Convert a 3-color image (JPG) to separate FITS images

This example opens an RGB JPEG image and writes out each channel as a separate FITS (image) file.

This example uses pillow to read the image, matplotlib.pyplot to display the image, and to save FITS files.

By: Erik Bray, Adrian Price-Whelan

License: BSD

import numpy as np
from PIL import Image
from import fits

Set up matplotlib and use a nicer set of plot parameters

import matplotlib.pyplot as plt
from astropy.visualization import astropy_mpl_style

Load and display the original 3-color jpeg image:

image ='Hs-2009-14-a-web.jpg')
xsize, ysize = image.size
print("Image size: {} x {}".format(xsize, ysize))


Image size: 400 x 232

<matplotlib.image.AxesImage object at 0x7fa0764faf98>

Split the three channels (RGB) and get the data as Numpy arrays. The arrays are flattened, so they are 1-dimensional:

r, g, b = image.split()
r_data = np.array(r.getdata()) # data is now an array of length ysize*xsize
g_data = np.array(g.getdata())
b_data = np.array(b.getdata())



Reshape the image arrays to be 2-dimensional:

r_data = r_data.reshape(ysize, xsize)
g_data = g_data.reshape(ysize, xsize)
b_data = b_data.reshape(ysize, xsize)

Write out the channels as separate FITS images

red = fits.PrimaryHDU(data=r_data)
red.header['LATOBS'] = "32:11:56" # add spurious header info
red.header['LONGOBS'] = "110:56"

green = fits.PrimaryHDU(data=g_data)
green.header['LATOBS'] = "32:11:56"
green.header['LONGOBS'] = "110:56"

blue = fits.PrimaryHDU(data=b_data)
blue.header['LATOBS'] = "32:11:56"
blue.header['LONGOBS'] = "110:56"

Delete the files created

import os

Total running time of the script: ( 0 minutes 0.258 seconds)

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