#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\S_SAR\AHV\landCover.xml"

    tree_obj = ET.parse(xmlpath)
    csv_header = ['sar_img_name', 'phenology_id', 'phenology_name', 'roi_polygon']
    csvpath = r"E:\MicroWorkspace\S_SAR\AHV\newimage.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)