Repository: SpectacularAI/3dgs-deblur
Branch: main
Commit: f7df8e0b8287
Files: 26
Total size: 121.4 KB
Directory structure:
gitextract_h5evqv1k/
├── .gitignore
├── .gitmodules
├── CITATION.bib
├── LICENSE
├── NOTICE
├── README.md
├── combine.py
├── download_data.py
├── parse_outputs.py
├── process_deblur_nerf_inputs.py
├── process_sai_custom.py
├── process_sai_inputs.py
├── process_synthetic_inputs.py
├── render_model.py
├── render_video.py
├── run_colmap.py
├── scripts/
│ ├── compile_comparison_video.sh
│ ├── install.sh
│ ├── process_and_train_sai_custom.sh
│ ├── process_and_train_video.sh
│ ├── process_smartphone_dataset.sh
│ ├── render_and_compile_comparison_video.sh
│ ├── render_and_train_comparison_sai_custom.sh
│ └── render_and_train_comparison_video.sh
├── train.py
└── train_eval_split_by_blur_score.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*~
__pycache__/
*.pyc
.vscode
outputs/
renders/
data/
venv/
static/
source_videos
source_videos/
overlay_images/
index.html
================================================
FILE: .gitmodules
================================================
[submodule "nerfstudio"]
path = nerfstudio
url = https://github.com/SpectacularAI/nerfstudio
[submodule "gsplat"]
path = gsplat
url = https://github.com/SpectacularAI/gsplat
================================================
FILE: CITATION.bib
================================================
@misc{seiskari2024gaussian,
title={Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natural Camera Motion},
author={Otto Seiskari and Jerry Ylilammi and Valtteri Kaatrasalo and Pekka Rantalankila and Matias Turkulainen and Juho Kannala and Arno Solin},
year={2024},
eprint={2403.13327},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
================================================
FILE: LICENSE
================================================
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
================================================
FILE: NOTICE
================================================
The file process_synthetic_inputs.py contains source code from Deblur-NeRF
https://github.com/limacv/Deblur-NeRF/blob/766ca3cfafa026ea45f75ee1d3186ec3d9e13d99/scripts/synthe2poses.py
And is used under the following license:
-----
MIT License
Copyright (c) 2020 bmild
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# Gaussian Splatting on the Move: <br> Blur and Rolling Shutter Compensation for Natural Camera Motion
[](https://arxiv.org/abs/2403.13327)
## Installation
Prerequisites: run on a Linux system with a recent NVidia RTX GPU with at least 8 GB of VRAM.
Git must be installed.
1. Activate a Conda environment with PyTorch that [supports Nerfstudio](https://github.com/nerfstudio-project/nerfstudio/?tab=readme-ov-file#dependencies)
2. Possibly required, depending on your environment: `conda install -c conda-forge gcc=12.1.0`
3. Run `./scripts/install.sh` (see steps within if something goes wrong)
## Training with custom data
**Custom video data** (_new in version 2_): The method can now be used for motion blur compensation with plain video data as follows
./scripts/process_and_train_video.sh /path/to/video.mp4
or for rolling shutter compensation as
ROLLING_SHUTTER=ON ./scripts/process_and_train_video.sh /path/to/video.mp4
Currently simultaneous motion blur and rolling-shutter compensation is only possible with known readout and exposure times. The easiest way to achieve this is using the Spectacular Rec application to record the data (see below).
**Spectacular Rec app** ([v1.0.0+ for Android](https://play.google.com/store/apps/details?id=com.spectacularai.rec), [v1.2.0+ for iOS](https://apps.apple.com/us/app/spectacular-rec/id6473188128)) is needed for simultaneous rolling shutter and motion blur compensation. This approach is also expected to give the best results if the data collection app can be chosen, since it also allows automatic blurry frame filtering and VIO-based velocity initialization, both of which improve the final reconstruction quality. Instructions below.
First, download and extract a recording created using the app, e.g., `/PATH/TO/spectacular-rec-MY_RECORDING`.
Then process as
./scripts/process_and_train_sai_custom.sh /PATH/TO/spectacular-rec-MY_RECORDING
or, for a faster version:
SKIP_COLMAP=ON ./scripts/process_and_train_sai_custom.sh /PATH/TO/spectacular-rec-MY_RECORDING
See the contents of the script for more details.
**Comparison videos** To train a custom recording with and without motion blur compensation and render a video comparing the two, use this script:
* motion blur OR rolling shutter, COLMAP-based, from video:
./scripts/render_and_compile_comparison_video.sh /path/to/video.mp4
ROLLING_SHUTTER=ON ./scripts/render_and_compile_comparison_video.sh /path/to/video.mp4
* motion blur AND rolling shutter compensations (needs Spectacular Rec data)
./scripts/render_and_train_comparison_sai_custom.sh /PATH/TO/spectacular-rec-MY_RECORDING
## Benchmark data
[](https://doi.org/10.5281/zenodo.10848124)
[](https://doi.org/10.5281/zenodo.10847884)
The inputs directly trainable with our fork of Nerfstudio are stored in `data/inputs-processed` folder.
Its subfolders are called "datasets" in these scripts.
The data can be automatically downloaded by first installing: `pip install unzip` and then running
python download_data.py --dataset synthetic
# or 'sai' for processed real world smartphone data
<details>
<summary> The data folder structure is as follows: </summary>
<pre>
<code>
<3dgs-deblur>
|---data
|---inputs-processed
|---colmap-sai-cli-vels-blur-scored/
|---iphone-lego1
|---images
|---image 0
|---image 1
|---...
|---sparse_pc.ply
|---transforms.json
|---...
|---synthetic-mb
|---cozyroom
|---images
|---image 0
|---image 1
|---...
|---sparse_pc.ply
|---transforms.json
|---...
|---...
|---...
</code>
</pre>
</details>
## Training
Example: List trainable variants for the `synthetic-mb` dataset:
python train.py --dataset=synthetic-mb
Train a single variant
python train.py --dataset=synthetic-mb --case=2
Common useful options:
* `--dry_run`
* `--preview` (show Viser during training)
Additionally, any folder of the form `data/inputs-processed/CASE` can be trained directly with Nerfstudio
using the `ns-train splatfacto --data data/inputs-processed/CASE ...`. Use `--help` and see `train.py` for
the recommended parameters.
## Viewing the results
Results are written to `data/outputs/` by dataset. You can also run these on another machine
and download these results on your machine. All of the below commands should then work for
locally examining the results.
### Numeric
List all numeric results
python parse_outputs.py
... or export to CSV
python parse_outputs.py -f csv > data/results.csv
### Visualizations
Off-the-shelf:
* Viser: `ns-viewer --load-config outputs/DATASET/VARIANT/splatfacto/TIMESTAMP/config.yml` (show actual results)
* Tensorboard: `tensorboard --logdir outputs/DATASET/VARIANT/splatfacto/TIMESTAMP` (prerequisite `pip install tensorboard`)
Custom:
* Created by `train.py --render_images ...`: Renders of evaluation images and predictions are available in `outputs/DATASET/VARIANT/splatfacto/TIMESTAMP` (`/renders`, or `/demo_video*.mp4` if `render_video.py` has been run, see below)
* Demo videos: see `render_video.py` and `scripts/render_and_combine_comparison_video.sh`
## Processing the raw benchmark input data
This method also creates the extra variants discussed in the appendix/supplementary material of the paper,
as well as all the relevant synthetic data variants.
### Synthetic data
For synthetic data, we use different re-rendered versions of the [Deblur-NeRF](https://limacv.github.io/deblurnerf/) synthetic dataset.
Note that there exists several, slightly different variation, which need to be trained with correct parameters for optimal results.
**Our Deblur-NeRF re-render** (uses $\gamma = 2.2$): Download and process as:
python download_data.py --dataset synthetic-raw
python process_synthetic_inputs.py
**Other variants**
1. Download the data and extract as `inputs-raw/FOLDER_NAME` (see options below)
2. Run
python process_deblur_nerf_inputs.py --dataset=FOLDER_NAME --manual_point_cloud all
This creates a dataset called `colmap-DATASET-synthetic-novel-view-manual-pc`
Note that it may be necessary to run the last command multiple times until COLMAP succeeds
in all cases (see also the `--case=N` argument in the script).
Supported datasets (TODO: a bit messy):
* Original Deblur-NeRF: `FOLDER_NAME` = `synthetic_camera_motion_blur`. Uses $\gamma = 2.2$.
* [BAD-NeRF](https://wangpeng000.github.io/BAD-NeRF/) re-render: `FOLDER_NAME` = `nerf_llff_data`. Uses $\gamma = 1$.
* [BAD-Gaussians](https://lingzhezhao.github.io/BAD-Gaussians/) re-render: `FOLDER_NAME` = `bad-nerf-gtK-colmap-nvs`
The last two are very similar except for the "Tanabata" scene, which is broken in the BAD-NeRF version:
the underlying 3D model is slightly different in the (sharp) and training (blurry) images (objects moved around).
### Smartphone data
Download as:
python download_data.py --dataset sai-raw
and then process and convert using the following script:
./scripts/process_smartphone_dataset.sh
# or
# EXTRA_VARIANTS=ON ./scripts/process_smartphone_dataset.sh
Note: all the components in this pipeline are not guaranteed to be deterministic, especially when executed on different machines.
Especially the COLMAP has a high level of randomness.
## Changelog
### Version 2 (2024-05)
* Angular and linear velocities added as optimizable variables, which can be initialized to zero if VIO-estimated velocity data is not available (i.e., no IMU data available)
* Added `--optimize-eval-cameras` mode, which allows optimizing evaluation camera poses and velocities (if `--optimize-eval-velocities=True`) without back-propagating information to the 3DSG reconstruction. This replaces the previous two-phase optimization mode (called "rolling shutter pose optimization" in the first paper revision)
* Method can be run in motion blur OR rolling-shutter mode form plain video without a known exposure or readout times. Added a helper script `process_and_train_video.sh` for this.
* Rebased on Nerfstudio version 1.1.0 and `gsplat` [409bcd3c](https://github.com/nerfstudio-project/gsplat/commit/409bcd3cf63491710444e60c29d3c44608d8eafd) (based on 0.1.11)
* Fixed a bug in pixel velocity formulas
* Tuned hyper-parameters (separate parameters for synthetic and real data)
* Using [optimizable background color](https://github.com/nerfstudio-project/nerfstudio/pull/3100) by [KevinXu02](https://github.com/KevinXu02) for synthetic data
* Using $\gamma \neq 1$ and `--min-rgb-level` only when motion blur compensation is enabled (for a more fair comparison to Splatfacto)
* Added conversion scripts for other common Deblur-NeRF dataset variants
### Version 1 (2024-03)
Initial release where IMU data was mandatory to run the method, and the uncertainties in VIO-estimated velocities were addressed with a custom regularization scheme (see §3.6 in the [first revision of the paper](https://arxiv.org/pdf/2403.13327v1)).
Based on Nerfstudio version 1.0.2 and `gsplat` 0.1.8.
## License
The code in this repository (except the `gh-pages` website branch) is licensed under Apache 2.0.
See `LICENSE` and `NOTICE` files for more information.
For the source code of the website and its license, see the [`gh-pages` branch](https://github.com/SpectacularAI/3dgs-deblur/tree/gh-pages).
The licenses of the datasets (CC BY-SA 4.0 & CC BY 4.0) are detailed on the Zenodo pages.
