"""Colormaps module."""
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.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")
# 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):
"""
Calculate cosine component for Boy's surface.
Parameters
----------
na : float
Amplitude parameter.
nd : float
Angle parameter in degrees.
Returns
-------
float
Cosine component value.
"""
return na * np.cos(nd * np.pi / 180.0)
[docs]
def ss(na, nd):
"""
Calculate sine component for Boy's surface.
Parameters
----------
na : float
Amplitude parameter.
nd : float
Angle parameter in degrees.
Returns
-------
float
Sine component value.
"""
return na * np.sin(nd * np.pi / 180.0)
[docs]
def boys2rgb(v):
"""
Convert orientation vectors to RGB colors using Boy's Surface.
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) or (3,)
Unit vectors (e.g., principal eigenvectors of tensor data) representing
one of the two directions of the undirected lines in a line field.
Returns
-------
array, shape (N, 3) or (3,)
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 = z2 * 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):
"""
Convert orientation vectors to RGB colors based on absolute values.
Parameters
----------
v : array, shape (N, 3) or (3,)
Vectors not necessarily normalized.
Returns
-------
array, shape (N, 3) or (3,)
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, out=np.full_like(v, np.nan, dtype=np.float64))
)
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,
out=np.full_like(v, np.nan, dtype=np.float64),
)
)
else:
raise OSError(
"Wrong vector dimension, It should be an array with a shape (N, 3)"
)
return orient
[docs]
def line_colors(streamlines, *, cmap="rgb_standard"):
"""
Create colors for streamlines based on their orientation.
Parameters
----------
streamlines : sequence of ndarrays
Sequence of streamlines, each represented as an ndarray.
cmap : str, optional
Colormap to use, either 'rgb_standard' or 'boys_standard'.
Returns
-------
ndarray
Array of colors for each streamline.
"""
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):
"""
Create a colormap callable similar to matplotlib.pyplot.get_cmap.
Parameters
----------
name : str
Name of the colormap.
Returns
-------
callable
A function that takes values between 0 and 1 and returns RGBA colors.
Notes
-----
This function creates a simple colormap function that emulates matplotlib's
behavior for use when matplotlib is not available.
"""
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.
Parameters
----------
v : array-like
Values between 0 and 1 to map to colors.
Returns
-------
ndarray
RGBA colors corresponding to the input values.
"""
rgba = np.ones((len(v), 4))
for i, color in enumerate(("red", "green", "blue")):
x, y0, _ = zip(*desc[color], strict=False)
# 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]
def create_colormap(v, *, name="plasma", auto=True):
"""
Create colors from a specific colormap.
Creates an array of shape (N,3) where every row gives the corresponding
r,g,b value. The colormaps used are similar to those of matplotlib.
Parameters
----------
v : (N,) array
Vector of values to be mapped to RGB colors according to colormap.
name : str, optional
Name of the colormap. Currently implemented: 'jet', 'blues',
'accent', 'bone' and matplotlib colormaps if matplotlib is
installed. For example, we suggest using 'plasma', 'viridis' or
'inferno'. Default is 'plasma'.
auto : bool, optional
If True, v is interpolated to [0, 1] from v.min() to v.max().
Default is True.
Returns
-------
ndarray, shape (N, 3)
Array of RGB colors corresponding to the input values.
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 = f"Colormap {name} is not yet implemented "
raise ValueError(e_s)
rgba = colormap(v)
rgb = rgba[:, :3].copy()
return rgb
def _lab_delta(x, y):
"""
Calculate CIELAB Delta E distance between colors.
Parameters
----------
x : ndarray, shape (..., 3)
First set of LAB color values.
y : ndarray, shape (..., 3)
Second set of LAB color values.
Returns
-------
ndarray
The Delta E distance between corresponding colors in x and 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):
"""
Calculate CIELAB Delta E distance between RGB colors.
Parameters
----------
x : ndarray, shape (..., 3)
First set of RGB color values.
y : ndarray, shape (..., 3)
Second set of RGB color values.
