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210 lines
7.3 KiB
Python
210 lines
7.3 KiB
Python
# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. 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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"""A set of standard datasets."""
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from __future__ import annotations
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import json
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from abc import abstractmethod
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, Dict, List, Literal, Optional, Type
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import torch
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from jaxtyping import Float
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from torch import Tensor
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import nerfstudio.configs.base_config as cfg
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from nerfstudio.cameras.cameras import Cameras
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from nerfstudio.configs.config_utils import to_immutable_dict
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from nerfstudio.data.scene_box import SceneBox
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@dataclass
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class Semantics:
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"""Dataclass for semantic labels."""
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filenames: List[Path]
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"""filenames to load semantic data"""
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classes: List[str]
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"""class labels for data"""
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colors: Tensor
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"""color mapping for classes"""
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mask_classes: List[str] = field(default_factory=lambda: [])
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"""classes to mask out from training for all modalities"""
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@dataclass
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class DataparserOutputs:
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"""Dataparser outputs for the which will be used by the DataManager
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for creating RayBundle and RayGT objects."""
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image_filenames: List[Path]
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"""Filenames for the images."""
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cameras: Cameras
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"""Camera object storing collection of camera information in dataset."""
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alpha_color: Optional[Float[Tensor, "3"]] = None
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"""Color of dataset background."""
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scene_box: SceneBox = SceneBox(aabb=torch.tensor([[-1, -1, -1], [1, 1, 1]]))
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"""Scene box of dataset. Used to bound the scene or provide the scene scale depending on model."""
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mask_filenames: Optional[List[Path]] = None
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"""Filenames for any masks that are required"""
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metadata: Dict[str, Any] = to_immutable_dict({})
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"""Dictionary of any metadata that be required for the given experiment.
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Will be processed by the InputDataset to create any additional tensors that may be required.
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"""
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dataparser_transform: Float[Tensor, "3 4"] = torch.eye(4)[:3, :]
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"""Transform applied by the dataparser."""
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dataparser_scale: float = 1.0
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"""Scale applied by the dataparser."""
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def as_dict(self) -> dict:
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"""Returns the dataclass as a dictionary."""
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return vars(self)
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def save_dataparser_transform(self, path: Path):
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"""Save dataparser transform to json file. Some dataparsers will apply a transform to the poses,
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this method allows the transform to be saved so that it can be used in other applications.
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Args:
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path: path to save transform to
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"""
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data = {
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"transform": self.dataparser_transform.tolist(),
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"scale": float(self.dataparser_scale),
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}
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if not path.parent.exists():
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path.parent.mkdir(parents=True)
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with open(path, "w", encoding="UTF-8") as file:
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json.dump(data, file, indent=4)
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def transform_poses_to_original_space(
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self,
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poses: Float[Tensor, "num_poses 3 4"],
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camera_convention: Literal["opengl", "opencv"] = "opencv",
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) -> Float[Tensor, "num_poses 3 4"]:
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"""
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Transforms the poses in the transformed space back to the original world coordinate system.
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Args:
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poses: Poses in the transformed space
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camera_convention: Camera system convention used for the transformed poses
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Returns:
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Original poses
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"""
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return transform_poses_to_original_space(
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poses,
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self.dataparser_transform,
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self.dataparser_scale,
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camera_convention=camera_convention,
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)
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@dataclass
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class DataParserConfig(cfg.InstantiateConfig):
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"""Basic dataset config"""
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_target: Type = field(default_factory=lambda: DataParser)
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"""_target: target class to instantiate"""
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data: Path = Path()
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"""Directory specifying location of data."""
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@dataclass
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class DataParser:
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"""A dataset.
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Args:
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config: datasetparser config containing all information needed to instantiate dataset
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Attributes:
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config: datasetparser config containing all information needed to instantiate dataset
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includes_time: Does the dataset include time information in the camera poses.
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"""
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config: DataParserConfig
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includes_time: bool = False
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def __init__(self, config: DataParserConfig):
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super().__init__()
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self.config = config
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@abstractmethod
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def _generate_dataparser_outputs(self, split: str = "train", **kwargs: Optional[Dict]) -> DataparserOutputs:
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"""Abstract method that returns the dataparser outputs for the given split.
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Args:
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split: Which dataset split to generate (train/test).
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kwargs: kwargs for generating dataparser outputs.
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Returns:
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DataparserOutputs containing data for the specified dataset and split
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"""
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def get_dataparser_outputs(self, split: str = "train", **kwargs: Optional[Dict]) -> DataparserOutputs:
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"""Returns the dataparser outputs for the given split.
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Args:
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split: Which dataset split to generate (train/test).
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kwargs: kwargs for generating dataparser outputs.
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Returns:
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DataparserOutputs containing data for the specified dataset and split
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"""
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dataparser_outputs = self._generate_dataparser_outputs(split, **kwargs)
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return dataparser_outputs
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def transform_poses_to_original_space(
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poses: Float[Tensor, "num_poses 3 4"],
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applied_transform: Float[Tensor, "3 4"],
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applied_scale: float,
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camera_convention: Literal["opengl", "opencv"] = "opencv",
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) -> Float[Tensor, "num_poses 3 4"]:
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"""
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Transforms the poses in the transformed space back to the original world coordinate system.
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Args:
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poses: Poses in the transformed space
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applied_transform: Transform matrix applied in the data processing step
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applied_scale: Scale used in the data processing step
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camera_convention: Camera system convention used for the transformed poses
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Returns:
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Original poses
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"""
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output_poses = torch.cat(
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(
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poses,
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torch.tensor([[[0, 0, 0, 1]]], dtype=poses.dtype, device=poses.device).repeat_interleave(len(poses), 0),
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),
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1,
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)
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output_poses[..., :3, 3] /= applied_scale
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inv_transform = torch.linalg.inv(
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torch.cat(
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(
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applied_transform,
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torch.tensor([[0, 0, 0, 1]], dtype=applied_transform.dtype, device=applied_transform.device),
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),
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0,
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)
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)
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output_poses = torch.einsum("ij,bjk->bik", inv_transform, output_poses)
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if camera_convention == "opencv":
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output_poses[..., 0:3, 1:3] *= -1
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elif camera_convention == "opengl":
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pass
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else:
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raise ValueError(f"Camera convention {camera_convention} is not supported.")
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return output_poses[:, :3]
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