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