from __future__ import absolute_import, print_function, division
from distutils import version
import os
import warnings
import numpy as np
from astropy import log
from astropy.io import fits
from astropy.coordinates import ICRS
from astropy.visualization import AsymmetricPercentileInterval, simple_norm
from reproject import reproject_interp
from reproject.mosaicking import find_optimal_celestial_wcs
def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
stretch='linear', vmid=None, exponent=2):
if vmin is None or vmax is None:
interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000)
try:
vmin_auto, vmax_auto = interval.get_limits(image)
except IndexError: # no valid values
vmin_auto = vmax_auto = 0
if vmin is None:
log.info("vmin = %10.3e (auto)" % vmin_auto)
vmin = vmin_auto
else:
log.info("vmin = %10.3e" % vmin)
if vmax is None:
log.info("vmax = %10.3e (auto)" % vmax_auto)
vmax = vmax_auto
else:
log.info("vmax = %10.3e" % vmax)
if stretch == 'arcsinh':
stretch = 'asinh'
normalizer = simple_norm(image, stretch=stretch, power=exponent,
asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False)
data = normalizer(image, clip=True).filled(0)
data = np.nan_to_num(data)
data = np.clip(data * 255., 0., 255.)
return data.astype(np.uint8)
[docs]def make_rgb_image(data, output, indices=(0, 1, 2), vmin_r=None, vmax_r=None,
pmin_r=0.25, pmax_r=99.75, stretch_r='linear', vmid_r=None,
exponent_r=2, vmin_g=None, vmax_g=None, pmin_g=0.25,
pmax_g=99.75, stretch_g='linear', vmid_g=None, exponent_g=2,
vmin_b=None, vmax_b=None, pmin_b=0.25, pmax_b=99.75,
stretch_b='linear', vmid_b=None, exponent_b=2,
make_nans_transparent=False, embed_avm_tags=True):
"""
Make an RGB image from a FITS RGB cube or from three FITS files.
Parameters
----------
data : str or tuple or list
If a string, this is the filename of an RGB FITS cube. If a tuple
or list, this should give the filename of three files to use for
the red, green, and blue channel.
output : str
The output filename. The image type (e.g. PNG, JPEG, TIFF, ...)
will be determined from the extension. Any image type supported by
the Python Imaging Library can be used.
indices : tuple, optional
If data is the filename of a FITS cube, these indices are the
positions in the third dimension to use for red, green, and
blue respectively. The default is to use the first three
indices.
vmin_r, vmin_g, vmin_b : float, optional
Minimum pixel value to use for the red, green, and blue channels.
If set to None for a given channel, the minimum pixel value for
that channel is determined using the corresponding pmin_x argument
(default).
vmax_r, vmax_g, vmax_b : float, optional
Maximum pixel value to use for the red, green, and blue channels.
If set to None for a given channel, the maximum pixel value for
that channel is determined using the corresponding pmax_x argument
(default).
pmin_r, pmin_r, pmin_g : float, optional
Percentile values used to determine for a given channel the
minimum pixel value to use for that channel if the corresponding
vmin_x is set to None. The default is 0.25% for all channels.
pmax_r, pmax_g, pmax_b : float, optional
Percentile values used to determine for a given channel the
maximum pixel value to use for that channel if the corresponding
vmax_x is set to None. The default is 99.75% for all channels.
stretch_r, stretch_g, stretch_b : { 'linear', 'log', 'sqrt', 'arcsinh', 'power' }
The stretch function to use for the different channels.
vmid_r, vmid_g, vmid_b : float, optional
Baseline values used for the log and arcsinh stretches. If
set to None, this is set to zero for log stretches and to
vmin - (vmax - vmin) / 30. for arcsinh stretches
exponent_r, exponent_g, exponent_b : float, optional
If stretch_x is set to 'power', this is the exponent to use.
make_nans_transparent : bool, optional
If set AND output is png, will add an alpha layer that sets pixels
containing a NaN to transparent.
embed_avm_tags : bool, optional
Whether to embed AVM tags inside the image - this can only be done for
JPEG and PNG files, and only if PyAVM is installed.
