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scikit-image/skimage/color/colorconv.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""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)
:license: modified BSD
References
----------
.. [1] Official specification of sRGB, IEC 61966-2-1:1999.
.. [2] http://www.poynton.com/ColorFAQ.html
.. [3] http://en.wikipedia.org/wiki/HSL_and_HSV
.. [4] http://en.wikipedia.org/wiki/CIE_1931_color_space
"""
from __future__ import division
import numpy as np
from scipy import linalg
from ..util import dtype
from skimage._shared.utils import deprecated
def guess_spatial_dimensions(image):
"""Make an educated guess about whether an image has a channels dimension.
Parameters
----------
image : ndarray
The input image.
Returns
-------
spatial_dims : int or None
The number of spatial dimensions of `image`. If ambiguous, the value
is ``None``.
Raises
------
ValueError
If the image array has less than two or more than four dimensions.
"""
if image.ndim == 2:
return 2
if image.ndim == 3 and image.shape[-1] != 3:
return 3
if image.ndim == 3 and image.shape[-1] == 3:
return None
if image.ndim == 4 and image.shape[-1] == 3:
return 3
else:
raise ValueError("Expected 2D, 3D, or 4D array, got %iD." % image.ndim)
def convert_colorspace(arr, fromspace, tospace):
"""Convert an image array to a new color space.
Parameters
----------
arr : array_like
The image to convert.
fromspace : str
The color space to convert from. Valid color space strings are
``['RGB', 'HSV', 'RGB CIE', 'XYZ']``. Value may also be specified as
lower case.
tospace : str
The color space to convert to. Valid color space strings are
``['RGB', 'HSV', 'RGB CIE', 'XYZ']``. Value may also be specified as
lower case.
Returns
-------
newarr : ndarray
The converted image.
Notes
-----
Conversion occurs 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
>>> lena = data.lena()
>>> lena_hsv = convert_colorspace(lena, 'RGB', 'HSV')
"""
fromdict = {'RGB': lambda im: im, 'HSV': hsv2rgb, 'RGB CIE': rgbcie2rgb,
'XYZ': xyz2rgb}
todict = {'RGB': lambda im: im, 'HSV': rgb2hsv, 'RGB CIE': rgb2rgbcie,
'XYZ': rgb2xyz}
fromspace = fromspace.upper()
tospace = tospace.upper()
if not fromspace in fromdict.keys():
raise ValueError('fromspace needs to be one of %s' % fromdict.keys())
if not tospace in todict.keys():
raise ValueError('tospace needs to be one of %s' % todict.keys())
return todict[tospace](fromdict[fromspace](arr))
def _prepare_colorarray(arr):
"""Check the shape of the array and convert it to
floating point representation.
"""
arr = np.asanyarray(arr)
if arr.ndim not in [3, 4] or arr.shape[-1] != 3:
msg = ("the input array must be have a shape == (.., ..,[ ..,] 3)), " +
"got (" + (", ".join(map(str, arr.shape))) + ")")
raise ValueError(msg)
return dtype.img_as_float(arr)
def rgb2hsv(rgb):
"""RGB to HSV color space conversion.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in HSV format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `rgb` is not a 3-D array of shape ``(.., .., 3)``.
Notes
-----
The conversion assumes an input data range of [0, 1] for all
color components.
Conversion between RGB and HSV color spaces results in some loss of
precision, due to integer arithmetic and rounding [1]_.
References
----------
.. [1] http://en.wikipedia.org/wiki/HSL_and_HSV
Examples
--------
>>> from skimage import color
>>> from skimage import data
>>> lena = data.lena()
>>> lena_hsv = color.rgb2hsv(lena)
"""
arr = _prepare_colorarray(rgb)
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
return out
def hsv2rgb(hsv):
"""HSV to RGB color space conversion.
Parameters
----------
hsv : array_like
The image in HSV format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `hsv` is not a 3-D array of shape ``(.., .., 3)``.
Notes
-----
The conversion assumes an input data range of ``[0, 1]`` for all
color components.
Conversion between RGB and HSV color spaces results in some loss of
precision, due to integer arithmetic and rounding [1]_.
