Source code for pytorch3d.implicitron.dataset.llff_dataset_map_provider

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

# pyre-unsafe

import numpy as np
import torch
from import registry

from .load_llff import load_llff_data

from .single_sequence_dataset import (

[docs] @registry.register class LlffDatasetMapProvider(SingleSceneDatasetMapProviderBase): """ Provides data for one scene from the LLFF dataset. Members: base_dir: directory holding the data for the scene. object_name: The name of the scene (e.g. "fern"). This is just used as a label. It will typically be equal to the name of the directory self.base_dir. path_manager_factory: Creates path manager which may be used for interpreting paths. n_known_frames_for_test: If set, training frames are included in the val and test datasets, and this many random training frames are added to each test batch. If not set, test batches each contain just a single testing frame. downscale_factor: determines image sizes. """ downscale_factor: int = 4 def _load_data(self) -> None: path_manager = self.path_manager_factory.get() images, poses, _ = load_llff_data( self.base_dir, factor=self.downscale_factor, path_manager=path_manager ) hwf = poses[0, :3, -1] poses = poses[:, :3, :4] llffhold = 8 i_test = np.arange(images.shape[0])[::llffhold] i_test_index = set(i_test.tolist()) i_train = np.array( [i for i in np.arange(images.shape[0]) if i not in i_test_index] ) i_split = (i_train, i_test, i_test) H, W, focal = hwf focal_ndc = 2 * focal / min(H, W) images = torch.from_numpy(images).permute(0, 3, 1, 2) poses = torch.from_numpy(poses) # pyre-ignore[16] self.poses = _interpret_blender_cameras(poses, focal_ndc) # pyre-ignore[16] self.images = images # pyre-ignore[16] self.fg_probabilities = None # pyre-ignore[16] self.i_split = i_split