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https://github.com/nerfstudio-project/nerfstudio.git
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132 lines
4.5 KiB
Python
132 lines
4.5 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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import copy
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from dataclasses import dataclass
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from typing import List, 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 (
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Camera,
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get_camera,
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get_intrinsics_from_intrinsics_matrix,
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)
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from nerfactory.cameras.utils import get_interpolated_poses_many
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@dataclass
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class CameraPath:
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"""A camera path."""
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cameras: List[Camera]
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def get_interpolated_camera_path(camera_a: Camera, camera_b: Camera, steps: int) -> CameraPath:
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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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CameraPath: A camera path.
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"""
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device = camera_a.device
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Ka = camera_a.get_intrinsics_matrix().cpu().numpy()
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pose_a = camera_a.get_camera_to_world_h().cpu().numpy()
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Kb = camera_b.get_intrinsics_matrix().cpu().numpy()
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pose_b = camera_b.get_camera_to_world_h().cpu().numpy()
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poses = [pose_a, pose_b]
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Ks = [Ka, Kb]
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poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps)
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cameras = []
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for pose, K in zip(poses, Ks):
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intrinsics = get_intrinsics_from_intrinsics_matrix(K).to(device).float()
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# TODO: this makes cx and cy an integer, but this code should be fixed
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# it was added to avoid floating point errors when rescaling and the image resolution
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# being different per rendered image in a camera path
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intrinsics[:2] = torch.round(intrinsics[:2])
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camera_to_world = torch.from_numpy(pose[:3]).to(device).float()
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camera = get_camera(intrinsics, camera_to_world, camera_index=camera_a.camera_index)
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cameras.append(camera)
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return CameraPath(cameras=cameras)
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def get_spiral_path(
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camera: Camera,
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steps: int = 30,
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radius: float = None,
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radiuses: Tuple[float] = None,
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rots: int = 2,
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zrate: float = 0.5,
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) -> CameraPath:
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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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CameraPath: 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_world[: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 = min(camera.fx, camera.fy)
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target = torch.tensor([0, 0, -focal], device=camera.device) # camera looking in -z direction
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c2wh_global = camera.get_camera_to_world_h()
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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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cameras = []
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for local_c2wh in local_c2whs:
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cam = copy.deepcopy(camera)
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c2wh = torch.matmul(c2wh_global, local_c2wh)
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cam.camera_to_world = c2wh[:3, :4]
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cameras.append(cam)
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return CameraPath(cameras=cameras)
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