================================================
FILE: combine.py
================================================
"""Combine COLMAP poses with sai-cli velocities"""
import os
import json
import shutil
def process(input_folder, args):
if args.override_calibration is None:
override_calibration = None
else:
with open(args.override_calibration, 'rt') as f:
calib_json = json.load(f)
calib_json_cam0, = calib_json['cameras']
override_calibration = calib_json_cam0
name = os.path.basename(os.path.normpath(input_folder))
print('name', name)
SAI_INPUT_ROOT = 'data/inputs-processed/' + args.dataset
def read_json(path):
with open(path) as f:
return json.load(f)
if args.sai_input_folder is None:
sai_folder = os.path.join(SAI_INPUT_ROOT, name)
else:
sai_folder = args.sai_input_folder
if args.pose_opt_pass_dir is None:
src_poses = read_json(os.path.join(input_folder, 'transforms.json'))
image_folder = os.path.join(input_folder, 'images')
ply_pc = os.path.join(input_folder, 'sparse_pc.ply')
else:
model_f = os.path.join(input_folder, args.model_name)
input_json_path = os.path.join(model_f, os.listdir(model_f)[0], 'transforms_train.json')
src_poses = { 'frames': read_json(input_json_path) }
image_folder = os.path.join(sai_folder, 'images')
ply_pc = os.path.join(sai_folder, 'sparse_pc.ply')
sai_poses = read_json(os.path.join(sai_folder, 'transforms.json'))
src_poses_by_filename = { './images/' + os.path.basename(f['file_path']): f for f in src_poses['frames'] }
if len(src_poses_by_filename) == 0:
print('skipping: no source poses found')
return
# print([(k, src_poses_by_filename[k]['file_path']) for k in sorted(src_poses_by_filename.keys())])
combined_frames = []
import numpy as np
frame_centers_sai = []
frame_centers_src = []
for sai_frame in sai_poses['frames']:
id = sai_frame['file_path']
if id.startswith('images'): id = './' + id
frame = src_poses_by_filename.get(id, None)
if frame is None:
print('warning: could not find source pose for %s, skipping' % id)
if not args.tolerate_missing: return
continue
# print('found frame', id)
if 'transform' in frame:
frame['transform_matrix'] = frame['transform']
frame['transform_matrix'].append([0, 0, 0, 1])
del frame['transform']
frame['file_path'] = id
frame_centers_sai.append(np.array(sai_frame['transform_matrix'])[:3, 3].tolist())
frame_centers_src.append(np.array(frame['transform_matrix'])[:3, 3].tolist())
for prop in ['camera_angular_velocity', 'camera_linear_velocity']:
if prop in sai_frame:
frame[prop] = sai_frame[prop]
for prop in ['motion_blur_score']:
if prop in sai_frame:
frame[prop] = sai_frame[prop]
for prop in ['colmap_im_id']:
if prop in frame:
del frame[prop]
combined_frames.append(frame)
# scale velocities to match COLMAP
frame_centers_sai = np.array(frame_centers_sai)
frame_centers_src = np.array(frame_centers_src)
frame_centers_sai -= np.mean(frame_centers_sai, axis=0)
frame_centers_src -= np.mean(frame_centers_src, axis=0)
scale_factor = np.sqrt(np.sum(frame_centers_src**2)) / np.sqrt(np.sum(frame_centers_sai**2))
print('scene scale factor %.12f' % scale_factor)
if args.pose_opt_pass_dir is None:
print('scaling linear velocities')
for frame in combined_frames:
# only linear velocity should be scaled
frame['camera_linear_velocity'] = [v * scale_factor for v in frame['camera_linear_velocity']]
processed_prefix = 'data/inputs-processed'
if args.pose_opt_pass_dir is not None:
output_prefix = os.path.join(processed_prefix, args.dataset + '-2nd-pass')
combined_poses = sai_poses
elif args.keep_intrinsics or override_calibration is not None:
combined_poses = sai_poses
if override_calibration is not None:
assert(override_calibration['model'] == 'brown-conrady')
def write_to_calib(names, values):
for i, n in enumerate(names):
combined_poses[n] = values[i]
write_to_calib('k1 k2 p1 p2 k3'.split(), override_calibration['distortionCoefficients'][:5])
write_to_calib('fl_x fl_y cx cy'.split(), [override_calibration[c] for c in 'focalLengthX focalLengthY principalPointX principalPointY'.split()])
if override_calibration is None and args.set_rolling_shutter_to is None:
intrinsics_postfix = 'orig'
else:
intrinsics_postfix = 'calib'
output_prefix = os.path.join(processed_prefix, 'colmap-' + args.dataset + '-' + intrinsics_postfix + '-intrinsics')
combined_poses['applied_transform'] = src_poses['applied_transform']
for prop in ['orientation_override', 'auto_scale_poses_override', 'fx', 'fy']:
if prop in combined_poses:
del combined_poses[prop]
else:
output_prefix = os.path.join(processed_prefix, 'colmap-' + args.dataset + '-vels')
combined_poses = src_poses
for prop in ['exposure_time', 'rolling_shutter_time']:
if prop in sai_poses:
combined_poses[prop] = sai_poses[prop]
combined_poses['frames'] = combined_frames
if args.set_rolling_shutter_to is not None:
combined_poses['rolling_shutter_time'] = args.set_rolling_shutter_to
if args.output_folder is None:
output_folder = os.path.join(output_prefix, name)
else:
output_folder = args.output_folder
print('Output folder: ' + output_folder)
if not args.dry_run:
if os.path.exists(output_folder): shutil.rmtree(output_folder)
shutil.copytree(image_folder, os.path.join(output_folder, 'images'))
# shutil.copytree(colmap_folder, os.path.join(output_folder, 'colmap'))
shutil.copyfile(ply_pc, os.path.join(output_folder, 'sparse_pc.ply'))
with open (os.path.join(output_folder, 'transforms.json'), 'w') as f:
json.dump(combined_poses, f, indent=4)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("input_folder", type=str, default=None, nargs='?')
parser.add_argument('sai_input_folder', default=None, nargs='?')
parser.add_argument('output_folder', default=None, nargs='?')
parser.add_argument('--dataset', default='sai-cli')
parser.add_argument('--set_rolling_shutter_to', default=None, type=float)
parser.add_argument('--keep_intrinsics', action='store_true')
parser.add_argument('--tolerate_missing', action='store_true')
parser.add_argument('--override_calibration', type=str, default=None)
parser.add_argument('--pose_opt_pass_dir', type=str, default=None)
parser.add_argument('--model_name', default='splatfacto')
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--case_number', type=int, default=-1)
args = parser.parse_args()
if args.input_folder in ['all']:
args.case_number = 0
args.input_folder = None
selected_cases = []
if args.input_folder is None:
if args.pose_opt_pass_dir is None:
src_folder = 'data/inputs-processed/colmap-' + args.dataset + '-imgs'
else:
src_folder = args.pose_opt_pass_dir
cases = [os.path.join(src_folder, f) for f in sorted(os.listdir(src_folder))]
if args.case_number == -1:
print('valid cases')
for i, c in enumerate(cases): print(str(i+1) + ':\t' + c)
elif args.case_number == 0:
selected_cases = cases
else:
selected_cases = [cases[args.case_number - 1]]
else:
selected_cases = [args.input_folder]
for case in selected_cases:
print('Processing ' + case)
process(case, args)
================================================
FILE: download_data.py
================================================
"""Script to download processed datasets."""
import os
import subprocess
from dataclasses import dataclass
from pathlib import Path
from typing import Literal
import tyro
@dataclass
class DownloadProcessedData:
save_dir: Path = Path(os.getcwd() + "/data")
"""Save directory. Default /data."""
dataset: Literal["synthetic", "sai", "synthetic-raw", "sai-raw", "all"] = "synthetic"
"""Dataset download name. Set to 'synthetic' to download all synthetic data. Set to 'spectacular' for real world smartphone captures."""
def main(self):
self.save_dir.mkdir(parents=True, exist_ok=True)
urls = {
"inputs-processed": {
"synthetic-all": "https://zenodo.org/records/10847884/files/processed-nerfstudio.zip",
"colmap-sai-cli-orig-intrinsics-blur-scored": "https://zenodo.org/records/10848124/files/colmap-sai-cli-orig-intrinsics-blur-scored.tar.xz",
"colmap-sai-cli-calib-intrinsics-blur-scored": "https://zenodo.org/records/10848124/files/colmap-sai-cli-calib-intrinsics-blur-scored.tar.xz",
"colmap-sai-cli-vels-blur-scored": "https://zenodo.org/records/10848124/files/colmap-sai-cli-vels-blur-scored.zip",
},
"inputs-raw": {
"spectacular-rec": "https://zenodo.org/records/10848124/files/spectacular-rec.zip",
"spectacular-rec-extras": "https://zenodo.org/records/10848124/files/spectacular-rec-extras.zip",
"synthetic-raw": "https://zenodo.org/records/10847884/files/renders.zip"
}
}
def download_dataset(dataset):
for subfolder, sub_urls in urls.items():
if dataset not in sub_urls: continue
save_dir = self.save_dir / subfolder
save_dir.mkdir(parents=True, exist_ok=True)
download_command = ["wget", "-P", str(self.save_dir), sub_urls[dataset]]
# download
try:
subprocess.run(download_command, check=True)
print("File file downloaded succesfully.")
except subprocess.CalledProcessError as e:
print(f"Error downloading file: {e}")
file_name = Path(sub_urls[dataset]).name
# subsubfolder for sai data
subsubfolder = dataset if "sai" in file_name or subfolder == "inputs-raw" else ""
if subsubfolder:
Path(self.save_dir / subfolder / subsubfolder).mkdir(
parents=True, exist_ok=True
)
# deal with zip or tar formats
if Path(sub_urls[dataset]).suffix == ".zip":
extract_command = [
"unzip",
self.save_dir / file_name,
"-d",
self.save_dir / Path(subfolder) / subsubfolder,
]
else:
extract_command = [
"tar",
"-xvJf",
self.save_dir / file_name,
"-C",
self.save_dir / Path(subfolder) / subsubfolder,
]
# extract
try:
subprocess.run(extract_command, check=True)
os.remove(self.save_dir / file_name)
print("Extraction complete.")
except subprocess.CalledProcessError as e:
print(f"Extraction failed: {e}")
def download_dataset_by_short_name(dataset):
if dataset == "synthetic":
for dataset in urls["inputs-processed"].keys():
if "synthetic" in dataset:
download_dataset(dataset)
elif dataset == "sai":
for dataset in urls["inputs-processed"].keys():
if "sai" in dataset:
download_dataset(dataset)
elif dataset == "synthetic-raw":
download_dataset("synthetic-raw")
elif dataset == "sai-raw":
download_dataset("spectacular-rec")
download_dataset("spectacular-rec-extras")
else:
raise NotImplementedError
if self.dataset == "all":
for ds in ["synthetic", "sai", "synthetic-raw", "sai-raw"]:
download_dataset_by_short_name(ds)
else:
download_dataset_by_short_name(self.dataset)
if __name__ == "__main__":
tyro.cli(DownloadProcessedData).main()
================================================
FILE: parse_outputs.py
================================================
"""Parse output metrics from JSON files"""
import os
import json
def parse_metrics(metrics_path):
with open(metrics_path) as f:
return json.load(f)
def find_and_parse_directories_containing_splatting_metrics(root_dir):
matching_dirs = []
def parse_dir(dirpath, filename):
run_name = dirpath[len(root_dir)+1:]
dataset, _, rest = run_name.partition('/')
rest_split = rest.split('/')
if len(rest_split) != 4: return None
variant, session, method, ts = rest_split
if method != 'splatfacto': return None
m = parse_metrics(os.path.join(dirpath, filename))
d = {
#'dataset': dataset[:1],
'dataset': dataset,
'variant': variant,
'session': session,
'path': dirpath,
'time': m.get('wall_clock_time_seconds', -1)
}
for k, v in m['results'].items(): d[k] = v
# print(d)
return d
for dirpath, _, filenames in os.walk(root_dir):
for filename in filenames:
# print(dirpath, filename)
if filename == 'metrics.json':
parsed = parse_dir(dirpath, filename)
if parsed is not None:
matching_dirs.append(parsed)
break
return sorted(matching_dirs, key=lambda x: x['path'])
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('dataset', type=str, nargs='?', default=None)
parser.add_argument('-f', '--output_format', choices=['csv', 'txt'], default='txt')
args = parser.parse_args()
import pandas as pd
pd.set_option("display.max_rows", None)
df = pd.DataFrame(find_and_parse_directories_containing_splatting_metrics('data/outputs'))
cols = 'dataset variant session psnr ssim lpips time'.split()
df = df[cols]
if args.dataset is not None:
df = df[df['dataset'] == args.dataset].drop('dataset', axis=1)
if args.output_format == 'csv':
print(df.to_csv(index=False))
elif args.output_format == 'txt':
print(df)
else:
raise ValueError(f'Unknown format: {args.output_format}')
================================================
FILE: process_deblur_nerf_inputs.py
================================================
"""Run COLMAP on a single sequence through Nerfstudio scripts"""
import os
import subprocess
import shutil
import tempfile
import json
from process_synthetic_inputs import generate_seed_points_match_and_triangulate
def process(input_folder, args, pass_no=1):
name = os.path.basename(os.path.normpath(input_folder))
# 'Wine' is 'Trolley' (see https://github.com/limacv/Deblur-NeRF/issues/39)
out_name = name.replace('blur', '').replace('2', '').replace('wine', 'trolley')
test_image_folder = None
first_pass_folder = None
input_image_folder = os.path.join(input_folder, 'images_1')
if args.hloc:
method = 'hloc'
else:
method = 'colmap'
if args.dataset == 'synthetic_camera_motion_blur':
paper = 'deblurnerf'
if args.dataset == 'synthetic_release':
paper = 'exblurf'
elif args.dataset == 'nerf_llff_data':
paper = 'bad-nerf'
elif args.dataset == 'synthetic-mb':
input_image_folder = os.path.join(input_folder, 'images')
paper = 'sai-mb'
elif args.dataset == 'synthetic-rs':
input_image_folder = os.path.join(input_folder, 'images')
paper = 'sai-rs'
elif args.dataset == 'bad-nerf-gtK-colmap-nvs':
# this data contains a fixed version of the Tanabata scene
# where the wine trolley is in the same place in sharp and blurry images
paper = 'bad-gaussians'
input_image_folder = os.path.join(input_folder, 'images')
elif args.dataset == 'colmap-bad-gaussians-synthetic-novel-view-deblurred-training':
input_image_folder = os.path.join(input_folder, 'images')
paper = 'mpr-deblurred'
basename = method + '-' + paper + '-synthetic'
if pass_no == 1:
if args.use_all_images:
dataset_name = basename + '-all'
else:
dataset_name = basename + '-novel-view-temp'
elif pass_no == 2:
first_pass_folder = os.path.join('data/inputs-processed/' + basename + '-novel-view-temp', out_name)
dataset_name = basename + '-novel-view'
elif pass_no == 3:
dataset_name = basename + '-deblurring'
input_image_folder = os.path.join(input_folder, 'images')
test_image_folder = os.path.join(input_folder, 'images_test')
else:
assert False
if pass_no != 1 or args.use_all_images:
if args.exact_intrinsics:
dataset_name += '-exact-intrinsics'
if args.manual_point_cloud:
dataset_name += '-manual-pc'
output_folder = os.path.join('data/inputs-processed/' + dataset_name, out_name)
temp_dir = tempfile.TemporaryDirectory()
n = 0
for index, f in enumerate(sorted(os.listdir(input_image_folder))):
if 'depth' in f: continue
if not args.dry_run:
new_name = f
if test_image_folder is not None:
new_name = 'train_' + f
if pass_no == 1 and index % 8 == 0 and not args.use_all_images:
continue
shutil.copyfile(os.path.join(input_image_folder, f), os.path.join(temp_dir.name, new_name))
n += 1
print('%d images (would be) copied in a temporary directory' % n)
# Print the path to the temporary directory
cmd = [
'ns-process-data',
'images',
'--data', temp_dir.name,
'--output-dir', output_folder,
'--num-downscales', '1',
'--matching-method', 'exhaustive',
'--camera-type', 'simple_pinhole',
]
if args.hloc:
cmd.extend([
'--feature-type', 'superpoint',
'--matcher-type', 'superpoint+lightglue',
])
if not args.post_process_only:
print(cmd)
if not args.dry_run:
if os.path.exists(output_folder):
shutil.rmtree(output_folder)
subprocess.check_call(cmd)
json_fn = os.path.join(output_folder, 'transforms.json')
if os.path.exists(json_fn):
with open(json_fn, 'r') as f:
transforms = json.load(f)
else:
transforms = { 'frames': [] }
assert args.dry_run
if test_image_folder is not None:
assert first_pass_folder is None
test_images = sorted(os.listdir(test_image_folder))
test_frames = []
if not any('train_' in f['file_path'] for f in transforms['frames']):
for index, frame in enumerate(sorted(transforms['frames'], key=lambda x: x['file_path'])):
orig_fn = test_images[index]
test_image_fn = 'eval_' + orig_fn
test_image_path = 'images/' + test_image_fn
if not args.dry_run:
shutil.copyfile(os.path.join(test_image_folder, orig_fn), os.path.join(output_folder, test_image_path))
if 'train_' not in frame['file_path']:
train_path = 'images/train_' + orig_fn
if not args.dry_run:
shutil.move(os.path.join(output_folder, frame['file_path']), os.path.join(output_folder, train_path))
frame['file_path'] = train_path
test_frame = { k: v for k, v in frame.items() }
test_frame['file_path'] = test_image_path
test_frames.append(test_frame)
transforms['frames'].extend(test_frames)
elif first_pass_folder is not None:
with open(os.path.join(first_pass_folder, 'transforms.json'), 'r') as f:
first_pass_transforms = json.load(f)
import numpy as np
to_pose_mat = lambda f : np.array(f['transform_matrix'])
get_frame_idx = lambda f: int(f['file_path'].split('_')[-1].split('.')[0], base=10) - 1
train_frame_c2ws = { get_frame_idx(f): to_pose_mat(f) for f in first_pass_transforms['frames'] }
all_frames_c2ws = { get_frame_idx(f): to_pose_mat(f) for f in transforms['frames'] }
combined_transforms = { k: v for k, v in first_pass_transforms.items() }
combined_transforms['frames'] = []
orig_index = 0
for index, frame in enumerate(sorted(transforms['frames'], key=lambda x: x['file_path'])):
#print(frame['file_path'])
if index % 8 == 0:
ref_frame = index - 1
ref_frame_orig_index = orig_index - 1
if ref_frame < 0:
ref_frame = index + 1
ref_frame_orig_index = orig_index # the next frame
# print(index, orig_index, ref_frame, ref_frame_orig_index)
pose_cur_pred_c2w = train_frame_c2ws[ref_frame_orig_index] @ np.linalg.inv(all_frames_c2ws[ref_frame]) @ all_frames_c2ws[index]
frame['transform_matrix'] = pose_cur_pred_c2w.tolist()
else:
frame['transform_matrix'] = train_frame_c2ws[orig_index].tolist()
orig_index += 1
combined_transforms['frames'].append(frame)
transforms = combined_transforms
if not args.dry_run:
shutil.copyfile(os.path.join(first_pass_folder, 'sparse_pc.ply'), os.path.join(output_folder, 'sparse_pc.ply'))
if args.exact_intrinsics:
KNOWN_INTRINSICS = {
"w": 600,
"h": 400,
"cx": 300.0,
"cy": 200.0,
"fl_x": 541.8502321581475,
"fl_y": 541.8502321581475,
"k1": 0,
"k2": 0,
"p1": 0,
"p2": 0,
}
for k, v in KNOWN_INTRINSICS.items():
transforms[k] = v
print('writing %s' % json_fn)
if not args.dry_run:
with open(json_fn, 'wt') as f:
json.dump(transforms, f, indent=4)
if pass_no == 1 and args.manual_point_cloud:
if os.path.exists(output_folder):
if not args.dry_run:
backup_ply = os.path.join(output_folder, 'sparse_pc_colmap.ply')
backup_json = os.path.join(output_folder, 'transforms_colmap.json')
if not os.path.exists(backup_ply):
ply_fn = os.path.join(output_folder, 'sparse_pc.ply')
assert os.path.exists(ply_fn) and os.path.exists(json_fn)
shutil.copyfile(ply_fn, backup_ply)
if not os.path.exists(backup_json):
shutil.copyfile(json_fn, backup_json)
generate_seed_points_match_and_triangulate(output_folder, dry_run=args.dry_run, visualize=args.dry_run)
else:
assert args.dry_run
temp_dir.cleanup()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("input_folder", type=str, default=None, nargs='?')
