修改正射插值模块，改为3次立方插值
tian jiax
parent
10b484e4bf
commit
477745e863
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@ -1590,6 +1590,18 @@ class IndirectOrthorectification(Orthorectification):
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print(os.system(exe_cmd))
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print("==========================================================================")
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def calInterpolation_cubic_Wgs84_rc_sar_sigma(self, parameter_path, dem_rc, in_sar, out_sar):
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'''
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# std::cout << "mode 10";
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# std::cout << "SIMOrthoProgram.exe 9 in_parameter_path in_rc_wgs84_path in_ori_sar_path out_orth_sar_path";
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'''
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exe_path = r".\baseTool\x64\Release\SIMOrthoProgram.exe"
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exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3} {4} {5}".format(exe_path, 9, parameter_path,
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dem_rc, in_sar, out_sar)
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print(exe_cmd)
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print(os.system(exe_cmd))
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print("==========================================================================")
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def getPowerTif(self,in_ori_path,out_power_path):
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'''
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std::cout << "mode 5: convert ori tiff to power tiff:";
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@ -487,6 +487,12 @@ class OrthoMain:
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pass
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def cut_dem(self, dem_merged_path, meta_file_path):
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left_up_lon = 0
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left_up_lat = 0
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def RD_process_handle(self):
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# RPC
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logger.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
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@ -498,6 +504,7 @@ class OrthoMain:
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out_dem_path = self.__workspace_ResampledDEM_path
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dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
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# self.cut_dem(dem_merged_path, meta_file_path)
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# 2、间接定位法求解行列坐标
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slc_paths = self.__in_processing_paras["SLC"]
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# 2.1 生成映射表
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@ -519,11 +526,11 @@ class OrthoMain:
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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
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logger.info('progress bar: 40%')
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clip_dem_reample_path=os.path.join(self.__workspace_Temporary_path, "SAR_dem.tiff")
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infooption=gdal.InfoOptions("-json")
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clip_dem_tif_info=gdal.Info(clip_dem_reample_path,options=infooption)
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dem_merged_info=gdal.Info(dem_merged_path,options=infooption)
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sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0]
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# clip_dem_reample_path=os.path.join(self.__workspace_Temporary_path, "SAR_dem.tiff")
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# infooption=gdal.InfoOptions("-json")
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# clip_dem_tif_info=gdal.Info(clip_dem_reample_path,options=infooption)
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# dem_merged_info=gdal.Info(dem_merged_path,options=infooption)
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# sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0]
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out_dir_path=self.__workspace_package_path.replace("\\","\\\\")
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this_outSpace_path = out_dir_path
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@ -566,6 +573,9 @@ class OrthoMain:
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GTC_rc_path=os.path.join(self.__workspace_package_path,"ori_sim-ortho.tif")
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GTC_out_path=self.__workspace_package_path
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parameter_path = os.path.join(self.__workspace_package_path, "orth_para.txt")
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dem_rc = os.path.join(self.__workspace_Temporary_path, "dem_rc.tiff")
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in_tif_paths = list(glob.glob(os.path.join(slc_paths, '*.tiff')))
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for in_tif_path in in_tif_paths:
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out_sar_path = os.path.join(GTC_out_path, os.path.split(in_tif_path)[1])
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@ -577,7 +587,8 @@ class OrthoMain:
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temp_slc_path=temp_slc_path.replace("_db.tif","-ortho.tif")
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#inter_Range2Geo(self,lon_lat_path , data_tiff , grid_path , space)
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Orthorectification.inter_Range2Geo(GTC_rc_path,out_power_path,temp_slc_path,Orthorectification.heightspace)
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# Orthorectification.inter_Range2Geo(GTC_rc_path,out_power_path,temp_slc_path,Orthorectification.heightspace)
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Orthorectification.calInterpolation_cubic_Wgs84_rc_sar_sigma(parameter_path, dem_rc, out_power_path, temp_slc_path)
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break
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#Orth_Slc.append(temp_slc_path)
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# power_list.append(out_power_path)
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@ -667,7 +678,7 @@ if __name__ == '__main__':
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except Exception:
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logger.exception("run-time error!")
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finally:
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OrthoMain.del_temp_workspace()
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# OrthoMain.del_temp_workspace()
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pass
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end = datetime.datetime.now()
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logger.info('running use time: %s ' % (end - start))
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@ -329,6 +329,8 @@ class ScatteringMain:
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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
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this_in_rpc_lon_lat_path = this_out_ori_sim_tiff
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parameter_path = os.path.join(self.__workspace_processing_path, "orth_para.txt")
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dem_rc = os.path.join(self.__workspace_preprocessing_path, "dem_rc.tiff")
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for in_tif_path in in_tif_paths:
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out_tif_path = os.path.join(self.__workspace_preprocessing_path,os.path.splitext(os.path.basename(in_tif_path))[0]) + r"_DB.tif"
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@ -344,7 +346,9 @@ class ScatteringMain:
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# 移动RPC
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#rpc_correction(in_tif_path,rpc_path,out_tif_path,dem_tif_file = None)
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Orthorectification.inter_Range2Geo(this_in_rpc_lon_lat_path,out_tif_path,tempout_tif_path,Orthorectification.heightspace)
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# Orthorectification.inter_Range2Geo(this_in_rpc_lon_lat_path,out_tif_path,tempout_tif_path,Orthorectification.heightspace)
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Orthorectification.calInterpolation_cubic_Wgs84_rc_sar_sigma(parameter_path, dem_rc, out_tif_path,
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tempout_tif_path)
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#shutil.move(rpc_path,out_tif_path.replace(".tiff",".rpc"))
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self.imageHandler.write_quick_view(tempout_tif_path, color_img=False)
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else:
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@ -384,9 +388,18 @@ class ScatteringMain:
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para_dict.update({"imageinfo_ProductLevel": "LEVEL3"})
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para_dict.update({"ProductProductionInfo_BandSelection": "1,2"})
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para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "DEM"})
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para_dict.update({"MetaInfo_UnitDes": "DB"}) # 设置单位
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CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
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temp_folder = os.path.join(self.__workspace_path, EXE_NAME, 'Output')
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out_xml = os.path.join(temp_folder, os.path.basename(meta_xml_path))
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if os.path.exists(temp_folder) is False:
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os.mkdir(temp_folder)
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# CreateProductXml(para_dict, model_path, out_xml).create_standard_xml()
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shutil.copy(meta_xml_path, out_xml)
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self.make_targz(self.__out_para, self.__workspace_processing_path)
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logger.info('process_handle finished!')
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logger.info('progress bar: 100%')
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return True
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@ -1588,6 +1588,18 @@ class IndirectOrthorectification(Orthorectification):
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print(os.system(exe_cmd))
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print("==========================================================================")
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def calInterpolation_cubic_Wgs84_rc_sar_sigma(self, parameter_path, dem_rc, in_sar, out_sar):
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'''
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# std::cout << "mode 10";
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# std::cout << "SIMOrthoProgram.exe 9 in_parameter_path in_rc_wgs84_path in_ori_sar_path out_orth_sar_path";
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'''
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exe_path = r".\baseTool\x64\Release\SIMOrthoProgram.exe"
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exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3} {4} {5}".format(exe_path, 9, parameter_path,
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dem_rc, in_sar, out_sar)
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print(exe_cmd)
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print(os.system(exe_cmd))
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print("==========================================================================")
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def getPowerTif(self,in_ori_path,out_power_path):
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'''
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std::cout << "mode 5: convert ori tiff to power tiff:";
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@ -15,6 +15,7 @@ from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource, InitPara
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from tool.algorithm.algtools.logHandler import LogHandler
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from tool.algorithm.image.ImageHandle import ImageHandler
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from tool.algorithm.block.blockprocess import BlockProcess
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from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml
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from tool.config.ConfigeHandle import Config as cf
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from tool.algorithm.xml.CreatMetafile import CreateMetafile
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from tool.algorithm.algtools.ROIAlg import ROIAlg as roi
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@ -91,7 +92,12 @@ class LeafIndexMain:
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self.__create_work_space()
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self.__task_id = self.__alg_xml_handler.get_task_id()
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self.__input_paras = self.__alg_xml_handler.get_input_paras() # 获取输入文件夹中的数据名、类型、地址
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self.__out_para = os.path.join(self.__workspace_path,EXE_NAME, 'Output', r"LeafAreaIndexProduct.tar.gz")
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# self.__out_para = os.path.join(self.__workspace_path,EXE_NAME, 'Output', r"LeafAreaIndexProduct.tar.gz")
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SrcImageName = os.path.split(self.__input_paras["SAR"]['ParaValue'])[1].split('.tar.gz')[0]
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result_name = SrcImageName + "-lef.tar.gz"
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# out_name = os.path.splitext(os.path.splitext(os.path.basename(self.__input_paras['SLC']['ParaValue']))[0])[0]
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# self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', "BackScatteringProduct.tar.gz")
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self.__out_para = os.path.join(self.__workspace_path, EXE_NAME, 'Output', result_name)
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self.__alg_xml_handler.write_out_para("LeafAreaIndexProduct", self.__out_para) # 写入输出参数
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Polarization = self.__input_paras['Polarization']['ParaValue']
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@ -315,9 +321,9 @@ class LeafIndexMain:
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block_size = self.BlockProcess.get_block_size(self.__rows, self.__cols)
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self.BlockProcess.cut(self.__workspace_maskcai_image_path, self.__workspace_cai_sartif_path, ['tif', 'tiff'], 'tif', block_size)
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self.BlockProcess.cut(self.__workspace_maskcai_localang_path, self.__workspace_cai_localang_path, ['tif', 'tiff'], 'tif', block_size)
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self.BlockProcess.cut(self.__workspace_maskcai_SoilMoi_path, self.__workspace_cai_SoilMoi_path, ['tif', 'tiff'], 'tif', block_size)
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self.BlockProcess.cut_new(self.__workspace_maskcai_image_path, self.__workspace_cai_sartif_path, ['tif', 'tiff'], 'tif', block_size)
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self.BlockProcess.cut_new(self.__workspace_maskcai_localang_path, self.__workspace_cai_localang_path, ['tif', 'tiff'], 'tif', block_size)
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self.BlockProcess.cut_new(self.__workspace_maskcai_SoilMoi_path, self.__workspace_cai_SoilMoi_path, ['tif', 'tiff'], 'tif', block_size)
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logger.info('mask data image success!')
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# 计算每小块的叶面积指数
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@ -349,14 +355,16 @@ class LeafIndexMain:
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logger.info('progress bar: 80%')
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ref_tif_path = self.__ref_img_path
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date_type = 'float32'
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self.BlockProcess.combine(self.__workspace_Leafindex_path, self.__cols, self.__rows, self.__workspace_processing_path + "combine", 'tif', ['tif'], date_type)
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self.BlockProcess.combine_new(self.__workspace_Leafindex_path, self.__cols, self.__rows, self.__workspace_processing_path + "combine", 'tif', ['tif'], date_type)
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# 添加地理信息
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out_path = self.__workspace_processing_path + "combine\\LeafAreaIndexProduct.tif"
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self.BlockProcess.assign_spatial_reference_byfile(ref_tif_path, out_path)
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# 截取roi区域
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bare_land_mask_path = self.cal_roi()
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product_path = self.__product_dic + "LeafAreaIndexProduct.tif"
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SrcImageName = os.path.split(self.__input_paras["SAR"]['ParaValue'])[1].split('.tar.gz')[0] + '-lef.tif'
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# product_path = self.__product_dic + "LeafAreaIndexProduct.tif"
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product_path = os.path.join(self.__product_dic, SrcImageName)
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roi.cal_roi(product_path, out_path, bare_land_mask_path, background_value=np.nan)
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logger.info('block_process finished!')
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@ -374,28 +382,41 @@ class LeafIndexMain:
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image_path = product_path
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out_path1 = os.path.join(tem_folder, "trans_geo_projcs.tif")
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out_path2 = os.path.join(tem_folder, "trans_projcs_geo.tif")
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par_dict = CreateDict(image_path, out_path1, out_path2).calu_nature(start)
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model_xml_path = os.path.join(tem_folder, "creat_standard.meta.xml") # 输出xml路径
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id_min = 0
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id_max = 0
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for id in cover_id_list:
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if id < id_min:
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id_min = id
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if id > id_max:
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id_max = id
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set_threshold = [id_max, id_min, threshold_of_ndvi_min, threshold_of_ndvi_max]
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CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, set_threshold, model_xml_path).create_standard_xml()
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# par_dict = CreateDict(image_path, out_path1, out_path2).calu_nature(start)
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# model_xml_path = os.path.join(tem_folder, "creat_standard.meta.xml") # 输出xml路径
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#
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# id_min = 0
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# id_max = 0
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# for id in cover_id_list:
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# if id < id_min:
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# id_min = id
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# if id > id_max:
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# id_max = id
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# set_threshold = [id_max, id_min, threshold_of_ndvi_min, threshold_of_ndvi_max]
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#
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# CreateStadardXmlFile(xml_path, self.alg_xml_path, par_dict, set_threshold, model_xml_path).create_standard_xml()
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#
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SrcImagePath = self.__input_paras["SAR"]['ParaValue']
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paths = SrcImagePath.split(';')
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SrcImageName=os.path.split(paths[0])[1].split('.tar.gz')[0]
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if len(paths) >= 2:
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for i in range(1, len(paths)):
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SrcImageName = SrcImageName+";"+os.path.split(paths[i])[1].split('.tar.gz')[0]
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meta_xml_path = self.__product_dic+EXE_NAME+"Product.meta.xml"
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CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process(SrcImageName)
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# if len(paths) >= 2:
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# for i in range(1, len(paths)):
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# SrcImageName = SrcImageName+";"+os.path.split(paths[i])[1].split('.tar.gz')[0]
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# meta_xml_path = self.__product_dic+EXE_NAME+"Product.meta.xml"
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# CreateMetafile(self.__processing_paras['META'], self.alg_xml_path, model_xml_path, meta_xml_path).process(SrcImageName)
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model_path = "./product.xml"
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meta_xml_path = os.path.join(self.__workspace_processing_path, SrcImageName + "-lef.meta.xml")
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para_dict = CreateMetaDict(image_path, self.__processing_paras['META'], self.__workspace_processing_path,
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out_path1, out_path2).calu_nature()
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para_dict.update({"imageinfo_ProductName": "叶面积指数"})
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para_dict.update({"imageinfo_ProductIdentifier": "LeafAreaIndex"})
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para_dict.update({"imageinfo_ProductLevel": "LEVEL3"})
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para_dict.update({"ProductProductionInfo_BandSelection": "1"})
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para_dict.update({"ProductProductionInfo_AuxiliaryDataDescription": "MeasuredData,NDVI,LandCover"})
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para_dict.update({"MetaInfo_Unit": "None"}) # 设置单位
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CreateProductXml(para_dict, model_path, meta_xml_path).create_standard_xml()
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# 文件夹打包
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file.make_targz(self.__out_para, self.__product_dic)
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@ -256,36 +256,6 @@ class BlockProcess:
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end_y) + '.' + out_type)
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# print(out_dir_images)
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# cut_factor_row = int(np.ceil(image.shape[0] / out_size))
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# cut_factor_clo = int(np.ceil(image.shape[1] / out_size))
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# for i in range(cut_factor_row):
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# for j in range(cut_factor_clo):
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#
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# if i == cut_factor_row - 1:
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# i = image.shape[0] / out_size - 1
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# else:
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# pass
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#
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# if j == cut_factor_clo - 1:
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# j = image.shape[1] / out_size - 1
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# else:
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# pass
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#
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# start_x = int(np.rint(i * out_size))
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# start_y = int(np.rint(j * out_size))
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# end_x = int(np.rint((i + 1) * out_size))
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# end_y = int(np.rint((j + 1) * out_size))
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# out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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# end_y) + '.' + out_type)
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# + '/' + img_name \
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# + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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# end_y) + '.' + out_type
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# temp_image = image[start_x:end_x, start_y:end_y]
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# out_image = Image.fromarray(temp_data)
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# out_image = Image.fromarray(temp_image)
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# out_image.save(out_dir_images)
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data = ImageHandler.get_data(each_dir)
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if ImageHandler.get_bands(each_dir) > 1:
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# temp_data = data[:,start_x:end_x, start_y:end_y]
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@ -445,12 +415,12 @@ class BlockProcess:
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# if __name__ == '__main__':
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# bp = BlockProcess()
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# # # cut
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# data_dir = r"D:\BaiduNetdiskDownload\HF\cut"
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# out_dir = r"D:\BaiduNetdiskDownload\HF\cut_out"
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# data_dir = r"D:\micro\WorkSpace\LandCover\Temporary\processing\feature_tif\cut"
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# out_dir = r"D:\micro\WorkSpace\LandCover\Temporary\processing\feature_tif\combine"
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# file_type = ['tif']
|
||||
# out_type = 'tif'
|
||||
# cut_size = 512
|
||||
#
|
||||
# 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"
|
||||
|
|
|
|||
|
|
@ -235,7 +235,7 @@ class MachineLeaning:
|
|||
@staticmethod
|
||||
def sel_optimal_feature_set(X_train, Y_train, threshold=0.01):
|
||||
"""
|
||||
筛选最优特征组合
|
||||
筛选最优特征组合(极度随机树）
|
||||
"""
|
||||
model = ExtraTreesClassifier()
|
||||
max = np.max(Y_train)
|
||||
|
|
|
|||
Loading…
Reference in New Issue