基于Python实现nc批量转tif格式

import os
import netCDF4 as nc
import numpy as np
from osgeo import gdal, osr, ogr
import glob

def nc2tif(data, Output_folder):
    tmp_data = nc.Dataset(data)  # 利用.Dataset()方法读取nc数据
    print('tmp_data', tmp_data)
 
    Lat_data = tmp_data.variables['lat'][:]
    Lon_data = tmp_data.variables['lon'][:]
    # print(Lat_data)
    # print(Lon_data)
 
    tmp_arr = np.asarray(tmp_data.variables['temp'])
 
    # 影像的左上角&右下角坐标
    Lonmin, Latmax, Lonmax, Latmin = [Lon_data.min(), Lat_data.max(), Lon_data.max(), Lat_data.min()]
    # print(Lonmin, Latmax, Lonmax, Latmin)
 
    # 分辨率计算
    Num_lat = len(Lat_data)  # 5146
    Num_lon = len(Lon_data)  # 7849
    Lat_res = (Latmax - Latmin) / (float(Num_lat) - 1)
    Lon_res = (Lonmax - Lonmin) / (float(Num_lon) - 1)
    # print(Num_lat, Num_lon)
    # print(Lat_res, Lon_res)
 
    for i in range(len(tmp_arr[:])):
        # i=0,1,2,3,4,5,6,7,8,9,...
        # 创建tif文件
        driver = gdal.GetDriverByName('GTiff')
        out_tif_name = Output_folder + '\\' + data.split('\\')[-1].split('.')[0] + '_' + str(i + 1) + '.tif'
        out_tif = driver.Create(out_tif_name, Num_lon, Num_lat, 1, gdal.GDT_Int16)
 
        # 设置影像的显示范围
        # Lat_re前需要添加负号
        geotransform = (Lonmin, Lon_res, 0.0, Latmax, 0.0, -Lat_res)
        out_tif.SetGeoTransform(geotransform)
 
        # 定义投影
        prj = osr.SpatialReference()
        prj.ImportFromEPSG(4326)  # WGS84
        out_tif.SetProjection(prj.ExportToWkt())
 
        # 数据导出
        out_tif.GetRasterBand(1).WriteArray(tmp_arr[i])  # 将数据写入内存，此时没有写入到硬盘
        out_tif.FlushCache()  # 将数据写入到硬盘
        out_tif = None  # 关闭tif文件
 
def main():
    Input_folder = r"E:\competition\航天宏图\2-meter air temperature_CMFD\Data_forcing_01yr_010deg\\"
    # Input_folder = r"E:\competition\航天宏图\2-meter air temperature_CMFD\Data_forcing_01mo_010deg\\"
    Output_folder = r"E:\competition\航天宏图\2-meter air temperature_CMFD\tif\\"
 
    # 读取所有数据
    data_list = glob.glob(os.path.join(Input_folder + '*.nc'))
    print(data_list)
 
    for i in range(len(data_list)):
        data = data_list[i]
        nc2tif(data, Output_folder)
        print(data + '转tif成功')

def NC_to_tiffs(data, Output_folder):
    nc_data_obj = nc.Dataset(data)
    Lon = nc_data_obj.variables['lon'][:]
    Lat = nc_data_obj.variables['lat'][:]
    ndvi_arr = np.asarray(nc_data_obj.variables['temp'])  
    ndvi_arr_float = ndvi_arr.astype(float) / 10000  之间
    # 影像的左上角和右下角坐标
    LonMin, LatMax, LonMax, LatMin = [Lon.min(), Lat.max(), Lon.max(), Lat.min()]
    # 分辨率计算
    N_Lat = len(Lat)
    N_Lon = len(Lon)
    Lon_Res = (LonMax - LonMin) / (float(N_Lon) - 1)
    Lat_Res = (LatMax - LatMin) / (float(N_Lat) - 1)
    for i in range(len(ndvi_arr[:])):
        driver = gdal.GetDriverByName('GTiff')
        out_tif_name = Output_folder + '\\' + data.split('\\')[-1].split('.')[0] + '_' + str(i + 1) + '.tif'
        out_tif = driver.Create(out_tif_name, N_Lon, N_Lat, 1, gdal.GDT_Float32)
       
        geotransform = (LonMin, Lon_Res, 0, LatMax, 0, -Lat_Res)
        out_tif.SetGeoTransform(geotransform)
      
        srs = osr.SpatialReference()
        srs.ImportFromEPSG(4326) 
        out_tif.SetProjection(srs.ExportToWkt()) 
        # 数据写出
        out_tif.GetRasterBand(1).WriteArray(ndvi_arr_float[i][::-1])  # 将数据写入内存，此时没有写入硬盘 此处[::-1]用于图像的垂直镜像对称，避免图像颠倒
        out_tif.FlushCache()  # 将数据写入硬盘
        out_tif = None  # 注意必须关闭tif文件

#  图像水平翻转
    im_data_hor = np.flip(im_data, axis=2)
    hor_path = train_image_path + "\\" + str(tran_num) + imageList[i][-4:]
    writeTiff(im_data_hor, im_geotrans, im_proj, hor_path)

 #  标签水平翻转
    Hor = cv2.flip(label, 1)
    hor_path = train_label_path + "\\" + str(tran_num) + labelList[i][-4:]
    cv2.imwrite(hor_path, Hor)
    tran_num += 1

 #  图像垂直翻转
    im_data_vec = np.flip(im_data, axis=1)
    vec_path = train_image_path + "\\" + str(tran_num) + imageList[i][-4:]
    writeTiff(im_data_vec, im_geotrans, im_proj, vec_path)

 #  标签垂直翻转
    Vec = cv2.flip(label, 0)
    vec_path = train_label_path + "\\" + str(tran_num) + labelList[i][-4:]
    cv2.imwrite(vec_path, Vec)
    tran_num += 1

 #  图像对角镜像
    im_data_dia = np.flip(im_data_vec, axis=2)
    dia_path = train_image_path + "\\" + str(tran_num) + imageList[i][-4:]
    writeTiff(im_data_dia, im_geotrans, im_proj, dia_path)

 #  标签对角镜像
    Dia = cv2.flip(label, -1)
    dia_path = train_label_path + "\\" + str(tran_num) + labelList[i][-4:]
    cv2.imwrite(dia_path, Dia)
    tran_num += 1