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# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Combine 3 images to produce a properly-scaled RGB image following
Lupton et al. (2004).
The three images must be aligned and have the same pixel scale and size.
For details, see : https://ui.adsabs.harvard.edu/abs/2004PASP..116..133L
"""
import numpy as np
from astropy.visualization.interval import ManualInterval, ZScaleInterval
from astropy.visualization.stretch import BaseStretch
from astropy.visualization.stretch import _prepare as _stretch_prepare
from .basic_rgb import RGBImageMapping
__all__ = [
"AsinhMapping",
"AsinhZScaleMapping",
"LinearMapping",
"LuptonAsinhStretch",
"LuptonAsinhZscaleStretch",
"Mapping",
"make_lupton_rgb",
]
def compute_intensity(image_r, image_g=None, image_b=None):
"""
Return a naive total intensity from the red, blue, and green intensities.
Parameters
----------
image_r : ndarray
Intensity of image to be mapped to red; or total intensity if
``image_g`` and ``image_b`` are None.
image_g : ndarray, optional
Intensity of image to be mapped to green.
image_b : ndarray, optional
Intensity of image to be mapped to blue.
Returns
-------
intensity : ndarray
Total intensity from the red, blue and green intensities, or
``image_r`` if green and blue images are not provided.
"""
if image_g is None or image_b is None:
if not (image_g is None and image_b is None):
raise ValueError(
"please specify either a single image or red, green, and blue images."
)
return image_r
intensity = (image_r + image_g + image_b) / 3.0
# Repack into whatever type was passed to us
return np.asarray(intensity, dtype=image_r.dtype)
class Mapping:
"""
Baseclass to map red, blue, green intensities into uint8 values.
Parameters
----------
minimum : float or sequence(3)
Intensity that should be mapped to black (a scalar or array for R, G, B).
image : ndarray, optional
An image used to calculate some parameters of some mappings.
"""
def __init__(self, minimum=None, image=None):
self._uint8Max = float(np.iinfo(np.uint8).max)
try:
len(minimum)
except TypeError:
minimum = 3 * [minimum]
if len(minimum) != 3:
raise ValueError("please provide 1 or 3 values for minimum.")
self.minimum = minimum
self._image = np.asarray(image)
def make_rgb_image(self, image_r, image_g, image_b):
"""
Convert 3 arrays, image_r, image_g, and image_b into an 8-bit RGB image.
Parameters
----------
image_r : ndarray
Image to map to red.
image_g : ndarray
Image to map to green.
image_b : ndarray
Image to map to blue.
Returns
-------
RGBimage : ndarray
RGB (integer, 8-bits per channel) color image as an NxNx3 numpy array.
"""
image_r = np.asarray(image_r)
image_g = np.asarray(image_g)
image_b = np.asarray(image_b)
if (image_r.shape != image_g.shape) or (image_g.shape != image_b.shape):
msg = "The image shapes must match. r: {}, g: {} b: {}"
raise ValueError(msg.format(image_r.shape, image_g.shape, image_b.shape))
return np.dstack(
self._convert_images_to_uint8(image_r, image_g, image_b)
).astype(np.uint8)
def intensity(self, image_r, image_g, image_b):
"""
Return the total intensity from the red, blue, and green intensities.
This is a naive computation, and may be overridden by subclasses.
Parameters
----------
image_r : ndarray
Intensity of image to be mapped to red; or total intensity if
``image_g`` and ``image_b`` are None.
image_g : ndarray, optional
Intensity of image to be mapped to green.
image_b : ndarray, optional
Intensity of image to be mapped to blue.
Returns
-------
intensity : ndarray
Total intensity from the red, blue and green intensities, or
``image_r`` if green and blue images are not provided.
"""
return compute_intensity(image_r, image_g, image_b)
def map_intensity_to_uint8(self, I):
"""
Return an array which, when multiplied by an image, returns that image
mapped to the range of a uint8, [0, 255] (but not converted to uint8).
The intensity is assumed to have had minimum subtracted (as that can be
done per-band).
Parameters
----------
I : ndarray
Intensity to be mapped.
Returns
-------
mapped_I : ndarray
``I`` mapped to uint8
"""
with np.errstate(invalid="ignore", divide="ignore"):
return np.clip(I, 0, self._uint8Max)
def _convert_images_to_uint8(self, image_r, image_g, image_b):
"""
Use the mapping to convert images image_r, image_g, and image_b to a triplet of uint8 images.
