# -*- coding: utf-8 -*-
"""
Color
=====
Provides tools for color manipulation.
Note
----
All numerical color values are by default returned using the range [0,1].
"""
__author__ = 'Christoph Kirst <christoph.kirst.ck@gmail.com>'
__license__ = 'GPLv3 - GNU General Public License v3 (see LICENSE)'
__copyright__ = 'Copyright © 2020 by Christoph Kirst'
__webpage__ = 'http://idisco.info'
__download__ = 'http://www.github.com/ChristophKirst/ClearMap2'
import numpy as np
import vispy as vp
import vispy.color as vpc
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as mpc
###############################################################################
### Color manipulation
###############################################################################
[docs]
def color(arg, alpha = None, as_int = False, int_type = int):
"""Converts color arguent to rgb values
Arguments
---------
arg : object
Color specifiction, e.g. a rgb tuple, color name from matplotlib or vispy, hex code
alpha : None, bool or float
Include the alpha value in the rgb tuple. If None and an alpha value exists,
it is kept, otherwise it is not included. If False the alpha value is not included.
If True, the alpha value is included and set to 1 in case its not given in the input.
as_int : bool
If True, return colors as integers in range 0 to 255.
Returns
-------
rgb : list of float or int
RBG or RGBA values for the colors.
"""
if isinstance(arg, (list, tuple, np.ndarray)):
carray = np.asarray(arg);
if alpha is None and carray.shape[-1] == 4:
alpha = True;
if carray.ndim > 1:
return np.array([color(c, alpha=alpha, as_int=as_int, int_type=int_type) for c in arg]);
try:
c = vpc.Color(arg);
if alpha is False or alpha is None:
c = c.rgb;
else:
c = c.rgba;
if alpha is not True:
c[-1] = alpha;
if as_int:
c = np.asarray(255 * c, dtype = int_type);
return c;
except:
pass;
try:
if alpha is False or alpha is None:
c = mpl.colors.to_rgb(arg);
else:
c = mpl.colors.to_rgba(arg);
if alpha is not True:
c[-1] = alpha;
if as_int:
c = np.asarray(255 * c, dtype = int_type);
return c;
except:
raise RuntimeError('Color %r not a valid color specification!' % arg);
[docs]
def hex_to_rgb(color, alpha = None, as_int = False):
"""Return rgb color from hex code
Arguments
---------
color : string
Hex code of the color
alpha : None, bool, int or float
Include the alpha value in the rgb tuple. If None the alpha value is included if given in the hex code,
if False no alpha value is given, if True the alpha value is included and set to 1 in case its not given in the input.
as_int : bool
If True return colors as integers in range 0 to 255.
Returns
-------
rgb : list of float or int
RBG or RGBA values for the colors.
"""
if not isinstance(color, str) or len(color) not in [6,7,8,9]:
raise ValueError('Color argument expected to be a valid hex string, found %r!' % color);
c = color.lstrip('#');
if len(c) == 8:
c = [int(c[i:i+2], 16) for i in (0, 2 ,4, 6)];
rgb = c[:-1];
a = c[-1];
else:
rgb = [int(c[i:i+2], 16) for i in (0, 2 ,4)];
a = 255;
if not as_int:
rgb = np.array(rgb, dtype = float);
rgb = rgb / 255;
a = float(a) / 255;
if alpha is None:
return rgb;
elif alpha is True:
return np.hstack((rgb,a));
else:
return np.hstack((rgb,alpha));
[docs]
def rgb_to_hex(rgb, alpha = None, as_int = False):
"""Return hex code from rgb or rgba colors
Arguments
---------
color : tuple of int or float
Hex code of the color
alpha : None, bool or float
Include the alpha value in the rgb tuple
as_int : bool
If True return colors as integers in range 0 to 255.
Returns
-------
rgb : list of float or int
RBG or RGBA values for the colors.
"""
if not hasattr(rgb, '__len__') or len(rgb) not in [3,4]:
raise ValueError('Color argument expected to be a valid rgb or rgba tuple, found %r!' % rgb);
if len(rgb) == 3:
r,g,b = rgb;
if as_int:
a = 255;
else:
a = 1.0;
else:
r,g,b,a = rgb;
if not as_int:
r,g,b,a = [int(255*x) for x in (r,g,b,a)];
if alpha is None:
return '#%02x%02x%02x' % (r, g, b);
elif alpha is True:
return '#%02x%02x%02x%02x' % (r, g, b, a);
else:
if as_int:
a = alpha;
else:
a = int(255*alpha);
return '#%02x%02x%02x%02x' % (r, g, b, a);
###############################################################################
### Color Maps
###############################################################################
[docs]
def colormap(cmap = None, alpha = True, as_int = False, int_type = int):
"""Convert argument to a color map function.
