dict.update({"imageinfo_corner_topLeft_latitude": point_upleft[1]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_topLeft_longitude": point_upleft[0]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_topRight_latitude": point_upright[1]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_topRight_longitude": point_upright[0]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_bottomLeft_latitude": point_downleft[1]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_bottomLeft_longitude": point_downleft[0]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_bottomRight_latitude": point_downright[1]})
|
||||||
|
self.para_dict.update({"imageinfo_corner_bottomRight_longitude": point_downright[0]})
|
||||||
|
|
||||||
|
longitude_max=np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).max()
|
||||||
|
longitude_min=np.array([point_upleft[0], point_upright[0], point_downleft[0], point_downright[0]]).min()
|
||||||
|
latitude_max=np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).max()
|
||||||
|
latitude_min=np.array([point_upleft[1], point_upright[1], point_downleft[1], point_downright[1]]).min()
|
||||||
|
imageinfo_center_latitude=(latitude_max+latitude_min)/2
|
||||||
|
imageinfo_center_longitude=(longitude_max+longitude_min)/2
|
||||||
|
self.para_dict.update({"imageinfo_center_latitude": imageinfo_center_latitude})
|
||||||
|
self.para_dict.update({"imageinfo_center_longitude": imageinfo_center_longitude})
|
||||||
|
|
||||||
|
self.para_dict.update({"productType": "GTC"}) # 设置产品类型
|
||||||
|
self.para_dict.update({"productFormat": "TIF"})
|
||||||
|
productGentime = datetime.datetime.now()
|
||||||
|
self.para_dict.update({"productGentime": productGentime})
|
||||||
|
msg=productGentime-start
|
||||||
|
self.para_dict.update({"productConsumeTime": msg})
|
||||||
|
self.para_dict.update({"unit": "none"}) # 设置单位
|
||||||
|
self.para_dict.update({"NoDataValue": "nan"})
|
||||||
|
self.para_dict.update({"productLevel": "1"}) # 设置图像位深度
|
||||||
|
|
||||||
|
image_array = self.ImageHandler.get_band_array(self.image_path)
|
||||||
|
try:
|
||||||
|
gdal_dtypes = {
|
||||||
|
'int8': gdal.GDT_Byte,
|
||||||
|
'unit16': gdal.GDT_UInt16,
|
||||||
|
'int16': gdal.GDT_Int16,
|
||||||
|
'unit32': gdal.GDT_UInt32,
|
||||||
|
'int32': gdal.GDT_Int32,
|
||||||
|
'float32': gdal.GDT_Float32,
|
||||||
|
'float64': gdal.GDT_Float64,
|
||||||
|
}
|
||||||
|
bit_dtypes = {
|
||||||
|
'int8': 8,
|
||||||
|
'unit16': 16,
|
||||||
|
'int16': 16,
|
||||||
|
'unit32': 32,
|
||||||
|
'int32': 32,
|
||||||
|
'float32': 32,
|
||||||
|
'float64': 64,
|
||||||
|
}
|
||||||
|
if not gdal_dtypes.get(image_array.dtype.name, None) is None:
|
||||||
|
bit_num = str(bit_dtypes[image_array.dtype.name])
|
||||||
|
datatype=bit_num+"bit"
|
||||||
|
else:
|
||||||
|
datatype = str(32) + "bit"
|
||||||
|
# datatype = str(gdal.GDT_Float32)+"bit"
|
||||||
|
self.para_dict.update({"imagebit": datatype})
|
||||||
|
except Exception:
|
||||||
|
self.para_dict.update({"imagebit": "None"})
|
||||||
|
|
||||||
|
HH, HV, VH ,VV= self.image_pair[0],self.image_pair[1],self.image_pair[2],self.image_pair[3]
