"""
LUT Processing
==============
Define the classes and definitions handling *LUT* processing:
- :class:`colour.LUT1D`
- :class:`colour.LUT3x1D`
- :class:`colour.LUT3D`
- :class:`colour.io.LUT_to_LUT`
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from copy import deepcopy
from operator import (
add,
iadd,
imul,
ipow,
isub,
itruediv,
mul,
pow,
sub,
truediv,
)
import numpy as np
from scipy.spatial import KDTree
from colour.algebra import (
Extrapolator,
LinearInterpolator,
linear_conversion,
table_interpolation_trilinear,
)
from colour.hints import (
Any,
ArrayLike,
List,
Literal,
NDArrayFloat,
Sequence,
Type,
cast,
)
from colour.utilities import (
as_array,
as_float_array,
as_int,
as_int_array,
as_int_scalar,
attest,
full,
is_iterable,
is_numeric,
multiline_repr,
multiline_str,
optional,
runtime_warning,
tsplit,
tstack,
usage_warning,
validate_method,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"AbstractLUT",
"LUT1D",
"LUT3x1D",
"LUT3D",
"LUT_to_LUT",
]
class AbstractLUT(ABC):
"""
Define the base class for *LUT*.
This is an :class:`ABCMeta` abstract class that must be inherited by
sub-classes.
Parameters
----------
table
Underlying *LUT* table.
name
*LUT* name.
dimensions
*LUT* dimensions, typically, 1 for a 1D *LUT*, 2 for a 3x1D *LUT* and 3
for a 3D *LUT*.
domain
*LUT* domain, also used to define the instantiation time default table
domain.
size
*LUT* size, also used to define the instantiation time default table
size.
comments
Comments to add to the *LUT*.
Attributes
----------
- :attr:`~colour.io.luts.lut.AbstractLUT.table`
- :attr:`~colour.io.luts.lut.AbstractLUT.name`
- :attr:`~colour.io.luts.lut.AbstractLUT.dimensions`
- :attr:`~colour.io.luts.lut.AbstractLUT.domain`
- :attr:`~colour.io.luts.lut.AbstractLUT.size`
- :attr:`~colour.io.luts.lut.AbstractLUT.comments`
Methods
-------
- :meth:`~colour.io.luts.lut.AbstractLUT.__init__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__str__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__repr__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__eq__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__ne__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__add__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__iadd__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__sub__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__isub__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__mul__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__imul__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__div__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__idiv__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__pow__`
- :meth:`~colour.io.luts.lut.AbstractLUT.__ipow__`
- :meth:`~colour.io.luts.lut.AbstractLUT.arithmetical_operation`
- :meth:`~colour.io.luts.lut.AbstractLUT.is_domain_explicit`
- :meth:`~colour.io.luts.lut.AbstractLUT.linear_table`
- :meth:`~colour.io.luts.lut.AbstractLUT.copy`
- :meth:`~colour.io.luts.lut.AbstractLUT.invert`
- :meth:`~colour.io.luts.lut.AbstractLUT.apply`
- :meth:`~colour.io.luts.lut.AbstractLUT.convert`
"""
def __init__(
self,
table: ArrayLike | None = None,
name: str | None = None,
dimensions: int | None = None,
domain: ArrayLike | None = None,
size: ArrayLike | None = None,
comments: Sequence | None = None,
) -> None:
self._name: str = f"Unity {size!r}" if table is None else f"{id(self)}"
self.name = optional(name, self._name)
self._dimensions = optional(dimensions, 0)
self._table: NDArrayFloat = self.linear_table(
optional(size, 0), optional(domain, np.array([]))
)
self.table = optional(table, self._table)
self._domain: NDArrayFloat = np.array([])
self.domain = optional(domain, self._domain)
self._comments: list = []
self.comments = cast(list, optional(comments, self._comments))
@property
def table(self) -> NDArrayFloat:
"""
Getter and setter property for the underlying *LUT* table.
Parameters
----------
value
Value to set the underlying *LUT* table with.
Returns
-------
:class:`numpy.ndarray`
Underlying *LUT* table.
"""
return self._table
@table.setter
def table(self, value: ArrayLike):
"""Setter for the **self.table** property."""
self._table = self._validate_table(value)
@property
def name(self) -> str:
"""
Getter and setter property for the *LUT* name.
Parameters
----------
value
Value to set the *LUT* name with.
Returns
-------
:class:`str`
*LUT* name.
"""
return self._name
@name.setter
def name(self, value: str):
"""Setter for the **self.name** property."""
attest(
isinstance(value, str),
f'"name" property: "{value}" type is not "str"!',
)
self._name = value
@property
def domain(self) -> NDArrayFloat:
"""
Getter and setter property for the *LUT* domain.
Parameters
----------
value
Value to set the *LUT* domain with.
Returns
-------
:class:`numpy.ndarray`
*LUT* domain.
"""
return self._domain
@domain.setter
def domain(self, value: ArrayLike):
"""Setter for the **self.domain** property."""
self._domain = self._validate_domain(value)
@property
def dimensions(self) -> int:
"""
Getter property for the *LUT* dimensions.
Returns
-------
:class:`int`
*LUT* dimensions.
"""
return self._dimensions
@property
def size(self) -> int:
"""
Getter property for the *LUT* size.
Returns
-------
:class:`int`
*LUT* size.
"""
return self._table.shape[0]
@property
def comments(self) -> list:
"""
Getter and setter property for the *LUT* comments.
Parameters
----------
value
Value to set the *LUT* comments with.
Returns
-------
:class:`list`
*LUT* comments.
"""
return self._comments
@comments.setter
def comments(self, value: Sequence):
"""Setter for the **self.comments** property."""
attest(
is_iterable(value),
f'"comments" property: "{value}" must be a sequence!',
)
self._comments = list(value)
def __str__(self) -> str:
"""
Return a formatted string representation of the *LUT*.
Returns
-------
:class:`str`
Formatted string representation.
"""
attributes = [
{
"formatter": lambda x: ( # noqa: ARG005
f"{self.__class__.__name__} - {self.name}"
),
"section": True,
},
{"line_break": True},
{"name": "dimensions", "label": "Dimensions"},
{"name": "domain", "label": "Domain"},
{
"label": "Size",
"formatter": lambda x: str(self.table.shape), # noqa: ARG005
},
]
if self.comments:
attributes.append(
{
"formatter": lambda x: "\n".join( # noqa: ARG005
[
f"Comment {str(i + 1).zfill(2)} : {comment}"
for i, comment in enumerate(self.comments)
]
),
}
)
return multiline_str(self, cast(List[dict], attributes))
def __repr__(self) -> str:
"""
Return an evaluable string representation of the *LUT*.
Returns
-------
:class:`str`
Evaluable string representation.
"""
attributes = [
{"name": "table"},
{"name": "name"},
{"name": "domain"},
{"name": "size"},
]
if self.comments:
attributes.append({"name": "comments"})
return multiline_repr(self, attributes)
def __eq__(self, other: Any) -> bool:
"""
Return whether the *LUT* is equal to given other object.
Parameters
----------
other
Object to test whether it is equal to the *LUT*.
