def test_convert_direct_keyword_argument_passing(self):
"""
Test :func:`colour.graph.conversion.convert` definition behaviour when
direct keyword arguments are passed.
"""
a = np.array([0.20654008, 0.12197225, 0.05136952])
illuminant = CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"][
"D50"
]
np.testing.assert_almost_equal(
convert(
a, "CIE XYZ", "CIE xyY", XYZ_to_xyY={"illuminant": illuminant}
),
convert(a, "CIE XYZ", "CIE xyY", illuminant=illuminant),
decimal=7,
)
# Illuminant "ndarray" is converted to tuple here so that it can
# be hashed by the "sd_to_XYZ" definition, this should never occur
# in practical application.
self.assertRaises(
AttributeError,
lambda: convert(
SDS_COLOURCHECKERS["ColorChecker N Ohta"]["dark skin"],
"Spectral Distribution",
"sRGB",
illuminant=tuple(illuminant),
),
)
def test_convert_direct_keyword_argument_passing(self):
"""
Tests :func:`colour.graph.conversion.convert` definition behaviour when
direct keyword arguments are passed.
"""
a = np.array([0.20654008, 0.12197225, 0.05136952])
illuminant = CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer'][
'D50']
np.testing.assert_almost_equal(convert(
a, 'CIE XYZ', 'CIE xyY', XYZ_to_xyY={'illuminant': illuminant}),
convert(a,
'CIE XYZ',
'CIE xyY',
illuminant=illuminant),
decimal=7)
if six.PY3: # pragma: no cover
# Illuminant "ndarray" is converted to tuple here so that it can
# be hashed by the "sd_to_XYZ" definition, this should never occur
# in practical application.
self.assertRaises(
AttributeError, lambda: convert(SDS_COLOURCHECKERS[
'ColorChecker N Ohta']['dark skin'],
'Spectral Distribution',
'sRGB',
illuminant=tuple(illuminant)))
def test_convert_direct_keyword_argument_passing(self):
"""
Tests :func:`colour.graph.conversion.convert` definition behaviour when
direct keyword arguments are passed.
"""
a = np.array([0.20654008, 0.12197225, 0.05136952])
illuminant = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']
np.testing.assert_almost_equal(
convert(
a, 'CIE XYZ', 'CIE xyY',
XYZ_to_xyY={'illuminant': illuminant}),
convert(a, 'CIE XYZ', 'CIE xyY', illuminant=illuminant),
decimal=7)
if six.PY3: # pragma: no cover
self.assertRaises(AttributeError, lambda: convert(
COLOURCHECKERS_SDS['ColorChecker N Ohta']['dark skin'],
'Spectral Distribution', 'sRGB',
illuminant=illuminant))
def test_convert(self):
"""
Tests :func:`colour.graph.conversion.convert` definition.
"""
RGB_a = convert(SDS_COLOURCHECKERS['ColorChecker N Ohta']['dark skin'],
'Spectral Distribution', 'sRGB')
np.testing.assert_almost_equal(
RGB_a, np.array([0.45675795, 0.30986982, 0.24861924]), decimal=7)
Jpapbp = convert(RGB_a, 'Output-Referred RGB', 'CAM16UCS')
np.testing.assert_almost_equal(
Jpapbp, np.array([0.39994810, 0.09206557, 0.08127526]), decimal=7)
RGB_b = convert(Jpapbp,
'CAM16UCS',
'sRGB',
verbose={'mode': 'Extended'})
