mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-12 02:45:34 +08:00
Matthew Tancik
04d0165a24
* Start camera refactor * Training runs, but transforms incorrect * Cameras render correctly in viewer. * Fix eval * cleanup * cleanup
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
# Copyright 2022 The Plenoptix Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Code for camera paths.
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"""
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from typing import Optional, Tuple
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import torch
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import nerfactory.cameras.utils as camera_utils
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from nerfactory.cameras.cameras import Cameras
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from nerfactory.cameras.utils import get_interpolated_poses_many
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def get_interpolated_camera_path(cameras: Cameras, steps: int) -> Cameras:
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"""Generate a camera path between two cameras.
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Args:
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camera_a: The first camera.
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camera_b: The second camera.
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steps: The number of steps to interpolate between the two cameras.
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Returns:
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A new set of cameras along a path.
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"""
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Ks = cameras.get_intrinsics_matrices().cpu().numpy()
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poses = cameras.camera_to_worlds().cpu().numpy()
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poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps)
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cameras = Cameras(fx=Ks[:, 0, 0], fy=Ks[:, 1, 1], cx=Ks[0, 0, 2], cy=Ks[0, 1, 2], camera_to_worlds=poses)
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return cameras
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def get_spiral_path(
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camera: Cameras,
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steps: int = 30,
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radius: Optional[float] = None,
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radiuses: Optional[Tuple[float]] = None,
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rots: int = 2,
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zrate: float = 0.5,
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) -> Cameras:
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"""
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Returns a list of camera in a sprial trajectory.
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Args:
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camera: The camera to start the spiral from.
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steps: The number of cameras in the generated path.
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radius: The radius of the spiral for all xyz directions.
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radiuses: The list of radii for the spiral in xyz directions.
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rots: The number of rotations to apply to the camera.
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zrate: How much to change the z position of the camera.
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Returns:
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A spiral camera path.
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"""
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assert radius is not None or radiuses is not None, "Either radius or radiuses must be specified."
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if radius is not None and radiuses is None:
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rad = torch.tensor([radius] * 3, device=camera.device)
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elif radiuses is not None and radius is None:
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rad = torch.tensor(radiuses, device=camera.device)
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else:
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raise ValueError("Only one of radius or radiuses must be specified.")
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# TODO: don't hardcode this. pass this in
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up = camera.camera_to_worlds[0, :3, 2] # scene is z up
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# up = camera.camera_to_world[:3, 1] # this will rotate 90 degrees
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focal = torch.min(camera.fx[0], camera.fy[0])
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target = torch.tensor([0, 0, -focal], device=camera.device) # camera looking in -z direction
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c2w = camera.camera_to_worlds[0]
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ones = torch.tensor([0, 0, 0, 1], device=c2w.device)[None]
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c2wh_global = torch.cat([c2w, ones], dim=0)
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local_c2whs = []
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for theta in torch.linspace(0.0, 2.0 * torch.pi * rots, steps + 1)[:-1]:
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center = (
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torch.tensor([torch.cos(theta), -torch.sin(theta), -torch.sin(theta * zrate)], device=camera.device) * rad
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)
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lookat = center - target
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c2w = camera_utils.viewmatrix(lookat, up, center)
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ones = torch.tensor([0, 0, 0, 1], device=c2w.device)[None]
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c2wh = torch.cat([c2w, ones], dim=0)
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local_c2whs.append(c2wh)
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new_c2ws = []
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for local_c2wh in local_c2whs:
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c2wh = torch.matmul(c2wh_global, local_c2wh)
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new_c2ws.append(c2wh[:3, :4])
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return Cameras(fx=camera.fx[0], fy=camera.fy[0], cx=camera.cx, cy=camera.cy, camera_to_worlds=new_c2ws)
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