Returns
-------
:class:`bool`
Whether given object is equal to the *LUT*.
"""
if isinstance(other, AbstractLUT) and all(
[
np.array_equal(self.table, other.table),
np.array_equal(self.domain, other.domain),
]
):
return True
return False
def __ne__(self, other: Any) -> bool:
"""
Return whether the *LUT* is not equal to given other object.
Parameters
----------
other
Object to test whether it is not equal to the *LUT*.
Returns
-------
:class:`bool`
Whether given object is not equal to the *LUT*.
"""
return not (self == other)
def __add__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for addition.
Parameters
----------
a
:math:`a` variable to add.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Variable added *LUT*.
"""
return self.arithmetical_operation(a, "+")
def __iadd__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for in-place addition.
Parameters
----------
a
:math:`a` variable to add in-place.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
In-place variable added *LUT*.
"""
return self.arithmetical_operation(a, "+", True)
def __sub__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for subtraction.
Parameters
----------
a
:math:`a` variable to subtract.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Variable subtracted *LUT*.
"""
return self.arithmetical_operation(a, "-")
def __isub__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for in-place subtraction.
Parameters
----------
a
:math:`a` variable to subtract in-place.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
In-place variable subtracted *LUT*.
"""
return self.arithmetical_operation(a, "-", True)
def __mul__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for multiplication.
Parameters
----------
a
:math:`a` variable to multiply by.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Variable multiplied *LUT*.
"""
return self.arithmetical_operation(a, "*")
def __imul__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for in-place multiplication.
Parameters
----------
a
:math:`a` variable to multiply by in-place.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
In-place variable multiplied *LUT*.
"""
return self.arithmetical_operation(a, "*", True)
def __div__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for division.
Parameters
----------
a
:math:`a` variable to divide by.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Variable divided *LUT*.
"""
return self.arithmetical_operation(a, "/")
def __idiv__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for in-place division.
Parameters
----------
a
:math:`a` variable to divide by in-place.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
In-place variable divided *LUT*.
"""
return self.arithmetical_operation(a, "/", True)
__itruediv__ = __idiv__
__truediv__ = __div__
def __pow__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for exponentiation.
Parameters
----------
a
:math:`a` variable to exponentiate by.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Variable exponentiated *LUT*.
"""
return self.arithmetical_operation(a, "**")
def __ipow__(self, a: ArrayLike | AbstractLUT) -> AbstractLUT:
"""
Implement support for in-place exponentiation.
Parameters
----------
a
:math:`a` variable to exponentiate by in-place.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
In-place variable exponentiated *LUT*.
"""
return self.arithmetical_operation(a, "**", True)
def arithmetical_operation(
self,
a: ArrayLike | AbstractLUT,
operation: Literal["+", "-", "*", "/", "**"],
in_place: bool = False,
) -> AbstractLUT:
"""
Perform given arithmetical operation with :math:`a` operand, the
operation can be either performed on a copy or in-place, must be
reimplemented by sub-classes.
Parameters
----------
a
Operand.
operation
Operation to perform.
in_place
Operation happens in place.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
*LUT*.
"""
operator, ioperator = {
"+": (add, iadd),
"-": (sub, isub),
"*": (mul, imul),
"/": (truediv, itruediv),
"**": (pow, ipow),
}[operation]
if in_place:
operand = a.table if isinstance(a, AbstractLUT) else as_float_array(a)
self.table = operator(self.table, operand)
return self
else:
copy = ioperator(self.copy(), a)
return copy
@abstractmethod
def _validate_table(self, table: ArrayLike) -> NDArrayFloat:
"""
Validate given table according to *LUT* dimensions.
Parameters
----------
table
Table to validate.
Returns
-------
:class:`numpy.ndarray`
Validated table as a :class:`ndarray` instance.
"""
@abstractmethod
def _validate_domain(self, domain: ArrayLike) -> NDArrayFloat:
"""
Validate given domain according to *LUT* dimensions.
Parameters
----------
domain
Domain to validate.
Returns
-------
:class:`numpy.ndarray`
Validated domain as a :class:`ndarray` instance.
"""
@abstractmethod
def is_domain_explicit(self) -> bool:
"""
Return whether the *LUT* domain is explicit (or implicit).
An implicit domain is defined by its shape only::
[[0 1]
[0 1]
[0 1]]
While an explicit domain defines every single discrete samples::
[[0.0 0.0 0.0]
[0.1 0.1 0.1]
[0.2 0.2 0.2]
[0.3 0.3 0.3]
[0.4 0.4 0.4]
[0.8 0.8 0.8]
[1.0 1.0 1.0]]
Returns
-------
:class:`bool`
Is *LUT* domain explicit.
"""
@staticmethod
@abstractmethod
def linear_table(
size: ArrayLike | None = None,
domain: ArrayLike | None = None,
) -> NDArrayFloat:
"""
Return a linear table of given size according to *LUT* dimensions.
Parameters
----------
size
Expected table size, for a 1D *LUT*, the number of output samples
:math:`n` is equal to ``size``, for a 3x1D *LUT* :math:`n` is equal
to ``size * 3`` or ``size[0] + size[1] + size[2]``, for a 3D *LUT*
:math:`n` is equal to ``size**3 * 3`` or
``size[0] * size[1] * size[2] * 3``.
domain
Domain of the table.
Returns
-------
:class:`numpy.ndarray`
Linear table.
"""
def copy(self) -> AbstractLUT:
"""
Return a copy of the sub-class instance.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
*LUT* copy.
"""
return deepcopy(self)
@abstractmethod
def invert(self, **kwargs: Any) -> AbstractLUT:
"""
Compute and returns an inverse copy of the *LUT*.
Other Parameters
----------------
kwargs
Keywords arguments.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Inverse *LUT* class instance.
"""
@abstractmethod
def apply(self, RGB: ArrayLike, **kwargs: Any) -> NDArrayFloat:
"""
Apply the *LUT* to given *RGB* colourspace array using given method.
Parameters
----------
RGB
*RGB* colourspace array to apply the *LUT* onto.
Other Parameters
----------------
direction
Whether the *LUT* should be applied in the forward or inverse
direction.
extrapolator
Extrapolator class type or object to use as extrapolating function.
extrapolator_kwargs
Arguments to use when instantiating or calling the extrapolating
function.
interpolator
Interpolator class type or object to use as interpolating function.
interpolator_kwargs
Arguments to use when instantiating or calling the interpolating
function.
Returns
-------
:class:`numpy.ndarray`
Interpolated *RGB* colourspace array.
"""
def convert(
self,
cls: Type[AbstractLUT],
force_conversion: bool = False,
**kwargs: Any,
) -> AbstractLUT:
"""
Convert the *LUT* to given ``cls`` class instance.
Parameters
----------
cls
*LUT* class instance.
force_conversion
Whether to force the conversion as it might be destructive.
Other Parameters
----------------
interpolator
Interpolator class type to use as interpolating function.
interpolator_kwargs
Arguments to use when instantiating the interpolating function.
size
Expected table size in case of an upcast to or a downcast from a
:class:`LUT3D` class instance.