|
||||||
|
if HH == 0:
|
||||||
|
HH = "delete"
|
||||||
|
else:
|
||||||
|
HH = "NULL"
|
||||||
|
self.para_dict.update({"imageinfo_QualifyValue_HH": HH})
|
||||||
|
if HV==0:
|
||||||
|
HV = "delete"
|
||||||
|
else:
|
||||||
|
HV = "NULL"
|
||||||
|
self.para_dict.update({"imageinfo_QualifyValue_HV": HV})
|
||||||
|
if VH==0:
|
||||||
|
VH = "delete"
|
||||||
|
else:
|
||||||
|
VH = "NULL"
|
||||||
|
self.para_dict.update({"imageinfo_QualifyValue_VH": VH})
|
||||||
|
if VV==0:
|
||||||
|
VV = "delete"
|
||||||
|
else:
|
||||||
|
VV = "NULL"
|
||||||
|
self.para_dict.update({"imageinfo_QualifyValue_VV": VV})
|
||||||
|
return self.para_dict
|
||||||
|
|
||||||
|
|
||||||
|
class CreateStadardXmlFile:
|
||||||
|
"""读取字典中的属性值，生成一个标准的xml文件"""
|
||||||
|
def __init__(self, xml_path, par_dict, path):
|
||||||
|
"""
|
||||||
|
par_dict:字典
|
||||||
|
path:xml模板输出路径
|
||||||
|
"""
|
||||||
|
self.par_dict = par_dict
|
||||||
|
self.path = path
|
||||||
|
shutil.copy(xml_path, path)
|
||||||
|
pass
|
||||||
|
|
||||||
|
def create_standard_xml(self):
|
||||||
|
"""将字典中的信息写入到copy的xml文件中"""
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(self.path) # 影像头文件
|
||||||
|
root = tree.getroot()
|
||||||
|
productinfo = root.find("productinfo")
|
||||||
|
for key, value in self.par_dict.items():
|
||||||
|
if key.split("_")[0] != "imageinfo":
|
||||||
|
productinfo.find(key).text = str(value)
|
||||||
|
elif key.split("_")[0] == "imageinfo":
|
||||||
|
imageinfo = productinfo.find("imageinfo")
|
||||||
|
if key.split("_")[1] in ["EarthModel", "ProjectModel", "width", "height", "widthspace", "heightspace",
|
||||||
|
"NearincidenceAngle", "FarIncidenceAngle", "Azimuth"]: # todo 新增xml字节
|
||||||
|
imageinfo.find(key.split("_")[1]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "center":
|
||||||
|
center = imageinfo.find("center")
|
||||||
|
center.find(key.split("_")[2]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "corner":
|
||||||
|
corner = imageinfo.find("corner")
|
||||||
|
corner.find(key.split("_")[2]).find(key.split("_")[3]).text = str(value)
|
||||||
|
elif key.split("_")[1] == "QualifyValue":
|
||||||
|
QualifyValue = imageinfo.find("QualifyValue")
|
||||||
|
if value =="delete":
|
||||||
|
element_QualifyValue = list(QualifyValue)
|
||||||
|
for i in element_QualifyValue:
|
||||||
|
if i.tag == key.split("_")[2]:
|
||||||
|
QualifyValue.remove(i)
|
||||||
|
else:
|
||||||
|
QualifyValue.find(key.split("_")[2]).text = str(value)
|
||||||
|
pass
|
||||||
|
processinfo = root.find("processinfo")
|
||||||
|
tree.write(self.path, encoding="utf-8", xml_declaration=True)
|
||||||
|
|
||||||
|
|
@ -0,0 +1,415 @@
|
||||||
|
# 一米正射辅助数据处理类
|
||||||
|
import time
|
||||||
|
import math
|
||||||
|
import numpy as np
|
||||||
|
from osgeo import gdal
|
||||||
|
from xml.etree.ElementTree import ElementTree
|
||||||
|
from scipy.optimize import leastsq
|
||||||
|
|
||||||
|
|
||||||
|
class OrthoAuxData:
|
||||||
|
def __init__(self):
|
||||||
|
pass
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def time_stamp(tm):
|
||||||
|
list = tm.split(':')
