def luminance_variance(a):
"""
Computes the Luminance variance of given :math:`a` 2-D array.
Parameters
----------
a : array_like
:math:`a` 2-D array to compute the Luminance variation.
Returns
-------
numeric
:math:`a` 2-D array Luminance variance.
Examples
--------
>>> a = np.tile(np.arange(5), (5, 1))
>>> luminance_variance(a) # doctest: +ELLIPSIS
12.2474487...
"""
x, y = np.mgrid[0:np.shape(a)[0], 0:np.shape(a)[1]]
x_centroid, y_centroid = centroid(a)
variance = np.sqrt(np.sum(
a * ((y - y_centroid) ** 2 + (x - x_centroid) ** 2)))
return variance
def light_probe_sampling_variance_minimization(
light_probe,
lights_count=16,
luminance_factors=DEFAULT_LUMINANCE_FACTORS):
"""
Sample given light probe to find lights using Viriyothai (2009) variance
minimization light probe sampling algorithm.
Parameters
----------
light_probe : array_like
Array to sample for lights.
lights_count : int
Amount of lights to generate.
luminance_factors : array_like
Weighting factors for Luminance conversion.
Returns
-------
list
list of :class:`Light_Specification` lights.
"""
light_probe = np.asarray(light_probe)
iterations = np.sqrt(lights_count).astype(np.int_)
if iterations ** 2 != lights_count:
warning(
'{0} lights requested, {1} will be effectively computed!'.format(
lights_count, iterations ** 2))
Y = np.dot(light_probe, luminance_factors)
regions = find_regions_variance_minimization(Y, iterations)
lights = []
for region in regions:
y_min, y_max, x_min, x_max = region
c = centroid(Y[y_min:y_max, x_min:x_max])
c = (c + np.array([y_min, x_min]))
lights.append(
Light_Specification(
(c / np.array(Y.shape))[::-1],
np.sum(np.sum(light_probe[y_min:y_max, x_min:x_max], 0), 0),
c))
return lights
