190 lines
10 KiB
Python
190 lines
10 KiB
Python
from tool.algorithm.algtools.MetaDataHandler import Calibration
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from tool.algorithm.polsarpro.AHVToPolsarpro import AHVToPolsarpro
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from tool.algorithm.polsarpro.pspLeeRefinedFilterT3 import LeeRefinedFilterT3
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from tool.algorithm.polsarpro.pspCloudePottierDecomposition import PspCloudePottierDecomposition
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from tool.algorithm.polsarpro.pspFreemanDecomposition import PspFreemanDecomposition
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from tool.algorithm.polsarpro.pspYamaguchiDecomposition import PspYamaguchiDecomposition
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from tool.algorithm.polsarpro.pspTouziDecomposition import PspTouziDecomposition
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from tool.algorithm.polsarpro.bin2tif import write_bin_to_tif
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from tool.algorithm.polsarpro.pspHAAlphaDecomposition import PspHAAlphaDecomposition
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from tool.algorithm.xml.AlgXmlHandle import InitPara
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import logging
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import os
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import shutil
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import glob
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logger = logging.getLogger("mylog")
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class CreateFeature:
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"""
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生产特征
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"""
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def __init__(self, debug = False, exe_dir = ''):
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self._debug = debug
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self._exe_dir = exe_dir
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pass
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def ahv_to_t3(self, workspace_processing_path, workspace_preprocessing_path, hh_hv_vh_vv_list, name='',FILTER_SIZE=3):
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# 全极化tif转bin格式T3数据
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atp = AHVToPolsarpro()
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atp = AHVToPolsarpro(hh_hv_vh_vv_list)
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lee_filter_path = os.path.join(workspace_processing_path, name, 'lee_filter\\') # workspace_processing_path + name + '\\lee_filter\\'
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if self._debug == False:
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t3_path = os.path.join(workspace_processing_path, name, 'psp_t3\\') # workspace_processing_path + name + '\\psp_t3\\'
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# atp.ahv_to_polsarpro_t3(t3_path, tif_path)
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polarization = ['HH', 'HV', 'VH', 'VV']
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if os.path.exists(workspace_preprocessing_path + name + '\\'):
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meta_xml_paths = list(glob.glob(os.path.join(workspace_preprocessing_path + name, '*.meta.xml')))
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meta_dic = InitPara.get_meta_dic_new(meta_xml_paths, name)
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calibration = Calibration.get_Calibration_coefficient(meta_dic['Origin_META'], polarization)
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tif_path = atp.calibration(calibration, workspace_preprocessing_path, name)
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atp.ahv_to_polsarpro_t3_veg(t3_path, tif_path)
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# Lee滤波
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leeFilter = LeeRefinedFilterT3()
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leeFilter.api_lee_refined_filter_T3('', t3_path, lee_filter_path, 0, 0, atp.rows(), atp.cols(), FILTER_SIZE)
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logger.info("refine_lee filter success!")
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return lee_filter_path
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def decompose(self,workspace_processing_path, name, t3_path, rows, cols, hh_hv_vh_vv_dic={},FeatureInput=['Freeman', 'Yamaguchi', 'Cloude']): # , 'Touzi'
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"""
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极化分解：Freeman、Touzi、Yamaguchi、Cloude
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:param t3_path: t3文件路径
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:param rows: 影像行数
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:return cols:影像列数
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"""
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# 计算特征组合
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exeDir = self._exe_dir
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outFolderDic = {}
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if 'Freeman' in FeatureInput:
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# freeman分解
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freemanOutDir = os.path.join(workspace_processing_path, name + '\\freeman\\')
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if self._debug == False:
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freemDecom = PspFreemanDecomposition(exeDir, t3_path, freemanOutDir)
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flag = freemDecom.api_freeman_decomposition_T3(0, 0, rows, cols)
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if not flag:
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logger.error('FreemanDecomposition err')
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return False, None
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outFolderDic['Freeman'] = freemanOutDir
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# Touzi分解
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if 'Touzi' in FeatureInput:
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touziOutDir = os.path.join(workspace_processing_path, name + '\\touzi\\')
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if not os.path.exists(touziOutDir):
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os.makedirs(touziOutDir)
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if self._debug == False:
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# touzi分解耗时较长，且对特征表达效果较差
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p = PspTouziDecomposition(hh_hv_vh_vv_dic, touziOutDir)
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p.Touzi_decomposition_multiprocessing()
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outFolderDic['Touzi'] = touziOutDir
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if 'Yamaguchi' in FeatureInput:
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# Yamaguchi分解
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yamaguchiOutDir = os.path.join(workspace_processing_path, name + '\\yamaguchi\\')
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if self._debug == False:
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yamaguchiDecom = PspYamaguchiDecomposition(exeDir, t3_path, yamaguchiOutDir)
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flag = yamaguchiDecom.api_yamaguchi_4components_decomposition_T3(0, 0, rows, cols)
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if not flag:
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logger.error('CloudePottierDecomposition err')
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return False, None
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outFolderDic['Yamaguchi'] = yamaguchiOutDir
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if 'Cloude' in FeatureInput:
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# CloudePottier分解
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cloudeOutDir = os.path.join(workspace_processing_path, name + '\\cloude\\')
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if self._debug == False:
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cloudeDecom = PspCloudePottierDecomposition(
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exeDir, t3_path, cloudeOutDir)
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flag = cloudeDecom.api_h_a_alpha_decomposition_T3(
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0, 0, rows, cols)
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if not flag:
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logger.error('CloudePottierDecomposition err')
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return False, None
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outFolderDic['Cloude'] = cloudeOutDir
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return True, outFolderDic
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def creat_h_a_alpha_features(self, t3_path, out_dir):
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logger.info('ahv transform to polsarpro T3 matrix success!')
