nerfbaselines.datasets

class nerfbaselines.datasets.Dataset(cameras: nerfbaselines.cameras.Cameras, file_paths: List[str], sampling_mask_paths: Optional[List[str]] = None, file_paths_root: Optional[pathlib.Path] = None, images: Optional[numpy.ndarray] = None, sampling_masks: Optional[numpy.ndarray] = None, points3D_xyz: Optional[numpy.ndarray] = None, points3D_rgb: Optional[numpy.ndarray] = None, metadata: Dict = <factory>, color_space: Optional[Literal['srgb', 'linear']] = None)

Bases: object

cameras: Cameras

color_space: Literal['srgb', 'linear'] | None = None

property expected_scene_scale

file_paths: List[str]

file_paths_root: Path | None = None

images: ndarray | None = None

load_features(required_features, supported_camera_models=None)

metadata: Dict

points3D_rgb: ndarray | None = None

points3D_xyz: ndarray | None = None

sampling_mask_paths: List[str] | None = None

sampling_masks: ndarray | None = None

exception nerfbaselines.datasets.DatasetNotFoundError

Bases: Exception

nerfbaselines.datasets.download_dataset(path: str, output: Path)

nerfbaselines.datasets.load_dataset(path: Path | str, split: str, features: FrozenSet[Literal['color', 'points3D_xyz']]) → Dataset

nerfbaselines.datasets.colmap

class nerfbaselines.datasets.colmap.Camera(id, model, width, height, params)

Bases: tuple

class nerfbaselines.datasets.colmap.Image(id, qvec, tvec, camera_id, name, xys, point3D_ids)

Bases: BaseImage

qvec2rotmat()

class nerfbaselines.datasets.colmap.Point3D(id, xyz, rgb, error, image_ids, point2D_idxs)

Bases: tuple

nerfbaselines.datasets.colmap.load_colmap_dataset(path: Path, images_path: Path | None = None, split: str | None = None, test_indices: Indices | None = None, features: FrozenSet[Literal['color', 'points3D_xyz']] | None = None)

nerfbaselines.datasets.colmap.padded_stack(tensors: List[ndarray]) → ndarray

nerfbaselines.datasets.colmap.qvec2rotmat(qvec)

nerfbaselines.datasets.colmap.read_cameras_binary(path_to_model_file)

see: src/base/reconstruction.cc

void Reconstruction::WriteCamerasBinary(const std::string& path) void Reconstruction::ReadCamerasBinary(const std::string& path)

nerfbaselines.datasets.colmap.read_cameras_text(path)

see: src/base/reconstruction.cc

void Reconstruction::WriteCamerasText(const std::string& path) void Reconstruction::ReadCamerasText(const std::string& path)

nerfbaselines.datasets.colmap.read_images_binary(path_to_model_file)

see: src/base/reconstruction.cc

void Reconstruction::ReadImagesBinary(const std::string& path) void Reconstruction::WriteImagesBinary(const std::string& path)

nerfbaselines.datasets.colmap.read_images_text(path)

see: src/base/reconstruction.cc

void Reconstruction::ReadImagesText(const std::string& path) void Reconstruction::WriteImagesText(const std::string& path)

nerfbaselines.datasets.colmap.read_points3D_binary(path_to_model_file)

see: src/base/reconstruction.cc

void Reconstruction::ReadPoints3DBinary(const std::string& path) void Reconstruction::WritePoints3DBinary(const std::string& path)

nerfbaselines.datasets.colmap.read_points3D_text(path)

see: src/base/reconstruction.cc

void Reconstruction::ReadPoints3DText(const std::string& path) void Reconstruction::WritePoints3DText(const std::string& path)

nerfbaselines.datasets.mipnerf360

nerfbaselines.datasets.mipnerf360.download_mipnerf360_dataset(path: str, output: Path)

nerfbaselines.datasets.mipnerf360.load_colmap_dataset(path: Path, images_path: Path | None = None, split: str | None = None, test_indices: Indices | None = None, features: FrozenSet[Literal['color', 'points3D_xyz']] | None = None)

nerfbaselines.datasets.mipnerf360.load_mipnerf360_dataset(path: Path, split: str, **kwargs)

nerfbaselines.datasets.mipnerf360.single(xs)

nerfbaselines.datasets.nerfstudio

nerfbaselines.datasets.nerfstudio.download_capture_name(output: Path, file_id_or_zip_url)

Download specific captures a given dataset and capture name.

nerfbaselines.datasets.nerfstudio.download_nerfstudio_dataset(path: str, output: Path)

Download data in the Nerfstudio format. If you are interested in the Nerfstudio Dataset subset from the SIGGRAPH 2023 paper, you can obtain that by using –capture-name nerfstudio-dataset or by visiting Google Drive directly at: https://drive.google.com/drive/folders/19TV6kdVGcmg3cGZ1bNIUnBBMD-iQjRbG?usp=drive_link.

nerfbaselines.datasets.nerfstudio.get_train_eval_split_all(image_filenames: List) → Tuple[ndarray, ndarray]

Get the train/eval split where all indices are used for both train and eval.

Parameters:

image_filenames – list of image filenames

nerfbaselines.datasets.nerfstudio.get_train_eval_split_filename(image_filenames: List) → Tuple[ndarray, ndarray]

Get the train/eval split based on the filename of the images.

Parameters:

image_filenames – list of image filenames

nerfbaselines.datasets.nerfstudio.get_train_eval_split_fraction(image_filenames: List, train_split_fraction: float) → Tuple[ndarray, ndarray]

Get the train/eval split fraction based on the number of images and the train split fraction.

Parameters:

• image_filenames – list of image filenames

• train_split_fraction – fraction of images to use for training

nerfbaselines.datasets.nerfstudio.get_train_eval_split_interval(image_filenames: List, eval_interval: float) → Tuple[ndarray, ndarray]

Get the train/eval split based on the interval of the images.

Parameters:

• image_filenames – list of image filenames

• eval_interval – interval of images to use for eval

nerfbaselines.datasets.nerfstudio.grab_file_id(zip_url: str) → str

Get the file id from the google drive zip url.

nerfbaselines.datasets.nerfstudio.load_from_json(filename: Path)

Load a dictionary from a JSON filename.

Parameters:

filename – The filename to load from.

nerfbaselines.datasets.nerfstudio.load_nerfstudio_dataset(path: Path, split: str, downscale_factor: int | None = None, features: FrozenSet[Literal['color', 'points3D_xyz']] | None = None, **kwargs)

nerfbaselines.datasets.nerfstudio.read_points3D_binary(path_to_model_file)

see: src/base/reconstruction.cc

void Reconstruction::ReadPoints3DBinary(const std::string& path) void Reconstruction::WritePoints3DBinary(const std::string& path)

nerfbaselines.datasets.nerfstudio.read_points3D_text(path)

see: src/base/reconstruction.cc

void Reconstruction::ReadPoints3DText(const std::string& path) void Reconstruction::WritePoints3DText(const std::string& path)