777 lines
24 KiB
Python
777 lines
24 KiB
Python
import os
|
||
import json
|
||
import functools
|
||
import colorsys
|
||
from pathlib import Path
|
||
from typing import Union, TextIO
|
||
|
||
import numpy as np
|
||
import scipy.interpolate as interp
|
||
import matplotlib as mpl
|
||
if os.name == 'posix' and 'DISPLAY' not in os.environ:
|
||
mpl.use('Agg')
|
||
import matplotlib.pyplot as plt
|
||
from matplotlib import cm
|
||
from PIL import Image
|
||
|
||
from ._typehints import FloatSequence, FileHandle
|
||
from . import util
|
||
from . import Table
|
||
|
||
_EPS = 216./24389.
|
||
_KAPPA = 24389./27.
|
||
_REF_WHITE = np.array([.95047, 1.00000, 1.08883]) # Observer = 2, Illuminant = D65
|
||
|
||
# ToDo (if needed)
|
||
# - support alpha channel (paraview/ASCII/input)
|
||
# - support NaN color (paraview)
|
||
|
||
class Colormap(mpl.colors.ListedColormap):
|
||
"""
|
||
Enhance matplotlib colormap functionality to be used within DAMASK.
|
||
|
||
Colors are internally stored as R(ed) G(green) B(lue) values.
|
||
The colormap can be used in matplotlib, seaborn, etc., or can
|
||
exported to file for external use.
|
||
|
||
References
|
||
----------
|
||
K. Moreland, Proceedings of the 5th International Symposium on Advances in Visual Computing, 2009
|
||
https://doi.org/10.1007/978-3-642-10520-3_9
|
||
|
||
P. Eisenlohr et al., International Journal of Plasticity 46:37–53, 2013
|
||
https://doi.org/10.1016/j.ijplas.2012.09.012
|
||
|
||
Matplotlib colormaps overview
|
||
https://matplotlib.org/stable/tutorials/colors/colormaps.html
|
||
|
||
"""
|
||
|
||
def __eq__(self,
|
||
other: object) -> bool:
|
||
"""Test equality of colormaps."""
|
||
if not isinstance(other, Colormap):
|
||
return NotImplemented
|
||
return len(self.colors) == len(other.colors) \
|
||
and bool(np.all(self.colors == other.colors))
|
||
|
||
def __add__(self,
|
||
other: 'Colormap') -> 'Colormap':
|
||
"""Concatenate."""
|
||
return Colormap(np.vstack((self.colors,other.colors)),
|
||
f'{self.name}+{other.name}')
|
||
|
||
def __iadd__(self,
|
||
other: 'Colormap') -> 'Colormap':
|
||
"""Concatenate (in-place)."""
|
||
return self.__add__(other)
|
||
|
||
def __mul__(self,
|
||
factor: int) -> 'Colormap':
|
||
"""Repeat."""
|
||
return Colormap(np.vstack([self.colors]*factor),f'{self.name}*{factor}')
|
||
|
||
def __imul__(self,
|
||
factor: int) -> 'Colormap':
|
||
"""Repeat (in-place)."""
|
||
return self.__mul__(factor)
|
||
|
||
def __invert__(self) -> 'Colormap':
|
||
"""Reverse."""
|
||
return self.reversed()
|
||
|
||
def __repr__(self) -> str:
|
||
"""Show as matplotlib figure."""
|
||
fig = plt.figure(self.name,figsize=(5,.5))
|
||
ax1 = fig.add_axes([0, 0, 1, 1])
|
||
ax1.set_axis_off()
|
||
ax1.imshow(np.linspace(0,1,self.N).reshape(1,-1),
|
||
aspect='auto', cmap=self, interpolation='nearest')
|
||
plt.show(block=False)
|
||
return f'Colormap: {self.name}'
|
||
|
||
|
||
@staticmethod
|
||
def from_range(low: FloatSequence,
|
||
high: FloatSequence,
|
||
name: str = 'DAMASK colormap',
|
||
N: int = 256,
|
||
model: str = 'rgb') -> 'Colormap':
|
||
"""
|
||
Create a perceptually uniform colormap between given (inclusive) bounds.
|
||
|
||
Parameters
|
||
----------
|
||
low : sequence of float, len (3)
|
||
Color definition for minimum value.
