修改分块大小，增加效率，正射新增裁剪dem，防止输入dem范围过大，影响效率

Ortho/Ortho.xml

@@ -1,7 +1,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <Root>
   <TaskID>CSAR_202107275419_0001-0</TaskID>
-  <WorkSpace>E:\Result_GF3\</WorkSpace>
+  <WorkSpace>D:\micro\WorkSpace\</WorkSpace>
   <AlgCompt>
     <DataTransModel>File</DataTransModel>
     <Artificial>ElementAlg</Artificial>
@@ -45,7 +45,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\GF3Data\vegetationPhenology\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764.tar.gz</ParaValue>
+        <ParaValue>D:\BaiduNetdiskDownload\西北新增CSAR盐碱度\GF3C_KSC_QPSI_008440_E86.2_N44.1_20231113_L1A_AHV_L10000215823.tar.gz</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>False</EnMultipleChoice> 
         <Control>File</Control> 
@@ -58,9 +58,9 @@
         <ParaChsName>DEM数字高程影像</ParaChsName>
         <Description>30m分辨率DEM数字高程影像tif</Description>
         <ParaType>File</ParaType>
-        <DataType>zip</DataType>
+        <DataType>File</DataType>
         <ParaSource>Cal</ParaSource>
-        <ParaValue>E:\GF3Data\vegetationPhenology\CSAR_vegetation_ASTGTM2_N26E100_dem.zip;E:\GF3Data\vegetationPhenology\CSAR_vegetation_ASTGTM2_N27E100_dem.zip</ParaValue>
+        <ParaValue>D:\BaiduNetdiskDownload\dem_xb</ParaValue>
         <EnModification>True</EnModification> 
         <EnMultipleChoice>True</EnMultipleChoice> 
         <Control>File</Control> 
@@ -92,7 +92,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Cal</ParaSource>
-        <ParaValue>E:\Result_GF3\Ortho\Output\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764-ortho.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\Ortho\Output\GF3C_KSC_QPSI_008440_E86.2_N44.1_20231113_L1A_AHV_L10000215823-ortho.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

Ortho/OrthoMain.py

@@ -13,6 +13,7 @@ import logging
 from tool.algorithm.block.blockprocess import BlockProcess
 from tool.algorithm.image.ImageHandle import ImageHandler
 from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml, OrthoAzimuth
+from tool.algorithm.xml.AnalysisXml import DictXml
 from tool.algorithm.algtools.PreProcess import PreProcess as pp
 import tarfile
 from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource  # 导入xml文件读取与检查文件
@@ -498,14 +499,19 @@ class OrthoMain:
         logger.info('progress bar :100％')
         return True
 
-        
         pass
 
-
     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, ori_sim, sim_ori):
         p = pp()
@@ -534,7 +540,7 @@ class OrthoMain:
         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
 
-        # self.cut_dem(dem_merged_path, meta_file_path)
+        dem_path = self.cut_dem(dem_merged_path, meta_file_path)
         # 2、间接定位法求解行列坐标
         slc_paths = self.__in_processing_paras["SLC"]
         # 2.1 生成映射表
@@ -553,7 +559,7 @@ class OrthoMain:
                 in_slc_path=os.path.join(slc_paths,slc_path)
                 break
         # 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
         
         logger.info('progress bar: 40%')
         # clip_dem_reample_path=os.path.join(self.__workspace_Temporary_path, "SAR_dem.tiff")
@@ -686,12 +692,12 @@ class OrthoMain:
         # 生成压缩包
         logger.info('progress bar :94％')
         logger.info('start make targz..')
-        self.del_floder(self.__workspace_unpack_path)
+        # self.del_floder(self.__workspace_unpack_path)
-        self.del_floder(self.__workspace_ResampledDEM_path)
+        # self.del_floder(self.__workspace_ResampledDEM_path)
-        self.del_floder(self.__workspace_LutImg_path)
+        # self.del_floder(self.__workspace_LutImg_path)
-        self.del_floder(self.__workspace_IncidenceImg_path)
+        # self.del_floder(self.__workspace_IncidenceImg_path)
-        self.del_floder(self.__workspace_SimImg_path)
+        # self.del_floder(self.__workspace_SimImg_path)
-        self.del_floder(self.__workspace_SARIntensity_path)
+        # self.del_floder(self.__workspace_SARIntensity_path)
         self.make_targz(self.__out_para, self.__workspace_package_path+"\\")
         logger.info('make targz finish')
         logger.info('progress bar :100％')

