From 73e6dcfa647f8d869496be5e9588fa9e4f88aa47 Mon Sep 17 00:00:00 2001
From: chenzenghui <chenzenghui@lamp.test>
Date: Mon, 15 Jan 2024 16:47:36 +0800
Subject: [PATCH] =?UTF-8?q?=E4=B8=8A=E4=BC=A0=E6=96=87=E4=BB=B6=E8=87=B3?=
 =?UTF-8?q?=20/?=
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提供ENVI ROI 格式 ->程序样本格式（找田嘉兴对接csv）的 工具；
---
 testxmlreading.py | 99 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 testxmlreading.py

diff --git a/testxmlreading.py b/testxmlreading.py
new file mode 100644
index 0000000..e05cc88
--- /dev/null
+++ b/testxmlreading.py
@@ -0,0 +1,99 @@
+#encoding=utf-8
+import os
+import xml.etree.ElementTree as ET
+import pandas as pd
+import csv
+
+def xml2csv(xmlpath):
+    tree_obj = ET.parse(xmlpath)
+
+    # 得到所有匹配Region 标签的Element对象的list集合
+    list_Region = tree_obj.findall("Region")
+    for Region in list_Region:
+        # 几何面对应的类(phenology_name)在标签<Region name="water" color="255,0,0">
+        Region_dict = Region.attrib
+        phenology_name = Region_dict.get("name")
+        print(phenology_name)
+        list_GeometryDef = Region.findall("GeometryDef")
+        list_Polygon = list_GeometryDef[0].findall("Polygon")  # 获得该类下的几何面list
+        for polygon in list_Polygon:
+            # 从polygon list中获取得到<Coordinates>标签的数据 注意是空格分隔的和csv中不同
+            Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
+            # POLYGON((119.035 31.51,119.035 31.50,119.033 31.50)) csv中
+            print("value")
+
+# 向csv中写
+def csvfile(csvpath,data):
+
+    with open(csvpath, 'a', newline='') as file:
+        # 2. step
+        writer = csv.writer(file)
+        # data example
+        #data = ["This", "is", "a", "Test"]
+        writer.writerow(data)
+
+
+# Define the structure of the data
+#data示例 student_header = ['name', 'age', 'major', 'minor']
+def csvcreateTitile(csvpath,data):
+    # 1. Open a new CSV file
+    with open(csvpath, 'w') as file:
+        # 2. Create a CSV writer
+        writer = csv.writer(file)
+        # 3. Write data to the file
+        writer.writerow(data)
+
+# 将列表中的坐标对转换为字符串
+def createcsv_roi_polygon(coordinates):
+    coord_str = ','.join([f'{coordinates[i]} {coordinates[i + 1]}' for i in range(0, len(coordinates), 2)])
+    # 构建最终的POLYGON字符串
+    polygon_str = f'POLYGON(({coord_str}))'
+    print(polygon_str)
+    return  polygon_str
+
+def get_Azimuth_incidence(Azimuth_path):
+    Azimuth_incidence = 0
+    if not os.path.exists(Azimuth_path):
+        print('get Azimuth_incidence failed!')
+        return Azimuth_incidence
+    with open(Azimuth_path) as f:
+        Azimuth_incidence = f.readline()
+        return Azimuth_incidence
+
+# if __name__ == '__main__':
+#     path = r"D:\micro\WorkSpace\Ortho1\Temporary\test.txt"
+#     value = get_Azimuth_incidence(path)
+#     print(value)
+if __name__ == '__main__':
+    xmlpath = r"E:\MicroWorkspace\GF3-Deformation\GF3-yuan\micro-check\GF3_KSC_QPSI_036065_E116.4_N44.2_20230616_L1A_AHV_L10006792277.xml"
+
+    tree_obj = ET.parse(xmlpath)
+    # csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
+    csv_header = ['parent_id', 'id', 'covernm', 'roi_polygon']
+    csvpath = r"E:\MicroWorkspace\GF3-Deformation\GF3-yuan\micro-check\GF3_KSC_QPSI_036065_E116.4_N44.2_20230616_L1A_AHV_L10006792277.csv"
+    # csvcreateTitile(csvpath,csv_header)
+    csvfile(csvpath,csv_header)
+    # 得到所有匹配Region 标签的Element对象的list集合
+    list_Region = tree_obj.findall("Region")
+    name_list = {}
+    count = 10
+    for Region in list_Region:
+        # 几何面对应的类(phenology_name)在标签<Region name="water" color="255,0,0">
+        Region_dict = Region.attrib
+        phenology_name = Region_dict.get("name")
+
+        if not phenology_name in name_list.keys():
+            name_list.update({phenology_name: count})
+            count += 10
+        print(phenology_name)
+        # list_GeometryDef = Region.findall("GeometryDef")
+        list_Polygon = Region.findall(".//Polygon")  # 获得该类下的几何面list
+
+        for polygon in list_Polygon:
+            # 从polygon list中获取得到<Coordinates>标签的数据 注意是空格分隔的和csv中不同
+            Coordinates_list = coordinates = polygon.find('.//Coordinates').text.strip().split()
+            # 将坐标和ploygon对应的写入到.csv中
+            polygon_str=createcsv_roi_polygon(Coordinates_list)
+            # POLYGON((119.035  31.51,119.035 31.50,119.033 31.50)) csv中
+            data = ['0', name_list.get(phenology_name), phenology_name, polygon_str]
+            csvfile(csvpath, data)