Returns
-------
ndarray
The Delta E distance between corresponding colors in x and y.
"""
labX = _rgb2lab(x)
labY = _rgb2lab(y)
return _lab_delta(labX, labY)
def _rgb2xyz(rgb):
"""
Convert from RGB to XYZ color space.
Parameters
----------
rgb : ndarray, shape (..., 3)
RGB values in range [0, 255].
Returns
-------
ndarray, shape (..., 3)
XYZ values.
"""
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):
"""
Convert from XYZ to CIELAB color space.
Parameters
----------
xyz : ndarray, shape (..., 3)
XYZ values.
Returns
-------
ndarray, shape (..., 3)
LAB values.
"""
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):
"""
Convert from CIELAB to XYZ color space.
Parameters
----------
lab : ndarray, shape (..., 3)
LAB values.
Returns
-------
ndarray, shape (..., 3)
XYZ values.
"""
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):
"""
Convert from XYZ to RGB color space.
Parameters
----------
xyz : ndarray, shape (..., 3)
XYZ values.
Returns
-------
ndarray, shape (..., 3)
RGB values in range [0, 255].
"""
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):
"""
Convert from RGB to CIELAB color space.
Parameters
----------
rgb : ndarray, shape (..., 3)
RGB values in range [0, 255].
Returns
-------
ndarray, shape (..., 3)
LAB values.
"""
tmp = _rgb2xyz(rgb)
return _xyz2lab(tmp)
def _lab2rgb(lab):
"""
Convert from CIELAB to RGB color space.
Parameters
----------
lab : ndarray, shape (..., 3)
LAB values.
Returns
-------
ndarray, shape (..., 3)
RGB values in range [0, 255].
"""
tmp = _lab2xyz(lab)
return _xyz2rgb(tmp)
[docs]
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 is (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 RGB colors.
Otherwise, yields the next RGB color maximally perceptually
distinct from previous ones.
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).
Examples
--------
>>> from fury.colormap import distinguishable_colormap
>>> # Generate 5 colors
>>> _ = [c for i, c in zip(range(5), distinguishable_colormap())]
"""
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():
"""Generate the next color in the sequence."""
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(), strict=False)
]
return _generate_next_color()
[docs]
def hex_to_rgb(color):
"""
Convert hexadecimal color code to RGB values.
Parameters
----------
color : str
String containing hexcode of color (can also start with a hash).
Returns
-------
ndarray, shape (3,)
Array of RGB values (between 0 and 1) corresponding to the hexcode.
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 normalize_colors(colors, n_points=None):
"""
Normalize colors to float32 arrays in [0, 1] range.
Accepts colors in multiple formats and converts them to a consistent
float32 ndarray with values in [0, 1], suitable for the GPU pipeline.
Parameters
----------
colors : tuple, list, ndarray, str, or None
Input colors. Supported formats:
- Hex string: ``"#FF0000"`` or list of hex strings.
- Numeric RGB ``[0, 255]``: values > 1.0 are divided by 255.
- Numeric RGB ``[0, 1]``: float values <= 1.0 pass through.
- RGBA variants of the above.
- ``None``: returns default red ``[[1.0, 0.0, 0.0]]``.
n_points : int, optional
Expected number of points. If given and a single color is provided,
it will be broadcast (tiled) to ``(n_points, C)``. If the number of
colors doesn't match ``n_points`` and isn't 1, a ``ValueError``
is raised.
Returns
-------
ndarray, shape (N, 3) or (N, 4), dtype float32
Normalized colors in [0, 1].
"""
def _tile_colors(colors):
"""
Tile a single color when a point count is requested.
Parameters
----------
colors : ndarray
Color array to validate and tile.
Returns
-------
ndarray
Color array broadcast to ``n_points`` when needed.