|
||||||
|
sec = math.ceil(float(list[2]))
|
||||||
|
tm1 = list[0] + ':' + list[1] + ':' + str(sec)
|
||||||
|
tmArr = time.strptime(tm1, "%Y-%m-%d %H:%M:%S")
|
||||||
|
# tmArr = time.strptime(tm1, "%Y-%m-%d %H:%M:%S.%f")
|
||||||
|
ts = float(time.mktime(tmArr)) # 转换为时间戳
|
||||||
|
return ts
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_meta(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
T = []
|
||||||
|
Xs = []
|
||||||
|
Ys = []
|
||||||
|
Zs = []
|
||||||
|
Vsx = []
|
||||||
|
Vsy = []
|
||||||
|
Vsz = []
|
||||||
|
GPS_data = root.find('GPS')
|
||||||
|
for child in GPS_data:
|
||||||
|
Xs.append(float(child.find('xPosition').text))
|
||||||
|
Ys.append(float(child.find('yPosition').text))
|
||||||
|
Zs.append(float(child.find('zPosition').text))
|
||||||
|
Vsx.append(float(child.find('xVelocity').text))
|
||||||
|
Vsy.append(float(child.find('yVelocity').text))
|
||||||
|
Vsz.append(float(child.find('zVelocity').text))
|
||||||
|
tm = child.find('TimeStamp').text
|
||||||
|
ts = OrthoAuxData.time_stamp(tm)
|
||||||
|
T.append(ts)
|
||||||
|
meta_data = [Xs, Ys, Zs, Vsx, Vsy, Vsz]
|
||||||
|
return T, meta_data
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_control_points(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
center = imageinfo.find('center')
|
||||||
|
corner = imageinfo.find('corner')
|
||||||
|
ctrl_pts = [[] for i in range(2)]
|
||||||
|
ctrl_pts[0].append(float(center.find('longitude').text))
|
||||||
|
ctrl_pts[1].append(float(center.find('latitude').text))
|
||||||
|
|
||||||
|
for child in corner:
|
||||||
|
ctrl_pts[0].append(float(child.find('longitude').text))
|
||||||
|
ctrl_pts[1].append(float(child.find('latitude').text))
|
||||||
|
return ctrl_pts
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_dem(dem_resampled_path, flag=1):
|
||||||
|
in_ds = gdal.Open(dem_resampled_path)
|
||||||
|
gt = list(in_ds.GetGeoTransform())
|
||||||
|
bands_num = in_ds.RasterCount
|
||||||
|
x_size = in_ds.RasterXSize
|
||||||
|
y_size = in_ds.RasterYSize
|
||||||
|
pstn_arr = np.zeros([y_size, x_size, 3], dtype=np.float)
|
||||||
|
for i in range(1, bands_num + 1):
|
||||||
|
data = in_ds.GetRasterBand(i).ReadAsArray(0, 0, x_size, y_size)
|
||||||
|
for y in range(y_size):
|
||||||
|
for x in range(x_size):
|
||||||
|
longitude = gt[0] + x * gt[1]
|
||||||
|
latitude = gt[3] + y * gt[5]
|
||||||
|
altitude = data[y, x]
|
||||||
|
if flag == 1:
|
||||||
|
pstn = OrthoAuxData.LLA2XYZ(longitude, latitude, altitude)
|
||||||
|
else:
|
||||||
|
pstn = [longitude, latitude, altitude]
|
||||||
|
pstn_arr[y, x, 0] = pstn[0]
|
||||||
|
pstn_arr[y, x, 1] = pstn[1]
|
||||||
|
pstn_arr[y, x, 2] = pstn[2]
|
||||||
|
|
||||||
|
del in_ds, data
|
||||||
|
return pstn_arr
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_demM(dem_resampled_path, part_cnt, r_cnt, c_cnt, flag=1):
|
||||||
|
in_ds = gdal.Open(dem_resampled_path)
|
||||||
|
gt = list(in_ds.GetGeoTransform())
|
||||||
|
bands_num = in_ds.RasterCount
|
||||||
|
x_size = in_ds.RasterXSize // part_cnt
|
||||||
|
y_size = in_ds.RasterYSize // part_cnt
|
||||||
|
