colour.LUT3D#

class colour.LUT3D(table: ArrayLike | None = None, name: str | None = None, domain: ArrayLike | None = None, size: ArrayLike | None = None, comments: Sequence | None = None)[source]#

Bases: AbstractLUT

Define the base class for a 3D LUT.

Parameters:

  • table (ArrayLike | None) – Underlying LUT table.

  • name (str | None) – LUT name.

  • domain (ArrayLike | None) – LUT domain, also used to define the instantiation time default table domain.

  • size (ArrayLike | None) – Size of the instantiation time default table, default to 33.

  • comments (Sequence | None) – Comments to add to the LUT.

Methods

Examples

Instantiating a unity LUT with a table with 16x16x16x3 elements:

>>> print(LUT3D(size=16))
LUT3D - Unity 16
----------------

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)))  
LUT3D - ...
--------...

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
--------------

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.
__init__(table: ArrayLike | None = None, name: str | None = None, domain: ArrayLike | None = None, size: ArrayLike | None = None, comments: Sequence | None = None) → None[source]#
Parameters:

  • table (ArrayLike | None)

  • name (str | None)

  • domain (ArrayLike | None)

  • size (ArrayLike | None)

  • comments (Sequence | None)

Return type:

None

is_domain_explicit() → bool[source]#

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:

Is LUT domain explicit.

Return type:

bool

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
static linear_table(size: ArrayLike | None = None, domain: ArrayLike | None = None) → NDArrayFloat[source]#

Return a linear table, the number of output samples \(n\) is equal to size**3 * 3 or size[0] * size[1] * size[2] * 3.

Parameters:

  • size (ArrayLike | None) – Expected table size, default to 33.

  • domain (ArrayLike | None) – Domain of the table.

Returns:

Linear table with size**3 * 3 or size[0] * size[1] * size[2] * 3 samples.

Return type:

numpy.ndarray

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. ]],

        [[-0.1,  1.4, -0.4],
         [-0.1,  1.4,  2.8],
         [-0.1,  1.4,  6. ]],

        [[-0.1,  3. , -0.4],
         [-0.1,  3. ,  2.8],
         [-0.1,  3. ,  6. ]]],


       [[[ 0.7, -0.2, -0.4],
         [ 0.7, -0.2,  2.8],
         [ 0.7, -0.2,  6. ]],

        [[ 0.7,  1.4, -0.4],
         [ 0.7,  1.4,  2.8],
         [ 0.7,  1.4,  6. ]],

        [[ 0.7,  3. , -0.4],
         [ 0.7,  3. ,  2.8],
         [ 0.7,  3. ,  6. ]]],


       [[[ 1.5, -0.2, -0.4],
         [ 1.5, -0.2,  2.8],
         [ 1.5, -0.2,  6. ]],

        [[ 1.5,  1.4, -0.4],
         [ 1.5,  1.4,  2.8],
         [ 1.5,  1.4,  6. ]],

        [[ 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. ]],

        [[-0.1,  1.4, -0.4],
         [-0.1,  1.4,  6. ]],

        [[-0.1,  3. , -0.4],
         [-0.1,  3. ,  6. ]]],


       [[[ 0.7, -0.2, -0.4],
         [ 0.7, -0.2,  6. ]],

        [[ 0.7,  1.4, -0.4],
         [ 0.7,  1.4,  6. ]],

        [[ 0.7,  3. , -0.4],
         [ 0.7,  3. ,  6. ]]],


       [[[ 1.5, -0.2, -0.4],
         [ 1.5, -0.2,  6. ]],

        [[ 1.5,  1.4, -0.4],
         [ 1.5,  1.4,  6. ]],

        [[ 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. ]],

        [[-0.1,  1.4, -0.4],
         [-0.1,  1.4,  6. ]],

        [[-0.1,  3. , -0.4],
         [-0.1,  3. ,  6. ]]],


       [[[ 0.7, -0.2, -0.4],
         [ 0.7, -0.2,  6. ]],

        [[ 0.7,  1.4, -0.4],
         [ 0.7,  1.4,  6. ]],

        [[ 0.7,  3. , -0.4],
         [ 0.7,  3. ,  6. ]]],


       [[[ 1.5, -0.2, -0.4],
         [ 1.5, -0.2,  6. ]],

        [[ 1.5,  1.4, -0.4],
         [ 1.5,  1.4,  6. ]],

        [[ 1.5,  3. , -0.4],
         [ 1.5,  3. ,  6. ]]]])
invert(**kwargs: Any) → LUT3D[source]#

Compute and returns an inverse copy of the LUT.

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, \(2^{\sqrt{size_{LUT}} + 1} + 1\) will be used instead.

  • kwargs (Any)

Returns:

Inverse LUT class instance.

Return type:

colour.LUT3D

Examples

>>> LUT = LUT3D()
>>> print(LUT)
LUT3D - Unity 33
----------------

Dimensions : 3
Domain     : [[ 0.  0.  0.]
              [ 1.  1.  1.]]
Size       : (33, 33, 33, 3)
>>> print(LUT.invert())
LUT3D - Unity 33 - Inverse
--------------------------

Dimensions : 3
Domain     : [[ 0.  0.  0.]
              [ 1.  1.  1.]]
Size       : (108, 108, 108, 3)
apply(RGB: ArrayLike, **kwargs: Any) → NDArrayFloat[source]#

Apply the LUT to given RGB colourspace array using given method.

Parameters:

  • RGB (ArrayLike) – RGB colourspace array to apply the LUT onto.

  • 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, \(2^{\sqrt{size_{LUT}} + 1} + 1\) will be used instead.

  • kwargs (Any)

Returns:

Interpolated RGB colourspace array.

Return type:

numpy.ndarray

Examples

>>> LUT = LUT3D(LUT3D.linear_table() ** (1 / 2.2))
>>> RGB = np.array([0.18, 0.18, 0.18])
>>> LUT.apply(RGB)  
array([ 0.4583277...,  0.4583277...,  0.4583277...])
>>> LUT.apply(LUT.apply(RGB), direction="Inverse")
... 
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)  
array([ 0.2996370..., -0.0901332..., -0.3949770...])