# -*- coding: UTF-8 -*-
"""
@Project:__init__.py 
@File:create_csv_data.py
@Function:
@Contact:
@Author:SHJ
@Date:2021/9/27 10:17 
@Version:1.0.0
"""
from osgeo import gdal
from osgeo import osr
import numpy as np
import math
import datetime
import csv
import random
from tool.algorithm.image.ImageHandle import ImageHandler

def get_data(filename):
    """
    :param filename: tif路径
    :return: 获取所有波段的数据
    """
    gdal.AllRegister()
    dataset = gdal.Open(filename)
    if dataset is None:
        return None
    im_width = dataset.RasterXSize
    im_height = dataset.RasterYSize
    im_data = dataset.ReadAsArray(0, 0, im_width, im_height)
    del dataset
    return im_data

def create_data(dir,tif_name_list):
   HH = get_data(dir+tif_name_list[0])
   HV = get_data(dir+tif_name_list[1])
   VH = get_data(dir+tif_name_list[2])
   VV = get_data(dir+tif_name_list[3])

   data = HH[0]+HH[1]+HV[0]+HV[1]+VH[0]+VH[1]+VV[0]+VV[1]
   # data = HH+HV+VH+VV
   data = np.abs(data)

   max = int(np.max(data))
   min = int(np.min(data))
   range = int(max) - int(min)
   data1 = data - int(min)
   normalization_data = (data1) /range

   return normalization_data

def create_data1(dir,tif_name_list):
   HH = get_data(dir+tif_name_list[0])
   HV = get_data(dir+tif_name_list[1])
   VH = get_data(dir+tif_name_list[2])
   VV = get_data(dir+tif_name_list[3])

   # data = HH[0]+HH[1]+HV[0]+HV[1]+VH[0]+VH[1]+VV[0]+VV[1]
   data = HH+HV+VH+VV
   data = np.abs(data)
   return data

def img2lonlat(dir,tif_name_list,csv_path,lon_num,lat_num):
    gdal.AllRegister()

    dataset = gdal.Open(dir+tif_name_list[0])
    rows = dataset.RasterYSize
    cols = dataset.RasterXSize
    data = create_data(dir,tif_name_list)
    # data = create_data1(dir,tif_name_list)
    trans = dataset.GetGeoTransform()

    f = open(csv_path, 'w', newline='')
    csv_writer = csv.writer(f)
    csv_writer.writerow(['data_time', 'lon', 'lat', 'value'])

    rows_invert = math.floor(rows/lat_num)
    cols_invert = math.floor(cols / lon_num)
    for r in range(lat_num):
        for c in range(lon_num):
            row = math.floor(rows_invert * r)
            col = math.floor(cols_invert * c)

            lon = trans[0] + col * trans[1] + row * trans[2]
            lat = trans[3] + col * trans[4] + row * trans[5]

            localtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')

            # value = 5  # data[row, col]
            value = random.uniform(6.5, 7.5)      # 苏州地区ph为6.5-7.5
            data_list = [localtime, lon, lat, value]
            csv_writer.writerow(data_list)
    pass

if __name__ == '__main__':

    dir = r'E:/Micro/datas/soil_geo/input_back/GF3_KAS_QPSI_009890_E102.3_N33.6_20180626_L4_AHV_L10003284994/'
    tif_name_list = ['GF3_KAS_QPSI_009890_E102.3_N33.6_20180626_L4_HH_L10003284994.tif',
                     'GF3_KAS_QPSI_009890_E102.3_N33.6_20180626_L4_HV_L10003284994.tif',
                     'GF3_KAS_QPSI_009890_E102.3_N33.6_20180626_L4_VH_L10003284994.tif',
                     'GF3_KAS_QPSI_009890_E102.3_N33.6_20180626_L4_VV_L10003284994.tif']

    csv_path = dir + '\SoilSalinityMeasureData_GF3_KAS_QPSI_009890_E102.3_N33.6_20180626_L1A_AHV_L10003284994_RPC.csv'
    lon_num = 20
    lat_num = 20
    img2lonlat(dir,tif_name_list,csv_path,lon_num,lat_num)
    pass