GDAL multiband synthesis tool

A set of The coordinate system is the same 、 The number of ranks is the same Of Single band tif image Merge into Multiband tif image

On the first code

#!D:\ProgramData\Anaconda3\python.exe
#  Appoint python Indicator 

import argparse
import os
import re
from osgeo import gdal
from osgeo import osr
from osgeo import gdal_array
from tqdm import tqdm

#  Set parameters 
parser = argparse.ArgumentParser(
    ' Multi band synthesis tool ： Combine multiple single bands tif The files are merged into one multi band tif file , To be merged tif The file must have the same spatial reference and number of rows and columns ')
parser.add_argument('--inputpath', type=str,
                    required=True, help=' Include to be merged tif File directory ')
parser.add_argument('--filter', type=str, default=None,
                    help=' Filter conditions , It should be a regular expression , Filter the files in the input directory ')
parser.add_argument('--outpath', type=str, default='./',
                    help=' Directory of output files , Default to current directory ')
parser.add_argument('--outname', type=str, required=True, help=' Output file name ')
args = parser.parse_args()

#  To obtain parameters 
inputpath = args.inputpath
filterstr = args.filter
outpath = args.outpath
outname = args.outname

#  Filter files 
files = os.listdir(inputpath)
#  Can only handle tif File format 
files = list(filter(lambda file: file.endswith('.tif'), files))
if len(files) == 0:
    print(' Can only handle tif file !')
    exit()
#  Filter eligible files based on the passed in regular expression 
files = list(filter(lambda file: re.search(filterstr, file), files))
if len(files) == 0:
    print(' There are no pending files that meet the filter criteria !')
    exit()
#  Filter spatial references and row / column numbers with processed images 
gdal.AllRegister()
srs = None
gt=None
row = -9999
col = -9999
dtypes = []
for file in files:
    ds = gdal.Open(os.path.join(inputpath, file))
    band = gdal.Dataset.GetRasterBand(ds, 1)
    dtypes.append(band.DataType)
    if row == -9999 and col == -9999:
        col = band.XSize
        row = band.YSize
        srs = gdal.Dataset.GetSpatialRef(ds)
        gt=gdal.Dataset.GetGeoTransform(ds)
    else:
        if row != band.YSize or col != band.XSize:
            print(' The images to be merged must have the same row and column number !')
            exit()
        elif not(osr.SpatialReference.IsSame(srs, gdal.Dataset.GetSpatialRef(ds))):
            print(' The images to be merged must have the same spatial reference !')
            exit()
        else:
            pass

driver = gdal.GetDriverByName('GTiff')
N = len(files)
#  Set the data type of the output file 
dMax = max(dtypes)
#  Estimate the size of the output file , Based on this, decide whether to create BIGTIFF
GDT_dict = dict([
    (1,8),
    (2,16),
    (3,16),
    (4,32),
    (5,32),
    (6,32),
    (7,64),
    (8,16),
    (9,32),
    (10,32),
    (11,64)])
outSize=GDT_dict[dMax]*col*row/8/1024/1024/1024*N
bigTiffFlag='YES' if outSize>=4 else 'NO'
outds = gdal.Driver.Create(driver, os.path.join(
    outpath, outname), col, row, N, dMax, ["COMPRESS=LZW", f"BIGTIFF={
      bigTiffFlag}"])
gdal.Dataset.SetSpatialRef(outds,srs)
gdal.Dataset.SetGeoTransform(outds,gt)

#  Start merging files 
print(' The documents to be merged are :\n{}'.format('\n'.join(files)))
pbar = tqdm(files, colour='blue')
for file in pbar:
    pbar.set_description(' Dealing with {}'.format(file))
    filearr = gdal_array.LoadFile(os.path.join(inputpath, file))
    i=files.index(file)
    outband=gdal.Dataset.GetRasterBand(outds,i+1)
    gdal.Band.WriteArray(outband,filearr)


print('finished!')

Example

bands_composite.py --inputpath=../months_lzw --filter=odiac2020b_1km_excl_intl_00[0-9]{
    2}.tif --outpath=./ --outname=2000.tif

Results output

View the multi band output tif image