# NOTE: The "CIE XYZ" tristimulus values to "sRGB" matrix is given
# rounded at 4 decimals as per "IEC 61966-2-1:1999" and thus preventing
# exact roundtrip.
np.testing.assert_allclose(RGB_a, RGB_b, rtol=1e-5, atol=1e-5)
np.testing.assert_almost_equal(
convert('#808080', 'Hexadecimal', 'Scene-Referred RGB'),
np.array([0.21586050, 0.21586050, 0.21586050]),
decimal=7)
self.assertAlmostEqual(convert('#808080', 'Hexadecimal',
'RGB Luminance'),
0.21586050,
places=7)
np.testing.assert_almost_equal(
convert(
convert(np.array([0.5, 0.5, 0.5]), 'Output-Referred RGB',
'Scene-Referred RGB'), 'RGB', 'YCbCr'),
np.array([0.49215686, 0.50196078, 0.50196078]),
decimal=7)
np.testing.assert_almost_equal(
convert(
RGB_a,
'RGB',
'Scene-Referred RGB',
RGB_to_RGB={'output_colourspace': RGB_COLOURSPACE_ACES2065_1}),
np.array([0.36364180, 0.31715308, 0.25888531]),
decimal=7)
def plot_hull_section_colours(
hull: trimesh.Trimesh, # type: ignore[name-defined] # noqa
model: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD",
"CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY",
"CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99",
"Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT",
"IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB",
"hdr-IPT", ], str, ] = "CIE xyY",
axis: Union[Literal["+z", "+x", "+y"], str] = "+z",
origin: Floating = 0.5,
normalise: Boolean = True,
section_colours: Optional[Union[ArrayLike, str]] = None,
section_opacity: Floating = 1,
convert_kwargs: Optional[Dict] = None,
samples: Integer = 256,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the section colours of given *trimesh* hull along given axis and
origin.
Parameters
----------
hull
*Trimesh* hull.
model
Colourspace model, see :attr:`colour.COLOURSPACE_MODELS` attribute for
the list of supported colourspace models.
axis
Axis the hull section will be normal to.
origin
Coordinate along ``axis`` at which to plot the hull section.
normalise
Whether to normalise ``axis`` to the extent of the hull along it.
section_colours
Colours of the hull section, if ``section_colours`` is set to *RGB*,
the colours will be computed according to the corresponding
coordinates.
section_opacity
Opacity of the hull section colours.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
samples
Samples count on one axis when computing the hull section colours.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour.models import RGB_COLOURSPACE_sRGB
>>> from colour.utilities import is_trimesh_installed
>>> vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
>>> XYZ_vertices = RGB_to_XYZ(
... vertices['position'] + 0.5,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
... )
>>> if is_trimesh_installed:
... import trimesh
... hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
... plot_hull_section_colours(hull, section_colours='RGB')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Hull_Section_Colours.png
:align: center
:alt: plot_hull_section_colours
"""
axis = validate_method(
axis,
["+z", "+x", "+y"],
'"{0}" axis is invalid, it must be one of {1}!',
)
hull = hull.copy()
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
section_colours = cast(
ArrayLike,
optional(section_colours,
HEX_to_RGB(CONSTANTS_COLOUR_STYLE.colour.average)),
)
convert_kwargs = optional(convert_kwargs, {})
# Luminance / Lightness reordered along "z" axis.
with suppress_warnings(python_warnings=True):
ijk_vertices = colourspace_model_axis_reorder(
convert(hull.vertices, "CIE XYZ", model, **convert_kwargs), model)
ijk_vertices = np.nan_to_num(ijk_vertices)
ijk_vertices *= COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[
model]
hull.vertices = ijk_vertices
if axis == "+x":
index_origin = 0
elif axis == "+y":
index_origin = 1
elif axis == "+z":
index_origin = 2
plane = MAPPING_AXIS_TO_PLANE[axis]
section = hull_section(hull, axis, origin, normalise)
padding = 0.1 * np.mean(
COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[model])
min_x = np.min(ijk_vertices[..., plane[0]]) - padding
max_x = np.max(ijk_vertices[..., plane[0]]) + padding
min_y = np.min(ijk_vertices[..., plane[1]]) - padding
max_y = np.max(ijk_vertices[..., plane[1]]) + padding
extent = (min_x, max_x, min_y, max_y)
use_RGB_section_colours = str(section_colours).upper() == "RGB"
if use_RGB_section_colours:
ii, jj = np.meshgrid(
np.linspace(min_x, max_x, samples),
np.linspace(max_y, min_y, samples),
)
ij = tstack([ii, jj])
ijk_section = full((samples, samples, 3),
np.median(section[..., index_origin]))
ijk_section[..., plane] = ij
ijk_section /= COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[