backScattering/BackScatteringMain.py

@@ -12,6 +12,7 @@ import logging
 from tool.algorithm.algtools.logHandler import LogHandler
 from tool.algorithm.block.blockprocess import BlockProcess
 from tool.algorithm.xml.AlgXmlHandle import ManageAlgXML, CheckSource
+from tool.algorithm.xml.AnalysisXml import DictXml
 from tool.algorithm.xml.CreateMetaDict import CreateMetaDict, CreateProductXml, OrthoAzimuth
 from tool.algorithm.image.ImageHandle import ImageHandler
 from tool.algorithm.algtools.PreProcess import PreProcess as pp
@@ -277,6 +278,18 @@ class ScatteringMain:
         pp().cut_img(sim_ori_process, sim_ori, shp_path)
         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 process_handle(self, start):
         in_tif_paths = list(glob.glob(os.path.join(self.__in_processing_paras['SLC'], '*.tif')))
         if in_tif_paths == []:
@@ -319,6 +332,7 @@ class ScatteringMain:
 
         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)
 
         in_slc_path = None
         for slc_path in in_tif_paths:
@@ -328,7 +342,7 @@ class ScatteringMain:
                 break
 
         # 获取校正模型后
-        Orthorectification.preCaldem_sar_rc(dem_merged_path, in_slc_path, self.__workspace_preprocessing_path,
+        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
         logger.info('progress bar: 40%')
         # clip_dem_reample_path=os.path.join(self.__workspace_preprocessing_path, "SAR_dem.tiff")

soilSalinity-Train_predict/SoilSalinityPredict.xml

@@ -39,7 +39,7 @@
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
 		<ParaValue>
-          F:\2023xibei\GF3C_KSC_QPSI_008440_E86.0_N44.7_20231113_L1A_AHV_L10000215825-ortho.tar.gz
+          F:\2024xibei\GF3B_KSC_QPSI_010328_E86.1_N44.3_20231109_L1A_AHV_L10000262133-ortho.tar.gz
         </ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
@@ -110,7 +110,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\2023xibei\S2_NDVImed2.tif</ParaValue>
+		<ParaValue>F:\2024xibei\S2_NDVImed.tif</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -140,7 +140,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\micro\WorkSpace\SoilSalinityPredict\Output\GF3C_KSC_QPSI_008440_E86.0_N44.7_20231113_L1A_AHV_L10000215825-ortho-SSAA.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SoilSalinityPredict\Output\GF3B_KSC_QPSI_010328_E86.1_N44.3_20231109_L1A_AHV_L10000262133-ortho-SSAA.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

soilSalinity/SoilSalinity.xml

@@ -1,7 +1,7 @@
 <?xml version='1.0' encoding='utf-8'?>
 <Root>
   <TaskID>CSAR_202107275419_0001-0</TaskID>
-  <WorkSpace>D:\soilSanlinity\</WorkSpace>
+  <WorkSpace>D:\micro\WorkSpace\</WorkSpace>
   <AlgCompt>
     <DataTransModel>File</DataTransModel>
     <Artificial>ElementAlg</Artificial>
@@ -80,7 +80,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>E:\C-SARNDVINDWIfugaileixingDEM\fugaileixing.tif</ParaValue>
+		<ParaValue>F:\2023xibei\fugaileixing.tif</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -108,7 +108,7 @@
         <ParaType>File</ParaType>
         <DataType>tif</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>E:\C-SARNDVINDWIfugaileixingDEM\S2_NDVImed2.tif</ParaValue>
+		<ParaValue>F:\2023xibei\S2_NDVImed2.tif</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -138,7 +138,7 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-        <ParaValue>D:\soilSanlinity\SoilSalinity\Output\GF3C_KSC_QPSI_008440_E86.0_N44.7_20231113_L1A_AHV_L10000215825-ortho-SSAA.tar.gz</ParaValue>
+        <ParaValue>D:\micro\WorkSpace\SoilSalinity\Output\GF3C_KSC_QPSI_008440_E86.0_N44.7_20231113_L1A_AHV_L10000215825-ortho-SSAA.tar.gz</ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>

