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import numpy as np
from .load_llff import load_llff_data
from .load_blender import load_blender_data
from .load_nsvf import load_nsvf_data
from .load_blendedmvs import load_blendedmvs_data
from .load_tankstemple import load_tankstemple_data
from .load_deepvoxels import load_dv_data
from .load_co3d import load_co3d_data
from .load_nerfpp import load_nerfpp_data
from .load_replica import load_replica_data
from .load_lerf import load_lerf_data
def load_data(args):
K, depths = None, None
near_clip = None
if args.dataset_type == 'llff':
images, depths, poses, bds, render_poses, i_test = load_llff_data(
args.datadir, args.factor, args.width, args.height,
recenter=True, bd_factor=args.bd_factor,
spherify=args.spherify,
load_depths=args.load_depths,
movie_render_kwargs=args.movie_render_kwargs, args=args)
hwf = poses[0,:3,-1]
poses = poses[:,:3,:4]
print('Loaded llff', images.shape, render_poses.shape, hwf, args.datadir)
if not isinstance(i_test, list):
i_test = [i_test]
if args.llffhold > 0:
print('Auto LLFF holdout,', args.llffhold)
i_test = np.arange(images.shape[0])[::args.llffhold]
# i_test = [1, 2]
# i_test = []
i_val = i_test
i_train = np.array([i for i in np.arange(int(images.shape[0])) if
(i not in i_test and i not in i_val)])
print('DEFINING BOUNDS')
if args.ndc:
near = 0.
far = 1.
else:
near_clip = max(np.ndarray.min(bds) * .9, 0)
_far = max(np.ndarray.max(bds) * 1., 0)
near = 0
far = inward_nearfar_heuristic(poses[i_train, :3, 3])[1]
print('near_clip', near_clip)
print('original far', _far)
print('NEAR FAR', near, far)
if depths == 0:
depths = np.zeros_like(images[..., :1])
elif args.dataset_type == 'blender':
images, poses, render_poses, hwf, i_split = load_blender_data(args.datadir, args.half_res, args.testskip, args=args)
print('Loaded blender', images.shape, render_poses.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
near, far = 2., 6.
if images.shape[-1] == 4:
if args.white_bkgd:
images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
else:
images = images[...,:3]*images[...,-1:]
elif args.dataset_type == 'blendedmvs':
images, poses, render_poses, hwf, K, i_split = load_blendedmvs_data(args.datadir)
print('Loaded blendedmvs', images.shape, render_poses.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
near, far = inward_nearfar_heuristic(poses[i_train, :3, 3])
assert images.shape[-1] == 3
elif args.dataset_type == 'tankstemple':
images, poses, render_poses, hwf, K, i_split = load_tankstemple_data(
args.datadir, movie_render_kwargs=args.movie_render_kwargs)
print('Loaded tankstemple', images.shape, render_poses.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
# i_test = [0]
near, far = inward_nearfar_heuristic(poses[i_train, :3, 3], ratio=0)
near_clip = near
if images.shape[-1] == 4:
if args.white_bkgd:
images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
else:
images = images[...,:3]*images[...,-1:]
elif args.dataset_type == 'nsvf':
images, poses, render_poses, hwf, i_split = load_nsvf_data(args.datadir)
print('Loaded nsvf', images.shape, render_poses.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
near, far = inward_nearfar_heuristic(poses[i_train, :3, 3])
near_clip = near
if images.shape[-1] == 4:
if args.white_bkgd:
images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
else:
images = images[...,:3]*images[...,-1:]
elif args.dataset_type == 'deepvoxels':
images, poses, render_poses, hwf, i_split = load_dv_data(scene=args.scene, basedir=args.datadir, testskip=args.testskip)
print('Loaded deepvoxels', images.shape, render_poses.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
hemi_R = np.mean(np.linalg.norm(poses[:,:3,-1], axis=-1))
near = hemi_R - 1
far = hemi_R + 1
assert args.white_bkgd
assert images.shape[-1] == 3
elif args.dataset_type == 'co3d':
# each image can be in different shapes and intrinsics
images, masks, poses, render_poses, hwf, K, i_split = load_co3d_data(args)
print('Loaded co3d', args.datadir, args.annot_path, args.sequence_name)
i_train, i_val, i_test = i_split
near, far = inward_nearfar_heuristic(poses[i_train, :3, 3], ratio=0)
for i in range(len(images)):
if args.white_bkgd:
images[i] = images[i] * masks[i][...,None] + (1.-masks[i][...,None])
else:
images[i] = images[i] * masks[i][...,None]
elif args.dataset_type == 'nerfpp':
images, poses, render_poses, hwf, K, i_split = load_nerfpp_data(args.datadir)
print('Loaded nerf_pp', images.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
near_clip, far = inward_nearfar_heuristic(poses[i_train, :3, 3], ratio=0.02)
near = 0
elif args.dataset_type == 'replica':
images, poses, render_poses, hwf, i_split = load_replica_data(args.datadir, args.half_res, args.testskip, args=args, \
spherify=args.spherify,movie_render_kwargs=args.movie_render_kwargs)
print('Loaded replica', images.shape, render_poses.shape, hwf, args.datadir)
i_train, i_val, i_test = i_split
near, far = inward_nearfar_heuristic(poses[i_train, :3, 3], ratio=0)
near_clip = near
print('NEAR FAR', near, far)
if images.shape[-1] == 4:
if args.white_bkgd:
images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
else:
images = images[...,:3]*images[...,-1:]
elif args.dataset_type == 'lerf':
images, poses, render_poses, hwf, K, i_split = load_lerf_data(args.datadir, args.factor,movie_render_kwargs=args.movie_render_kwargs)
print('Loaded lerf', images.shape, render_poses.shape, hwf[:2], args.datadir)
i_train, i_val, i_test = i_split
near, far = inward_nearfar_heuristic(poses[i_train, :3, 3], ratio=0)
near_clip = near
print('NEAR FAR', near, far)
if images.shape[-1] == 4:
if args.white_bkgd:
images = images[...,:3]*images[...,-1:] + (1.-images[...,-1:])
else:
images = images[...,:3]*images[...,-1:]
else:
raise NotImplementedError(f'Unknown dataset type {args.dataset_type} exiting')
# Cast intrinsics to right types
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
HW = np.array([im.shape[:2] for im in images])
irregular_shape = (images.dtype is np.dtype('object'))
if K is None:
K = np.array([
[focal, 0, 0.5*W],
[0, focal, 0.5*H],
[0, 0, 1]
])
if len(K.shape) == 2:
Ks = K[None].repeat(len(poses), axis=0)
else:
Ks = K
render_poses = render_poses[...,:4]
data_dict = dict(
hwf=hwf, HW=HW, Ks=Ks,
near=near, far=far, near_clip=near_clip,
i_train=i_train, i_val=i_val, i_test=i_test,
poses=poses, render_poses=render_poses,
images=images, depths=depths,
irregular_shape=irregular_shape
)
return data_dict
def inward_nearfar_heuristic(cam_o, ratio=0.05):
dist = np.linalg.norm(cam_o[:,None] - cam_o, axis=-1)
far = dist.max() # could be too small to exist the scene bbox
# it is only used to determined scene bbox
# lib/dvgo use 1e9 as far
near = far * ratio
return near, far
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