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--dataset', default='synthetic_camera_motion_blur')
parser.add_argument('--post_process_only', action='store_true')
parser.add_argument('--manual_point_cloud', action='store_true')
parser.add_argument('--deblurring_version', action='store_true')
parser.add_argument('--exact_intrinsics', action='store_true')
parser.add_argument('--hloc', action='store_true')
parser.add_argument('--use_all_images', action='store_true',
help='Use both blurry training and sharp test images for training pose registration')
parser.add_argument('--case_number', type=int, default=-1)
args = parser.parse_args()
if args.input_folder in ['all']:
args.case_number = 0
args.input_folder = None
selected_cases = []
misc = False
if args.dataset.endswith('/'): args.dataset = args.dataset[:-1]
if args.input_folder is None:
sai_dataset = args.dataset.startswith('synthetic-')
if sai_dataset:
input_root = os.path.join('data/inputs-processed/', args.dataset)
else:
input_root = os.path.join('data/inputs-raw/', args.dataset)
cases = [os.path.join(input_root, f)
for f in sorted(os.listdir(input_root))
if f.startswith('blur') or sai_dataset or args.dataset == 'colmap-bad-gaussians-synthetic-novel-view-deblurred-training'
]
if args.case_number == -1:
print('valid cases')
for i, c in enumerate(cases): print(str(i+1) + ':\t' + c)
elif args.case_number == 0:
selected_cases = cases
else:
selected_cases = [cases[args.case_number - 1]]
else:
selected_cases = [args.input_folder]
for case in selected_cases:
print('Processing ' + case)
process(case, args)
if not args.use_all_images:
if args.deblurring_version:
process(case, args, pass_no=3)
else:
process(case, args, pass_no=2)
================================================
FILE: process_sai_custom.py
================================================
"""Process a single custom SAI input"""
import os
import subprocess
import shutil
import json
import tempfile
from process_sai_inputs import SAI_CLI_PROCESS_PARAMS
DEFAULT_OUT_FOLDER = 'data/inputs-processed/custom'
def ensure_exposure_time(target, input_folder):
trans_fn = os.path.join(target, 'transforms.json')
with open(trans_fn) as f:
transforms = json.load(f)
if 'exposure_time' in transforms: return
with open(os.path.join(input_folder, 'data.jsonl')) as f:
for line in f:
d = json.loads(line)
if 'frames' in d:
e = d['frames'][0].get('exposureTimeSeconds', None)
if e is not None:
print('got exposure time %g from data.jsonl' % e)
transforms['exposure_time'] = e
with open(trans_fn, 'wt') as f:
json.dump(transforms, f, indent=4)
return
raise RuntimeError("no exposure time available")
def process(args):
def maybe_run_cmd(cmd):
print('COMMAND:', cmd)
if not args.dry_run: subprocess.check_call(cmd)
def maybe_unzip(fn):
name = os.path.basename(fn)
if name.endswith('.zip'):
name = name[:-4]
tempdir = tempfile.mkdtemp()
input_folder = os.path.join(tempdir, 'recording')
extract_command = [
"unzip",
fn,
"-d",
input_folder,
]
maybe_run_cmd(extract_command)
if not args.dry_run:
# handle folder inside zip
for f in os.listdir(input_folder):
if f == name:
input_folder = os.path.join(input_folder, f)
break
else:
input_folder = fn
return name, input_folder
sai_params = json.loads(json.dumps(SAI_CLI_PROCESS_PARAMS))
sai_params['key_frame_distance'] = args.key_frame_distance
tempdir = None
name, input_folder = maybe_unzip(args.spectacular_rec_input_folder_or_zip)
sai_params_list = []
for k, v in sai_params.items():
if k == 'internal':
for k2, v2 in v.items():
sai_params_list.append(f'--{k}={k2}:{v2}')
else:
if v is None:
sai_params_list.append(f'--{k}')
else:
sai_params_list.append(f'--{k}={v}')
result_name = name
if args.output_folder is None:
final_target = os.path.join(DEFAULT_OUT_FOLDER, result_name)
else:
final_target = args.output_folder
if not args.skip_colmap:
if tempdir is None: tempdir = tempfile.mkdtemp()
target = os.path.join(tempdir, 'sai-cli', result_name)
else:
target = final_target
cmd = [
'sai-cli', 'process',
input_folder,
target
] + sai_params_list
if args.preview:
cmd.extend(['--preview', '--preview3d'])
if os.path.exists(target): shutil.rmtree(target)
maybe_run_cmd(cmd)
if not args.dry_run: ensure_exposure_time(target, input_folder)
if not args.skip_colmap:
colmap_target = os.path.join(tempdir, 'colmap-sai-cli-imgs', result_name)
colmap_cmd = [
'python', 'run_colmap.py',
target,
colmap_target
]
maybe_run_cmd(colmap_cmd)
combine_cmd = [
'python', 'combine.py',
colmap_target,
target,
final_target,
'--tolerate_missing'
]
if args.keep_intrinsics:
combine_cmd.append('--keep_intrinsics')
if os.path.exists(final_target): shutil.rmtree(final_target)
maybe_run_cmd(combine_cmd)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("spectacular_rec_input_folder_or_zip", type=str)
parser.add_argument("output_folder", type=str, default=None, nargs='?')
parser.add_argument('--preview', action='store_true')
parser.add_argument('--skip_colmap', action='store_true')
parser.add_argument('--keep_intrinsics', action='store_true')
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--key_frame_distance', type=float, default=0.1,
help="Minimum key frame distance in meters, default (0.1), increase for larger scenes")
args = parser.parse_args()
process(args)
================================================
FILE: process_sai_inputs.py
================================================
"""Process raw input data to the main benchmark format"""
import os
import subprocess
import shutil
import json
SAI_CLI_PROCESS_PARAMS = {
'image_format': 'png',
'no_undistort': None,
'key_frame_distance': 0.1,
'internal': {
'maxKeypoints': 2000,
'optimizerMaxIterations': 50,
}
}
DATASET_SPECIFIC_PARAMETERS = {}
def process_subfolders(spec, output_folder, method='sai', only_this_case_number=None, dry_run=False, preview=False):
def process(folder, counter, prefix, named):
if named:
name = os.path.basename(folder)
else:
name = "%02d" % counter
if prefix is not None:
name = prefix + '-' + name
sai_params = json.loads(json.dumps(SAI_CLI_PROCESS_PARAMS)) # deep copy
out_dataset_folder = output_folder
if args.no_blur_score_filter:
out_dataset_folder += '-no-blur-select'
sai_params['blur_filter_range'] = 0
sai_params['internal']['keyFrameCandidateSelectionBufferSize'] = 1
for k, v in DATASET_SPECIFIC_PARAMETERS.get(prefix, {}).items():
if k == 'internal':
for k2, v2 in v.items():
sai_params['internal'][k2] = v2
else:
sai_params[k] = v
sai_params_list = []
for k, v in sai_params.items():
if k == 'internal':
for k2, v2 in v.items():
sai_params_list.append(f'--{k}={k2}:{v2}')
else:
if v is None:
sai_params_list.append(f'--{k}')
else:
sai_params_list.append(f'--{k}={v}')
target = os.path.join(out_dataset_folder, name.replace('_', '-').replace('-capture', ''))
if method == 'sai':
cmd = [
'sai-cli', 'process',
folder,
target
] + sai_params_list
if preview:
cmd.extend(['--preview', '--preview3d'])
elif method == 'colmap-video':
[
'ns-process-data',
'video',
'--data', os.path.join(folder, 'data.mp4'),
'--output-dir', target
]
else:
assert(False)
if dry_run:
print(cmd)
return
print(f"Processing: {folder} -> {target}")
if os.path.exists(target): shutil.rmtree(target)
subprocess.check_call(cmd)
counter = 1
for (base_folder, prefix, named) in spec:
items = os.listdir(base_folder)
directories = sorted([item for item in items if os.path.isdir(os.path.join(base_folder, item))])
dir_counter = 1
# Loop through each directory and run a command
for directory in directories:
full_path = os.path.join(base_folder, directory)
if only_this_case_number is None or only_this_case_number == counter:
print('case %d: %s' % (counter, full_path))
process(full_path, dir_counter, prefix, named)
counter += 1
dir_counter += 1
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--case_number", type=int, default=None)
parser.add_argument('--method', choices={'sai', 'colmap-video'}, default='sai')
parser.add_argument('--no_blur_score_filter', action='store_true')
parser.add_argument('--preview', action='store_true')
parser.add_argument('--dry_run', action='store_true')
args = parser.parse_args()
if args.method == 'sai':
out_folder ='data/inputs-processed/sai-cli'
elif args.method == 'colmap-video':
out_folder ='data/inputs-processed/colmap-video'
else:
assert(False)
process_subfolders([
('data/inputs-raw/spectacular-rec', None, True),
],
out_folder,
method=args.method,
only_this_case_number=args.case_number,
dry_run=args.dry_run,
preview=args.preview)
================================================
FILE: process_synthetic_inputs.py
================================================
"""Process raw synthetic input data to the main benchmark format"""
import os
import json
import shutil
import cv2
import numpy as np
POSE_POSITION_NOISE_REL = 0.05
POSE_ORIENTATION_NOISE_DEG = 1
INTRINSIC_NOISE_REL = 0.01
def rotation_matrix_to_rotvec(R):
# Using a proven/stable algorithm. Other options are sketchy for small rotation
from scipy.spatial.transform import Rotation
return Rotation.from_matrix(R).as_rotvec()
def quaternion_to_rotation_matrix(q_wxyz):
q = q_wxyz
return np.array([
[q[0]*q[0]+q[1]*q[1]-q[2]*q[2]-q[3]*q[3], 2*q[1]*q[2] - 2*q[0]*q[3], 2*q[1]*q[3] + 2*q[0]*q[2]],
[2*q[1]*q[2] + 2*q[0]*q[3], q[0]*q[0] - q[1]*q[1] + q[2]*q[2] - q[3]*q[3], 2*q[2]*q[3] - 2*q[0]*q[1]],
[2*q[1]*q[3] - 2*q[0]*q[2], 2*q[2]*q[3] + 2*q[0]*q[1], q[0]*q[0] - q[1]*q[1] - q[2]*q[2] + q[3]*q[3]]
])
def deterministic_uniform_rand_generator(seed=1000):
"""
A simple pseudorandom number generator that returns the
same random sequence on all machines. The quality of these
random numbers is low but this is fine for this particular
application.
