mirror of
https://github.com/nerfstudio-project/nerfstudio.git
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73fedc7593
* initial commit for viewer refactor * updating name change to nerfactory * adding headers * making the rendering fix which broke from a merge * things are starting to work. removing old commands now * removed prints * cleanup * updating state works now even for nested dictionaries * updating state in redux. listening for state changes. trying to draw three.js now * added drawing functionality. need to fix translation of frustums and image planes * images and frustums drawn correctly * fixing lint * github actions * back to npm * fixing eslint * remove package.json from root * new favicon * some viewer cleanup * notebook * fixed camera drawings speed to avoid redraw * trying to get Leva to update * quick fix * synced config panel * adding notebook * fixing output print error
76 lines
2.4 KiB
Python
76 lines
2.4 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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"""Generic utility functions
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"""
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import torch
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def get_chunks(
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lst: List[float], num_chunks: Optional[int] = None, size_of_chunk: Optional[int] = None
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) -> List[List[float]]:
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"""Returns list of n elements, constaining a sublist.
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Args:
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lst: List to be chunked up
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num_chunks: number of chunks to split list into
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size_of_chunk: size of each chunk
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"""
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if num_chunks:
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assert not size_of_chunk
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size = len(lst) // num_chunks
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if size_of_chunk:
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assert not num_chunks
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size = size_of_chunk
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chunks = []
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for i in range(0, len(lst), size):
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chunks.append(lst[i : i + size])
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return chunks
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def get_intrinsics_matrix_and_camera_to_world_h(
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camera_object: Dict[str, Any], image_height: int
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Returns the camera intrinsics matrix and the camera to world homogeneous matrix.
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Args:
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camera_object: a Camera object.
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image_size: the size of the image (height, width)
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"""
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# intrinsics
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fov = camera_object["fov"]
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aspect = camera_object["aspect"]
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image_width = aspect * image_height
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pp_w = image_width / 2.0
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pp_h = image_height / 2.0
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focal_length = pp_h / np.tan(fov * (np.pi / 180.0) / 2.0)
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intrinsics_matrix = torch.tensor([[focal_length, 0, pp_w], [0, focal_length, pp_h], [0, 0, 1]]).float()
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# extrinsics
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camera_to_world_h = torch.tensor(get_chunks(camera_object["matrix"], size_of_chunk=4)).T.float()
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camera_to_world_h = torch.stack(
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[
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camera_to_world_h[0, :],
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camera_to_world_h[2, :],
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camera_to_world_h[1, :],
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camera_to_world_h[3, :],
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],
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dim=0,
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)
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return intrinsics_matrix, camera_to_world_h
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