#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Colour Models Volume Plotting
=============================
Defines colour models volume and gamut plotting objects:
- :func:`RGB_colourspaces_gamuts_plot`
- :func:`RGB_scatter_plot`
"""
from __future__ import division
import matplotlib
import numpy as np
import pylab
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from colour.models import RGB_to_XYZ
from colour.models.common import (
COLOURSPACE_MODELS_LABELS,
XYZ_to_colourspace_model)
from colour.plotting import (
DEFAULT_PLOTTING_ILLUMINANT,
camera,
cube,
decorate,
display,
get_RGB_colourspace,
get_cmfs,
grid)
from colour.utilities import Structure, tsplit, tstack
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2017 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['common_colourspace_model_axis_reorder',
'nadir_grid',
'RGB_identity_cube',
'RGB_colourspaces_gamuts_plot',
'RGB_scatter_plot']
[docs]def common_colourspace_model_axis_reorder(a, model=None):
"""
Reorder axis of given colourspace model :math:`a` values accordingly to its
most common volume plotting axis order.
Parameters
----------
a : array_like
Colourspace model values :math:`a`.
model : unicode, optional
**{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',
'IPT', 'Hunter Lab', 'Hunter Rdab'}**
Colourspace model.
Returns
-------
Figure
Reordered colourspace model values.
Examples
--------
>>> a = np.array([0, 1, 2])
>>> common_colourspace_model_axis_reorder(a)
array([0, 1, 2])
>>> common_colourspace_model_axis_reorder(a, 'CIE Lab')
array([1, 2, 0])
>>> common_colourspace_model_axis_reorder(a, 'CIE LCHab')
array([1, 2, 0])
>>> common_colourspace_model_axis_reorder(a, 'CIE Luv')
array([1, 2, 0])
>>> common_colourspace_model_axis_reorder(a, 'CIE LCHab')
array([1, 2, 0])
>>> common_colourspace_model_axis_reorder(a, 'IPT')
array([1, 2, 0])
"""
if model in ('CIE Lab', 'CIE LCHab', 'CIE Luv', 'CIE LCHuv', 'IPT',
'Hunter Lab', 'Hunter Rdab'):
i, j, k = tsplit(a)
a = tstack((j, k, i))
return a
[docs]def nadir_grid(limits=None, segments=10, labels=None, axes=None, **kwargs):
"""
Returns a grid on *xy* plane made of quad geometric elements and its
associated faces and edges colours. Ticks and labels are added to the
given axes accordingly to the extended grid settings.
Parameters
----------
limits : array_like, optional
Extended grid limits.
segments : int, optional
Edge segments count for the extended grid.
labels : array_like, optional
Axis labels.
axes : matplotlib.axes.Axes, optional
Axes to add the grid.
Other Parameters
----------------
grid_face_colours : array_like, optional
Grid face colours array such as
`grid_face_colours = (0.25, 0.25, 0.25)`.
grid_edge_colours : array_like, optional
Grid edge colours array such as
`grid_edge_colours = (0.25, 0.25, 0.25)`.
grid_face_alpha : numeric, optional
Grid face opacity value such as `grid_face_alpha = 0.1`.
grid_edge_alpha : numeric, optional
Grid edge opacity value such as `grid_edge_alpha = 0.5`.
x_axis_colour : array_like, optional
*X* axis colour array such as `x_axis_colour = (0.0, 0.0, 0.0, 1.0)`.
y_axis_colour : array_like, optional
*Y* axis colour array such as `y_axis_colour = (0.0, 0.0, 0.0, 1.0)`.
x_ticks_colour : array_like, optional
*X* axis ticks colour array such as
`x_ticks_colour = (0.0, 0.0, 0.0, 0.85)`.
y_ticks_colour : array_like, optional
*Y* axis ticks colour array such as
`y_ticks_colour = (0.0, 0.0, 0.0, 0.85)`.
x_label_colour : array_like, optional
*X* axis label colour array such as
`x_label_colour = (0.0, 0.0, 0.0, 0.85)`.
y_label_colour : array_like, optional
*Y* axis label colour array such as
`y_label_colour = (0.0, 0.0, 0.0, 0.85)`.
ticks_and_label_location : array_like, optional
Location of the *X* and *Y* axis ticks and labels such as
`ticks_and_label_location = ('-x', '-y')`.