|
||
high : sequence of float, len (3)
|
||
Color definition for maximum value.
|
||
name : str, optional
|
||
Name of the colormap. Defaults to 'DAMASK colormap'.
|
||
N : int, optional
|
||
Number of color quantization levels. Defaults to 256.
|
||
model : {'rgb', 'hsv', 'hsl', 'xyz', 'lab', 'msh'}
|
||
Color model used for input color definitions. Defaults to 'rgb'.
|
||
The available color models are:
|
||
- 'rgb': Red Green Blue.
|
||
- 'hsv': Hue Saturation Value.
|
||
- 'hsl': Hue Saturation Luminance.
|
||
- 'xyz': CIE Xyz.
|
||
- 'lab': CIE Lab.
|
||
- 'msh': Msh (for perceptual uniform interpolation).
|
||
|
||
Returns
|
||
-------
|
||
new : damask.Colormap
|
||
Colormap within given bounds.
|
||
|
||
Examples
|
||
--------
|
||
>>> import damask
|
||
>>> damask.Colormap.from_range((0,0,1),(0,0,0),'blue_to_black')
|
||
|
||
"""
|
||
toMsh = dict(
|
||
rgb=Colormap._rgb2msh,
|
||
hsv=Colormap._hsv2msh,
|
||
hsl=Colormap._hsl2msh,
|
||
xyz=Colormap._xyz2msh,
|
||
lab=Colormap._lab2msh,
|
||
msh=lambda x:x,
|
||
)
|
||
|
||
if model.lower() not in toMsh:
|
||
raise ValueError(f'invalid color model "{model}"')
|
||
|
||
low_high = np.vstack((low,high)).astype(float)
|
||
out_of_bounds = np.bool_(False)
|
||
|
||
if model.lower() == 'rgb':
|
||
out_of_bounds = np.any(low_high<0) or np.any(low_high>1)
|
||
elif model.lower() == 'hsv':
|
||
out_of_bounds = np.any(low_high<0) or np.any(low_high>[360,1,1])
|
||
elif model.lower() == 'hsl':
|
||
out_of_bounds = np.any(low_high<0) or np.any(low_high>[360,1,1])
|
||
elif model.lower() == 'lab':
|
||
out_of_bounds = np.any(low_high[:,0]<0)
|
||
|
||
if out_of_bounds:
|
||
raise ValueError(f'{model.upper()} colors {low_high[0]} | {low_high[1]} are out of bounds')
|
||
|
||
low_,high_ = map(toMsh[model.lower()],low_high)
|
||
msh = map(functools.partial(Colormap._interpolate_msh,low=low_,high=high_),np.linspace(0,1,N))
|
||
rgb = np.array(list(map(Colormap._msh2rgb,msh)))
|
||
|
||
return Colormap(rgb,name=name)
|
||
|
||
|
||
@staticmethod
|
||
def from_predefined(name: str,
|
||
N: int = 256) -> 'Colormap':
|
||
"""
|
||
Select from a set of predefined colormaps.
|
||
|
||
Predefined colormaps (Colormap.predefined) include
|
||
native matplotlib colormaps and common DAMASK colormaps.
|
||
|
||
Parameters
|
||
----------
|
||
name : str
|
||
Name of the colormap.
|
||
N : int, optional
|
||
Number of color quantization levels. Defaults to 256.
|
||
This parameter is not used for matplotlib colormaps
|
||
that are of type `ListedColormap`.
|
||
|
||
Returns
|
||
-------
|
||
new : damask.Colormap
|
||
Predefined colormap.
|
||
|
||
Examples
|
||
--------
|
||
>>> import damask
|
||
>>> damask.Colormap.from_predefined('strain')
|
||
|
||
"""
|
||
try:
|
||
# matplotlib presets
|
||
colormap = cm.__dict__[name]
|
||
return Colormap(np.array(list(map(colormap,np.linspace(0,1,N)))
|
||
if isinstance(colormap,mpl.colors.LinearSegmentedColormap) else
|
||
colormap.colors),
|
||
name=name)
|
||
except KeyError:
|
||
# DAMASK presets
|
||
definition = Colormap._predefined_DAMASK[name]
|
||
return Colormap.from_range(definition['low'],definition['high'],name,N)
|
||
|
||
|
||
def at(self,
|
||
fraction : Union[float,FloatSequence]) -> np.ndarray:
|
||
"""
|
||
Interpolate color at fraction.
