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colorconv.py
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colorconv.py
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"""Functions for converting between color spaces.
The "central" color space in this module is RGB, more specifically the linear
sRGB color space using D65 as a white-point [1]_. This represents a
standard monitor (w/o gamma correction). For a good FAQ on color spaces see
[2]_.
The API consists of functions to convert to and from RGB as defined above, as
well as a generic function to convert to and from any supported color space
(which is done through RGB in most cases).
Supported color spaces
----------------------
* RGB : Red Green Blue.
Here the sRGB standard [1]_.
* HSV : Hue, Saturation, Value.
Uniquely defined when related to sRGB [3]_.
* RGB CIE : Red Green Blue.
The original RGB CIE standard from 1931 [4]_. Primary colors are 700 nm
(red), 546.1 nm (blue) and 435.8 nm (green).
* XYZ CIE : XYZ
Derived from the RGB CIE color space. Chosen such that
``x == y == z == 1/3`` at the whitepoint, and all color matching
functions are greater than zero everywhere.
* LAB CIE : Lightness, a, b
Colorspace derived from XYZ CIE that is intended to be more
perceptually uniform
* LUV CIE : Lightness, u, v
Colorspace derived from XYZ CIE that is intended to be more
perceptually uniform
* LCH CIE : Lightness, Chroma, Hue
Defined in terms of LAB CIE. C and H are the polar representation of
a and b. The polar angle C is defined to be on ``(0, 2*pi)``
:author: Nicolas Pinto (rgb2hsv)
:author: Ralf Gommers (hsv2rgb)
:author: Travis Oliphant (XYZ and RGB CIE functions)
:author: Matt Terry (lab2lch)
:author: Alex Izvorski (yuv2rgb, rgb2yuv and related)
:license: modified BSD
References
----------
.. [1] Official specification of sRGB, IEC 61966-2-1:1999.
.. [2] http://www.poynton.com/ColorFAQ.html
.. [3] https://en.wikipedia.org/wiki/HSL_and_HSV
.. [4] https://en.wikipedia.org/wiki/CIE_1931_color_space
"""
from warnings import warn
import numpy as np
from scipy import linalg
from .._shared.utils import (_supported_float_type, channel_as_last_axis,
identity, reshape_nd, slice_at_axis)
from ..util import dtype, dtype_limits
def convert_colorspace(arr, fromspace, tospace, *, channel_axis=-1):
"""Convert an image array to a new color space.
Valid color spaces are:
'RGB', 'HSV', 'RGB CIE', 'XYZ', 'YUV', 'YIQ', 'YPbPr', 'YCbCr', 'YDbDr'
Parameters
----------
arr : (..., 3, ...) array_like
The image to convert. By default, the final dimension denotes
channels.
fromspace : str
The color space to convert from. Can be specified in lower case.
tospace : str
The color space to convert to. Can be specified in lower case.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The converted image. Same dimensions as input.
Raises
------
ValueError
If fromspace is not a valid color space
ValueError
If tospace is not a valid color space
Notes
-----
Conversion is performed through the "central" RGB color space,
i.e. conversion from XYZ to HSV is implemented as ``XYZ -> RGB -> HSV``
instead of directly.
Examples
--------
>>> from skimage import data
>>> img = data.astronaut()
>>> img_hsv = convert_colorspace(img, 'RGB', 'HSV')
"""
fromdict = {'rgb': identity, 'hsv': hsv2rgb, 'rgb cie': rgbcie2rgb,
'xyz': xyz2rgb, 'yuv': yuv2rgb, 'yiq': yiq2rgb,
'ypbpr': ypbpr2rgb, 'ycbcr': ycbcr2rgb, 'ydbdr': ydbdr2rgb}
todict = {'rgb': identity, 'hsv': rgb2hsv, 'rgb cie': rgb2rgbcie,
'xyz': rgb2xyz, 'yuv': rgb2yuv, 'yiq': rgb2yiq,
'ypbpr': rgb2ypbpr, 'ycbcr': rgb2ycbcr, 'ydbdr': rgb2ydbdr}
fromspace = fromspace.lower()
tospace = tospace.lower()
if fromspace not in fromdict:
msg = f'`fromspace` has to be one of {fromdict.keys()}'
raise ValueError(msg)
if tospace not in todict:
msg = f'`tospace` has to be one of {todict.keys()}'
raise ValueError(msg)
return todict[tospace](
fromdict[fromspace](arr, channel_axis=channel_axis),
channel_axis=channel_axis
)
def _prepare_colorarray(arr, force_copy=False, *, channel_axis=-1):
"""Check the shape of the array and convert it to
floating point representation.