"""
# see https://en.cppreference.com/w/cpp/numeric/random/linear_congruential_engine
a, c, m = 48271, 0, 2147483647
x = seed + 1
uniform_steps = 999
while True:
x = (a * x + c) % m
yield float(x % uniform_steps) / uniform_steps
def process(data_path, target, noisy_poses=False, noisy_intrinsics=False):
"""
# --- Based on
# https://github.com/limacv/Deblur-NeRF/blob/766ca3cfafa026ea45f75ee1d3186ec3d9e13d99/scripts/synthe2poses.py
# and used under the following license
MIT License
Copyright (c) 2020 bmild
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
print(f"Processing: {data_path} -> {target}")
if os.path.exists(target): shutil.rmtree(target)
input_path = data_path
json_path = os.path.join(input_path, "transforms.json")
out_path = os.path.join(target, "images")
converted_json_path = os.path.join(target, "transforms.json")
os.makedirs(out_path, exist_ok=True)
rand = deterministic_uniform_rand_generator()
def rand3():
nonlocal rand
return np.array([next(rand) for _ in range(3)]) * 2 - 1
def convert_pose_c2w(pose, scaling):
pose = np.array(pose)
pose[:3, :] *= scaling
return pose
def get_scaling(m):
return 1.0 / np.sqrt((m[:3,:3].transpose() @ m[:3,:3])[0,0])
with open(json_path, 'r') as metaf:
meta = json.load(metaf)
frames_data = meta["frames"]
fov = meta["fov"]
h, w = meta['h'], meta['w']
exposure_time = meta["exposure_time"]
rolling_shutter_time = meta["rolling_shutter_time"]
focal_length = w / 2 / np.tan(fov / 2)
if noisy_intrinsics:
# slight (fixed) error in intrinsics
intrinsic_noisy_scaling_x = 1 + INTRINSIC_NOISE_REL
intrinsic_noisy_scaling_y = 1 - INTRINSIC_NOISE_REL
else:
intrinsic_noisy_scaling_x = 1
intrinsic_noisy_scaling_y = 1
converted_meta = {
"aabb_scale": 16,
"w": w,
"h": h,
"cx": w/2,
"cy": h/2,
"orientation_override": "none",
"exposure_time": exposure_time,
"rolling_shutter_time": rolling_shutter_time,
"fl_x": focal_length * intrinsic_noisy_scaling_x,
"fl_y": focal_length * intrinsic_noisy_scaling_y,
"k1": 0,
"k2": 0,
"p1": 0,
"p2": 0,
"frames": []
}
scaling = None
cam_positions = []
for frame_data in frames_data:
pose = np.array(frame_data["transform_matrix"])
if scaling is None:
scaling = get_scaling(pose)
pose = convert_pose_c2w(pose, scaling)
cam_positions.append(pose[:3, 3])
img_path = os.path.join(data_path, frame_data["filename"])
img_name = os.path.basename(img_path)
img_out = os.path.join(out_path, img_name)
if frame_data["blurcount"] == 0:
img = cv2.imread(img_path)
cv2.imwrite(img_out, img)
velocity_cam = np.array([0, 0, 0])
ang_vel_cam = np.array([0, 0, 0])
else:
img = cv2.imread(img_path)
blur_poses = []
for bluri in range(frame_data["blurcount"]):
blur_poses.append(convert_pose_c2w(frame_data['blur_matrices'][bluri], scaling))
velocity_w = (blur_poses[-1][:3, 3] - blur_poses[0][:3, 3]) / (exposure_time + rolling_shutter_time)
rot = blur_poses[-1][:3, :3] @ blur_poses[0][:3, :3].transpose()
rot_vec = rotation_matrix_to_rotvec(rot)
# print(rot, rot_vec, np.linalg.norm(rot_vec))
ang_vel_w = rot_vec / (exposure_time + rolling_shutter_time)
R_w2c = pose[:3, :3].transpose()
velocity_cam = R_w2c @ velocity_w
ang_vel_cam = R_w2c @ ang_vel_w
# print(velocity_cam, ang_vel_cam)
cv2.imwrite(img_out, img)
print(f"frame {img_name} saved!")
converted_meta["frames"].append({
"camera_linear_velocity": velocity_cam.tolist(),
"camera_angular_velocity": ang_vel_cam.tolist(),
"file_path": f"./images/{img_name}",
"transform_matrix": pose.tolist()
})
if noisy_poses:
center = np.mean(cam_positions, axis=0)
scene_motion_scale = np.max(np.linalg.norm(cam_positions - center, axis=1))
pos_noise_scale = POSE_POSITION_NOISE_REL * scene_motion_scale
print('center point of scene cameras %s scale %g, pose noise scale +-%g' % (
str(center.tolist()),
scene_motion_scale,
pos_noise_scale))
for f in converted_meta['frames']:
pose = np.array(f['transform_matrix'])
pose[:3, 3] + rand3() * pos_noise_scale
noise_ang = 0
while noise_ang < 1e-6:
noise_rot_vec = rand3() * POSE_ORIENTATION_NOISE_DEG / 180.0 * np.pi
noise_ang = np.linalg.norm(noise_rot_vec)
noise_rot_dir = noise_rot_vec / noise_ang
noise_quat = [np.cos(noise_ang*0.5)] + (np.sin(noise_ang*0.5) * noise_rot_dir).tolist()
noise_R = quaternion_to_rotation_matrix(noise_quat)
pose[:3, :3] = pose[:3, :3] @ noise_R
f['transform_matrix'] = pose.tolist()
with open(converted_json_path, 'wt') as f:
json.dump(converted_meta, f, indent=4)
def point_cloud_to_ply(xyzrgbs, out_fn):
with open(out_fn, 'wt') as f:
f.write('\n'.join([
'ply',
'format ascii 1.0',
'element vertex %d' % len(xyzrgbs),
'property float x',
'property float y',
'property float z',
'property uint8 red',
'property uint8 green',
'property uint8 blue',
'end_header'
]) + '\n')
for r in xyzrgbs:
for i in range(3): r[i+3] = int(r[i+3])
f.write(' '.join([str(v) for v in r]) + '\n')
def triangulate_point(o1, d1, o2, d2):
A = np.stack([d1, -d2]).T
b = o2 - o1
x, _, _, _ = np.linalg.lstsq(A, b, rcond=None)
P1 = o1 + x[0] * d1
P2 = o2 + x[1] * d2
P = (P1 + P2) / 2
return P
def reproject_point(p, c2w, intrinsics):
p_cam = c2w[:3, :3].transpose() @ (p - c2w[:3, 3])
MIN_D = 1e-6
if -p_cam[2] <= MIN_D: return None
p_img = p_cam[:2] / -p_cam[2]
p_px = [p_img[0] * intrinsics['fl_x'] + intrinsics['cx'], -p_img[1] * intrinsics['fl_y'] + intrinsics['cy']]
return p_px
def reprojection_error(p_reproj, p_orig):
if p_reproj is None: return 1e6
return np.linalg.norm(p_reproj - np.array(p_orig))
def triangulate(points1, points2, c2w_i, c2w_j, matches, intrinsics, reprojection_error_pixels):
filtered_matches = []
points3d = []
rejected_matches = []
for match in matches:
i, j = match.queryIdx, match.trainIdx
def to_dir(p):
px = (p[0] - intrinsics['cx']) / intrinsics['fl_x']
py = -(p[1] - intrinsics['cy']) / intrinsics['fl_y']
h = [px, py, -1]
return np.array(h) / np.linalg.norm(h)
p1 = points1[i].pt
p2 = points2[j].pt
dir_i_cam = to_dir(p1)
dir_j_cam = to_dir(p2)
dir_i = c2w_i[:3, :3] @ dir_i_cam
dir_j = c2w_j[:3, :3] @ dir_j_cam
P = triangulate_point(c2w_i[:3, 3], dir_i, c2w_j[:3, 3], dir_j)
rp1 = reproject_point(P, c2w_i, intrinsics)
rp2 = reproject_point(P, c2w_j, intrinsics)
err = max(
reprojection_error(rp1, p1),
reprojection_error(rp2, p2))
if err > reprojection_error_pixels:
rejected_matches.append((match, rp1, rp2))
continue
filtered_matches.append(match)
points3d.append(P)
return filtered_matches, points3d, rejected_matches
def generate_seed_points_match_and_triangulate(target, visualize=False, dry_run=False, reprojection_error_pixels=10):
json_path = os.path.join(target, "transforms.json")
def is_eval_frame(i, frame):
if i % 8 == 0:
if 'camera_linear_velocity' in frame:
vel = np.linalg.norm(frame['camera_linear_velocity']) + np.linalg.norm(frame['camera_angular_velocity'])
assert(vel == 0)
return True
return False
with open(json_path, 'rt') as f: transforms = json.load(f)
training_frames = [f for i, f in enumerate(sorted(transforms['frames'], key=lambda fr: fr['file_path'])) if not is_eval_frame(i, f)]
transforms['ply_file_path'] = './sparse_pc.ply'
converted_json = transforms
images = [cv2.imread(os.path.join(target, frame['file_path'])) for frame in training_frames]
# --- By ChatGPT
def find_keypoints_and_descriptors(images, detector):
"""Find keypoints and descriptors for each image using the given detector."""
keypoints_and_descriptors = []
for image in images:
keypoints, descriptors = detector.detectAndCompute(image, None)
keypoints_and_descriptors.append((keypoints, descriptors))
return keypoints_and_descriptors
def match_descriptors_and_triangulate(descriptor_pairs, matcher, frames, intrinsics):
"""Match descriptors between all pairs of images."""
matches = {}
n = len(descriptor_pairs)
for i in range(n):
for j in range(i+1, n):
matches_ij = matcher.match(descriptor_pairs[i][1], descriptor_pairs[j][1])
matches_ij = sorted(matches_ij, key=lambda x: x.distance)
c2w_i = np.array(frames[i]['transform_matrix'])
c2w_j = np.array(frames[j]['transform_matrix'])
matches_ij, points3d, rejected_matches = triangulate(
descriptor_pairs[i][0],
descriptor_pairs[j][0],
c2w_i, c2w_j,
matches_ij, intrinsics, reprojection_error_pixels)
matches[(i, j)] = (matches_ij, points3d, rejected_matches)
return matches
def visualize_matches(images, keypoints_and_descriptors, matches, pair):
"""Visualize the matches for a specific pair of images."""
img1, img2 = images[pair[0]], images[pair[1]]
kp1, kp2 = keypoints_and_descriptors[pair[0]][0], keypoints_and_descriptors[pair[1]][0]
matches_ij, points, rejected_matches = matches[pair]
img_matches = cv2.drawMatches(img1, kp1, img2, kp2, matches_ij, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
for rm in rejected_matches:
match, rp1, rp2 = rm
p1_orig = tuple(map(int, kp1[match.queryIdx].pt))
p2_orig_x, p2_orig_y = tuple(map(int, kp2[match.trainIdx].pt))
p2_orig_x += img1.shape[1]
p2_orig = (p2_orig_x, p2_orig_y)
cv2.circle(img_matches, p1_orig, 3, (0, 0, 255), 1)
cv2.circle(img_matches, p2_orig, 3, (0, 0, 255), 1)
if rp1 is not None:
cv2.line(img_matches, p1_orig, tuple(map(int, rp1)), (0, 0, 255), 1)
if rp2 is not None:
rp2_x, rp2_y = tuple(map(int, rp2))
cv2.line(img_matches, p2_orig, (rp2_x + img1.shape[1], rp2_y), (0, 0, 255), 1)
cv2.imshow(f"Matches between image {pair[0]} and {pair[1]}", img_matches)
cv2.waitKey(0)
cv2.destroyAllWindows()
detector = cv2.SIFT_create()
print('finding keypoints and descriptors...')
keypoints_and_descriptors = find_keypoints_and_descriptors(images, detector)
print('matching descriptors...')