"""
image_r = image_r - self.minimum[0] # n.b. makes copy
image_g = image_g - self.minimum[1]
image_b = image_b - self.minimum[2]
fac = self.map_intensity_to_uint8(self.intensity(image_r, image_g, image_b))
image_rgb = [image_r, image_g, image_b]
for c in image_rgb:
c *= fac
with np.errstate(invalid="ignore"):
c[c < 0] = 0 # individual bands can still be < 0, even if fac isn't
pixmax = self._uint8Max
# copies -- could work row by row to minimise memory usage
r0, g0, b0 = image_rgb
# n.b. np.where can't and doesn't short-circuit
with np.errstate(invalid="ignore", divide="ignore"):
for i, c in enumerate(image_rgb):
c = np.where(
r0 > g0,
np.where(
r0 > b0,
np.where(r0 >= pixmax, c * pixmax / r0, c),
np.where(b0 >= pixmax, c * pixmax / b0, c),
),
np.where(
g0 > b0,
np.where(g0 >= pixmax, c * pixmax / g0, c),
np.where(b0 >= pixmax, c * pixmax / b0, c),
),
).astype(np.uint8)
c[c > pixmax] = pixmax
image_rgb[i] = c
return image_rgb
class LinearMapping(Mapping):
"""
A linear map of red, blue, green intensities into uint8 values.
A linear stretch from [minimum, maximum].
If one or both are omitted use image min and/or max to set them.
Parameters
----------
minimum : float
Intensity that should be mapped to black (a scalar or array for R, G, B).
maximum : float
Intensity that should be mapped to white (a scalar).
"""
def __init__(self, minimum=None, maximum=None, image=None):
if minimum is None or maximum is None:
if image is None:
raise ValueError(
"you must provide an image if you don't "
"set both minimum and maximum"
)
if minimum is None:
minimum = image.min()
if maximum is None:
maximum = image.max()
Mapping.__init__(self, minimum=minimum, image=image)
self.maximum = maximum
if maximum is None:
self._range = None
else:
if maximum == minimum:
raise ValueError("minimum and maximum values must not be equal")
self._range = float(maximum - minimum)
def map_intensity_to_uint8(self, I):
# n.b. np.where can't and doesn't short-circuit
with np.errstate(invalid="ignore", divide="ignore"):
return np.where(
I <= 0,
0,
np.where(
I >= self._range, self._uint8Max / I, self._uint8Max / self._range
),
)
class AsinhMapping(Mapping):
"""
A mapping for an asinh stretch (preserving colours independent of brightness).
x = asinh(Q (I - minimum)/stretch)/Q
This reduces to a linear stretch if Q == 0
See https://ui.adsabs.harvard.edu/abs/2004PASP..116..133L
Parameters
----------
minimum : float
Intensity that should be mapped to black (a scalar or array for R, G, B).
stretch : float
The linear stretch of the image.
Q : float
The asinh softening parameter.
"""
def __init__(self, minimum, stretch, Q=8):
Mapping.__init__(self, minimum)
# 32bit floating point machine epsilon; sys.float_info.epsilon is 64bit
epsilon = 1.0 / 2**23
if abs(Q) < epsilon:
Q = 0.1
else:
Qmax = 1e10
if Q > Qmax:
Q = Qmax
frac = 0.1 # gradient estimated using frac*stretch is _slope
self._slope = frac * self._uint8Max / np.arcsinh(frac * Q)
self._soften = Q / float(stretch)
def map_intensity_to_uint8(self, I):
# n.b. np.where can't and doesn't short-circuit
with np.errstate(invalid="ignore", divide="ignore"):
return np.where(I <= 0, 0, np.arcsinh(I * self._soften) * self._slope / I)
class AsinhZScaleMapping(AsinhMapping):
"""
A mapping for an asinh stretch, estimating the linear stretch by zscale.
x = asinh(Q (I - z1)/(z2 - z1))/Q
Parameters
----------
image1 : ndarray or a list of arrays
The image to analyse, or a list of 3 images to be converted to
an intensity image.
image2 : ndarray, optional
the second image to analyse (must be specified with image3).
image3 : ndarray, optional
the third image to analyse (must be specified with image2).
Q : float, optional
The asinh softening parameter. Default is 8.
pedestal : float or sequence(3), optional
The value, or array of 3 values, to subtract from the images; or None.
