clean

dataLoader/nsvf.py

@@ -44,7 +44,7 @@ def __init__(self, datadir, split='train', downsample=1.0, wh=[800,800], is_stac
  self.define_transforms()
 
  self.white_bg = True
- self.near_far = [0.5,5.0]
+ self.near_far = [0.5,6.0]
  self.scene_bbox = torch.from_numpy(np.loadtxt(f'{self.root_dir}/bbox.txt')).float()[:6].view(2,3)
  self.blender2opencv = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
  self.read_meta()
@@ -111,15 +111,10 @@ def read_meta(self):
  c2w = torch.FloatTensor(c2w)
  self.poses.append(c2w) # C2W
  rays_o, rays_d = get_rays(self.directions, c2w) # both (h*w, 3)
- # vdirs = rays_d / torch.norm(rays_d, dim=-1, keepdim=True)
- # t_minmax = aabb(rays_o, vdirs, self.scene_bbox)
  self.all_rays += [torch.cat([rays_o, rays_d], 1)] # (h*w, 8)
 
 # w2c = torch.inverse(c2w)
-# depth = (w2c[:3,:3] @corners.t() + w2c[:3,3:])[2]
-# near_far = [max(0.0, torch.min(depth)), torch.max(depth)]
-# print(near_far)
-
+#
 
  self.poses = torch.stack(self.poses)
  if 'train' == self.split:

dataLoader/tankstemple.py

@@ -160,16 +160,13 @@ def read_meta(self):
  c2w = torch.FloatTensor(c2w)
  self.poses.append(c2w) # C2W
  rays_o, rays_d = get_rays(self.directions, c2w) # both (h*w, 3)
- # vdirs = rays_d / torch.norm(rays_d, dim=-1, keepdim=True)
- # t_minmax = aabb(rays_o, vdirs, self.scene_bbox)
  self.all_rays += [torch.cat([rays_o, rays_d], 1)] # (h*w, 8)
 
  self.poses = torch.stack(self.poses)
 
  center = torch.mean(self.scene_bbox, dim=0)
  radius = torch.norm(self.scene_bbox[1]-center)*1.2
  up = torch.mean(self.poses[:, :3, 1], dim=0).tolist()
- # print(center,radius,up,-0.2*up[1])
  pos_gen = circle(radius=radius, h=-0.2*up[1], axis='y')
  self.render_path = gen_path(pos_gen, up=up,frames=200)
  self.render_path[:, :3, 3] += center

models/tensoRF.py

@@ -217,13 +217,6 @@ def compute_densityfeature(self, xyz_sampled):
  align_corners=True).view(-1, *xyz_sampled.shape[:1])
  sigma_feature = sigma_feature + torch.sum(plane_coef_point * line_coef_point, dim=0)
 
- # if 0==idx_plane:
- # sigma_feature = sigma_feature + torch.sum((plane_coef_point * line_coef_point)[[10, 0, 6, 4]], dim=0)
- # elif 1==idx_plane:
- # sigma_feature = sigma_feature + torch.sum((plane_coef_point * line_coef_point)[[7, 6, 5, 8]], dim=0)
- # else:
- # sigma_feature = sigma_feature + torch.sum((plane_coef_point * line_coef_point)[[15, 12, 3, 9]], dim=0)
-
  return sigma_feature
 
 
@@ -312,8 +305,6 @@ def shrink(self, new_aabb):
 
 
 
-
-
 class TensorCP(TensorBase):
  def __init__(self, aabb, gridSize, device, **kargs):
  super(TensorCP, self).__init__(aabb, gridSize, device, **kargs)
@@ -354,10 +345,6 @@ def compute_densityfeature(self, xyz_sampled):
  align_corners=True).view(-1, *xyz_sampled.shape[:1])
  line_coef_point = line_coef_point * F.grid_sample(self.density_line[2], coordinate_line[[2]],
  align_corners=True).view(-1, *xyz_sampled.shape[:1])
-
- # line_feats = F.grid_sample(self.line_coef[:, -self.density_n_comp:], coordinate_line, align_corners=True).view(
- # 3, self.density_n_comp, *xyz_sampled.shape[:1])
-
  sigma_feature = torch.sum(line_coef_point, dim=0)
 