"""
arr = np.asanyarray(arr)
if arr.shape[channel_axis] != 3:
msg = (f'the input array must have size 3 along `channel_axis`, '
f'got {arr.shape}')
raise ValueError(msg)
float_dtype = _supported_float_type(arr.dtype)
if float_dtype == np.float32:
_func = dtype.img_as_float32
else:
_func = dtype.img_as_float64
return _func(arr, force_copy=force_copy)
def _validate_channel_axis(channel_axis, ndim):
if not isinstance(channel_axis, int):
raise TypeError("channel_axis must be an integer")
if channel_axis < -ndim or channel_axis >= ndim:
raise np.AxisError("channel_axis exceeds array dimensions")
def rgba2rgb(rgba, background=(1, 1, 1), *, channel_axis=-1):
"""RGBA to RGB conversion using alpha blending [1]_.
Parameters
----------
rgba : (..., 4, ...) array_like
The image in RGBA format. By default, the final dimension denotes
channels.
background : array_like
The color of the background to blend the image with (3 floats
between 0 to 1 - the RGB value of the background).
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Raises
------
ValueError
If `rgba` is not at least 2D with shape (..., 4, ...).
References
----------
.. [1] https://en.wikipedia.org/wiki/Alpha_compositing#Alpha_blending
Examples
--------
>>> from skimage import color
>>> from skimage import data
>>> img_rgba = data.logo()
>>> img_rgb = color.rgba2rgb(img_rgba)
"""
arr = np.asanyarray(rgba)
_validate_channel_axis(channel_axis, arr.ndim)
channel_axis = channel_axis % arr.ndim
if arr.shape[channel_axis] != 4:
msg = (f'the input array must have size 4 along `channel_axis`, '
f'got {arr.shape}')
raise ValueError(msg)
float_dtype = _supported_float_type(arr.dtype)
if float_dtype == np.float32:
arr = dtype.img_as_float32(arr)
else:
arr = dtype.img_as_float64(arr)
background = np.ravel(background).astype(arr.dtype)
if len(background) != 3:
raise ValueError('background must be an array-like containing 3 RGB '
f'values. Got {len(background)} items')
if np.any(background < 0) or np.any(background > 1):
raise ValueError('background RGB values must be floats between '
'0 and 1.')
# reshape background for broadcasting along non-channel axes
background = reshape_nd(background, arr.ndim, channel_axis)
alpha = arr[slice_at_axis(slice(3, 4), axis=channel_axis)]
channels = arr[slice_at_axis(slice(3), axis=channel_axis)]
out = np.clip((1 - alpha) * background + alpha * channels,
a_min=0, a_max=1)
return out
@channel_as_last_axis()
def rgb2hsv(rgb, *, channel_axis=-1):
"""RGB to HSV color space conversion.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in HSV format. Same dimensions as input.
Raises
------
ValueError
If `rgb` is not at least 2-D with shape (..., 3, ...).
Notes
-----
Conversion between RGB and HSV color spaces results in some loss of
precision, due to integer arithmetic and rounding [1]_.
References
----------
.. [1] https://en.wikipedia.org/wiki/HSL_and_HSV
Examples
--------
>>> from skimage import color
>>> from skimage import data
>>> img = data.astronaut()
>>> img_hsv = color.rgb2hsv(img)
"""
input_is_one_pixel = rgb.ndim == 1
if input_is_one_pixel:
rgb = rgb[np.newaxis, ...]
arr = _prepare_colorarray(rgb, channel_axis=-1)
out = np.empty_like(arr)
# -- V channel
out_v = arr.max(-1)
# -- S channel
delta = arr.ptp(-1)
# Ignore warning for zero divided by zero
old_settings = np.seterr(invalid='ignore')
out_s = delta / out_v
out_s[delta == 0.] = 0.