References
----------
.. [1] http://en.wikipedia.org/wiki/HSL_and_HSV
Examples
--------
>>> from skimage import data
>>> lena = data.lena()
>>> lena_hsv = rgb2hsv(lena)
>>> lena_rgb = hsv2rgb(lena_hsv)
"""
arr = _prepare_colorarray(hsv)
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.dstack([hi, hi, hi]).astype(np.uint8) % 6
out = np.choose(hi, [np.dstack((v, t, p)),
np.dstack((q, v, p)),
np.dstack((p, v, t)),
np.dstack((p, q, v)),
np.dstack((t, p, v)),
np.dstack((v, p, q))])
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 http://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 = np.dot(rgbcie_from_xyz, xyz_from_rgb)
rgb_from_rgbcie = np.dot(rgb_from_xyz, xyz_from_rgbcie)
gray_from_rgb = np.array([[0.2125, 0.7154, 0.0721],
[0, 0, 0],
[0, 0, 0]])
# CIE LAB constants for Observer= 2A, Illuminant= D65
lab_ref_white = np.array([0.95047, 1., 1.08883])
# 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:
# http://www.dentistry.bham.ac.uk/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 : array_like
The input array.
Returns
-------
out : ndarray, dtype=float
The converted array.
"""
arr = _prepare_colorarray(arr)
arr = np.swapaxes(arr, 0, -1)
oldshape = arr.shape
arr = np.reshape(arr, (3, -1))
out = np.dot(matrix, arr)
out.shape = oldshape
out = np.swapaxes(out, -1, 0)
return np.ascontiguousarray(out)
def xyz2rgb(xyz):
"""XYZ to RGB color space conversion.
Parameters
----------
xyz : array_like
The image in XYZ format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `xyz` is not a 3-D array of 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] http://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2xyz, xyz2rgb
>>> lena = data.lena()
>>> lena_xyz = rgb2xyz(lena)
>>> lena_rgb = xyz2rgb(lena_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
return arr
def rgb2xyz(rgb):
"""RGB to XYZ color space conversion.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3- or 4-D array of shape
``(.., ..,[ ..,] 3)``.
Returns
-------
out : ndarray
The image in XYZ format, in a 3- or 4-D array of shape
``(.., ..,[ ..,] 3)``.
Raises
------
ValueError
If `rgb` is not a 3- or 4-D array of 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] http://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> lena = data.lena()
>>> lena_xyz = rgb2xyz(lena)
"""
# 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).copy()
mask = arr > 0.04045
arr[mask] = np.power((arr[mask] + 0.055) / 1.055, 2.4)
arr[~mask] /= 12.92
return _convert(xyz_from_rgb, arr)
def rgb2rgbcie(rgb):
"""RGB to RGB CIE color space conversion.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in RGB CIE format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `rgb` is not a 3-D array of shape ``(.., .., 3)``.
References
----------
.. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2rgbcie
>>> lena = data.lena()
>>> lena_rgbcie = rgb2rgbcie(lena)
"""
return _convert(rgbcie_from_rgb, rgb)
def rgbcie2rgb(rgbcie):
"""RGB CIE to RGB color space conversion.
Parameters
----------
rgbcie : array_like
The image in RGB CIE format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `rgbcie` is not a 3-D array of shape ``(.., .., 3)``.
References
----------
.. [1] http://en.wikipedia.org/wiki/CIE_1931_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2rgbcie, rgbcie2rgb
>>> lena = data.lena()
>>> lena_rgbcie = rgb2rgbcie(lena)
>>> lena_rgb = rgbcie2rgb(lena_rgbcie)
"""
return _convert(rgb_from_rgbcie, rgbcie)
def rgb2gray(rgb):
"""Compute luminance of an RGB image.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``,
or in RGBA format with shape ``(.., .., 4)``.
Returns
-------
out : ndarray
The luminance image, a 2-D array.
Raises
------
ValueError
If `rgb2gray` is not a 3-D array of shape ``(.., .., 3)`` or
``(.., .., 4)``.
References
----------
.. [1] http://www.poynton.com/PDFs/ColorFAQ.pdf
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.
