mirror of
https://github.com/nerfstudio-project/nerfstudio.git
synced 2026-11-13 03:14:00 +08:00
Matthew Tancik
304a759e85
Contract the scene to a sphere of radius 2. This is the same contraction used in MipNeRF 360. Add mipnerf 360 graph and config. It currently doesn't contain the other advancements made in the paper (sample proposal network, regularizers)
119 lines
3.9 KiB
Python
119 lines
3.9 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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"""
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Code for loading mipnerf 360 dataset formats.
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"""
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import os
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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 pyrad.data.structs import DatasetInputs
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def load_mipnerf_360_data(
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basedir: str,
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downscale_factor: int = 1,
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split: str = "train",
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val_skip: int = 8,
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auto_scale: bool = True,
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) -> DatasetInputs:
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"""Processes mipnerf 360 data.
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Example data can be downloaded from https://jonbarron.info/mipnerf360/.
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TODO: code currently assumes images were previously downscaled.
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Args:
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data_directory (str): Location of data
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downscale_factor (int, optional): How much to downscale images. Must be a factor of 2. Defaults to 1.
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split (str, optional): Which dataset split to generate.
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val_skip (int, optional): 1/val_skip images to use for validation. Defaults to 8.
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auto_scale (boolean, optional): Scale based on pose bounds. Defaults to True.
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aabb_scale (float, optional): Scene scale, Defaults to 1.0.
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Returns:
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DatasetInputs
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"""
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image_dir = os.path.join(basedir, "images")
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if downscale_factor > 1:
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image_dir += f"_{downscale_factor}"
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if not os.path.exists(image_dir):
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raise ValueError(f"Image directory {image_dir} doesn't exist")
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valid_formats = [".jpg", ".png"]
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image_filenames = []
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for f in os.listdir(image_dir):
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ext = os.path.splitext(f)[1]
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if ext.lower() not in valid_formats:
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continue
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image_filenames.append(os.path.join(image_dir, f))
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image_filenames = sorted(image_filenames)
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num_images = len(image_filenames)
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poses_data = np.load(os.path.join(basedir, "poses_bounds.npy"))
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poses = poses_data[:, :-2].reshape([-1, 3, 5]).astype(np.float32)
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bounds = poses_data[:, -2:].transpose([1, 0])
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if num_images != poses.shape[0]:
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raise RuntimeError(f"Different number of images ({num_images}), and poses ({poses.shape[0]})")
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idx_test = np.arange(num_images)[::val_skip]
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idx_train = np.array([i for i in np.arange(num_images) if i not in idx_test])
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idx = idx_train if split == "train" else idx_test
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image_filenames = np.array(image_filenames)[idx]
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poses = poses[idx]
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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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poses[:, :2, 4] = np.array([image_height, image_width])
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poses[:, 2, 4] = poses[:, 2, 4] * 1.0 / downscale_factor
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# Reorder pose to match our convention
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poses = np.concatenate([poses[:, :, 1:2], -poses[:, :, 0:1], poses[:, :, 2:]], axis=-1)
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# Scale factor used in mipnerf
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if auto_scale:
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scale_factor = 1 / (np.min(bounds) * 0.75)
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poses[:, :3, 3] *= scale_factor
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bounds *= scale_factor
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# Center poses
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poses[:, :3, 3] = poses[:, :3, 3] - np.mean(poses[:, :3, :], axis=0)[:, 3]
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focal_length = poses[0, -1, -1]
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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, :4]) # camera to world transform
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num_cameras = len(image_filenames)
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num_intrinsics_params = 3
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intrinsics = torch.ones((num_cameras, num_intrinsics_params), dtype=torch.float32)
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intrinsics *= torch.tensor([cx, cy, focal_length])
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dataset_inputs = DatasetInputs(
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image_filenames=image_filenames,
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downscale_factor=1,
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intrinsics=intrinsics,
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camera_to_world=camera_to_world,
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)
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return dataset_inputs
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