Returns
-------
tuple
Grid quads, faces colours, edges colours.
Examples
--------
>>> nadir_grid(segments=1)
(array([[[-1. , -1. , 0. ],
[ 1. , -1. , 0. ],
[ 1. , 1. , 0. ],
[-1. , 1. , 0. ]],
<BLANKLINE>
[[-1. , -1. , 0. ],
[ 0. , -1. , 0. ],
[ 0. , 0. , 0. ],
[-1. , 0. , 0. ]],
<BLANKLINE>
[[-1. , 0. , 0. ],
[ 0. , 0. , 0. ],
[ 0. , 1. , 0. ],
[-1. , 1. , 0. ]],
<BLANKLINE>
[[ 0. , -1. , 0. ],
[ 1. , -1. , 0. ],
[ 1. , 0. , 0. ],
[ 0. , 0. , 0. ]],
<BLANKLINE>
[[ 0. , 0. , 0. ],
[ 1. , 0. , 0. ],
[ 1. , 1. , 0. ],
[ 0. , 1. , 0. ]],
<BLANKLINE>
[[-1. , -0.001, 0. ],
[ 1. , -0.001, 0. ],
[ 1. , 0.001, 0. ],
[-1. , 0.001, 0. ]],
<BLANKLINE>
[[-0.001, -1. , 0. ],
[ 0.001, -1. , 0. ],
[ 0.001, 1. , 0. ],
[-0.001, 1. , 0. ]]]), array([[ 0.25, 0.25, 0.25, 0.1 ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 0. ],
[ 0. , 0. , 0. , 1. ],
[ 0. , 0. , 0. , 1. ]]), array([[ 0.5 , 0.5 , 0.5 , 0.5 ],
[ 0.75, 0.75, 0.75, 0.25],
[ 0.75, 0.75, 0.75, 0.25],
[ 0.75, 0.75, 0.75, 0.25],
[ 0.75, 0.75, 0.75, 0.25],
[ 0. , 0. , 0. , 1. ],
[ 0. , 0. , 0. , 1. ]]))
"""
if limits is None:
limits = np.array([[-1, 1], [-1, 1]])
if labels is None:
labels = ('x', 'y')
extent = np.max(np.abs(limits[..., 1] - limits[..., 0]))
settings = Structure(
**{'grid_face_colours': (0.25, 0.25, 0.25),
'grid_edge_colours': (0.50, 0.50, 0.50),
'grid_face_alpha': 0.1,
'grid_edge_alpha': 0.5,
'x_axis_colour': (0.0, 0.0, 0.0, 1.0),
'y_axis_colour': (0.0, 0.0, 0.0, 1.0),
'x_ticks_colour': (0.0, 0.0, 0.0, 0.85),
'y_ticks_colour': (0.0, 0.0, 0.0, 0.85),
'x_label_colour': (0.0, 0.0, 0.0, 0.85),
'y_label_colour': (0.0, 0.0, 0.0, 0.85),
'ticks_and_label_location': ('-x', '-y')})
settings.update(**kwargs)
# Outer grid.
quads_g = grid(origin=(-extent / 2, -extent / 2),
width=extent,
height=extent,
height_segments=segments,
width_segments=segments)
RGB_g = np.ones((quads_g.shape[0], quads_g.shape[-1]))
RGB_gf = RGB_g * settings.grid_face_colours
RGB_gf = np.hstack((RGB_gf,
np.full((RGB_gf.shape[0], 1),
settings.grid_face_alpha,
np.float_)))
RGB_ge = RGB_g * settings.grid_edge_colours
RGB_ge = np.hstack((RGB_ge,
np.full((RGB_ge.shape[0], 1),
settings.grid_edge_alpha,
np.float_)))
# Inner grid.
quads_gs = grid(origin=(-extent / 2, -extent / 2),
width=extent,
height=extent,
height_segments=segments * 2,
width_segments=segments * 2)
RGB_gs = np.ones((quads_gs.shape[0], quads_gs.shape[-1]))
RGB_gsf = RGB_gs * 0
RGB_gsf = np.hstack((RGB_gsf,
np.full((RGB_gsf.shape[0], 1), 0, np.float_)))
RGB_gse = np.clip(RGB_gs *
settings.grid_edge_colours * 1.5, 0, 1)
RGB_gse = np.hstack((RGB_gse,
np.full((RGB_gse.shape[0], 1),
settings.grid_edge_alpha / 2,
np.float_)))
