Source code for nerfstudio.cameras.camera_paths

# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
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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],[0],[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, )