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, 0.0, cx, 0.0], [0.0, fy, cy, 0], [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, 1.0],

]

)

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)) / 255.0 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)) / 255.0 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)) / 255.0 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)