Returns
-------
:class:`colour.io.luts.lut.AbstractLUT`
Converted *LUT* class instance.
Warnings
--------
Some conversions are destructive and raise a :class:`ValueError`
exception by default.
Raises
------
ValueError
If the conversion is destructive.
"""
return LUT_to_LUT(
self,
cls,
force_conversion,
**kwargs, # pyright: ignore
)
[docs]
class LUT1D(AbstractLUT):
"""
Define the base class for a 1D *LUT*.
Parameters
----------
table
Underlying *LUT* table.
name
*LUT* name.
domain
*LUT* domain, also used to define the instantiation time default table
domain.
size
Size of the instantiation time default table, default to 10.
comments
Comments to add to the *LUT*.
Methods
-------
- :meth:`~colour.LUT1D.__init__`
- :meth:`~colour.LUT1D.is_domain_explicit`
- :meth:`~colour.LUT1D.linear_table`
- :meth:`~colour.LUT1D.invert`
- :meth:`~colour.LUT1D.apply`
Examples
--------
Instantiating a unity LUT with a table with 16 elements:
>>> print(LUT1D(size=16))
LUT1D - Unity 16
----------------
<BLANKLINE>
Dimensions : 1
Domain : [ 0. 1.]
Size : (16,)
Instantiating a LUT using a custom table with 16 elements:
>>> print(LUT1D(LUT1D.linear_table(16) ** (1 / 2.2))) # doctest: +ELLIPSIS
LUT1D - ...
--------...
<BLANKLINE>
Dimensions : 1
Domain : [ 0. 1.]
Size : (16,)
Instantiating a LUT using a custom table with 16 elements, custom name,
custom domain and comments:
>>> from colour.algebra import spow
>>> domain = np.array([-0.1, 1.5])
>>> print(
... LUT1D(
... spow(LUT1D.linear_table(16, domain), 1 / 2.2),
... "My LUT",
... domain,
... comments=["A first comment.", "A second comment."],
... )
... )
LUT1D - My LUT
--------------
<BLANKLINE>
Dimensions : 1
Domain : [-0.1 1.5]
Size : (16,)
Comment 01 : A first comment.
Comment 02 : A second comment.
"""
[docs]
def __init__(
self,
table: ArrayLike | None = None,
name: str | None = None,
domain: ArrayLike | None = None,
size: ArrayLike | None = None,
comments: Sequence | None = None,
) -> None:
domain = as_float_array(optional(domain, np.array([0, 1])))
size = optional(size, 10)
super().__init__(table, name, 1, domain, size, comments)
def _validate_table(self, table: ArrayLike) -> NDArrayFloat:
"""
Validate given table is a 1D array.
Parameters
----------
table
Table to validate.
Returns
-------
:class:`numpy.ndarray`
Validated table as a :class:`ndarray` instance.
"""
table = as_float_array(table)
attest(len(table.shape) == 1, "The table must be a 1D array!")
return table
def _validate_domain(self, domain: ArrayLike) -> NDArrayFloat:
"""
Validate given domain.
Parameters
----------
domain
Domain to validate.
Returns
-------
:class:`numpy.ndarray`
Validated domain as a :class:`ndarray` instance.
"""
domain = as_float_array(domain)
attest(len(domain.shape) == 1, "The domain must be a 1D array!")
attest(
domain.shape[0] >= 2,
"The domain column count must be equal or greater than 2!",
)
return domain
[docs]
def is_domain_explicit(self) -> bool:
"""
Return whether the *LUT* domain is explicit (or implicit).
An implicit domain is defined by its shape only::
[0 1]
While an explicit domain defines every single discrete samples::
[0.0 0.1 0.2 0.4 0.8 1.0]
Returns
-------
:class:`bool`
Is *LUT* domain explicit.
Examples
--------
>>> LUT1D().is_domain_explicit()
False
>>> table = domain = np.linspace(0, 1, 10)
>>> LUT1D(table, domain=domain).is_domain_explicit()
True
"""
return len(self.domain) != 2
[docs]
@staticmethod
def linear_table(
size: ArrayLike | None = None,
domain: ArrayLike | None = None,
) -> NDArrayFloat:
"""
Return a linear table, the number of output samples :math:`n` is equal
to ``size``.
Parameters
----------
size
Expected table size, default to 10.
domain
Domain of the table.
Returns
-------
:class:`numpy.ndarray`
Linear table with ``size`` samples.
Examples
--------
>>> LUT1D.linear_table(5, np.array([-0.1, 1.5]))
array([-0.1, 0.3, 0.7, 1.1, 1.5])
>>> LUT1D.linear_table(domain=np.linspace(-0.1, 1.5, 5))
array([-0.1, 0.3, 0.7, 1.1, 1.5])
"""
size = optional(size, 10)
domain = as_float_array(optional(domain, np.array([0, 1])))
if len(domain) != 2:
return domain
else:
attest(is_numeric(size), "Linear table size must be a numeric!")
return np.linspace(domain[0], domain[1], as_int_scalar(size))
[docs]
def invert(self, **kwargs: Any) -> LUT1D: # noqa: ARG002
"""
Compute and returns an inverse copy of the *LUT*.
Other Parameters
----------------
kwargs
Keywords arguments, only given for signature compatibility with
the :meth:`AbstractLUT.invert` method.
Returns
-------
:class:`colour.LUT1D`
Inverse *LUT* class instance.
Examples
--------
>>> LUT = LUT1D(LUT1D.linear_table() ** (1 / 2.2))
>>> print(LUT.table) # doctest: +ELLIPSIS
[ 0. ... 0.3683438... 0.5047603... 0.6069133... \
0.6916988... 0.7655385...
0.8316843... 0.8920493... 0.9478701... 1. ]
>>> print(LUT.invert()) # doctest: +ELLIPSIS
LUT1D - ... - Inverse
--------...----------
<BLANKLINE>
Dimensions : 1
Domain : [ 0. 0.3683438... 0.5047603... 0.6069133... \
0.6916988... 0.7655385...
0.8316843... 0.8920493... 0.9478701... 1. ]
Size : (10,)
>>> print(LUT.invert().table) # doctest: +ELLIPSIS
[ 0. ... 0.1111111... 0.2222222... 0.3333333... \
0.4444444... 0.5555555...
0.6666666... 0.7777777... 0.8888888... 1. ]
"""
if self.is_domain_explicit():
domain = self.domain
else:
domain_min, domain_max = self.domain
domain = np.linspace(domain_min, domain_max, self.size)
LUT_i = LUT1D(
table=domain,
name=f"{self.name} - Inverse",
domain=self.table,
)
return LUT_i
[docs]
def apply(self, RGB: ArrayLike, **kwargs: Any) -> NDArrayFloat:
"""
Apply the *LUT* to given *RGB* colourspace array using given method.
Parameters
----------
RGB
*RGB* colourspace array to apply the *LUT* onto.
Other Parameters
----------------
direction
Whether the *LUT* should be applied in the forward or inverse
direction.
extrapolator
Extrapolator class type or object to use as extrapolating function.
extrapolator_kwargs
Arguments to use when instantiating or calling the extrapolating
function.
interpolator
Interpolator class type to use as interpolating function.
interpolator_kwargs
Arguments to use when instantiating the interpolating function.