Examples
--------
>>> from skimage.color import rgb2gray
>>> from skimage import data
>>> lena = data.lena()
>>> lena_gray = rgb2gray(lena)
"""
if rgb.ndim == 2:
return rgb
return _convert(gray_from_rgb, rgb[:, :, :3])[..., 0]
rgb2grey = rgb2gray
def gray2rgb(image):
"""Create an RGB representation of a gray-level image.
Parameters
----------
image : array_like
Input image of shape ``(M, N [, P])``.
Returns
-------
rgb : ndarray
RGB image of shape ``(M, N, [, P], 3)``.
Raises
------
ValueError
If the input is not a 2- or 3-dimensional image.
"""
if np.squeeze(image).ndim == 3 and image.shape[2] in (3, 4):
return image
elif image.ndim != 1 and np.squeeze(image).ndim in (1, 2, 3):
image = image[..., np.newaxis]
return np.concatenate(3 * (image,), axis=-1)
else:
raise ValueError("Input image expected to be RGB, RGBA or gray.")
def xyz2lab(xyz):
"""XYZ to CIE-LAB color space conversion.
Parameters
----------
xyz : array_like
The image in XYZ format, in a 3- or 4-D array of shape
``(.., ..,[ ..,] 3)``.
Returns
-------
out : ndarray
The image in CIE-LAB format, in a 3- or 4-D array of shape
``(.., ..,[ ..,] 3)``.
Raises
------
ValueError
If `xyz` is not a 3-D array of shape ``(.., ..,[ ..,] 3)``.
Notes
-----
Observer= 2A, Illuminant= D65
CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883
References
----------
.. [1] http://www.easyrgb.com/index.php?X=MATH&H=07#text7
.. [2] http://en.wikipedia.org/wiki/Lab_color_space
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2xyz, xyz2lab
>>> lena = data.lena()
>>> lena_xyz = rgb2xyz(lena)
>>> lena_lab = xyz2lab(lena_xyz)
"""
arr = _prepare_colorarray(xyz)
# scale by CIE XYZ tristimulus values of the reference white point
arr = arr / lab_ref_white
# Nonlinear distortion and linear transformation
mask = arr > 0.008856
arr[mask] = np.power(arr[mask], 1. / 3.)
arr[~mask] = 7.787 * arr[~mask] + 16. / 116.
x, y, z = arr[..., 0], arr[..., 1], arr[..., 2]
# Vector scaling
L = (116. * y) - 16.
a = 500.0 * (x - y)
b = 200.0 * (y - z)
return np.concatenate([x[..., np.newaxis] for x in [L, a, b]], axis=-1)
def lab2xyz(lab):
"""CIE-LAB to XYZcolor space conversion.
Parameters
----------
lab : array_like
The image in lab format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in XYZ format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `lab` is not a 3-D array of shape ``(.., .., 3)``.
Notes
-----
Observer = 2A, Illuminant = D65
CIE XYZ tristimulus values x_ref = 95.047, y_ref = 100., z_ref = 108.883
References
----------
.. [1] http://www.easyrgb.com/index.php?X=MATH&H=07#text7
.. [2] http://en.wikipedia.org/wiki/Lab_color_space
"""
arr = _prepare_colorarray(lab).copy()
L, a, b = arr[:, :, 0], arr[:, :, 1], arr[:, :, 2]
y = (L + 16.) / 116.
x = (a / 500.) + y
z = y - (b / 200.)
out = np.dstack([x, y, z])
mask = out > 0.2068966
out[mask] = np.power(out[mask], 3.)
out[~mask] = (out[~mask] - 16.0 / 116.) / 7.787
# rescale Observer= 2 deg, Illuminant= D65
out *= lab_ref_white
return out
def rgb2lab(rgb):
"""RGB to lab color space conversion.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3- or 4-D array of shape
``(.., ..,[ ..,] 3)``.
Returns
-------
out : ndarray
The image in Lab format, in a 3- or 4-D array of shape
``(.., ..,[ ..,] 3)``.
Raises
------
ValueError
If `rgb` is not a 3- or 4-D array of shape ``(.., ..,[ ..,] 3)``.
Notes
-----
This function uses rgb2xyz and xyz2lab.
"""
return xyz2lab(rgb2xyz(rgb))
def lab2rgb(lab):
"""Lab to RGB color space conversion.
Parameters
----------
lab : array_like
The image in Lab format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `lab` is not a 3-D array of shape ``(.., .., 3)``.
