Fix intrinsics problem

run_nerf.py

@@ -7,7 +7,6 @@ import time
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm, trange
 
 import matplotlib.pyplot as plt
@@ -17,6 +16,7 @@ from run_nerf_helpers import *
 from load_llff import load_llff_data
 from load_deepvoxels import load_dv_data
 from load_blender import load_blender_data
+from load_LINEMOD import load_LINEMOD_data
 
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -66,7 +66,7 @@ def batchify_rays(rays_flat, chunk=1024*32, **kwargs):
     return all_ret
 
 
-def render(H, W, focal, chunk=1024*32, rays=None, c2w=None, ndc=True,
+def render(H, W, K, chunk=1024*32, rays=None, c2w=None, ndc=True,
                   near=0., far=1.,
                   use_viewdirs=False, c2w_staticcam=None,
                   **kwargs):
@@ -94,7 +94,7 @@ def render(H, W, focal, chunk=1024*32, rays=None, c2w=None, ndc=True,
     """
     if c2w is not None:
         # special case to render full image
-        rays_o, rays_d = get_rays(H, W, focal, c2w)
+        rays_o, rays_d = get_rays(H, W, K, c2w)
     else:
         # use provided ray batch
         rays_o, rays_d = rays
@@ -104,14 +104,14 @@ def render(H, W, focal, chunk=1024*32, rays=None, c2w=None, ndc=True,
         viewdirs = rays_d
         if c2w_staticcam is not None:
             # special case to visualize effect of viewdirs
-            rays_o, rays_d = get_rays(H, W, focal, c2w_staticcam)
+            rays_o, rays_d = get_rays(H, W, K, c2w_staticcam)
         viewdirs = viewdirs / torch.norm(viewdirs, dim=-1, keepdim=True)
         viewdirs = torch.reshape(viewdirs, [-1,3]).float()
 
     sh = rays_d.shape # [..., 3]
     if ndc:
         # for forward facing scenes
-        rays_o, rays_d = ndc_rays(H, W, focal, 1., rays_o, rays_d)
+        rays_o, rays_d = ndc_rays(H, W, K[0][0], 1., rays_o, rays_d)
 
     # Create ray batch
     rays_o = torch.reshape(rays_o, [-1,3]).float()
@@ -134,7 +134,7 @@ def render(H, W, focal, chunk=1024*32, rays=None, c2w=None, ndc=True,
     return ret_list + [ret_dict]
 
 
-def render_path(render_poses, hwf, chunk, render_kwargs, gt_imgs=None, savedir=None, render_factor=0):
+def render_path(render_poses, hwf, K, chunk, render_kwargs, gt_imgs=None, savedir=None, render_factor=0):
 
     H, W, focal = hwf
 
@@ -151,7 +151,7 @@ def render_path(render_poses, hwf, chunk, render_kwargs, gt_imgs=None, savedir=N
     for i, c2w in enumerate(tqdm(render_poses)):
         print(i, time.time() - t)
         t = time.time()
-        rgb, disp, acc, _ = render(H, W, focal, chunk=chunk, c2w=c2w[:3,:4], **render_kwargs)
+        rgb, disp, acc, _ = render(H, W, K, chunk=chunk, c2w=c2w[:3,:4], **render_kwargs)
         rgbs.append(rgb.cpu().numpy())
         disps.append(disp.cpu().numpy())
         if i==0:
@@ -537,7 +537,7 @@ def train():
     args = parser.parse_args()
 
     # Load data
-
+    K = None
     if args.dataset_type == 'llff':
         images, poses, bds, render_poses, i_test = load_llff_data(args.datadir, args.factor,
                                                                   recenter=True, bd_factor=.75,
@@ -579,6 +579,17 @@ def train():
         else:
             images = images[...,:3]
 
+    elif args.dataset_type == 'LINEMOD':
+        images, poses, render_poses, hwf, K, i_split, near, far = load_LINEMOD_data(args.datadir, args.half_res, args.testskip)
+        print(f'Loaded LINEMOD, images shape: {images.shape}, hwf: {hwf}, K: {K}')
+        print(f'[CHECK HERE] near: {near}, far: {far}.')
+        i_train, i_val, i_test = i_split
+
+        if args.white_bkgd:
+            images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
+        else:
+            images = images[...,:3]
+
     elif args.dataset_type == 'deepvoxels':
 
         images, poses, render_poses, hwf, i_split = load_dv_data(scene=args.shape,
@@ -601,6 +612,13 @@ def train():
     H, W = int(H), int(W)
     hwf = [H, W, focal]
 
+    if K is None:
+        K = np.array([
+            [focal, 0, 0.5*W],
+            [0, focal, 0.5*H],
+            [0, 0, 1]
+        ])
+
     if args.render_test:
         render_poses = np.array(poses[i_test])
 
@@ -647,7 +665,7 @@ def train():
             os.makedirs(testsavedir, exist_ok=True)
             print('test poses shape', render_poses.shape)
 
