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evonneng
d50c71ac43
* fixing docstrings and small nits; up to fileds * finished adding and fixing docstrings
115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
# Copyright 2022 The Plenoptix Team. 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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from __future__ import annotations
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import logging
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from dataclasses import dataclass
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from pathlib import Path
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import numpy as np
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import torch
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from nerfactory.cameras import utils as camera_utils
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from nerfactory.cameras.cameras import CAMERA_MODEL_TO_TYPE, Cameras, CameraType
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from nerfactory.configs import base as cfg
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from nerfactory.datamanagers.dataparsers.base import DataParser
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from nerfactory.datamanagers.structs import DatasetInputs, SceneBounds
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from nerfactory.utils.io import get_absolute_path, load_from_json
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@dataclass
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class Nerfactory(DataParser):
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"""Nerfactory Dataset"""
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config: cfg.NerfactoryDataParserConfig
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def _generate_dataset_inputs(self, split="train"):
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abs_dir = get_absolute_path(self.config.data_directory)
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meta = load_from_json(abs_dir / "transforms.json")
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image_filenames = []
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poses = []
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num_skipped_image_filenames = 0
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for frame in meta["frames"]:
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fname = abs_dir / Path(frame["file_path"])
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if self.config.downscale_factor > 1:
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fname = abs_dir / f"images_{self.config.downscale_factor}" / Path(frame["file_path"]).name
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else:
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fname = abs_dir / Path(frame["file_path"])
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if not fname:
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num_skipped_image_filenames += 1
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else:
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image_filenames.append(fname)
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poses.append(np.array(frame["transform_matrix"]))
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if num_skipped_image_filenames >= 0:
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logging.info("Skipping %s files in dataset split %s.", num_skipped_image_filenames, split)
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assert (
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len(image_filenames) != 0
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), """
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No image files found.
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You should check the file_paths in the transforms.json file to make sure they are correct.
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"""
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poses = torch.from_numpy(np.array(poses).astype(np.float32))
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poses = camera_utils.auto_orient_poses(poses, method=self.config.orientation_method)
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# Scale poses
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scale_factor = 1.0 / torch.max(torch.abs(poses[:, :3, 3]))
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poses[:, :3, 3] *= scale_factor
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# in x,y,z order
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# assumes that the scene is centered at the origin
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aabb_scale = self.config.scene_scale
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scene_bounds = SceneBounds(
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aabb=torch.tensor(
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[[-aabb_scale, -aabb_scale, -aabb_scale], [aabb_scale, aabb_scale, aabb_scale]], dtype=torch.float32
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)
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)
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if "camera_model" in meta:
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camera_type = CAMERA_MODEL_TO_TYPE[meta["camera_model"]]
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else:
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camera_type = CameraType.PERSPECTIVE
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distortion_params = camera_utils.get_distortion_params(
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k1=float(meta["k1"]) if "k1" in meta else 0.0,
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k2=float(meta["k2"]) if "k2" in meta else 0.0,
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k3=float(meta["k3"]) if "k3" in meta else 0.0,
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k4=float(meta["k4"]) if "k4" in meta else 0.0,
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p1=float(meta["p1"]) if "p1" in meta else 0.0,
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p2=float(meta["p2"]) if "p2" in meta else 0.0,
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)
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cameras = Cameras(
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fx=float(meta["fl_x"]),
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fy=float(meta["fl_y"]),
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cx=float(meta["cx"]),
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cy=float(meta["cy"]),
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distortion_params=distortion_params,
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height=int(meta["h"]),
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width=int(meta["w"]),
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camera_to_worlds=poses[:, :3, :4],
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camera_type=camera_type,
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)
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cameras.rescale_output_resolution(scaling_factor=1.0 / self.config.downscale_factor)
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dataset_inputs = DatasetInputs(
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image_filenames=image_filenames,
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cameras=cameras,
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scene_bounds=scene_bounds,
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)
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return dataset_inputs
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