# -- H channel
# red is max
idx = (arr[..., 0] == out_v)
out[idx, 0] = (arr[idx, 1] - arr[idx, 2]) / delta[idx]
# green is max
idx = (arr[..., 1] == out_v)
out[idx, 0] = 2. + (arr[idx, 2] - arr[idx, 0]) / delta[idx]
# blue is max
idx = (arr[..., 2] == out_v)
out[idx, 0] = 4. + (arr[idx, 0] - arr[idx, 1]) / delta[idx]
out_h = (out[..., 0] / 6.) % 1.
out_h[delta == 0.] = 0.
np.seterr(**old_settings)
# -- output
out[..., 0] = out_h
out[..., 1] = out_s
out[..., 2] = out_v
# # remove NaN
out[np.isnan(out)] = 0
if input_is_one_pixel:
out = np.squeeze(out, axis=0)
return out
@channel_as_last_axis()
def hsv2rgb(hsv, *, channel_axis=-1):
"""HSV to RGB color space conversion.
Parameters
----------
hsv : (..., 3, ...) array_like
The image in HSV format. By default, the final dimension denotes
channels.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Raises
------
ValueError
If `hsv` is not at least 2-D with shape (..., 3, ...).
Notes
-----
Conversion between RGB and HSV color spaces results in some loss of
precision, due to integer arithmetic and rounding [1]_.
References
----------
.. [1] https://en.wikipedia.org/wiki/HSL_and_HSV
Examples
--------
>>> from skimage import data
>>> img = data.astronaut()
>>> img_hsv = rgb2hsv(img)
>>> img_rgb = hsv2rgb(img_hsv)
"""
arr = _prepare_colorarray(hsv, channel_axis=-1)
hi = np.floor(arr[..., 0] * 6)
f = arr[..., 0] * 6 - hi
p = arr[..., 2] * (1 - arr[..., 1])
q = arr[..., 2] * (1 - f * arr[..., 1])
t = arr[..., 2] * (1 - (1 - f) * arr[..., 1])
v = arr[..., 2]
hi = np.stack([hi, hi, hi], axis=-1).astype(np.uint8) % 6
out = np.choose(
hi, np.stack([np.stack((v, t, p), axis=-1),
np.stack((q, v, p), axis=-1),
np.stack((p, v, t), axis=-1),
np.stack((p, q, v), axis=-1),
np.stack((t, p, v), axis=-1),
np.stack((v, p, q), axis=-1)]))
return out
# ---------------------------------------------------------------
# Primaries for the coordinate systems
# ---------------------------------------------------------------
cie_primaries = np.array([700, 546.1, 435.8])
sb_primaries = np.array([1. / 155, 1. / 190, 1. / 225]) * 1e5
# ---------------------------------------------------------------
# Matrices that define conversion between different color spaces
# ---------------------------------------------------------------
# From sRGB specification
xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423],
[0.212671, 0.715160, 0.072169],
[0.019334, 0.119193, 0.950227]])
rgb_from_xyz = linalg.inv(xyz_from_rgb)
# From https://en.wikipedia.org/wiki/CIE_1931_color_space
# Note: Travis's code did not have the divide by 0.17697
xyz_from_rgbcie = np.array([[0.49, 0.31, 0.20],
[0.17697, 0.81240, 0.01063],
[0.00, 0.01, 0.99]]) / 0.17697
rgbcie_from_xyz = linalg.inv(xyz_from_rgbcie)
# construct matrices to and from rgb:
rgbcie_from_rgb = rgbcie_from_xyz @ xyz_from_rgb
rgb_from_rgbcie = rgb_from_xyz @ xyz_from_rgbcie
gray_from_rgb = np.array([[0.2125, 0.7154, 0.0721],
[0, 0, 0],
[0, 0, 0]])
yuv_from_rgb = np.array([[ 0.299 , 0.587 , 0.114 ],
[-0.14714119, -0.28886916, 0.43601035 ],
[ 0.61497538, -0.51496512, -0.10001026 ]])
rgb_from_yuv = linalg.inv(yuv_from_rgb)
yiq_from_rgb = np.array([[0.299 , 0.587 , 0.114 ],
[0.59590059, -0.27455667, -0.32134392],
[0.21153661, -0.52273617, 0.31119955]])
rgb_from_yiq = linalg.inv(yiq_from_rgb)
ypbpr_from_rgb = np.array([[ 0.299 , 0.587 , 0.114 ],
[-0.168736,-0.331264, 0.5 ],
[ 0.5 ,-0.418688,-0.081312]])
rgb_from_ypbpr = linalg.inv(ypbpr_from_rgb)
ycbcr_from_rgb = np.array([[ 65.481, 128.553, 24.966],
[ -37.797, -74.203, 112.0 ],
[ 112.0 , -93.786, -18.214]])
rgb_from_ycbcr = linalg.inv(ycbcr_from_rgb)
ydbdr_from_rgb = np.array([[ 0.299, 0.587, 0.114],
[ -0.45 , -0.883, 1.333],
[ -1.333, 1.116, 0.217]])
rgb_from_ydbdr = linalg.inv(ydbdr_from_rgb)
