mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 11:24:00 +08:00
Ethan Weber
1e22984d87
* some initial code for viewer. copying from other repos * adding electron support * moving some test files around. need to rewrite these * camera traj. test cases. starting to transmit images * still working on vis * reorganized viewer and figured out websocket sending from client. need to do with camera pose * black * temporarily ignore pylinting for viewer * ignore viewer * fixed array * license and fixed ignore in pyproject.toml * improved syntax
66 lines
2.0 KiB
Python
66 lines
2.0 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 List
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import torch
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from pyrad.cameras.cameras import Camera, get_camera, get_intrinsics_from_intrinsics_matrix
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from pyrad.cameras.transformations import get_interpolated_poses, get_interpolated_poses_many
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import numpy as np
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class CameraPath:
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"""
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Base class for camera path.
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"""
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def __init__(self) -> None:
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pass
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class InterpolatedCameraPath(CameraPath):
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"""
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Class to interpolate between two cameras.
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"""
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def __init__(self, camera_a: Camera, camera_b: Camera) -> None:
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super().__init__()
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self.camera_a = camera_a
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self.camera_b = camera_b
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def get_path(self, steps: int) -> List[Camera]:
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"""
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Returns a list of cameras.
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"""
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K_a = self.camera_a.get_intrinsics_matrix()
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pose_a = self.camera_a.get_camera_to_world_h()
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K_b = self.camera_b.get_intrinsics_matrix()
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pose_b = self.camera_b.get_camera_to_world_h()
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poses = [pose_a, pose_b]
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Ks = [K_a, K_b]
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poses, Ks = get_interpolated_poses_many(poses, Ks, steps_per_transition=steps)
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# create a list of cameras
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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)
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camera_to_world = torch.from_numpy(pose[:3])
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camera = get_camera(intrinsics, camera_to_world, None)
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cameras.append(camera)
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return cameras
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