#bf_matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)
bf_matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=True)
matches = match_descriptors_and_triangulate(keypoints_and_descriptors, bf_matcher, training_frames, transforms)
if visualize:
visualize_matches(images, keypoints_and_descriptors, matches, (0, 1))
xyzrgbs = []
for i in range(len(images)):
for j in range(i+1, len(images)):
matches_ij, points, rejected_matches = matches[(i, j)]
for (k, match) in enumerate(matches_ij):
p = points[k]
kp1 = keypoints_and_descriptors[i][0][match.queryIdx].pt
color = images[i][int(kp1[1]), int(kp1[0]), [2, 1, 0]]
xyzrgbs.append(p.tolist() + color.tolist())
print('Triangulated %d points' % len(xyzrgbs))
if not dry_run:
with open(json_path, 'wt') as f:
json.dump(converted_json, f, indent=4)
seed_ply_path = os.path.join(target, "sparse_pc.ply")
point_cloud_to_ply(xyzrgbs, seed_ply_path)
def process_dataset_folder(
base_folder,
output_folder,
subfolder,
points_only=False,
noisy_poses=False,
noisy_intrinsics=False,
dry_run=False,
visualize=False):
items = os.listdir(base_folder)
directories = sorted([item for item in items if os.path.isdir(os.path.join(base_folder, item))])
for directory in directories:
print(directory)
full_path = os.path.join(base_folder, directory, subfolder)
if not os.path.exists(full_path): continue
out_path = os.path.join(output_folder, directory)
if not points_only and not dry_run:
process(full_path, out_path, noisy_poses=noisy_poses, noisy_intrinsics=noisy_intrinsics)
if os.path.exists(out_path):
generate_seed_points_match_and_triangulate(out_path, visualize=visualize)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--points_only', action='store_true')
parser.add_argument('--visualize', action='store_true')
args = parser.parse_args()
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-posenoise',
subfolder='raw_clear',
noisy_poses=True,
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-rs',
subfolder='raw_rs',
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-mb',
subfolder='raw_mb',
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-mb-posenoise',
subfolder='raw_mb',
noisy_poses=True,
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-clear',
subfolder='raw_clear',
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-mbrs',
subfolder='raw_mbrs',
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-mbrs-posenoise',
subfolder='raw_mbrs',
noisy_poses=True,
**vars(args))
process_dataset_folder(
'data/inputs-raw/synthetic-raw',
'data/inputs-processed/synthetic-mbrs-pose-calib-noise',
subfolder='raw_mbrs',
noisy_poses=True,
noisy_intrinsics=True,
**vars(args))
================================================
FILE: render_model.py
================================================
"""Load g model and render all outputs to disc"""
from dataclasses import dataclass
from pathlib import Path
import torch
import tyro
import os
import numpy as np
import shutil
from nerfstudio.cameras.cameras import Cameras
from nerfstudio.models.splatfacto import SplatfactoModel
from nerfstudio.utils.eval_utils import eval_setup
from nerfstudio.utils import colormaps
from nerfstudio.data.datasets.base_dataset import InputDataset
from PIL import Image
from torch import Tensor
from typing import List, Literal, Optional, Union
def save_img(image, image_path, verbose=True) -> None:
"""helper to save images
Args:
image: image to save (numpy, Tensor)
image_path: path to save
verbose: whether to print save path
Returns:
None
"""
if image.shape[-1] == 1 and torch.is_tensor(image):
image = image.repeat(1, 1, 3)
if torch.is_tensor(image):
image = image.detach().cpu().numpy() * 255
image = image.astype(np.uint8)
if not Path(os.path.dirname(image_path)).exists():
Path(os.path.dirname(image_path)).mkdir(parents=True)
im = Image.fromarray(image)
if verbose:
print("saving to: ", image_path)
im.save(image_path)
# Depth Scale Factor m to mm
SCALE_FACTOR = 0.001
SAVE_RAW_DEPTH = False
def save_depth(depth, depth_path, verbose=True, scale_factor=SCALE_FACTOR) -> None:
"""helper to save metric depths
Args:
depth: image to save (numpy, Tensor)
depth_path: path to save
verbose: whether to print save path
scale_factor: depth metric scaling factor
Returns:
None
"""
if torch.is_tensor(depth):
depth = depth.float() / scale_factor
depth = depth.detach().cpu().numpy()
else:
depth = depth / scale_factor
if not Path(os.path.dirname(depth_path)).exists():
Path(os.path.dirname(depth_path)).mkdir(parents=True)
if verbose:
print("saving to: ", depth_path)
np.save(depth_path, depth)
def save_outputs_helper(
rgb_out: Optional[Tensor],
gt_img: Optional[Tensor],
depth_color: Optional[Tensor],
depth_gt_color: Optional[Tensor],
depth_gt: Optional[Tensor],
depth: Optional[Tensor],
normal_gt: Optional[Tensor],
normal: Optional[Tensor],
render_output_path: Path,
image_name: Optional[str],
) -> None:
"""Helper to save model rgb/depth/gt outputs to disk
Args:
rgb_out: rgb image
gt_img: gt rgb image
depth_color: colored depth image
depth_gt_color: gt colored depth image
depth_gt: gt depth map
depth: depth map
render_output_path: save directory path
image_name: stem of save name
Returns:
None
"""
if image_name is None:
image_name = ""
if rgb_out is not None and gt_img is not None:
# easier consecutive compare
save_img(rgb_out, os.getcwd() + f"/{render_output_path}/{image_name}_pred.png", False)
save_img(gt_img, os.getcwd() + f"/{render_output_path}/{image_name}_gt.png", False)
if depth_color is not None:
save_img(
depth_color,
os.getcwd()
+ f"/{render_output_path}/pred/depth/colorised/{image_name}.png",
False,
)
if depth_gt_color is not None:
save_img(
depth_gt_color,
os.getcwd() + f"/{render_output_path}/gt/depth/colorised/{image_name}.png",
False,
)
if depth_gt is not None:
# save metric depths
save_depth(
depth_gt,
os.getcwd() + f"/{render_output_path}/gt/depth/raw/{image_name}.npy",
False,
)
if SAVE_RAW_DEPTH:
if depth is not None:
save_depth(
depth,
os.getcwd() + f"/{render_output_path}/pred/depth/raw/{image_name}.npy",
False,
)
if normal is not None:
save_normal(
normal,
os.getcwd() + f"/{render_output_path}/pred/normal/{image_name}.png",
verbose=False,
)
if normal_gt is not None:
save_normal(
normal_gt,
os.getcwd() + f"/{render_output_path}/gt/normal/{image_name}.png",
verbose=False,
)
@dataclass
class RenderModel:
"""Render outputs of a GS model."""
load_config: Path = Path("outputs/")
"""Path to the config YAML file."""
output_dir: Path = Path("./data/renders/")
"""Path to the output directory."""
set: Literal["train", "eval"] = "eval"
"""Dataset to test with (train or eval)"""
output_same_dir: bool = True
"""Output to the subdirectory of the load_config path"""
def main(self):
if self.output_same_dir:
self.output_dir = os.path.join(os.path.dirname(self.load_config), 'renders')
if os.path.exists(self.output_dir):
shutil.rmtree(self.output_dir)
os.makedirs(self.output_dir)
print('writing %s' % str(self.output_dir))
_, pipeline, _, _ = eval_setup(self.load_config)
assert isinstance(pipeline.model, SplatfactoModel)
model: SplatfactoModel = pipeline.model
dataset: InputDataset
with torch.no_grad():
if self.set == "train":
dataset = pipeline.datamanager.train_dataset
images = pipeline.datamanager.cached_train
elif self.set == "eval":
dataset = pipeline.datamanager.eval_dataset
images = pipeline.datamanager.cached_eval
else:
raise RuntimeError("Invalid set")
cameras: Cameras = dataset.cameras # type: ignore
for image_idx in range(len(dataset)): # type: ignore
data = images[image_idx]
# process batch gt data
mask = None
if "mask" in data:
mask = data["mask"]
gt_img = 256 - data["image"] # not sure why negative
if "sensor_depth" in data:
depth_gt = data["sensor_depth"]
depth_gt_color = colormaps.apply_depth_colormap(
data["sensor_depth"]
)
else:
depth_gt = None
depth_gt_color = None
if "normal" in data:
normal_gt = data["normal"]
else:
normal_gt = None
# process pred outputs
camera = cameras[image_idx : image_idx + 1].to("cpu")
#if self.set == "train":
# camera idx is used to fetch camera optimizer adjustments
# and should not be used for 'eval' data
camera.metadata['cam_idx'] = image_idx
outputs = model.get_outputs_for_camera(camera=camera)
rgb_out, depth_out = outputs["rgb"], outputs["depth"]
normal = None
if "normal" in outputs:
normal = outputs["normal"]
seq_name = Path(dataset.image_filenames[image_idx])
image_name = f"{seq_name.stem}"
depth_color = colormaps.apply_depth_colormap(depth_out)
depth = depth_out.detach().cpu().numpy()
if mask is not None:
rgb_out = rgb_out * mask
gt_img = gt_img * mask
if depth_color is not None:
depth_color = depth_color * mask
if depth_gt_color is not None:
depth_gt_color = depth_gt_color * mask
if depth_gt is not None:
depth_gt = depth_gt * mask
if depth is not None:
depth = depth * mask
if normal_gt is not None:
normal_gt = normal_gt * mask
if normal is not None:
normal = normal * mask
# save all outputs
save_outputs_helper(
rgb_out,
gt_img,
depth_color,
depth_gt_color,
depth_gt,
depth,
normal_gt,
normal,
self.output_dir,
image_name,
)
if __name__ == "__main__":
tyro.cli(RenderModel).main()
================================================
FILE: render_video.py
================================================
"""Generate demo video camera trajectory"""
import os
import json
import subprocess
import numpy as np
class SplineInterpolator:
def __init__(self, target, frames_per_transition):
self.target = target
self.positions = []
self.orientations = []
self.loop = False
self.tension = 0.0
self.model_frame = None
self.frames_per_transition = frames_per_transition
def push(self, frame):
from scipy.spatial.transform import Rotation
m = np.array(frame['camera_to_world'])
self.positions.append(m[:3, 3].tolist())
q_xyzw = Rotation.from_matrix(m[:3, :3]).as_quat().tolist()
self.orientations.append(q_xyzw)
if self.model_frame is None:
self.model_frame = frame
def finish(self):
import splines
import splines.quaternion
from scipy.spatial.transform import Rotation
# as in Nerfstudio
end_cond = "closed" if self.loop else "natural"
orientation_spline = splines.quaternion.KochanekBartels(
[
splines.quaternion.UnitQuaternion.from_unit_xyzw(q)
for q in self.orientations
],
tcb=(self.tension, 0.0, 0.0),
endconditions=end_cond,
)
position_spline = splines.KochanekBartels(
self.positions,
tcb=(self.tension, 0.0, 0.0),
endconditions=end_cond,
)
n = len(self.positions)
for t in np.linspace(0, n-1, num=(n-1)*self.frames_per_transition, endpoint=True):
f = { k: v for k, v in self.model_frame.items() }
q = orientation_spline.evaluate(t)
p = position_spline.evaluate(t)
m = np.eye(4)
m[:3, 3] = p
m[:3, :3] = Rotation.from_quat([*q.vector, q.scalar]).as_matrix()
f['camera_to_world'] = m.tolist()
self.target.append(f)
def look_at(cam_pos, cam_target, up_dir=np.array([0, 0, 1])):
z = cam_target - cam_pos
z = z / np.linalg.norm(z)
x = np.cross(z, up_dir)
x = x / np.linalg.norm(x)
y = np.cross(z, x)
y = y / np.linalg.norm(y)
m = np.eye(4)
m[:3, 3] = cam_pos
m[:3, :3] = np.column_stack((x, -y, -z))
return m
def get_original_length_seconds(raw_input_data_jsonl):
with open(raw_input_data_jsonl, 'rt') as f:
first_ts = None
for line in f:
d = json.loads(line)
if 'time' in d:
last_ts = d['time']
if first_ts is None:
first_ts = last_ts
return last_ts - first_ts
def add_velocities(camera_path, loop=False):
from scipy.spatial.transform import Rotation
path = camera_path['camera_path']
for i in range(len(path)):
if loop:
i_prev = (i - 1) % len(path)
i_next = (i + 1) % len(path)
else:
i_prev = max(0, i - 1)
i_next = min(len(path) - 1, i + 1)
delta_t = i_next - i_prev
prev_pose = np.array(path[i_prev]['camera_to_world'])
next_pose = np.array(path[i_next]['camera_to_world'])
velocity_w = (next_pose[:3, 3] - prev_pose[:3, 3]) / delta_t
cur_pose = np.array(path[i]['camera_to_world'])
rot = next_pose[:3, :3] @ prev_pose[:3, :3].transpose()
rot_vec = Rotation.from_matrix(rot).as_rotvec()
ang_vel_w = rot_vec / delta_t
R_w2c = cur_pose[:3, :3].transpose()
velocity_cam = R_w2c @ velocity_w
ang_vel_cam = R_w2c @ ang_vel_w
path[i]['camera_linear_velocity'] = velocity_cam.tolist()
path[i]['camera_angular_velocity'] = ang_vel_cam.tolist()
def process(out_folder, args):
import numpy as np
path = os.path.normpath(out_folder)
name = os.path.basename(path)
variant_folder = os.path.split(path)[0]
# variant = os.path.basename(variant_folder)
dataset_folder = os.path.split(variant_folder)[0]
dataset = os.path.basename(dataset_folder)
result_folder = os.path.join(out_folder, 'splatfacto', os.listdir(os.path.join(out_folder, 'splatfacto'))[0])
config_file = os.path.join(result_folder, 'config.yml')
input_folder = os.path.join('data/inputs-processed', dataset, name)
with open(os.path.join(input_folder, 'transforms.json'), 'rt') as f:
transforms = json.load(f)
with open(os.path.join(result_folder, 'dataparser_transforms.json'), 'rt') as f:
parser_transforms = json.load(f)
def transform_func(m):
if 'applied_transform' in transforms:
M1 = np.array(transforms['applied_transform'] + [[0,0,0,1]])
else:
M1 = np.eye(4)
M = np.array(parser_transforms['transform'] + [[0,0,0,1]])
m = np.array(m)
M = M @ np.linalg.inv(M1)
m = M @ m
m[:3, 3] *= parser_transforms['scale']
return m
if args.original_trajectory:
raw_input_data_jsonl = os.path.join('data', 'inputs-raw', 'spectacular-rec', name, 'data.jsonl')
if os.path.exists(raw_input_data_jsonl):
length_seconds = get_original_length_seconds(raw_input_data_jsonl)
print('original length %g' % length_seconds)
else:
length_seconds = len(transforms['frames']) * 0.3
print('approx. length %g' % length_seconds)
length_seconds /= args.playback_speed
def get_frame_number(frame):
return int(frame['file_path'].rpartition('_')[-1].split('.')[0])
frames = sorted(transforms['frames'], key=get_frame_number)
frames = frames[::args.key_frame_stride]
if args.max_duration is not None:
max_frames = round(args.max_duration / length_seconds * len(frames))
if max_frames < len(frames):
length_seconds = length_seconds * max_frames / len(frames)
print('keeping %d/%d key frames to cut duration to %g' % (max_frames, len(frames), length_seconds))
frames = frames[:max_frames]
frame_poses = [transform_func(frame['transform_matrix']) for frame in frames]
loop = False
else:
length_seconds = args.artificial_length_seconds
loop = True
rough_up_dir = np.array([0, 0, 1])
frame_poses_np = [transform_func(frame['transform_matrix']) for frame in transforms['frames']]
scene_cam_center = np.mean([m[:3, 3] for m in frame_poses_np], axis=0)
scene_cam_mean_dir = np.mean([-m[:3, 2] for m in frame_poses_np], axis=0)
scene_cam_mean_dir = scene_cam_mean_dir / np.linalg.norm(scene_cam_mean_dir)
scene_scale = np.max([np.linalg.norm(m[:3, 3] - scene_cam_center) for m in frame_poses_np])
cam_target = scene_cam_center + scene_cam_mean_dir * scene_scale * args.artificial_relative_look_at_distance
left = np.cross(rough_up_dir, scene_cam_mean_dir)
left = left / np.linalg.norm(left)
up = np.cross(scene_cam_mean_dir, left)
up_dim = np.max(np.abs(np.dot([m[:3, 3] - scene_cam_center for m in frame_poses_np], up)))
left_dim = np.max(np.abs(np.dot([m[:3, 3] - scene_cam_center for m in frame_poses_np], left)))
frame_poses = []
for t in np.linspace(0, 2*np.pi, endpoint=False, num=100):
frame_poses.append(look_at(
scene_cam_center + args.artificial_relative_motion_scale * (
up_dim * up * np.sin(t * args.artificial_y_rounds) +
left_dim * left * np.cos(t)
),
cam_target,
rough_up_dir
))
center_cam_to_world = look_at(scene_cam_center, cam_target, rough_up_dir)
fov = 2.0 * np.arctan(0.5 * transforms['h'] / transforms['fl_y']) / np.pi * 180.0 / args.zoom
frames_per_transition = round((length_seconds * args.fps) / (len(frame_poses) - 1))
width = transforms['w']
height = transforms['h']
if args.resolution is not None:
width, height = [int(x) for x in args.resolution.split('x')]
aspect = width / float(height)
cam_path = {
'render_width': width,
'render_height': height,
'fps': args.fps,
'seconds': length_seconds,
'camera_path': []
}
interpolator = SplineInterpolator(cam_path['camera_path'], frames_per_transition=frames_per_transition)
interpolator.loop = loop
for pose in frame_poses:
# print(frame['file_path'])
interpolator.push({
'aspect': aspect,
'fov': fov,
'camera_to_world': pose
})
interpolator.finish()
add_velocities(cam_path)
cam_path['rolling_shutter_time'] = args.rolling_shutter_time
cam_path['exposure_time'] = args.exposure_time
if args.artificial_keep_center_pose:
for c in cam_path['camera_path']: c['camera_to_world'] = center_cam_to_world.tolist()
trajectory_file = os.path.join(result_folder, 'demo_video_camera_path.json')
if args.output_video_file is None:
video_fn = ['demo_video']
if args.rolling_shutter_time > 0:
video_fn.append('rs')
if args.exposure_time > 0:
video_fn.append('mb')
video_file = os.path.join(result_folder, '-'.join(video_fn) + '.mp4')
else:
video_file = args.output_video_file
render_cmd = [
'ns-render',
'camera-path',
'--load-config', config_file,
'--camera-path-filename', trajectory_file,
'--output-path', video_file
]
if args.video_crf is not None:
render_cmd.extend(['--crf', str(args.video_crf)])
if not args.dry_run:
with open(trajectory_file, 'wt') as f:
json.dump(cam_path, f, indent=4)
subprocess.check_call(render_cmd)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("input_folder", type=str, default=None, nargs='?')