# CIE LAB constants for Observer=2A, Illuminant=D65
# NOTE: this is actually the XYZ values for the illuminant above.
lab_ref_white = np.array([0.95047, 1., 1.08883])
# XYZ coordinates of the illuminants, scaled to [0, 1]. For each illuminant I
# we have:
#
# illuminant[I]['2'] corresponds to the XYZ coordinates for the 2 degree
# field of view.
#
# illuminant[I]['10'] corresponds to the XYZ coordinates for the 10 degree
# field of view.
#
# illuminant[I]['R'] corresponds to the XYZ coordinates for R illuminants
# in grDevices::convertColor
#
# The XYZ coordinates are calculated from [1], using the formula:
#
# X = x * ( Y / y )
# Y = Y
# Z = ( 1 - x - y ) * ( Y / y )
#
# where Y = 1. The only exception is the illuminant "D65" with aperture angle
# 2, whose coordinates are copied from 'lab_ref_white' for
# backward-compatibility reasons.
#
# References
# ----------
# .. [1] https://en.wikipedia.org/wiki/Standard_illuminant
illuminants = \
{"A": {'2': (1.098466069456375, 1, 0.3558228003436005),
'10': (1.111420406956693, 1, 0.3519978321919493),
'R': (1.098466069456375, 1, 0.3558228003436005)},
"B": {'2': (0.9909274480248003, 1, 0.8531327322886154),
'10': (0.9917777147717607, 1, 0.8434930535866175),
'R': (0.9909274480248003, 1, 0.8531327322886154)},
"C": {'2': (0.980705971659919, 1, 1.1822494939271255),
'10': (0.9728569189782166, 1, 1.1614480488951577),
'R': (0.980705971659919, 1, 1.1822494939271255)},
"D50": {'2': (0.9642119944211994, 1, 0.8251882845188288),
'10': (0.9672062750333777, 1, 0.8142801513128616),
'R': (0.9639501491621826, 1, 0.8241280285499208)},
"D55": {'2': (0.956797052643698, 1, 0.9214805860173273),
'10': (0.9579665682254781, 1, 0.9092525159847462),
'R': (0.9565317453467969, 1, 0.9202554587037198)},
"D65": {'2': (0.95047, 1., 1.08883), # This was: `lab_ref_white`
'10': (0.94809667673716, 1, 1.0730513595166162),
'R': (0.9532057125493769, 1, 1.0853843816469158)},
"D75": {'2': (0.9497220898840717, 1, 1.226393520724154),
'10': (0.9441713925645873, 1, 1.2064272211720228),
'R': (0.9497220898840717, 1, 1.226393520724154)},
"E": {'2': (1.0, 1.0, 1.0),
'10': (1.0, 1.0, 1.0),
'R': (1.0, 1.0, 1.0)}}
def get_xyz_coords(illuminant, observer, dtype=float):
"""Get the XYZ coordinates of the given illuminant and observer [1]_.
Parameters
----------
illuminant : {"A", "B", "C", "D50", "D55", "D65", "D75", "E"}, optional
The name of the illuminant (the function is NOT case sensitive).
observer : {"2", "10", "R"}, optional
One of: 2-degree observer, 10-degree observer, or 'R' observer as in
R function grDevices::convertColor.
dtype: dtype, optional
Output data type.
Returns
-------
out : array
Array with 3 elements containing the XYZ coordinates of the given
illuminant.
Raises
------
ValueError
If either the illuminant or the observer angle are not supported or
unknown.