Arguments
---------
cmap : None, string, tuple or list of colors
Color map specification. If a tuple is given a colormap that linearly interpolates is returned.
alpha : None, bool, int or float
Include the alpha value in the color map.
as_int : bool
If True return color maps as integers in range 0 to 255.
int_type : dtype
The type to use for integer color maps.
Returns
-------
cmap : function
A color map function that maps floats in [0,1] to a RBG or RGBA tuples.
"""
# default color map
if cmap is None:
return colormap('gray');
# linearly interpolated color maps
if isinstance(cmap, (list, tuple, np.ndarray)):
cols = [color(a) for a in cmap];
cmap = vpc.Colormap(cols, interpolation = 'linear');
return colormap(cmap, alpha = alpha, as_int = as_int);
# vispy color maps
cmps = vpc.get_colormaps()
if isinstance(cmap, str) and cmap in cmps.keys():
cmap = vpc.colormap.get_colormap(cmap);
if isinstance(cmap, vp.color.BaseColormap):
if alpha is False or alpha is None:
if as_int:
def cm(x):
return np.array(255*cmap.map(np.array(x))[:,:3], dtype = int_type);
else:
def cm(x):
return cmap.map(np.array(x))[:,:3];
elif alpha is True:
if as_int:
def cm(x):
return np.array(255*cmap.map(np.array(x)), dtype = int_type);
else:
def cm(x):
return cmap.map(np.array(x));
else:
if as_int:
def cm(x):
c = np.array(255 * cmap.map(np.array(x)), dtype = int_type);
c[:,-1] = alpha;
return c;
else:
def cm(x):
c = cmap.map(np.array(x));
c[:,-1] = alpha;
return c;
return cm
# matplotlib color maps
if isinstance(cmap, str) and cmap in plt.cm.cmap_d.keys():
cmap = plt.get_cmap(cmap);
if isinstance(cmap, str) and cmap in colormaps:
cmap = colormaps[cmap];
if isinstance(cmap, mpl.colors.Colormap):
if alpha is False or alpha is None:
if as_int:
def cm(x):
return np.array(255*cmap(np.array(x))[:,:3], dtype = int_type);
else:
def cm(x):
return cmap(np.array(x))[:,:3];
elif alpha is True:
if as_int:
def cm(x):
return np.array(255 * cmap(np.array(x)), dtype = int_type);
else:
def cm(x):
return cmap(np.array(x));
else:
if as_int:
def cm(x):
c = np.array(255*cmap(np.array(x)), dtype = int_type);
c[:,-1] = alpha;
return c;
else:
def cm(x):
c = cmap(np.array(x));
c[:,-1] = alpha;
return c;
return cm
# custom color maps
if cmap in ['boys', 'orientation', orientation_to_boys, orientation_to_rgb]:
if cmap == 'boys':
cmap = orientation_to_boys;
elif cmap == 'orientation':
cmap = orientation_to_rgb;
if alpha is False or alpha is None:
if as_int:
def cm(x):
return np.array(255*cmap(np.array(x)), dtype = int);
else:
def cm(x):
return cmap(np.array(x))[:,:3];
elif alpha is True:
if as_int:
def cm(x):
return np.array(255 * cmap(np.array(x)), dtype = int);
else:
def cm(x):
return cmap(np.array(x));
else:
if as_int:
def cm(x):
c = np.array(255*cmap(np.array(x)), dtype = int);
c[:,-1] = alpha;
return c;
else:
def cm(x):
c = cmap(np.array(x));
c[:,-1] = alpha;
return c;
else:
raise RuntimeError('Colormap specification %r is invalid!' % cmap);
color_map = colormap
# special color maps
[docs]
def orientation_to_boys(orientations, alpha = None, as_int = False, normalize = True):
"""Colormap mapping 3d orientation vectors into rgb colors.
Arguments
---------
orientations : array
List of 3d vectors as nx3 array
alpha : None, bool, int or float
Include the alpha value in the color map.
as_int : bool
If True return color maps as integers in range 0 to 255.
normalize : bool
Normalize vectors to unit length. set to False to avoid overhead
when passing normalized vectors.
Returns
-------
colors : array
RGB colors for the orientation vectors according to Boys coloring.
Note
----
The Boys color map is a smooth way of coloring orientations.
