import json
from os.path import join as pjoin
from warnings import warn
import numpy as np
from scipy import linalg
from fury.data import DATA_DIR
from fury.decorators import warn_on_args_to_kwargs
from fury.lib import LookupTable
# Allow import, but disable doctests if we don't have matplotlib
from fury.optpkg import optional_package
cm, have_matplotlib, _ = optional_package("matplotlib.cm")
[docs]
@warn_on_args_to_kwargs()
def colormap_lookup_table(
*,
scale_range=(0, 1),
hue_range=(0.8, 0),
saturation_range=(1, 1),
value_range=(0.8, 0.8),
):
"""Lookup table for the colormap.
Parameters
----------
scale_range : tuple
It can be anything e.g. (0, 1) or (0, 255). Usually it is the minimum
and maximum value of your data. Default is (0, 1).
hue_range : tuple of floats
HSV values (min 0 and max 1). Default is (0.8, 0).
saturation_range : tuple of floats
HSV values (min 0 and max 1). Default is (1, 1).
value_range : tuple of floats
HSV value (min 0 and max 1). Default is (0.8, 0.8).
Returns
-------
lookup_table : LookupTable
"""
lookup_table = LookupTable()
lookup_table.SetRange(scale_range)
lookup_table.SetTableRange(scale_range)
lookup_table.SetHueRange(hue_range)
lookup_table.SetSaturationRange(saturation_range)
lookup_table.SetValueRange(value_range)
lookup_table.Build()
return lookup_table
[docs]
def cc(na, nd):
return na * np.cos(nd * np.pi / 180.0)
[docs]
def ss(na, nd):
return na * np.sin(nd * np.pi / 180.0)
[docs]
def boys2rgb(v):
"""Boys 2 rgb cool colormap
Maps a given field of undirected lines (line field) to rgb
colors using Boy's Surface immersion of the real projective
plane.
Boy's Surface is one of the three possible surfaces
obtained by gluing a Mobius strip to the edge of a disk.
The other two are the crosscap and Roman surface,
Steiner surfaces that are homeomorphic to the real
projective plane (Pinkall 1986). The Boy's surface
is the only 3D immersion of the projective plane without
singularities.
Visit http://www.cs.brown.edu/~cad/rp2coloring for further details.
Cagatay Demiralp, 9/7/2008.
Code was initially in matlab and was rewritten in Python for fury by
the FURY Team. Thank you Cagatay for putting this online.
Parameters
----------
v : array, shape (N, 3) of unit vectors (e.g., principal eigenvectors of
tensor data) representing one of the two directions of the
undirected lines in a line field.
Returns
-------
c : array, shape (N, 3) matrix of rgb colors corresponding to the vectors
given in V.
Examples
--------
>>> from fury import colormap
>>> v = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> c = colormap.boys2rgb(v)
"""
if v.ndim == 1:
x = v[0]
y = v[1]
z = v[2]
if v.ndim == 2:
x = v[:, 0]
y = v[:, 1]
z = v[:, 2]
x2 = x**2
y2 = y**2
z2 = z**2
x3 = x * x2
y3 = y * y2
z3 = z * z2
z4 = z * z2
xy = x * y
xz = x * z
yz = y * z
hh1 = 0.5 * (3 * z2 - 1) / 1.58
hh2 = 3 * xz / 2.745
hh3 = 3 * yz / 2.745
hh4 = 1.5 * (x2 - y2) / 2.745
hh5 = 6 * xy / 5.5
hh6 = (1 / 1.176) * 0.125 * (35 * z4 - 30 * z2 + 3)
hh7 = 2.5 * x * (7 * z3 - 3 * z) / 3.737
hh8 = 2.5 * y * (7 * z3 - 3 * z) / 3.737
hh9 = ((x2 - y2) * 7.5 * (7 * z2 - 1)) / 15.85
hh10 = ((2 * xy) * (7.5 * (7 * z2 - 1))) / 15.85
hh11 = 105 * (4 * x3 * z - 3 * xz * (1 - z2)) / 59.32
hh12 = 105 * (-4 * y3 * z + 3 * yz * (1 - z2)) / 59.32
s0 = -23.0
s1 = 227.9
s2 = 251.0
s3 = 125.0
ss23 = ss(2.71, s0)
cc23 = cc(2.71, s0)
ss45 = ss(2.12, s1)
cc45 = cc(2.12, s1)
ss67 = ss(0.972, s2)
cc67 = cc(0.972, s2)
ss89 = ss(0.868, s3)
cc89 = cc(0.868, s3)
X = 0.0
X = X + hh2 * cc23