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logger.info('progress bar: 20%')
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h_a_alpha_decomposition_T3_path = os.path.join(self._exe_dir, 'h_a_alpha_decomposition_T3.exe')
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h_a_alpha_eigenvalue_set_T3_path = os.path.join(self._exe_dir, 'h_a_alpha_eigenvalue_set_T3.exe')
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h_a_alpha_eigenvector_set_T3_path = os.path.join(self._exe_dir, 'h_a_alpha_eigenvector_set_T3.exe')
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if self._debug == False:
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haa = PspHAAlphaDecomposition(normalization=True)
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haa.api_creat_h_a_alpha_features(h_a_alpha_out_dir=out_dir,
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h_a_alpha_decomposition_T3_path=h_a_alpha_decomposition_T3_path ,
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h_a_alpha_eigenvalue_set_T3_path=h_a_alpha_eigenvalue_set_T3_path ,
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h_a_alpha_eigenvector_set_T3_path=h_a_alpha_eigenvector_set_T3_path,
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polsarpro_in_dir=t3_path)
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def cereat_features_dic(self,outFolderDic, feature_tif_dir):
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if not os.path.exists(feature_tif_dir):
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os.makedirs(feature_tif_dir)
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feature_tif_paths = {}
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for key in outFolderDic:
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feature_bin_dic = outFolderDic[key]
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if key == 'Touzi':
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for path in list(glob.glob(os.path.join(feature_bin_dic, '*.tif'))):
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name = os.path.split(path)[1].split('.')[0]
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if self._debug == False:
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shutil.copyfile(path, os.path.join(feature_tif_dir, name + '.tif')) # feature_tif_dir + '\\' + name + '.tif')
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feature_tif_paths.update({name: os.path.join(feature_tif_dir, name + '.tif')}) # feature_tif_dir + '\\' + name + '.tif'
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else:
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feature_tif_paths.update(write_bin_to_tif(feature_tif_dir, feature_bin_dic))
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return feature_tif_paths
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@staticmethod
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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']):
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hh_hv_vh_vv_dic = {}
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hh_hv_vh_vv_dic.update({'HH': hh_hv_vh_vv_list[0]})
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hh_hv_vh_vv_dic.update({'HV': hh_hv_vh_vv_list[1]})
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hh_hv_vh_vv_dic.update({'VH': hh_hv_vh_vv_list[2]})
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hh_hv_vh_vv_dic.update({'VV': hh_hv_vh_vv_list[3]})
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t3_path = os.path.join(workspace_processing_path, name, "lee_filter") # workspace_processing_path + name + "\\lee_filter"
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feature_tif_dir = os.path.join(workspace_processing_path, name, 'features') # workspace_processing_path + name + "\\features"
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cfeature = CreateFeature(debug, exe_dir)
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cfeature.creat_h_a_alpha_features(t3_path, feature_tif_dir)
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t3_path = cfeature.ahv_to_t3(workspace_processing_path, workspace_preprocessing_path, hh_hv_vh_vv_list, name, FILTER_SIZE)
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flag, outFolderDic = cfeature.decompose(workspace_processing_path, name, t3_path, rows, cols, hh_hv_vh_vv_dic, FeatureInput) # , 'Touzi'
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cfeature.cereat_features_dic(outFolderDic, feature_tif_dir)
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return feature_tif_dir
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if __name__ == '__main__':
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# # 实例1：
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# exe_dir = os.getcwd()
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# dir = r'D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\preprocessed/'
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# hh_hv_vh_vv_list = [dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HH_preprocessed.tif',
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# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_HV_preprocessed.tif',
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# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VH_preprocessed.tif',
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# dir +'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC_VV_preprocessed.tif']
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#
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# workspace_processing_path= r"D:\MicroWorkspace\product\C-SAR\VegetationPhenology\Temporary\processing/"
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# name= 'GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_AHV_L10003515422_RPC'
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# hh_hv_vh_vv_dic = {}
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# hh_hv_vh_vv_dic.update({'HH': hh_hv_vh_vv_list[0]})
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# hh_hv_vh_vv_dic.update({'HV': hh_hv_vh_vv_list[1]})
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# hh_hv_vh_vv_dic.update({'VH': hh_hv_vh_vv_list[2]})
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# hh_hv_vh_vv_dic.update({'VV': hh_hv_vh_vv_list[3]})
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# t3_path = workspace_processing_path + name + "\\lee_filter"
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# feature_tif_dir = workspace_processing_path + name + "\\features"
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#
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# cfeature = CreateFeature(False, exe_dir)
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#
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# cfeature.creat_h_a_alpha_features(t3_path, feature_tif_dir)
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#
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# t3_path = cfeature.ahv_to_t3(workspace_processing_path, hh_hv_vh_vv_list, name, 3)
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# flag, outFolderDic = cfeature.decompose(workspace_processing_path, name, t3_path, 997, 1227, hh_hv_vh_vv_dic, FeatureInput=['Freeman', 'Touzi', 'Yamaguchi', 'Cloude'])
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#
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# feature_tifs_dic = cfeature.cereat_features_dic(outFolderDic, feature_tif_dir)
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pass |