Returns
-------
:class:`numpy.ndarray`
Interpolated *RGB* colourspace array.
Examples
--------
>>> LUT = LUT1D(LUT1D.linear_table() ** (1 / 2.2))
>>> RGB = np.array([0.18, 0.18, 0.18])
*LUT* applied to the given *RGB* colourspace in the forward direction:
>>> LUT.apply(RGB) # doctest: +ELLIPSIS
array([ 0.4529220..., 0.4529220..., 0.4529220...])
*LUT* applied to the modified *RGB* colourspace in the inverse
direction:
>>> LUT.apply(LUT.apply(RGB), direction="Inverse")
... # doctest: +ELLIPSIS
array([ 0.18..., 0.18..., 0.18...])
"""
direction = validate_method(
kwargs.get("direction", "Forward"), ("Forward", "Inverse")
)
interpolator = kwargs.get("interpolator", LinearInterpolator)
interpolator_kwargs = kwargs.get("interpolator_kwargs", {})
extrapolator = kwargs.get("extrapolator", Extrapolator)
extrapolator_kwargs = kwargs.get("extrapolator_kwargs", {})
LUT = self.invert() if direction == "inverse" else self
if LUT.is_domain_explicit():
samples = LUT.domain
else:
domain_min, domain_max = LUT.domain
samples = np.linspace(domain_min, domain_max, LUT.size)
RGB_interpolator = extrapolator(
interpolator(samples, LUT.table, **interpolator_kwargs),
**extrapolator_kwargs,
)
return RGB_interpolator(RGB)
[docs]
class LUT3x1D(AbstractLUT):
"""
Define the base class for a 3x1D *LUT*.
Parameters
----------
table
Underlying *LUT* table.
name
*LUT* name.
domain
*LUT* domain, also used to define the instantiation time default table
domain.
size
Size of the instantiation time default table, default to 10.
comments
Comments to add to the *LUT*.
Methods
-------
- :meth:`~colour.LUT3x1D.__init__`
- :meth:`~colour.LUT3x1D.is_domain_explicit`
- :meth:`~colour.LUT3x1D.linear_table`
- :meth:`~colour.LUT3x1D.invert`
- :meth:`~colour.LUT3x1D.apply`
Examples
--------
Instantiating a unity LUT with a table with 16x3 elements:
>>> print(LUT3x1D(size=16))
LUT3x1D - Unity 16
------------------
<BLANKLINE>
Dimensions : 2
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (16, 3)
Instantiating a LUT using a custom table with 16x3 elements:
>>> print(LUT3x1D(LUT3x1D.linear_table(16) ** (1 / 2.2)))
... # doctest: +ELLIPSIS
LUT3x1D - ...
----------...
<BLANKLINE>
Dimensions : 2
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (16, 3)
Instantiating a LUT using a custom table with 16x3 elements, custom name,
custom domain and comments:
>>> from colour.algebra import spow
>>> domain = np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]])
>>> print(
... LUT3x1D(
... spow(LUT3x1D.linear_table(16), 1 / 2.2),
... "My LUT",
... domain,
... comments=["A first comment.", "A second comment."],
... )
... )
LUT3x1D - My LUT
----------------
<BLANKLINE>
Dimensions : 2
Domain : [[-0.1 -0.2 -0.4]
[ 1.5 3. 6. ]]
Size : (16, 3)
Comment 01 : A first comment.
Comment 02 : A second comment.
"""
[docs]
def __init__(
self,
table: ArrayLike | None = None,
name: str | None = None,
domain: ArrayLike | None = None,
size: ArrayLike | None = None,
comments: Sequence | None = None,
) -> None:
domain = as_float_array(optional(domain, [[0, 0, 0], [1, 1, 1]]))
size = optional(size, 10)
super().__init__(table, name, 2, domain, size, comments)
def _validate_table(self, table: ArrayLike) -> NDArrayFloat:
"""
Validate given table is a 3x1D array.
Parameters
----------
table
Table to validate.
Returns
-------
:class:`numpy.ndarray`
Validated table as a :class:`ndarray` instance.
"""
table = as_float_array(table)
attest(len(table.shape) == 2, "The table must be a 2D array!")
return table
def _validate_domain(self, domain: ArrayLike) -> NDArrayFloat:
"""
Validate given domain.
Parameters
----------
domain
Domain to validate.
Returns
-------
:class:`numpy.ndarray`
Validated domain as a :class:`ndarray` instance.
"""
domain = as_float_array(domain)
attest(len(domain.shape) == 2, "The domain must be a 2D array!")
attest(
domain.shape[0] >= 2,
"The domain row count must be equal or greater than 2!",
)
attest(domain.shape[1] == 3, "The domain column count must be equal to 3!")
return domain
[docs]
def is_domain_explicit(self) -> bool:
"""
Return whether the *LUT* domain is explicit (or implicit).
An implicit domain is defined by its shape only::
[[0 1]
[0 1]
[0 1]]
While an explicit domain defines every single discrete samples::
[[0.0 0.0 0.0]
[0.1 0.1 0.1]
[0.2 0.2 0.2]
[0.3 0.3 0.3]
[0.4 0.4 0.4]
[0.8 0.8 0.8]
[1.0 1.0 1.0]]
Returns
-------
:class:`bool`
Is *LUT* domain explicit.
Examples
--------
>>> LUT3x1D().is_domain_explicit()
False
>>> samples = np.linspace(0, 1, 10)
>>> table = domain = tstack([samples, samples, samples])
>>> LUT3x1D(table, domain=domain).is_domain_explicit()
True
"""
return self.domain.shape != (2, 3)
[docs]
@staticmethod
def linear_table(
size: ArrayLike | None = None,
domain: ArrayLike | None = None,
) -> NDArrayFloat:
"""
Return a linear table, the number of output samples :math:`n` is equal
to ``size * 3`` or ``size[0] + size[1] + size[2]``.
Parameters
----------
size
Expected table size, default to 10.
domain
Domain of the table.
Returns
-------
:class:`numpy.ndarray`
Linear table with ``size * 3`` or ``size[0] + size[1] + size[2]``
samples.
Warnings
--------
If ``size`` is non uniform, the linear table will be padded
accordingly.
