def run_mitsuba(min_elev, max_elev, min_azim, max_azim, radius):
for k in kinds:
for i, elev in enumerate(np.linspace(min_elev, max_elev, PER)):
for j, azim in enumerate(np.linspace(min_azim, max_azim, PER)):
ox, oy, oz = elaz_to_xyz(elev, azim, radius)
lx, ly, lz = elaz_to_xyz(elev, azim, 1.05 * radius)
scene = get_scene(ox, oy, oz, lx, ly, lz, k)
out = render_torch(scene, spp=32)
for _ in range(N-1):
out += render_torch(scene, spp=32)
out /= N
out = torch.cat([
out[..., :3],
out[..., 3].unsqueeze(-1) > 0,
], dim=-1)
save_image(f"{k}_{i:03}_{j:03}.png", out)
Thread.thread().file_resolver().append('cbox')
scene = load_file('cbox/cbox.xml')
# Find differentiable scene parameters
params = traverse(scene)
# Discard all parameters except for one we want to differentiate
params.keep(['red.reflectance.value'])
# Print the current value and keep a backup copy
param_ref = params['red.reflectance.value'].torch()
print(param_ref)
# Render a reference image (no derivatives used yet)
image_ref = render_torch(scene, spp=8)
crop_size = scene.sensors()[0].film().crop_size()
write_bitmap('out_ref.png', image_ref, crop_size)
# Change the left wall into a bright white surface
params['red.reflectance.value'] = [.9, .9, .9]
params.update()
# Which parameters should be exposed to the PyTorch optimizer?
params_torch = params.torch()
# Construct a PyTorch Adam optimizer that will adjust 'params_torch'
opt = torch.optim.Adam(params_torch.values(), lr=.2)
objective = torch.nn.MSELoss()
time_a = time.time()
sensors = " ".join([sensor.format(cam_origin=o) for o in cams_origins])
scene = scene.format(sensors=sensors,
shape=sdf if sdf_file else mesh,
integrator=integrator)
return load_string(scene,
sdf_file=sdf_file,
interpol=interpolation,
fov=fov,
res=cam_res)
scene_target = create_scene(None, interpolation, cams_origins, fov, img_res)
images_ref = list(
render_torch(scene_target, spp=spp_ref, sensor_index=i)
for i in range(len(cams_origins)))
crop_size = scene_target.sensors()[0].film().crop_size()
for i in range(len(cams_origins)):
write_bitmap(f'{out_path}{i}_target.png', images_ref[i], crop_size)
# Find differentiable scene parameters
sdf_file = "data/sdf/init.vol" if restart_epoch == 0 else f"{out_path}sdf_e{restart_epoch}.vol"
scene_init = create_scene(sdf_file, interpolation, cams_origins, fov, img_res)
params = traverse(scene_init)
#print(params)
params.keep(['SDF.data', 'SDF.bsdf.reflectance.data'])
if restart_epoch > 0 and increase_res:
sdf = params['SDF.data'].numpy().reshape([sdf_res] * 3)
sdf = double_sdf_res(sdf)
scene = scene.format(sensors=sensors,
shape=sdf if sdf_file else mesh,
integrator=integrator)
return load_string(scene,
sdf_file=sdf_file,
interpol=interpolation,
n_cams=n_cams,
fov=fov,
res=cam_res)
scene_target = create_scene(None, interpolation, n_cams, fov, img_res)
images_ref = list(
render_torch(scene_target, spp=64, sensor_index=i) for i in range(n_cams))
crop_size = scene_target.sensors()[0].film().crop_size()
for i in range(n_cams):
write_bitmap(f'{out_path}{i}_target.png', images_ref[i], crop_size)
# Find differentiable scene parameters
sdf_file = "data/sdf/init.vol" if restart_epoch == 0 else f"{out_path}sdf_e{restart_epoch}.vol"
scene_init = create_scene(sdf_file, interpolation, n_cams, fov, img_res)
params = traverse(scene_init)
if restart_epoch > 0 and increase_res:
params = traverse(scene_init)
sdf = params['SDF.data'].numpy().reshape([sdf_res] * 3)
sdf = double_sdf_res(sdf)
sdf_res = sdf.shape[0]
sdf_file = f'{out_path}sdf_e{restart_epoch}_x2.vol'