|
||
|
||
Parameters
|
||
----------
|
||
fraction : float or sequence of float
|
||
Fractional coordinate(s) to evaluate Colormap at.
|
||
|
||
Returns
|
||
-------
|
||
color : numpy.ndarray, shape(...,4)
|
||
RGBA values of interpolated color(s).
|
||
|
||
Examples
|
||
--------
|
||
>>> import damask
|
||
>>> cmap = damask.Colormap.from_predefined('gray')
|
||
>>> cmap.at(0.5)
|
||
array([0.5, 0.5, 0.5, 1. ])
|
||
>>> 'rgb({},{},{})'.format(*cmap.at(0.5))
|
||
'rgb(0.5,0.5,0.5)'
|
||
|
||
"""
|
||
return interp.interp1d(np.linspace(0,1,self.N),
|
||
self.colors,
|
||
axis=0,
|
||
assume_sorted=True)(fraction)
|
||
|
||
|
||
def shade(self,
|
||
field: np.ndarray,
|
||
bounds: FloatSequence = None,
|
||
gap: float = None) -> Image:
|
||
"""
|
||
Generate PIL image of 2D field using colormap.
|
||
|
||
Parameters
|
||
----------
|
||
field : numpy.ndarray, shape (:,:)
|
||
Data to be shaded.
|
||
bounds : sequence of float, len (2), optional
|
||
Value range (left,right) spanned by colormap.
|
||
gap : field.dtype, optional
|
||
Transparent value. NaN will always be rendered transparent.
|
||
|
||
Returns
|
||
-------
|
||
PIL.Image
|
||
RGBA image of shaded data.
|
||
|
||
"""
|
||
mask = np.logical_not(np.isnan(field) if gap is None else \
|
||
np.logical_or (np.isnan(field), field == gap)) # mask NaN (and gap if present)
|
||
|
||
l,r = (field[mask].min(),field[mask].max()) if bounds is None else \
|
||
(bounds[0],bounds[1])
|
||
|
||
if abs(delta := r-l) * 1e8 <= (avg := 0.5*abs(r+l)): # delta is similar to numerical noise
|
||
l,r = (l-0.5*avg*np.sign(delta),r+0.5*avg*np.sign(delta)) # extend range to have actual data centered within
|
||
|
||
return Image.fromarray(
|
||
(np.dstack((
|
||
self.colors[(np.round(np.clip((field-l)/delta,0.0,1.0)*(self.N-1))).astype(np.uint16),:3],
|
||
mask.astype(float)
|
||
)
|
||
)*255
|
||
).astype(np.uint8),
|
||
mode='RGBA')
|
||
|
||
|
||
def reversed(self,
|
||
name: str = None) -> 'Colormap':
|
||
"""
|
||
Reverse.
|
||
|
||
Parameters
|
||
----------
|
||
name : str, optional
|
||
Name of the reversed colormap.
|
||
Defaults to parent colormap name + '_r'.
|
||
|
||
Returns
|
||
-------
|
||
damask.Colormap
|
||
Reversed colormap.
|
||
|
||
Examples
|
||
--------
|
||
>>> import damask
|
||
>>> damask.Colormap.from_predefined('stress').reversed()
|
||
|
||
"""
|
||
rev = super().reversed(name)
|
||
return Colormap(np.array(rev.colors),rev.name[:-4] if rev.name.endswith('_r_r') else rev.name)
|
||
|
||
|
||
def _get_file_handle(self,
|
||
fname: Union[FileHandle, None],
|
||
suffix: str = '') -> TextIO:
|
||
"""
|
||
Provide file handle.
|
||
|
||
Parameters
|
||
----------
|
||
fname : file, str, pathlib.Path, or None
|
||
Name or handle of file.
|
||
If None, colormap name + suffix.
|
||
suffix: str, optional
|
||
Extension to use for colormap file.
|
||
|
||
Returns
|
||
-------
|
||
f : file object
|
||
File handle with write access.
|
||
|
||
"""
|
||
if fname is None:
|
||
return open(self.name.replace(' ','_')+suffix, 'w', newline='\n')
|
||
elif isinstance(fname, (str, Path)):
|
||
return open(Path(fname).expanduser(), 'w', newline='\n')
|
||
else:
|
||
return fname
|
||
|
||
|
||
def save_paraview(self,
|
||
fname: FileHandle = None):
|
||
"""
|
||
Save as JSON file for use in Paraview.