parser.add_argument('--output_variant_folder', default='data/outputs/colmap-sai-cli-imgs/baseline', type=str)
parser.add_argument('-o', '--output_video_file', default=None, type=str)
parser.add_argument('--key_frame_stride', default=3, type=int)
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--original_trajectory', action='store_true')
parser.add_argument('--fps', default=30, type=int)
parser.add_argument('--playback_speed', default=0.5, type=float)
parser.add_argument('--artificial_relative_motion_scale', default=0.6, type=float)
parser.add_argument('--artificial_relative_look_at_distance', default=3, type=float)
parser.add_argument('--artificial_y_rounds', default=1, type=int)
parser.add_argument('--artificial_length_seconds', default=8, type=float)
parser.add_argument('--artificial_keep_center_pose', action='store_true')
parser.add_argument('--rolling_shutter_time', default=0.0, type=float)
parser.add_argument('--max_duration', default=None, type=float)
parser.add_argument('--resolution', type=str, default=None)
parser.add_argument('--exposure_time', default=0.0, type=float)
parser.add_argument('--zoom', default=1.0, type=float)
parser.add_argument('--video_crf', default=None, type=int)
parser.add_argument('--case_number', type=int, default=-1)
args = parser.parse_args()
if args.input_folder in ['all']:
args.case_number = 0
args.input_folder = None
selected_cases = []
if args.input_folder is None:
src_folder = args.output_variant_folder
cases = [os.path.join(src_folder, f) for f in sorted(os.listdir(src_folder))]
if args.case_number == -1:
print('valid cases')
for i, c in enumerate(cases): print(str(i+1) + ':\t' + c)
elif args.case_number == 0:
selected_cases = cases
else:
selected_cases = [cases[args.case_number - 1]]
else:
selected_cases = [args.input_folder]
for case in selected_cases:
print('Processing ' + case)
process(case, args)
================================================
FILE: run_colmap.py
================================================
"""Run COLMAP on a single sequence through Nerfstudio scripts"""
import os
import subprocess
import shutil
import sys
import tempfile
def process(input_folder, args):
name = os.path.basename(os.path.normpath(input_folder))
postf = 'colmap-' + args.dataset + '-imgs'
if args.output_folder is None:
output_folder = os.path.join('data/inputs-processed/' + postf, name)
else:
output_folder = args.output_folder
input_image_folder = os.path.join(input_folder, 'images')
temp_dir = tempfile.TemporaryDirectory()
n = 0
for f in os.listdir(input_image_folder):
if 'depth' in f: continue
if not args.dry_run:
shutil.copyfile(os.path.join(input_image_folder, f), os.path.join(temp_dir.name, f))
n += 1
print('%d images (would be) copied in a temporary directory' % n)
# Print the path to the temporary directory
cmd = [
'ns-process-data',
'images',
'--data', temp_dir.name,
'--output-dir', output_folder
]
print(cmd)
success = False
if not args.dry_run:
for itr in range(args.max_retries):
if os.path.exists(output_folder):
shutil.rmtree(output_folder)
ret = subprocess.run(cmd, check=True, capture_output=True)
success = any([b'CONGRATS' in s for s in [ret.stdout, ret.stderr]]) # hacky
if success:
break
else:
print('COLMAP failed')
print('--- stdout ---')
sys.stdout.buffer.write(ret.stdout)
print('--- stderr ---')
sys.stderr.buffer.write(ret.stderr)
if itr != args.max_retries - 1:
print('Retrying...')
if not success:
raise RuntimeError('Could not get COLMAP to succeed')
temp_dir.cleanup()
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("input_folder", type=str, default=None, nargs='?')
parser.add_argument("output_folder", type=str, default=None, nargs='?')
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--dataset', default='sai-cli')
parser.add_argument('--case_number', type=int, default=-1)
parser.add_argument('--max_retries', type=int, default=1)
args = parser.parse_args()
if args.input_folder in ['all']:
args.case_number = 0
args.input_folder = None
selected_cases = []
misc = False
PROCESSED_PREFIX = 'data/inputs-processed/'
if args.dataset.startswith(PROCESSED_PREFIX):
args.dataset = args.dataset[len(PROCESSED_PREFIX):]
if args.dataset.endswith('/'): args.dataset = args.dataset[:-1]
if args.input_folder is None:
input_root = os.path.join(PROCESSED_PREFIX, args.dataset)
cases = [os.path.join(input_root, f) for f in sorted(os.listdir(input_root))]
if args.case_number == -1:
print('valid cases')
for i, c in enumerate(cases): print(str(i+1) + ':\t' + c)
elif args.case_number == 0:
selected_cases = cases
else:
selected_cases = [cases[args.case_number - 1]]
else:
selected_cases = [args.input_folder]
for case in selected_cases:
print('Processing ' + case)
process(case, args)
================================================
FILE: scripts/compile_comparison_video.sh
================================================
#!/bin/bash
set -eux
INPUT_BASELINE="$1"
INPUT_OURS="$2"
OUTPUT="$3"
: "${OURS_NAME:=Deblurred}"
#: "${VIDEO_MODE:=HALF}"
: "${VIDEO_MODE:=SWEEP}"
: "${DRAW_TEXT:=ON}"
: "${DRAW_BAR:=ON}"
: "${CROP_TO_HD_ASPECT:=ON}"
if [ $CROP_TO_HD_ASPECT == "ON" ]; then
BASE_FILTER="
[0:v]crop=iw:'min(ih,iw/16*9)'[base];\
[1:v]crop=iw:'min(ih,iw/16*9)'[ours]"
else
BASE_FILTER="[0:v]copy[base];[1:v]copy[ours]"
fi
if [ $DRAW_TEXT == "ON" ]; then
BASE_FILTER="
$BASE_FILTER;\
[base]drawtext=text='Baseline':fontcolor=white:fontsize=h/50:x=w/50:y=h/50[base];\
[ours]drawtext=text='$OURS_NAME':fontcolor=white:fontsize=h/50:x=w-tw-w/50:y=h/50[ours]"
fi
if [ $DRAW_BAR == "ON" ]; then
BASE_FILTER="
$BASE_FILTER;\
color=0x80ff80,format=rgba[bar];\
[bar][base]scale2ref[bar][base];\
[bar]crop=iw:ih/200:0:0[bar];\
[ours][bar]overlay=x=0:y=0[ours]"
fi
case $VIDEO_MODE in
HALF)
VIDEO_FILTER="
$BASE_FILTER;\
[base]crop=iw/2:ih:0:0[left_crop];\
[ours]crop=iw/2:ih:iw/2:0[right_crop];\
[left_crop][right_crop]hstack"
;;
SWEEP)
LEN=8
VIDEO_FILTER="
$BASE_FILTER;\
color=0x00000000,format=rgba,scale=[black];\
color=0xffffffff,format=rgba[white];\
[black][base]scale2ref[black][base];\
[white][base]scale2ref[white][base];\
[white][black]blend=all_expr='if(lte(X,W*abs(1-mod(T,$LEN)/$LEN*2)),B,A)'[mask];\
[ours][mask]alphamerge[overlayalpha]; \
[base][overlayalpha]overlay=shortest=1"
;;
*)
echo -n "unknown video mode $VIDEO_MODE"
exit 1
;;
esac
ffmpeg -i "$INPUT_BASELINE" -i "$INPUT_OURS" -filter_complex "$VIDEO_FILTER" -hide_banner -y "$OUTPUT"
================================================
FILE: scripts/install.sh
================================================
#!/bin/bash
set -eux
: "${BUILD_NERFSTUDIO:=ON}"
: "${INSTALL_SAI:=ON}"
# You may also need to run this
# pip install --upgrade pip setuptools
if [ $BUILD_NERFSTUDIO == "ON" ]; then
# Install the custom fork of Nerfstudio
cd nerfstudio
pip install -e .
cd ..
fi
# ... then install the custom gsplat (order may matter here!)
if [ $BUILD_NERFSTUDIO == "ON" ]; then
cd gsplat
pip install -e .
cd ..
fi
if [ $INSTALL_SAI == "ON" ]; then
pip install spectacularAI[full]==1.31.0
fi
================================================
FILE: scripts/process_and_train_sai_custom.sh
================================================
#!/bin/bash
# Process and train a custom recording created with Spectacular Rec.
#
# This version uses both motion blur compensation only and should work
# well with iPhone data and other devices with short rolling shutter
# readout times (or global shutter cameras)
#
# Run as
#
# ./scripts/process_and_train_sai_custom.sh /PATH/TO/RECORDING.zip
#
# or, in headless mode
#
# SAI_PREVIEW=OFF ./scripts/process_and_train_sai_custom.sh \
# /PATH/TO/RECORDING.zip
set -eux
NAME_W_EXT=`basename "$1"`
NAME=${NAME_W_EXT%.zip}
: "${SAI_PREVIEW:=ON}"
: "${SKIP_COLMAP:=OFF}"
if [ $SAI_PREVIEW == "ON" ]; then
PREVIEW_FLAG="--preview"
else
PREVIEW_FLAG=""
fi
if [ $SKIP_COLMAP == "ON" ]; then
COLMAP_FLAG="--skip_colmap"
else
COLMAP_FLAG=""
fi
python process_sai_custom.py "$1" $COLMAP_FLAG $PREVIEW_FLAG
python train.py data/inputs-processed/custom/$NAME --no_eval --train_all $PREVIEW_FLAG
================================================
FILE: scripts/process_and_train_video.sh
================================================
#!/bin/bash
# Process and train deblurred 3DGS from a video.
# set ROLLING_SHUTTER=ON to train a rolling shutter compensated model instead
# of a deblurred one. For simultaneous MB and RS compensation, see
# process_and_train_sai_custom.sh
set -eux
NAME_W_EXT=`basename "$1"`
NAME="${NAME_W_EXT%.*}"
: "${ROLLING_SHUTTER:=OFF}"
: "${PREVIEW:=ON}"
if [ $PREVIEW == "ON" ]; then
PREVIEW_FLAG="--preview"
else
PREVIEW_FLAG=""
fi
if [ $ROLLING_SHUTTER == "ON" ]; then
MODE_FLAGS="--no_motion_blur"
else
MODE_FLAGS="--no_rolling_shutter"
fi
mkdir -p "data/inputs-processed/custom"
TARGET_DIR="data/inputs-processed/custom/$NAME"
ns-process-data video --num-frames-target 100 --data "$1" --output-dir "$TARGET_DIR"
python train.py "$TARGET_DIR" $MODE_FLAGS --velocity_opt_zero_init --train_all --no_eval $PREVIEW_FLAG
================================================
FILE: scripts/process_smartphone_dataset.sh
================================================
#!/bin/bash
set -eux
# Process raw input data. If set to OFF, then sai-cli and
# colmap-sai-cli-imgs intermediary datasets must have been
# fully generated or downloaded
: "${PROCESS_RAW:=ON}"
# Extra variants in the supplementary
: "${EXTRA_VARIANTS:=OFF}"
# Show preview in sai-cli
: "${PREVIEW:=ON}"
if [ $PREVIEW == "ON" ]; then
PREVIEW_FLAG="--preview"
else
PREVIEW_FLAG=""
fi
if [ $PROCESS_RAW == "ON" ]; then
# Process and convert using the Spectacular AI SDK to get VIO velocity and pose estimates
python process_sai_inputs.py $PREVIEW_FLAG
# you can also run individual failing cases with: python run_colmap.py all --case=N
python run_colmap.py all --max_retries=10
fi
rm -rf data/inputs-processed/colmap-sai-cli-vels*
rm -rf data/inputs-processed/colmap-sai-cli-orig-intrinsics*
rm -rf data/inputs-processed/sai-cli-blur-scored
# --- real data, COLMAP intrinsics
python combine.py all
python train_eval_split_by_blur_score.py colmap-sai-cli-vels all
# --- real data, factory intrinsics
python combine.py --keep_intrinsics all
python train_eval_split_by_blur_score.py colmap-sai-cli-orig-intrinsics all
# --- real data, calibrated intrinsics
rm -rf data/inputs-processed/colmap-sai-cli-calib-intrinsics*
for i in 1 2 3 4 5; do
python combine.py --case=$i --keep_intrinsics --set_rolling_shutter_to=0.005
done
for i in 6 7 8; do
python combine.py --case=$i --override_calibration=data/inputs-raw/spectacular-rec-extras/calibration/manual-calibration-result-pixel5.json
done
for i in 9 10 11; do
python combine.py --case=$i --override_calibration=data/inputs-raw/spectacular-rec-extras/calibration/manual-calibration-result-s20.json
done
python train_eval_split_by_blur_score.py colmap-sai-cli-calib-intrinsics all
if [ $EXTRA_VARIANTS == "ON" ]; then
rm -rf data/inputs-processed/colmap-sai-cli-no-blur-select-imgs*
if [ $PROCESS_RAW == "ON" ]; then
# --- real data, no blur score filter
python process_sai_inputs.py --no_blur_score_filter $PREVIEW_FLAG
fi
# NOTE: run this until success
python run_colmap.py --dataset=sai-cli-no-blur-select all --max_retries=10
# --- real data, no blur score filter, COLMAP intrinsics
python combine.py --dataset=sai-cli-no-blur-select all
# --- real data, no blur score filter, factory intrinsics
python combine.py --keep_intrinsics --dataset=sai-cli-no-blur-select all
fi
================================================
FILE: scripts/render_and_compile_comparison_video.sh
================================================
#!/bin/bash
set -eux
INPUT_BASE="$1"
INPUT_OURS="$2"