model]
XYZ_section = convert(
colourspace_model_axis_reorder(ijk_section, model, "Inverse"),
model,
"CIE XYZ",
**convert_kwargs,
)
RGB_section = XYZ_to_plotting_colourspace(XYZ_section)
else:
section_colours = np.hstack([section_colours, section_opacity])
facecolor = "none" if use_RGB_section_colours else section_colours
polygon = Polygon(
section[..., plane],
facecolor=facecolor,
edgecolor="none",
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
axes.add_patch(polygon)
if use_RGB_section_colours:
image = axes.imshow(
np.clip(RGB_section, 0, 1),
interpolation="bilinear",
extent=extent,
clip_path=None,
alpha=section_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
image.set_clip_path(polygon)
settings = {
"axes": axes,
"bounding_box": extent,
}
settings.update(kwargs)
return render(**settings)
def plot_hull_section_contour(
hull: trimesh.Trimesh, # type: ignore[name-defined] # noqa
model: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD",
"CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY",
"CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99",
"Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT",
"IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB",
"hdr-IPT", ], str, ] = "CIE xyY",
axis: Union[Literal["+z", "+x", "+y"], str] = "+z",
origin: Floating = 0.5,
normalise: Boolean = True,
contour_colours: Optional[Union[ArrayLike, str]] = None,
contour_opacity: Floating = 1,
convert_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the section contour of given *trimesh* hull along given axis and
origin.
Parameters
----------
hull
*Trimesh* hull.
model
Colourspace model, see :attr:`colour.COLOURSPACE_MODELS` attribute for
the list of supported colourspace models.
axis
Axis the hull section will be normal to.
origin
Coordinate along ``axis`` at which to plot the hull section.
normalise
Whether to normalise ``axis`` to the extent of the hull along it.
contour_colours
Colours of the hull section contour, if ``contour_colours`` is set to
*RGB*, the colours will be computed according to the corresponding
coordinates.
contour_opacity
Opacity of the hull section contour.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour.models import RGB_COLOURSPACE_sRGB
>>> from colour.utilities import is_trimesh_installed
>>> vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
>>> XYZ_vertices = RGB_to_XYZ(
... vertices['position'] + 0.5,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
... )
>>> if is_trimesh_installed:
... import trimesh
... hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
... plot_hull_section_contour(hull, contour_colours='RGB')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Hull_Section_Contour.png
:align: center
:alt: plot_hull_section_contour
"""
hull = hull.copy()
contour_colours = cast(
Union[ArrayLike, str],
optional(contour_colours, CONSTANTS_COLOUR_STYLE.colour.dark),
)
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
convert_kwargs = optional(convert_kwargs, {})
# Luminance / Lightness is re-ordered along "z-up" axis.
with suppress_warnings(python_warnings=True):
ijk_vertices = colourspace_model_axis_reorder(
convert(hull.vertices, "CIE XYZ", model, **convert_kwargs), model)
ijk_vertices = np.nan_to_num(ijk_vertices)
ijk_vertices *= COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[
model]
hull.vertices = ijk_vertices
plane = MAPPING_AXIS_TO_PLANE[axis]
padding = 0.1 * np.mean(
COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[model])
min_x = np.min(ijk_vertices[..., plane[0]]) - padding
max_x = np.max(ijk_vertices[..., plane[0]]) + padding
min_y = np.min(ijk_vertices[..., plane[1]]) - padding
max_y = np.max(ijk_vertices[..., plane[1]]) + padding
extent = (min_x, max_x, min_y, max_y)
use_RGB_contour_colours = str(contour_colours).upper() == "RGB"
section = hull_section(hull, axis, origin, normalise)
if use_RGB_contour_colours:
ijk_section = section / (
COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[model])
XYZ_section = convert(
colourspace_model_axis_reorder(ijk_section, model, "Inverse"),
model,
"CIE XYZ",
**convert_kwargs,
)
contour_colours = np.clip(XYZ_to_plotting_colourspace(XYZ_section), 0,
1)
section = np.reshape(section[..., plane], (-1, 1, 2))
line_collection = LineCollection(
np.concatenate([section[:-1], section[1:]], axis=1),
colors=contour_colours,
alpha=contour_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
)
axes.add_collection(line_collection)
settings = {
"axes": axes,
"bounding_box": extent,
}
settings.update(kwargs)
return render(**settings)