x = [[i] * y_size for i in range(x_size)]
|
||||||
|
y = [[i] * x_size for i in range(y_size)]
|
||||||
|
x = np.array(x)
|
||||||
|
x = x.T
|
||||||
|
y = np.array(y)
|
||||||
|
x_off = c_cnt * x_size
|
||||||
|
y_off = r_cnt * y_size
|
||||||
|
gt[0] = gt[0] + c_cnt * x_size * gt[1]
|
||||||
|
gt[3] = gt[3] + r_cnt * y_size * gt[5]
|
||||||
|
for i in range(1, bands_num + 1):
|
||||||
|
data = in_ds.GetRasterBand(i).ReadAsArray(x_off, y_off, x_size, y_size)
|
||||||
|
altitude = data / 255 * 1024
|
||||||
|
longitude = gt[0] + x * gt[1]
|
||||||
|
latitude = gt[3] + y * gt[5]
|
||||||
|
if flag == 1:
|
||||||
|
pstn = OrthoAuxData.LLA2XYZM(longitude, latitude, altitude)
|
||||||
|
else:
|
||||||
|
pstn = [longitude, latitude, altitude]
|
||||||
|
|
||||||
|
del in_ds, data
|
||||||
|
return pstn
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def read_dem_row(dem_resampled_path, p, flag=1):
|
||||||
|
in_ds = gdal.Open(dem_resampled_path)
|
||||||
|
gt = list(in_ds.GetGeoTransform())
|
||||||
|
bands_num = in_ds.RasterCount
|
||||||
|
x_size = in_ds.RasterXSize
|
||||||
|
y_size = in_ds.RasterYSize
|
||||||
|
|
||||||
|
x = [[i] for i in range(x_size)]
|
||||||
|
x = np.array(x)
|
||||||
|
x = x.T
|
||||||
|
y = np.ones((1, x_size)) * p
|
||||||
|
x_off = 0
|
||||||
|
y_off = p
|
||||||
|
for i in range(1, bands_num + 1):
|
||||||
|
data = in_ds.GetRasterBand(i).ReadAsArray(x_off, y_off, x_size, 1)
|
||||||
|
altitude = data
|
||||||
|
longitude = gt[0] + x * gt[1]
|
||||||
|
latitude = gt[3] + y * gt[5]
|
||||||
|
if flag == 1:
|
||||||
|
pstn = OrthoAuxData.LLA2XYZM(longitude, latitude, altitude)
|
||||||
|
else:
|
||||||
|
pstn = [longitude, latitude, altitude]
|
||||||
|
|
||||||
|
del in_ds, data
|
||||||
|
return pstn
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def orbit_fitting(time_array, meta_data):
|
||||||
|
# 最小二乘法求解轨道参数
|
||||||
|
T0 = (time_array[0] + time_array[len(time_array)-1]) / 2
|
||||||
|
t = []
|
||||||
|
for i in range(len(time_array)):
|
||||||
|
t.append(time_array[i]-T0)
|
||||||
|
|
||||||
|
def func(p, x):
|
||||||
|
w3, w2, w1, w0 = p
|
||||||
|
return w3*x**3 + w2*x**2 + w1*x + w0
|
||||||
|
|
||||||
|
def error(p, x, y):
|
||||||
|
return func(p, x) - y
|
||||||
|
|
||||||
|
orbital_paras = []
|
||||||
|
for j in range(len(meta_data)):
|
||||||
|
p0 = [1, 2, 3, 4]
|
||||||
|
x = np.array(t)
|
||||||
|
y = np.array(meta_data[j])
|
||||||
|
Para = leastsq(error, p0, args=(x, y))
|
||||||
|
orbital_paras.append(Para[0])
|
||||||
|
print(Para[0], Para[1])
|
||||||
|
|
||||||
|
return orbital_paras, T0
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_PRF(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
sensor = root.find('sensor')
|
||||||
|
waveParams = sensor.find('waveParams')
|
||||||
|
PRF = float(waveParams.find('wave').find('prf').text)
|
||||||
|
return PRF
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_delta_R(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
sensor = root.find('sensor')
|
||||||
|
pulseWidth = float(sensor.find('waveParams').find('wave').find('pulseWidth').text)
|
||||||
|
