def range(self, value):
if value is not None:
if not np.all(np.isfinite(value)):
warning('"range" variable is not finite, '
'unpredictable results may occur!\n{0}'.format(value))
# TODO: `self.range` is a copy of `value` to avoid side effects,
# Is it a smart way to avoid them?
value = np.copy(np.asarray(value))
if self._domain is not None:
assert value.size == self._domain.size, (
'"domain" and "range" variables must have same size!')
value.setflags(write=False)
self._range = value
self._create_function()
def range(self, value):
if value is not None:
if not np.all(np.isfinite(value)):
warning('"range" variable is not finite, '
'unpredictable results may occur!\n{0}'.format(value))
# TODO: `self.range` is a copy of `value` to avoid side effects,
# Is it a smart way to avoid them?
value = np.copy(np.asarray(value))
if self._domain is not None:
assert value.size == self._domain.size, (
'"domain" and "range" variables must have same size!')
value.setflags(write=False)
self._range = value
self._create_function()
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
def main():
"""
Starts the application.
Return
------
bool
Definition success.
"""
arguments = command_line_arguments()
if arguments.version:
print("{0} - {1}".format(__application_name__, __version__))
return True
settings = json.load(open(SETTINGS_FILE))
if arguments.settings_file is not None:
assert os.path.exists(arguments.settings_file), '"{0}" file doesn\'t exists!'.format(arguments.settings_file)
settings.update(json.load(open(arguments.settings_file)))
input_linearity = arguments.input_linearity.lower()
if input_linearity == "linear":
input_linear = True
elif input_linearity == "oecf":
input_linear = False
else:
input_extension = os.path.splitext(arguments.input_image)[1].lower()
if input_extension in LINEAR_IMAGE_FORMATS:
input_linear = True
else:
input_linear = False
if arguments.input_image is not None:
assert os.path.exists(arguments.input_image), '"{0}" input image doesn\'t exists!'.format(arguments.input_image)
image_path = arguments.input_image
else:
image_path = DEFAULT_IMAGE_PATH
if is_openimageio_installed:
image = read_image(str(image_path))
if not input_linear:
colourspace = RGB_COLOURSPACES[arguments.input_oecf]
image = colourspace.inverse_transfer_function(image)
# Keeping RGB channels only.
image = image[..., 0:3]
image = image[:: int(arguments.input_resample), :: int(arguments.input_resample)]
else:
warning('"OpenImageIO" is not available, image reading is not supported, ' "falling back to some random noise!")
image = None
if not arguments.enable_warnings:
warnings.filterwarnings("ignore")
ColourAnalysis(
image,
arguments.input_image,
arguments.input_colourspace,
arguments.input_oecf,
input_linear,
arguments.reference_colourspace,
arguments.correlate_colourspace,
settings,
arguments.layout,
)
return run()
def main():
"""
Starts the application.
Return
------
bool
Definition success.
"""
arguments = command_line_arguments()
if arguments.version:
print('{0} - {1}'.format(__application_name__, __version__))
return True
settings = json.load(open(SETTINGS_FILE))
if arguments.settings_file is not None:
assert os.path.exists(arguments.settings_file), (
'"{0}" file doesn\'t exists!'.format(arguments.settings_file))
settings.update(json.load(open(arguments.settings_file)))
input_linearity = arguments.input_linearity.lower()
if input_linearity == 'linear':
input_linear = True
elif input_linearity == 'oecf':
input_linear = False
else:
input_extension = os.path.splitext(arguments.input_image)[1].lower()
if input_extension in LINEAR_IMAGE_FORMATS:
input_linear = True
else:
input_linear = False
if arguments.input_image is not None:
assert os.path.exists(arguments.input_image), (
'"{0}" input image doesn\'t exists!'.format(arguments.input_image))
image_path = arguments.input_image
else:
image_path = DEFAULT_IMAGE_PATH
try:
image = read_image(str(image_path))
if not input_linear:
colourspace = RGB_COLOURSPACES[arguments.input_oecf]
image = colourspace.decoding_cctf(image)
# Keeping RGB channels only.
image = image[..., 0:3]
image = image[::int(arguments.input_resample),
::int(arguments.input_resample)]
except ImportError:
warning(
'"OpenImageIO" is not available, image reading is not supported, '
'falling back to some random noise!')
image = None
if not arguments.enable_warnings:
warnings.filterwarnings("ignore")
ColourAnalysis(image,
arguments.input_image,
arguments.input_colourspace,
arguments.input_oecf,
input_linear,
arguments.reference_colourspace,
arguments.correlate_colourspace,
settings,
arguments.layout)
return run()