References
----------
.. [1] https://en.wikipedia.org/wiki/Standard_illuminant
"""
illuminant = illuminant.upper()
observer = observer.upper()
try:
return np.asarray(illuminants[illuminant][observer], dtype=dtype)
except KeyError:
raise ValueError(f'Unknown illuminant/observer combination '
f'(`{illuminant}`, `{observer}`)')
# Haematoxylin-Eosin-DAB colorspace
# From original Ruifrok's paper: A. C. Ruifrok and D. A. Johnston,
# "Quantification of histochemical staining by color deconvolution,"
# Analytical and quantitative cytology and histology / the International
# Academy of Cytology [and] American Society of Cytology, vol. 23, no. 4,
# pp. 291-9, Aug. 2001.
rgb_from_hed = np.array([[0.65, 0.70, 0.29],
[0.07, 0.99, 0.11],
[0.27, 0.57, 0.78]])
hed_from_rgb = linalg.inv(rgb_from_hed)
# Following matrices are adapted form the Java code written by G.Landini.
# The original code is available at:
# https://web.archive.org/web/20160624145052/http://www.mecourse.com/landinig/software/cdeconv/cdeconv.html
# Hematoxylin + DAB
rgb_from_hdx = np.array([[0.650, 0.704, 0.286],
[0.268, 0.570, 0.776],
[0.0, 0.0, 0.0]])
rgb_from_hdx[2, :] = np.cross(rgb_from_hdx[0, :], rgb_from_hdx[1, :])
hdx_from_rgb = linalg.inv(rgb_from_hdx)
# Feulgen + Light Green
rgb_from_fgx = np.array([[0.46420921, 0.83008335, 0.30827187],
[0.94705542, 0.25373821, 0.19650764],
[0.0, 0.0, 0.0]])
rgb_from_fgx[2, :] = np.cross(rgb_from_fgx[0, :], rgb_from_fgx[1, :])
fgx_from_rgb = linalg.inv(rgb_from_fgx)
# Giemsa: Methyl Blue + Eosin
rgb_from_bex = np.array([[0.834750233, 0.513556283, 0.196330403],
[0.092789, 0.954111, 0.283111],
[0.0, 0.0, 0.0]])
rgb_from_bex[2, :] = np.cross(rgb_from_bex[0, :], rgb_from_bex[1, :])
bex_from_rgb = linalg.inv(rgb_from_bex)
# FastRed + FastBlue + DAB
rgb_from_rbd = np.array([[0.21393921, 0.85112669, 0.47794022],
[0.74890292, 0.60624161, 0.26731082],
[0.268, 0.570, 0.776]])
rbd_from_rgb = linalg.inv(rgb_from_rbd)
# Methyl Green + DAB
rgb_from_gdx = np.array([[0.98003, 0.144316, 0.133146],
[0.268, 0.570, 0.776],
[0.0, 0.0, 0.0]])
rgb_from_gdx[2, :] = np.cross(rgb_from_gdx[0, :], rgb_from_gdx[1, :])
gdx_from_rgb = linalg.inv(rgb_from_gdx)
# Hematoxylin + AEC
rgb_from_hax = np.array([[0.650, 0.704, 0.286],
[0.2743, 0.6796, 0.6803],
[0.0, 0.0, 0.0]])
rgb_from_hax[2, :] = np.cross(rgb_from_hax[0, :], rgb_from_hax[1, :])
hax_from_rgb = linalg.inv(rgb_from_hax)
# Blue matrix Anilline Blue + Red matrix Azocarmine + Orange matrix Orange-G
rgb_from_bro = np.array([[0.853033, 0.508733, 0.112656],
[0.09289875, 0.8662008, 0.49098468],
[0.10732849, 0.36765403, 0.9237484]])
bro_from_rgb = linalg.inv(rgb_from_bro)
# Methyl Blue + Ponceau Fuchsin
rgb_from_bpx = np.array([[0.7995107, 0.5913521, 0.10528667],
[0.09997159, 0.73738605, 0.6680326],
[0.0, 0.0, 0.0]])
rgb_from_bpx[2, :] = np.cross(rgb_from_bpx[0, :], rgb_from_bpx[1, :])
bpx_from_rgb = linalg.inv(rgb_from_bpx)
# Alcian Blue + Hematoxylin
rgb_from_ahx = np.array([[0.874622, 0.457711, 0.158256],
[0.552556, 0.7544, 0.353744],
[0.0, 0.0, 0.0]])
rgb_from_ahx[2, :] = np.cross(rgb_from_ahx[0, :], rgb_from_ahx[1, :])
ahx_from_rgb = linalg.inv(rgb_from_ahx)
# Hematoxylin + PAS
rgb_from_hpx = np.array([[0.644211, 0.716556, 0.266844],
[0.175411, 0.972178, 0.154589],
[0.0, 0.0, 0.0]])
rgb_from_hpx[2, :] = np.cross(rgb_from_hpx[0, :], rgb_from_hpx[1, :])
hpx_from_rgb = linalg.inv(rgb_from_hpx)
# -------------------------------------------------------------
# The conversion functions that make use of the matrices above
# -------------------------------------------------------------
def _convert(matrix, arr):
"""Do the color space conversion.
