140 lines
5.8 KiB
Python
140 lines
5.8 KiB
Python
import glob
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import struct
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import numpy as np
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from tool.algorithm.algtools.MetaDataHandler import MetaDataHandler
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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.image.ImageHandle import ImageHandler
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from tool.algorithm.polsarpro.pspSurfaceInversion import SurfaceInversionHisto
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from tool.algorithm.polsarpro.pspSurfaceInversion import SurfaceInversionDubois
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from tool.algorithm.polsarpro.pspSurfaceInversion import SurfaceInversionOh
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from tool.algorithm.polsarpro.pspSurfaceInversion import SurfaceInversionOh2004
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from tool.algorithm.polsarpro.bin2tif import write_bin_to_tif_soil
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from tool.algorithm.algtools.oh2004 import oh2004
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from tool.algorithm.algtools.MetaDataHandler import Calibration
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import os
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from osgeo import osr
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import logging
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import xml.etree.ElementTree as ET
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logger = logging.getLogger("mylog")
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FILTER_SIZE = 3
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class SoilMoistureTool:
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def __init__(self, tif_path, out_path, cols, rows, incidence_file, xml_file):
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self.__workspace_processing_path = tif_path
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self.__workspace_preprocessed_path = out_path
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self.imageHandler = ImageHandler()
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# self.ref_img = hh_path
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self.xml_path = xml_file
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self.__cols = cols # self.imageHandler.get_img_width(self.ref_img)
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self.__rows = rows # self.imageHandler.get_img_height(self.ref_img)
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self.incidence_file = incidence_file # self.create_incidence(incidence_xml)
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def ahv_to_t3(self):
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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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t3_path = os.path.join(self.__workspace_preprocessed_path,
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'psp_t3\\')
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polarization = ['HH', 'HV', 'VH', 'VV']
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calibration = Calibration.get_Calibration_coefficient(self.xml_path, polarization)
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tif_path = atp.calibration(calibration, in_ahv_dir=self.__workspace_processing_path)
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atp.ahv_to_polsarpro_t3_soil(t3_path, tif_path)
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# Lee滤波
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leeFilter = LeeRefinedFilterT3()
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lee_filter_path = os.path.join(self.__workspace_processing_path,
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'lee_filter\\')
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leeFilter.api_lee_refined_filter_T3('', t3_path, lee_filter_path, 0, 0, atp.rows(), atp.cols())
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logger.info("refine_lee filter success!")
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# logging.info("refine_lee filter success!")
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return lee_filter_path
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# def create_incidence(self, incidence_xml):
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# tree = ET.parse(incidence_xml)
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# root = tree.getroot()
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# import numpy as np
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# arr = np.zeros((self.__rows, self.__cols), dtype=float)
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# i = 0
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# for child in root:
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# if child.tag == 'incidenceValue':
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# arr[0, i] = float(child.text)
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# i += 1
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#
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# arr[1:, :] = arr[0, :]
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# geo_trans = self.imageHandler.get_geotransform(self.ref_img)
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# wgs84 = osr.SpatialReference()
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# wgs84.SetWellKnownGeogCS("WGS84")
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# proj = wgs84.ExportToWkt()
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# incidence_file = os.path.join(self.__workspace_preprocessed_path, 'incidence.tif')
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# self.imageHandler.write_img(incidence_file, proj, geo_trans, arr)
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# return incidence_file
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def __write_img_bin(self, im, file_path):
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"""
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写入影像到bin文件中，保存为float32类型
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:param im : 影像矩阵数据，暂支持单通道影像数据
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:param file_path: bin文件的完整路径
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"""
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with open(file_path, 'wb') as f:
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self._rows = im.shape[0]
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self._cols = im.shape[1]
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for row in range(self._rows):
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im_bin = struct.pack("f" * self._cols, *np.reshape(im[row, :], (self._cols, 1), order='F'))
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f.write(im_bin)
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f.close()
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def soil_oh2004(self):
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lee_file = self.ahv_to_t3()
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radarCenterFrequency = MetaDataHandler.get_RadarCenterFrequency(self.xml_path)
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# lamda = MetaDataHandler.get_lamda(self.xml_path)
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lamda = float(299792458 / radarCenterFrequency)
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f = oh2004.lamda2freq(lamda) / 1e9
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if not os.path.exists(self.__workspace_preprocessed_path):
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os.mkdir(self.__workspace_preprocessed_path)
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incidence_dat = os.path.join(self.__workspace_preprocessed_path, 'incidence.dat')
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im_proj, im_geotrans, im_arr = ImageHandler().read_img(self.incidence_file)
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ImageHandler().write_img_envi(incidence_dat, im_proj, im_geotrans, im_arr)
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# self.__write_img_bin(im_arr, incidence_dat)
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# incidence_bin = r'D:\micro\WorkSpace\soil_test\processing\in_angle.dat'
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oh2004Filter = SurfaceInversionOh2004()
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oh2004_path = os.path.join(self.__workspace_preprocessed_path, '0h2004\\')
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if not os.path.exists(oh2004_path):
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os.mkdir(oh2004_path)
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oh2004Filter.api_surface_inversion_oh2004('', lee_file, oh2004_path, incidence_dat, 0, 0, self.__rows, self.__cols, f, 0)
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oh2004_tifpath = os.path.join(self.__workspace_preprocessed_path, 'oh2004tif\\')
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write_bin_to_tif_soil(oh2004_tifpath, oh2004_path)
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logger.info("bin to tif successful! ")
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return oh2004_tifpath
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if __name__ == '__main__':
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tif_path = r'F:\20230605\ortho\tif'
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put_path = r'F:\20230605\back\oh2004'
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cols = 7670
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rows = 7443
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incidence_file = r'F:\20230605\ortho\RD_localincidentAngle.tiff'
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xml_file = r'F:\20230605\ortho\GF3C_MYC_QPSI_005390_E121.7_N41.1_20230415_L1A_AHV_L10000135644.meta.xml'
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soil = SoilMoistureTool(tif_path, put_path, cols, rows, incidence_file, xml_file)
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soil.soil_oh2004()
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# fn = r'F:\MicroWorkspace\mico_datas\GF3A_nanjing\PIESAR\test\GF3_SAY_QPSI_011444_E118.9_N31.4_20181012_L1A_HH_L10003515422.img'
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# fn1 = r'F:\MicroWorkspace\mico_datas\GF3A_nanjing\PIESAR\test11.bin'
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# data = ImageHandler().get_data(fn)
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# data.tofile(fn1)
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