microproduct/tool/algorithm/polsarpro/createfeature.py

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