Source code for nerfstudio.data.dataparsers.dnerf_dataparser

# Copyright 2022 the Regents of the University of California, Nerfstudio Team and contributors. All rights reserved.
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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 nerfstudio.data.dataparsers.base_dataparser import DataParser, DataParserConfig, DataparserOutputs
from nerfstudio.data.scene_box import SceneBox
from nerfstudio.utils.colors import get_color
from nerfstudio.utils.io 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) self.data: Path = config.data 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(self.data / f"transforms_{split}.json") image_filenames = [] poses = [] times = [] for frame in meta["frames"]: fname = self.data / 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