Implementing-cryptography-using-python

Chapter 6 ■ Using Cryptography with Images 195
Working with large images
While we are on the topic of images and the ability to hide data inside them,
I want to introduce you to the FITS image type. FITS stand for Flexible Image
Transport System; it is an open standard that defines a digital file format that
is used for the transmission, processing, and storage of data within a single
file. FITS is typically used for digital files produced in astronomical research
or satellite data. The amount of data inside the file can be astronomical, pun
intended. The data inside the file can be represented in several dimensional
formats. The important aspect to understand about how this format works is
that there is human-readable text stored in the header portion of the file format.
The header details may list any parameters that could tell where the image was
taken, which satellite or telescope produced it, and any number of pieces of
metadata that the author wants to include; the only limit to the header metadata
is that the label must be eight characters or less. A FITS file consists of one or
more Header + Data Units; these are known as HDUs. The first HDU that is
created is called the primary HDU or primary array. The primary HDU can
be empty or contain an N-dimensional array of pixels such as a 1-D spectrum,
a 2-D image, or a 3-D data cube. The best way to get an understanding of the
FITS standard is to dive in with some Python code; in order to follow along
with the following code, you will need to include the astropy library. Another
library that you can use is PyFITS. The .io FITS module is identical to PyFITS
except by name. PyFITS was ported into astropy. You can do this by typing pip
install astropy. Let's create a FITS file that stores some random numpy array in
a file named random_array.fits. The output of the file will give you the default
header information along with the contents of the data.
import numpy as np
from astropy.io import fits
file_name = "chapter6/fits/random_array.fits"
hdu = fits.PrimaryHDU()
hdu.data = np.random.random((128,128))
# Note that setting the data automatically populates the header with
basic information:
hdu.writeto(file_name, overwrite=True)
data = fits.getdata(file_name)
header = fits.getheader(file_name)
print(header)
print()
print(data)
Now that you understand the components that make up a FITS file, we can
explore FITS for images where you might want to keep some of the header