# zoom 2x original focal length to highlight details, slow speed (approx.)
RENDER_ARGS="--zoom=1.5 --original_trajectory --playback_speed=0.25"
NAME=`basename "$INPUT_BASE"`
mkdir -p data/renders
BASE_VID="data/renders/$NAME-baseline.mp4"
OURS_VID="data/renders/$NAME-deblurred.mp4"
COMP_VID="data/renders/$NAME-comparison.mp4"
python render_video.py $RENDER_ARGS "$INPUT_BASE" -o "$BASE_VID"
python render_video.py $RENDER_ARGS "$INPUT_OURS" -o "$OURS_VID"
./scripts/compile_comparison_video.sh "$BASE_VID" "$OURS_VID" "$COMP_VID"
================================================
FILE: scripts/render_and_train_comparison_sai_custom.sh
================================================
#!/bin/bash
# Process and train a custom recording created with Spectacular Rec.
# Trains two versions: baseline and deblurred and renders a video that
# shows their differences. With normal, not-very-blurry recordings, the
# expected improvement is subtle but noticeable.
set -eu
NAME_W_EXT=`basename "$1"`
NAME=${NAME_W_EXT%.zip}
echo "============= Training motion-blur compensated model =========="
# Note: do not set SKIP_COLMAP here: the 3DGS reconstruction may work
# fine but the comparison video will often be misaligned
./scripts/process_and_train_sai_custom.sh "$1"
echo "============= Training baseline model =========="
python train.py data/inputs-processed/custom/$NAME \
--no_eval --train_all --no_rolling_shutter --no_pose_opt --no_motion_blur --no_velocity_opt --preview
echo "============= Rendering comparison video =========="
./scripts/render_and_compile_comparison_video.sh \
"data/outputs/custom/baseline/$NAME" \
"data/outputs/custom/pose_opt-motion_blur-rolling_shutter-velocity_opt/$NAME"
echo "Success: see data/renders/$NAME-comparison.mp4"
================================================
FILE: scripts/render_and_train_comparison_video.sh
================================================
#!/bin/bash
# Process and train 3DGS from a video with and without deblurring
# (or rolling shutter compensation if ROLLINGS_SHUTTER=ON) and
# render a comparison video
set -eux
NAME_W_EXT=`basename "$1"`
NAME="${NAME_W_EXT%.*}"
: "${ROLLING_SHUTTER:=OFF}"
if [ $ROLLING_SHUTTER == "ON" ]; then
MODE_NAME="rolling_shutter"
export OURS_NAME="Compensated"
else
MODE_NAME="motion_blur"
export OURS_NAME="Deblurred"
fi
export ROLLING_SHUTTER
echo "============= Training $MODE_NAME compensated model =========="
./scripts/process_and_train_video.sh "$1"
echo "============= Training baseline model =========="
TARGET_DIR="data/inputs-processed/custom/$NAME"
python train.py "$TARGET_DIR" --no_rolling_shutter --no_pose_opt \
--no_motion_blur --no_velocity_opt --train_all --no_eval
echo "============= Rendering comparison video =========="
./scripts/render_and_compile_comparison_video.sh \
"data/outputs/custom/baseline/$NAME" \
"data/outputs/custom/pose_opt-${MODE_NAME}-velocity_opt-zero_init/$NAME"
echo "Success: see data/renders/$NAME-comparison.mp4"
================================================
FILE: train.py
================================================
"""Train a single instance"""
import os
import subprocess
import shutil
import sys
import time
import datetime
import json
import re
DATASET_SPECIFIC_PARAMETERS = {
r".*synthetic.*": [
# '--max-num-iterations', '20000', # this would be enough, usually
'--pipeline.model.num-downscales', '0', # low resolution -> no downscaling
# These help reconstructing large areas with very smooth color,
# i.e., the synthetic sky. With defaults, large holes can easily appear
'--pipeline.model.background-color', 'auto',
'--pipeline.model.cull-scale-thresh', '2.0',
# Evaluation data is known to be static. Don't try to optimize camera velocities
'--pipeline.model.optimize-eval-velocities=False',
# Hight motion blur, needs more samples
'--pipeline.model.blur-samples=10',
]
}
def print_cmd(cmd):
print('RUNNING COMMAND: ' + ' '.join(cmd))
def flags_to_variant_name_and_cmd(args):
cmd = []
variant = []
use_gamma_correction = False
optimize_eval_cameras = False
if not args.get('no_pose_opt', False):
optimize_eval_cameras = True
variant.append('pose_opt')
cmd.extend([
'--pipeline.model.camera-optimizer.mode=SO3xR3',
## '--pipeline.model.sh-degree=0'
])
if not args.get('no_motion_blur', False):
variant.append('motion_blur')
# default blur samples: 5
use_gamma_correction = not args.get('no_gamma', False)
if not use_gamma_correction:
variant.append('no_gamma')
else:
cmd.append('--pipeline.model.blur-samples=0')
if not args.get('no_rolling_shutter', False):
variant.append('rolling_shutter')
else:
cmd.append('--pipeline.model.rolling-shutter-compensation=False')
if use_gamma_correction:
# min RGB level only seems necessary with gamma correction
cmd.append('--pipeline.model.min-rgb-level=10')
else:
cmd.append('--pipeline.model.gamma=1')
if not args.get('no_velocity_opt', False):
optimize_eval_cameras = True
cmd.append('--pipeline.model.camera-velocity-optimizer.enabled=True')
variant.append('velocity_opt')
if args.get('velocity_opt_zero_init', False):
cmd.append('--pipeline.model.camera-velocity-optimizer.zero-initial-velocities=True')
variant.append('zero_init')
if len(variant) == 0:
variant.append('baseline')
return '-'.join(variant), cmd, optimize_eval_cameras
def evaluate(output_folder, elapsed_time, dry_run=False, render_images=True):
result_paths = find_config_path(output_folder)
if result_paths is None:
if dry_run: return
assert(False)
out_path, config_path = result_paths
metrics_path = os.path.join(out_path, 'metrics.json')
elapsed_time
eval_cmd = [
'ns-eval',
'--load-config', config_path,
'--output-path', metrics_path
]
print_cmd(eval_cmd)
if not dry_run:
subprocess.check_call(eval_cmd)
with open(metrics_path) as f:
metrics = json.load(f)
metrics['wall_clock_time_seconds'] = elapsed_time
with open(metrics_path, 'w') as f:
json.dump(metrics, f, indent=4)
if render_images:
render_cmd = [
'python', 'render_model.py',
'--load-config', config_path
]
print_cmd(render_cmd)
if not dry_run:
subprocess.check_call(render_cmd)
def process(input_folder, args):
name = os.path.split(input_folder)[-1]
cmd = [
'ns-train',
'splatfacto',
'--data', input_folder,
'--viewer.quit-on-train-completion', 'True',
'--pipeline.model.rasterize-mode', 'antialiased',
'--pipeline.model.use-scale-regularization', 'True',
# '--logging.local-writer.max-log-size=0'
]
for pattern, values in DATASET_SPECIFIC_PARAMETERS.items():
if re.match(pattern, args.dataset):
cmd.extend(values)
if '--max-num-iterations' not in cmd:
if args.draft:
cmd.extend(['--max-num-iterations', '3000'])
else:
cmd.extend(['--max-num-iterations', '20000'])
if args.preview:
cmd.extend([
'--vis=viewer+tensorboard',
'--viewer.websocket-host=127.0.0.1'
])
else:
cmd.append('--vis=tensorboard')
variant, variant_cmd, optimize_eval_cameras = flags_to_variant_name_and_cmd(vars(args))
cmd.extend(variant_cmd)
if args.case_number is None:
dataset_folder = 'custom'
else:
dataset_folder = args.dataset
variant_folder = os.path.join(dataset_folder, variant)
output_prefix = 'data/outputs'
# note: 'name' is automatically added by Nerfstudio
output_root = os.path.join(output_prefix, variant_folder)
cmd.extend(['--output-dir', output_root])
cmd.extend([
'nerfstudio-data',
'--orientation-method', 'none',
])
if args.train_all:
cmd.extend([
'--eval-mode', 'all'
])
optimize_eval_cameras = False
elif '-scored' in args.input_folder or args.dataset == 'colmap-bad-nerf-synthetic-deblurring':
cmd.extend([
'--eval-mode', 'filename'
])
else:
cmd.extend([
'--eval-mode', 'interval',
'--eval-interval', '8'
])
#cmd.extend(['--eval-mode', 'all'])
if optimize_eval_cameras:
cmd.extend([
'--optimize-eval-cameras', 'True',
])
print_cmd(cmd)
output_folder = os.path.join(output_root, name)
elapsed_time = 0
if not args.dry_run and not args.eval_only:
if os.path.exists(output_folder):
shutil.rmtree(output_folder)
start_time = time.time()
subprocess.check_call(cmd)
end_time = time.time()
elapsed_time = end_time - start_time
print('Training time: %s' % str(datetime.timedelta(seconds=elapsed_time)))
if not args.no_eval:
evaluate(output_folder, elapsed_time,
dry_run=args.dry_run,
render_images=args.render_images)
def find_config_path(output_folder):
model_folder = os.path.join(output_folder, 'splatfacto')
paths = []
if os.path.exists(model_folder):
for subdir in os.listdir(model_folder):
out_path = os.path.join(model_folder, subdir)
config_path = os.path.join(out_path, 'config.yml')
if os.path.exists(config_path):
paths.append((out_path, config_path))
if len(paths) == 0: return None
assert(len(paths) == 1)
return paths[0]
def add_velocity_opt_variants(variants, dataset):
has_velocity_info = ('sai-' in dataset
or 'spectacular-rec' in dataset
or ('synthetic-' in dataset and 'colmap' not in dataset and 'hloc' not in dataset)
)
new_variants = []
for v in variants:
v1 = v.copy()
no_velocity_to_optimize = 'no_rolling_shutter' in v and 'no_motion_blur' in v
if has_velocity_info or no_velocity_to_optimize:
v1.add('no_velocity_opt')
new_variants.append(v1)
if no_velocity_to_optimize: continue
if has_velocity_info:
new_variants.append(v)
v2 = v.copy()
v2.add('velocity_opt_zero_init')
new_variants.append(v2)
return new_variants
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
# note: velocity optimization arguments are auto-added to all of these
baseline = {
'no_pose_opt',
'no_motion_blur',
'no_rolling_shutter'
}
no_rolling_shutter_variants = [
baseline,
{ 'no_rolling_shutter', 'no_pose_opt' },
{ 'no_rolling_shutter', 'no_motion_blur' },
{ 'no_rolling_shutter' }
]
full_variants = no_rolling_shutter_variants + [
{ 'no_pose_opt', 'no_motion_blur' },
{ 'no_pose_opt' },
{ 'no_motion_blur' },
set([])
]
default_variants = full_variants
bad_nerf_variants = [
baseline,
{ 'no_rolling_shutter', 'no_pose_opt' },
{ 'no_rolling_shutter' }
]
add_popt = lambda a: a + [o - {'no_pose_opt'} for o in a if 'no_pose_opt' in o]
variants_by_dataset = {
'synthetic-clear': [
baseline
],
'synthetic-mb': add_popt([
baseline,
{ 'no_pose_opt', 'no_rolling_shutter' }
]),
'synthetic-rs': add_popt([
baseline,
{ 'no_pose_opt', 'no_motion_blur' }
]),
'synthetic-posenoise': add_popt([
baseline,
{ 'no_rolling_shutter', 'no_motion_blur' }
]),
'synthetic-mbrs': add_popt([
baseline,
{ 'no_pose_opt' },
{ 'no_pose_opt', 'no_motion_blur' },
{ 'no_pose_opt', 'no_rolling_shutter' }
]),
'synthetic-posenoise-2nd-pass': [
baseline
],
'colmap-bad-nerf-synthetic-deblurring': bad_nerf_variants,
'colmap-bad-nerf-synthetic-novel-view': bad_nerf_variants,
'colmap-bad-nerf-synthetic-novel-view-manual-pc': add_popt(bad_nerf_variants),
'colmap-exblurf-synthetic-novel-view-manual-pc': bad_nerf_variants,
'hloc-exblurf-synthetic-novel-view-manual-pc': bad_nerf_variants,
'hloc-bad-nerf-synthetic-novel-view-manual-pc': bad_nerf_variants,
'hloc-bad-nerf-synthetic-novel-view-exact-intrinsics-manual-pc': bad_nerf_variants,
'hloc-bad-gaussians-synthetic-novel-view-manual-pc': bad_nerf_variants,
'colmap-bad-gaussians-synthetic-novel-view-manual-pc': bad_nerf_variants,
'colmap-mpr-deblurred-synthetic-all-manual-pc': bad_nerf_variants,
'colmap-mpr-deblurred-synthetic-novel-view-manual-pc': bad_nerf_variants + [{ 'no_rolling_shutter', 'no_motion_blur' }],
}
parser.add_argument("input_folder", type=str, default=None, nargs='?')