 

models/tensorBase.py

@@ -272,7 +272,6 @@ def sample_ray_ndc(self, rays_o, rays_d, is_train=True, N_samples=-1):
 
  rays_pts = rays_o[..., None, :] + rays_d[..., None, :] * interpx[..., None]
  mask_outbbox = ((self.aabb[0] > rays_pts) | (rays_pts > self.aabb[1])).any(dim=-1)
- # print(rays_pts.shape, interpx.shape, mask_outbbox.shape, N_samples)
  return rays_pts, interpx, ~mask_outbbox
 
  def sample_ray(self, rays_o, rays_d, is_train=True, N_samples=-1):
@@ -305,16 +304,13 @@ def updateAlphaMask(self, gridSize=(200,200,200)):
 
  total_voxels = gridSize[0] * gridSize[1] * gridSize[2]
 
- # voxel_size = self.aabbSize / (torch.tensor(gridSize).to(self.device)-1)
- # voxel_norm = torch.norm(voxel_size)
-
  samples = torch.stack(torch.meshgrid(
  torch.linspace(0, 1, gridSize[0]),
  torch.linspace(0, 1, gridSize[1]),
  torch.linspace(0, 1, gridSize[2]),
  ), -1).to(self.device)
  dense_xyz = self.aabb[0] * (1-samples) + self.aabb[1] * samples
- # dense_xyz = dense_xyz.view(-1,3)
+
  dense_xyz = dense_xyz.transpose(0,2).contiguous()
  alpha = torch.zeros_like(dense_xyz[...,0])
  for i in range(gridSize[2]):
@@ -397,8 +393,7 @@ def compute_alpha(self, xyz_locs, length=1):
  validsigma = self.feature2density(sigma_feature)
  sigma[alpha_mask] = validsigma
 
- # density = F.grid_sample(grid_rgba.density_volume, dense_xyz.view(1,-1,1,1,3)).view(-1) *4/350*25
- # print(density.shape)
+
  alpha = 1 - torch.exp(-sigma*length).view(xyz_locs.shape[:-1])
 
  return alpha

renderer.py

@@ -143,36 +143,3 @@ def evaluation_path(test_dataset,tensorf, c2ws, renderer, savePath=None, N_vis=5
 
  return PSNRs
 
-def rgba_evaluation(test_dataset, grid_rgba, world2ndc, args, savePath=None, N_vis=5, prtx='', white_bg=False, N_samples=256):
- PSNRs, rgb_maps = [], []
- with torch.no_grad():
- try:
- tqdm._instances.clear()
- except Exception:
- pass
-
- img_eval_interval = 1 if N_vis < 0 else test_dataset.all_rays.shape[0] // N_vis
- idxs = list(range(0, test_dataset.all_rays.shape[0], img_eval_interval))
- for idx, samples in tqdm(enumerate(test_dataset.all_rays[0::img_eval_interval])):
-
- W, H = test_dataset.img_wh
- rays = samples.view(-1, 8)
-
- rgb_map, _, depth_map, _, _ = grid_rgba.render(rays, world2ndc, chunk=8192, lindisp=args.lindisp, perturb=0.0, white_bg = white_bg, N_samples=N_samples)
- rgb_map = rgb_map.clamp(0.0, 1.0)
-
- rgb_map, depth_map = rgb_map.reshape(H, W, 3).cpu(), depth_map.reshape(H, W).cpu()
-
- depth_map, _ = visualize_depth_numpy(depth_map.numpy())
- if len(test_dataset.all_rgbs):
- loss = torch.mean((rgb_map - test_dataset.all_rgbs[idxs[idx]].view(H, W, 3)) ** 2)
- PSNRs.append(-10.0 * np.log(loss.item()) / np.log(10.0))
-
- rgb_map = (rgb_map.numpy() * 255).astype('uint8')
- rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
- rgb_maps.append(rgb_map)
- if savePath is not None:
- imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
-
- imageio.mimwrite(f'{savePath}/{prtx}_video.mp4', np.stack(rgb_maps), fps=30, quality=10)
- return PSNRs

train.py

@@ -291,9 +291,6 @@ def reconstruction(args):
  torch.set_default_dtype(torch.float32)
  torch.manual_seed(20121202)
  np.random.seed(20121202)
- # torch.cuda.manual_seed(20121202)
- # random.seed(20121202)
- # torch.backends.cudnn.deterministic = True
 
  args = config_parser()
  print(args)