Source code for colour_hdri.sampling.grossberg2003

# -*- coding: utf-8 -*-
"""
Grossberg (2003) Histogram Based Image Sampling
===============================================

Defines *Grossberg (2003)* histogram based image sampling objects:

-   :func:`colour_hdri.samples_Grossberg2003`

References
----------
-   :cite:`Banterle2014a` : Banterle, F., & Benedetti, L. (2014). PICCANTE: An
    Open and Portable Library for HDR Imaging.
-   :cite:`Grossberg2003g` : Grossberg, M. D., & Nayar, S. K. (2003).
    Determining the camera response from images: What is knowable? IEEE
    Transactions on Pattern Analysis and Machine Intelligence, 25(11),
    1455-1467. doi:10.1109/TPAMI.2003.1240119
"""

import numpy as np

from colour.utilities import as_float_array, tsplit, tstack

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2015-2021 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-developers@colour-science.org'
__status__ = 'Production'

__all__ = ['samples_Grossberg2003']


[docs]def samples_Grossberg2003(image_stack, samples=1000, n=256): """ Returns the samples for given image stack intensity histograms using *Grossberg (2003)* method. Parameters ---------- image_stack : array_like Stack of single channel or multi-channel floating point images. samples : int, optional Samples count. n : int, optional Histograms bins count. Returns ------- ndarray Intensity histograms samples. References ---------- :cite:`Banterle2014a`, :cite:`Grossberg2003g` """ image_stack = as_float_array(image_stack) if image_stack.ndim == 3: channels_c = 1 else: channels_c = image_stack.shape[-2] cdf_i = [] for image in tsplit(image_stack): histograms = tstack( [np.histogram(image[..., c], n, range=(0, 1))[0] for c in np.arange(channels_c)]) # yapf: disable cdf = np.cumsum(histograms, axis=0) cdf_i.append(cdf.astype(np.float_) / np.max(cdf, axis=0)) samples_cdf_i = np.zeros((samples, len(cdf_i), channels_c)) samples_u = np.linspace(0, 1, samples) for i in np.arange(samples): for j in np.arange(channels_c): for k, cdf in enumerate(cdf_i): samples_cdf_i[i, k, j] = np.argmin( np.abs(cdf[:, j] - samples_u[i])) return samples_cdf_i