# Axis.
thickness = extent / 1000
quad_x = grid(origin=(limits[0, 0], -thickness / 2),
width=extent,
height=thickness)
RGB_x = np.ones((quad_x.shape[0], quad_x.shape[-1] + 1))
RGB_x = RGB_x * settings.x_axis_colour
quad_y = grid(origin=(-thickness / 2, limits[1, 0]),
width=thickness,
height=extent)
RGB_y = np.ones((quad_y.shape[0], quad_y.shape[-1] + 1))
RGB_y = RGB_y * settings.y_axis_colour
if axes is not None:
# Ticks.
x_s = 1 if '+x' in settings.ticks_and_label_location else -1
y_s = 1 if '+y' in settings.ticks_and_label_location else -1
for i, axis in enumerate('xy'):
h_a = 'center' if axis == 'x' else 'left' if x_s == 1 else 'right'
v_a = 'center'
ticks = list(sorted(set(quads_g[..., 0, i])))
ticks += [ticks[-1] + ticks[-1] - ticks[-2]]
for tick in ticks:
x = (limits[1, 1 if x_s == 1 else 0] + (x_s * extent / 25)
if i else tick)
y = (tick if i else
limits[0, 1 if y_s == 1 else 0] + (y_s * extent / 25))
tick = int(tick) if np.float_(tick).is_integer() else tick
c = settings['{0}_ticks_colour'.format(axis)]
axes.text(x, y, 0, tick, 'x',
horizontalalignment=h_a,
verticalalignment=v_a,
color=c,
clip_on=True)
# Labels.
for i, axis in enumerate('xy'):
h_a = 'center' if axis == 'x' else 'left' if x_s == 1 else 'right'
v_a = 'center'
x = (limits[1, 1 if x_s == 1 else 0] + (x_s * extent / 10)
if i else 0)
y = (0 if i else
limits[0, 1 if y_s == 1 else 0] + (y_s * extent / 10))
c = settings['{0}_label_colour'.format(axis)]
axes.text(x, y, 0, labels[i], 'x',
horizontalalignment=h_a,
verticalalignment=v_a,
color=c,
size=20,
clip_on=True)
quads = np.vstack((quads_g, quads_gs, quad_x, quad_y))
RGB_f = np.vstack((RGB_gf, RGB_gsf, RGB_x, RGB_y))
RGB_e = np.vstack((RGB_ge, RGB_gse, RGB_x, RGB_y))
return quads, RGB_f, RGB_e
[docs]def RGB_identity_cube(plane=None,
width_segments=16,
height_segments=16,
depth_segments=16):
"""
Returns an *RGB* identity cube made of quad geometric elements and its
associated *RGB* colours.
Parameters
----------
plane : array_like, optional
Any combination of **{'+x', '-x', '+y', '-y', '+z', '-z'}**,
Included grids in the cube construction.
width_segments: int, optional
Cube segments, quad counts along the width.
height_segments: int, optional
Cube segments, quad counts along the height.
depth_segments: int, optional
Cube segments, quad counts along the depth.
Returns
-------
tuple
Cube quads, *RGB* colours.
Examples
--------
>>> vertices, RGB = RGB_identity_cube(None, 1, 1, 1)
>>> vertices
array([[[ 0., 0., 0.],
[ 1., 0., 0.],
[ 1., 1., 0.],
[ 0., 1., 0.]],
<BLANKLINE>
[[ 0., 0., 1.],
[ 1., 0., 1.],
[ 1., 1., 1.],
[ 0., 1., 1.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 1., 0., 0.],
[ 1., 0., 1.],
[ 0., 0., 1.]],
<BLANKLINE>
[[ 0., 1., 0.],
[ 1., 1., 0.],
[ 1., 1., 1.],
[ 0., 1., 1.]],
<BLANKLINE>
[[ 0., 0., 0.],
[ 0., 1., 0.],
[ 0., 1., 1.],
[ 0., 0., 1.]],
<BLANKLINE>
[[ 1., 0., 0.],
[ 1., 1., 0.],
[ 1., 1., 1.],
[ 1., 0., 1.]]])