Examples
--------
>>> LUT3x1D.linear_table(5, np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]]))
array([[-0.1, -0.2, -0.4],
[ 0.3, 0.6, 1.2],
[ 0.7, 1.4, 2.8],
[ 1.1, 2.2, 4.4],
[ 1.5, 3. , 6. ]])
>>> LUT3x1D.linear_table(
... np.array([5, 3, 2]),
... np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]]),
... )
array([[-0.1, -0.2, -0.4],
[ 0.3, 1.4, 6. ],
[ 0.7, 3. , nan],
[ 1.1, nan, nan],
[ 1.5, nan, nan]])
>>> domain = np.array(
... [
... [-0.1, -0.2, -0.4],
... [0.3, 1.4, 6.0],
... [0.7, 3.0, np.nan],
... [1.1, np.nan, np.nan],
... [1.5, np.nan, np.nan],
... ]
... )
>>> LUT3x1D.linear_table(domain=domain)
array([[-0.1, -0.2, -0.4],
[ 0.3, 1.4, 6. ],
[ 0.7, 3. , nan],
[ 1.1, nan, nan],
[ 1.5, nan, nan]])
"""
size = optional(size, 10)
domain = as_float_array(optional(domain, [[0, 0, 0], [1, 1, 1]]))
if domain.shape != (2, 3):
return domain
else:
size_array = np.tile(size, 3) if is_numeric(size) else as_int_array(size)
R, G, B = tsplit(domain)
samples = [
np.linspace(a[0], a[1], size_array[i]) for i, a in enumerate([R, G, B])
]
if len(np.unique(size_array)) != 1:
runtime_warning(
"Table is non uniform, axis will be "
'padded with "NaNs" accordingly!'
)
samples = [
np.pad(
axis,
(0, np.max(size_array) - len(axis)), # pyright: ignore
mode="constant",
constant_values=np.nan,
)
for axis in samples
]
return tstack(samples)
[docs]
def invert(self, **kwargs: Any) -> LUT3x1D: # noqa: ARG002
"""
Compute and returns an inverse copy of the *LUT*.
Other Parameters
----------------
kwargs
Keywords arguments, only given for signature compatibility with
the :meth:`AbstractLUT.invert` method.
Returns
-------
:class:`colour.LUT3x1D`
Inverse *LUT* class instance.
Examples
--------
>>> LUT = LUT3x1D(LUT3x1D.linear_table() ** (1 / 2.2))
>>> print(LUT.table)
[[ 0. 0. 0. ]
[ 0.36834383 0.36834383 0.36834383]
[ 0.50476034 0.50476034 0.50476034]
[ 0.60691337 0.60691337 0.60691337]
[ 0.69169882 0.69169882 0.69169882]
[ 0.76553851 0.76553851 0.76553851]
[ 0.83168433 0.83168433 0.83168433]
[ 0.89204934 0.89204934 0.89204934]
[ 0.94787016 0.94787016 0.94787016]
[ 1. 1. 1. ]]
>>> print(LUT.invert()) # doctest: +ELLIPSIS
LUT3x1D - ... - Inverse
----------...----------
<BLANKLINE>
Dimensions : 2
Domain : [[ 0. ... 0. ... 0. ...]
[ 0.3683438... 0.3683438... 0.3683438...]
[ 0.5047603... 0.5047603... 0.5047603...]
[ 0.6069133... 0.6069133... 0.6069133...]
[ 0.6916988... 0.6916988... 0.6916988...]
[ 0.7655385... 0.7655385... 0.7655385...]
[ 0.8316843... 0.8316843... 0.8316843...]
[ 0.8920493... 0.8920493... 0.8920493...]
[ 0.9478701... 0.9478701... 0.9478701...]
[ 1. ... 1. ... 1. ...]]
Size : (10, 3)
>>> print(LUT.invert().table) # doctest: +ELLIPSIS
[[ 0. ... 0. ... 0. ...]
[ 0.1111111... 0.1111111... 0.1111111...]
[ 0.2222222... 0.2222222... 0.2222222...]
[ 0.3333333... 0.3333333... 0.3333333...]
[ 0.4444444... 0.4444444... 0.4444444...]
[ 0.5555555... 0.5555555... 0.5555555...]
[ 0.6666666... 0.6666666... 0.6666666...]
[ 0.7777777... 0.7777777... 0.7777777...]
[ 0.8888888... 0.8888888... 0.8888888...]
[ 1. ... 1. ... 1. ...]]
"""
size = self.table.size // 3
if self.is_domain_explicit():
domain = [
axes[: (~np.isnan(axes)).cumsum().argmax() + 1]
for axes in np.transpose(self.domain)
]
else:
domain_min, domain_max = self.domain
domain = [np.linspace(domain_min[i], domain_max[i], size) for i in range(3)]
LUT_i = LUT3x1D(
table=tstack(domain),
name=f"{self.name} - Inverse",
domain=self.table,
)
return LUT_i
[docs]
def apply(self, RGB: ArrayLike, **kwargs: Any) -> NDArrayFloat:
"""
Apply the *LUT* to given *RGB* colourspace array using given method.
Parameters
----------
RGB
*RGB* colourspace array to apply the *LUT* onto.
Other Parameters
----------------
direction
Whether the *LUT* should be applied in the forward or inverse
direction.
extrapolator
Extrapolator class type or object to use as extrapolating function.
extrapolator_kwargs
Arguments to use when instantiating or calling the extrapolating
function.
interpolator
Interpolator class type to use as interpolating function.
interpolator_kwargs
Arguments to use when instantiating the interpolating function.
Returns
-------
:class:`numpy.ndarray`
Interpolated *RGB* colourspace array.
Examples
--------
>>> LUT = LUT3x1D(LUT3x1D.linear_table() ** (1 / 2.2))
>>> RGB = np.array([0.18, 0.18, 0.18])
>>> LUT.apply(RGB) # doctest: +ELLIPSIS
array([ 0.4529220..., 0.4529220..., 0.4529220...])
>>> LUT.apply(LUT.apply(RGB), direction="Inverse")
... # doctest: +ELLIPSIS
array([ 0.18..., 0.18..., 0.18...])
>>> from colour.algebra import spow
>>> domain = np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]])
>>> table = spow(LUT3x1D.linear_table(domain=domain), 1 / 2.2)
>>> LUT = LUT3x1D(table, domain=domain)
>>> RGB = np.array([0.18, 0.18, 0.18])
>>> LUT.apply(RGB) # doctest: +ELLIPSIS
array([ 0.4423903..., 0.4503801..., 0.3581625...])
>>> domain = np.array(
... [
... [-0.1, -0.2, -0.4],
... [0.3, 1.4, 6.0],
... [0.7, 3.0, np.nan],
... [1.1, np.nan, np.nan],
... [1.5, np.nan, np.nan],
... ]
... )
>>> table = spow(LUT3x1D.linear_table(domain=domain), 1 / 2.2)
>>> LUT = LUT3x1D(table, domain=domain)
>>> RGB = np.array([0.18, 0.18, 0.18])
>>> LUT.apply(RGB) # doctest: +ELLIPSIS
array([ 0.2996370..., -0.0901332..., -0.3949770...])
"""
direction = validate_method(
kwargs.get("direction", "Forward"), ("Forward", "Inverse")
)
interpolator = kwargs.get("interpolator", LinearInterpolator)
interpolator_kwargs = kwargs.get("interpolator_kwargs", {})
extrapolator = kwargs.get("extrapolator", Extrapolator)
extrapolator_kwargs = kwargs.get("extrapolator_kwargs", {})
R, G, B = tsplit(RGB)
LUT = self.invert() if direction == "inverse" else self
size = LUT.table.size // 3
if LUT.is_domain_explicit():
samples = [
axes[: (~np.isnan(axes)).cumsum().argmax() + 1]
for axes in np.transpose(LUT.domain)
]
R_t, G_t, B_t = (
axes[: len(samples[i])]
for i, axes in enumerate(np.transpose(LUT.table))
)
else:
domain_min, domain_max = LUT.domain
samples = [
np.linspace(domain_min[i], domain_max[i], size) for i in range(3)
]
R_t, G_t, B_t = tsplit(LUT.table)
s_R, s_G, s_B = samples
RGB_i = [
extrapolator(
interpolator(a[0], a[1], **interpolator_kwargs),
**extrapolator_kwargs,
)(a[2])
for a in zip((s_R, s_G, s_B), (R_t, G_t, B_t), (R, G, B))
]
return tstack(RGB_i)
[docs]
class LUT3D(AbstractLUT):
"""
Define the base class for a 3D *LUT*.
