# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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""" Data parser for NeRF-OSR datasets
Presented in the paper: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/
"""
from __future__ import annotations
import glob
import os
from dataclasses import dataclass, field
from pathlib import Path
from typing import List, Literal, Tuple, Type
import numpy as np
import torch
from nerfstudio.cameras import camera_utils
from nerfstudio.cameras.cameras import Cameras, CameraType
from nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
from nerfstudio.data.scene_box import SceneBox
def _find_files(directory: str, exts: List[str]):
"""Find all files in a directory that have a certain file extension.
Args:
directory : The directory to search for files.
exts : A list of file extensions to search for. Each file extension should be in the form '*.ext'.
Returns:
A list of file paths for all the files that were found. The list is sorted alphabetically.
"""
if os.path.isdir(directory):
# types should be ['*.png', '*.jpg', '*.JPG', '*.PNG']
files_grabbed = []
for ext in exts:
files_grabbed.extend(glob.glob(os.path.join(directory, ext)))
if len(files_grabbed) > 0:
files_grabbed = sorted(files_grabbed)
return files_grabbed
return []
def _parse_osm_txt(filename: str):
"""Parse a text file containing numbers and return a 4x4 numpy array of float32 values.
Args:
filename : a file containing numbers in a 4x4 matrix.
Returns:
A numpy array of shape [4, 4] containing the numbers from the file.
"""
assert os.path.isfile(filename)
with open(filename, encoding="UTF-8") as f:
nums = f.read().split()
return np.array([float(x) for x in nums]).reshape([4, 4]).astype(np.float32)
[docs]def get_camera_params(
scene_dir: str, split: Literal["train", "validation", "test"]
) -> Tuple[torch.Tensor, torch.Tensor, int]:
"""Load camera intrinsic and extrinsic parameters for a given scene split.
Args"
scene_dir : The directory containing the scene data.
split : The split for which to load the camera parameters.
Returns
A tuple containing the intrinsic parameters (as a torch.Tensor of shape [N, 4, 4]),
the camera-to-world matrices (as a torch.Tensor of shape [N, 4, 4]), and the number of cameras (N).
"""
split_dir = f"{scene_dir}/{split}"
# camera parameters files
intrinsics_files = _find_files(f"{split_dir}/intrinsics", exts=["*.txt"])
pose_files = _find_files(f"{split_dir}/pose", exts=["*.txt"])
num_cams = len(pose_files)
intrinsics = []
camera_to_worlds = []
for i in range(num_cams):
intrinsics.append(_parse_osm_txt(intrinsics_files[i]))
pose = _parse_osm_txt(pose_files[i])
# convert from COLMAP/OpenCV to nerfstudio camera (OpenGL/Blender)
pose[0:3, 1:3] *= -1
camera_to_worlds.append(pose)
intrinsics = torch.from_numpy(np.stack(intrinsics).astype(np.float32)) # [N, 4, 4]
camera_to_worlds = torch.from_numpy(np.stack(camera_to_worlds).astype(np.float32)) # [N, 4, 4]
return intrinsics, camera_to_worlds, num_cams
[docs]@dataclass
class NeRFOSRDataParserConfig(DataParserConfig):
"""Nerfstudio dataset config"""
_target: Type = field(default_factory=lambda: NeRFOSR)
"""target class to instantiate"""
data: Path = Path("data/NeRF-OSR/Data/")
"""Directory specifying location of data."""
scene: str = "stjacob"
"""Which scene to load"""
scene_scale: float = 1.0
"""How much to scale the region of interest by."""
scale_factor: float = 1.0
"""How much to scale the camera origins by."""
use_masks: bool = False
"""Whether to use masks."""
orientation_method: Literal["pca", "up", "vertical", "none"] = "vertical"
"""The method to use for orientation."""
center_method: Literal["poses", "focus", "none"] = "focus"
"""The method to use for centering."""
auto_scale_poses: bool = True
"""Whether to automatically scale the poses to fit in +/- 1 bounding box."""
[docs]@dataclass
class NeRFOSR(DataParser):
"""NeRFOSR Dataparser
Presented in the paper: https://4dqv.mpi-inf.mpg.de/NeRF-OSR/
Some of this code comes from https://github.com/r00tman/NeRF-OSR/blob/main/data_loader_split.py
Source data convention is:
camera coordinate system: x-->right, y-->down, z-->scene (opencv/colmap convention)
poses is camera-to-world
masks are 0 for dynamic content, 255 for static content
"""
config: NeRFOSRDataParserConfig
def _generate_dataparser_outputs(self, split="train"):
data = self.config.data
scene = self.config.scene
split = "validation" if split == "val" else split
if scene == "trevi":
scene_dir = f"{data}/{scene}/final_clean"
split_dir = f"{data}/{scene}/final_clean/{split}"
else:
scene_dir = f"{data}/{scene}/final"
split_dir = f"{data}/{scene}/final/{split}"
# get all split cam params
intrinsics_train, camera_to_worlds_train, n_train = get_camera_params(scene_dir, "train")
intrinsics_val, camera_to_worlds_val, n_val = get_camera_params(scene_dir, "validation")
intrinsics_test, camera_to_worlds_test, _ = get_camera_params(scene_dir, "test")
# combine all cam params
intrinsics = torch.cat([intrinsics_train, intrinsics_val, intrinsics_test], dim=0)
camera_to_worlds = torch.cat([camera_to_worlds_train, camera_to_worlds_val, camera_to_worlds_test], dim=0)
camera_to_worlds, _ = camera_utils.auto_orient_and_center_poses(
camera_to_worlds,
method=self.config.orientation_method,
center_method=self.config.center_method,
)
# Scale poses
scale_factor = 1.0
if self.config.auto_scale_poses:
scale_factor /= torch.max(torch.abs(camera_to_worlds[:, :3, 3]))
camera_to_worlds[:, :3, 3] *= scale_factor * self.config.scale_factor
if split == "train":
camera_to_worlds = camera_to_worlds[:n_train]
intrinsics = intrinsics[:n_train]
elif split == "validation":
camera_to_worlds = camera_to_worlds[n_train : n_train + n_val]
intrinsics = intrinsics[n_train : n_train + n_val]
elif split == "test":
camera_to_worlds = camera_to_worlds[n_train + n_val :]
intrinsics = intrinsics[n_train + n_val :]
cameras = Cameras(
camera_to_worlds=camera_to_worlds[:, :3, :4],
fx=intrinsics[:, 0, 0],
fy=intrinsics[:, 1, 1],
cx=intrinsics[:, 0, 2],
cy=intrinsics[:, 1, 2],
camera_type=CameraType.PERSPECTIVE,
)
# in x,y,z order
# assumes that the scene is centered at the origin
aabb_scale = self.config.scene_scale
scene_box = SceneBox(
aabb=torch.tensor(
[[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
)
)
# --- images ---
image_filenames = _find_files(f"{split_dir}/rgb", exts=["*.png", "*.jpg", "*.JPG", "*.PNG"])
# --- masks ---
mask_filenames = []
if self.config.use_masks:
mask_filenames = _find_files(f"{split_dir}/mask", exts=["*.png", "*.jpg", "*.JPG", "*.PNG"])
dataparser_outputs = DataparserOutputs(
image_filenames=image_filenames,
cameras=cameras,
scene_box=scene_box,
mask_filenames=mask_filenames if len(mask_filenames) > 0 else None,
dataparser_scale=self.config.scale_factor,
)
return dataparser_outputs