X = X + hh3 * ss23
X = X + hh5 * cc45
X = X + hh4 * ss45
X = X + hh7 * cc67
X = X + hh8 * ss67
X = X + hh10 * cc89
X = X + hh9 * ss89
Y = 0.0
Y = Y + hh2 * -ss23
Y = Y + hh3 * cc23
Y = Y + hh5 * -ss45
Y = Y + hh4 * cc45
Y = Y + hh7 * -ss67
Y = Y + hh8 * cc67
Y = Y + hh10 * -ss89
Y = Y + hh9 * cc89
Z = 0.0
Z = Z + hh1 * -2.8
Z = Z + hh6 * -0.5
Z = Z + hh11 * 0.3
Z = Z + hh12 * -2.5
# scale and normalize to fit
# in the rgb space
w_x = 4.1925
trl_x = -2.0425
w_y = 4.0217
trl_y = -1.8541
w_z = 4.0694
trl_z = -2.1899
if v.ndim == 2:
N = len(x)
C = np.zeros((N, 3))
C[:, 0] = 0.9 * np.abs(((X - trl_x) / w_x)) + 0.05
C[:, 1] = 0.9 * np.abs(((Y - trl_y) / w_y)) + 0.05
C[:, 2] = 0.9 * np.abs(((Z - trl_z) / w_z)) + 0.05
if v.ndim == 1:
C = np.zeros((3,))
C[0] = 0.9 * np.abs(((X - trl_x) / w_x)) + 0.05
C[1] = 0.9 * np.abs(((Y - trl_y) / w_y)) + 0.05
C[2] = 0.9 * np.abs(((Z - trl_z) / w_z)) + 0.05
return C
[docs]
def orient2rgb(v):
"""Get Standard orientation 2 rgb colormap.
v : array, shape (N, 3) of vectors not necessarily normalized
Returns
-------
c : array, shape (N, 3) matrix of rgb colors corresponding to the vectors
given in V.
Examples
--------
>>> from fury import colormap
>>> v = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> c = colormap.orient2rgb(v)
"""
if v.ndim == 1:
r = np.linalg.norm(v)
orient = np.abs(np.divide(v, r, where=r != 0))
elif v.ndim == 2:
orientn = np.sqrt(v[:, 0] ** 2 + v[:, 1] ** 2 + v[:, 2] ** 2)
orientn.shape = orientn.shape + (1,)
orient = np.abs(np.divide(v, orientn, where=orientn != 0))
else:
raise IOError(
"Wrong vector dimension, It should be an array" " with a shape (N, 3)"
)
return orient
[docs]
@warn_on_args_to_kwargs()
def line_colors(streamlines, *, cmap="rgb_standard"):
"""Create colors for streamlines to be used in actor.line.
Parameters
----------
streamlines : sequence of ndarrays
cmap : ('rgb_standard', 'boys_standard')
Returns
-------
colors : ndarray
"""
if cmap == "rgb_standard":
col_list = [
orient2rgb(streamline[-1] - streamline[0]) for streamline in streamlines
]
if cmap == "boys_standard":
col_list = [
boys2rgb(streamline[-1] - streamline[0]) for streamline in streamlines
]
return np.vstack(col_list)
lowercase_cm_name = {"blues": "Blues", "accent": "Accent"}
dipy_cmaps = None
[docs]
def get_cmap(name):
"""Make a callable, similar to maptlotlib.pyplot.get_cmap."""
if name.lower() == "accent":
warn(
"The `Accent` colormap is deprecated as of version"
+ " 0.2 of Fury and will be removed in a future "
+ "version. Please use another colormap",
PendingDeprecationWarning,
stacklevel=2,
)
global dipy_cmaps
if dipy_cmaps is None:
filename = pjoin(DATA_DIR, "dipy_colormaps.json")
with open(filename) as f:
dipy_cmaps = json.load(f)
desc = dipy_cmaps.get(name)
if desc is None:
return None
def simple_cmap(v):
"""Emulate matplotlib colormap callable."""
rgba = np.ones((len(v), 4))
for i, color in enumerate(("red", "green", "blue")):
x, y0, _ = zip(*desc[color])
# Matplotlib allows more complex colormaps, but for users who do
# not have Matplotlib fury makes a few simple colormaps available.
# These colormaps are simple because y0 == y1, and therefore we
# ignore y1 here.
rgba[:, i] = np.interp(v, x, y0)
return rgba
return simple_cmap
[docs]
@warn_on_args_to_kwargs()
def create_colormap(v, *, name="plasma", auto=True):
"""Create colors from a specific colormap and return it
as an array of shape (N,3) where every row gives the corresponding
r,g,b value. The colormaps we use are similar with those of matplotlib.