Parameters
----------
table
Underlying *LUT* table.
name
*LUT* name.
domain
*LUT* domain, also used to define the instantiation time default table
domain.
size
Size of the instantiation time default table, default to 33.
comments
Comments to add to the *LUT*.
Methods
-------
- :meth:`~colour.LUT3D.__init__`
- :meth:`~colour.LUT3D.is_domain_explicit`
- :meth:`~colour.LUT3D.linear_table`
- :meth:`~colour.LUT3D.invert`
- :meth:`~colour.LUT3D.apply`
Examples
--------
Instantiating a unity LUT with a table with 16x16x16x3 elements:
>>> print(LUT3D(size=16))
LUT3D - Unity 16
----------------
<BLANKLINE>
Dimensions : 3
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (16, 16, 16, 3)
Instantiating a LUT using a custom table with 16x16x16x3 elements:
>>> print(LUT3D(LUT3D.linear_table(16) ** (1 / 2.2))) # doctest: +ELLIPSIS
LUT3D - ...
--------...
<BLANKLINE>
Dimensions : 3
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (16, 16, 16, 3)
Instantiating a LUT using a custom table with 16x16x16x3 elements, custom
name, custom domain and comments:
>>> from colour.algebra import spow
>>> domain = np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]])
>>> print(
... LUT3D(
... spow(LUT3D.linear_table(16), 1 / 2.2),
... "My LUT",
... domain,
... comments=["A first comment.", "A second comment."],
... )
... )
LUT3D - My LUT
--------------
<BLANKLINE>
Dimensions : 3
Domain : [[-0.1 -0.2 -0.4]
[ 1.5 3. 6. ]]
Size : (16, 16, 16, 3)
Comment 01 : A first comment.
Comment 02 : A second comment.
"""
[docs]
def __init__(
self,
table: ArrayLike | None = None,
name: str | None = None,
domain: ArrayLike | None = None,
size: ArrayLike | None = None,
comments: Sequence | None = None,
) -> None:
domain = as_float_array(optional(domain, [[0, 0, 0], [1, 1, 1]]))
size = optional(size, 33)
super().__init__(table, name, 3, domain, size, comments)
def _validate_table(self, table: ArrayLike) -> NDArrayFloat:
"""
Validate given table is a 4D array and that its dimensions are equal.
Parameters
----------
table
Table to validate.
Returns
-------
:class:`numpy.ndarray`
Validated table as a :class:`ndarray` instance.
"""
table = as_float_array(table)
attest(len(table.shape) == 4, "The table must be a 4D array!")
return table
def _validate_domain(self, domain: ArrayLike) -> NDArrayFloat:
"""
Validate given domain.
Parameters
----------
domain
Domain to validate.
Returns
-------
:class:`numpy.ndarray`
Validated domain as a :class:`ndarray` instance.
Notes
-----
- A :class:`LUT3D` class instance must use an implicit domain.
"""
domain = as_float_array(domain)
attest(len(domain.shape) == 2, "The domain must be a 2D array!")
attest(
domain.shape[0] >= 2,
"The domain row count must be equal or greater than 2!",
)
attest(domain.shape[1] == 3, "The domain column count must be equal to 3!")
return domain
[docs]
def is_domain_explicit(self) -> bool:
"""
Return whether the *LUT* domain is explicit (or implicit).
An implicit domain is defined by its shape only::
[[0 0 0]
[1 1 1]]
While an explicit domain defines every single discrete samples::
[[0.0 0.0 0.0]
[0.1 0.1 0.1]
[0.2 0.2 0.2]
[0.3 0.3 0.3]
[0.4 0.4 0.4]
[0.8 0.8 0.8]
[1.0 1.0 1.0]]
Returns
-------
:class:`bool`
Is *LUT* domain explicit.
Examples
--------
>>> LUT3D().is_domain_explicit()
False
>>> domain = np.array([[-0.1, -0.2, -0.4], [0.7, 1.4, 6.0], [1.5, 3.0, np.nan]])
>>> LUT3D(domain=domain).is_domain_explicit()
True
"""
return self.domain.shape != (2, 3)
[docs]
@staticmethod
def linear_table(
size: ArrayLike | None = None,
domain: ArrayLike | None = None,
) -> NDArrayFloat:
"""
Return a linear table, the number of output samples :math:`n` is equal
to ``size**3 * 3`` or ``size[0] * size[1] * size[2] * 3``.
Parameters
----------
size
Expected table size, default to 33.
domain
Domain of the table.
Returns
-------
:class:`numpy.ndarray`
Linear table with ``size**3 * 3`` or
``size[0] * size[1] * size[2] * 3`` samples.
