NITFPythonGDAL.py

# example script on how to read a NITF file in Python
# tested with UNICRON 2008 data that has been truthed
# converted to Python 3.X tested with Python 3.7 from
# Anaconda 12/2018

import gdal
from gdalconst import *
import argparse
import sys
import numpy as np
import matplotlib.pyplot as plt
#import cv2 as cv # only needed if you want to convert the image

if __name__ == "__main__":
	parser = argparse.ArgumentParser()
	parser.add_argument("-f", "--fileNameNITF",
		help="name of NITF file to read full path",
		action="store", required=True)
	args = parser.parse_args()
	data = {}
	data['args'] = args
	data['fileNameNITF'] = args.fileNameNITF
	dataset = gdal.Open( data['fileNameNITF'], GA_ReadOnly )
	if dataset is None:
		print("dataset is None that is bad")
		sys.exit(2)
	print('Driver: %s / %s' % (dataset.GetDriver().ShortName, dataset.GetDriver().LongName))
	print('Size is %d x %d x %d' % (dataset.RasterXSize, dataset.RasterYSize, dataset.RasterCount))
	print('Projection is %s' % (dataset.GetProjection()))

	geotransform = dataset.GetGeoTransform()
	if not geotransform is None:
		print('Origin = (%d, %d)' % (geotransform[0], geotransform[3]))
		print('Pixel Size = (%d, %d)' % (geotransform[1] ,geotransform[5]))

	band = dataset.GetRasterBand(1)

	print('Band Type= %s' % (gdal.GetDataTypeName(band.DataType)))

	min = band.GetMinimum()
	max = band.GetMaximum()
	if min is None or max is None:
		(min,max) = band.ComputeRasterMinMax(1)
	print('Min=%.3f, Max=%.3f' % (min, max))

	if band.GetOverviewCount() > 0:
		print('Band has %d overviews' % (band.GetOverviewCount()))

	if not band.GetRasterColorTable() is None:
		print('Band has a color table with %d entries' % (band.GetRasterColorTable().GetCount()))

	# Read the data from the NITF file and place into a numpy array
	# ReadAsArray(self, xoff=0, yoff=0, win_xsize=None, win_ysize=None, 
	# buf_xsize=None, buf_ysize=None, buf_obj=None)	
	# band.XSize is how many pixels in left to right
	# band.YSize is how many pixels up to down
	# this grabs the OSU stadium in frame ~/data/CLIF2007/20071028142502-01000100-VIS.ntf.r0
	# you can display the entire frame but matplot lib is slow
	#scanlineArray = band.ReadAsArray(6000, 5000, 1000, 1000, 
	#	1000, 1000)
	#
	# reads the entire image at full resolution, this will put the track on the
	# correct vehicle
	scanlineArray = band.ReadAsArray(0, 0, dataset.RasterXSize, 
		dataset.RasterYSize, dataset.RasterXSize, dataset.RasterYSize)
	print(scanlineArray.shape)
	print(type(scanlineArray[0][0]))
	# this line saves the image data as a jpg a good way to do conversion, if 
	# you want to get the data out of the NITF file format, you can use OpenCV
	# and imwrite the image out as a jpeg file
	# cv.imwrite("/Users/rovitotv/temp/output.jpg", scanlineArray)

	plt.imshow(scanlineArray, cmap=plt.cm.gray)
	plt.show()