References
----------
[1] https://ieeexplore.ieee.org/abstract/document/5290761
"""
def cc(na, nd):
return (na * np.cos(nd * np.pi / 180.0))
def ss(na, nd):
return na * np.sin(nd * np.pi / 180.0);
x = orientations[...,0]
y = orientations[...,1]
z = orientations[...,2]
if normalize:
norm = np.linalg.norm(orientations, axis=-1)
x = x / norm
y = y / norm
z = z / norm
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 = .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) * .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(.972, s2)
cc67 = cc(.972, s2)
ss89 = ss(.868, s3)
cc89 = cc(.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 alpha is None or alpha is False:
C = np.zeros((3,) + x.shape);
elif alpha is True:
C = np.ones((4,) + x.shape);
else:
C = np.ones((4,) + x.shape);
C[3] = 1.0;
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
ids = np.logical_and(x == 0, np.logical_and(y == 0, z == 0));
C[:,ids] = 0;
C = np.moveaxis(C, 0, -1);
if as_int:
C = np.array(255 * C, dtype = int);
return C;
[docs]
def orientation_to_rgb(orientation, alpha = None, as_int = False):
"""Color map mapping 3d orientation vectors into rgb colors using normalized coordinates.
Arguments
---------
orientations : array
List of 3d vectors as nx3 array.
alpha : None, bool, int or float
Include the alpha value in the color map.
as_int : bool
If True return color maps as integers in range 0 to 255.
Returns
-------
colors : array
RGB colors for the orientation vectors according to standard coloring.
Note
----
This color map is not continuous.
"""
rgb = np.asarray(orientation, dtype = float);
rgb = np.abs(rgb.T / np.linalg.norm(rgb, axis = 1)).T;
if alpha is None or alpha is False:
if as_int:
return np.array(255*rgb, dtype = int);
else:
return rgb;
else:
if alpha is True:
alpha = 1.0;
rgb = np.concatenate([rgb, alpha * np.ones((len(rgb),1))], axis = 1);
if as_int:
return np.array(255*rgb, dtype = int);
else:
return rgb;
_twilight_data = [
[0.88575015840754434, 0.85000924943067835, 0.8879736506427196],
[0.88378520195539056, 0.85072940540310626, 0.88723222096949894],
[0.88172231059285788, 0.85127594077653468, 0.88638056925514819],
[0.8795410528270573, 0.85165675407495722, 0.8854143767924102],
[0.87724880858965482, 0.85187028338870274, 0.88434120381311432],
[0.87485347508575972, 0.85191526123023187, 0.88316926967613829],
[0.87233134085124076, 0.85180165478080894, 0.88189704355001619],
[0.86970474853509816, 0.85152403004797894, 0.88053883390003362],
[0.86696015505333579, 0.8510896085314068, 0.87909766977173343],
[0.86408985081463996, 0.85050391167507788, 0.87757925784892632],
[0.86110245436899846, 0.84976754857001258, 0.87599242923439569],
[0.85798259245670372, 0.84888934810281835, 0.87434038553446281],
[0.85472593189256985, 0.84787488124672816, 0.8726282980930582],
[0.85133714570857189, 0.84672735796116472, 0.87086081657350445],
[0.84780710702577922, 0.8454546229209523, 0.86904036783694438],
[0.8441261828674842, 0.84406482711037389, 0.86716973322690072],
[0.84030420805957784, 0.8425605950855084, 0.865250882410458],
[0.83634031809191178, 0.84094796518951942, 0.86328528001070159],
[0.83222705712934408, 0.83923490627754482, 0.86127563500427884],
[0.82796894316013536, 0.83742600751395202, 0.85922399451306786],
[0.82357429680252847, 0.83552487764795436, 0.85713191328514948],
[0.81904654677937527, 0.8335364929949034, 0.85500206287010105],
[0.81438982121143089, 0.83146558694197847, 0.85283759062147024],
[0.8095999819094809, 0.82931896673505456, 0.85064441601050367],
[0.80469164429814577, 0.82709838780560663, 0.84842449296974021],
[0.79967075421267997, 0.82480781812080928, 0.84618210029578533],
[0.79454305089231114, 0.82245116226304615, 0.84392184786827984],
[0.78931445564608915, 0.82003213188702007, 0.8416486380471222],
[0.78399101042764918, 0.81755426400533426, 0.83936747464036732],
[0.77857892008227592, 0.81502089378742548, 0.8370834463093898],
[0.77308416590170936, 0.81243524735466011, 0.83480172950579679],
[0.76751108504417864, 0.8098007598713145, 0.83252816638059668],
[0.76186907937980286, 0.80711949387647486, 0.830266486168872],
[0.75616443584381976, 0.80439408733477935, 0.82802138994719998],