Parameters
----------
v : (N,) array
vector of values to be mapped in RGB colors according to colormap
name : str.
Name of the colormap. Currently implemented: 'jet', 'blues',
'accent', 'bone' and matplotlib colormaps if you have matplotlib
installed. For example, we suggest using 'plasma', 'viridis' or
'inferno'. 'jet' is popular but can be often misleading and we will
deprecate it the future.
auto : bool,
if auto is True then v is interpolated to [0, 1] from v.min()
to v.max()
Notes
-----
FURY supports a few colormaps for those who do not use Matplotlib, for
more colormaps consider downloading Matplotlib (see matplotlib.org).
"""
if not have_matplotlib:
msg = "You do not have Matplotlib installed. Some colormaps"
msg += " might not work for you. Consider downloading Matplotlib."
warn(msg, stacklevel=2)
if name.lower() == "jet":
msg = "Jet is a popular colormap but can often be misleading"
msg += "Use instead plasma, viridis, hot or inferno."
warn(msg, PendingDeprecationWarning, stacklevel=2)
if v.ndim > 1:
msg = "This function works only with 1d arrays. Use ravel()"
raise ValueError(msg)
if auto:
v = np.interp(v, [v.min(), v.max()], [0, 1])
else:
v = np.clip(v, 0, 1)
# For backwards compatibility with lowercase names
newname = lowercase_cm_name.get(name) or name
colormap = getattr(cm, newname) if have_matplotlib else get_cmap(newname)
if colormap is None:
e_s = "Colormap {} is not yet implemented ".format(name)
raise ValueError(e_s)
rgba = colormap(v)
rgb = rgba[:, :3].copy()
return rgb
def _lab_delta(x, y):
dL = y[:, 0] - x[:, 0] # L
dA = y[:, 1] - x[:, 1] # A
dB = y[:, 2] - x[:, 2] # B
return np.sqrt(dL**2 + dA**2 + dB**2)
def _rgb_lab_delta(x, y):
labX = _rgb2lab(x)
labY = _rgb2lab(y)
return _lab_delta(labX, labY)
def _rgb2xyz(rgb):
var_R = rgb[:, 0] / 255 # R from 0 to 255
var_G = rgb[:, 1] / 255 # G from 0 to 255
var_B = rgb[:, 2] / 255 # B from 0 to 255
idx = var_R > 0.04045
var_R[idx] = ((var_R[idx] + 0.055) / 1.055) ** 2.4
idx = np.logical_not(idx)
var_R[idx] = var_R[idx] / 12.92
idx = var_G > 0.04045
var_G[idx] = ((var_G[idx] + 0.055) / 1.055) ** 2.4
idx = np.logical_not(idx)
var_G[idx] = var_G[idx] / 12.92
idx = var_B > 0.04045
var_B[idx] = ((var_B[idx] + 0.055) / 1.055) ** 2.4
idx = np.logical_not(idx)
var_B[idx] = var_B[idx] / 12.92
var_R = var_R * 100
var_G = var_G * 100
var_B = var_B * 100
# Observer. = Illuminant = D65
X = var_R * 0.4124 + var_G * 0.3576 + var_B * 0.1805
Y = var_R * 0.2126 + var_G * 0.7152 + var_B * 0.0722
Z = var_R * 0.0193 + var_G * 0.1192 + var_B * 0.9505
return np.c_[X, Y, Z]
def _xyz2lab(xyz):
ref_X = 095.047
ref_Y = 100.000
ref_Z = 108.883
var_X = xyz[:, 0] / ref_X
var_Y = xyz[:, 1] / ref_Y
var_Z = xyz[:, 2] / ref_Z
idx = var_X > 0.008856
var_X[idx] = var_X[idx] ** (1 / 3)
idx = np.logical_not(idx)
var_X[idx] = (7.787 * var_X[idx]) + (16.0 / 116.0)
idx = var_Y > 0.008856
var_Y[idx] = var_Y[idx] ** (1 / 3)
idx = np.logical_not(idx)
var_Y[idx] = (7.787 * var_Y[idx]) + (16.0 / 116.0)
idx = var_Z > 0.008856
var_Z[idx] = var_Z[idx] ** (1 / 3)
idx = np.logical_not(idx)
var_Z[idx] = (7.787 * var_Z[idx]) + (16.0 / 116.0)
L = (116 * var_Y) - 16
A = 500 * (var_X - var_Y)