Notes
-----
This function uses lab2xyz and xyz2rgb.
"""
return xyz2rgb(lab2xyz(lab))
def xyz2luv(xyz):
"""XYZ to CIE-Luv color space conversion.
Parameters
----------
xyz : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in XYZ format. Final dimension denotes
channels.
Returns
-------
out : (M, N, [P,] 3) ndarray
The image in CIE-Luv format. Same dimensions as input.
Raises
------
ValueError
If `xyz` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``.
Notes
-----
XYZ conversion weights use Observer = 2A. Reference whitepoint for D65
Illuminant, with XYZ tristimulus values of ``(95.047, 100., 108.883)``.
References
----------
.. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
.. [2] http://en.wikipedia.org/wiki/CIELUV
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2xyz, xyz2luv
>>> lena = data.lena()
>>> lena_xyz = rgb2xyz(lena)
>>> lena_luv = xyz2luv(lena_xyz)
"""
arr = _prepare_colorarray(xyz)
# extract channels
x, y, z = arr[..., 0], arr[..., 1], arr[..., 2]
eps = np.finfo(np.float).eps
# compute y_r and L
L = y / lab_ref_white[1]
mask = L > 0.008856
L[mask] = 116. * np.power(L[mask], 1. / 3.) - 16.
L[~mask] = 903.3 * L[~mask]
u0 = 4*lab_ref_white[0] / np.dot([1, 15, 3], lab_ref_white)
v0 = 9*lab_ref_white[1] / np.dot([1, 15, 3], lab_ref_white)
# u' and v' helper functions
def fu(X, Y, Z):
return (4.*X) / (X + 15.*Y + 3.*Z + eps)
def fv(X, Y, Z):
return (9.*Y) / (X + 15.*Y + 3.*Z + eps)
# compute u and v using helper functions
u = 13.*L * (fu(x, y, z) - u0)
v = 13.*L * (fv(x, y, z) - v0)
return np.concatenate([q[..., np.newaxis] for q in [L, u, v]], axis=-1)
def luv2xyz(luv):
"""CIE-Luv to XYZ color space conversion.
Parameters
----------
luv : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in CIE-Luv format. Final dimension denotes
channels.
Returns
-------
out : (M, N, [P,] 3) ndarray
The image in XYZ format. Same dimensions as input.
Raises
------
ValueError
If `luv` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``.
Notes
-----
XYZ conversion weights use Observer = 2A. Reference whitepoint for D65
Illuminant, with XYZ tristimulus values of ``(95.047, 100., 108.883)``.
References
----------
.. [1] http://www.easyrgb.com/index.php?X=MATH&H=16#text16
.. [2] http://en.wikipedia.org/wiki/CIELUV
"""
arr = _prepare_colorarray(luv).copy()
L, u, v = arr[:, :, 0], arr[:, :, 1], arr[:, :, 2]
eps = np.finfo(np.float).eps
# compute y
y = L.copy()
mask = y > 7.999625
y[mask] = np.power((y[mask]+16.) / 116., 3.)
y[~mask] = y[~mask] / 903.3
y *= lab_ref_white[1]
# reference white x,z
uv_weights = [1, 15, 3]
u0 = 4*lab_ref_white[0] / np.dot(uv_weights, lab_ref_white)
v0 = 9*lab_ref_white[1] / np.dot(uv_weights, lab_ref_white)
# compute intermediate values
a = u0 + u / (13.*L + eps)
b = v0 + v / (13.*L + eps)
c = 3*y * (5*b-3)
# compute x and z
z = ((a-4)*c - 15*a*b*y) / (12*b)
x = -(c/b + 3.*z)
return np.concatenate([q[..., np.newaxis] for q in [x, y, z]], axis=-1)
def rgb2luv(rgb):
"""RGB to CIE-Luv color space conversion.
Parameters
----------
rgb : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in RGB format. Final dimension denotes
channels.
Returns
-------
out : (M, N, [P,] 3) ndarray
The image in CIE Luv format. Same dimensions as input.
Raises
------
ValueError
If `rgb` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``.
Notes
-----
This function uses rgb2xyz and xyz2luv.