soilSalinity/SoilSalinityMain.py

@@ -315,6 +315,7 @@ class SalinityMain:
         rows = self.imageHandler.get_img_height(self.__preprocessed_paras['HH'])
         cols = self.imageHandler.get_img_width(self.__preprocessed_paras['HH'])
         block_size = bp.get_block_size(rows, cols)
+        logger.info('block size is :%s', block_size)
         bp.cut(out_dir, self.__workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
         img_dir, img_name = bp.get_file_names(self.__workspace_block_tif_path, ['tif'])
         dir_dict = bp.get_same_img(img_dir, img_name)
@@ -337,6 +338,7 @@ class SalinityMain:
             features_array[np.isnan(features_array)] = 0.0
             features_array[np.isinf(features_array)] = 0.0
             self.imageHandler.write_img(features_path, "", [0, 0, 1, 0, 0, 1], features_array)
+
         logger.info('create features matrix success!')
 
         # for n in range(block_num):

soilSalinity/config.ini

@@ -8,7 +8,6 @@ productLevel = 5
 exe_name = SoilSalinity
 # 开启调试模式则不删除临时工作区，True:开启调试，False:不开启调试
 debug = False
-
 ######2-算法处理参数######
 pixelspace = 5
 ######3-算法结果参数######

soilSalinity/pspHAAlphaDecomposition.py

@@ -98,6 +98,7 @@ class PspHAAlphaDecomposition:
             self.api_trans_T3_to_tif(h_a_alpha_out_dir, polsarpro_in_dir)
         if is_read_to_dic:
             h_a_alpha_features.update(self.api_read_T3_matrix(polsarpro_in_dir))
+        logger.info('run bin to tif success!')
         return h_a_alpha_features
 
     def api_h_a_alpha_decomposition_T3(self, h_a_alpha_out_dir, h_a_alpha_decomposition_T3_path, polsarpro_in_dir, is_trans_to_tif=True, is_read_to_dic=False, *args):

surfaceRoughness_oh2004/SurfaceRoughnessMain.py

@@ -50,7 +50,6 @@ else:
     DEBUG = False
 file =fileHandle(DEBUG)
 EXE_NAME = cf.get('exe_name')
-FILTER_SIZE = int(cf.get('filter_size'))
 soil_moisture_value_min = float(cf.get('product_value_min'))
 soil_moisture_value_max = float(cf.get('product_value_max'))
 pixelspace=float(cf.get('pixelspace'))

tool/algorithm/algtools/PreProcess.py

@@ -376,6 +376,31 @@ class PreProcess:
             return {}
         return cutted_img_paths
 
+    def cut_imgs_VP(self, out_dir, para_names, processing_paras, shp_path, img_name):
+        """
+        使用矢量数据裁剪影像
+        :param para_names:需要检查的参数名称
+        :param shp_path：裁剪的shp文件
+        """
+        if len(para_names) == 0:
+            return {}
+        cutted_img_paths = {}
+        try:
+            for name in para_names:
+                if name == 'Covering':
+                    img_name = img_name.split('_')[6] + '_'
+                    output_path = os.path.join(out_dir, img_name + name + '_cut.tif')
+                else:
+                    output_path = os.path.join(out_dir, name + '_cut.tif')
+                input_path = processing_paras[name]
+                self.cut_img(output_path, input_path, shp_path)
+                cutted_img_paths.update({name: output_path})
+                logger.info('cut %s success!', name)
+        except BaseException:
+            logger.error('cut_img failed!')
+            return {}
+        return cutted_img_paths
+
     @staticmethod
     def cut_img(output_path, input_path, shp_path):
         """

tool/algorithm/block/blockprocess.py

@@ -25,13 +25,14 @@ class BlockProcess:
 
     @staticmethod
     def get_block_size(rows, cols):
-        block_size = 512
+        block_size = 1024
-        if (rows // 1024) <= 5 and (cols // 1024) <= 5:
+        if rows <= 2048 or cols <= 2048:
             block_size = 512
-        elif 5 < (rows // 1024) < 10 and 5 < (cols // 1024) < 10:
+        if (rows // 1024) <= 5 or (cols // 1024) <= 5:
             block_size = 1024
-        else:
+        elif 5 < (rows // 1024) and 5 < (cols // 1024):
             block_size = 2048
+
         return block_size
 