Parameters
----------
matrix : array_like
The 3x3 matrix to use.
arr : (..., 3, ...) array_like
The input array. By default, the final dimension denotes
channels.
Returns
-------
out : (..., 3, ...) ndarray
The converted array. Same dimensions as input.
"""
arr = _prepare_colorarray(arr)
return arr @ matrix.T.astype(arr.dtype)
@channel_as_last_axis()
def xyz2rgb(xyz, *, channel_axis=-1):
"""XYZ to RGB color space conversion.
Parameters
----------
xyz : (..., 3, ...) array_like
The image in XYZ format. By default, the final dimension denotes
channels.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Raises
------
ValueError
If `xyz` is not at least 2-D with shape (..., 3, ...).
Notes
-----
The CIE XYZ color space is derived from the CIE RGB color space. Note
however that this function converts to sRGB.
References
----------
.. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2xyz, xyz2rgb
>>> img = data.astronaut()
>>> img_xyz = rgb2xyz(img)
>>> img_rgb = xyz2rgb(img_xyz)
"""
# Follow the algorithm from http://www.easyrgb.com/index.php
# except we don't multiply/divide by 100 in the conversion
arr = _convert(rgb_from_xyz, xyz)
mask = arr > 0.0031308
arr[mask] = 1.055 * np.power(arr[mask], 1 / 2.4) - 0.055
arr[~mask] *= 12.92
np.clip(arr, 0, 1, out=arr)
return arr
@channel_as_last_axis()
def rgb2xyz(rgb, *, channel_axis=-1):
"""RGB to XYZ color space conversion.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in XYZ format. Same dimensions as input.
Raises
------
ValueError
If `rgb` is not at least 2-D with shape (..., 3, ...).
Notes
-----
The CIE XYZ color space is derived from the CIE RGB color space. Note
however that this function converts from sRGB.
References
----------
.. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> img = data.astronaut()
>>> img_xyz = rgb2xyz(img)
"""
# Follow the algorithm from http://www.easyrgb.com/index.php
# except we don't multiply/divide by 100 in the conversion
arr = _prepare_colorarray(rgb, channel_axis=-1).copy()
mask = arr > 0.04045
arr[mask] = np.power((arr[mask] + 0.055) / 1.055, 2.4)
arr[~mask] /= 12.92
return arr @ xyz_from_rgb.T.astype(arr.dtype)
@channel_as_last_axis()
def rgb2rgbcie(rgb, *, channel_axis=-1):
"""RGB to RGB CIE color space conversion.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB CIE format. Same dimensions as input.
Raises
------
ValueError
If `rgb` is not at least 2-D with shape (..., 3, ...).
References
----------
.. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2rgbcie
>>> img = data.astronaut()
>>> img_rgbcie = rgb2rgbcie(img)
"""
return _convert(rgbcie_from_rgb, rgb)
@channel_as_last_axis()
def rgbcie2rgb(rgbcie, *, channel_axis=-1):
"""RGB CIE to RGB color space conversion.
Parameters
----------
rgbcie : (..., 3, ...) array_like
The image in RGB CIE format. By default, the final dimension denotes
channels.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Raises
------
ValueError
If `rgbcie` is not at least 2-D with shape (..., 3, ...).
References
----------
.. [1] https://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2rgbcie, rgbcie2rgb
>>> img = data.astronaut()
>>> img_rgbcie = rgb2rgbcie(img)
>>> img_rgb = rgbcie2rgb(img_rgbcie)
"""
return _convert(rgb_from_rgbcie, rgbcie)
@channel_as_last_axis(multichannel_output=False)
def rgb2gray(rgb, *, channel_axis=-1):
"""Compute luminance of an RGB image.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
Returns
-------
out : ndarray
The luminance image - an array which is the same size as the input
array, but with the channel dimension removed.
