Read GeoTiff and convert numpy.array to GeoTiff.

tif_tools.py

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os.path
import re

from osgeo import gdal
from osgeo import gdal_array
from osgeo import osr

def get_gain_band(input_file):
    """get GAIN_BAND from meta file (*.tif.txt)"""
     # define file name of *.tif.txt
    ifile_txt = re.sub(r'.tif', '.tif.txt', input_file)
    ld = open(ifile_txt)
    lines = ld.readlines()
    ld.close()
    
    gain_band = []
    for line in lines:
        if line.find("GAIN_BAND") >= 0:
             gain_band.append(float((re.split(' ', line)[1]).strip()))
    return gain_band

def tif2array(input_file, calc_gain=True):
    """
    read GeoTiff and convert to numpy.ndarray.

    Inputs:
        input_file (str) : the name of input GeoTiff file.
        calc_gain (bool) : wheter calc GAIN to DN  or not (defaul:True).
    return:
        image(np.array) : image for each bands
        dataset : for gdal's data drive.
    """
    dataset = gdal.Open(input_file, gdal.GA_ReadOnly)
    # Allocate our array using the first band's datatype
    image_datatype = dataset.GetRasterBand(1).DataType
    image = np.zeros((dataset.RasterYSize, dataset.RasterXSize, dataset.RasterCount),
                     dtype=float)
    
    if calc_gain == True:
        # get gain
        gain = get_gain_band(input_file)
    
    # Loop over all bands in dataset
    for b in range(dataset.RasterCount):
        # Remember, GDAL index is on 1, but Python is on 0 -- so we add 1 for our GDAL calls
        band = dataset.GetRasterBand(b + 1)
        # Read in the band's data into the third dimension of our array
        if calc_gain == True:
            # calc gain value for each bands
            image[:, :, b] = band.ReadAsArray() * gain[b]
        else:
            image[:, :, b] = band.ReadAsArray()
    return image, dataset

def read_training_data(training_data, input_file, band_num):
    """read training data.
    input: training_data ... file name of training_data
          input_file ... file name of input img
    """
    df=pd.read_table(training_data ,sep=',', header=None)
    scene = os.path.basename(input_file)
    # get only training data for input image.
    # we assume last columns is scene_name. so df.iloc[,-1].
    training_data_dataframe = df[ df.iloc[:,-1].str.contains(scene)]
    # convert np.array
    training_data=training_data_dataframe.as_matrix()
    # category
    category_label = np.array(training_data[:,1], dtype=int)
    #feature: np.array(band1_gain, band2_gain, band3_gain, band4_gain..., bandn_gain)
    feature = np.array(training_data[:,5:5+band_num], dtype=float)
    
    return category_label, feature
def array2raster(newRasterfn, dataset, array, dtype):
    """
    save GTiff file from numpy.array
    input:
        newRasterfn: save file name
        dataset : original tif file
        array : numpy.array
        dtype: Byte or Float32.
    """
    cols = array.shape[1]
    rows = array.shape[0]
    originX, pixelWidth, b, originY, d, pixelHeight = dataset.GetGeoTransform() 

    driver = gdal.GetDriverByName('GTiff')

    # set data type to save.
    GDT_dtype = gdal.GDT_Unknown
    if dtype == "Byte": 
        GDT_dtype = gdal.GDT_Byte
    elif dtype == "Float32":
        GDT_dtype = gdal.GDT_Float32
    else:
        print("Not supported data type.")

    # set number of band.
    if array.ndim == 2:
        band_num = 1
    else:
        band_num = array.shape[2]

    outRaster = driver.Create(newRasterfn, cols, rows, band_num, GDT_dtype)
    outRaster.SetGeoTransform((originX, pixelWidth, 0, originY, 0, pixelHeight))

    # Loop over all bands.
    for b in range(band_num):
        outband = outRaster.GetRasterBand(b + 1)
        # Read in the band's data into the third dimension of our array
        if band_num == 1:
            outband.WriteArray(array)
        else:
            outband.WriteArray(array[:,:,b])

    # setteing srs from input tif file.
    prj=dataset.GetProjection()
    outRasterSRS = osr.SpatialReference(wkt=prj)
    outRaster.SetProjection(outRasterSRS.ExportToWkt())
    outband.FlushCache()

Super helpful. Thanks for sharing!

thanx for sharing but if it had documentation with it then it would have been more helpfull.

Super useful, Thank you