Source code for colour_hdri.sampling.grossberg2003
"""
Grossberg (2003) Histogram Based Image Sampling
===============================================
Defines the *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
"""
from __future__ import annotations
import numpy as np
from colour.hints import ArrayLike, NDArrayFloat
from colour.utilities import as_float_array, tsplit, tstack
__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__ = [
"samples_Grossberg2003",
]
[docs]
def samples_Grossberg2003(
image_stack: ArrayLike, samples: int = 1000, n: int = 256
) -> NDArrayFloat:
"""
Return the samples for given image stack intensity histograms using
*Grossberg (2003)* method.
Parameters
----------
image_stack
Stack of single channel or multi-channel floating point images.
samples
Samples count.
n
Histograms bins count.
Returns
-------
:class:`numpy.ndarray`
Intensity histograms samples.
References
----------
:cite:`Banterle2014a`, :cite:`Grossberg2003g`
"""
image_stack = as_float_array(image_stack)
channels_c = 1 if image_stack.ndim == 3 else 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)
]
)
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