-            rgbs, _ = render_path(render_poses, hwf, args.chunk, render_kwargs_test, gt_imgs=images, savedir=testsavedir, render_factor=args.render_factor)
+            rgbs, _ = render_path(render_poses, hwf, K, args.chunk, render_kwargs_test, gt_imgs=images, savedir=testsavedir, render_factor=args.render_factor)
             print('Done rendering', testsavedir)
             imageio.mimwrite(os.path.join(testsavedir, 'video.mp4'), to8b(rgbs), fps=30, quality=8)
 
@@ -659,7 +677,7 @@ def train():
     if use_batching:
         # For random ray batching
         print('get rays')
-        rays = np.stack([get_rays_np(H, W, focal, p) for p in poses[:,:3,:4]], 0) # [N, ro+rd, H, W, 3]
+        rays = np.stack([get_rays_np(H, W, K, p) for p in poses[:,:3,:4]], 0) # [N, ro+rd, H, W, 3]
         print('done, concats')
         rays_rgb = np.concatenate([rays, images[:,None]], 1) # [N, ro+rd+rgb, H, W, 3]
         rays_rgb = np.transpose(rays_rgb, [0,2,3,1,4]) # [N, H, W, ro+rd+rgb, 3]
@@ -673,6 +691,7 @@ def train():
         i_batch = 0
 
     # Move training data to GPU
+    if use_batching:
         images = torch.Tensor(images).to(device)
     poses = torch.Tensor(poses).to(device)
     if use_batching:
@@ -710,10 +729,11 @@ def train():
             # Random from one image
             img_i = np.random.choice(i_train)
             target = images[img_i]
+            target = torch.Tensor(target).to(device)
             pose = poses[img_i, :3,:4]
 
             if N_rand is not None:
-                rays_o, rays_d = get_rays(H, W, focal, torch.Tensor(pose))  # (H, W, 3), (H, W, 3)
+                rays_o, rays_d = get_rays(H, W, K, torch.Tensor(pose))  # (H, W, 3), (H, W, 3)
 
                 if i < args.precrop_iters:
                     dH = int(H//2 * args.precrop_frac)
@@ -737,7 +757,7 @@ def train():
                 target_s = target[select_coords[:, 0], select_coords[:, 1]]  # (N_rand, 3)
 
         #####  Core optimization loop  #####
-        rgb, disp, acc, extras = render(H, W, focal, chunk=args.chunk, rays=batch_rays,
+        rgb, disp, acc, extras = render(H, W, K, chunk=args.chunk, rays=batch_rays,
                                                 verbose=i < 10, retraw=True,
                                                 **render_kwargs_train)
 
@@ -782,7 +802,7 @@ def train():
         if i%args.i_video==0 and i > 0:
             # Turn on testing mode
             with torch.no_grad():
-                rgbs, disps = render_path(render_poses, hwf, args.chunk, render_kwargs_test)
+                rgbs, disps = render_path(render_poses, hwf, K, args.chunk, render_kwargs_test)
             print('Done, saving', rgbs.shape, disps.shape)
             moviebase = os.path.join(basedir, expname, '{}_spiral_{:06d}_'.format(expname, i))
             imageio.mimwrite(moviebase + 'rgb.mp4', to8b(rgbs), fps=30, quality=8)
@@ -800,7 +820,7 @@ def train():
             os.makedirs(testsavedir, exist_ok=True)
             print('test poses shape', poses[i_test].shape)
             with torch.no_grad():
-                render_path(torch.Tensor(poses[i_test]).to(device), hwf, args.chunk, render_kwargs_test, gt_imgs=images[i_test], savedir=testsavedir)
+                render_path(torch.Tensor(poses[i_test]).to(device), hwf, K, args.chunk, render_kwargs_test, gt_imgs=images[i_test], savedir=testsavedir)
             print('Saved test set')
 
 

run_nerf_helpers.py

@@ -153,11 +153,11 @@ class NeRF(nn.Module):
 
 
 # Ray helpers
-def get_rays(H, W, focal, c2w):
+def get_rays(H, W, K, c2w):
     i, j = torch.meshgrid(torch.linspace(0, W-1, W), torch.linspace(0, H-1, H))  # pytorch's meshgrid has indexing='ij'
     i = i.t()
     j = j.t()
-    dirs = torch.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -torch.ones_like(i)], -1)
+    dirs = torch.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -torch.ones_like(i)], -1)
     # Rotate ray directions from camera frame to the world frame
     rays_d = torch.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)  # dot product, equals to: [c2w.dot(dir) for dir in dirs]
     # Translate camera frame's origin to the world frame. It is the origin of all rays.
@@ -165,9 +165,9 @@ def get_rays(H, W, focal, c2w):
     return rays_o, rays_d
 
 
-def get_rays_np(H, W, focal, c2w):
+def get_rays_np(H, W, K, c2w):
     i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')
-    dirs = np.stack([(i-W*.5)/focal, -(j-H*.5)/focal, -np.ones_like(i)], -1)
+    dirs = np.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -np.ones_like(i)], -1)
     # Rotate ray directions from camera frame to the world frame
     rays_d = np.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)  # dot product, equals to: [c2w.dot(dir) for dir in dirs]
     # Translate camera frame's origin to the world frame. It is the origin of all rays.