>>> RGB
array([[ 0.5, 0.5, 0. ],
[ 0.5, 0.5, 1. ],
[ 0.5, 0. , 0.5],
[ 0.5, 1. , 0.5],
[ 0. , 0.5, 0.5],
[ 1. , 0.5, 0.5]])
"""
quads = cube(plane=plane,
width=1,
height=1,
depth=1,
width_segments=width_segments,
height_segments=height_segments,
depth_segments=depth_segments)
RGB = np.average(quads, axis=-2)
return quads, RGB
[docs]def RGB_colourspaces_gamuts_plot(colourspaces=None,
reference_colourspace='CIE xyY',
segments=8,
display_grid=True,
grid_segments=10,
spectral_locus=False,
spectral_locus_colour=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces gamuts in given reference colourspace.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot the gamuts.
reference_colourspace : unicode, optional
**{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',
'IPT', 'Hunter Lab', 'Hunter Rdab'}**,
Reference colourspace to plot the gamuts into.
segments : int, optional
Edge segments count for each *RGB* colourspace cubes.
display_grid : bool, optional
Display a grid at the bottom of the *RGB* colourspace cubes.
grid_segments : bool, optional
Edge segments count for the grid.
spectral_locus : bool, optional
Is spectral locus line plotted.
spectral_locus_colour : array_like, optional
Spectral locus line colour.
cmfs : unicode, optional
Standard observer colour matching functions used for spectral locus.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`nadir_grid`},
Please refer to the documentation of the previously listed definitions.
face_colours : array_like, optional
Face colours array such as `face_colours = (None, (0.5, 0.5, 1.0))`.
edge_colours : array_like, optional
Edge colours array such as `edge_colours = (None, (0.5, 0.5, 1.0))`.
face_alpha : numeric, optional
Face opacity value such as `face_alpha = (0.5, 1.0)`.
edge_alpha : numeric, optional
Edge opacity value such as `edge_alpha = (0.0, 1.0)`.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_gamuts_plot(c) # doctest: +SKIP
"""
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg')
count_c = len(colourspaces)
settings = Structure(
**{'face_colours': [None] * count_c,
'edge_colours': [None] * count_c,
'face_alpha': [1] * count_c,
'edge_alpha': [1] * count_c,
'title': '{0} - {1} Reference Colourspace'.format(
', '.join(colourspaces), reference_colourspace)})
settings.update(kwargs)
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
illuminant = DEFAULT_PLOTTING_ILLUMINANT
points = np.zeros((4, 3))
if spectral_locus:
cmfs = get_cmfs(cmfs)
XYZ = cmfs.values
points = common_colourspace_model_axis_reorder(
XYZ_to_colourspace_model(
XYZ, illuminant, reference_colourspace),
reference_colourspace)
points[np.isnan(points)] = 0
c = ((0.0, 0.0, 0.0, 0.5)
if spectral_locus_colour is None else
spectral_locus_colour)
pylab.plot(points[..., 0],
points[..., 1],
points[..., 2],
color=c,
linewidth=2,
zorder=1)
pylab.plot((points[-1][0], points[0][0]),
(points[-1][1], points[0][1]),
(points[-1][2], points[0][2]),
color=c,
linewidth=2,
zorder=1)
quads, RGB_f, RGB_e = [], [], []
for i, colourspace in enumerate(colourspaces):
colourspace = get_RGB_colourspace(colourspace)
quads_c, RGB = RGB_identity_cube(width_segments=segments,
height_segments=segments,
depth_segments=segments)
XYZ = RGB_to_XYZ(
quads_c,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix)
quads.extend(common_colourspace_model_axis_reorder(
XYZ_to_colourspace_model(
XYZ, colourspace.whitepoint, reference_colourspace),
reference_colourspace))
if settings.face_colours[i] is not None:
RGB = np.ones(RGB.shape) * settings.face_colours[i]
RGB_f.extend(np.hstack(
(RGB, np.full((RGB.shape[0], 1),
settings.face_alpha[i],
np.float_))))
if settings.edge_colours[i] is not None:
RGB = np.ones(RGB.shape) * settings.edge_colours[i]
RGB_e.extend(np.hstack(
(RGB, np.full((RGB.shape[0], 1),
settings.edge_alpha[i],
np.float_))))
quads = np.asarray(quads)
