python多线程方法详解

处理多个数据和多文件时，使用for循环的速度非常慢，此时需要用多线程来加速运行进度，常用的模块为multiprocess和joblib，下面对两种包我常用的方法进行说明。

1、模块安装

pip install multiprocessing
pip install joblib

2、以分块计算NDVI为例

首先导入需要的包

import numpy as np
from osgeo import gdal
import time
from multiprocessing import cpu_count
from multiprocessing import Pool
from joblib import Parallel, delayed

定义GdalUtil类，以读取遥感数据

class GdalUtil:
    def __init__(self):
        pass
    @staticmethod
    def read_file(raster_file, read_band=None):
        """读取栅格数据"""
        # 注册栅格驱动
        gdal.AllRegister()
        gdal.SetConfigOption('gdal_FILENAME_IS_UTF8', 'YES')
        # 打开输入图像
        dataset = gdal.Open(raster_file, gdal.GA_ReadOnly)
        if dataset == None:
            print('打开图像{0} 失败.\n', raster_file)
        # 列
        raster_width = dataset.RasterXSize
        # 行
        raster_height = dataset.RasterYSize
        # 读取数据
        if read_band == None:
            data_array = dataset.ReadAsArray(0, 0, raster_width, raster_height)
        else:
            band = dataset.GetRasterBand(read_band)
            data_array = band.ReadAsArray(0, 0, raster_width, raster_height)
        return data_array

    @staticmethod
    def read_block_data(dataset, band_num, cols_read, rows_read, start_col=0, start_row=0):
        band = dataset.GetRasterBand(band_num)
        res_data = band.ReadAsArray(start_col, start_row, cols_read, rows_read)
        return res_data

    @staticmethod
    def get_raster_band(raster_path):
        # 注册栅格驱动
        gdal.AllRegister()
        gdal.SetConfigOption('gdal_FILENAME_IS_UTF8', 'YES')
        # 打开输入图像
        dataset = gdal.Open(raster_path, gdal.GA_ReadOnly)
        if dataset == None:
            print('打开图像{0} 失败.\n', raster_path)
        raster_band = dataset.RasterCount
        return raster_band

    @staticmethod
    def get_file_size(raster_path):
        """获取栅格仿射变换参数"""
        # 注册栅格驱动
        gdal.AllRegister()
        gdal.SetConfigOption('gdal_FILENAME_IS_UTF8', 'YES')

        # 打开输入图像
        dataset = gdal.Open(raster_path, gdal.GA_ReadOnly)
        if dataset == None:
            print('打开图像{0} 失败.\n', raster_path)
        # 列
        raster_width = dataset.RasterXSize
        # 行
        raster_height = dataset.RasterYSize
        return raster_width, raster_height

    @staticmethod
    def get_file_geotransform(raster_path):
        """获取栅格仿射变换参数"""
        # 注册栅格驱动
        gdal.AllRegister()
        gdal.SetConfigOption('gdal_FILENAME_IS_UTF8', 'YES')

        # 打开输入图像
        dataset = gdal.Open(raster_path, gdal.GA_ReadOnly)
        if dataset == None:
            print('打开图像{0} 失败.\n', raster_path)

        # 获取输入图像仿射变换参数
        input_geotransform = dataset.GetGeoTransform()
        return input_geotransform

    @staticmethod
    def get_file_proj(raster_path):
        """获取栅格图像空间参考"""
        # 注册栅格驱动
        gdal.AllRegister()
        gdal.SetConfigOption('gdal_FILENAME_IS_UTF8', 'YES')

        # 打开输入图像
        dataset = gdal.Open(raster_path, gdal.GA_ReadOnly)
        if dataset == None:
            print('打开图像{0} 失败.\n', raster_path)

        # 获取输入图像空间参考
        input_project = dataset.GetProjection()
        return input_project

    @staticmethod
    def write_file(dataset, geotransform, project, output_path, out_format='GTiff', eType=gdal.GDT_Float32):
        """写入栅格"""
        if np.ndim(dataset) == 3:
            out_band, out_rows, out_cols = dataset.shape
        else:
            out_band = 1
            out_rows, out_cols = dataset.shape

