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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"""Data parser for blender dataset"""
from __future__ import annotations

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

import imageio
import numpy as np
import open3d as o3d
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 BlenderDataParserConfig(DataParserConfig): """Blender dataset parser config""" _target: Type = field(default_factory=lambda: Blender) """target class to instantiate""" data: Path = Path("data/blender/lego") """Directory specifying location of data.""" scale_factor: float = 1.0 """How much to scale the camera origins by.""" alpha_color: Optional[str] = "white" """alpha color of background, when set to None, InputDataset that consumes DataparserOutputs will not attempt to blend with alpha_colors using image's alpha channel data. Thus rgba image will be directly used in training. """ ply_path: Optional[Path] = None """Path to PLY file to load 3D points from, defined relative to the dataset directory. This is helpful for Gaussian splatting and generally unused otherwise. If `None`, points are initialized randomly."""
[docs]@dataclass class Blender(DataParser): """Blender Dataset Some of this code comes from """ config: BlenderDataParserConfig def __init__(self, config: BlenderDataParserConfig): super().__init__(config=config) Path = self.scale_factor: float = config.scale_factor self.alpha_color = config.alpha_color if self.alpha_color is not None: self.alpha_color_tensor = get_color(self.alpha_color) else: self.alpha_color_tensor = None def _generate_dataparser_outputs(self, split="train"): meta = load_from_json( / f"transforms_{split}.json") image_filenames = [] poses = [] for frame in meta["frames"]: fname = / Path(frame["file_path"].replace("./", "") + ".png") image_filenames.append(fname) poses.append(np.array(frame["transform_matrix"])) poses = np.array(poses).astype(np.float32) img_0 = imageio.v2.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, ) metadata = {} if self.config.ply_path is not None: metadata.update(self._load_3D_points( / self.config.ply_path)) dataparser_outputs = DataparserOutputs( image_filenames=image_filenames, cameras=cameras, alpha_color=self.alpha_color_tensor, scene_box=scene_box, dataparser_scale=self.scale_factor, metadata=metadata, ) return dataparser_outputs def _load_3D_points(self, ply_file_path: Path): pcd = points3D = torch.from_numpy(np.asarray(pcd.points, dtype=np.float32) * self.config.scale_factor) points3D_rgb = torch.from_numpy((np.asarray(pcd.colors) * 255).astype(np.uint8)) out = { "points3D_xyz": points3D, "points3D_rgb": points3D_rgb, } return out