quads[np.isnan(quads)] = 0
if quads.size != 0:
for i, axis in enumerate('xyz'):
min_a = np.min(np.vstack((quads[..., i], points[..., i])))
max_a = np.max(np.vstack((quads[..., i], points[..., i])))
getattr(axes, 'set_{}lim'.format(axis))((min_a, max_a))
labels = COLOURSPACE_MODELS_LABELS[reference_colourspace]
for i, axis in enumerate('xyz'):
getattr(axes, 'set_{}label'.format(axis))(labels[i])
if display_grid:
if reference_colourspace == 'CIE Lab':
limits = np.array([[-450, 450], [-450, 450]])
elif reference_colourspace == 'CIE Luv':
limits = np.array([[-650, 650], [-650, 650]])
elif reference_colourspace == 'CIE UVW':
limits = np.array([[-850, 850], [-850, 850]])
elif reference_colourspace in ('Hunter Lab', 'Hunter Rdab'):
limits = np.array([[-250, 250], [-250, 250]])
else:
limits = np.array([[-1.5, 1.5], [-1.5, 1.5]])
quads_g, RGB_gf, RGB_ge = nadir_grid(
limits, grid_segments, labels, axes, **settings)
quads = np.vstack((quads_g, quads))
RGB_f = np.vstack((RGB_gf, RGB_f))
RGB_e = np.vstack((RGB_ge, RGB_e))
collection = Poly3DCollection(quads)
collection.set_facecolors(RGB_f)
collection.set_edgecolors(RGB_e)
axes.add_collection3d(collection)
settings.update({
'camera_aspect': 'equal',
'no_axes': True})
settings.update(kwargs)
camera(**settings)
decorate(**settings)
return display(**settings)
[docs]def RGB_scatter_plot(RGB,
colourspace,
reference_colourspace='CIE xyY',
colourspaces=None,
segments=8,
display_grid=True,
grid_segments=10,
spectral_locus=False,
spectral_locus_colour=None,
points_size=12,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspace array in a scatter plot.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
colourspace : RGB_Colourspace
*RGB* colourspace of the *RGB* array.
reference_colourspace : unicode, optional
**{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',
'IPT', 'Hunter Lab', 'Hunter Rdab'}**,
Reference colourspace for colour conversion.
colourspaces : array_like, optional
*RGB* colourspaces to plot the gamuts.
segments : int, optional
Edge segments count for each *RGB* colourspace cubes.
display_grid : bool, optional
Display a grid at the bottom of the *RGB* colourspace cubes.
grid_segments : bool, optional
Edge segments count for the grid.
spectral_locus : bool, optional
Is spectral locus line plotted.
spectral_locus_colour : array_like, optional
Spectral locus line colour.
points_size : numeric, optional
Scatter points size.
cmfs : unicode, optional
Standard observer colour matching functions used for spectral locus.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`RGB_colourspaces_gamuts_plot`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = 'Rec. 709'
>>> RGB_scatter_plot(c) # doctest: +SKIP
"""
colourspace = get_RGB_colourspace(colourspace)
if colourspaces is None:
colourspaces = (colourspace.name,)
count_c = len(colourspaces)
settings = Structure(
**{'face_colours': [None] * count_c,
'edge_colours': [(0.25, 0.25, 0.25)] * count_c,
'face_alpha': [0.0] * count_c,
'edge_alpha': [0.1] * count_c,
'standalone': False})
settings.update(kwargs)
RGB_colourspaces_gamuts_plot(
colourspaces=colourspaces,
reference_colourspace=reference_colourspace,
segments=segments,
display_grid=display_grid,
grid_segments=grid_segments,
spectral_locus=spectral_locus,
spectral_locus_colour=spectral_locus_colour,
cmfs=cmfs,
**settings)
XYZ = RGB_to_XYZ(
RGB,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix)
points = common_colourspace_model_axis_reorder(
XYZ_to_colourspace_model(
XYZ, colourspace.whitepoint, reference_colourspace),
reference_colourspace)
axes = matplotlib.pyplot.gca()
axes.scatter(points[..., 0],
points[..., 1],
points[..., 2],
color=np.reshape(RGB, (-1, 3)),
s=points_size)
settings.update({'standalone': True})
settings.update(kwargs)
camera(**settings)
decorate(**settings)
return display(**settings)