[0.75040346765406696, 0.80162699008965321, 0.82579737851082424],
[0.74459247771890169, 0.79882047719583249, 0.82359867586156521],
[0.73873771700494939, 0.79597665735031009, 0.82142922780433014],
[0.73284543645523459, 0.79309746468844067, 0.81929263384230377],
[0.72692177512829703, 0.7901846863592763, 0.81719217466726379],
[0.72097280665536778, 0.78723995923452639, 0.81513073920879264],
[0.71500403076252128, 0.78426487091581187, 0.81311116559949914],
[0.70902078134539304, 0.78126088716070907, 0.81113591855117928],
[0.7030297722540817, 0.77822904973358131, 0.80920618848056969],
[0.6970365443886174, 0.77517050008066057, 0.80732335380063447],
[0.69104641009309098, 0.77208629460678091, 0.80548841690679074],
[0.68506446154395928, 0.7689774029354699, 0.80370206267176914],
[0.67909554499882152, 0.76584472131395898, 0.8019646617300199],
[0.67314422559426212, 0.76268908733890484, 0.80027628545809526],
[0.66721479803752815, 0.7595112803730375, 0.79863674654537764],
[0.6613112930078745, 0.75631202708719025, 0.7970456043491897],
[0.65543692326454717, 0.75309208756768431, 0.79550271129031047],
[0.64959573004253479, 0.74985201221941766, 0.79400674021499107],
[0.6437910831099849, 0.7465923800833657, 0.79255653201306053],
[0.63802586828545982, 0.74331376714033193, 0.79115100459573173],
[0.6323027138710603, 0.74001672160131404, 0.78978892762640429],
[0.62662402022604591, 0.73670175403699445, 0.78846901316334561],
[0.62099193064817548, 0.73336934798923203, 0.78718994624696581],
[0.61540846411770478, 0.73001995232739691, 0.78595022706750484],
[0.60987543176093062, 0.72665398759758293, 0.78474835732694714],
[0.60439434200274855, 0.7232718614323369, 0.78358295593535587],
[0.5989665814482068, 0.71987394892246725, 0.78245259899346642],
[0.59359335696837223, 0.7164606049658685, 0.78135588237640097],
[0.58827579780555495, 0.71303214646458135, 0.78029141405636515],
[0.58301487036932409, 0.70958887676997473, 0.77925781820476592],
[0.5778116438998202, 0.70613106157153982, 0.77825345121025524],
[0.5726668948158774, 0.7026589535425779, 0.77727702680911992],
[0.56758117853861967, 0.69917279302646274, 0.77632748534275298],
[0.56255515357219343, 0.69567278381629649, 0.77540359142309845],
[0.55758940419605174, 0.69215911458254054, 0.7745041337932782],
[0.55268450589347129, 0.68863194515166382, 0.7736279426902245],
[0.54784098153018634, 0.68509142218509878, 0.77277386473440868],
[0.54305932424018233, 0.68153767253065878, 0.77194079697835083],
[0.53834015575176275, 0.67797081129095405, 0.77112734439057717],
[0.53368389147728401, 0.67439093705212727, 0.7703325054879735],
[0.529090861832473, 0.67079812302806219, 0.76955552292313134],
[0.52456151470593582, 0.66719242996142225, 0.76879541714230948],
[0.52009627392235558, 0.66357391434030388, 0.76805119403344102],
[0.5156955988596057, 0.65994260812897998, 0.76732191489596169],
[0.51135992541601927, 0.65629853981831865, 0.76660663780645333],
[0.50708969576451657, 0.65264172403146448, 0.76590445660835849],
[0.5028853540415561, 0.64897216734095264, 0.76521446718174913],
[0.49874733661356069, 0.6452898684900934, 0.76453578734180083],
[0.4946761847863938, 0.64159484119504429, 0.76386719002130909],
[0.49067224938561221, 0.63788704858847078, 0.76320812763163837],
[0.4867359599430568, 0.63416646251100506, 0.76255780085924041],
[0.4828677867260272, 0.6304330455306234, 0.76191537149895305],
[0.47906816236197386, 0.62668676251860134, 0.76128000375662419],
[0.47533752394906287, 0.62292757283835809, 0.76065085571817748],
[0.47167629518877091, 0.61915543242884641, 0.76002709227883047],
[0.46808490970531597, 0.61537028695790286, 0.75940789891092741],
[0.46456376716303932, 0.61157208822864151, 0.75879242623025811],
[0.46111326647023881, 0.607760777169989, 0.75817986436807139],
[0.45773377230160567, 0.60393630046586455, 0.75756936901859162],
[0.45442563977552913, 0.60009859503858665, 0.75696013660606487],
[0.45118918687617743, 0.59624762051353541, 0.75635120643246645],
[0.44802470933589172, 0.59238331452146575, 0.75574176474107924],
[0.44493246854215379, 0.5885055998308617, 0.7551311041857901],
[0.44191271766696399, 0.58461441100175571, 0.75451838884410671],