"""
try:
from PIL import Image
except ImportError:
try:
import Image
except ImportError:
raise ImportError("The Python Imaging Library (PIL) is required to make an RGB image")
if isinstance(data, str):
image = fits.getdata(data)
image_r = image[indices[0], :, :]
image_g = image[indices[1], :, :]
image_b = image[indices[2], :, :]
# Read in header
header = fits.getheader(data)
# Remove information about third dimension
header['NAXIS'] = 2
for key in ['NAXIS', 'CTYPE', 'CRPIX', 'CRVAL', 'CUNIT', 'CDELT', 'CROTA']:
for coord in range(3, 6):
name = key + str(coord)
if name in header:
header.__delitem__(name)
elif (type(data) == list or type(data) == tuple) and len(data) == 3:
filename_r, filename_g, filename_b = data
image_r = fits.getdata(filename_r)
image_g = fits.getdata(filename_g)
image_b = fits.getdata(filename_b)
# Read in header
header = fits.getheader(filename_r)
else:
raise Exception("data should either be the filename of a FITS cube or a list/tuple of three images")
# are we making a transparent layer?
do_alpha = make_nans_transparent and output.lower().endswith('.png')
if do_alpha:
log.info("Making alpha layer")
# initialize alpha layer
image_alpha = np.empty_like(image_r, dtype=np.uint8)
image_alpha[:] = 255
# look for nans in images
for im in [image_r, image_g, image_b]:
image_alpha[np.isnan(im)] = 0
log.info("Red:")
image_r = Image.fromarray(_data_stretch(image_r, vmin=vmin_r, vmax=vmax_r,
pmin=pmin_r, pmax=pmax_r, stretch=stretch_r,
vmid=vmid_r, exponent=exponent_r))
log.info("Green:")
image_g = Image.fromarray(_data_stretch(image_g,
vmin=vmin_g, vmax=vmax_g,
pmin=pmin_g, pmax=pmax_g,
stretch=stretch_g,
vmid=vmid_g,
exponent=exponent_g))
log.info("Blue:")
image_b = Image.fromarray(_data_stretch(image_b,
vmin=vmin_b, vmax=vmax_b,
pmin=pmin_b, pmax=pmax_b,
stretch=stretch_b,
vmid=vmid_b,
exponent=exponent_b))
img = Image.merge("RGB", (image_r, image_g, image_b))
if do_alpha:
# convert to RGBA and add alpha layer
image_alpha = Image.fromarray(image_alpha)
img.convert("RGBA")
img.putalpha(image_alpha)
img = img.transpose(Image.FLIP_TOP_BOTTOM)
img.save(output)
if embed_avm_tags:
try:
import pyavm
except ImportError:
warnings.warn("PyAVM 0.9.1 or later is not installed, so AVM tags will not be embedded in RGB image")
return
if version.LooseVersion(pyavm.__version__) < version.LooseVersion('0.9.1'):
warnings.warn("PyAVM 0.9.1 or later is not installed, so AVM tags will not be embedded in RGB image")
return
from pyavm import AVM
if output.lower().endswith(('.jpg', '.jpeg', '.png')):
avm = AVM.from_header(header)
avm.embed(output, output)
else:
warnings.warn("AVM tags will not be embedded in RGB image, as only JPEG and PNG files are supported")
[docs]def make_rgb_cube(files, output, north=True):
"""
Make an RGB data cube from a list of three FITS images.
This method can read in three FITS files with different
projections/sizes/resolutions and uses the `reproject
<https://reproject.readthedocs.io/en/stable/>`_ package to reproject them all
to the same projection.
Two files are produced by this function. The first is a three-dimensional
FITS cube with a filename give by ``output``, where the third dimension
contains the different channels. The second is a two-dimensional FITS
image with a filename given by ``output`` with a `_2d` suffix. This file
contains the mean of the different channels, and is required as input to
FITSFigure if show_rgb is subsequently used to show a color image
generated from the FITS cube (to provide the correct WCS information to
FITSFigure).
Parameters
----------
files : tuple or list
A list of the filenames of three FITS filename to reproject.
The order is red, green, blue.
output : str
The filename of the output RGB FITS cube.
north : bool, optional
Whether to rotate the image so that north is up. By default, this is
assumed to be 'north' in the ICRS frame, but you can also pass any
astropy :class:`~astropy.coordinates.BaseCoordinateFrame` to indicate
to use the north of that frame.
"""
# Check that input files exist
for f in files:
if not os.path.exists(f):
raise Exception("File does not exist : " + f)
if north is not False:
frame = ICRS() if north is True else north
auto_rotate = False
else:
frame = None
auto_rotate = True
# Find optimal WCS and shape based on input images
wcs, shape = find_optimal_celestial_wcs(files, frame=frame, auto_rotate=auto_rotate)
header = wcs.to_header()
# Generate empty datacube
image_cube = np.zeros((len(files),) + shape, dtype=np.float32)
# Loop through files and reproject
for i, filename in enumerate(files):
image_cube[i, :, :] = reproject_interp(filename, wcs, shape_out=shape)[0]
# Write out final cube
fits.writeto(output, image_cube, header, overwrite=True)
# Write out collapsed version of cube
fits.writeto(output.replace('.fits', '_2d.fits'),
np.mean(image_cube, axis=0), header, overwrite=True)