def plot_RGB_scatter(
RGB: ArrayLike,
colourspace: Union[RGB_Colourspace, str, Sequence[Union[RGB_Colourspace,
str]]],
reference_colourspace: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS",
"CAM16LCD", "CAM16SCD", "CAM16UCS",
"CIE XYZ", "CIE xyY", "CIE Lab",
"CIE Luv", "CIE UCS", "CIE UVW",
"DIN99", "Hunter Lab", "Hunter Rdab",
"ICaCb", "ICtCp", "IPT", "IgPgTg",
"Jzazbz", "OSA UCS", "Oklab",
"hdr-CIELAB", "hdr-IPT", ],
str, ] = "CIE xyY",
colourspaces: Optional[Union[RGB_Colourspace, str,
Sequence[Union[RGB_Colourspace,
str]]]] = None,
segments: Integer = 8,
show_grid: Boolean = True,
grid_segments: Integer = 10,
show_spectral_locus: Boolean = False,
spectral_locus_colour: Optional[Union[ArrayLike, str]] = None,
points_size: Floating = 12,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
chromatically_adapt: Boolean = False,
convert_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given *RGB* colourspace array in a scatter plot.
Parameters
----------
RGB
*RGB* colourspace array.
colourspace
*RGB* colourspace of the *RGB* array. ``colourspace`` can be of any
type or form supported by the
:func:`colour.plotting.filter_RGB_colourspaces` definition.
reference_colourspace
Reference colourspace model to plot the gamuts into, see
:attr:`colour.COLOURSPACE_MODELS` attribute for the list of supported
colourspace models.
colourspaces
*RGB* colourspaces to plot the gamuts. ``colourspaces`` elements
can be of any type or form supported by the
:func:`colour.plotting.filter_RGB_colourspaces` definition.
segments
Edge segments count for each *RGB* colourspace cubes.
show_grid
Whether to show a grid at the bottom of the *RGB* colourspace cubes.
grid_segments
Edge segments count for the grid.
show_spectral_locus
Whether to show the spectral locus.
spectral_locus_colour
Spectral locus colour.
points_size
Scatter points size.
cmfs
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
chromatically_adapt
Whether to chromatically adapt the *RGB* colourspaces given in
``colourspaces`` to the whitepoint of the default plotting colourspace.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_RGB_colourspaces_gamuts`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> RGB = np.random.random((128, 128, 3))
>>> plot_RGB_scatter(RGB, 'ITU-R BT.709') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...Axes3DSubplot...>)
.. image:: ../_static/Plotting_Plot_RGB_Scatter.png
:align: center
:alt: plot_RGB_scatter
"""
colourspace = cast(
RGB_Colourspace,
first_item(filter_RGB_colourspaces(colourspace).values()),
)
colourspaces = cast(List[str], optional(colourspaces, [colourspace.name]))
convert_kwargs = optional(convert_kwargs, {})
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,
})
settings.update(kwargs)
settings["standalone"] = False
plot_RGB_colourspaces_gamuts(
colourspaces=colourspaces,
reference_colourspace=reference_colourspace,
segments=segments,
show_grid=show_grid,
grid_segments=grid_segments,
show_spectral_locus=show_spectral_locus,
spectral_locus_colour=spectral_locus_colour,
cmfs=cmfs,
chromatically_adapt=chromatically_adapt,
**settings,
)
XYZ = RGB_to_XYZ(
RGB,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.matrix_RGB_to_XYZ,
)
convert_settings = {"illuminant": colourspace.whitepoint}
convert_settings.update(convert_kwargs)
points = colourspace_model_axis_reorder(
convert(XYZ, "CIE XYZ", reference_colourspace, **convert_settings),
reference_colourspace,
)
axes = plt.gca()
axes.scatter(
points[..., 0],
points[..., 1],
points[..., 2],
color=np.reshape(RGB, (-1, 3)),
s=points_size,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_scatter,
)
settings.update({"axes": axes, "standalone": True})
settings.update(kwargs)
return render(**settings)
def plot_RGB_colourspaces_gamuts(
colourspaces: Union[RGB_Colourspace, str, Sequence[Union[RGB_Colourspace,
str]]],
reference_colourspace: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS",
"CAM16LCD", "CAM16SCD", "CAM16UCS",
"CIE XYZ", "CIE xyY", "CIE Lab",
"CIE Luv", "CIE UCS", "CIE UVW",
"DIN99", "Hunter Lab", "Hunter Rdab",
"ICaCb", "ICtCp", "IPT", "IgPgTg",
"Jzazbz", "OSA UCS", "Oklab",
"hdr-CIELAB", "hdr-IPT", ],
str, ] = "CIE xyY",
segments: Integer = 8,
show_grid: Boolean = True,
grid_segments: Integer = 10,
show_spectral_locus: Boolean = False,
spectral_locus_colour: Optional[Union[ArrayLike, str]] = None,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
chromatically_adapt: Boolean = False,
convert_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given *RGB* colourspaces gamuts in given reference colourspace.