     # def get_block_size(rows, cols, block_size_config):

tool/algorithm/ml/machineLearning.py

@@ -70,7 +70,7 @@ class MachineLeaning:
         # 特征分块
         bp = BlockProcess()
         block_size = bp.get_block_size(rows, cols)
-
+        logger.info('block size is :%s', block_size)
         bp.cut(feature_tif_dir, workspace_block_tif_path, ['tif', 'tiff'], 'tif', block_size)
         img_dir, img_name = bp.get_file_names(workspace_block_tif_path, ['tif'])
         dir_dict_all = bp.get_same_img(img_dir, img_name)
@@ -209,7 +209,7 @@ class MachineLeaning:
 
             band = ImageHandler.get_bands(path)
             if band == 1:
-                features_array = np.zeros((1, 1024, 1024), dtype=float)
+                features_array = np.zeros((1, block_size, block_size), dtype=float)
                 feature_array = ImageHandler.get_data(path)
                 features_array[0, :, :] = feature_array
             else:

tool/algorithm/xml/AnalysisXml.py

@@ -45,14 +45,14 @@ class DictXml:
         point_downright = [float(bottomRight.find("longitude").text), float(bottomRight.find("latitude").text)]
         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) - 0.4, float(topLeft.find("latitude").text) + 0.4]
-        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) + 0.4, float(topRight.find("latitude").text) + 0.4]
-        point_downleft_buf = [float(bottomLeft.find("longitude").text) - 0.5,
+        point_downleft_buf = [float(bottomLeft.find("longitude").text) - 0.4,
-                              float(bottomLeft.find("latitude").text) - 0.5]
+                              float(bottomLeft.find("latitude").text) - 0.4]
-        point_downright_buf = [float(bottomRight.find("longitude").text) + 0.5,
+        point_downright_buf = [float(bottomRight.find("longitude").text) + 0.4,
-                               float(bottomRight.find("latitude").text) - 0.5]
+                               float(bottomRight.find("latitude").text) - 0.4]
-        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, scopes_buf
 
 
 class xml_extend:

vegetationPhenology/VegetationPhenology.xml

@@ -37,7 +37,9 @@
         <ParaType>File</ParaType>
         <DataType>tar.gz</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>F:\VegetationPhenology-likun\lijiang\GF3B_KSC_QPSI_007906_E100.2_N27.0_20230525_L1A_AHV_L10000190531-ortho.tar.gz</ParaValue>
+		<ParaValue>
+          F:\VegetationPhenology-likun\lijiang\GF3B_KSC_QPSI_007906_E100.2_N27.0_20230525_L1A_AHV_L10000190531-ortho.tar.gz
+        </ParaValue>
         <MaxValue>DEFAULT</MaxValue>
         <MinValue>DEFAULT</MinValue>
         <OptionValue>DEFAULT</OptionValue>
@@ -107,13 +109,12 @@
         <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）
 		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(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(11)、反熵A(12)、平均散射角α(13)</Description>
         <ParaType>Value</ParaType>
         <DataType>string</DataType>
 		<ParaSource>Man</ParaSource>
-		<ParaValue>0,1,2,7,8,9,10</ParaValue>
+		<ParaValue>0,1,2</ParaValue>
         <EnModification>True</EnModification>
         <EnMultipleChoice>True</EnMultipleChoice>
         <Control>UploadInput</Control>

vegetationPhenology/VegetationPhenologyMain.py

@@ -7,6 +7,7 @@
 @Date ：2021/9/6
 @Version ：1.0.0
 """
+import csv
 import glob
 import logging
 import os
@@ -169,8 +170,8 @@ class PhenologyMain:
             # 计算图像的轮廓,并求相交区域
             intersect_shp_path = self.__workspace_preprocessing_path + 'IntersectPolygon.shp'
             scopes_roi = p.cal_intersect_shp(intersect_shp_path, para_names_geo, self.__processing_paras, scopes)
-            cutted_img_paths = p.cut_imgs(self.__workspace_preprocessing_path, para_names_geo, self.__processing_paras,
+            cutted_img_paths = p.cut_imgs_VP(self.__workspace_preprocessing_path, para_names_geo, self.__processing_paras,
-                                          intersect_shp_path)
+                                          intersect_shp_path, name)
             self.__preprocessed_paras.update({name + '_sim_ori': cutted_img_paths.get(name + '_sim_ori')})
             self.__preprocessed_paras.update({name + '_Covering': cutted_img_paths.get('Covering')})
 