Examples
--------
>>> LUT3D.linear_table(3, np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]]))
array([[[[-0.1, -0.2, -0.4],
[-0.1, -0.2, 2.8],
[-0.1, -0.2, 6. ]],
<BLANKLINE>
[[-0.1, 1.4, -0.4],
[-0.1, 1.4, 2.8],
[-0.1, 1.4, 6. ]],
<BLANKLINE>
[[-0.1, 3. , -0.4],
[-0.1, 3. , 2.8],
[-0.1, 3. , 6. ]]],
<BLANKLINE>
<BLANKLINE>
[[[ 0.7, -0.2, -0.4],
[ 0.7, -0.2, 2.8],
[ 0.7, -0.2, 6. ]],
<BLANKLINE>
[[ 0.7, 1.4, -0.4],
[ 0.7, 1.4, 2.8],
[ 0.7, 1.4, 6. ]],
<BLANKLINE>
[[ 0.7, 3. , -0.4],
[ 0.7, 3. , 2.8],
[ 0.7, 3. , 6. ]]],
<BLANKLINE>
<BLANKLINE>
[[[ 1.5, -0.2, -0.4],
[ 1.5, -0.2, 2.8],
[ 1.5, -0.2, 6. ]],
<BLANKLINE>
[[ 1.5, 1.4, -0.4],
[ 1.5, 1.4, 2.8],
[ 1.5, 1.4, 6. ]],
<BLANKLINE>
[[ 1.5, 3. , -0.4],
[ 1.5, 3. , 2.8],
[ 1.5, 3. , 6. ]]]])
>>> LUT3D.linear_table(
... np.array([3, 3, 2]),
... np.array([[-0.1, -0.2, -0.4], [1.5, 3.0, 6.0]]),
... )
array([[[[-0.1, -0.2, -0.4],
[-0.1, -0.2, 6. ]],
<BLANKLINE>
[[-0.1, 1.4, -0.4],
[-0.1, 1.4, 6. ]],
<BLANKLINE>
[[-0.1, 3. , -0.4],
[-0.1, 3. , 6. ]]],
<BLANKLINE>
<BLANKLINE>
[[[ 0.7, -0.2, -0.4],
[ 0.7, -0.2, 6. ]],
<BLANKLINE>
[[ 0.7, 1.4, -0.4],
[ 0.7, 1.4, 6. ]],
<BLANKLINE>
[[ 0.7, 3. , -0.4],
[ 0.7, 3. , 6. ]]],
<BLANKLINE>
<BLANKLINE>
[[[ 1.5, -0.2, -0.4],
[ 1.5, -0.2, 6. ]],
<BLANKLINE>
[[ 1.5, 1.4, -0.4],
[ 1.5, 1.4, 6. ]],
<BLANKLINE>
[[ 1.5, 3. , -0.4],
[ 1.5, 3. , 6. ]]]])
>>> domain = np.array([[-0.1, -0.2, -0.4], [0.7, 1.4, 6.0], [1.5, 3.0, np.nan]])
>>> LUT3D.linear_table(domain=domain)
array([[[[-0.1, -0.2, -0.4],
[-0.1, -0.2, 6. ]],
<BLANKLINE>
[[-0.1, 1.4, -0.4],
[-0.1, 1.4, 6. ]],
<BLANKLINE>
[[-0.1, 3. , -0.4],
[-0.1, 3. , 6. ]]],
<BLANKLINE>
<BLANKLINE>
[[[ 0.7, -0.2, -0.4],
[ 0.7, -0.2, 6. ]],
<BLANKLINE>
[[ 0.7, 1.4, -0.4],
[ 0.7, 1.4, 6. ]],
<BLANKLINE>
[[ 0.7, 3. , -0.4],
[ 0.7, 3. , 6. ]]],
<BLANKLINE>
<BLANKLINE>
[[[ 1.5, -0.2, -0.4],
[ 1.5, -0.2, 6. ]],
<BLANKLINE>
[[ 1.5, 1.4, -0.4],
[ 1.5, 1.4, 6. ]],
<BLANKLINE>
[[ 1.5, 3. , -0.4],
[ 1.5, 3. , 6. ]]]])
"""
size = optional(size, 33)
domain = as_float_array(optional(domain, [[0, 0, 0], [1, 1, 1]]))
if domain.shape != (2, 3):
samples = list(
np.flip(
# NOTE: "dtype=object" is required for ragged array support
# in "Numpy" 1.24.0.
as_array(
[
axes[: (~np.isnan(axes)).cumsum().argmax() + 1]
for axes in np.transpose(domain)
],
dtype=object, # pyright: ignore
),
-1,
)
)
size_array = as_int_array([len(axes) for axes in samples])
else:
size_array = np.tile(size, 3) if is_numeric(size) else as_int_array(size)
R, G, B = tsplit(domain)
size_array = np.flip(size_array, -1)
samples = [
np.linspace(a[0], a[1], size_array[i]) for i, a in enumerate([B, G, R])
]
table = np.flip(
np.reshape(
np.transpose(np.meshgrid(*samples, indexing="ij")),
np.hstack([np.flip(size_array, -1), 3]),
),
-1,
)
return table
[docs]
def invert(self, **kwargs: Any) -> LUT3D:
"""
Compute and returns an inverse copy of the *LUT*.
Other Parameters
----------------
extrapolate
Whether to extrapolate the *LUT* when computing its inverse.
Extrapolation is performed by reflecting the *LUT* cube along its 8
faces. Note that the domain is extended beyond [0, 1], thus the
*LUT* might not be handled properly in other software.
interpolator
Interpolator class type or object to use as interpolating function.
query_size
Number of points to query in the KDTree, their mean is computed,
resulting in a smoother result.
size
Size of the inverse *LUT*. With the given implementation, it is
good practise to double the size of the inverse *LUT* to provide a
smoother result. If ``size`` is not given,
:math:`2^{\\sqrt{size_{LUT}} + 1} + 1` will be used instead.
Returns
-------
:class:`colour.LUT3D`
Inverse *LUT* class instance.
Examples
--------
>>> LUT = LUT3D()
>>> print(LUT)
LUT3D - Unity 33
----------------
<BLANKLINE>
Dimensions : 3
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (33, 33, 33, 3)
>>> print(LUT.invert())
LUT3D - Unity 33 - Inverse
--------------------------
<BLANKLINE>
Dimensions : 3
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (108, 108, 108, 3)
"""
if self.is_domain_explicit():
raise NotImplementedError(
'Inverting a "LUT3D" with an explicit domain is not implemented!'
)
interpolator = kwargs.get("interpolator", table_interpolation_trilinear)
extrapolate = kwargs.get("extrapolate", False)
query_size = kwargs.get("query_size", 3)
LUT = self.copy()
source_size = LUT.size
target_size = kwargs.get("size", (as_int(2 ** (np.sqrt(source_size) + 1) + 1)))
if target_size > 129: # pragma: no cover
usage_warning("LUT3D inverse computation time could be excessive!")
if extrapolate:
LUT.table = np.pad(
LUT.table,
[(1, 1), (1, 1), (1, 1), (0, 0)],
"reflect",
reflect_type="odd",
)
LUT.domain[0] -= 1 / (source_size - 1)
LUT.domain[1] += 1 / (source_size - 1)
# "LUT_t" is an intermediate LUT with a size equal to that of the
# final inverse LUT which is usually larger than the input LUT.
# The intent is to smooth the inverse LUT's table by increasing the
# resolution of the KDTree.
LUT_t = LUT3D(size=target_size, domain=LUT.domain)
table = np.reshape(LUT_t.table, (-1, 3))
LUT_t.table = LUT.apply(LUT_t.table, interpolator=interpolator)
tree = KDTree(np.reshape(LUT_t.table, (-1, 3)))
# "LUT_q" stores the indexes of the KDTree query, i.e., the closest
# entry of "LUT_t" for any searched table sample.
LUT_q = LUT3D(size=target_size, domain=LUT.domain)
query = tree.query(table, query_size)[-1]
if query_size == 1:
LUT_q.table = np.reshape(
table[query], (target_size, target_size, target_size, 3)
)
else:
LUT_q.table = np.reshape(
np.mean(table[query], axis=-2),
(target_size, target_size, target_size, 3),
)
LUT_q.name = f"{self.name} - Inverse"
return LUT_q
[docs]
def apply(self, RGB: ArrayLike, **kwargs: Any) -> NDArrayFloat:
"""
Apply the *LUT* to given *RGB* colourspace array using given method.
Parameters
----------
RGB
*RGB* colourspace array to apply the *LUT* onto.
Other Parameters
----------------
direction
Whether the *LUT* should be applied in the forward or inverse
direction.
extrapolate
Whether to extrapolate the *LUT* when computing its inverse.