[0.43896563958048396, 0.58070969241098491, 0.75390276208285945],
[0.43609138958356369, 0.57679137998186081, 0.7532834105961016],
[0.43329008867358393, 0.57285941625606673, 0.75265946532566674],
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[0.57313845754107873, 0.18597036007065024, 0.31357126868520002],
[0.57715550812992045, 0.18940601489760422, 0.31339704333572083],
[0.58112932761586555, 0.19288548904692518, 0.31322399394183942],
[0.58506024396466882, 0.19640737049066315, 0.31305401163732732],
[0.58894861935544707, 0.19997020971775276, 0.31288922211590126],
[0.59279480536520257, 0.20357251410079796, 0.31273234839304942],
[0.59659918109122367, 0.207212956082026, 0.31258523031121233],
[0.60036213010411577, 0.21089030138947745, 0.31244934410414688],
[0.60408401696732739, 0.21460331490206347, 0.31232652641170694],
[0.60776523994818654, 0.21835070166659282, 0.31221903291870201],
[0.6114062072731884, 0.22213124697023234, 0.31212881396435238],
[0.61500723236391375, 0.22594402043981826, 0.31205680685765741],
[0.61856865258877192, 0.22978799249179921, 0.31200463838728931],
[0.62209079821082613, 0.2336621873300741, 0.31197383273627388],
[0.62557416500434959, 0.23756535071152696, 0.31196698314912269],
[0.62901892016985872, 0.24149689191922535, 0.31198447195645718],
[0.63242534854210275, 0.24545598775548677, 0.31202765974624452],
[0.6357937104834237, 0.24944185818822678, 0.31209793953300591],
[0.6391243387840212, 0.25345365461983138, 0.31219689612063978],
[0.642417577481186, 0.257490519876798, 0.31232631707560987],
[0.64567349382645434, 0.26155203161615281, 0.31248673753935263],
[0.64889230169458245, 0.26563755336209077, 0.31267941819570189],
[0.65207417290277303, 0.26974650525236699, 0.31290560605819168],
[0.65521932609327127, 0.27387826652410152, 0.3131666792687211],
[0.6583280801134499, 0.27803210957665631, 0.3134643447952643],
[0.66140037532601781, 0.28220778870555907, 0.31379912926498488],
[0.66443632469878844, 0.28640483614256179, 0.31417223403606975],
[0.66743603766369131, 0.29062280081258873, 0.31458483752056837],
[0.67039959547676198, 0.29486126309253047, 0.31503813956872212],
[0.67332725564817331, 0.29911962764489264, 0.31553372323982209],
[0.67621897924409746, 0.30339762792450425, 0.3160724937230589],
[0.67907474028157344, 0.30769497879760166, 0.31665545668946665],
[0.68189457150944521, 0.31201133280550686, 0.31728380489244951],
[0.68467850942494535, 0.31634634821222207, 0.31795870784057567],
[0.68742656435169625, 0.32069970535138104, 0.31868137622277692],
[0.6901389321505248, 0.32507091815606004, 0.31945332332898302],
[0.69281544846764931, 0.32945984647042675, 0.3202754315314667],
[0.69545608346891119, 0.33386622163232865, 0.32114884306985791],
[0.6980608153581771, 0.33828976326048621, 0.32207478855218091],
[0.70062962477242097, 0.34273019305341756, 0.32305449047765694],
[0.70316249458814151, 0.34718723719597999, 0.32408913679491225],
[0.70565951122610093, 0.35166052978120937, 0.32518014084085567],
[0.70812059568420482, 0.35614985523380299, 0.32632861885644465],
[0.7105456546582587, 0.36065500290840113, 0.32753574162788762],
[0.71293466839773467, 0.36517570519856757, 0.3288027427038317],
[0.71528760614847287, 0.36971170225223449, 0.3301308728723546],
[0.71760444908133847, 0.37426272710686193, 0.33152138620958932],
[0.71988521490549851, 0.37882848839337313, 0.33297555200245399],
[0.7221299918421461, 0.38340864508963057, 0.33449469983585844],
[0.72433865647781592, 0.38800301593162145, 0.33607995965691828],
[0.72651122900227549, 0.3926113126792577, 0.3377325942005665],
[0.72864773856716547, 0.39723324476747235, 0.33945384341064017],
[0.73074820754845171, 0.401868526884681, 0.3412449533046818],
[0.73281270506268747, 0.4065168468778026, 0.34310715173410822],
[0.73484133598564938, 0.41117787004519513, 0.34504169470809071],
[0.73683422173585866, 0.41585125850290111, 0.34704978520758401],
[0.73879140024599266, 0.42053672992315327, 0.34913260148542435],
[0.74071301619506091, 0.4252339389526239, 0.35129130890802607],
[0.7425992159973317, 0.42994254036133867, 0.35352709245374592],
[0.74445018676570673, 0.43466217184617112, 0.35584108091122535],
[0.74626615789163442, 0.43939245044973502, 0.35823439142300639],
