# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Code for camera paths.
"""
from typing import Any, Dict, Optional, Tuple
import torch
import nerfstudio.utils.poses as pose_utils
from nerfstudio.cameras import camera_utils
from nerfstudio.cameras.camera_utils import get_interpolated_poses_many
from nerfstudio.cameras.cameras import Cameras, CameraType
from nerfstudio.viewer_legacy.server.utils import three_js_perspective_camera_focal_length
[docs]def get_interpolated_camera_path(cameras: Cameras, steps: int, order_poses: bool) -> Cameras:
"""Generate a camera path between two cameras. Uses the camera type of the first camera
Args:
cameras: Cameras object containing intrinsics of all cameras.
steps: The number of steps to interpolate between the two cameras.
Returns:
A new set of cameras along a path.
"""
Ks = cameras.get_intrinsics_matrices()
poses = cameras.camera_to_worlds
poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps, order_poses=order_poses)
cameras = Cameras(
fx=Ks[:, 0, 0],
fy=Ks[:, 1, 1],
cx=Ks[0, 0, 2],
cy=Ks[0, 1, 2],
camera_type=cameras.camera_type[0],
camera_to_worlds=poses,
)
return cameras
[docs]def get_spiral_path(
camera: Cameras,
steps: int = 30,
radius: Optional[float] = None,
radiuses: Optional[Tuple[float]] = None,
rots: int = 2,
zrate: float = 0.5,
) -> Cameras:
"""
Returns a list of camera in a spiral trajectory.
Args:
camera: The camera to start the spiral from.
steps: The number of cameras in the generated path.
radius: The radius of the spiral for all xyz directions.
radiuses: The list of radii for the spiral in xyz directions.
rots: The number of rotations to apply to the camera.
zrate: How much to change the z position of the camera.
Returns:
A spiral camera path.
"""
assert radius is not None or radiuses is not None, "Either radius or radiuses must be specified."
assert camera.ndim == 1, "We assume only one batch dim here"
if radius is not None and radiuses is None:
rad = torch.tensor([radius] * 3, device=camera.device)
elif radiuses is not None and radius is None:
rad = torch.tensor(radiuses, device=camera.device)
else:
raise ValueError("Only one of radius or radiuses must be specified.")
up = camera.camera_to_worlds[0, :3, 2] # scene is z up
focal = torch.min(camera.fx[0], camera.fy[0])
target = torch.tensor([0, 0, -focal], device=camera.device) # camera looking in -z direction
c2w = camera.camera_to_worlds[0]
c2wh_global = pose_utils.to4x4(c2w)
local_c2whs = []
for theta in torch.linspace(0.0, 2.0 * torch.pi * rots, steps + 1)[:-1]:
center = (
torch.tensor([torch.cos(theta), -torch.sin(theta), -torch.sin(theta * zrate)], device=camera.device) * rad
)
lookat = center - target
c2w = camera_utils.viewmatrix(lookat, up, center)
c2wh = pose_utils.to4x4(c2w)
local_c2whs.append(c2wh)
new_c2ws = []
for local_c2wh in local_c2whs:
c2wh = torch.matmul(c2wh_global, local_c2wh)
new_c2ws.append(c2wh[:3, :4])
new_c2ws = torch.stack(new_c2ws, dim=0)
times = None
if camera.times is not None:
times = torch.linspace(0, 1, steps)[:, None]
return Cameras(
fx=camera.fx[0],
fy=camera.fy[0],
cx=camera.cx[0],
cy=camera.cy[0],
camera_to_worlds=new_c2ws,
times=times,
)
[docs]def get_path_from_json(camera_path: Dict[str, Any]) -> Cameras:
"""Takes a camera path dictionary and returns a trajectory as a Camera instance.
Args:
camera_path: A dictionary of the camera path information coming from the viewer.
Returns:
A Cameras instance with the camera path.
"""
image_height = camera_path["render_height"]
image_width = camera_path["render_width"]
if "camera_type" not in camera_path:
camera_type = CameraType.PERSPECTIVE
elif camera_path["camera_type"] == "fisheye":
camera_type = CameraType.FISHEYE
elif camera_path["camera_type"] == "equirectangular":
camera_type = CameraType.EQUIRECTANGULAR
elif camera_path["camera_type"].lower() == "omnidirectional":
camera_type = CameraType.OMNIDIRECTIONALSTEREO_L
elif camera_path["camera_type"].lower() == "vr180":
camera_type = CameraType.VR180_L
else:
camera_type = CameraType.PERSPECTIVE
c2ws = []
fxs = []
fys = []
for camera in camera_path["camera_path"]:
# pose
c2w = torch.tensor(camera["camera_to_world"]).view(4, 4)[:3]
c2ws.append(c2w)
if camera_type in [
CameraType.EQUIRECTANGULAR,
CameraType.OMNIDIRECTIONALSTEREO_L,
CameraType.OMNIDIRECTIONALSTEREO_R,
CameraType.VR180_L,
CameraType.VR180_R,
]:
fxs.append(image_width / 2)
fys.append(image_height)
else:
# field of view
fov = camera["fov"]
focal_length = three_js_perspective_camera_focal_length(fov, image_height)
fxs.append(focal_length)
fys.append(focal_length)
# Iff ALL cameras in the path have a "time" value, construct Cameras with times
if all("render_time" in camera for camera in camera_path["camera_path"]):
times = torch.tensor([camera["render_time"] for camera in camera_path["camera_path"]])
else:
times = None
camera_to_worlds = torch.stack(c2ws, dim=0)
fx = torch.tensor(fxs)
fy = torch.tensor(fys)
return Cameras(
fx=fx,
fy=fy,
cx=image_width / 2,
cy=image_height / 2,
camera_to_worlds=camera_to_worlds,
camera_type=camera_type,
times=times,
)