B = 200 * (var_Y - var_Z)
return np.c_[L, A, B]
def _lab2xyz(lab):
var_Y = (lab[:, 0] + 16) / 116.0
var_X = lab[:, 1] / 500.0 + var_Y
var_Z = var_Y - lab[:, 2] / 200.0
if var_Y**3 > 0.008856:
var_Y = var_Y**3
else:
var_Y = (var_Y - 16.0 / 116.0) / 7.787
if var_X**3 > 0.008856:
var_X = var_X**3
else:
var_X = (var_X - 16.0 / 116.0) / 7.787
if var_Z**3 > 0.008856:
var_Z = var_Z**3
else:
var_Z = (var_Z - 16.0 / 116.0) / 7.787
ref_X = 095.047
ref_Y = 100.000
ref_Z = 108.883
X = ref_X * var_X
Y = ref_Y * var_Y
Z = ref_Z * var_Z
return np.c_[X, Y, Z]
def _xyz2rgb(xyz):
var_X = xyz[:, 0] / 100 # X from 0 to 95.047
var_Y = xyz[:, 1] / 100 # Y from 0 to 100.000
var_Z = xyz[:, 2] / 100 # Z from 0 to 108.883
var_R = var_X * 03.2406 + var_Y * -1.5372 + var_Z * -0.4986
var_G = var_X * -0.9689 + var_Y * 01.8758 + var_Z * 00.0415
var_B = var_X * 00.0557 + var_Y * -0.2040 + var_Z * 01.0570
if var_R > 0.0031308:
var_R = 1.055 * (var_R ** (1 / 2.4)) - 0.055
else:
var_R = 12.92 * var_R
if var_G > 0.0031308:
var_G = 1.055 * (var_G ** (1 / 2.4)) - 0.055
else:
var_G = 12.92 * var_G
if var_B > 0.0031308:
var_B = 1.055 * (var_B ** (1 / 2.4)) - 0.055
else:
var_B = 12.92 * var_B
R = var_R * 255
G = var_G * 255
B = var_B * 255
return np.c_[R, G, B]
def _rgb2lab(rgb):
tmp = _rgb2xyz(rgb)
return _xyz2lab(tmp)
def _lab2rgb(lab):
tmp = _lab2xyz(lab)
return _xyz2rgb(tmp)
[docs]
@warn_on_args_to_kwargs()
def distinguishable_colormap(*, bg=(0, 0, 0), exclude=None, nb_colors=None):
"""Generate colors that are maximally perceptually distinct.
This function generates a set of colors which are distinguishable
by reference to the "Lab" color space, which more closely matches
human color perception than RGB. Given an initial large list of possible
colors, it iteratively chooses the entry in the list that is farthest (in
Lab space) from all previously-chosen entries. While this "greedy"
algorithm does not yield a global maximum, it is simple and efficient.
Moreover, the sequence of colors is consistent no matter how many you
request, which facilitates the users' ability to learn the color order
and avoids major changes in the appearance of plots when adding or
removing lines.
Parameters
----------
bg : tuple (optional)
Background RGB color, to make sure that your colors are also
distinguishable from the background. Default: (0, 0, 0).
exclude : list of tuples (optional)
Additional RGB colors to be distinguishable from.
nb_colors : int (optional)
Number of colors desired. Default: generate as many colors as needed.
Returns
-------
iterable of ndarray
If `nb_colors` is provided, returns a list of RBG colors.
Otherwise, yields the next RBG color maximally perceptually
distinct from previous ones.
Examples
--------
>>> from fury.colormap import distinguishable_colormap
>>> # Generate 5 colors
>>> _ = [c for i, c in zip(range(5), distinguishable_colormap())]
Notes
-----
Code was initially in matlab and was rewritten in Python for dipy by
the Dipy Team. Thank you Tim Holy for putting this online. Visit
http://www.mathworks.com/matlabcentral/fileexchange/29702 for the
original implementation (v1.2), 14 Dec 2010 (Updated 07 Feb 2011).