bandWidth = float(sensor.find('waveParams').find('wave').find('bandWidth').text)
|
||||||
|
c = 299792458
|
||||||
|
delta_R = c / (1000000 * 2 * bandWidth)
|
||||||
|
return delta_R
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_doppler_rate_coef(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
processinfo = root.find('processinfo')
|
||||||
|
doppler = processinfo.find('DopplerRateValuesCoefficients')
|
||||||
|
t0 = float(processinfo.find('DopplerParametersReferenceTime').text)
|
||||||
|
r0 = float(doppler.find('r0').text)
|
||||||
|
r1 = float(doppler.find('r1').text)
|
||||||
|
r2 = float(doppler.find('r2').text)
|
||||||
|
r3 = float(doppler.find('r3').text)
|
||||||
|
r4 = float(doppler.find('r4').text)
|
||||||
|
|
||||||
|
return t0, np.array([r0, r1, r2, r3, r4]).reshape(5, 1)
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_doppler_center_coef(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
processinfo = root.find('processinfo')
|
||||||
|
doppler = processinfo.find('DopplerCentroidCoefficients')
|
||||||
|
b0 = float(doppler.find('d0').text)
|
||||||
|
b1 = float(doppler.find('d1').text)
|
||||||
|
b2 = float(doppler.find('d2').text)
|
||||||
|
return b0, b1, b2
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_lamda(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
sensor = root.find('sensor')
|
||||||
|
λ = float(sensor.find('lamda').text)
|
||||||
|
return λ
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_t0(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
tm = imageinfo.find('imagingTime').find('start').text
|
||||||
|
t0 = OrthoAuxData.time_stamp(tm)
|
||||||
|
return t0
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_start_and_end_time(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
tm0 = imageinfo.find('imagingTime').find('start').text
|
||||||
|
tm1 = imageinfo.find('imagingTime').find('end').text
|
||||||
|
starttime = OrthoAuxData.time_stamp(tm0)
|
||||||
|
endtime = OrthoAuxData.time_stamp(tm1)
|
||||||
|
return starttime, endtime
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_width_and_height(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
width = int(imageinfo.find('width').text)
|
||||||
|
height = int(imageinfo.find('height').text)
|
||||||
|
return width, height
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_R0(meta_file_path):
|
||||||
|
tree = ElementTree()
|
||||||
|
tree.parse(meta_file_path)
|
||||||
|
root = tree.getroot()
|
||||||
|
imageinfo = root.find('imageinfo')
|
||||||
|
R0 = float(imageinfo.find('nearRange').text)
|
||||||
|
return R0
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_h():
|
||||||
|
h = 6.6
|
||||||
|
return h
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def LLA2XYZ(longitude, latitude, altitude):
|
||||||
|
'''
|
||||||
|
WGS-84坐标系下：经纬度坐标转空间直角坐标
|
||||||
|
'''
|
||||||
|
# 经纬度余弦值
|
||||||
|
cosLat = math.cos(latitude * math.pi / 180)
|
||||||
|
sinLat = math.sin(latitude * math.pi / 180)
|
||||||
|
cosLon = math.cos(longitude * math.pi / 180)
|
||||||
|
sinLon = math.sin(longitude * math.pi / 180)
|
||||||
|
# WGS84坐标系参数
|
||||||
|
