import pyredner
import numpy as np
import torch
# Optimize for a textured plane in a specular reflection
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/teapot_specular.xml')
# The last material is the teapot material, set it to a specular material
scene.materials[-1].diffuse_reflectance = \
torch.tensor([0.15, 0.2, 0.15], device = pyredner.get_device())
scene.materials[-1].specular_reflectance = \
torch.tensor([0.8, 0.8, 0.8], device = pyredner.get_device())
scene.materials[-1].roughness = \
torch.tensor([0.0001], device = pyredner.get_device())
args=pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = 2)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_teapot_specular/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_teapot_specular/target.png')
target = pyredner.imread('results/test_teapot_specular/target.exr')
if pyredner.get_use_gpu():
filedata = urllib.request.urlretrieve('https://benedikt-bitterli.me/resources/mitsuba/living-room-3.zip', 'living-room-3.zip')
print('Unzipping living-room-3.zip')
zip_ref = zipfile.ZipFile('living-room-3.zip', 'r')
zip_ref.extractall('scenes/')
print('Copying scene file')
copyfile('scenes/living-room-3-scene.xml', 'scenes/living-room-3/scene.xml')
print('Removing zip file')
os.remove('living-room-3.zip')
# Optimize for camera pose
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/living-room-3/scene.xml')
print('scene loaded')
max_bounces = 6
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = max_bounces)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_living_room/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_living_room/target.png')
target = pyredner.imread('results/test_living_room/target.exr')
if pyredner.get_use_gpu():
import pyredner
import numpy as np
import torch
import scipy
import scipy.ndimage
# Optimize for material parameters and camera pose
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/teapot.xml')
# The last material is the teapot material, set it to the target
scene.materials[-1].diffuse_reflectance = \
pyredner.Texture(torch.tensor([0.3, 0.2, 0.2], device = pyredner.get_device()))
scene.materials[-1].specular_reflectance = \
pyredner.Texture(torch.tensor([0.6, 0.6, 0.6], device = pyredner.get_device()))
scene.materials[-1].roughness = \
pyredner.Texture(torch.tensor([0.05], device = pyredner.get_device()))
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 2)
# Alias of the render function
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_teapot_reflectance/target.exr')
# Joint material & camera pose estimation with global illumination
# using coarse to fine estimation
# This time we will learn the following through a Cornell box example.
# - Loading from a Mitsuba scene file
# - Global illumination and glossy/specular materials
# - Coarse to fine estimation with a Gaussian pyramid loss
# - Incorporate box constraints in your optimizer
# In addition to Wavefront obj file, redner also supports loading from a Mitsuba
# scene file. Currently we only support a limited amount of features. In particular
# we only support two kinds of materials: diffuse and roughplastic. Note that the
# "alpha" values in roughplastic is the square root of the roughness. See cbox.xml
# for how a Mitsuba scene file should look like.
# We can load a scene using pyredner.load_mitsuba() utility, and render it as usual.
scene = pyredner.load_mitsuba('cbox/cbox.xml')
scene_args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = 5) # Set max_bounces = 5 for global illumination
render = pyredner.RenderFunction.apply
img = render(0, *scene_args)
pyredner.imwrite(img.cpu(), 'results/coarse_to_fine_estimation/target.exr')
pyredner.imwrite(img.cpu(), 'results/coarse_to_fine_estimation/target.png')
target = pyredner.imread('results/coarse_to_fine_estimation/target.exr')
if pyredner.get_use_gpu():
target = target.cuda()
# Now let's generate an initial guess by perturbing the reference.
# Let's set all the diffuse color to gray by manipulating material.diffuse_reflectance.
# We also store all the material variables to optimize in a list.
import scipy.ndimage.filters
import pyredner
import numpy as np
import torch
pyredner.set_use_gpu(torch.cuda.is_available())
scene = pyredner.load_mitsuba('scenes/bunny_box.xml')
scene.shapes[-1].vertices += torch.tensor([0, 0.01, 0],
device=pyredner.get_device())
args=pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 6)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_bunny_box/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_bunny_box/target.png')
target = pyredner.imread('results/test_bunny_box/target.exr')
target = target.cuda(pyredner.get_device())
bunny_vertices = scene.shapes[-1].vertices.clone()
bunny_translation = torch.tensor([0.1, 0.4, 0.1],
device=pyredner.get_device(),
requires_grad=True)
bunny_rotation = torch.tensor([-0.2, 0.1, -0.1],
device=pyredner.get_device(),
requires_grad=True)
print('Unzipping living-room-3.zip')
zip_ref = zipfile.ZipFile('living-room-3.zip', 'r')
zip_ref.extractall('scenes/')
print('Copying scene file')
copyfile('scenes/living-room-3-scene.xml',
'scenes/living-room-3/scene.xml')
print('Removing zip file')
os.remove('living-room-3.zip')
# Optimize for camera pose
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/living-room-3/scene.xml')
print('scene loaded')
max_bounces = 6
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = max_bounces)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_living_room/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_living_room/target.png')
target = pyredner.imread('results/test_living_room/target.exr')
if pyredner.get_use_gpu():
import pyredner
import numpy as np
import torch
import scipy
# Optimize for material parameters and camera pose
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/teapot.xml')
# The last material is the teapot material, set it to the target
scene.materials[-1].diffuse_reflectance = \
pyredner.Texture(torch.tensor([0.3, 0.2, 0.2], device = pyredner.get_device()))
scene.materials[-1].specular_reflectance = \
pyredner.Texture(torch.tensor([0.6, 0.6, 0.6], device = pyredner.get_device()))
scene.materials[-1].roughness = \
pyredner.Texture(torch.tensor([0.05], device = pyredner.get_device()))
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 2)
# Alias of the render function
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_teapot_reflectance/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_teapot_reflectance/target.png')
import pyredner
import numpy as np
import torch
# Optimize for a textured plane in a specular reflection
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Load the scene from a Mitsuba scene file
scene = pyredner.load_mitsuba('scenes/teapot_specular.xml')
# The last material is the teapot material, set it to a specular material
scene.materials[-1].diffuse_reflectance = \
pyredner.Texture(torch.tensor([0.15, 0.2, 0.15], device = pyredner.get_device()))
scene.materials[-1].specular_reflectance = \
pyredner.Texture(torch.tensor([0.8, 0.8, 0.8], device = pyredner.get_device()))
scene.materials[-1].roughness = \
pyredner.Texture(torch.tensor([0.0001], device = pyredner.get_device()))
args=pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 512,
max_bounces = 2)
render = pyredner.RenderFunction.apply
# Render our target. The first argument is the seed for RNG in the renderer.
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_teapot_specular/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_teapot_specular/target.png')
target = pyredner.imread('results/test_teapot_specular/target.exr')
if pyredner.get_use_gpu():