Notes
-----
pedestal, if not None, is removed from the images when calculating the
zscale stretch, and added back into Mapping.minimum[]
"""
def __init__(self, image1, image2=None, image3=None, Q=8, pedestal=None):
if image2 is None or image3 is None:
if not (image2 is None and image3 is None):
raise ValueError(
"please specify either a single image or three images."
)
image = [image1]
else:
image = [image1, image2, image3]
if pedestal is not None:
try:
len(pedestal)
except TypeError:
pedestal = 3 * [pedestal]
if len(pedestal) != 3:
raise ValueError("please provide 1 or 3 pedestals.")
image = list(image) # needs to be mutable
for i, im in enumerate(image):
if pedestal[i] != 0.0:
image[i] = im - pedestal[i] # n.b. a copy
else:
pedestal = len(image) * [0.0]
image = compute_intensity(*image)
zscale_limits = ZScaleInterval().get_limits(image)
zscale = LinearMapping(*zscale_limits, image=image)
# zscale.minimum is always a triple
stretch = zscale.maximum - zscale.minimum[0]
minimum = zscale.minimum
for i, level in enumerate(pedestal):
minimum[i] += level
AsinhMapping.__init__(self, minimum, stretch, Q)
self._image = image
class LuptonAsinhStretch(BaseStretch):
r"""
A modified asinh stretch, with some changes to the constants
relative to `~astropy.visualization.AsinhStretch`.
The stretch is given by:
.. math::
& y = {\rm asinh}\left(\frac{Q * x}{stretch}\right) *
\frac{frac}{{\rm asinh}(frac * Q)} \\
& frac = 0.1
Parameters
----------
stretch : float, optional
Linear stretch of the image. ``stretch`` must be greater than 0.
Default is 5.
Q : float, optional
The asinh softening parameter. ``Q`` must be greater than 0.
Default is 8.
Notes
-----
Based on the asinh stretch presented in Lupton et al. 2004
(https://ui.adsabs.harvard.edu/abs/2004PASP..116..133L).
Examples
--------
.. plot::
:show-source-link:
import numpy as np
from astropy.visualization import LuptonAsinhStretch
from matplotlib import pyplot as plt
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(8, 8),
layout='constrained')
ax = ax.ravel()
x = np.linspace(0, 1, 100)
stretches = (0.05, 0.1, 0.2, 0.5, 1, 5, 10)
Qs = (2, 5, 8, 10)
for i, Q in enumerate(Qs):
for st in stretches:
stretch = LuptonAsinhStretch(stretch=st, Q=Q)
label = f'{st=}'
ax[i].plot(x, stretch(x, clip=True), label=label)
ax[i].axis('equal')
ax[i].plot(x, x, ls='dotted', color='k', alpha=0.3)
ax[i].set_xlim(0, 1)
ax[i].set_ylim(0, 1)
ax[i].set_xlabel('Input Value')
ax[i].set_ylabel('Output Value')
ax[i].set_title(f'{stretch.__class__.__name__}, {Q=}')
ax[i].legend(loc='lower right', fontsize=8)
"""
def __init__(self, stretch=5, Q=8):
super().__init__()
if stretch < 0:
raise ValueError(f"Stretch must be non-negative! {stretch=}")
if Q < 0:
raise ValueError(f"Q must be non-negative! {Q=}")
# 32bit floating point machine epsilon; sys.float_info.epsilon is 64bit
epsilon = 1.0 / 2**23
if abs(Q) < epsilon:
Q = 0.1
else:
Qmax = 1e10
if Q > Qmax:
Q = Qmax
self.stretch = stretch
self.Q = Q
frac = 0.1
self._slope = frac / np.arcsinh(frac * Q)
self._soften = Q / float(stretch)
def __call__(self, values, clip=False, out=None):
values = _stretch_prepare(values, clip=clip, out=out)
np.multiply(values, self._soften, out=values)
np.arcsinh(values, out=values)
np.multiply(values, self._slope, out=values)
return values
class LuptonAsinhZscaleStretch(LuptonAsinhStretch):
r"""
A modified asinh stretch, where the linear stretch is calculated using
zscale.