[0.74804739275559562, 0.44413297780351974, 0.36070813602540136],
[0.74979420547170472, 0.44888333481548809, 0.36326337558360278],
[0.75150685045891663, 0.45364314496866825, 0.36590112443835765],
[0.75318566369046569, 0.45841199172949604, 0.36862236642234769],
[0.75483105066959544, 0.46318942799460555, 0.3714280448394211],
[0.75644341577140706, 0.46797501437948458, 0.37431909037543515],
[0.75802325538455839, 0.4727682731566229, 0.37729635531096678],
[0.75957111105340058, 0.47756871222057079, 0.380360657784311],
[0.7610876378057071, 0.48237579130289127, 0.38351275723852291],
[0.76257333554052609, 0.48718906673415824, 0.38675335037837993],
[0.76402885609288662, 0.49200802533379656, 0.39008308392311997],
[0.76545492593330511, 0.49683212909727231, 0.39350254000115381],
[0.76685228950643891, 0.5016608471009063, 0.39701221751773474],
[0.76822176599735303, 0.50649362371287909, 0.40061257089416885],
[0.7695642334401418, 0.5113298901696085, 0.40430398069682483],
[0.77088091962302474, 0.51616892643469103, 0.40808667584648967],
[0.77217257229605551, 0.5210102658711383, 0.41196089987122869],
[0.77344021829889886, 0.52585332093451564, 0.41592679539764366],
[0.77468494746063199, 0.53069749384776732, 0.41998440356963762],
[0.77590790730685699, 0.53554217882461186, 0.42413367909988375],
[0.7771103295521099, 0.54038674910561235, 0.42837450371258479],
[0.77829345807633121, 0.54523059488426595, 0.432706647838971],
[0.77945862731506643, 0.55007308413977274, 0.43712979856444761],
[0.78060774749483774, 0.55491335744890613, 0.44164332426364639],
[0.78174180478981836, 0.55975098052594863, 0.44624687186865436],
[0.78286225264440912, 0.56458533111166875, 0.45093985823706345],
[0.78397060836414478, 0.56941578326710418, 0.45572154742892063],
[0.78506845019606841, 0.5742417003617839, 0.46059116206904965],
[0.78615737132332963, 0.5790624629815756, 0.46554778281918402],
[0.78723904108188347, 0.58387743744557208, 0.47059039582133383],
[0.78831514045623963, 0.58868600173562435, 0.47571791879076081],
[0.78938737766251943, 0.5934875421745599, 0.48092913815357724],
[0.79045776847727878, 0.59828134277062461, 0.48622257801969754],
[0.79152832843475607, 0.60306670593147205, 0.49159667021646397],
[0.79260034304237448, 0.60784322087037024, 0.49705020621532009],
[0.79367559698664958, 0.61261029334072192, 0.50258161291269432],
[0.79475585972654039, 0.61736734400220705, 0.50818921213102985],
[0.79584292379583765, 0.62211378808451145, 0.51387124091909786],
[0.79693854719951607, 0.62684905679296699, 0.5196258425240281],
[0.79804447815136637, 0.63157258225089552, 0.52545108144834785],
[0.7991624518501963, 0.63628379372029187, 0.53134495942561433],
[0.80029415389753977, 0.64098213306749863, 0.53730535185141037],
[0.80144124292560048, 0.64566703459218766, 0.5433300863249918],
[0.80260531146112946, 0.65033793748103852, 0.54941691584603647],
[0.80378792531077625, 0.65499426549472628, 0.55556350867083815],
[0.80499054790810298, 0.65963545027564163, 0.56176745110546977],
[0.80621460526927058, 0.66426089585282289, 0.56802629178649788],
[0.8074614045096935, 0.6688700095398864, 0.57433746373459582],
[0.80873219170089694, 0.67346216702194517, 0.58069834805576737],
[0.81002809466520687, 0.67803672673971815, 0.58710626908082753],
[0.81135014011763329, 0.68259301546243389, 0.59355848909050757],
[0.81269922039881493, 0.68713033714618876, 0.60005214820435104],
[0.81407611046993344, 0.69164794791482131, 0.6065843782630862],
[0.81548146627279483, 0.69614505508308089, 0.61315221209322646],
[0.81691575775055891, 0.70062083014783982, 0.61975260637257923],
[0.81837931164498223, 0.70507438189635097, 0.62638245478933297],
[0.81987230650455289, 0.70950474978787481, 0.63303857040067113],
[0.8213947205565636, 0.7139109141951604, 0.63971766697672761],
[0.82294635110428427, 0.71829177331290062, 0.6464164243818421],
[0.8245268129450285, 0.72264614312088882, 0.65313137915422603],
[0.82613549710580259, 0.72697275518238258, 0.65985900156216504],
[0.8277716072353446, 0.73127023324078089, 0.66659570204682972],
[0.82943407816481474, 0.7355371221572935, 0.67333772009301907],
[0.83112163529096306, 0.73977184647638616, 0.68008125203631464],
