You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
111 lines
3.8 KiB
Python
111 lines
3.8 KiB
Python
import os
|
|
import numpy as np
|
|
import imageio
|
|
|
|
|
|
def load_dv_data(scene='cube', basedir='/data/deepvoxels', testskip=8):
|
|
|
|
|
|
def parse_intrinsics(filepath, trgt_sidelength, invert_y=False):
|
|
# Get camera intrinsics
|
|
with open(filepath, 'r') as file:
|
|
f, cx, cy = list(map(float, file.readline().split()))[:3]
|
|
grid_barycenter = np.array(list(map(float, file.readline().split())))
|
|
near_plane = float(file.readline())
|
|
scale = float(file.readline())
|
|
height, width = map(float, file.readline().split())
|
|
|
|
try:
|
|
world2cam_poses = int(file.readline())
|
|
except ValueError:
|
|
world2cam_poses = None
|
|
|
|
if world2cam_poses is None:
|
|
world2cam_poses = False
|
|
|
|
world2cam_poses = bool(world2cam_poses)
|
|
|
|
print(cx,cy,f,height,width)
|
|
|
|
cx = cx / width * trgt_sidelength
|
|
cy = cy / height * trgt_sidelength
|
|
f = trgt_sidelength / height * f
|
|
|
|
fx = f
|
|
if invert_y:
|
|
fy = -f
|
|
else:
|
|
fy = f
|
|
|
|
# Build the intrinsic matrices
|
|
full_intrinsic = np.array([[fx, 0., cx, 0.],
|
|
[0., fy, cy, 0],
|
|
[0., 0, 1, 0],
|
|
[0, 0, 0, 1]])
|
|
|
|
return full_intrinsic, grid_barycenter, scale, near_plane, world2cam_poses
|
|
|
|
|
|
def load_pose(filename):
|
|
assert os.path.isfile(filename)
|
|
nums = open(filename).read().split()
|
|
return np.array([float(x) for x in nums]).reshape([4,4]).astype(np.float32)
|
|
|
|
|
|
H = 512
|
|
W = 512
|
|
deepvoxels_base = '{}/train/{}/'.format(basedir, scene)
|
|
|
|
full_intrinsic, grid_barycenter, scale, near_plane, world2cam_poses = parse_intrinsics(os.path.join(deepvoxels_base, 'intrinsics.txt'), H)
|
|
print(full_intrinsic, grid_barycenter, scale, near_plane, world2cam_poses)
|
|
focal = full_intrinsic[0,0]
|
|
print(H, W, focal)
|
|
|
|
|
|
def dir2poses(posedir):
|
|
poses = np.stack([load_pose(os.path.join(posedir, f)) for f in sorted(os.listdir(posedir)) if f.endswith('txt')], 0)
|
|
transf = np.array([
|
|
[1,0,0,0],
|
|
[0,-1,0,0],
|
|
[0,0,-1,0],
|
|
[0,0,0,1.],
|
|
])
|
|
poses = poses @ transf
|
|
poses = poses[:,:3,:4].astype(np.float32)
|
|
return poses
|
|
|
|
posedir = os.path.join(deepvoxels_base, 'pose')
|
|
poses = dir2poses(posedir)
|
|
testposes = dir2poses('{}/test/{}/pose'.format(basedir, scene))
|
|
testposes = testposes[::testskip]
|
|
valposes = dir2poses('{}/validation/{}/pose'.format(basedir, scene))
|
|
valposes = valposes[::testskip]
|
|
|
|
imgfiles = [f for f in sorted(os.listdir(os.path.join(deepvoxels_base, 'rgb'))) if f.endswith('png')]
|
|
imgs = np.stack([imageio.imread(os.path.join(deepvoxels_base, 'rgb', f))/255. for f in imgfiles], 0).astype(np.float32)
|
|
|
|
|
|
testimgd = '{}/test/{}/rgb'.format(basedir, scene)
|
|
imgfiles = [f for f in sorted(os.listdir(testimgd)) if f.endswith('png')]
|
|
testimgs = np.stack([imageio.imread(os.path.join(testimgd, f))/255. for f in imgfiles[::testskip]], 0).astype(np.float32)
|
|
|
|
valimgd = '{}/validation/{}/rgb'.format(basedir, scene)
|
|
imgfiles = [f for f in sorted(os.listdir(valimgd)) if f.endswith('png')]
|
|
valimgs = np.stack([imageio.imread(os.path.join(valimgd, f))/255. for f in imgfiles[::testskip]], 0).astype(np.float32)
|
|
|
|
all_imgs = [imgs, valimgs, testimgs]
|
|
counts = [0] + [x.shape[0] for x in all_imgs]
|
|
counts = np.cumsum(counts)
|
|
i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]
|
|
|
|
imgs = np.concatenate(all_imgs, 0)
|
|
poses = np.concatenate([poses, valposes, testposes], 0)
|
|
|
|
render_poses = testposes
|
|
|
|
print(poses.shape, imgs.shape)
|
|
|
|
return imgs, poses, render_poses, [H,W,focal], i_split
|
|
|
|
|