"""
return xyz2luv(rgb2xyz(rgb))
def luv2rgb(luv):
"""Luv to RGB color space conversion.
Parameters
----------
luv : (M, N, [P,] 3) array_like
The 3 or 4 dimensional image in CIE Luv format. Final dimension denotes
channels.
Returns
-------
out : (M, N, [P,] 3) ndarray
The image in RGB format. Same dimensions as input.
Raises
------
ValueError
If `luv` is not a 3-D or 4-D array of shape ``(M, N, [P,] 3)``.
Notes
-----
This function uses luv2xyz and xyz2rgb.
"""
return xyz2rgb(luv2xyz(luv))
def rgb2hed(rgb):
"""RGB to Haematoxylin-Eosin-DAB (HED) color space conversion.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in HED format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `rgb` is not a 3-D array of shape ``(.., .., 3)``.
References
----------
.. [1] 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.
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2hed
>>> ihc = data.immunohistochemistry()
>>> ihc_hed = rgb2hed(ihc)
"""
return separate_stains(rgb, hed_from_rgb)
def hed2rgb(hed):
"""Haematoxylin-Eosin-DAB (HED) to RGB color space conversion.
Parameters
----------
hed : array_like
The image in the HED color space, in a 3-D array of shape ``(.., .., 3)``.
Returns
-------
out : ndarray
The image in RGB, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `hed` is not a 3-D array of shape ``(.., .., 3)``.
References
----------
.. [1] 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.
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2hed, hed2rgb
>>> ihc = data.immunohistochemistry()
>>> ihc_hed = rgb2hed(ihc)
>>> ihc_rgb = hed2rgb(ihc_hed)
"""
return combine_stains(hed, rgb_from_hed)
def separate_stains(rgb, conv_matrix):
"""RGB to stain color space conversion.
Parameters
----------
rgb : array_like
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
conv_matrix: ndarray
The stain separation matrix as described by G. Landini [1]_.
Returns
-------
out : ndarray
The image in stain color space, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `rgb` is not a 3-D array of shape ``(.., .., 3)``.
Notes
-----
Stain separation matrices available in the ``color`` module and their
respective colorspace:
* ``hed_from_rgb``: Hematoxylin + Eosin + DAB
* ``hdx_from_rgb``: Hematoxylin + DAB
* ``fgx_from_rgb``: Feulgen + Light Green
* ``bex_from_rgb``: Giemsa stain : Methyl Blue + Eosin
* ``rbd_from_rgb``: FastRed + FastBlue + DAB
* ``gdx_from_rgb``: Methyl Green + DAB
* ``hax_from_rgb``: Hematoxylin + AEC
* ``bro_from_rgb``: Blue matrix Anilline Blue + Red matrix Azocarmine\
+ Orange matrix Orange-G
* ``bpx_from_rgb``: Methyl Blue + Ponceau Fuchsin
* ``ahx_from_rgb``: Alcian Blue + Hematoxylin
* ``hpx_from_rgb``: Hematoxylin + PAS
References
----------
.. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
Examples
--------
>>> from skimage import data
>>> from skimage.color import separate_stains, hdx_from_rgb
>>> ihc = data.immunohistochemistry()
>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
"""
rgb = dtype.img_as_float(rgb, force_copy=True)
rgb += 2
stains = np.dot(np.reshape(-np.log(rgb), (-1, 3)), conv_matrix)
return np.reshape(stains, rgb.shape)
def combine_stains(stains, conv_matrix):
"""Stain to RGB color space conversion.
Parameters
----------
stains : array_like
The image in stain color space, in a 3-D array of shape ``(.., .., 3)``.
conv_matrix: ndarray
The stain separation matrix as described by G. Landini [1]_.
Returns
-------
out : ndarray
The image in RGB format, in a 3-D array of shape ``(.., .., 3)``.
Raises
------
ValueError
If `stains` is not a 3-D array of shape ``(.., .., 3)``.
