parser.add_option("-s", "--sample-count", dest="sampleCount", default=256)
parser.add_option("-c",
"--distribution",
dest="distribution",
default=None,
type="str")
parser.add_option("-o", "--output", dest="output", type="str")
parser.add_option("-d", "--dry-run", action="store_true", dest="dryRun")
(options, args) = parser.parse_args()
directory = args[0]
dataset = Dataset(directory)
if options.superIndex == -1 and options.index == -1:
targetBSDF = dataset.lastAvailableBSDF()
else:
targetBSDF = dataset.BSDFAt(iteration=options.index,
superiteration=options.superIndex)
print targetBSDF
if options.dryRun:
sys.exit(0)
testset = dataset.testSet()
sphereXML = os.path.dirname(__file__) + "/data/sphere-embeddable.xml"
sphereXMLP = os.path.dirname(__file__) + "/data/sphere-postprocessed.xml"
testset.embedOnto(sphereXML, sphereXMLP)
renderable = testset.renderables[0]
# Plots high sample count last image.
parser = optparse.OptionParser()
parser.add_option("-s", "--samples", dest="samples", default=2048, type="int")
parser.add_option("-c", "--distribution", dest="distribution", default=None)
(options, args) = parser.parse_args()
directory = args[0]
dataset = Dataset(directory)
# TODO: Temporary
copyfile(dataset.testSet().targetMeshPath, "/tmp/mts_mesh_intensity_slot_0.ply")
renderable = dataset.testSet().renderable(1)
pt0 = np.array(dataset.lastAvailableBSDF())
pt1 = np.array(dataset.testSet().targetBSDF())
paramList = dataset.testSet().parameterList()
#print paramList
#print pt0
#print toMap(paramList, pt1)
if dataset.testSet().bsdfAdaptiveSampled:
adaptiveParamList = dataset.bsdfAdaptiveSamplingParameterList
print adaptiveParamList
adaptiveParamMap = toMap(adaptiveParamList, len(adaptiveParamList) * [1.0])
else:
adaptiveParamMap = {}
renderable.setEmbeddedParameter("sampleCount", options.samples)
renderable.setParameter("blockSize", 8)
profiles = [{
"name":
"invAlpha",
"sampleWeights":
invAlpha(dataset.testSet().bsdfDictionary).tolist(),
"type":
"bsdf-adaptive"
}, {
"name":
"uniform",
"sampleWeights": [1.0] * len(dataset.testSet().bsdfDictionary["elements"]),
"type":
"bsdf-adaptive"
}, {
"name": "bsdfWeight",
"sampleWeights": dataset.lastAvailableBSDF(),
"type": "bsdf-adaptive"
}]
uniform = np.stack(
[np.ones(
(dataset.testSet().targetWidth, dataset.testSet().targetHeight))] *
dataset.testSet().numLights(),
axis=0)
np.save(directory + "/uniform.npy", uniform)
spatialProfiles = [{
"name": "spatialUniform",
"type": "spatial-custom",
"spatial-sampler": directory + "/uniform.npy"
}, {
"name": "spatialAdaptive",