mask = (xyz[0,:]<0) | (xyz[1,:]<0) | (xyz[2,:]<0) | (xyz[0,:]>x_lim) | (xyz[1,:]>y_lim) | (xyz[2,:]>z_lim)
xyz[:,mask] = 0
sigma = self.img[tuple(xyz)]
# Anything out of bounds set back to 0
sigma[mask] = 0.0
sigma = sigma.reshape(1, -1, 1)
rgb = torch.ones(sigma.size(0), sigma.size(1), 3).to(device)
return torch.cat((rgb, sigma), dim=-1).to(device)
image = CTImage(torch.tensor(arr).to(device))
renderer = NeRFRenderer(
n_coarse=64, n_fine=32, n_fine_depth=16, depth_std=0.01, sched=[], white_bkgd=False, eval_batch_size=50000
).to(device=device)
render_par = renderer.bind_parallel(image, [0], simple_output=True).eval()
render_rays = util.gen_rays(render_poses, W, H, focal, z_near, z_far).to(device=device)
all_rgb_fine = []
for rays in tqdm(torch.split(render_rays.view(-1, 8), 80000, dim=0)):
rgb, _depth = render_par(rays[None])
all_rgb_fine.append(rgb[0])
_depth = None
rgb_fine = torch.cat(all_rgb_fine)
frames = (rgb_fine.view(num_views, H, W, 3).cpu().numpy() * 255).astype(
np.uint8
)
im_name = "raw_data"
focal = torch.tensor(focal, dtype=torch.float32, device=device)
# Render training data or load in if already rendered
# if os.path.exists(os.path.join(output, f'training_ct_{H}.pkl')):
# ct_gt = torch.load(os.path.join(output, f'training_ct_{H}.pkl'))
# else:
image = CTImage(torch.tensor(arr).to(device))
renderer = NeRFRenderer(n_coarse=512,
depth_std=0.01,
sched=[],
white_bkgd=False,
composite_x_ray=False,
eval_batch_size=50000,
lindisp=True).to(device=device)
render_par = renderer.bind_parallel(image, [0], simple_output=True).eval()
render_rays = util.gen_rays_variable_sensor(render_poses, width_pixels,
height_pixels, width, height,
focal, z_near, z_far).to(device)
all_rgb_fine = []
for rays in tqdm(torch.split(render_rays.view(-1, 8), 80000, dim=0)):
rgb, _depth = render_par(rays[None])
all_rgb_fine.append(rgb[0])
_depth = None
rgb_fine = torch.cat(all_rgb_fine)
# rgb_fine = torch.clamp(1 - rgb_fine, 0, 1)
ct_gt_min = rgb_fine.min()
ct_gt_max = rgb_fine.max()