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evonneng
d50c71ac43
* fixing docstrings and small nits; up to fileds * finished adding and fixing docstrings
91 lines
3.2 KiB
Python
91 lines
3.2 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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"""Data parser for blender dataset"""
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from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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import imageio
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import numpy as np
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import torch
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from nerfactory.cameras.cameras import 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.colors import get_color
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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 Blender(DataParser):
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"""Blender Dataset
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Some of this code comes from https://github.com/yenchenlin/nerf-pytorch/blob/master/load_blender.py#L37.
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"""
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def __init__(self, config: cfg.BlenderDataParserConfig):
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super().__init__(config=config)
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self.data_directory: Path = config.data_directory
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self.scale_factor: float = config.scale_factor
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self.alpha_color = config.alpha_color
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def _generate_dataset_inputs(self, split="train"):
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if self.alpha_color is not None:
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alpha_color_tensor = get_color(self.alpha_color)
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else:
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alpha_color_tensor = None
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abs_dir = get_absolute_path(self.data_directory)
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meta = load_from_json(abs_dir / f"transforms_{split}.json")
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image_filenames = []
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poses = []
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for frame in meta["frames"]:
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fname = abs_dir / Path(frame["file_path"].replace("./", "") + ".png")
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image_filenames.append(fname)
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poses.append(np.array(frame["transform_matrix"]))
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poses = np.array(poses).astype(np.float32)
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img_0 = imageio.imread(image_filenames[0])
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image_height, image_width = img_0.shape[:2]
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camera_angle_x = float(meta["camera_angle_x"])
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focal_length = 0.5 * image_width / np.tan(0.5 * camera_angle_x)
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cx = image_width / 2.0
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cy = image_height / 2.0
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camera_to_world = torch.from_numpy(poses[:, :3]) # camera to world transform
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# in x,y,z order
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camera_to_world[..., 3] *= self.scale_factor
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scene_bounds = SceneBounds(aabb=torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]], dtype=torch.float32))
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cameras = Cameras(
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camera_to_worlds=camera_to_world,
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fx=focal_length,
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fy=focal_length,
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cx=cx,
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cy=cy,
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camera_type=CameraType.PERSPECTIVE,
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)
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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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alpha_color=alpha_color_tensor,
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scene_bounds=scene_bounds,
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)
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return dataset_inputs
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