|
||
|
||
Parameters
|
||
----------
|
||
fname : file, str, or pathlib.Path, optional
|
||
File to store results. Defaults to colormap name + '.json'.
|
||
|
||
"""
|
||
colors = []
|
||
for i,c in enumerate(np.round(self.colors,6).tolist()):
|
||
colors+=[i]+c
|
||
|
||
out = [{
|
||
'Creator':util.execution_stamp('Colormap'),
|
||
'ColorSpace':'RGB',
|
||
'Name':self.name,
|
||
'DefaultMap':True,
|
||
'RGBPoints':colors
|
||
}]
|
||
|
||
fhandle = self._get_file_handle(fname,'.json')
|
||
json.dump(out,fhandle,indent=4)
|
||
fhandle.write('\n')
|
||
|
||
|
||
def save_ASCII(self,
|
||
fname: FileHandle = None):
|
||
"""
|
||
Save as ASCII file.
|
||
|
||
Parameters
|
||
----------
|
||
fname : file, str, or pathlib.Path, optional
|
||
File to store results. Defaults to colormap name + '.txt'.
|
||
|
||
"""
|
||
labels = {'RGBA':4} if self.colors.shape[1] == 4 else {'RGB': 3}
|
||
t = Table(labels,self.colors,f'Creator: {util.execution_stamp("Colormap")}')
|
||
t.save(self._get_file_handle(fname,'.txt'))
|
||
|
||
|
||
def save_GOM(self, fname: FileHandle = None):
|
||
"""
|
||
Save as ASCII file for use in GOM Aramis.
|
||
|
||
Parameters
|
||
----------
|
||
fname : file, str, or pathlib.Path, optional
|
||
File to store results. Defaults to colormap name + '.legend'.
|
||
|
||
"""
|
||
# ToDo: test in GOM
|
||
GOM_str = '1 1 {name} 9 {name} '.format(name=self.name.replace(" ","_")) \
|
||
+ '0 1 0 3 0 0 -1 9 \\ 0 0 0 255 255 255 0 0 255 ' \
|
||
+ f'30 NO_UNIT 1 1 64 64 64 255 1 0 0 0 0 0 0 3 0 {self.N}' \
|
||
+ ' '.join([f' 0 {c[0]} {c[1]} {c[2]} 255 1' for c in reversed((self.colors*255).astype(int))]) \
|
||
+ '\n'
|
||
|
||
self._get_file_handle(fname,'.legend').write(GOM_str)
|
||
|
||
|
||
def save_gmsh(self,
|
||
fname: FileHandle = None):
|
||
"""
|
||
Save as ASCII file for use in gmsh.
|
||
|
||
Parameters
|
||
----------
|
||
fname : file, str, or pathlib.Path, optional
|
||
File to store results. Defaults to colormap name + '.msh'.
|
||
|
||
"""
|
||
# ToDo: test in gmsh
|
||
gmsh_str = 'View.ColorTable = {\n' \
|
||
+'\n'.join([f'{c[0]},{c[1]},{c[2]},' for c in self.colors[:,:3]*255]) \
|
||
+'\n}\n'
|
||
self._get_file_handle(fname,'.msh').write(gmsh_str)
|
||
|
||
|
||
@staticmethod
|
||
def _interpolate_msh(frac: float,
|
||
low: np.ndarray,
|
||
high: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Interpolate in Msh color space.
|
||
|
||
This interpolation gives a perceptually uniform colormap.
|
||
|
||
References
|
||
----------
|
||
https://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf
|
||
https://www.kennethmoreland.com/color-maps/diverging_map.py
|
||
|
||
"""
|
||
def rad_diff(a,b):
|
||
return abs(a[2]-b[2])
|
||
|
||
def adjust_hue(msh_sat, msh_unsat):
|
||
"""If saturation of one of the two colors is much less than the other, hue of the less."""