@@ -373,7 +374,9 @@ class PhenologyMain:
                 """
         # 生成每个时相的特征， 并提取训练集和测试集
         # 每个时相的影像生成特征图
-        X_train, Y_train = None, None
+
+        X_train = []
+        Y_train = []
         flag = True
         total_name_list = []
         X_test_dic = {}
@@ -381,35 +384,45 @@ class PhenologyMain:
 
             feature_dir, train_data_dic = self.create_feature_single_tar(name)
             # 生成训练集
-            X_train_part, Y_train_part, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_dir, 0.08, 0.85)
+            # X_train_part, Y_train_part, optimal_feature = ml.gene_optimal_train_set(train_data_dic, feature_dir, 0.08, 0.7)
+            X_train_part, Y_train_part = ml.gene_train_set(train_data_dic, feature_dir)
             name_list = ml.get_name_list(feature_dir)
-            if len(optimal_feature) <= 0:
+
-                logger.error('特征筛选结果为空，无可用特征作为训练集')
-                continue
             # 生成测试集合
+            # X_test_path_list = ml.gene_test_set(feature_dir, optimal_feature)
+
+            # X_test_dic.update({name: X_test_path_list})
+            X_test_dic.update({name + '_features': feature_dir})
+
+            if X_train == []:
+                X_train = X_train_part
+                Y_train = Y_train_part
+                total_name_list = name_list
+            else:
+                X_train = np.vstack((X_train, X_train_part))
+                Y_train = np.hstack((Y_train, Y_train_part))
+                total_name_list = total_name_list + name_list
+
+        logger.info("generate train and test set success!")
+        logger.info('progress bar: 30%')
+
+        optimal_X_train, optimal_Y_train, optimal_feature = ml.sel_optimal_feature(X_train, Y_train, total_name_list, correlation_threshold=0.7)
+
+        # RF
+        clf = ml.trainRF(optimal_X_train, optimal_Y_train)
+        logger.info('RF train success!')
+
+        # 测试数据
+        logger.info('mode testing')
+        for name in self.__processing_paras['name_list']:
+            feature_dir = X_test_dic.get(name + '_features')
             X_test_path_list = ml.gene_test_set(feature_dir, optimal_feature)
-
-            X_test_dic.update({name: X_test_path_list})
-
-            X_train = X_train_part
-            Y_train = Y_train_part
-
-            logger.info("generate train and test set success!")
-            logger.info('progress bar: 30%')
-
-            # RF
-            clf = ml.trainRF(X_train, Y_train)
-            logger.info('RF train success!')
-            logger.info('progress bar: 80%')
-
-            # 测试数据
-            logger.info('mode testing')
-
             in_tif_paths = list(glob.glob(os.path.join(feature_dir, '*.tif')))
             rows = ImageHandler.get_img_height(in_tif_paths[0])
             cols = ImageHandler.get_img_width(in_tif_paths[0])
             proj_geo, geo_geo, cover_data_geo = self.imageHandler.read_img(in_tif_paths[0])
             product_path = ml.predict_VP(clf, X_test_path_list, name, self.__workspace_processing_path, rows, cols)
+            # product_path = ml.predict_VP(clf, X_test_dic.get(name), name, self.__workspace_processing_path, rows, cols)
 