Raises
------
ValueError
If `rgb` is not at least 2-D with shape (..., 3, ...).
Notes
-----
The weights used in this conversion are calibrated for contemporary
CRT phosphors::
Y = 0.2125 R + 0.7154 G + 0.0721 B
If there is an alpha channel present, it is ignored.
References
----------
.. [1] http://poynton.ca/PDFs/ColorFAQ.pdf
Examples
--------
>>> from skimage.color import rgb2gray
>>> from skimage import data
>>> img = data.astronaut()
>>> img_gray = rgb2gray(img)
"""
rgb = _prepare_colorarray(rgb)
coeffs = np.array([0.2125, 0.7154, 0.0721], dtype=rgb.dtype)
return rgb @ coeffs
def gray2rgba(image, alpha=None, *, channel_axis=-1):
"""Create a RGBA representation of a gray-level image.
Parameters
----------
image : array_like
Input image.
alpha : array_like, optional
Alpha channel of the output image. It may be a scalar or an
array that can be broadcast to ``image``. If not specified it is
set to the maximum limit corresponding to the ``image`` dtype.
channel_axis : int, optional
This parameter indicates which axis of the output array will correspond
to channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
rgba : ndarray
RGBA image. A new dimension of length 4 is added to input
image shape.
"""
arr = np.asarray(image)
alpha_min, alpha_max = dtype_limits(arr, clip_negative=False)
if alpha is None:
alpha = alpha_max
if not np.can_cast(alpha, arr.dtype):
warn(f'alpha cannot be safely cast to image dtype {arr.dtype.name}',
stacklevel=2)
if np.isscalar(alpha):
alpha = np.full(arr.shape, alpha, dtype=arr.dtype)
elif alpha.shape != arr.shape:
raise ValueError("alpha.shape must match image.shape")
rgba = np.stack((arr,) * 3 + (alpha,), axis=channel_axis)
return rgba
def gray2rgb(image, *, channel_axis=-1):
"""Create an RGB representation of a gray-level image.
Parameters
----------
image : array_like
Input image.
channel_axis : int, optional
This parameter indicates which axis of the output array will correspond
to channels.
Returns
-------
rgb : (..., 3, ...) ndarray
RGB image. A new dimension of length 3 is added to input image.
Notes
-----
If the input is a 1-dimensional image of shape ``(M, )``, the output
will be shape ``(M, 3)``.
"""
return np.stack(3 * (image,), axis=channel_axis)
@channel_as_last_axis()
def xyz2lab(xyz, illuminant="D65", observer="2", *, channel_axis=-1):
"""XYZ to CIE-LAB color space conversion.
Parameters
----------
xyz : (..., 3, ...) array_like
The image in XYZ format. By default, the final dimension denotes
channels.
illuminant : {"A", "B", "C", "D50", "D55", "D65", "D75", "E"}, optional
The name of the illuminant (the function is NOT case sensitive).
observer : {"2", "10", "R"}, optional
One of: 2-degree observer, 10-degree observer, or 'R' observer as in
R function grDevices::convertColor.
channel_axis : int, optional
This parameter indicates which axis of the array corresponds to
channels.
.. versionadded:: 0.19
``channel_axis`` was added in 0.19.
Returns
-------
out : (..., 3, ...) ndarray
The image in CIE-LAB format. Same dimensions as input.
Raises
------
ValueError
If `xyz` is not at least 2-D with shape (..., 3, ...).
ValueError
If either the illuminant or the observer angle is unsupported or
unknown.
Notes
-----
By default Observer="2", Illuminant="D65". CIE XYZ tristimulus values
x_ref=95.047, y_ref=100., z_ref=108.883. See function `get_xyz_coords` for
a list of supported illuminants.
References
----------
.. [1] http://www.easyrgb.com/en/math.php
.. [2] https://en.wikipedia.org/wiki/CIELAB_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2xyz, xyz2lab
>>> img = data.astronaut()
>>> img_xyz = rgb2xyz(img)
>>> img_lab = xyz2lab(img_xyz)
"""
arr = _prepare_colorarray(xyz, channel_axis=-1)