Parameters
----------
colourspaces
*RGB* colourspaces to plot the gamuts. ``colourspaces`` elements
can be of any type or form supported by the
:func:`colour.plotting.filter_RGB_colourspaces` definition.
reference_colourspace
Reference colourspace model to plot the gamuts into, see
:attr:`colour.COLOURSPACE_MODELS` attribute for the list of supported
colourspace models.
segments
Edge segments count for each *RGB* colourspace cubes.
show_grid
Whether to show a grid at the bottom of the *RGB* colourspace cubes.
grid_segments
Edge segments count for the grid.
show_spectral_locus
Whether to show the spectral locus.
spectral_locus_colour
Spectral locus colour.
cmfs
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
chromatically_adapt
Whether to chromatically adapt the *RGB* colourspaces given in
``colourspaces`` to the whitepoint of the default plotting colourspace.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
Other Parameters
----------------
edge_colours
Edge colours array such as `edge_colours = (None, (0.5, 0.5, 1.0))`.
edge_alpha
Edge opacity value such as `edge_alpha = (0.0, 1.0)`.
face_alpha
Face opacity value such as `face_alpha = (0.5, 1.0)`.
face_colours
Face colours array such as `face_colours = (None, (0.5, 0.5, 1.0))`.
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.volume.nadir_grid`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...Axes3DSubplot...>)
.. image:: ../_static/Plotting_Plot_RGB_Colourspaces_Gamuts.png
:align: center
:alt: plot_RGB_colourspaces_gamuts
"""
colourspaces = cast(
List[RGB_Colourspace],
list(filter_RGB_colourspaces(colourspaces).values()),
)
convert_kwargs = optional(convert_kwargs, {})
count_c = len(colourspaces)
title = (
f"{', '.join([colourspace.name for colourspace in colourspaces])} "
f"- {reference_colourspace} Reference Colourspace")
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
convert_settings = {"illuminant": illuminant}
convert_settings.update(convert_kwargs)
settings = Structure(
**{
"face_colours": [None] * count_c,
"edge_colours": [None] * count_c,
"face_alpha": [1] * count_c,
"edge_alpha": [1] * count_c,
"title": title,
})
settings.update(kwargs)
figure = plt.figure()
axes = figure.add_subplot(111, projection="3d")
points = zeros((4, 3))
if show_spectral_locus:
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
XYZ = cmfs.values
points = colourspace_model_axis_reorder(
convert(XYZ, "CIE XYZ", reference_colourspace, **convert_settings),
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)
axes.plot(
points[..., 0],
points[..., 1],
points[..., 2],
color=c,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
axes.plot(
(points[-1][0], points[0][0]),
(points[-1][1], points[0][1]),
(points[-1][2], points[0][2]),
color=c,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
plotting_colourspace = CONSTANTS_COLOUR_STYLE.colour.colourspace
quads_c: List = []
RGB_cf: List = []
RGB_ce: List = []
for i, colourspace in enumerate(colourspaces):
if chromatically_adapt and not np.array_equal(
colourspace.whitepoint, plotting_colourspace.whitepoint):
colourspace = colourspace.chromatically_adapt(
plotting_colourspace.whitepoint,
plotting_colourspace.whitepoint_name,