The stretch is given by:
.. math::
& y = {\rm asinh}\left(\frac{Q * x}{stretch}\right) *
\frac{frac}{{\rm asinh}(frac * Q)} \\
& frac = 0.1 \\
& stretch = z2 - z1
Parameters
----------
image1 : ndarray or array-like
The image to analyse, or a list of 3 images to be converted to
an intensity image.
Q : float, optional
The asinh softening parameter. ``Q`` must be greater than 0.
Default is 8.
pedestal : or array-like, optional
The value, or array of 3 values, to subtract from the images(s)
before determining the zscaling. Default is None (nothing subtracted).
"""
def __init__(self, image, Q=8, pedestal=None):
# copy because of in-place operations after
image = np.array(image, copy=True, dtype=float)
_raiseerr = False
if len(image.shape) == 2:
image = [image]
elif len(image.shape) == 3:
if image.shape[0] != 3:
_raiseerr = True
else:
_raiseerr = True
if _raiseerr:
raise ValueError(
"Input 'image' must be a single image "
"or a stack/3xMxN array of 3 images! "
f"{image.shape=}"
)
image = list(image) # needs to be mutable
if pedestal is not None:
try:
len(pedestal)
except TypeError:
pedestal = 3 * [pedestal]
if len(pedestal) != 3:
raise ValueError(
"pedestal must be 1 or 3 values, matching the image input."
)
for i, im in enumerate(image):
if pedestal[i] != 0.0:
image[i] = im - pedestal[i] # n.b. a copy
image = compute_intensity(*image)
zscale_limits = ZScaleInterval().get_limits(image)
_stretch = zscale_limits[1] - zscale_limits[0]
self._image = image
super().__init__(stretch=_stretch, Q=Q)
class RGBImageMappingLupton(RGBImageMapping):
"""
Class to map red, blue, green images into either a normalized float or
an 8-bit image, by performing optional clipping and applying
a scaling function to each band in non-independent manner that depends
on the other bands, following the scaling scheme presented in
Lupton et al. 2004.
Parameters
----------
interval : `~astropy.visualization.BaseInterval` subclass instance or array-like, optional
The interval object to apply to the data (either a single instance or
an array for R, G, B). Default is
`~astropy.visualization.ManualInterval`.
stretch : `~astropy.visualization.BaseStretch` subclass instance
The stretch object to apply to the data. The default is
`~astropy.visualization.AsinhLuptonStretch`.
"""
def __init__(
self,
interval=ManualInterval(vmin=0, vmax=None),
stretch=LuptonAsinhStretch(stretch=5, Q=8),
):
super().__init__(interval=interval, stretch=stretch)
self._pixmax = 1.0
def intensity(self, image_r, image_g, image_b):
"""
Return the total intensity from the red, blue, and green intensities.
This is a naive computation, and may be overridden by subclasses.
Parameters
----------
image_r : ndarray
Intensity of image to be mapped to red; or total intensity if
``image_g`` and ``image_b`` are None.
image_g : ndarray, optional
Intensity of image to be mapped to green.
image_b : ndarray, optional
Intensity of image to be mapped to blue.
Returns
-------
intensity : ndarray
Total intensity from the red, blue and green intensities, or
``image_r`` if green and blue images are not provided.
"""
return compute_intensity(image_r, image_g, image_b)
def apply_mappings(self, image_r, image_g, image_b):
"""
Apply mapping stretch and intervals to convert images image_r, image_g,
and image_b to a triplet of normalized images, following the scaling
scheme presented in Lupton et al. 2004.
Compared to astropy's ImageNormalize which first normalizes images
by cropping and linearly mapping onto [0.,1.] and then applies
a specified stretch algorithm, the Lupton et al. algorithm applies
stretching to a multi-color intensity and then computes per-band
scaled images with bound cropping.
This is modified here by allowing for different minimum values
for each of the input r, g, b images, and then computing
the intensity on the subtracted images.
Parameters
----------
image_r : ndarray
Intensity of image to be mapped to red
image_g : ndarray
Intensity of image to be mapped to green.
image_b : ndarray
Intensity of image to be mapped to blue.
