nerfbaselines.types

class nerfbaselines.types.CurrentProgress(i: int, total: int, image_i: int, image_total: int)

Bases: object

i: int

image_i: int

image_total: int

total: int

class nerfbaselines.types.Dataset(camera_poses: numpy.ndarray, camera_intrinsics_normalized: numpy.ndarray, camera_distortions: nerfbaselines.distortion.Distortions, image_sizes: numpy.ndarray | None, nears_fars: numpy.ndarray, file_paths: List[str], sampling_mask_paths: List[str] | None = None, file_paths_root: pathlib.Path | None = None, images: numpy.array | None = None, sampling_masks: numpy.array | None = None, points3D_xyz: numpy.ndarray | None = None, points3D_rgb: numpy.ndarray | None = None, metadata: dict | None = <factory>, color_space: Literal['srgb', 'linear'] | None = None)

Bases: object

camera_distortions: Distortions

property camera_intrinsics

camera_intrinsics_normalized: ndarray

camera_poses: ndarray

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

file_paths: List[str]

file_paths_root: Path | None = None

image_sizes: ndarray | None

images: array | None = None

metadata: dict | None

nears_fars: ndarray

points3D_rgb: ndarray | None = None

points3D_xyz: ndarray | None = None

sampling_mask_paths: List[str] | None = None

sampling_masks: array | None = None

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

Bases: Protocol

abstract property info: MethodInfo

Get method defaults for the trainer.

Returns:

Method info.

abstract render(poses: ndarray, intrinsics: ndarray, sizes: ndarray, nears_fars: ndarray, distortions: Distortions | None = None, progress_callback: Callable[[CurrentProgress], None] | None = None) → Iterable[ndarray]

Render images.

Parameters:

• poses – Camera poses.

• intrinsics – Camera intrinsics.

• sizes – Image sizes.

• nears_fars – Near and far planes.

• distortions – Distortions.

• progress_callback – Callback for progress.

abstract save(path: str)

Save model.

Parameters:

path – Path to save.

abstract setup_train(train_dataset: Dataset, *, num_iterations: int)

Setup training data, model, optimizer, etc.

Parameters:

• train_dataset – Training dataset.

• num_iterations – Number of iterations to train.

abstract train_iteration(step: int)

Train one iteration.

Parameters:

step – Current step.

class nerfbaselines.types.MethodInfo(loaded_step: int | None = None, num_iterations: int | None = None, batch_size: int | None = None, eval_batch_size: int | None = None, required_features: FrozenSet[Literal['color', 'points3D_xyz']] = <factory>, supports_undistortion: bool = False)

Bases: object

batch_size: int | None = None

eval_batch_size: int | None = None

loaded_step: int | None = None

num_iterations: int | None = None

required_features: FrozenSet[Literal['color', 'points3D_xyz']]

supports_undistortion: bool = False

class nerfbaselines.types.RayMethod(batch_size, seed: int = 42)

Bases: Method

render(poses: ndarray, intrinsics: ndarray, sizes: ndarray, nears_fars: ndarray, distortions: Distortions | None = None, progress_callback: Callable[[CurrentProgress], None] | None = None) → Iterable[ndarray]

Render images.

Parameters:

• poses – Camera poses.

• intrinsics – Camera intrinsics.

• sizes – Image sizes.

• nears_fars – Near and far planes.

• distortions – Distortions.

• progress_callback – Callback for progress.

abstract render_rays(origins: ndarray, directions: ndarray, nears_fars: ndarray)

Render rays.

Parameters:

• origins – Ray origins.

• directions – Ray directions.

• nears_fars – Near and far planes.

setup_train(train_dataset: Dataset, *, num_iterations: int)

Setup training data, model, optimizer, etc.

Parameters:

• train_dataset – Training dataset.

• num_iterations – Number of iterations to train.

train_iteration(step: int)

Train one iteration.

Parameters:

step – Current step.

abstract train_iteration_rays(step: int, origins: ndarray, directions: ndarray, nears_fars: ndarray, colors: ndarray)

Train one iteration.

Parameters:

• step – Current step.

• origins – Ray origins.

• directions – Ray directions.

• nears_fars – Near and far planes.

• colors – Colors.

nerfbaselines.types.batched(array, batch_size)