Convert DN to Reflection using GAIN BAND (I think it's more faster than using GRASS GIS and gdal_merge.py)

conevert_DN.py

#!/usr/bin/env pytho3
# 2017/05/24 Jin Katagi
# convert DN to reflectance.
# usage) python3 convert_DN.py

import subprocess
import os
import re
from src import tif_tools


def get_scene_list(input_dir):
    """ get tif list from input_dir (helper function)."""
    scene_list = os.listdir(input_dir)
    # read only *.tif
    scene_list = [f for f in scene_list if re.match(r'.*tif$', f)]
    return scene_list

def calc_GAIN(input_dir, scene_name, output_dir):
    """ calc GAIN to DN bands and save as GeoTiff. 
    Inputs:
        input_dir (str) : path to scene_name dir.
        scene_naem (str) : name of the input img.
        output_dir (str) : path to output dir.
    """
    # read input image and calc GAIN.
    print("read and calc GAIN:{}".format(scene_name))
    band, dataset = tif_tools.tif2array(input_dir + "/" + scene_name)

    savename=output_dir + "/" +  scene_name
    # save img
    print("save:{}".format(scene_name))
    tif_tools.array2raster(savename, dataset, band, dtype="Float32")


def main():
    input_dir="/path/to/input_img/dir"
    output_dir="./path/to/output_img/dir"

    # make output dir.
    subprocess.run(args=["mkdir", "-p", output_dir])

    # get *.tif list.
    scene_list = get_scene_list(input_dir)

    for scene_name in scene_list:
        calc_GAIN(input_dir, scene_name, output_dir)


if __name__ == '__main__':
    main()