Returns
-------
image_rgb : ndarray
Triplet of mapped images based on the specified (per-band)
intervals and the stretch function
Notes
-----
The Lupton et al 2004 algorithm is computed with the following steps:
1. Shift each band with the minimum values
2. Compute the intensity I and stretched intensity f(I)
3. Compute the ratio of the stretched intensity to intensity f(I)/I,
and clip to a lower bound of 0
4. Compute the scaled band images by multiplying with the ratio f(I)/I
5. Clip each band to a lower bound of 0
6. Scale down pixels where max(R,G,B)>1 by the value max(R,G,B)
"""
image_r = np.array(image_r, copy=True)
image_g = np.array(image_g, copy=True)
image_b = np.array(image_b, copy=True)
# Subtract per-band minima
image_rgb = [image_r, image_g, image_b]
for i, img in enumerate(image_rgb):
vmin, _ = self.intervals[i].get_limits(img)
image_rgb[i] = np.subtract(img, vmin)
image_rgb = np.asarray(image_rgb)
# Determine the intensity and streteched intensity
Int = self.intensity(*image_rgb)
fI = self.stretch(Int, clip=False)
# Get normalized fI, and clip to lower bound of 0:
fInorm = np.where(Int <= 0, 0, np.true_divide(fI, Int))
# Compute X = x * f(I) / I for each filter x=(r,g,b)
np.multiply(image_rgb, fInorm, out=image_rgb)
# Clip individual bands to minimum of 0, as
# individual bands can be < 0 even if fI/I isn't.
image_rgb = np.clip(image_rgb, 0.0, None)
# Determine the max of all 3 bands at each position
maxRGB = np.max(image_rgb, axis=0)
with np.errstate(invalid="ignore", divide="ignore"):
image_rgb = np.where(
maxRGB > self._pixmax,
np.true_divide(image_rgb * self._pixmax, maxRGB),
image_rgb,
)
return np.asarray(image_rgb)
def make_lupton_rgb( # ruff: ignore[PLR0917]
image_r,
image_g,
image_b,
interval=None,
stretch_object=None,
minimum=None,
stretch=5,
Q=8,
filename=None,
output_dtype=np.uint8,
):
r"""
Return a Red/Green/Blue color image from 3 images using interconnected
band scaling, and an arbitrary stretch function (by default, an asinh stretch).
The input images can be int or float, and in any range or bit-depth.
For a more detailed look at the use of this method, see the document
:ref:`astropy:astropy-visualization-rgb`.
Parameters
----------
image_r : ndarray
Image to map to red.
image_g : ndarray
Image to map to green.
image_b : ndarray
Image to map to blue.
interval : `~astropy.visualization.BaseInterval` subclass instance or array-like, optional
The interval object to apply to the data (either a single instance or
an array for R, G, B). Default is
`~astropy.visualization.ManualInterval` with vmin=0.
stretch_object : `~astropy.visualization.BaseStretch` subclass instance, optional
The stretch object to apply to the data. If set, the input values of
``minimum``, ``stretch``, and ``Q`` will be ignored.
For the Lupton scheme, this would be an instance of
`~astropy.visualization.LuptonAsinhStretch`, but alternatively
`~astropy.visualization.LuptonAsinhZscaleStretch` or some other
stretch can be used.
minimum : float or array-like, optional
Deprecated. Intensity that should be mapped to black (a scalar or
array of R, G, B). If `None`, each image's minimum value is used.
Default is None.
stretch : float, optional
The linear stretch of the image. Default is 5
Q : float, optional
The asinh softening parameter. Default is 8.
filename : str, optional
Write the resulting RGB image to a file (file type determined
from extension).
output_dtype : numpy scalar type, optional
Image output data type. Default is np.uint8.
Returns
-------
rgb : ndarray
RGB color image as an NxNx3 numpy array, with the specified
data type format
"""
if stretch_object is None:
stretch_object = LuptonAsinhStretch(stretch=stretch, Q=Q)
if interval is None:
# Only use minimum if interval is not specified:
if minimum is not None:
# Backwards compatibility:
try:
len(minimum)
except TypeError:
minimum = 3 * [minimum]
if len(minimum) != 3:
raise ValueError("please provide 1 or 3 values for minimum.")
interval = []
for i in range(3):
interval.append(ManualInterval(vmin=minimum[i], vmax=None))
else:
# Default option:
interval = ManualInterval(vmin=0, vmax=None)
lup_map = RGBImageMappingLupton(
interval=interval,
stretch=stretch_object,
)
rgb = lup_map.make_rgb_image(image_r, image_g, image_b, output_dtype=output_dtype)
if filename:
import matplotlib.image
matplotlib.image.imsave(filename, rgb, origin="lower")
return rgb
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