nerfbaselines.types

class nerfbaselines.types.Dataset

Bases: _IncompleteDataset

cameras: Cameras

image_paths: List[str]

image_paths_root: str

images: ndarray | List[ndarray]

images_points3D_indices: List[ndarray] | None

metadata: Dict

points3D_rgb: ndarray | None

points3D_xyz: ndarray | None

sampling_mask_paths: List[str] | None

sampling_mask_paths_root: str | None

sampling_masks: ndarray | List[ndarray] | None

class nerfbaselines.types.DatasetSpecMetadata

Bases: TypedDict

default_metric: str

description: str

id: str

link: str

metrics: List[str]

name: str

paper_authors: List[str]

paper_link: str

paper_title: str

scenes: List[Dict[str, str]]

class nerfbaselines.types.DownloadDatasetFunction(*args, **kwargs)

Bases: Protocol

class nerfbaselines.types.EvaluationProtocol(*args, **kwargs)

Bases: Protocol

accumulate_metrics(metrics: Iterable[Dict[str, float | int]]) → Dict[str, float | int]

evaluate(predictions: Iterable[RenderOutput], dataset: Dataset) → Iterable[Dict[str, float | int]]

get_name() → str

render(method: Method, dataset: Dataset) → Iterable[RenderOutput]

class nerfbaselines.types.GenericCameras(*args, **kwargs)

Bases: Protocol[TTensor_co]

apply(fn: Callable[[TTensor_co, str], TTensor]) → GenericCameras[TTensor]

property camera_types: TTensor_co

Camera types, [N]

classmethod cat(values: Sequence[Self]) → Self

property distortion_parameters: TTensor_co

Distortion parameters, [N, num_params]

property image_sizes: TTensor_co

Image sizes, [N, 2]

property intrinsics: TTensor_co

Intrinsics, [N, (fx,fy,cx,cy)]

item() → Self

Returns a single camera if there is only one. Otherwise raises an error.

property metadata: TTensor_co | None

Metadata, [N, …]

property nears_fars: TTensor_co | None

Near and far planes, [N, 2]

property poses: TTensor_co

Camera-to-world matrices, [N, (R, t)]

replace(**changes) → Self

class nerfbaselines.types.GenericCamerasImpl(poses: +TTensor_co, intrinsics: +TTensor_co, camera_types: +TTensor_co, distortion_parameters: +TTensor_co, image_sizes: +TTensor_co, nears_fars: Optional[+TTensor_co], metadata: Optional[+TTensor_co] = None)

Bases: Generic[TTensor_co]

apply(fn: Callable[[TTensor_co, str], TTensor]) → GenericCamerasImpl[TTensor]

camera_types: TTensor_co

classmethod cat(values: Sequence[Self]) → Self

distortion_parameters: TTensor_co

image_sizes: TTensor_co

intrinsics: TTensor_co

item()

metadata: TTensor_co | None = None

nears_fars: TTensor_co | None

poses: TTensor_co

replace(**changes) → Self

class nerfbaselines.types.ImageSetInterpolationSource

Bases: TypedDict

default_appearance: NotRequired[TrajectoryFrameAppearance | None]

default_fov: float

default_transition_duration: float

interpolation: Literal['none']

keyframes: List[TrajectoryKeyframe]

type: Literal['interpolation']

class nerfbaselines.types.KochanekBartelsInterpolationSource

Bases: TypedDict

default_appearance: NotRequired[TrajectoryFrameAppearance | None]

default_fov: float

default_transition_duration: float

interpolation: Literal['kochanek-bartels']

is_cycle: bool

keyframes: List[TrajectoryKeyframe]

tension: float

type: Literal['interpolation']

class nerfbaselines.types.LoadDatasetFunction(*args, **kwargs)

Bases: Protocol

class nerfbaselines.types.Logger(*args, **kwargs)

Bases: Protocol

add_embedding(tag: str, embeddings: ndarray, step: int, *, images: List[ndarray] | None = None, labels: None | List[Dict[str, str]] | List[str] = None) → None

add_event(step: int) → ContextManager[LoggerEvent]

add_image(tag: str, image: ndarray, step: int, *, display_name: str | None = None, description: str | None = None) → None

add_scalar(tag: str, value: float | int, step: int) → None

add_text(tag: str, text: str, step: int) → None

class nerfbaselines.types.LoggerEvent(*args, **kwargs)

Bases: Protocol

add_embedding(tag: str, embeddings: ndarray, *, images: List[ndarray] | None = None, labels: None | List[Dict[str, str]] | List[str] = None) → None

add_histogram(tag: str, values: ndarray, *, num_bins: int | None = None) → None

add_image(tag: str, image: ndarray, display_name: str | None = None, description: str | None = None, **kwargs) → None

add_plot(tag: str, *data: ndarray, axes_labels: Sequence[str] | None = None, title: str | None = None, **kwargs) → None

add_scalar(tag: str, value: float | int) → None

add_text(tag: str, text: str) → None

class nerfbaselines.types.Trajectory

Bases: TypedDict

appearances: NotRequired[List[TrajectoryFrameAppearance]]

camera_model: Literal['pinhole', 'opencv', 'opencv_fisheye', 'full_opencv']

fps: float

frames: List[TrajectoryFrame]

image_size: Tuple[int, int]

source: NotRequired[ImageSetInterpolationSource | KochanekBartelsInterpolationSource | None]

class nerfbaselines.types.TrajectoryFrame

Bases: TypedDict

appearance_weights: NotRequired[ndarray]

intrinsics: ndarray

pose: ndarray

class nerfbaselines.types.TrajectoryFrameAppearance

Bases: TypedDict

embedding: ndarray | None

embedding_train_index: int | None

class nerfbaselines.types.TrajectoryKeyframe

Bases: TypedDict

appearance: NotRequired[TrajectoryFrameAppearance]

fov: float | None

pose: ndarray

transition_duration: NotRequired[float | None]

class nerfbaselines.types.UnloadedDataset

Bases: _IncompleteDataset

cameras: Cameras

image_paths: List[str]

image_paths_root: str

images: NotRequired[ndarray | List[ndarray] | None]

images_points3D_indices: List[ndarray] | None

metadata: Dict

points3D_rgb: ndarray | None

points3D_xyz: ndarray | None

sampling_mask_paths: List[str] | None

sampling_mask_paths_root: str | None

sampling_masks: ndarray | List[ndarray] | None

nerfbaselines.types.camera_model_from_int(i: int) → Literal['pinhole', 'opencv', 'opencv_fisheye', 'full_opencv']

nerfbaselines.types.camera_model_to_int(camera_model: Literal['pinhole', 'opencv', 'opencv_fisheye', 'full_opencv']) → int

nerfbaselines.types.new_cameras(*, poses: ndarray, intrinsics: ndarray, camera_types: ndarray, distortion_parameters: ndarray, image_sizes: ndarray, nears_fars: ndarray | None = None, metadata: ndarray | None = None) → Cameras