[0.83283277185777982, 0.74397271817459876, 0.68682235874648545],
[0.8345656905566583, 0.7481379479992134, 0.69355697649863846],
[0.83631898844737929, 0.75226548952875261, 0.70027999028864962],
[0.83809123476131964, 0.75635314860808633, 0.70698561390212977],
[0.83987839884120874, 0.76039907199779677, 0.71367147811129228],
[0.84167750766845151, 0.76440101200982946, 0.72033299387284622],
[0.84348529222933699, 0.76835660399870176, 0.72696536998972039],
[0.84529810731955113, 0.77226338601044719, 0.73356368240541492],
[0.84711195507965098, 0.77611880236047159, 0.74012275762807056],
[0.84892245563117641, 0.77992021407650147, 0.74663719293664366],
[0.85072697023178789, 0.78366457342383888, 0.7530974636118285],
[0.85251907207708444, 0.78734936133548439, 0.7594994148789691],
[0.85429219611470464, 0.79097196777091994, 0.76583801477914104],
[0.85604022314725403, 0.79452963601550608, 0.77210610037674143],
[0.85775662943504905, 0.79801963142713928, 0.77829571667247499],
[0.8594346370300241, 0.8014392309950078, 0.78439788751383921],
[0.86107117027565516, 0.80478517909812231, 0.79039529663736285],
[0.86265601051127572, 0.80805523804261525, 0.796282666437655],
[0.86418343723941027, 0.81124644224653542, 0.80204612696863953],
[0.86564934325605325, 0.81435544067514909, 0.80766972324164554],
[0.86705314907048503, 0.81737804041911244, 0.81313419626911398],
[0.86839954695818633, 0.82030875512181523, 0.81841638963128993],
[0.86969131502613806, 0.82314158859569164, 0.82350476683173168],
[0.87093846717297507, 0.82586857889438514, 0.82838497261149613],
[0.87215331978454325, 0.82848052823709672, 0.8330486712880828],
[0.87335171360916275, 0.83096715251272624, 0.83748851001197089],
[0.87453793320260187, 0.83331972948645461, 0.84171925358069011],
[0.87571458709961403, 0.8355302318472394, 0.84575537519027078],
[0.87687848451614692, 0.83759238071186537, 0.84961373549150254],
[0.87802298436649007, 0.83950165618540074, 0.85330645352458923],
[0.87913244240792765, 0.84125554884475906, 0.85685572291039636],
[0.88019293315695812, 0.84285224824778615, 0.86027399927156634],
[0.88119169871341951, 0.84429066717717349, 0.86356595168669881],
[0.88211542489401606, 0.84557007254559347, 0.86673765046233331],
[0.88295168595448525, 0.84668970275699273, 0.86979617048190971],
[0.88369127145898041, 0.84764891761519268, 0.87274147101441557],
[0.88432713054113543, 0.84844741572055415, 0.87556785228242973],
[0.88485138159908572, 0.84908426422893801, 0.87828235285372469],
[0.88525897972630474, 0.84955892810989209, 0.88088414794024839],
[0.88554714811952384, 0.84987174283631584, 0.88336206121170946],
[0.88571155122845646, 0.85002186115856315, 0.88572538990087124]];
colormaps = {name : mpc.ListedColormap(data, name=name) for name, data in (('twilight', _twilight_data),)};
###############################################################################
### Utility and Lookup tables
###############################################################################
[docs]
def lighter(arg, fraction = 0.3):
col = color(arg);
delta = np.ones(3) - col[...,:3];
col[...,:3] = col[...,:3] + fraction * delta;
return col;
[docs]
def darker(arg, fraction = 0.3):
col = color(arg);
col[...,:3] = col[...,:3] * fraction;
return col;
[docs]
def rgb_LUT():
"""Returns a lookup table listing all integer rgb colors
Note
----
Useful to color data and plot in imagej.
"""
xl = np.mgrid[0:256, 0:256, 0:256]
lut = np.vstack((xl[0].reshape(1, 256**3),
xl[1].reshape(1, 256**3),
xl[2].reshape(1, 256**3),
255 * np.ones((1, 256**3)))).T
return lut.astype('int32')
[docs]
def rgb_to_LUT(rgb):
"""Converts the rgb colors to the index color in the full rgb color look up table
Arguments
---------
rgb : array
Integer rgb values as nX3 array
Returns
-------
index : array
The indices of the rgb values in the full rgb LUT.
"""
r,g,b = rgb.T;
return 256**2 * r + 256 * g + b
[docs]
def write_LUT(filename, colors):
"""Write a lut lookup table file from a list of colors for use with imagej
Arguments
---------
filename : str
file name of the lookup table
colors : array
Colors as nX3 array to write to te ookup table
Returns
-------
filename : str
Filename of the lookup table.