|
||
if msh_sat[0] >= msh_unsat[0]:
|
||
return msh_sat[2]
|
||
|
||
hSpin = msh_sat[1]/np.sin(msh_sat[1])*np.sqrt(msh_unsat[0]**2.0-msh_sat[0]**2)/msh_sat[0]
|
||
if msh_sat[2] < - np.pi/3.0: hSpin *= -1.0
|
||
return msh_sat[2] + hSpin
|
||
|
||
lo = np.array(low)
|
||
hi = np.array(high)
|
||
|
||
if (lo[1] > 0.05 and hi[1] > 0.05 and rad_diff(lo,hi) > np.pi/3.0):
|
||
M_mid = max(lo[0],hi[0],88.0)
|
||
if frac < 0.5:
|
||
hi = np.array([M_mid,0.0,0.0])
|
||
frac *= 2.0
|
||
else:
|
||
lo = np.array([M_mid,0.0,0.0])
|
||
frac = 2.0*frac - 1.0
|
||
if lo[1] < 0.05 < hi[1]:
|
||
lo[2] = adjust_hue(hi,lo)
|
||
elif hi[1] < 0.05 < lo[1]:
|
||
hi[2] = adjust_hue(lo,hi)
|
||
|
||
return (1.0 - frac) * lo + frac * hi
|
||
|
||
|
||
_predefined_mpl= {'Perceptually Uniform Sequential': [
|
||
'viridis', 'plasma', 'inferno', 'magma', 'cividis'],
|
||
'Sequential': [
|
||
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
|
||
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
|
||
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'],
|
||
'Sequential (2)': [
|
||
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
|
||
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
|
||
'hot', 'afmhot', 'gist_heat', 'copper'],
|
||
'Diverging': [
|
||
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
|
||
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'],
|
||
'Cyclic': ['twilight', 'twilight_shifted', 'hsv'],
|
||
'Qualitative': [
|
||
'Pastel1', 'Pastel2', 'Paired', 'Accent',
|
||
'Dark2', 'Set1', 'Set2', 'Set3',
|
||
'tab10', 'tab20', 'tab20b', 'tab20c'],
|
||
'Miscellaneous': [
|
||
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
|
||
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg',
|
||
'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar']}
|
||
|
||
_predefined_DAMASK = {'orientation': {'low': [0.933334,0.878432,0.878431], # noqa
|
||
'high': [0.250980,0.007843,0.000000]},
|
||
'strain': {'low': [0.941177,0.941177,0.870588],
|
||
'high': [0.266667,0.266667,0.000000]},
|
||
'stress': {'low': [0.878432,0.874511,0.949019],
|
||
'high': [0.000002,0.000000,0.286275]}}
|
||
|
||
predefined = dict(**{'DAMASK':list(_predefined_DAMASK)},**_predefined_mpl)
|
||
|
||
|
||
@staticmethod
|
||
def _hsv2rgb(hsv: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Hue Saturation Value to Red Green Blue.
|
||
|
||
Parameters
|
||
----------
|
||
hsv : numpy.ndarray, shape (3)
|
||
HSV values.
|
||
|
||
Returns
|
||
-------
|
||
rgb : numpy.ndarray, shape (3)
|
||
RGB values.
|
||
|
||
"""
|
||
return np.array(colorsys.hsv_to_rgb(hsv[0]/360.,hsv[1],hsv[2]))
|
||
|
||
@staticmethod
|
||
def _rgb2hsv(rgb: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Red Green Blue to Hue Saturation Value.
|
||
|
||
Parameters
|
||
----------
|
||
rgb : numpy.ndarray, shape (3)
|
||
RGB values.
|
||
|
||
Returns
|
||
-------
|
||
hsv : numpy.ndarray, shape (3)
|
||
HSV values.
|
||
|
||
"""
|
||
h,s,v = colorsys.rgb_to_hsv(rgb[0],rgb[1],rgb[2])
|
||
return np.array([h*360,s,v])
|
||
|
||
|
||
@staticmethod
|
||
def _hsl2rgb(hsl: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Hue Saturation Luminance to Red Green Blue.
|
||
|
||
Parameters
|
||
----------
|
||
hsl : numpy.ndarray, shape (3)
|
||
HSL values.
|
||
|
||
Returns
|
||
-------
|
||
rgb : numpy.ndarray, shape (3)
|
||
RGB values.
|
||
|
||
"""
|
||
return np.array(colorsys.hls_to_rgb(hsl[0]/360.,hsl[2],hsl[1]))
|
||
|
||
@staticmethod
|
||
def _rgb2hsl(rgb: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Red Green Blue to Hue Saturation Luminance.
