update render path

.gitignore

@@ -1 +1,2 @@
-log
+log
+run_commands

configs/big_scene/fangcang/all.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/fangcang/group1.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/fangcang/group2.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/fangcang/group3.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/fangcang/group4.txt

@@ -13,8 +13,9 @@ update_AlphaMask_list = [2000,4000]
 
 N_vis = 5
 vis_every = 10000
-
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/outdoor_r2_agi.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/skdfs/1.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

configs/big_scene/skdfs/3.txt

@@ -14,7 +14,9 @@ update_AlphaMask_list = [2000,4000]
 N_vis = 5
 vis_every = 10000
 
+render_train = 1
 render_test = 1
+render_path = 1
 
 n_lamb_sigma = [16,16,16]
 n_lamb_sh = [48,48,48]

dataLoader/big_scene.py

@@ -22,7 +22,10 @@ def __init__(self, datadir, split='train', downsample=1.0, is_stack=False, N_vis
 
  self.scene_bbox = torch.Tensor([[-4.0, -4.0, -4.0,], [4.0, 4.0, 4.0]])
  self.blender2opencv = np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
- self.read_meta()
+ if self.split == 'render':
+ self.read_meta_render()
+ else:
+ self.read_meta()
  self.define_proj_mat()
 
  self.white_bg = False
@@ -96,7 +99,48 @@ def read_meta(self):
  self.all_rays = torch.stack(self.all_rays, 0)
  self.all_rgbs = torch.stack(self.all_rgbs, 0).reshape(-1, *self.img_wh[::-1], 3) # (len(self.meta['frames]),h,w,3)
 
-
+ def read_meta_render(self):
+
+ with open(os.path.join(self.root_dir, "cam_tra", "camera_render.json"), 'r') as f:
+ self.meta = json.load(f)
+
+ w = int(self.meta['w'] / self.downsample)
+ h = int(self.meta['h'] / self.downsample)
+ self.img_wh = [w, h]
+ self.focal = self.meta['fl_x'] * (w / self.meta['w'])
+
+ # ray directions for all pixels, same for all images (same H, W, focal)
+ self.directions = get_ray_directions(h, w, [self.focal, self.focal]) # (h, w, 3)
+ self.directoins = self.directions / torch.norm(self.directions, dim = -1, keepdim=True)
+ self.intrinsics = torch.tensor([[self.focal,0,w/2],[0,self.focal,h/2],[0,0,1]]).float()
+
+ self.image_paths = []
+ self.poses = []
+ self.all_rays = []
+ self.all_rgbs = []
+ self.all_masks = []
+ self.all_depths = []
+
+ img_eval_interval = 1 if self.N_vis <0 else len(self.meta['frames']) // self.N_vis
+
+ idxs = list(range(0, len(self.meta['frames']), img_eval_interval))
+ for i in tqdm(idxs, desc=f'Loading data {self.split} ({len(idxs)})'): #img_list:#
+
+ frame = self.meta['frames'][i]
+ pose = np.array(frame['transform_matrix']) @ self.blender2opencv
+ c2w = torch.FloatTensor(pose)
+ self.poses +=[c2w]
+
+ rays_o, rays_d = get_rays(self.directions, c2w) # both (h*w, 3)
+ self.all_rays += [torch.cat([rays_o, rays_d], 1)] # (h*w, 6)
+
+ self.poses = torch.stack(self.poses)
+ if not self.is_stack:
+ self.all_rays = torch.cat(self.all_rays, 0) # (len(self.meta['frames']) * h * w, 3)
+
+ else:
+ self.all_rays = torch.stack(self.all_rays, 0)
+
  def define_proj_mat(self):
  self.proj_mat = self.intrinsics.unsqueeze(0) @ torch.inverse(self.poses)[:, :3]
 
@@ -113,6 +157,13 @@ def __getitem__(self, idx):
  sample = {'rays': self.all_rays[idx],
  'rgbs': self.all_rgbs[idx]}
 
+ #elif self.split == 'test':
+ # rays = self.all_rays[idx]
+#
+ # sample = {'rays': self.all_rays[idx],
+ # }
+
+
  else: # create data for each image separately
 
  img = self.all_rgbs[idx]

train.py

@@ -287,15 +287,19 @@ def reconstruction(args):
  os.makedirs(f'{logfolder}/imgs_test_all', exist_ok=True)
  PSNRs_test = evaluation(test_dataset,tensorf, args, renderer, f'{logfolder}/imgs_test_all/',
  N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
+ #render_dataset = dataset(args.datadir, split= 'render', downsample = 1.0, is_stack = True)
+ # PSNRs_test = evaluation(render_dataset,tensorf, args, renderer, f'{logfolder}/imgs_test_all/',
+ # N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
  summary_writer.add_scalar('test/psnr_all', np.mean(PSNRs_test), global_step=iteration)
  print(f'======> {args.expname} test all psnr: {np.mean(PSNRs_test)} <========================')
 
  if args.render_path:
- c2ws = test_dataset.render_path
- # c2ws = test_dataset.poses
+ render_dataset = dataset(args.datadir, split= 'render', downsample = 1.0, is_stack = True)
+ #c2ws = render_dataset.render_path
+ c2ws = render_dataset.poses
  print('========>',c2ws.shape)
  os.makedirs(f'{logfolder}/imgs_path_all', exist_ok=True)
- evaluation_path(test_dataset,tensorf, c2ws, renderer, f'{logfolder}/imgs_path_all/',
+ evaluation_path(render_dataset,tensorf, c2ws, renderer, f'{logfolder}/imgs_path_all/',
  N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)