"""
if exclude is None:
exclude = []
NB_DIVISIONS = 30 # This constant come from the original code.
# Generate a sizable number of RGB triples. This represents our space of
# possible choices. By starting in RGB space, we ensure that all of the
# colors can be generated by the monitor.
colors_to_exclude = np.array([bg] + exclude)
# Divisions along each axis in RGB space.
x = np.linspace(0, 1, NB_DIVISIONS)
R, G, B = np.meshgrid(x, x, x)
rgb = np.c_[R.flatten(), G.flatten(), B.flatten()]
lab = _rgb2lab(rgb)
bglab = _rgb2lab(colors_to_exclude)
def _generate_next_color():
lastlab = bglab[0]
mindist2 = np.ones(len(rgb)) * np.inf
for bglab_i in bglab[1:]:
dist2 = np.sum((lab - bglab_i) ** 2, axis=1)
# Dist2 to closest previously-chosen color.
mindist2 = np.minimum(dist2, mindist2)
while True:
dX = lab - lastlab # Displacement of last from all colors on list.
dist2 = np.sum(dX**2, axis=1) # Square distance.
# Dist2 to closest previously-chosen color.
mindist2 = np.minimum(dist2, mindist2)
# Find the entry farthest from all previously-chosen colors.
idx = np.argmax(mindist2)
yield rgb[idx]
lastlab = lab[idx]
if nb_colors is not None:
return [c for i, c in zip(range(nb_colors), _generate_next_color())]
return _generate_next_color()
[docs]
def hex_to_rgb(color):
"""Converts Hexadecimal color code to rgb()
color : string containing hexcode of color (can also start with a hash)
Returns
-------
c : array, shape(1, 3) matrix of rbg colors corresponding to the
hexcode string given in color.
Examples
--------
>>> from fury import colormap
>>> color = "#FFFFFF"
>>> c = colormap.hex_to_rgb(color)
>>> from fury import colormap
>>> color = "FFFFFF"
>>> c = colormap.hex_to_rgb(color)
"""
if color[0] == "#":
color = color[1:]
r = int("0x" + color[0:2], 0) / 255
g = int("0x" + color[2:4], 0) / 255
b = int("0x" + color[4:6], 0) / 255
return np.array([r, g, b])
[docs]
def rgb2hsv(rgb):
"""RGB to HSV color space conversion.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
Returns
-------
out : (..., 3, ...) ndarray
The image in HSV format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
input_is_one_pixel = rgb.ndim == 1
if input_is_one_pixel:
rgb = rgb[np.newaxis, ...]
out = np.empty_like(rgb)
# -- V channel
out_v = rgb.max(-1)
# -- S channel
delta = np.ptp(rgb, -1)
# Ignore warning for zero divided by zero
old_settings = np.seterr(invalid="ignore")
out_s = delta / out_v
out_s[delta == 0.0] = 0.0
# -- H channel
# red is max
idx = rgb[..., 0] == out_v
out[idx, 0] = (rgb[idx, 1] - rgb[idx, 2]) / delta[idx]
# green is max
idx = rgb[..., 1] == out_v
out[idx, 0] = 2.0 + (rgb[idx, 2] - rgb[idx, 0]) / delta[idx]
# blue is max
idx = rgb[..., 2] == out_v
out[idx, 0] = 4.0 + (rgb[idx, 0] - rgb[idx, 1]) / delta[idx]
out_h = (out[..., 0] / 6.0) % 1.0
out_h[delta == 0.0] = 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
[docs]
def hsv2rgb(hsv):
"""HSV to RGB color space conversion.
Parameters
----------
hsv : (..., 3, ...) array_like
The image in HSV format. By default, the final dimension denotes
channels.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
hi = np.floor(hsv[..., 0] * 6)
f = hsv[..., 0] * 6 - hi
p = hsv[..., 2] * (1 - hsv[..., 1])
q = hsv[..., 2] * (1 - f * hsv[..., 1])
t = hsv[..., 2] * (1 - (1 - f) * hsv[..., 1])
v = hsv[..., 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
# 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)
[docs]
def xyz2rgb(xyz):
"""XYZ to RGB color space conversion.