"""
if n_points is None:
return colors
if len(colors) == 1:
return np.tile(colors, (n_points, 1))
if len(colors) != n_points:
raise ValueError(
f"Number of colors ({len(colors)}) must be 1 or match "
f"n_points ({n_points})."
)
return colors
if colors is None:
colors = np.array([[1.0, 0.0, 0.0]], dtype=np.float32)
return _tile_colors(colors)
if isinstance(colors, str):
colors = np.array([hex_to_rgb(colors)], dtype=np.float32)
return _tile_colors(colors)
if isinstance(colors, (list, tuple)) and len(colors) > 0:
if isinstance(colors[0], str):
colors = np.array([hex_to_rgb(c) for c in colors], dtype=np.float32)
return _tile_colors(colors)
colors = np.asarray(colors, dtype=np.float64)
if colors.size == 0:
return colors.astype(np.float32).reshape(-1, 3)
if colors.shape[-1] not in (3, 4):
raise ValueError(f"colors must have 3 or 4 channels; got {colors.shape[-1]}.")
if colors.ndim == 1:
colors = colors.reshape(1, -1)
if colors.max() > 1.0:
colors = colors / 255.0
colors = colors.astype(np.float32)
return _tile_colors(colors)
[docs]
def rgb2hsv(rgb):
"""
Convert RGB color values to HSV color space.
Parameters
----------
rgb : ndarray (..., 3, ...)
The image in RGB format. By default, the final dimension denotes
channels.
Returns
-------
ndarray (..., 3, ...)
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):
"""
Convert HSV color values to RGB color space.
Parameters
----------
hsv : ndarray (..., 3, ...)
The image in HSV format. By default, the final dimension denotes
channels.
Returns
-------
ndarray (..., 3, ...)
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):
"""
Convert XYZ color values to RGB color space.
Parameters
----------
xyz : ndarray (..., 3, ...)
The image in XYZ format. By default, the final dimension denotes
channels.
Returns
-------
ndarray (..., 3, ...)
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):
"""
Convert RGB color values to XYZ color space.
Parameters
----------
rgb : (..., 3, ...) array_like
The image in RGB format. By default, the final dimension denotes
channels.
Returns
-------
ndarray (..., 3, ...)
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.
Parameters
----------
illuminant : {"A", "B", "C", "D50", "D55", "D65", "D75", "E"}
The name of the illuminant (the function is NOT case sensitive).
observer : {"2", "10", "R"}
One of: 2-degree observer, 10-degree observer, or 'R' observer as in
R function grDevices::convertColor.
Returns
-------
ndarray
Array with 3 elements containing the XYZ coordinates of the given
illuminant.
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.
"""
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 (`{illuminant}`, `{observer}`)"
) from err
[docs]
def xyz2lab(xyz, *, illuminant="D65", observer="2"):
"""
Convert XYZ color values to CIE-LAB color space.
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).
Default is "D65".
observer : {"2", "10", "R"}, optional
One of: 2-degree observer, 10-degree observer, or 'R' observer as in
R function grDevices::convertColor. Default is "2".
Returns
-------
ndarray (..., 3, ...)
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]
def lab2xyz(lab, *, illuminant="D65", observer="2"):
"""
Convert CIE-LAB color values to XYZ color space.
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).
Default is "D65".
observer : {"2", "10", "R"}, optional
The aperture angle of the observer. Default is "2".
Returns
-------
ndarray (..., 3, ...)
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]
def rgb2lab(rgb, *, illuminant="D65", observer="2"):
"""
Convert from RGB color space to CIE-Lab color space.
Converts 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).
Default is "D65".
observer : {"2", "10", "R"}, optional
The aperture angle of the observer. Default is "2".
Returns
-------
ndarray (..., 3, ...)
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]
def lab2rgb(lab, *, illuminant="D65", observer="2"):
"""
Convert from CIE-Lab color space to RGB color space.
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).
Default is "D65".
observer : {"2", "10", "R"}, optional
The aperture angle of the observer. Default is "2".
Returns
-------
ndarray (..., 3, ...)
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))