|
||
|
||
Parameters
|
||
----------
|
||
rgb : numpy.ndarray, shape (3)
|
||
RGB values.
|
||
|
||
Returns
|
||
-------
|
||
hsl : numpy.ndarray, shape (3)
|
||
HSL values.
|
||
|
||
"""
|
||
h,l,s = colorsys.rgb_to_hls(rgb[0],rgb[1],rgb[2])
|
||
return np.array([h*360,s,l])
|
||
|
||
|
||
@staticmethod
|
||
def _xyz2rgb(xyz: np.ndarray) -> np.ndarray:
|
||
"""
|
||
CIE Xyz to Red Green Blue.
|
||
|
||
Parameters
|
||
----------
|
||
xyz : numpy.ndarray, shape (3)
|
||
CIE Xyz values.
|
||
|
||
Returns
|
||
-------
|
||
rgb : numpy.ndarray, shape (3)
|
||
RGB values.
|
||
|
||
References
|
||
----------
|
||
https://www.easyrgb.com/en/math.php
|
||
|
||
"""
|
||
rgb_lin = np.dot(np.array([
|
||
[ 3.240969942,-1.537383178,-0.498610760],
|
||
[-0.969243636, 1.875967502, 0.041555057],
|
||
[ 0.055630080,-0.203976959, 1.056971514]
|
||
]),xyz)
|
||
with np.errstate(invalid='ignore'):
|
||
rgb = np.where(rgb_lin>0.0031308,rgb_lin**(1.0/2.4)*1.0555-0.0555,rgb_lin*12.92)
|
||
|
||
return np.clip(rgb,0.,1.)
|
||
|
||
@staticmethod
|
||
def _rgb2xyz(rgb: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Red Green Blue to CIE Xyz.
|
||
|
||
Parameters
|
||
----------
|
||
rgb : numpy.ndarray, shape (3)
|
||
RGB values.
|
||
|
||
Returns
|
||
-------
|
||
xyz : numpy.ndarray, shape (3)
|
||
CIE Xyz values.
|
||
|
||
References
|
||
----------
|
||
https://www.easyrgb.com/en/math.php
|
||
|
||
"""
|
||
rgb_lin = np.where(rgb>0.04045,((rgb+0.0555)/1.0555)**2.4,rgb/12.92)
|
||
return np.dot(np.array([
|
||
[0.412390799,0.357584339,0.180480788],
|
||
[0.212639006,0.715168679,0.072192315],
|
||
[0.019330819,0.119194780,0.950532152]
|
||
]),rgb_lin)
|
||
|
||
|
||
@staticmethod
|
||
def _lab2xyz(lab: np.ndarray,
|
||
ref_white: np.ndarray = _REF_WHITE) -> np.ndarray:
|
||
"""
|
||
CIE Lab to CIE Xyz.
|
||
|
||
Parameters
|
||
----------
|
||
lab : numpy.ndarray, shape (3)
|
||
CIE lab values.
|
||
ref_white : numpy.ndarray, shape (3)
|
||
Reference white, default value is the standard 2° observer for D65.
|
||
|
||
Returns
|
||
-------
|
||
xyz : numpy.ndarray, shape (3)
|
||
CIE Xyz values.
|
||
|
||
References
|
||
----------
|
||
http://www.brucelindbloom.com/index.html?Eqn_Lab_to_XYZ.html
|
||
|
||
"""
|
||
f_x = (lab[0]+16.)/116. + lab[1]/500.
|
||
f_z = (lab[0]+16.)/116. - lab[2]/200.
|
||
|
||
return np.array([
|
||
f_x**3. if f_x**3. > _EPS else (116.*f_x-16.)/_KAPPA,
|
||
((lab[0]+16.)/116.)**3 if lab[0]>_KAPPA*_EPS else lab[0]/_KAPPA,
|
||
f_z**3. if f_z**3. > _EPS else (116.*f_z-16.)/_KAPPA
|
||
])*ref_white
|
||
|
||
@staticmethod
|
||
def _xyz2lab(xyz: np.ndarray,
|
||
ref_white: np.ndarray = _REF_WHITE) -> np.ndarray:
|
||
"""
|
||
CIE Xyz to CIE Lab.
