Source code for

# 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.
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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"""Data parser for blender dataset"""
from __future__ import annotations

from dataclasses import dataclass, field
from pathlib import Path
from typing import Type

import imageio
import numpy as np
import torch

from nerfstudio.cameras.cameras import Cameras, CameraType
from import DataParser, DataParserConfig, DataparserOutputs
from import SceneBox
from nerfstudio.utils.colors import get_color
from import load_from_json

[docs]@dataclass class DNeRFDataParserConfig(DataParserConfig): """D-NeRF dataset parser config""" _target: Type = field(default_factory=lambda: DNeRF) """target class to instantiate""" data: Path = Path("data/dnerf/lego") """Directory specifying location of data.""" scale_factor: float = 1.0 """How much to scale the camera origins by.""" alpha_color: str = "white" """alpha color of background"""
[docs]@dataclass class DNeRF(DataParser): """DNeRF Dataset""" config: DNeRFDataParserConfig includes_time: bool = True def __init__(self, config: DNeRFDataParserConfig): super().__init__(config=config) Path = self.scale_factor: float = config.scale_factor self.alpha_color = config.alpha_color def _generate_dataparser_outputs(self, split="train"): if self.alpha_color is not None: alpha_color_tensor = get_color(self.alpha_color) else: alpha_color_tensor = None meta = load_from_json( / f"transforms_{split}.json") image_filenames = [] poses = [] times = [] for frame in meta["frames"]: fname = / Path(frame["file_path"].replace("./", "") + ".png") image_filenames.append(fname) poses.append(np.array(frame["transform_matrix"])) times.append(frame["time"]) poses = np.array(poses).astype(np.float32) times = torch.tensor(times, dtype=torch.float32) img_0 = imageio.imread(image_filenames[0]) image_height, image_width = img_0.shape[:2] camera_angle_x = float(meta["camera_angle_x"]) focal_length = 0.5 * image_width / np.tan(0.5 * camera_angle_x) cx = image_width / 2.0 cy = image_height / 2.0 camera_to_world = torch.from_numpy(poses[:, :3]) # camera to world transform # in x,y,z order camera_to_world[..., 3] *= self.scale_factor scene_box = SceneBox(aabb=torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]], dtype=torch.float32)) cameras = Cameras( camera_to_worlds=camera_to_world, fx=focal_length, fy=focal_length, cx=cx, cy=cy, camera_type=CameraType.PERSPECTIVE, times=times, ) dataparser_outputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, alpha_color=alpha_color_tensor, scene_box=scene_box, dataparser_scale=self.scale_factor, ) return dataparser_outputs