Notes
-----
Stain combination matrices available in the ``color`` module and their
respective colorspace:
* ``rgb_from_hed``: Hematoxylin + Eosin + DAB
* ``rgb_from_hdx``: Hematoxylin + DAB
* ``rgb_from_fgx``: Feulgen + Light Green
* ``rgb_from_bex``: Giemsa stain : Methyl Blue + Eosin
* ``rgb_from_rbd``: FastRed + FastBlue + DAB
* ``rgb_from_gdx``: Methyl Green + DAB
* ``rgb_from_hax``: Hematoxylin + AEC
* ``rgb_from_bro``: Blue matrix Anilline Blue + Red matrix Azocarmine\
+ Orange matrix Orange-G
* ``rgb_from_bpx``: Methyl Blue + Ponceau Fuchsin
* ``rgb_from_ahx``: Alcian Blue + Hematoxylin
* ``rgb_from_hpx``: Hematoxylin + PAS
References
----------
.. [1] http://www.dentistry.bham.ac.uk/landinig/software/cdeconv/cdeconv.html
Examples
--------
>>> from skimage import data
>>> from skimage.color import (separate_stains, combine_stains,
... hdx_from_rgb, rgb_from_hdx)
>>> ihc = data.immunohistochemistry()
>>> ihc_hdx = separate_stains(ihc, hdx_from_rgb)
>>> ihc_rgb = combine_stains(ihc_hdx, rgb_from_hdx)
"""
from ..exposure import rescale_intensity
stains = dtype.img_as_float(stains)
logrgb2 = np.dot(-np.reshape(stains, (-1, 3)), conv_matrix)
rgb2 = np.exp(logrgb2)
return rescale_intensity(np.reshape(rgb2 - 2, stains.shape), in_range=(-1, 1))
def lab2lch(lab):
"""CIE-LAB to CIE-LCH color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
Parameters
----------
lab : array_like
The N-D image in CIE-LAB format. The last (``N+1``-th) dimension must
have at least 3 elements, corresponding to the ``L``, ``a``, and ``b``
color channels. Subsequent elements are copied.
Returns
-------
out : ndarray
The image in LCH format, in a N-D array with same shape as input `lab`.
Raises
------
ValueError
If `lch` does not have at least 3 color channels (i.e. l, a, b).
Notes
-----
The Hue is expressed as an angle between ``(0, 2*pi)``
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2lab, lab2lch
>>> lena = data.lena()
>>> lena_lab = rgb2lab(lena)
>>> lena_lch = lab2lch(lena_lab)
"""
lch = _prepare_lab_array(lab)
a, b = lch[..., 1], lch[..., 2]
lch[..., 1], lch[..., 2] = _cart2polar_2pi(a, b)
return lch
def _cart2polar_2pi(x, y):
"""convert cartesian coordiantes to polar (uses non-standard theta range!)
NON-STANDARD RANGE! Maps to ``(0, 2*pi)`` rather than usual ``(-pi, +pi)``
"""
r, t = np.hypot(x, y), np.arctan2(y, x)
t += np.where(t < 0., 2 * np.pi, 0)
return r, t
def lch2lab(lch):
"""CIE-LCH to CIE-LAB color space conversion.
LCH is the cylindrical representation of the LAB (Cartesian) colorspace
Parameters
----------
lch : array_like
The N-D image in CIE-LCH format. The last (``N+1``-th) dimension must
have at least 3 elements, corresponding to the ``L``, ``a``, and ``b``
color channels. Subsequent elements are copied.
Returns
-------
out : ndarray
The image in LAB format, with same shape as input `lch`.
Raises
------
ValueError
If `lch` does not have at least 3 color channels (i.e. l, c, h).
Examples
--------
>>> from skimage import data
>>> from skimage.color import rgb2lab, lch2lab
>>> lena = data.lena()
>>> lena_lab = rgb2lab(lena)
>>> lena_lch = lab2lch(lena_lab)
>>> lena_lab2 = lch2lab(lena_lch)
"""
lch = _prepare_lab_array(lch)
c, h = lch[..., 1], lch[..., 2]
lch[..., 1], lch[..., 2] = c * np.cos(h), c * np.sin(h)
return lch
def _prepare_lab_array(arr):
"""Ensure input for lab2lch, lch2lab are well-posed.
Arrays must be in floating point and have at least 3 elements in
last dimension. Return a new array.
"""
arr = np.asarray(arr)
shape = arr.shape
if shape[-1] < 3:
raise ValueError('Input array has less than 3 color channels')
return dtype.img_as_float(arr, force_copy=True)
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