|
||
|
||
Parameters
|
||
----------
|
||
xyz : numpy.ndarray, shape (3)
|
||
CIE Xyz values.
|
||
ref_white : numpy.ndarray, shape (3)
|
||
Reference white, default value is the standard 2° observer for D65.
|
||
|
||
Returns
|
||
-------
|
||
lab : numpy.ndarray, shape (3)
|
||
CIE lab values.
|
||
|
||
References
|
||
----------
|
||
http://www.brucelindbloom.com/index.html?Eqn_Lab_to_XYZ.html
|
||
|
||
"""
|
||
f = np.where(xyz/ref_white > _EPS,(xyz/ref_white)**(1./3.),(_KAPPA*xyz/ref_white+16.)/116.)
|
||
|
||
return np.array([
|
||
116.0 * f[1] - 16.0,
|
||
500.0 * (f[0] - f[1]),
|
||
200.0 * (f[1] - f[2])
|
||
])
|
||
|
||
|
||
@staticmethod
|
||
def _lab2msh(lab: np.ndarray) -> np.ndarray:
|
||
"""
|
||
CIE Lab to Msh.
|
||
|
||
Parameters
|
||
----------
|
||
lab : numpy.ndarray, shape (3)
|
||
CIE lab values.
|
||
|
||
Returns
|
||
-------
|
||
msh : numpy.ndarray, shape (3)
|
||
Msh values.
|
||
|
||
References
|
||
----------
|
||
https://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf
|
||
https://www.kennethmoreland.com/color-maps/diverging_map.py
|
||
|
||
"""
|
||
M = np.linalg.norm(lab)
|
||
return np.array([
|
||
M,
|
||
np.arccos(lab[0]/M) if M>1e-8 else 0.,
|
||
np.arctan2(lab[2],lab[1]) if M>1e-8 else 0.,
|
||
])
|
||
|
||
@staticmethod
|
||
def _msh2lab(msh: np.ndarray) -> np.ndarray:
|
||
"""
|
||
Msh to CIE Lab.
|
||
|
||
Parameters
|
||
----------
|
||
msh : numpy.ndarray, shape (3)
|
||
Msh values.
|
||
|
||
Returns
|
||
-------
|
||
lab : numpy.ndarray, shape (3)
|
||
CIE lab values.
|
||
|
||
References
|
||
----------
|
||
https://www.kennethmoreland.com/color-maps/ColorMapsExpanded.pdf
|
||
https://www.kennethmoreland.com/color-maps/diverging_map.py
|
||
|
||
"""
|
||
return np.array([
|
||
msh[0] * np.cos(msh[1]),
|
||
msh[0] * np.sin(msh[1]) * np.cos(msh[2]),
|
||
msh[0] * np.sin(msh[1]) * np.sin(msh[2])
|
||
])
|
||
|
||
@staticmethod
|
||
def _lab2rgb(lab: np.ndarray) -> np.ndarray:
|
||
return Colormap._xyz2rgb(Colormap._lab2xyz(lab))
|
||
|
||
@staticmethod
|
||
def _rgb2lab(rgb: np.ndarray) -> np.ndarray:
|
||
return Colormap._xyz2lab(Colormap._rgb2xyz(rgb))
|
||
|
||
@staticmethod
|
||
def _msh2rgb(msh: np.ndarray) -> np.ndarray:
|
||
return Colormap._lab2rgb(Colormap._msh2lab(msh))
|
||
|
||
@staticmethod
|
||
def _rgb2msh(rgb: np.ndarray) -> np.ndarray:
|
||
return Colormap._lab2msh(Colormap._rgb2lab(rgb))
|
||
|
||
@staticmethod
|
||
def _hsv2msh(hsv: np.ndarray) -> np.ndarray:
|
||
return Colormap._rgb2msh(Colormap._hsv2rgb(hsv))
|
||
|
||
@staticmethod
|
||
def _hsl2msh(hsl: np.ndarray) -> np.ndarray:
|
||
return Colormap._rgb2msh(Colormap._hsl2rgb(hsl))
|
||
|
||
@staticmethod
|
||
def _xyz2msh(xyz: np.ndarray) -> np.ndarray:
|
||
return Colormap._lab2msh(Colormap._xyz2lab(xyz))
|