# -*- coding: utf-8 -*-
"""
HDRI / Radiance Image Generation
================================
Defines HDRI / radiance image generation objects:
- :func:`colour_hdri.image_stack_to_radiance_image`
See Also
--------
`Colour - HDRI - Examples Jupyter Notebooks
<https://github.com/colour-science/colour-hdri/\
blob/master/colour_hdri/examples>`_
References
----------
- :cite:`Banterle2011n` : Banterle, F., Artusi, A., Debattista, K., &
Chalmers, A. (2011). 2.1.1 Generating HDR Content by Combining Multiple
Exposures. Advanced High Dynamic Range Imaging. A K Peters/CRC Press.
ISBN:978-1568817194
"""
from __future__ import division, unicode_literals
import numpy as np
from colour.utilities import tsplit, tstack, warning
from colour_hdri.generation import weighting_function_Debevec1997
from colour_hdri.utilities import average_luminance
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2015-2019 - Colour Developers'
__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = ['image_stack_to_radiance_image']
[docs]def image_stack_to_radiance_image(
image_stack,
weighting_function=weighting_function_Debevec1997,
weighting_average=False,
camera_response_functions=None):
"""
Generates a HDRI / radiance image from given image stack.
Parameters
----------
image_stack : colour_hdri.ImageStack
Stack of single channel or multi-channel floating point images. The
stack is assumed to be representing linear values except if
``camera_response_functions`` argument is provided.
weighting_function : callable, optional
Weighting function :math:`w`.
weighting_average : bool, optional
Enables weighting function :math:`w` computation on channels average
instead of on a per channel basis.
camera_response_functions : array_like, optional
Camera response functions :math:`g(z)` of the imaging system / camera
if the stack is representing non linear values.
Returns
-------
ndarray
Radiance image.
Warning
-------
If the image stack contains images with negative or equal to zero values,
unpredictable results may occur and NaNs might be generated. It is
thus recommended to encode the images in a wider RGB colourspace or clamp
negative values.
References
----------
:cite:`Banterle2011n`
"""
image_c = None
weight_c = None
for i, image in enumerate(image_stack):
if image_c is None:
image_c = np.zeros(image.data.shape)
weight_c = np.zeros(image.data.shape)
L = 1 / average_luminance(image.metadata.f_number,
image.metadata.exposure_time,
image.metadata.iso)
if np.any(image.data <= 0):
warning('"{0}" image channels contain negative or equal to zero '
'values, unpredictable results may occur! Please consider '
'encoding your images in a wider gamut RGB colourspace or '
'clamp negative values.'.format(image.path))
if weighting_average and image.data.ndim == 3:
average = np.average(image.data, axis=-1)
weights = weighting_function(average)
weights = np.rollaxis(weights[np.newaxis], 0, 3)
if i == 0:
weights[average >= 0.5] = 1
if i == len(image_stack) - 1:
weights[average <= 0.5] = 1
else:
weights = weighting_function(image.data)
if i == 0:
weights[image.data >= 0.5] = 1
if i == len(image_stack) - 1:
weights[image.data <= 0.5] = 1
image_data = image.data
if camera_response_functions is not None:
samples = np.linspace(0, 1, camera_response_functions.shape[0])
R, G, B = tsplit(image.data)
R = np.interp(R, samples, camera_response_functions[..., 0])
G = np.interp(G, samples, camera_response_functions[..., 1])
B = np.interp(B, samples, camera_response_functions[..., 2])
image_data = tstack([R, G, B])
image_c += weights * image_data / L
weight_c += weights
if image_c is not None:
image_c /= weight_c
return image_c