             proj, geo, cover_data = self.imageHandler.read_img(product_path)
             # 形态学（闭运算）去roi区域噪点
@@ -417,7 +430,8 @@ class PhenologyMain:
             kernel = np.ones((5, 5), np.uint8)
             cover_data = cv2.erode(cv2.dilate(cover_data, kernel), kernel)
             cover_data = np.int16(cover_data)
-            for id, class_id in zip(train_data_dic['ids'], train_data_dic['class_ids']):
+            train_dic = self.get_train_dic(self.__processing_paras['MeasuredData'])
+            for id, class_id in zip(train_dic['ids'], train_dic['class_ids']):
                 cover_data[np.where(cover_data == id)] = class_id
             roi_img = self.imageHandler.get_band_array(self.create_roi(in_tif_paths[0]))
             # 获取影像roi区域
@@ -443,64 +457,11 @@ class PhenologyMain:
         # CreateProductXml(para_dict, model_path, out_xml).create_standard_xml()
         shutil.copy(meta_xml_path, out_xml)
         logger.info('mode test success!')
+        logger.info('progress bar: 80%')
         # 文件夹打包
         file.make_targz(self.__out_para, self.__product_dic)
         logger.info('progress bar: 100%')
 
-        # """
-        # 算法主处理函数
-        # :return: True or False
-        # """
-        # # 生成每个时相的特征， 并提取训练集和测试集
-        # # 每个时相的影像生成特征图
-        # X_train, Y_train = None, None
-        # flag = True
-        # total_name_list = []
-        # test_data = []
-        # X_test_dic = {}
-        # for name in self.__processing_paras['name_list']:
-        #     feature_dir, train_data_dic, test_data_part, type_map = self.create_feature_single_tar(name)
-        #     #生成训练集
-        #     X_train_part, Y_train_part = ml.gene_train_set(train_data_dic, feature_dir)
-        #     name_list = ml.get_name_list(feature_dir)
-        #     # 生成测试集合
-        #     rows, cols = self.get_name_rows_cols(name)
-        #     name_featuresPath_dic_part = ml.vegetationPhenology_combine_feature(feature_dir, self.__workspace_processing_path, name, rows, cols, DEBUG)
-        #     X_test_dic_part = self.gene_test_set(test_data_part, name_featuresPath_dic_part, name)
-        #     X_test_dic.update(X_test_dic_part)
-        #     if flag:
-        #         X_train = X_train_part
-        #         Y_train = Y_train_part
-        #         total_name_list = name_list
-        #         flag = False
-        #         test_data = test_data_part
-        #     else:
-        #         X_train = np.vstack((X_train, X_train_part))
-        #         Y_train = np.hstack((Y_train, Y_train_part))
-        #         total_name_list = total_name_list + name_list
-        #         test_data = test_data + test_data_part
-        #
-        # logger.info("create_features success!")
-        # logger.info('progress bar: 20%')
-        #
-        # optimal_X_train, optimal_Y_train, optimal_feature = ml.sel_optimal_feature(X_train, Y_train, total_name_list, correlation_threshold=0.7)
-        #
-        # logger.info("generate train and test set success!")
-        # logger.info('progress bar: 30%')
-        #
-        # #RF
-        # clf = ml.trainRF(optimal_X_train, optimal_Y_train)
-        # logger.info('svm train success!')
-        # logger.info('progress bar: 80%')
-        #
-        # # 测试数据
-        # logger.info('mode testing')
-        # product_path = self.predict(clf, X_test_dic, optimal_feature, type_map, start)
-        # logger.info('mode test success!')
-        # self.create_meta_file(product_path)
-        # # 文件夹打包
-        # file.make_targz(self.__out_para, self.__product_dic)
-        # logger.info('progress bar: 100%')
 
     def predict(self, mode, X_test_dic, validity_list, type_map, start):
         # 测试数据
@@ -558,6 +519,23 @@ class PhenologyMain:
 
         return product_geo_path
 
+    def get_train_dic(self, csv_path):
+        reader = csv.reader(open(csv_path, newline=''))
+        ids = []
+        class_ids = []
+        csv_list = []
+        for line_data in reader:
+            csv_list.append(line_data)
+        for data in csv_list[1:]:
+            ids.append(data[0])
+            class_ids.append(data[1])
+
+        train_data_dic = {}
+        train_data_dic.update({"ids": ids})
+        train_data_dic.update({"class_ids": class_ids})
+
+        return train_data_dic
+
     def resampleImgs(self, name, refer_img_path):
 
         cover_rampling_path = os.path.join(self.__workspace_processing_path, name + "_cover.tif")