Source code for colour_hdri.plotting.tonemapping

"""
Tonemapping Operators Plotting
==============================

Define the tonemapping operators plotting objects:

-   :func:`colour_hdri.plotting.plot_tonemapping_operator_image`
"""

from __future__ import annotations

import matplotlib.pyplot as plt
import matplotlib.ticker
import numpy as np
from colour.hints import Any, ArrayLike, Callable, Dict, Tuple
from colour.plotting import (
    CONSTANTS_COLOUR_STYLE,
    artist,
    override_style,
    render,
)
from colour.utilities import as_float_array
from matplotlib.axes import Axes
from matplotlib.figure import Figure

__author__ = "Colour Developers"
__copyright__ = "Copyright 2015 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__ = [
    "plot_tonemapping_operator_image",
]


[docs] @override_style() def plot_tonemapping_operator_image( image: ArrayLike, luminance_function: ArrayLike, log_scale: bool = False, cctf_encoding: Callable = CONSTANTS_COLOUR_STYLE.colour.colourspace.cctf_encoding, **kwargs: Any, ) -> Tuple[Figure, Axes]: """ Plot given tonemapped image with superimposed luminance mapping function. Parameters ---------- image Tonemapped image to plot. luminance_function Luminance mapping function. log_scale Use a log scale for plotting the luminance mapping function. cctf_encoding Encoding colour component transfer function / opto-electronic transfer function used for plotting. Other Parameters ---------------- kwargs {:func:`colour.plotting.render`}, Please refer to the documentation of the previously listed definition. Returns ------- :class:`tuple` Current figure and axes. """ image = as_float_array(image) luminance_function = as_float_array(luminance_function) settings: Dict[str, Any] = {"uniform": True} settings.update(kwargs) figure, axes = artist(**settings) shape = image.shape bounding_box = (0.0, 1.0, 0.0, 1.0) image = np.clip(cctf_encoding(image), 0, 1) axes.imshow( image, aspect=shape[0] / shape[1], extent=bounding_box, interpolation="nearest", ) axes.plot( np.linspace(0, 1, len(luminance_function)), luminance_function, color="red", ) settings = { "axes": axes, "bounding_box": bounding_box, "x_ticker": True, "y_ticker": True, "x_label": "Input Luminance", "y_label": "Output Luminance", } settings.update(kwargs) if log_scale: settings.update( { "x_label": "$log_2$ Input Luminance", "x_ticker_locator": matplotlib.ticker.AutoMinorLocator( 0.5 # pyright: ignore ), } ) plt.gca().set_xscale("log", basex=2) plt.gca().xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter()) return render(**settings)