rad = 6378137.0 #地球赤道平均半径
|
||||||
|
f = 1.0/298.257224 #WGS84椭球扁率
|
||||||
|
C = 1.0/math.sqrt(cosLat*cosLat + (1-f)*(1-f)*sinLat*sinLat)
|
||||||
|
S = (1-f)*(1-f)*C
|
||||||
|
h = altitude
|
||||||
|
# 计算XYZ坐标
|
||||||
|
X = (rad * C + h) * cosLat * cosLon
|
||||||
|
Y = (rad * C + h) * cosLat * sinLon
|
||||||
|
Z = (rad * S + h) * sinLat
|
||||||
|
# return np.array([X, Y, Z]).reshape(1,3)
|
||||||
|
return [X, Y, Z]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def LLA2XYZM(longitude, latitude, altitude):
|
||||||
|
# 经纬度余弦值
|
||||||
|
cosLat = np.cos(latitude * math.pi / 180).reshape(-1,1)
|
||||||
|
sinLat = np.sin(latitude * math.pi / 180).reshape(-1,1)
|
||||||
|
cosLon = np.cos(longitude * math.pi / 180).reshape(-1,1)
|
||||||
|
sinLon = np.sin(longitude * math.pi / 180).reshape(-1,1)
|
||||||
|
# WGS84坐标系参数
|
||||||
|
rad = 6378137.0 #地球赤道平均半径
|
||||||
|
f = 1.0/298.257224 #WGS84椭球扁率
|
||||||
|
C = 1.0/(np.sqrt(cosLat*cosLat + (1-f)*(1-f)*sinLat*sinLat)).reshape(-1,1)
|
||||||
|
S = (1-f)*(1-f)*C
|
||||||
|
h = altitude.reshape(-1,1)
|
||||||
|
# 计算XYZ坐标
|
||||||
|
X = (rad * C + h) * cosLat * cosLon
|
||||||
|
Y = (rad * C + h) * cosLat * sinLon
|
||||||
|
Z = (rad * S + h) * sinLat
|
||||||
|
return [X, Y, Z]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def XYZ2LLA(X, Y, Z):
|
||||||
|
''' 大地坐标系转经纬度
|
||||||
|
适用于WGS84坐标系
|
||||||
|
args:
|
||||||
|
x,y,z
|
||||||
|
return:
|
||||||
|
lat,long,altitude
|
||||||
|
'''
|
||||||
|
# WGS84坐标系的参数
|
||||||
|
a = 6378137.0 # 椭球长半轴
|
||||||
|
b = 6356752.314245 # 椭球短半轴
|
||||||
|
ea = np.sqrt((a ** 2 - b ** 2) / a ** 2)
|
||||||
|
eb = np.sqrt((a ** 2 - b ** 2) / b ** 2)
|
||||||
|
p = np.sqrt(X ** 2 + Y ** 2)
|
||||||
|
theta = np.arctan2(Z * a, p * b)
|
||||||
|
|
||||||
|
# 计算经纬度及海拔
|
||||||
|
longitude = np.arctan2(Y, X)
|
||||||
|
latitude = np.arctan2(Z + eb ** 2 * b * np.sin(theta) ** 3, p - ea ** 2 * a * np.cos(theta) ** 3)
|
||||||
|
N = a / np.sqrt(1 - ea ** 2 * np.sin(latitude) ** 2)
|
||||||
|
altitude = p / np.cos(latitude) - N
|
||||||
|
|
||||||
|
# return np.array([np.degrees(latitude), np.degrees(longitude), altitude])
|
||||||
|
return [np.degrees(longitude), np.degrees(latitude), altitude]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def XYZ2LLAM(X, Y, Z):
|
||||||
|
''' 大地坐标系转经纬度
|
||||||
|
适用于WGS84坐标系
|
||||||
|
args:
|
||||||
|
x,y,z
|
||||||
|
return:
|
||||||
|
lat,long,altitude
|
||||||
|
'''
|
||||||
|
# WGS84坐标系的参数
|
||||||
|
a = 6378137.0 # 椭球长半轴
|
||||||
|
b = 6356752.314245 # 椭球短半轴
|
||||||
|
ea = np.sqrt((a ** 2 - b ** 2) / a ** 2)
|
||||||
|
eb = np.sqrt((a ** 2 - b ** 2) / b ** 2)
|
||||||
|
p = np.sqrt(X ** 2 + Y ** 2)
|
||||||
|
theta = np.arctan2(Z * a, p * b)
|
||||||
|
|
||||||
|
# 计算经纬度及海拔
|
||||||
|
longitude = np.arctan2(Y, X)
|
||||||
|
latitude = np.arctan2(Z + eb ** 2 * b * np.sin(theta) ** 3, p - ea ** 2 * a * np.cos(theta) ** 3)
|
||||||
|
N = a / np.sqrt(1 - ea ** 2 * np.sin(latitude) ** 2)
|
||||||
|
altitude = p / np.cos(latitude) - N
|
||||||
|
|
||||||
|
# return np.array([np.degrees(latitude), np.degrees(longitude), altitude])
|
||||||
|