Parameters
----------
xyz : (..., 3, ...) array_like
The image in XYZ format. By default, the final dimension denotes
channels.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
arr = xyz @ rgb_from_xyz.T.astype(xyz.dtype)
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
[docs]
def rgb2xyz(rgb):
"""RGB to XYZ color space conversion.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
Returns
-------
out : (..., 3, ...) ndarray
The image in XYZ format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
rgb = rgb.astype(float)
mask = rgb > 0.04045
rgb[mask] = np.power((rgb[mask] + 0.055) / 1.055, 2.4)
rgb[~mask] /= 12.92
return rgb @ xyz_from_rgb.T.astype(rgb.dtype)
# XYZ coordinates of the illuminants, scaled to [0, 1]. For each illuminant I.
# Original Implementation of this object is from scikit-image package.
# it can be found at:
# https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
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, 1.0, 1.08883),
"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)},
}
[docs]
def get_xyz_coords(illuminant, observer):
"""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.
Returns
-------
out : array
Array with 3 elements containing the XYZ coordinates of the given
illuminant.
Notes
-----
Original Implementation from scikit-image package.
it can be found here:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
References
----------
.. [1] scikit-image, `colorconv.py` source code
"""
illuminant = illuminant.upper()
observer = observer.upper()
try:
return np.asarray(illuminants[illuminant][observer], dtype=float)
except KeyError as err:
raise ValueError(
f"Unknown illuminant/observer combination "
f"(`{illuminant}`, `{observer}`)"
) from err
[docs]
@warn_on_args_to_kwargs()
def xyz2lab(xyz, *, illuminant="D65", observer="2"):
"""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.
Returns
-------
out : (..., 3, ...) ndarray
The image in CIE-LAB format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
xyz_ref_white = get_xyz_coords(illuminant, observer)
# scale by CIE XYZ tristimulus values of the reference white point
arr = xyz / xyz_ref_white
# Nonlinear distortion and linear transformation
mask = arr > 0.008856
arr[mask] = np.cbrt(arr[mask])
arr[~mask] = 7.787 * arr[~mask] + 16.0 / 116.0
x, y, z = arr[..., 0], arr[..., 1], arr[..., 2]
# Vector scaling
L = (116.0 * y) - 16.0
a = 500.0 * (x - y)
b = 200.0 * (y - z)
return np.concatenate([x[..., np.newaxis] for x in [L, a, b]], axis=-1)
[docs]
@warn_on_args_to_kwargs()
def lab2xyz(lab, *, illuminant="D65", observer="2"):
"""CIE-LAB to XYZcolor space conversion.
Parameters
----------
lab : (..., 3, ...) array_like
The image in Lab 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
The aperture angle of the observer.
Returns
-------
out : (..., 3, ...) ndarray
The image in XYZ format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
L, a, b = lab[..., 0], lab[..., 1], lab[..., 2]
y = (L + 16.0) / 116.0
x = (a / 500.0) + y
z = y - (b / 200.0)
if np.any(z < 0):
invalid = np.nonzero(z < 0)
warn(
"Color data out of range: Z < 0 in %s pixels" % invalid[0].size,
stacklevel=2,
)
z[invalid] = 0
out = np.stack([x, y, z], axis=-1)
mask = out > 0.2068966
out[mask] = np.power(out[mask], 3.0)
out[~mask] = (out[~mask] - 16.0 / 116.0) / 7.787
# rescale to the reference white (illuminant)
xyz_ref_white = get_xyz_coords(illuminant, observer)
out *= xyz_ref_white
return out
[docs]
@warn_on_args_to_kwargs()
def rgb2lab(rgb, *, illuminant="D65", observer="2"):
"""Conversion from the sRGB color space (IEC 61966-2-1:1999)
to the CIE Lab colorspace under the given illuminant and observer.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB 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
The aperture angle of the observer.
Returns
-------
out : (..., 3, ...) ndarray
The image in Lab format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
return xyz2lab(rgb2xyz(rgb), illuminant=illuminant, observer=observer)
[docs]
@warn_on_args_to_kwargs()
def lab2rgb(lab, *, illuminant="D65", observer="2"):
"""Lab to RGB color space conversion.
Parameters
----------
lab : (..., 3, ...) array_like
The image in Lab 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
The aperture angle of the observer.
Returns
-------
out : (..., 3, ...) ndarray
The image in RGB format. Same dimensions as input.
Notes
-----
Original Implementation from scikit-image package.
it can be found at:
https://github.com/scikit-image/scikit-image/blob/main/skimage/color/colorconv.py
This implementation might have been modified.
"""
return xyz2rgb(lab2xyz(lab, illuminant=illuminant, observer=observer))