        # 创建指定输出格式的驱动
        out_driver = gdal.GetDriverByName(out_format)
        if out_driver == None:
            print('格式%s 不支持Creat()方法.\n', out_format)
            return

        out_dataset = out_driver.Create(output_path, xsize=out_cols,
                                        ysize=out_rows, bands=out_band,
                                        eType=eType)
        # 设置输出图像的仿射参数
        out_dataset.SetGeoTransform(geotransform)

        # 设置输出图像的投影参数
        out_dataset.SetProjection(project)

        # 写出数据
        if out_band == 1:
            out_dataset.GetRasterBand(1).WriteArray(dataset)
        else:
            for i in range(out_band):
                out_dataset.GetRasterBand(i + 1).WriteArray(dataset[i])
        del out_dataset

定义计算NDVI的函数

def cal_ndvi(multi):
    '''
    计算高分NDVI
    :param multi:格式为列表，依次包含[遥感文件路径，开始行号，开始列号，待读的行数，待读的列数]
    :return: NDVI数组
    '''
    input_file, start_col, start_row, cols_step, rows_step = multi
    dataset = gdal.Open(input_file, gdal.GA_ReadOnly)
    nir_data = GdalUtil.read_block_data(dataset, 4, cols_step, rows_step, start_col=start_col, start_row=start_row)
    red_data = GdalUtil.read_block_data(dataset, 3, cols_step, rows_step, start_col=start_col, start_row=start_row)
    ndvi = (nir_data - red_data) / (nir_data + red_data)
    ndvi[(ndvi > 1.5) | (ndvi < -1)] = 0
    return ndvi

定义主函数

if __name__ == "__main__":
    input_file = r'D:\originalData\GF1\namucuo2021.tif'
    output_file = r'D:\originalData\GF1\namucuo2021_ndvi.tif'
    method = 'joblib'
    # method = 'multiprocessing'
    # 获取文件主要信息
    raster_cols, raster_rows = GdalUtil.get_file_size(input_file)
    geotransform = GdalUtil.get_file_geotransform(input_file)
    project = GdalUtil.get_file_proj(input_file)
    # 定义分块大小
    rows_block_size = 50
    cols_block_size = 50
    multi = []
    for j in range(0, raster_rows, rows_block_size):
        for i in range(0, raster_cols, cols_block_size):
            if j + rows_block_size < raster_rows:
                rows_step = rows_block_size
            else:
                rows_step = raster_rows - j
            # 数据横向步长
            if i + cols_block_size < raster_cols:
                cols_step = cols_block_size
            else:
                cols_step = raster_cols - i
            temp_multi = [input_file, i, j, cols_step, rows_step]
            multi.append(temp_multi)

    t1 = time.time()
    if method == 'multiprocessing':
        # multiprocessing方法
        pool = Pool(processes=cpu_count()-1)
        # 注意map函数中传入的参数应该是可迭代对象，如list;返回值为list
        res = pool.map(cal_ndvi, multi)
        pool.close()
        pool.join()
    else:
        # joblib方法
        res = Parallel(n_jobs=-1)(delayed(cal_ndvi)(input_list) for input_list in multi)

    t2 = time.time()
    print("Total time:" + (t2 - t1).__str__())

    # 将multiprocessing中的结果提取出来，放回对应的矩阵位置中
    out_data = np.zeros([raster_rows, raster_cols], dtype='float')
    for result, input_multi in zip(res, multi):
        start_col = input_multi[1]
        start_row = input_multi[2]
        cols_step = input_multi[3]
        rows_step = input_multi[4]
        out_data[start_row:start_row + rows_step, start_col:start_col + cols_step] = result

    GdalUtil.write_file(out_data, geotransform, project, output_file)

双重for循环时，两层for循环都使用multiprocessing时会报错，这时可以外层for循环使用joblib方法，内层for循环改为multiprocessing方法，不会报错

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