"""
with open(filename, 'w') as f:
for c in colors:
f.write(('%d' % c[0]).rjust(4) + ('%d' % c[1]).rjust(5) + ('%d' % c[2]).rjust(5));
f.write('\n');
f.close();
return filename;
[docs]
def write_PAL(filename, colors):
"""Write a pal pallette file from a list of colors for use with e.g. imaris
Arguments
---------
filename : str
file name of the lookup table
colors : array
Colors as nX3 array to write to te ookup table
Returns
-------
filename : str
Filename of the lookup table.
"""
with open(filename, 'w') as f:
for c in colors:
f.write(('%3.3f' % c[0]).rjust(10) + ('%3.3f' % c[1]).rjust(11) + ('%3.3f' % c[2]).rjust(11));
f.write('\n');
f.close();
return filename;
[docs]
def rand_cmap(n_labels, map_type='bright', first_color_black=True, last_color_black=False, cmap_name='Paired'):
"""
Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks
Arguments
---------
n_labels : int
Number of labels (size of colormap)
map_type : str
'bright' for strong colors, 'soft' for pastel colors
first_color_black : bool
Option to use first color as black, True or False
last_color_black : bool
Option to use last color as black, True or False
Returns
-------
random_colormap : list of RGB colors
colormap for matplotlib
"""
label_cmap_names = ('Pastel1', 'Pastel2', 'Accent', 'Paired', 'Dark2', 'Set1', 'Set2', 'Set3', 'Tab10')
pastel_cmap_names = ('Pastel1', 'Pastel2', 'Set3')
bright_cmap_names = list(set(label_cmap_names) - set(pastel_cmap_names))
black = [0, 0, 0]
if n_labels == 0:
return [black]
assert cmap_name in label_cmap_names
if map_type == 'pastel' and cmap_name not in pastel_cmap_names:
cmap_name = 'Set3'
elif map_type == 'bright' and cmap_name not in bright_cmap_names:
cmap_name = 'Accent'
cmap_colors = np.array(mpl.cm.get_cmap(cmap_name).colors)
n_repeats = n_labels // cmap_colors.shape[0] + 1 # Overshoot
colors = np.tile(cmap_colors, (n_repeats, 1))[:n_labels, :] # Trim
if first_color_black:
colors[0, :] = black
if last_color_black:
colors[-1, :] = black
return colors
[docs]
def gray_image_to_rgb(image, color_name, pseudo_z_score=False, normalize=False, range_max=None):
"""
Convert a grayscale image to a colored RGB image.
Parameters:
image (numpy.ndarray): The grayscale image to convert.
color (str): The color to convert to. Must be one of 'cyan', 'magenta', 'yellow', 'red', 'green', 'blue'.
Returns:
numpy.ndarray: The converted RGB image.
"""
if pseudo_z_score and normalize:
raise ValueError('Cannot normalize and pseudo z-score at the same time.')
output_shape = image.shape
blank = np.zeros(output_shape, dtype=image.dtype)
if pseudo_z_score:
n_sds = 4
high_intensity = (image.mean() + n_sds * image.std())
if range_max is None:
max_val = np.iinfo(image.dtype).max
else:
max_val = range_max
image = image / high_intensity * (max_val / 2)
if normalize:
image = (image - np.min(image)) / (np.max(image) - np.min(image))
color_dict = {
'cyan': (0, 1, 1),
'magenta': (1, 0, 1),
'yellow': (1, 1, 0),
'red': (1, 0, 0),
'green': (0, 1, 0),
'blue': (0, 0, 1)
}
try:
color_order = color_dict[color_name]
except KeyError:
raise ValueError(f'Invalid color name: {color_name}, should be one of {color_dict.keys()}')
image = np.dstack((color_order[0]*image + (1-color_order[0])*blank,
color_order[1]*image + (1-color_order[1])*blank,
color_order[2]*image + (1-color_order[2])*blank))
return image
###############################################################################
### Tests
###############################################################################
def _test():
import ClearMap.Visualization.Color as col
from importlib import reload
reload(col);
col.hex_to_rgb('FFFFFF')
#orientation color maps
#import numpy as np
import ClearMap.Visualization.Mesh.IcoSphere as ics
vertices, faces = ics.icosphere(n_refinements = 7)
vertex_colors = col.orientation_to_boys(vertices, alpha=1.0)
import ClearMap.Visualization.Plot3d as p3d
p = p3d.plot_mesh_3d(vertices, faces, vertex_colors=vertex_colors)
vertex_colors = col.orientation_to_rgb(vertices, alpha=1.0)
p = p3d.plot_mesh_3d(vertices, faces, vertex_colors=vertex_colors)
p3d.set_light_to_camera(p)