)
quads_cb, RGB = RGB_identity_cube(
width_segments=segments,
height_segments=segments,
depth_segments=segments,
)
XYZ = RGB_to_XYZ(
quads_cb,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.matrix_RGB_to_XYZ,
)
convert_settings = {"illuminant": colourspace.whitepoint}
convert_settings.update(convert_kwargs)
quads_c.extend(
colourspace_model_axis_reorder(
convert(XYZ, "CIE XYZ", reference_colourspace,
**convert_settings),
reference_colourspace,
))
if settings.face_colours[i] is not None:
RGB = ones(RGB.shape) * settings.face_colours[i]
RGB_cf.extend(
np.hstack([RGB,
full((RGB.shape[0], 1), settings.face_alpha[i])]))
if settings.edge_colours[i] is not None:
RGB = ones(RGB.shape) * settings.edge_colours[i]
RGB_ce.extend(
np.hstack([RGB,
full((RGB.shape[0], 1), settings.edge_alpha[i])]))
quads = as_float_array(quads_c)
RGB_f = as_float_array(RGB_cf)
RGB_e = as_float_array(RGB_ce)
quads[np.isnan(quads)] = 0
if quads.size != 0:
for i, axis in enumerate("xyz"):
min_a = np.minimum(np.min(quads[..., i]), np.min(points[..., i]))
max_a = np.maximum(np.max(quads[..., i]), np.max(points[..., i]))
getattr(axes, f"set_{axis}lim")((min_a, max_a))
labels = np.array(
COLOURSPACE_MODELS_AXIS_LABELS[reference_colourspace])[as_int_array(
colourspace_model_axis_reorder([0, 1, 2], reference_colourspace))]
for i, axis in enumerate("xyz"):
getattr(axes, f"set_{axis}label")(labels[i])
if show_grid:
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({
"axes": axes,
"axes_visible": False,
"camera_aspect": "equal"
})
settings.update(kwargs)
return render(**settings)
def test_convert(self):
"""Test :func:`colour.graph.conversion.convert` definition."""
RGB_a = convert(
SDS_COLOURCHECKERS["ColorChecker N Ohta"]["dark skin"],
"Spectral Distribution",
"sRGB",
)
np.testing.assert_almost_equal(
RGB_a, np.array([0.45675795, 0.30986982, 0.24861924]), decimal=7
)
Jpapbp = convert(RGB_a, "Output-Referred RGB", "CAM16UCS")
np.testing.assert_almost_equal(
Jpapbp, np.array([0.39994810, 0.09206557, 0.08127526]), decimal=7
)
RGB_b = convert(
Jpapbp, "CAM16UCS", "sRGB", verbose={"mode": "Extended"}
)
# NOTE: The "CIE XYZ" tristimulus values to "sRGB" matrix is given
# rounded at 4 decimals as per "IEC 61966-2-1:1999" and thus preventing
# exact roundtrip.
np.testing.assert_allclose(RGB_a, RGB_b, rtol=1e-5, atol=1e-5)
np.testing.assert_almost_equal(
convert("#808080", "Hexadecimal", "Scene-Referred RGB"),
np.array([0.21586050, 0.21586050, 0.21586050]),
decimal=7,
)
self.assertAlmostEqual(
convert("#808080", "Hexadecimal", "RGB Luminance"),
0.21586050,
places=7,
)
np.testing.assert_almost_equal(
convert(
convert(
np.array([0.5, 0.5, 0.5]),
"Output-Referred RGB",
"Scene-Referred RGB",
),
"RGB",
"YCbCr",
),
np.array([0.49215686, 0.50196078, 0.50196078]),
decimal=7,
)
np.testing.assert_almost_equal(
convert(
RGB_a,
"RGB",
"Scene-Referred RGB",
RGB_to_RGB={"output_colourspace": RGB_COLOURSPACE_ACES2065_1},
),
np.array([0.36364180, 0.31715308, 0.25888531]),
decimal=7,
)
# Consistency check to verify that all the colour models are properly
# named in the graph:
for model in COLOURSPACE_MODELS:
convert(
np.array([0.20654008, 0.12197225, 0.05136952]),
"CIE XYZ",
model,
)