Extrapolation is performed by reflecting the *LUT* cube along its 8
faces.
interpolator
Interpolator object to use as interpolating function.
interpolator_kwargs
Arguments to use when calling the interpolating function.
query_size
Number of points to query in the KDTree, their mean is computed,
resulting in a smoother result.
size
Size of the inverse *LUT*. With the given implementation, it is
good practise to double the size of the inverse *LUT* to provide a
smoother result. If ``size`` is not given,
:math:`2^{\\sqrt{size_{LUT}} + 1} + 1` will be used instead.
Returns
-------
:class:`numpy.ndarray`
Interpolated *RGB* colourspace array.
Examples
--------
>>> LUT = LUT3D(LUT3D.linear_table() ** (1 / 2.2))
>>> RGB = np.array([0.18, 0.18, 0.18])
>>> LUT.apply(RGB) # doctest: +ELLIPSIS
array([ 0.4583277..., 0.4583277..., 0.4583277...])
>>> LUT.apply(LUT.apply(RGB), direction="Inverse")
... # doctest: +ELLIPSIS
array([ 0.1781995..., 0.1809414..., 0.1809513...])
>>> from colour.algebra import spow
>>> domain = np.array(
... [
... [-0.1, -0.2, -0.4],
... [0.3, 1.4, 6.0],
... [0.7, 3.0, np.nan],
... [1.1, np.nan, np.nan],
... [1.5, np.nan, np.nan],
... ]
... )
>>> table = spow(LUT3D.linear_table(domain=domain), 1 / 2.2)
>>> LUT = LUT3D(table, domain=domain)
>>> RGB = np.array([0.18, 0.18, 0.18])
>>> LUT.apply(RGB) # doctest: +ELLIPSIS
array([ 0.2996370..., -0.0901332..., -0.3949770...])
"""
direction = validate_method(
kwargs.get("direction", "Forward"), ("Forward", "Inverse")
)
interpolator = kwargs.get("interpolator", table_interpolation_trilinear)
interpolator_kwargs = kwargs.get("interpolator_kwargs", {})
R, G, B = tsplit(RGB)
settings = {"interpolator": interpolator}
settings.update(**kwargs)
LUT = self.invert(**settings) if direction == "inverse" else self
if LUT.is_domain_explicit():
domain_min = LUT.domain[0, ...]
domain_max = [
axes[: (~np.isnan(axes)).cumsum().argmax() + 1][-1]
for axes in np.transpose(LUT.domain)
]
usage_warning(
f'"LUT" was defined with an explicit domain but requires an '
f"implicit domain to be applied. The following domain will be "
f"used: {np.vstack([domain_min, domain_max])}"
)
else:
domain_min, domain_max = LUT.domain
RGB_l = [
linear_conversion(j, (domain_min[i], domain_max[i]), (0, 1))
for i, j in enumerate((R, G, B))
]
RGB_i = interpolator(tstack(RGB_l), LUT.table, **interpolator_kwargs)
return RGB_i
[docs]
def LUT_to_LUT(
LUT,
cls: Type[AbstractLUT],
force_conversion: bool = False,
**kwargs: Any,
) -> AbstractLUT:
"""
Convert given *LUT* to given ``cls`` class instance.
Parameters
----------
cls
*LUT* class instance.
force_conversion
Whether to force the conversion if destructive.
Other Parameters
----------------
channel_weights
Channel weights in case of a downcast from a :class:`LUT3x1D` or
:class:`LUT3D` class instance.
interpolator
Interpolator class type to use as interpolating function.
interpolator_kwargs
Arguments to use when instantiating the interpolating function.
size
Expected table size in case of an upcast to or a downcast from a
:class:`LUT3D` class instance.
Returns
-------
:class:`colour.LUT1D` or :class:`colour.LUT3x1D` or :class:`colour.LUT3D`
Converted *LUT* class instance.
Warnings
--------
Some conversions are destructive and raise a :class:`ValueError` exception
by default.
Raises
------
ValueError
If the conversion is destructive.
Examples
--------
>>> print(LUT_to_LUT(LUT1D(), LUT3D, force_conversion=True))
LUT3D - Unity 10 - Converted 1D to 3D
-------------------------------------
<BLANKLINE>
Dimensions : 3
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (33, 33, 33, 3)
>>> print(LUT_to_LUT(LUT3x1D(), LUT1D, force_conversion=True))
LUT1D - Unity 10 - Converted 3x1D to 1D
---------------------------------------
<BLANKLINE>
Dimensions : 1
Domain : [ 0. 1.]
Size : (10,)
>>> print(LUT_to_LUT(LUT3D(), LUT1D, force_conversion=True))
LUT1D - Unity 33 - Converted 3D to 1D
-------------------------------------
<BLANKLINE>
Dimensions : 1
Domain : [ 0. 1.]
Size : (10,)
"""
ranks = {LUT1D: 1, LUT3x1D: 2, LUT3D: 3}
path = (ranks[LUT.__class__], ranks[cls]) # pyright: ignore
path_verbose = [f"{element}D" if element != 2 else "3x1D" for element in path]
if path in ((1, 3), (2, 1), (2, 3), (3, 1), (3, 2)) and not force_conversion:
raise ValueError(
f'Conversion of a "LUT" {path_verbose[0]} to a "LUT" '
f"{path_verbose[1]} is destructive, please use the "
f'"force_conversion" argument to proceed!'
)
suffix = f" - Converted {path_verbose[0]} to {path_verbose[1]}"
name = f"{LUT.name}{suffix}"
# Same dimension conversion, returning a copy.
if len(set(path)) == 1:
LUT = LUT.copy()
LUT.name = name
else:
size = kwargs.get("size", 33 if cls is LUT3D else 10)
if "size" in kwargs:
del kwargs["size"]
channel_weights = as_float_array(kwargs.get("channel_weights", full(3, 1 / 3)))
if "channel_weights" in kwargs:
del kwargs["channel_weights"]
if isinstance(LUT, LUT1D):
if cls is LUT3x1D:
domain = tstack([LUT.domain, LUT.domain, LUT.domain])
table = tstack([LUT.table, LUT.table, LUT.table])
elif cls is LUT3D:
domain = tstack([LUT.domain, LUT.domain, LUT.domain])
table = LUT3D.linear_table(size, domain)
table = LUT.apply(table, **kwargs)
elif isinstance(LUT, LUT3x1D):
if cls is LUT1D:
domain = np.sum(LUT.domain * channel_weights, axis=-1)
table = np.sum(LUT.table * channel_weights, axis=-1)
elif cls is LUT3D:
domain = LUT.domain
table = LUT3D.linear_table(size, domain)
table = LUT.apply(table, **kwargs)
elif isinstance(LUT, LUT3D):
if cls is LUT1D:
domain = np.sum(LUT.domain * channel_weights, axis=-1)
table = LUT1D.linear_table(size, domain)
table = LUT.apply(tstack([table, table, table]), **kwargs)
table = np.sum(table * channel_weights, axis=-1)
elif cls is LUT3x1D:
domain = LUT.domain
table = LUT3x1D.linear_table(size, domain)
table = LUT.apply(table, **kwargs)
LUT = cls(
table=table,
name=name,
domain=domain,
size=table.shape[0],
comments=LUT.comments,
) # pyright: ignore
return LUT