return [np.degrees(longitude), np.degrees(latitude), altitude]
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def world2Pixel(geoMatrix, x, y):
|
||||||
|
"""
|
||||||
|
使用GDAL库的geomatrix对象((gdal.GetGeoTransform()))计算地理坐标的像素位置
|
||||||
|
"""
|
||||||
|
ulx = geoMatrix[0]
|
||||||
|
uly = geoMatrix[3]
|
||||||
|
xDist = geoMatrix[1]
|
||||||
|
yDist = geoMatrix[5]
|
||||||
|
rtnX = geoMatrix[2]
|
||||||
|
rtnY = geoMatrix[4]
|
||||||
|
pixel = int((x - ulx) / xDist)
|
||||||
|
line = int((uly - y) / abs(yDist))
|
||||||
|
return pixel, line
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def sar_intensity_synthesis(in_sar_tif, out_sar_tif):
|
||||||
|
# 获取SLC格式SAR影像的相关信息
|
||||||
|
in_ds = gdal.Open(in_sar_tif)
|
||||||
|
bands_num = in_ds.RasterCount
|
||||||
|
rows = in_ds.RasterYSize
|
||||||
|
columns = in_ds.RasterXSize
|
||||||
|
proj = in_ds.GetProjection()
|
||||||
|
geotrans = in_ds.GetGeoTransform()
|
||||||
|
|
||||||
|
# 创建输出的SAR强度图
|
||||||
|
gtiff_driver = gdal.GetDriverByName('GTiff')
|
||||||
|
out_ds = gtiff_driver.Create(out_sar_tif, columns, rows, 1)
|
||||||
|
out_ds.SetProjection(proj)
|
||||||
|
out_ds.SetGeoTransform(geotrans)
|
||||||
|
|
||||||
|
# 输出SAR强度图
|
||||||
|
in_data1 = in_ds.GetRasterBand(1).ReadAsArray(0, 0, columns, rows)
|
||||||
|
in_data1 = in_data1/10
|
||||||
|
in_data1 = np.power(10, in_data1)
|
||||||
|
in_data2 = in_ds.GetRasterBand(2).ReadAsArray(0, 0, columns, rows)
|
||||||
|
in_data2 = in_data2 / 10
|
||||||
|
in_data2 = np.power(10, in_data2)
|
||||||
|
out_data = np.sqrt(in_data1**2 + in_data2**2)
|
||||||
|
out_ds.GetRasterBand(1).WriteArray(out_data)
|
||||||
|
|
||||||
|
del in_ds, out_ds
|
||||||
Binary file not shown.
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|
|
@ -0,0 +1,19 @@
|
||||||
|
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
|
||||||
|
<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
|
||||||
|
<assemblyIdentity type="win32" name="BackScatteringMain" processorArchitecture="amd64" version="1.0.0.0"/>
|
||||||
|
<dependency>
|
||||||
|
<dependentAssembly>
|
||||||
|
<assemblyIdentity type="win32" name="Microsoft.Windows.Common-Controls" language="*" processorArchitecture="*" version="6.0.0.0" publicKeyToken="6595b64144ccf1df"/>
|
||||||
|
<compatibility xmlns="urn:schemas-microsoft-com:compatibility.v1"/>
|
||||||
|
</dependentAssembly>
|
||||||
|
</dependency>
|
||||||
|
<compatibility xmlns="urn:schemas-microsoft-com:compatibility.v1">
|
||||||
|
<application>
|
||||||
|
<supportedOS Id="{e2011457-1546-43c5-a5fe-008deee3d3f0}"/>
|
||||||
|
<supportedOS Id="{35138b9a-5d96-4fbd-8e2d-a2440225f93a}"/>
|
||||||
|
<supportedOS Id="{4a2f28e3-53b9-4441-ba9c-d69d4a4a6e38}"/>
|
||||||
|
<supportedOS Id="{1f676c76-80e1-4239-95bb-83d0f6d0da78}"/>
|
||||||
|
<supportedOS Id="{8e0f7a12-bfb3-4fe8-b9a5-48fd50a15a9a}"/>
|
||||||
|
</application>
|
||||||
|
</compatibility>
|
||||||
|
</assembly>
|
||||||
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Load Diff
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Load Diff
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue