KJ135
/
microproduct-s-sar
mirror of http://172.16.0.12:5000/WBJY/microproduct-s-sar.git
4
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

更新后向散射添加配准

dev
tian jiax 2024-08-26 19:20:59 +08:00
parent ebd2ff5516
commit 07bf00a634
17 changed files with 504 additions and 98 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
Ortho/Ortho-S-SAR.xml
View File

@ -1,7 +1,7 @@
<?xml version='1.0' encoding='utf-8'?> <?xml version='1.0' encoding='utf-8'?>
<Root> <Root>
<TaskID>CSAR_202107275419_0001-0</TaskID> <TaskID>CSAR_202107275419_0001-0</TaskID>
<WorkSpace>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\</WorkSpace> <WorkSpace>D:\micro\SWork\</WorkSpace>
<AlgCompt> <AlgCompt>
<DataTransModel>File</DataTransModel> <DataTransModel>File</DataTransModel>
<Artificial>ElementAlg</Artificial> <Artificial>ElementAlg</Artificial>
@ -37,7 +37,7 @@
<GPU>无需求</GPU> <GPU>无需求</GPU>
</Environment> </Environment>
<Utility Satellite="GF3" Sensor="MSS" Resolution="1" /> <Utility Satellite="GF3" Sensor="MSS" Resolution="1" />
<Inputs ParameterNum="3"> <Inputs ParameterNum="4">
<Parameter> <Parameter>
<ParaName>SLC</ParaName> <ParaName>SLC</ParaName>
<ParaChsName>SLC元文件</ParaChsName> <ParaChsName>SLC元文件</ParaChsName>
@ -45,7 +45,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\MicroWorkspace\S_SAR\yuan\HJ2E_KSC_STRIP_006199_E85.8_N44.2_20231124_SLC_HHHV_L10000135594.tar.gz</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\HJ2E_KRN_QPS_008852_E110.6_N20.1_20240515_SLC_AHV_L10000208028.tar.gz</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>File</Control> <Control>File</Control>
@ -60,7 +60,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tif</DataType> <DataType>tif</DataType>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\MicroWorkspace\S_SAR\yuan\dem\dem_cut1.tif</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\dem\109E15N_COP30.tif</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>True</EnMultipleChoice> <EnMultipleChoice>True</EnMultipleChoice>
<Control>File</Control> <Control>File</Control>
@ -68,6 +68,25 @@
<InputNum>0</InputNum> <InputNum>0</InputNum>
<DateFrom>DEM</DateFrom> <DateFrom>DEM</DateFrom>
</Parameter> </Parameter>
<Parameter>
<ParaName>baseMap</ParaName>
<ParaChsName>哨兵底图数据</ParaChsName>
<Description>同地区哨兵地图用于影像配准</Description>
<ParaType>File</ParaType>
<DataType>File</DataType>
<ParaSource>Cal</ParaSource>
<OptionValue>DEFAULT</OptionValue>
<MinValue>DEFAULT</MinValue>
<MaxValue>DEFAULT</MaxValue>
<ParaValue>
F:\MicroWorkspace\20240814tw\S1A</ParaValue>
<EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice>
<Control>File</Control>
<InputType>Aux</InputType>
<InputNum>0</InputNum>
<DateFrom>baseMap</DateFrom>
</Parameter>
<Parameter> <Parameter>
<ParaName>CorrectMethod</ParaName> <ParaName>CorrectMethod</ParaName>
<ParaChsName>选择校正方法</ParaChsName> <ParaChsName>选择校正方法</ParaChsName>
@ -92,7 +111,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\Ortho\Output\HJ2E_KSC_STRIP_006199_E85.8_N44.2_20231124_SLC_HHHV_L10000135594-Ortho.tar.gz</ParaValue> <ParaValue>D:\micro\SWork\Ortho\Output\HJ2E_KRN_QPS_008852_E110.6_N20.1_20240515_SLC_AHV_L10000208028-Ortho.tar.gz</ParaValue>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>

BIN
Ortho/Ortho.rar
View File

Binary file not shown.

84
Ortho/OrthoAlg.py
View File

@ -121,7 +121,57 @@ class ScatteringAlg:
# db_arr[np.isinf(db_arr)] = -9999 # db_arr[np.isinf(db_arr)] = -9999
ImageHandler.write_img(db_path, proj, geotrans, db_arr, -9999) ImageHandler.write_img(db_path, proj, geotrans, db_arr, -9999)
@staticmethod
def sar_backscattering_coef_RPC(in_sar_tif, meta_file_path, out_sar_tif, replece_VV=False, is_DB=True):
# 读取原始SAR影像
proj, geotrans, in_data = ImageHandler.read_img(in_sar_tif)
# 计算强度信息
I = np.array(in_data[0], dtype="float32")
Q = np.array(in_data[1], dtype="float32")
where_9999_0 = np.where(I == -9999)
where_9999_1 = np.where(Q == -9999)
I[where_9999_0] = 1.0
Q[where_9999_1] = 1.0
I2 = np.square(I)
Q2 = np.square(Q)
intensity_arr = I2 + Q2
# 获取极化类型
if 'HH' in os.path.basename(in_sar_tif):
polarization = 'HH'
elif 'HV' in os.path.basename(in_sar_tif):
polarization = 'HV'
elif 'VH' in os.path.basename(in_sar_tif):
polarization = 'VH'
elif 'VV' in os.path.basename(in_sar_tif):
polarization = 'VV'
if replece_VV:
polarization = 'HV' # 土壤水分算法中可能会用HV替换VV
elif 'DH' in os.path.basename(in_sar_tif):
polarization = 'HH'
else:
raise Exception('there are not HH、HV、VH、VV in path:', in_sar_tif)
# 获取参数
QualifyValue = MetaDataHandler.get_QualifyValue(meta_file_path, polarization)
# Kdb = MetaDataHandler.get_Kdb(meta_file_path, polarization)
Kdb = 0
# 计算后向散射系数
# 对数形式
coef_arr = 10 * (np.log10(intensity_arr * ((QualifyValue / 32767) ** 2))) - Kdb
coef_arr[np.isnan(coef_arr)] = 0
coef_arr[np.isinf(coef_arr)] = 0
coef_arr[where_9999_0] = 0
coef_arr[where_9999_1] = 0
## 输出的SAR后向散射系数产品
ImageHandler.write_img(out_sar_tif, proj, geotrans, coef_arr, 0)
return True
@ -1008,6 +1058,34 @@ class DEMProcess(object):
time.sleep(3) time.sleep(3)
#gdal.CloseDir(out_DEM) #gdal.CloseDir(out_DEM)
return out_DEM return out_DEM
@staticmethod
def bsMap_merged(in_bsMap_path, meta_file_path, out_bsMap_path):
'''
DEM重采样函数，默认坐标系为WGS84
agrs:
in_dem_path: 输入的DEM文件夹路径
meta_file_path: 输入的xml元文件路径
out_dem_path: 输出的DEM文件夹路径
'''
# 读取文件夹中所有的DEM
bsMap_file_paths = [os.path.join(in_bsMap_path, dem_name) for dem_name in os.listdir(in_bsMap_path) if
dem_name.find(".tif") >= 0 and dem_name.find(".tif.") == -1]
spatialreference = osr.SpatialReference()
spatialreference.SetWellKnownGeogCS("WGS84") # 设置地理坐标，单位为度 degree # 设置投影坐标，单位为度 degree
spatialproj = spatialreference.ExportToWkt() # 导出投影结果
# 将DEM拼接成一张大图
mergeFile = gdal.BuildVRT(os.path.join(out_bsMap_path, "mergedBsMap_VRT.tif"), bsMap_file_paths)
out_DEM = os.path.join(out_bsMap_path, "MergedBsMap.tif")
gdal.Warp(out_DEM,
mergeFile,
format="GTiff",
dstSRS=spatialproj,
dstNodata=-9999,
outputType=gdal.GDT_Float32)
time.sleep(3)
# gdal.CloseDir(out_DEM)
return out_DEM
@staticmethod @staticmethod
def dem_resampled(in_dem_path,out_dem_path,samling_f): def dem_resampled(in_dem_path,out_dem_path,samling_f):
in_dem=gdal.Open(in_dem_path,gdalconst.GA_ReadOnly) in_dem=gdal.Open(in_dem_path,gdalconst.GA_ReadOnly)
@ -1659,6 +1737,12 @@ class IndirectOrthorectification(Orthorectification):
print(os.system(exe_cmd)) print(os.system(exe_cmd))
print("==========================================================================") print("==========================================================================")
def get_offset(self, baseMap, in_sar, in_sar_sigma):
exe = r".\baseTool\x64\calOffset\calOffset.exe"
exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3}".format(exe, baseMap, in_sar, in_sar_sigma)
print(exe_cmd)
print(os.system(exe_cmd))
print("==========================================================================")
def lee_process_sar(self,in_sar, out_sar, win_size, noise_var): def lee_process_sar(self,in_sar, out_sar, win_size, noise_var):
''' '''

83
Ortho/OrthoMain.py
View File

@ -191,6 +191,7 @@ class OrthoMain:
self.__workspace_Temporary_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary") self.__workspace_Temporary_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary")
self.__workspace_unpack_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "unpack") self.__workspace_unpack_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "unpack")
self.__workspace_ResampledDEM_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestDEM') self.__workspace_ResampledDEM_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestDEM')
self.__workspace_baseMap_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'baseMap')
self.__workspace_LutImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestLut') self.__workspace_LutImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestLut')
self.__workspace_IncidenceImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestInc') self.__workspace_IncidenceImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestInc')
self.__workspace_SimImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestSim') self.__workspace_SimImg_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'TestSim')
@ -202,7 +203,8 @@ class OrthoMain:
self.__workspace_unpack_path, self.__workspace_ResampledDEM_path, self.__workspace_unpack_path, self.__workspace_ResampledDEM_path,
self.__workspace_LutImg_path, self.__workspace_IncidenceImg_path, self.__workspace_LutImg_path, self.__workspace_IncidenceImg_path,
self.__workspace_SimImg_path, self.__workspace_SARIntensity_path, self.__workspace_SimImg_path, self.__workspace_SARIntensity_path,
self.__workspace_package_path, self.__workspace_origin_path] self.__workspace_package_path, self.__workspace_origin_path,
self.__workspace_baseMap_path]
for path in path_list: for path in path_list:
if os.path.exists(path): if os.path.exists(path):
@ -391,7 +393,7 @@ class OrthoMain:
if CorrectMethod.get('CorrectMethod') == '1' or CorrectMethod.get('CorrectMethod') == 1: if CorrectMethod.get('CorrectMethod') == '1' or CorrectMethod.get('CorrectMethod') == 1:
logger.info("CorrectMethod is RPC!") logger.info("CorrectMethod is RPC!")
return self.RPC_process_handle() return self.RD_process_handle()
elif CorrectMethod.get('CorrectMethod') == '2' or CorrectMethod.get('CorrectMethod') == 2: elif CorrectMethod.get('CorrectMethod') == '2' or CorrectMethod.get('CorrectMethod') == 2:
logger.info("CorrectMethod is RD!") logger.info("CorrectMethod is RD!")
@ -401,8 +403,6 @@ class OrthoMain:
raise Exception('No CorrectMethod') raise Exception('No CorrectMethod')
def RPC_process_handle(self): def RPC_process_handle(self):
logger.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')) logger.info(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
# print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')) # print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f'))
@ -516,8 +516,16 @@ class OrthoMain:
def cut_dem(self, dem_merged_path, meta_file_path): def cut_dem(self, dem_merged_path, meta_file_path):
left_up_lon = 0 _, scopes = DictXml(meta_file_path).get_extend()
left_up_lat = 0 intersect_polygon = pp().intersect_polygon(scopes)
if intersect_polygon is None:
raise Exception('cal intersect box fail!')
shp_path = os.path.join(self.__workspace_Temporary_path, 'IntersectPolygon.shp')
if pp().write_polygon_shp(shp_path, intersect_polygon, 4326) is False:
raise Exception('create intersect shp fail!')
dem_process = os.path.join(self.__workspace_Temporary_path, 'dem_cut.tif')
pp().cut_img(dem_process, dem_merged_path, shp_path)
return dem_process
def process_sim_ori(self, sim_ori): def process_sim_ori(self, sim_ori):
@ -534,6 +542,48 @@ class OrthoMain:
pp().cut_img(sim_ori_process, sim_ori, shp_path) pp().cut_img(sim_ori_process, sim_ori, shp_path)
return sim_ori_process return sim_ori_process
def correct_sim_ori(self, Orthorectification, slc_paths, bsMap_merged_path, out_dir_path):
# 对映射表进行校正
sim_ori_tiff = out_dir_path + "\\" + "RD_sim_ori.tif"
out_sim_ori = out_dir_path + "\\" + "sim_ori-ortho.tif"
parameter_path = os.path.join(self.__workspace_package_path, "orth_para.txt")
in_tif_paths = list(glob.glob(os.path.join(slc_paths, '*.tiff')))
out_rpc_db = os.path.join(self.__workspace_baseMap_path, 'rpc_line.tif')
alg.sar_backscattering_coef_RPC(in_tif_paths[0], self.__in_processing_paras['META'], out_rpc_db)
db_tif_path = os.path.join(self.__workspace_baseMap_path, 'rpc_db_geo.tif')
Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, sim_ori_tiff, out_rpc_db,
db_tif_path)
dataset = ImageHandler().get_dataset(db_tif_path)
baseMapCut = os.path.join(self.__workspace_baseMap_path, 'baseMapCut.tif')
inputCut = os.path.join(self.__workspace_baseMap_path, 'inputCut.tif')
baseMapResample = os.path.join(self.__workspace_baseMap_path, 'baseMapCut_Resample.tif')
shpCenterFile = os.path.join(self.__workspace_baseMap_path, 'shpCenter.shp')
center_scopes = (ImageHandler().get_center_scopes(dataset),)
intersect_polygon = pp().intersect_polygon(center_scopes)
if intersect_polygon is None:
raise Exception('create intersect shp fail!')
if pp().write_polygon_shp(shpCenterFile, intersect_polygon, 4326) is False:
raise Exception('create intersect shp fail!')
pp().cut_img(baseMapCut, bsMap_merged_path, shpCenterFile)
pp().cut_img(inputCut, db_tif_path, shpCenterFile)
pp().resampling_by_scale(baseMapCut, baseMapResample, inputCut)
in_sar_png = self.imageHandler.write_view(inputCut)
baseMap_png = self.imageHandler.write_view(baseMapResample)
Orthorectification.get_offset(in_sar_png, baseMap_png, inputCut)
off_txt = os.path.join(os.path.dirname(inputCut), 'off.txt')
with open(off_txt, 'r') as f:
data = f.readlines()
x = float(data[0])
y = float(data[1])
im_proj, im_geotrans, im_arr = self.imageHandler.read_img(sim_ori_tiff)
lon_new = im_geotrans[0] + x
lat_new = im_geotrans[3] - y
im_geosNew = [lon_new, im_geotrans[1], im_geotrans[2], lat_new, im_geotrans[4], im_geotrans[5]]
ImageHandler().write_img(out_sim_ori, im_proj, im_geosNew, im_arr)
os.remove(sim_ori_tiff)
return out_sim_ori
def RD_process_handle(self): def RD_process_handle(self):
# RPC # RPC
@ -546,7 +596,11 @@ class OrthoMain:
out_dem_path = self.__workspace_ResampledDEM_path out_dem_path = self.__workspace_ResampledDEM_path
dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
# self.cut_dem(dem_merged_path, meta_file_path) bsMap = self.__in_processing_paras['baseMap']
ortho_bsMap_path = self.__workspace_baseMap_path
bsMap_merged_path = DEMProcess.bsMap_merged(bsMap, meta_file_path, ortho_bsMap_path)
dem_path = self.cut_dem(dem_merged_path, meta_file_path)
# 2、间接定位法求解行列坐标 # 2、间接定位法求解行列坐标
slc_paths = self.__in_processing_paras["SLC"] slc_paths = self.__in_processing_paras["SLC"]
# 2.1 生成映射表 # 2.1 生成映射表
@ -565,8 +619,9 @@ class OrthoMain:
in_slc_path=os.path.join(slc_paths,slc_path) in_slc_path=os.path.join(slc_paths,slc_path)
break break
# 获取校正模型后 # 获取校正模型后
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 Orthorectification.preCaldem_sar_rc(dem_path,in_slc_path,self.__workspace_Temporary_path,self.__workspace_package_path.replace("\\","\\\\")) # 初步筛选坐标范围
out_dir_path = self.__workspace_package_path.replace("\\", "\\\\")
sim_ori_rpc = self.correct_sim_ori(Orthorectification, slc_paths, bsMap_merged_path, out_dir_path)
logger.info('progress bar: 40%') logger.info('progress bar: 40%')
# clip_dem_reample_path=os.path.join(self.__workspace_Temporary_path, "SAR_dem.tiff") # clip_dem_reample_path=os.path.join(self.__workspace_Temporary_path, "SAR_dem.tiff")
# infooption=gdal.InfoOptions("-json") # infooption=gdal.InfoOptions("-json")
@ -574,7 +629,7 @@ class OrthoMain:
# dem_merged_info=gdal.Info(dem_merged_path,options=infooption) # dem_merged_info=gdal.Info(dem_merged_path,options=infooption)
# sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0] # sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0]
out_dir_path=self.__workspace_package_path.replace("\\","\\\\")
this_outSpace_path = out_dir_path this_outSpace_path = out_dir_path
this_out_dem_slantRange_path = out_dir_path + "\\" + "dem_slantRange.tiff"#// 地形斜距 this_out_dem_slantRange_path = out_dir_path + "\\" + "dem_slantRange.tiff"#// 地形斜距
this_out_plant_slantRange_path = out_dir_path + "\\" + "flat_slantRange.tiff"#// 平地斜距 this_out_plant_slantRange_path = out_dir_path + "\\" + "flat_slantRange.tiff"#// 平地斜距
@ -619,10 +674,10 @@ class OrthoMain:
this_out_ori_sim_tiff = out_dir_path + "\\" + "RD_ori_sim.tif"#// 局地入射角 this_out_ori_sim_tiff = out_dir_path + "\\" + "RD_ori_sim.tif"#// 局地入射角
if (os.path.exists(this_out_ori_sim_tiff)): if (os.path.exists(this_out_ori_sim_tiff)):
shutil.move(this_out_ori_sim_tiff, out_dir_path + "\\" + "ori_sim-ortho.tif") shutil.move(this_out_ori_sim_tiff, out_dir_path + "\\" + "ori_sim-ortho.tif")
#
this_out_sim_ori_tiff = out_dir_path + "\\" + "RD_sim_ori.tif" # // 局地入射角 # this_out_sim_ori_tiff = out_dir_path + "\\" + "RD_sim_ori.tif" # // 局地入射角
if (os.path.exists(this_out_sim_ori_tiff)): # if (os.path.exists(this_out_sim_ori_tiff)):
shutil.move(this_out_sim_ori_tiff, out_dir_path + "\\" + "sim_ori-ortho.tif") # shutil.move(this_out_sim_ori_tiff, out_dir_path + "\\" + "sim_ori-ortho.tif")
# GTC 入射角 # GTC 入射角
GTC_rc_path=os.path.join(self.__workspace_package_path,"ori_sim-ortho.tif") GTC_rc_path=os.path.join(self.__workspace_package_path,"ori_sim-ortho.tif")

31
backScattering/BackScattering-S-SAR.xml
View File

@ -1,7 +1,7 @@
<?xml version='1.0' encoding='utf-8'?> <?xml version='1.0' encoding='utf-8'?>
<Root> <Root>
<TaskID>CSAR_202107275419_0001-0</TaskID> <TaskID>CSAR_202107275419_0001-0</TaskID>
<WorkSpace>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\</WorkSpace> <WorkSpace>D:\micro\SWork\</WorkSpace>
<AlgCompt> <AlgCompt>
<DataTransModel>File</DataTransModel> <DataTransModel>File</DataTransModel>
<Artificial>ElementAlg</Artificial> <Artificial>ElementAlg</Artificial>
@ -34,7 +34,7 @@
<GPU>无需求</GPU> <GPU>无需求</GPU>
</Environment> </Environment>
<Utility Satellite="GF3" Sensor="MSS" Resolution="1" /> <Utility Satellite="GF3" Sensor="MSS" Resolution="1" />
<Inputs ParameterNum="2"> <Inputs ParameterNum="3">
<Parameter> <Parameter>
<ParaName>SLC</ParaName> <ParaName>SLC</ParaName>
<ParaChsName>SLC影像文件</ParaChsName> <ParaChsName>SLC影像文件</ParaChsName>
@ -42,7 +42,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\MicroWorkspace\S_SAR\HJ2E_MYC_STRIP_008031_E110.8_N19.8_20240323_SLC_HHHV_L10000185601.tar.gz</ParaValue> <ParaValue>F:\MicroWorkspace\20240826Ortho\HJ2E_KSC_QPS_008535_E116.5_N43.9_20240425_SLC_AHV_L10000199516.tar.gz</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>File</Control> <Control>File</Control>
@ -55,9 +55,9 @@
<ParaChsName>DEM数字高程影像</ParaChsName> <ParaChsName>DEM数字高程影像</ParaChsName>
<Description>30m分辨率DEM数字高程影像</Description> <Description>30m分辨率DEM数字高程影像</Description>
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tif</DataType> <DataType>File</DataType>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\MicroWorkspace\COPDEM\COPDEM_Int16\109E15N_COP30.tif</ParaValue> <ParaValue>F:\MicroWorkspace\20240826Ortho\dem</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>True</EnMultipleChoice> <EnMultipleChoice>True</EnMultipleChoice>
<Control>File</Control> <Control>File</Control>
@ -65,6 +65,25 @@
<InputNum>0</InputNum> <InputNum>0</InputNum>
<DateFrom>DEM</DateFrom> <DateFrom>DEM</DateFrom>
</Parameter> </Parameter>
<Parameter>
<ParaName>baseMap</ParaName>
<ParaChsName>哨兵底图数据</ParaChsName>
<Description>同地区哨兵地图用于影像配准</Description>
<ParaType>File</ParaType>
<DataType>File</DataType>
<ParaSource>Cal</ParaSource>
<OptionValue>DEFAULT</OptionValue>
<MinValue>DEFAULT</MinValue>
<MaxValue>DEFAULT</MaxValue>
<ParaValue>
F:\MicroWorkspace\20240826Ortho\S1GBM</ParaValue>
<EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice>
<Control>File</Control>
<InputType>Aux</InputType>
<InputNum>0</InputNum>
<DateFrom>baseMap</DateFrom>
</Parameter>
</Inputs> </Inputs>
<Outputs ParameterNum="1"> <Outputs ParameterNum="1">
<Parameter> <Parameter>
@ -74,7 +93,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\BackScattering\Output\HJ2E_MYC_STRIP_008031_E110.8_N19.8_20240323_SLC_HHHV_L10000185601-cal.tar.gz</ParaValue> <ParaValue>D:\micro\SWork\BackScattering\Output\HJ2E_KSC_QPS_008535_E116.5_N43.9_20240425_SLC_AHV_L10000199516-cal.tar.gz</ParaValue>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>

87
backScattering/BackScatteringAlg.py
View File

@ -115,6 +115,58 @@ class ScatteringAlg:
ImageHandler.write_img(out_sar_tif, proj, geotrans, tif_array, 0) ImageHandler.write_img(out_sar_tif, proj, geotrans, tif_array, 0)
return True return True
@staticmethod
def sar_backscattering_coef_RPC(in_sar_tif, meta_file_path, out_sar_tif, replece_VV=False, is_DB=True):
# 读取原始SAR影像
proj, geotrans, in_data = ImageHandler.read_img(in_sar_tif)
# 计算强度信息
I = np.array(in_data[0], dtype="float32")
Q = np.array(in_data[1], dtype="float32")
where_9999_0 = np.where(I == -9999)
where_9999_1 = np.where(Q == -9999)
I[where_9999_0] = 1.0
Q[where_9999_1] = 1.0
I2 = np.square(I)
Q2 = np.square(Q)
intensity_arr = I2 + Q2
# 获取极化类型
if 'HH' in os.path.basename(in_sar_tif):
polarization = 'HH'
elif 'HV' in os.path.basename(in_sar_tif):
polarization = 'HV'
elif 'VH' in os.path.basename(in_sar_tif):
polarization = 'VH'
elif 'VV' in os.path.basename(in_sar_tif):
polarization = 'VV'
if replece_VV:
polarization = 'HV' # 土壤水分算法中可能会用HV替换VV
elif 'DH' in os.path.basename(in_sar_tif):
polarization = 'HH'
else:
raise Exception('there are not HH、HV、VH、VV in path:', in_sar_tif)
# 获取参数
QualifyValue = MetaDataHandler.get_QualifyValue(meta_file_path, polarization)
# Kdb = MetaDataHandler.get_Kdb(meta_file_path, polarization)
Kdb = 0
# 计算后向散射系数
# 对数形式
coef_arr = 10 * (np.log10(intensity_arr * ((QualifyValue / 32767) ** 2))) - Kdb
coef_arr[np.isnan(coef_arr)] = 0
coef_arr[np.isinf(coef_arr)] = 0
coef_arr[where_9999_0] = 0
coef_arr[where_9999_1] = 0
## 输出的SAR后向散射系数产品
ImageHandler.write_img(out_sar_tif, proj, geotrans, coef_arr, 0)
return True
@staticmethod @staticmethod
def lin_to_db(lin_path, db_path): def lin_to_db(lin_path, db_path):
proj, geotrans, in_data = ImageHandler.read_img(lin_path) proj, geotrans, in_data = ImageHandler.read_img(lin_path)
@ -1023,6 +1075,34 @@ class DEMProcess(object):
time.sleep(3) time.sleep(3)
#gdal.CloseDir(out_DEM) #gdal.CloseDir(out_DEM)
return out_DEM return out_DEM
@staticmethod
def bsMap_merged(in_bsMap_path, meta_file_path, out_bsMap_path):
'''
DEM重采样函数，默认坐标系为WGS84
agrs:
in_dem_path: 输入的DEM文件夹路径
meta_file_path: 输入的xml元文件路径
out_dem_path: 输出的DEM文件夹路径
'''
# 读取文件夹中所有的DEM
bsMap_file_paths = [os.path.join(in_bsMap_path, dem_name) for dem_name in os.listdir(in_bsMap_path) if
dem_name.find(".tif") >= 0 and dem_name.find(".tif.") == -1]
spatialreference = osr.SpatialReference()
spatialreference.SetWellKnownGeogCS("WGS84") # 设置地理坐标，单位为度 degree # 设置投影坐标，单位为度 degree
spatialproj = spatialreference.ExportToWkt() # 导出投影结果
# 将DEM拼接成一张大图
mergeFile = gdal.BuildVRT(os.path.join(out_bsMap_path, "mergedBsMap_VRT.tif"), bsMap_file_paths)
out_DEM = os.path.join(out_bsMap_path, "MergedBsMap.tif")
gdal.Warp(out_DEM,
mergeFile,
format="GTiff",
dstSRS=spatialproj,
dstNodata=-9999,
outputType=gdal.GDT_Float32)
time.sleep(3)
# gdal.CloseDir(out_DEM)
return out_DEM
@staticmethod @staticmethod
def dem_resampled(in_dem_path,out_dem_path,samling_f): def dem_resampled(in_dem_path,out_dem_path,samling_f):
in_dem=gdal.Open(in_dem_path,gdalconst.GA_ReadOnly) in_dem=gdal.Open(in_dem_path,gdalconst.GA_ReadOnly)
@ -1644,6 +1724,13 @@ class IndirectOrthorectification(Orthorectification):
print(os.system(exe_cmd)) print(os.system(exe_cmd))
print("==========================================================================") print("==========================================================================")
def get_offset(self, baseMap, in_sar, in_sar_sigma):
exe = r".\baseTool\x64\calOffset\calOffset.exe"
exe_cmd = r"set PROJ_LIB=.\baseTool\x64\Release; & {0} {1} {2} {3}".format(exe, baseMap, in_sar, in_sar_sigma)
print(exe_cmd)
print(os.system(exe_cmd))
print("==========================================================================")
def calInterpolation_bil_Wgs84_rc_sar_sigma(self, parameter_path, dem_rc, in_sar, out_sar): def calInterpolation_bil_Wgs84_rc_sar_sigma(self, parameter_path, dem_rc, in_sar, out_sar):
''' '''
# std::cout << "mode 11"; # std::cout << "mode 11";

72
backScattering/BackScatteringMain.py
View File

@ -209,8 +209,10 @@ class ScatteringMain:
self.__workspace_preprocessed_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "preprocessed") # self.__workspace_path + EXE_NAME + r"\Temporary\preprocessed""\\" self.__workspace_preprocessed_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "preprocessed") # self.__workspace_path + EXE_NAME + r"\Temporary\preprocessed""\\"
self.__workspace_processing_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary","processing\\") #self.__workspace_path + EXE_NAME + r"\Temporary\processing""\\" self.__workspace_processing_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary","processing\\") #self.__workspace_path + EXE_NAME + r"\Temporary\processing""\\"
self.__workspace_origin_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "origin") self.__workspace_origin_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", "origin")
self.__workspace_baseMap_path = os.path.join(self.__workspace_path, EXE_NAME, "Temporary", 'baseMap')
path_list = [self.__workspace_preprocessing_path, self.__workspace_preprocessed_path, path_list = [self.__workspace_preprocessing_path, self.__workspace_preprocessed_path,
self.__workspace_processing_path, self.__workspace_origin_path] self.__workspace_processing_path, self.__workspace_origin_path,
self.__workspace_baseMap_path]
for path in path_list: for path in path_list:
if os.path.exists(path): if os.path.exists(path):
@ -282,6 +284,61 @@ class ScatteringMain:
pp().cut_img(sim_ori_process, sim_ori, shp_path) pp().cut_img(sim_ori_process, sim_ori, shp_path)
return sim_ori_process return sim_ori_process
def cut_dem(self, dem_merged_path, meta_file_path):
_, scopes = DictXml(meta_file_path).get_extend()
intersect_polygon = pp().intersect_polygon(scopes)
if intersect_polygon is None:
raise Exception('cal intersect box fail!')
shp_path = os.path.join(self.__workspace_preprocessing_path, 'IntersectPolygon.shp')
if pp().write_polygon_shp(shp_path, intersect_polygon, 4326) is False:
raise Exception('create intersect shp fail!')
dem_process = os.path.join(self.__workspace_preprocessing_path, 'dem_cut.tif')
pp().cut_img(dem_process, dem_merged_path, shp_path)
return dem_process
def correct_sim_ori(self, Orthorectification, slc_paths, bsMap_merged_path, out_dir_path):
# 对映射表进行校正
sim_ori_tiff = out_dir_path + "\\" + "RD_sim_ori.tif"
out_sim_ori = out_dir_path + "\\" + "sim_ori-ortho.tif"
parameter_path = os.path.join(self.__workspace_processing_path, "orth_para.txt")
in_tif_paths = list(glob.glob(os.path.join(slc_paths, '*.tiff')))
out_rpc_db = os.path.join(self.__workspace_baseMap_path, 'rpc_line.tif')
alg.sar_backscattering_coef_RPC(in_tif_paths[0], self.__in_processing_paras['META'], out_rpc_db)
db_tif_path = os.path.join(self.__workspace_baseMap_path, 'rpc_db_geo.tif')
Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, sim_ori_tiff, out_rpc_db,
db_tif_path)
dataset = ImageHandler().get_dataset(db_tif_path)
baseMapCut = os.path.join(self.__workspace_baseMap_path, 'baseMapCut.tif')
inputCut = os.path.join(self.__workspace_baseMap_path, 'inputCut.tif')
baseMapResample = os.path.join(self.__workspace_baseMap_path, 'baseMapCut_Resample.tif')
shpCenterFile = os.path.join(self.__workspace_baseMap_path, 'shpCenter.shp')
center_scopes = (ImageHandler().get_center_scopes(dataset),)
intersect_polygon = pp().intersect_polygon(center_scopes)
if intersect_polygon is None:
raise Exception('create intersect shp fail!')
if pp().write_polygon_shp(shpCenterFile, intersect_polygon, 4326) is False:
raise Exception('create intersect shp fail!')
pp().cut_img(baseMapCut, bsMap_merged_path, shpCenterFile)
pp().cut_img(inputCut, db_tif_path, shpCenterFile)
pp().resampling_by_scale(baseMapCut, baseMapResample, inputCut)
in_sar_png = self.imageHandler.write_view(inputCut)
baseMap_png = self.imageHandler.write_view(baseMapResample)
Orthorectification.get_offset(in_sar_png,baseMap_png, inputCut)
off_txt = os.path.join(os.path.dirname(inputCut), 'off.txt')
with open(off_txt, 'r') as f:
data = f.readlines()
x = float(data[0])
y = float(data[1])
im_proj, im_geotrans, im_arr = self.imageHandler.read_img(sim_ori_tiff)
lon_new = im_geotrans[0] + x
lat_new = im_geotrans[3] - y
im_geosNew = [lon_new, im_geotrans[1], im_geotrans[2], lat_new, im_geotrans[4], im_geotrans[5]]
ImageHandler().write_img(out_sim_ori, im_proj, im_geosNew, im_arr)
os.remove(sim_ori_tiff)
return out_sim_ori
def process_handle(self,start): def process_handle(self,start):
in_tif_paths = list(glob.glob(os.path.join(self.__in_processing_paras['SLC'], '*.tif'))) in_tif_paths = list(glob.glob(os.path.join(self.__in_processing_paras['SLC'], '*.tif')))
if in_tif_paths == []: if in_tif_paths == []:
@ -320,6 +377,11 @@ class ScatteringMain:
meta_file_path = self.__in_processing_paras['META'] # .meta文件路径 meta_file_path = self.__in_processing_paras['META'] # .meta文件路径
out_dem_path = self.__workspace_preprocessing_path out_dem_path = self.__workspace_preprocessing_path
dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif dem_merged_path=DEMProcess.dem_merged(in_dem_path, meta_file_path, out_dem_path) # 生成TestDEM\mergedDEM_VRT.tif
dem_path = self.cut_dem(dem_merged_path, meta_file_path)
bsMap = self.__in_processing_paras['baseMap']
ortho_bsMap_path = self.__workspace_baseMap_path
bsMap_merged_path = DEMProcess.bsMap_merged(bsMap, meta_file_path, ortho_bsMap_path)
in_slc_path=None in_slc_path=None
for slc_path in in_tif_paths: for slc_path in in_tif_paths:
@ -328,7 +390,10 @@ class ScatteringMain:
break break
# 获取校正模型后 # 获取校正模型后
Orthorectification.preCaldem_sar_rc(dem_merged_path,in_slc_path,self.__workspace_preprocessing_path,self.__workspace_processing_path.replace("\\","\\\\")) # 初步筛选坐标范围 Orthorectification.preCaldem_sar_rc(dem_path,in_slc_path,self.__workspace_preprocessing_path,self.__workspace_processing_path.replace("\\","\\\\")) # 初步筛选坐标范围
out_dir_path = self.__workspace_processing_path.replace("\\", "\\\\")
sim_ori_rpc = self.correct_sim_ori(Orthorectification, self.__in_processing_paras['SLC'], bsMap_merged_path,
out_dir_path)
logger.info('progress bar: 40%') logger.info('progress bar: 40%')
# clip_dem_reample_path=os.path.join(self.__workspace_preprocessing_path, "SAR_dem.tiff") # clip_dem_reample_path=os.path.join(self.__workspace_preprocessing_path, "SAR_dem.tiff")
# infooption=gdal.InfoOptions("-json") # infooption=gdal.InfoOptions("-json")
@ -336,7 +401,6 @@ class ScatteringMain:
# dem_merged_info=gdal.Info(dem_merged_path,options=infooption) # dem_merged_info=gdal.Info(dem_merged_path,options=infooption)
# sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0] # sampling_f=clip_dem_tif_info['size'][0]/dem_merged_info['size'][0]
# 处理RD 的结果 # 处理RD 的结果
out_dir_path=self.__workspace_processing_path.replace("\\","\\\\")
this_outSpace_path = out_dir_path this_outSpace_path = out_dir_path
this_out_dem_slantRange_path = os.path.join(out_dir_path, "dem_slantRange.tiff") # out_dir_path + "\\" + "dem_slantRange.tiff"#// 地形斜距 this_out_dem_slantRange_path = os.path.join(out_dir_path, "dem_slantRange.tiff") # out_dir_path + "\\" + "dem_slantRange.tiff"#// 地形斜距
this_out_plant_slantRange_path = os.path.join(out_dir_path, "flat_slantRange.tiff") # out_dir_path + "\\" + "flat_slantRange.tiff"#// 平地斜距 this_out_plant_slantRange_path = os.path.join(out_dir_path, "flat_slantRange.tiff") # out_dir_path + "\\" + "flat_slantRange.tiff"#// 平地斜距
@ -408,7 +472,7 @@ class ScatteringMain:
# out_tif_path, # out_tif_path,
# lin_tif_path) # lin_tif_path)
Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, this_in_rpc_x_y_path, Orthorectification.calInterpolation_bil_Wgs84_rc_sar_sigma(parameter_path, sim_ori_rpc,
out_tif_path, out_tif_path,
lin_tif_path) lin_tif_path)
tempout_tif_path = os.path.join(self.__workspace_processing_path, tempout_tif_path = os.path.join(self.__workspace_processing_path,

BIN
backScattering/backScattering.rar
View File

Binary file not shown.

15
landcover-S-SAR/LandCover-S-SAR.xml
View File

@ -1,7 +1,7 @@
<?xml version='1.0' encoding='utf-8'?> <?xml version='1.0' encoding='utf-8'?>
<Root> <Root>
<TaskID>CSAR_202107275419_0001-0</TaskID> <TaskID>CSAR_202107275419_0001-0</TaskID>
<WorkSpace>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\</WorkSpace> <WorkSpace>D:\micro\SWork\</WorkSpace>
<AlgCompt> <AlgCompt>
<DataTransModel>File</DataTransModel> <DataTransModel>File</DataTransModel>
<Artificial>ElementAlg</Artificial> <Artificial>ElementAlg</Artificial>
@ -38,7 +38,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>F:\MicroWorkspace\S_SAR\AHV\HJ2E_MYC_QPS_001752_E118.0_N37.7_20230204_SLC_AHV_L10000010458-Ortho.tar.gz</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\HJ2E_KRN_QPS_008852_E110.6_N20.1_20240515_SLC_AHV_L10000208028-Ortho.tar.gz</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>File</Control> <Control>File</Control>
@ -51,9 +51,9 @@
<ParaChsName>标记数据</ParaChsName> <ParaChsName>标记数据</ParaChsName>
<Description>标记的样本数据</Description> <Description>标记的样本数据</Description>
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>zip</DataType> <DataType>csv</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>F:\al_zhongji\S-SAR-data\landCover\SSAR_landcover_landaCoverSample.zip</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\LandCover.csv</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>True</EnMultipleChoice> <EnMultipleChoice>True</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -81,9 +81,8 @@
<ParaChsName>极化特征组合</ParaChsName> <ParaChsName>极化特征组合</ParaChsName>
<Description>可选极化特征组合一、共14种特征（编号依次为0-13） <Description>可选极化特征组合一、共14种特征（编号依次为0-13）
Freeman：表面散射p_s(0)、偶次散射p_d(1)、体散射p_v(2); Freeman：表面散射p_s(0)、偶次散射p_d(1)、体散射p_v(2);
Touzi：散射角α_s(3)、散射相位ϕ_α(4)、目标散射对称度τ(5)、相对能量λ_i(6); Yamaguchi：表面散射f_s(3)、二次散射f_d(4)、体散射f_v(5)、螺旋体散射f_h(6);
Yamaguchi：表面散射f_s(7)、二次散射f_d(8)、体散射f_v(9)、螺旋体散射f_h(10); Cloude-Pottier：分解散射熵H(7)、反熵A(8)、平均散射角α(9)</Description>
Cloude-Pottier：分解散射熵H(11)、反熵A(12)、平均散射角α(13)</Description>
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>string</DataType> <DataType>string</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
@ -104,7 +103,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\LandCover\Output\HJ2E_MYC_QPS_001752_E118.0_N37.7_20230204_SLC_AHV_L10000010458-Ortho-LANDCLASS.tar.gz</ParaValue> <ParaValue>D:\micro\SWork\LandCover\Output\HJ2E_KRN_QPS_008852_E110.6_N20.1_20240515_SLC_AHV_L10000208028-Ortho-LANDCLASS.tar.gz</ParaValue>
</Parameter> </Parameter>
</Outputs> </Outputs>
</AlgCompt> </AlgCompt>

11
landcover-S-SAR/LandCoverAuxData.py
View File

@ -122,10 +122,17 @@ class LandCoverMeasCsv:
# raise Exception('there are empty data!', train_data) # raise Exception('there are empty data!', train_data)
if len(train_data_list) <= 1: if len(train_data_list) <= 1:
raise Exception('there is only one label type!', train_data_list) raise Exception('there is only one label type!', train_data_list)
num_list = []
for train_data in train_data_list:
if not len(train_data[3]) == 0:
num_list.append(len(train_data[3]))
try:
max_num = np.min(num_list)
except Exception as e:
print(e)
for train_data in train_data_list: for train_data in train_data_list:
logger.info(str(train_data[0]) + "," + str(train_data[2]) +"," + "num:" + str(len(train_data[3]))) logger.info(str(train_data[0]) + "," + str(train_data[2]) +"," + "num:" + str(len(train_data[3])))
max_num = self.__max_tran__num_per_class # max_num = self.__max_tran__num_per_class
logger.info("max number =" + str(max_num) + ", random select" + str(max_num) + " point as train data!") logger.info("max number =" + str(max_num) + ", random select" + str(max_num) + " point as train data!")
if (len(train_data[3]) > max_num): if (len(train_data[3]) > max_num):
train_data[3] = random.sample(train_data[3], max_num) train_data[3] = random.sample(train_data[3], max_num)

40
landcover-S-SAR/LandCoverMain.py
View File

@ -289,16 +289,11 @@ class LandCoverMain:
else: else:
self.__FeatureInput['Freeman'] = [i] self.__FeatureInput['Freeman'] = [i]
elif 3 <= i < 7: elif 3 <= i < 7:
if 'Touzi' in self.__FeatureInput:
self.__FeatureInput['Touzi'].append(i)
else:
self.__FeatureInput['Touzi'] = [i]
elif 7 <= i < 11:
if 'Yamaguchi' in self.__FeatureInput: if 'Yamaguchi' in self.__FeatureInput:
self.__FeatureInput['Yamaguchi'].append(i) self.__FeatureInput['Yamaguchi'].append(i)
else: else:
self.__FeatureInput['Yamaguchi'] = [i] self.__FeatureInput['Yamaguchi'] = [i]
elif 11 <= i < 14: elif 7 <= i < 10:
if 'Cloude' in self.__FeatureInput: if 'Cloude' in self.__FeatureInput:
self.__FeatureInput['Cloude'].append(i) self.__FeatureInput['Cloude'].append(i)
else: else:
@ -308,24 +303,19 @@ class LandCoverMain:
def __FeatureParaInit(self): def __FeatureParaInit(self):
self.__FeatureMap["Freeman"] = [0, 1, 2] self.__FeatureMap["Freeman"] = [0, 1, 2]
self.__FeatureMap["Touzi"] = [3, 4, 5, 6] self.__FeatureMap["Yamaguchi"] = [3, 4, 5, 6]
self.__FeatureMap["Yamaguchi"] = [7, 8, 9, 10] self.__FeatureMap["Cloude"] = [7, 8, 9]
self.__FeatureMap["Cloude"] = [11, 12, 13]
self.___FeatureFileNameMap[0] = ['Freeman', "Freeman_Odd.bin"] self.___FeatureFileNameMap[0] = ['Freeman', "Freeman_Odd.bin"]
self.___FeatureFileNameMap[1] = ['Freeman', "Freeman_Dbl.bin"] self.___FeatureFileNameMap[1] = ['Freeman', "Freeman_Dbl.bin"]
self.___FeatureFileNameMap[2] = ['Freeman', "Freeman_Vol.bin"] self.___FeatureFileNameMap[2] = ['Freeman', "Freeman_Vol.bin"]
self.___FeatureFileNameMap[3] = ['Touzi', "alpha.bin"] self.___FeatureFileNameMap[3] = ['Yamaguchi', "Yamaguchi4_Odd.bin"]
self.___FeatureFileNameMap[4] = ['Touzi', "phi.bin"] self.___FeatureFileNameMap[4] = ['Yamaguchi', "Yamaguchi4_Dbl.bin"]
self.___FeatureFileNameMap[5] = ['Touzi', "tau.bin"] self.___FeatureFileNameMap[5] = ['Yamaguchi', "Yamaguchi4_Vol.bin"]
self.___FeatureFileNameMap[6] = ['Touzi', "psi.bin"] self.___FeatureFileNameMap[6] = ['Yamaguchi', "Yamaguchi4_Hlx.bin"]
self.___FeatureFileNameMap[7] = ['Yamaguchi', "Yamaguchi4_Odd.bin"] self.___FeatureFileNameMap[7] = ['Cloude', "anisotropy.bin"]
self.___FeatureFileNameMap[8] = ['Yamaguchi', "Yamaguchi4_Dbl.bin"] self.___FeatureFileNameMap[8] = ['Cloude', "entropy.bin"]
self.___FeatureFileNameMap[9] = ['Yamaguchi', "Yamaguchi4_Vol.bin"] self.___FeatureFileNameMap[9] = ['Cloude', "alpha.bin"]
self.___FeatureFileNameMap[10] = ['Yamaguchi', "Yamaguchi4_Hlx.bin"]
self.___FeatureFileNameMap[11] = ['Cloude', "anisotropy.bin"]
self.___FeatureFileNameMap[12] = ['Cloude', "entropy.bin"]
self.___FeatureFileNameMap[13] = ['Cloude', "alpha.bin"]
def create_roi(self, img_path): def create_roi(self, img_path):
""" """
@ -515,11 +505,11 @@ class LandCoverMain:
train_data_list = pm.api_read_measure() train_data_list = pm.api_read_measure()
train_data_dic = csvh.trans_landCover_list2dic(train_data_list) train_data_dic = csvh.trans_landCover_list2dic(train_data_list)
csvh_roi = csvHandle(self.__rows_geo, self.__cols_geo) # csvh_roi = csvHandle(self.__rows_geo, self.__cols_geo)
# train_data_dic = csvh_roi.trans_landCover_measuredata_dic(csvh_roi.readcsv(self.__processing_paras['LabelData']), self.__preprocessed_paras['ori_sim'], MAX_TRAN_NUM) # train_data_dic = csvh_roi.trans_landCover_measuredata_dic(csvh_roi.readcsv(self.__processing_paras['LabelData']), self.__preprocessed_paras['ori_sim'], MAX_TRAN_NUM)
label_img = csvh_roi.get_roi_img() # label_img = csvh_roi.get_roi_img()
if(len(label_img) != 0): # if(len(label_img) != 0):
self.imageHandler.write_img(os.path.join(self.__workspace_processing_path, "label_img.tif"),"",[0,0,0,0,0,0],label_img) # self.imageHandler.write_img(os.path.join(self.__workspace_processing_path, "label_img.tif"),"",[0,0,0,0,0,0],label_img)
logger.info("read csv data success!") logger.info("read csv data success!")
logger.info('progress bar: 20%') logger.info('progress bar: 20%')
@ -550,7 +540,7 @@ class LandCoverMain:
logger.info('progress bar: 50%') logger.info('progress bar: 50%')
# 生成最优特征子集训练集 # 生成最优特征子集训练集
X_train, Y_train, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_geo, 0.07, 0.85) X_train, Y_train, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_geo, 0.5, 0.85)
# 训练模型 # 训练模型
# cost = self.__processing_paras["Cost"] # cost = self.__processing_paras["Cost"]

32
soilMoisture-S-SAR/SoilMoisture.xml
View File

@ -1,7 +1,7 @@
<?xml version='1.0' encoding='utf-8'?> <?xml version='1.0' encoding='utf-8'?>
<Root> <Root>
<TaskID>CSAR_202107275419_0001-0</TaskID> <TaskID>CSAR_202107275419_0001-0</TaskID>
<WorkSpace>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\</WorkSpace> <WorkSpace>D:\micro\SWork\</WorkSpace>
<AlgCompt> <AlgCompt>
<DataTransModel>File</DataTransModel> <DataTransModel>File</DataTransModel>
<Artificial>ElementAlg</Artificial> <Artificial>ElementAlg</Artificial>
@ -40,7 +40,7 @@
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue> <ParaValue>
F:\MicroWorkspace\S_SAR\AHV\HJ2E_MYC_QPS_001752_E118.0_N37.7_20230204_SLC_AHV_L10000010458-cal.tar.gz</ParaValue> F:\MicroWorkspace\20240814tw\HJ2E_KRN_QPS_008852_E110.6_N20.1_20240515_SLC_AHV_L10000208028-cal.tar.gz</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>File</Control> <Control>File</Control>
@ -53,12 +53,12 @@
<ParaChsName>地表覆盖类型数据</ParaChsName> <ParaChsName>地表覆盖类型数据</ParaChsName>
<Description>经过地理定标（WGS84）的地表覆盖类型数据</Description> <Description>经过地理定标（WGS84）的地表覆盖类型数据</Description>
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>zip</DataType> <DataType>tif</DataType>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\al_zhongji\S-SAR-data\soilMoisture\SSAR_soilMoisture_landCover.zip</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\global30.tif</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadTable</Control> <Control>UploadTable</Control>
@ -74,7 +74,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>string</DataType> <DataType>string</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>empty</ParaValue> <ParaValue>10;20;30;40;70;90</ParaValue>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>
@ -90,12 +90,12 @@
<ParaChsName>NDVI数据</ParaChsName> <ParaChsName>NDVI数据</ParaChsName>
<Description>经过地理定标（WGS84）的NDVI数据</Description> <Description>经过地理定标（WGS84）的NDVI数据</Description>
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>zip</DataType> <DataType>tif</DataType>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\al_zhongji\S-SAR-data\soilMoisture\SSAR_soilMoisture_NDVI.zip</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\S2_NDVImed.tif</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadTable</Control> <Control>UploadTable</Control>
@ -144,12 +144,12 @@
<ParaChsName>NDWI数据</ParaChsName> <ParaChsName>NDWI数据</ParaChsName>
<Description>经过地理定标（WGS84）的NDWI数据</Description> <Description>经过地理定标（WGS84）的NDWI数据</Description>
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>zip</DataType> <DataType>tif</DataType>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>
<ParaSource>Cal</ParaSource> <ParaSource>Cal</ParaSource>
<ParaValue>F:\al_zhongji\S-SAR-data\soilMoisture\SSAR_soilMoisture_NDWI.zip</ParaValue> <ParaValue>F:\MicroWorkspace\20240814tw\S2_NDWImed.tif</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadTable</Control> <Control>UploadTable</Control>
@ -164,7 +164,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>float</DataType> <DataType>float</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>-22.482554048434324</ParaValue> <ParaValue>-6.765447191190488</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -182,7 +182,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>float</DataType> <DataType>float</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>-10.72946251632336</ParaValue> <ParaValue>6.235438228712402</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -200,7 +200,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>float</DataType> <DataType>float</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>-0.08238130673792357</ParaValue> <ParaValue>861.6172697366004</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -218,7 +218,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>float</DataType> <DataType>float</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>1.0194495140476119</ParaValue> <ParaValue>-0.005217401735104278</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -236,7 +236,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>float</DataType> <DataType>float</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>6.107713980885245</ParaValue> <ParaValue>-29.78633099522965</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -254,7 +254,7 @@
<ParaType>Value</ParaType> <ParaType>Value</ParaType>
<DataType>float</DataType> <DataType>float</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>-4.496951628949385</ParaValue> <ParaValue>23.39131797361224</ParaValue>
<EnModification>True</EnModification> <EnModification>True</EnModification>
<EnMultipleChoice>False</EnMultipleChoice> <EnMultipleChoice>False</EnMultipleChoice>
<Control>UploadInput</Control> <Control>UploadInput</Control>
@ -274,7 +274,7 @@
<ParaType>File</ParaType> <ParaType>File</ParaType>
<DataType>tar.gz</DataType> <DataType>tar.gz</DataType>
<ParaSource>Man</ParaSource> <ParaSource>Man</ParaSource>
<ParaValue>D:\613NET\ComputingNode\ftproot\Production\PL_20240507155658_0002\SoilMoisture\Output\HJ2E_MYC_QPS_001752_E118.0_N37.7_20230204_SLC_AHV_L10000010458-cal-SMC.tar.gz</ParaValue> <ParaValue>D:\micro\SWork\SoilMoisture\Output\HJ2E_KRN_QPS_008852_E110.6_N20.1_20240515_SLC_AHV_L10000208028-cal-SMC.tar.gz</ParaValue>
<MaxValue>DEFAULT</MaxValue> <MaxValue>DEFAULT</MaxValue>
<MinValue>DEFAULT</MinValue> <MinValue>DEFAULT</MinValue>
<OptionValue>DEFAULT</OptionValue> <OptionValue>DEFAULT</OptionValue>

6
soilMoisture-S-SAR/SoilMoistureMain.py
View File

@ -382,8 +382,10 @@ class MoistureMain:
logger.info('cal soil_moisture success!') logger.info('cal soil_moisture success!')
proj, geos, data = self.imageHandler.read_img(product_path) proj, geos, data = self.imageHandler.read_img(product_path)
data[data < soil_moisture_value_min] = soil_moisture_value_min # data[data < soil_moisture_value_min] = soil_moisture_value_min
data[data > soil_moisture_value_max] = soil_moisture_value_max # data[data > soil_moisture_value_max] = soil_moisture_value_max
data[data < soil_moisture_value_min] = -9999
data[data > soil_moisture_value_max] = -9999
self.imageHandler.write_img(product_path, proj, geos, data) self.imageHandler.write_img(product_path, proj, geos, data)
# 生成快视图 # 生成快视图

78
tool/algorithm/image/ImageHandle.py
View File

@ -677,9 +677,85 @@ class ImageHandler:
scopes = [[min_lon, max_lat], [max_lon, max_lat], [min_lon, min_lat], [max_lon, min_lat]] scopes = [[min_lon, max_lat], [max_lon, max_lat], [min_lon, min_lat], [max_lon, min_lat]]
return scopes return scopes
def get_center_scopes(self, dataset):
if dataset is None:
return None
img_geotrans = dataset.GetGeoTransform()
if img_geotrans is None:
return None
width = dataset.RasterXSize # 栅格矩阵的列数
height = dataset.RasterYSize # 栅格矩阵的行数
x_split = int(width/5)
y_split = int(height/5)
img_col_start = x_split * 1
img_col_end = x_split * 3
img_row_start = y_split * 1
img_row_end = y_split *3
point_upleft = self.trans_rowcol2geo(img_geotrans, img_col_start, img_row_start)
point_upright = self.trans_rowcol2geo(img_geotrans, img_col_end, img_row_start)
point_downleft = self.trans_rowcol2geo(img_geotrans, img_col_start, img_row_end)
point_downright = self.trans_rowcol2geo(img_geotrans, img_col_end, img_row_end)
return [point_upleft, point_upright, point_downleft, point_downright]
def write_view(self, tif_path, color_img=False, quick_view_path=None):
"""
生成快视图,默认快视图和影像同路径且同名
:param tif_path:影像路径
:param color_img:是否生成随机伪彩色图
:param quick_view_path:快视图路径
"""
if quick_view_path is None:
quick_view_path = os.path.splitext(tif_path)[0]+'.jpg'
n = self.get_bands(tif_path)
if n == 1: # 单波段
t_data = self.get_data(tif_path)
else: # 多波段，转为强度数据
t_data = self.get_data(tif_path)
t_data = t_data.astype(float)
t_data = np.sqrt(t_data[0] ** 2 + t_data[1] ** 2)
t_data[np.isnan(t_data)] = 0
t_data[np.where(t_data == -9999)] = 0
t_r = self.get_img_height(tif_path)
t_c = self.get_img_width(tif_path)
q_r = t_r
q_c = t_c
if color_img is True:
# 生成伪彩色图
img = np.zeros((t_r, t_c, 3), dtype=np.uint8) # (高，宽，维度)
u = np.unique(t_data)
for i in u:
if i != 0:
w = np.where(t_data == i)
img[w[0], w[1], 0] = np.random.randint(0, 255) # 随机生成一个0到255之间的整数 可以通过挑参数设定不同的颜色范围
img[w[0], w[1], 1] = np.random.randint(0, 255)
img[w[0], w[1], 2] = np.random.randint(0, 255)
img = cv2.resize(img, (q_c, q_r)) # (宽，高)
cv2.imwrite(quick_view_path, img)
# cv2.imshow("result4", img)
# cv2.waitKey(0)
else:
# 灰度图
min = np.percentile(t_data, 2) # np.nanmin(t_data)
max = np.percentile(t_data, 98) # np.nanmax(t_data)
# if (max - min) < 256:
t_data = (t_data - min) / (max - min) * 255
out_img = Image.fromarray(t_data)
out_img = out_img.resize((q_c, q_r)) # 重采样
out_img = out_img.convert("L") # 转换成灰度图
out_img.save(quick_view_path)
return quick_view_path
if __name__ == '__main__': if __name__ == '__main__':
fn = r"D:\micro\SWork\Ortho\Output\HJ2E_KSC_STRIP_006199_E85.8_N44.2_20231124_SLC_HHHV_L10000135594-Ortho\HJ2E_KSC_STRIP_006199_E85.8_N44.2_20231124_SLC_h_h_L10000135594-Ortho.tif" fn = r"C:\Users\sxwcc\Downloads\HJ2E_MYC_QPS_001752_E118.0_N37.7_20230204_SLC_AHV_L10000010458-cal-SMC\HJ2E_MYC_QPS_001752_E118.0_N37.7_20230204_SLC_AHV_L10000010458-cal-SMC.tif"
ImageHandler().write_quick_view(fn) ImageHandler().write_quick_view(fn)
# path = r'D:\BaiduNetdiskDownload\GZ\lon.rdr' # path = r'D:\BaiduNetdiskDownload\GZ\lon.rdr'
# path2 = r'D:\BaiduNetdiskDownload\GZ\lat.rdr' # path2 = r'D:\BaiduNetdiskDownload\GZ\lat.rdr'

6
tool/algorithm/transforml1a/transHandle.py
View File

@ -147,8 +147,10 @@ class TransImgL1A:
# 处理最大最小范围 # 处理最大最小范围
xmin = 0 if 0 > xmin else xmin xmin = 0 if 0 > xmin else xmin
ymin = 0 if 0 > ymin else ymin ymin = 0 if 0 > ymin else ymin
xmax = ori2geo_img_width if ori2geo_img_width > xmax else xmax # xmax = ori2geo_img_width if ori2geo_img_width > xmax else xmax
ymax = ori2geo_img_height if ori2geo_img_height > ymax else ymax # ymax = ori2geo_img_height if ori2geo_img_height > ymax else ymax
xmax = xmax if ori2geo_img_width > xmax else ori2geo_img_width
ymax = ymax if ori2geo_img_height > ymax else ori2geo_img_height
# 判断条件 # 判断条件
xmax = xmax + 1 if xmax == xmin else xmax xmax = xmax + 1 if xmax == xmin else xmax

24
tool/algorithm/xml/AnalysisXml.py
View File

@ -45,14 +45,14 @@ class DictXml:
point_downright = [float(bottomRight.find("longitude").text), float(bottomRight.find("latitude").text)] point_downright = [float(bottomRight.find("longitude").text), float(bottomRight.find("latitude").text)]
scopes = [point_upleft, point_upright, point_downleft, point_downright] scopes = [point_upleft, point_upright, point_downleft, point_downright]
point_upleft_buf = [float(topLeft.find("longitude").text) - 0.5, float(topLeft.find("latitude").text) + 0.5] point_upleft_buf = [float(topLeft.find("longitude").text) - 0.6, float(topLeft.find("latitude").text) + 0.6]
point_upright_buf = [float(topRight.find("longitude").text) + 0.5, float(topRight.find("latitude").text) + 0.5] point_upright_buf = [float(topRight.find("longitude").text) + 0.6, float(topRight.find("latitude").text) + 0.6]
point_downleft_buf = [float(bottomLeft.find("longitude").text) - 0.5, point_downleft_buf = [float(bottomLeft.find("longitude").text) - 0.6,
float(bottomLeft.find("latitude").text) - 0.5] float(bottomLeft.find("latitude").text) - 0.6]
point_downright_buf = [float(bottomRight.find("longitude").text) + 0.5, point_downright_buf = [float(bottomRight.find("longitude").text) + 0.6,
float(bottomRight.find("latitude").text) - 0.5] float(bottomRight.find("latitude").text) - 0.6]
scopes_buf = [point_upleft_buf, point_upright_buf, point_downleft_buf, point_downright_buf] scopes_buf = ([point_upleft_buf, point_upright_buf, point_downleft_buf, point_downright_buf], )
return scopes_buf return scopes, scopes_buf
class xml_extend: class xml_extend:
@ -114,10 +114,10 @@ class xml_extend:
point_downright = [float(BottomRightLongitude.text), float(BottomRightLatitude.text)] point_downright = [float(BottomRightLongitude.text), float(BottomRightLatitude.text)]
scopes = [point_upleft, point_upright, point_downleft, point_downright] scopes = [point_upleft, point_upright, point_downleft, point_downright]
point_upleft_buf = [float(TopLeftLongitude.text) - 0.5, float(TopLeftLatitude.text) + 0.5] point_upleft_buf = [float(TopLeftLongitude.text) - 0.6, float(TopLeftLatitude.text) + 0.6]
point_upright_buf = [float(TopRightLongitude.text) + 0.5, float(TopRightLatitude.text) + 0.5] point_upright_buf = [float(TopRightLongitude.text) + 0.6, float(TopRightLatitude.text) + 0.6]
point_downleft_buf = [float(BottomLeftLongitude.text) - 0.5, float(BottomLeftLatitude.text) - 0.5] point_downleft_buf = [float(BottomLeftLongitude.text) - 0.6, float(BottomLeftLatitude.text) - 0.6]
point_downright_buf = [float(BottomRightLongitude.text) + 0.5, float(BottomRightLatitude.text) - 0.5] point_downright_buf = [float(BottomRightLongitude.text) + 0.6, float(BottomRightLatitude.text) - 0.6]
scopes_buf = [point_upleft_buf, point_upright_buf, point_downleft_buf, point_downright_buf] scopes_buf = [point_upleft_buf, point_upright_buf, point_downleft_buf, point_downright_buf]
return scopes return scopes

2
tool/csv/csvHandle.py
View File

@ -129,6 +129,8 @@ class csvHandle:
ch_names = [] ch_names = []
positions = [] positions = []
for data in train_data_list: for data in train_data_list:
if data[3] == []:
continue
ids.append(data[0]) ids.append(data[0])
class_ids.append(data[1]) class_ids.append(data[1])
ch_names.append(data[2]) ch_names.append(data[2])