parser.add_argument("--preview", action='store_true', help='show Viser preview')
parser.add_argument("--no_pose_opt", action='store_true')
parser.add_argument("--no_motion_blur", action='store_true')
parser.add_argument('--no_rolling_shutter', action='store_true')
parser.add_argument('--no_velocity_opt', action='store_true')
parser.add_argument('--velocity_opt_zero_init', action='store_true')
parser.add_argument('--dataset', type=str, default='colmap-sai-cli-vels-blur-scored')
parser.add_argument('--draft', action='store_true')
parser.add_argument('--no_gamma', action='store_true')
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--render_images', action='store_true')
parser.add_argument('--eval_only', action='store_true')
parser.add_argument('--no_eval', action='store_true')
parser.add_argument('--train_all', action='store_true')
parser.add_argument('--case_number', type=int, default=None)
args = parser.parse_args()
if args.input_folder is None and args.case_number is None:
args.case_number = -1
if args.case_number is not None:
INPUT_ROOT = 'data/inputs-processed/' + args.dataset
sessions = [os.path.join(INPUT_ROOT, f) for f in sorted(os.listdir(INPUT_ROOT))]
variants = add_velocity_opt_variants(variants_by_dataset.get(args.dataset, default_variants), args.dataset)
cases = [(s, v) for v in variants for s in sessions]
if args.case_number <= 0:
print('valid cases')
for i, (c, v) in enumerate(cases):
variant = flags_to_variant_name_and_cmd({k: True for k in v})[0]
print(str(i+1) + ':\t' + variant + '\t' + c)
sys.exit(0)
else:
args.input_folder, variant = cases[args.case_number - 1]
for p in variant: setattr(args, p, True)
print('Running %s %s' % (args.input_folder, str(variant)))
process(args.input_folder, args)
================================================
FILE: train_eval_split_by_blur_score.py
================================================
"""Combine COLMAP poses with sai-cli velocities"""
import os
import json
import shutil
def process(input_folder, output_prefix, args):
name = os.path.basename(os.path.normpath(input_folder))
print('name', name)
def read_json(folder):
with open(os.path.join(folder, 'transforms.json')) as f:
return json.load(f)
print(input_folder)
output_folder = os.path.join(output_prefix, name)
input_image_folder = os.path.join(input_folder, 'images')
output_image_folder = os.path.join(output_folder, 'images')
poses = read_json(input_folder)
poses['frames'].sort(key=lambda x: x['file_path'])
if not args.dry_run:
if os.path.exists(output_folder): shutil.rmtree(output_folder)
os.makedirs(output_image_folder)
ival_start = 0
while ival_start < len(poses['frames']):
ival_end = ival_start + args.interval
least_blur = sorted(poses['frames'][ival_start:ival_end], key=lambda x: x['motion_blur_score'])[0]['file_path']
for frame in poses['frames'][ival_start:ival_end]:
id = frame['file_path']
if id == least_blur:
new_name = f'eval_' + os.path.basename(id)
else:
new_name = f'train_' + os.path.basename(id)
old_file_name = os.path.join(input_image_folder, os.path.basename(id))
new_file_name = os.path.join(output_image_folder, new_name)
frame['file_path'] = os.path.join('images', new_name)
print("%s -> %s (%g)" % (old_file_name, new_file_name, frame['motion_blur_score']))
if not args.dry_run:
shutil.copyfile(old_file_name, new_file_name)
ival_start = ival_end
# colmap_folder = os.path.join(args.input_folder, 'colmap')
ply_pc = os.path.join(input_folder, 'sparse_pc.ply')
print('Output folder: ' + output_folder)
if not args.dry_run:
# shutil.copytree(colmap_folder, os.path.join(output_folder, 'colmap'))
shutil.copyfile(ply_pc, os.path.join(output_folder, 'sparse_pc.ply'))
with open (os.path.join(output_folder, 'transforms.json'), 'w') as f:
json.dump(poses, f, indent=4)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('dataset')
parser.add_argument("input_folder", type=str, default=None, nargs='?')
parser.add_argument('--interval', type=int, default=8)
parser.add_argument('--dry_run', action='store_true')
parser.add_argument('--case_number', type=int, default=-1)
args = parser.parse_args()
if args.input_folder in ['all']:
args.case_number = 0
args.input_folder = None
selected_cases = []
PROCESSED_PREFIX = 'data/inputs-processed/'
if args.dataset.startswith(PROCESSED_PREFIX):
args.dataset = args.dataset[len(PROCESSED_PREFIX):]
out_folder = os.path.join(PROCESSED_PREFIX, args.dataset + '-blur-scored')
if args.input_folder is None:
processed_prefix = os.path.join(PROCESSED_PREFIX, args.dataset)
cases = [os.path.join(processed_prefix, f) for f in sorted(os.listdir(processed_prefix))]
if args.case_number == -1:
print('valid cases')
for i, c in enumerate(cases): print(str(i+1) + ':\t' + c)
elif args.case_number == 0:
selected_cases = cases
else:
selected_cases = [cases[args.case_number - 1]]
else:
selected_cases = [args.input_folder]
for case in selected_cases:
print('Processing ' + case)
process(case, out_folder, args)
gitextract_h5evqv1k/ ├── .gitignore ├── .gitmodules ├── CITATION.bib ├── LICENSE ├── NOTICE ├── README.md ├── combine.py ├── download_data.py ├── parse_outputs.py ├── process_deblur_nerf_inputs.py ├── process_sai_custom.py ├── process_sai_inputs.py ├── process_synthetic_inputs.py ├── render_model.py ├── render_video.py ├── run_colmap.py ├── scripts/ │ ├── compile_comparison_video.sh │ ├── install.sh │ ├── process_and_train_sai_custom.sh │ ├── process_and_train_video.sh │ ├── process_smartphone_dataset.sh │ ├── render_and_compile_comparison_video.sh │ ├── render_and_train_comparison_sai_custom.sh │ └── render_and_train_comparison_video.sh ├── train.py └── train_eval_split_by_blur_score.py
SYMBOL INDEX (41 symbols across 12 files)
FILE: combine.py
function process (line 6) | def process(input_folder, args):
FILE: download_data.py
class DownloadProcessedData (line 12) | class DownloadProcessedData:
method main (line 18) | def main(self):
FILE: parse_outputs.py
function parse_metrics (line 5) | def parse_metrics(metrics_path):
function find_and_parse_directories_containing_splatting_metrics (line 9) | def find_and_parse_directories_containing_splatting_metrics(root_dir):
FILE: process_deblur_nerf_inputs.py
function process (line 10) | def process(input_folder, args, pass_no=1):
FILE: process_sai_custom.py
function ensure_exposure_time (line 12) | def ensure_exposure_time(target, input_folder):
function process (line 33) | def process(args):
FILE: process_sai_inputs.py
function process_subfolders (line 19) | def process_subfolders(spec, output_folder, method='sai', only_this_case...
FILE: process_synthetic_inputs.py
function rotation_matrix_to_rotvec (line 13) | def rotation_matrix_to_rotvec(R):
function quaternion_to_rotation_matrix (line 18) | def quaternion_to_rotation_matrix(q_wxyz):
function deterministic_uniform_rand_generator (line 26) | def deterministic_uniform_rand_generator(seed=1000):
function process (line 44) | def process(data_path, target, noisy_poses=False, noisy_intrinsics=False):
function point_cloud_to_ply (line 203) | def point_cloud_to_ply(xyzrgbs, out_fn):
function triangulate_point (line 221) | def triangulate_point(o1, d1, o2, d2):
function reproject_point (line 230) | def reproject_point(p, c2w, intrinsics):
function reprojection_error (line 240) | def reprojection_error(p_reproj, p_orig):
function triangulate (line 244) | def triangulate(points1, points2, c2w_i, c2w_j, matches, intrinsics, rep...
function generate_seed_points_match_and_triangulate (line 285) | def generate_seed_points_match_and_triangulate(target, visualize=False, ...
function process_dataset_folder (line 384) | def process_dataset_folder(
FILE: render_model.py
function save_img (line 21) | def save_img(image, image_path, verbose=True) -> None:
function save_depth (line 48) | def save_depth(depth, depth_path, verbose=True, scale_factor=SCALE_FACTO...
function save_outputs_helper (line 71) | def save_outputs_helper(
class RenderModel (line 150) | class RenderModel:
method main (line 162) | def main(self):
FILE: render_video.py
class SplineInterpolator (line 7) | class SplineInterpolator:
method __init__ (line 8) | def __init__(self, target, frames_per_transition):
method push (line 17) | def push(self, frame):
method finish (line 26) | def finish(self):
function look_at (line 62) | def look_at(cam_pos, cam_target, up_dir=np.array([0, 0, 1])):
function get_original_length_seconds (line 74) | def get_original_length_seconds(raw_input_data_jsonl):
function add_velocities (line 85) | def add_velocities(camera_path, loop=False):
function process (line 117) | def process(out_folder, args):
FILE: run_colmap.py
function process (line 8) | def process(input_folder, args):
FILE: train.py
function print_cmd (line 26) | def print_cmd(cmd):
function flags_to_variant_name_and_cmd (line 29) | def flags_to_variant_name_and_cmd(args):
function evaluate (line 78) | def evaluate(output_folder, elapsed_time, dry_run=False, render_images=T...
function process (line 111) | def process(input_folder, args):
function find_config_path (line 203) | def find_config_path(output_folder):
function add_velocity_opt_variants (line 216) | def add_velocity_opt_variants(variants, dataset):
FILE: train_eval_split_by_blur_score.py
function process (line 6) | def process(input_folder, output_prefix, args):
Condensed preview — 26 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (130K chars).
[
{
"path": ".gitignore",
"chars": 124,
"preview": "*~\n__pycache__/\n*.pyc\n.vscode\noutputs/\nrenders/\ndata/\nvenv/\nstatic/\nsource_videos\nsource_videos/\noverlay_images/\nindex.h"
},
{
"path": ".gitmodules",
"chars": 178,
"preview": "[submodule \"nerfstudio\"]\n\tpath = nerfstudio\n\turl = https://github.com/SpectacularAI/nerfstudio\n[submodule \"gsplat\"]\n\tpat"
},
{
"path": "CITATION.bib",
"chars": 395,
"preview": "@misc{seiskari2024gaussian,\n title={Gaussian Splatting on the Move: Blur and Rolling Shutter Compensation for Natur"
},
{
"path": "LICENSE",
"chars": 11357,
"preview": " Apache License\n Version 2.0, January 2004\n "
},
{
"path": "NOTICE",
"chars": 1294,
"preview": "The file process_synthetic_inputs.py contains source code from Deblur-NeRF\nhttps://github.com/limacv/Deblur-NeRF/blob/76"
},
{
"path": "README.md",
"chars": 9904,
"preview": "# Gaussian Splatting on the Move: <br> Blur and Rolling Shutter Compensation for Natural Camera Motion\n\n[![arXiv preprin"
},
{
"path": "combine.py",
"chars": 8098,
"preview": "\"\"\"Combine COLMAP poses with sai-cli velocities\"\"\"\nimport os\nimport json\nimport shutil\n\ndef process(input_folder, args):"
},
{
"path": "download_data.py",
"chars": 4679,
"preview": "\"\"\"Script to download processed datasets.\"\"\"\nimport os\nimport subprocess\nfrom dataclasses import dataclass\nfrom pathlib "
},
{
"path": "parse_outputs.py",
"chars": 2226,
"preview": "\"\"\"Parse output metrics from JSON files\"\"\"\nimport os\nimport json\n\ndef parse_metrics(metrics_path):\n with open(metrics"
},
{
"path": "process_deblur_nerf_inputs.py",
"chars": 10793,
"preview": "\"\"\"Run COLMAP on a single sequence through Nerfstudio scripts\"\"\"\nimport os\nimport subprocess\nimport shutil\nimport tempfi"
},
{
"path": "process_sai_custom.py",
"chars": 4538,
"preview": "\"\"\"Process a single custom SAI input\"\"\"\nimport os\nimport subprocess\nimport shutil\nimport json\nimport tempfile\n\nfrom proc"
},
{
"path": "process_sai_inputs.py",
"chars": 4106,
"preview": "\"\"\"Process raw input data to the main benchmark format\"\"\"\nimport os\nimport subprocess\nimport shutil\nimport json\n\nSAI_CLI"
},
{
"path": "process_synthetic_inputs.py",
"chars": 17536,
"preview": "\"\"\"Process raw synthetic input data to the main benchmark format\"\"\"\nimport os\nimport json\nimport shutil\nimport cv2\nimpor"
},
{
"path": "render_model.py",
"chars": 8552,
"preview": "\"\"\"Load g model and render all outputs to disc\"\"\"\n\nfrom dataclasses import dataclass\nfrom pathlib import Path\nimport tor"
},
{
"path": "render_video.py",
"chars": 12180,
"preview": "\"\"\"Generate demo video camera trajectory\"\"\"\nimport os\nimport json\nimport subprocess\nimport numpy as np\n\nclass SplineInte"
},
{
"path": "run_colmap.py",
"chars": 3415,
"preview": "\"\"\"Run COLMAP on a single sequence through Nerfstudio scripts\"\"\"\nimport os\nimport subprocess\nimport shutil\nimport sys\nim"
},
{
"path": "scripts/compile_comparison_video.sh",
"chars": 1766,
"preview": "#!/bin/bash\nset -eux\n\nINPUT_BASELINE=\"$1\"\nINPUT_OURS=\"$2\"\nOUTPUT=\"$3\"\n: \"${OURS_NAME:=Deblurred}\"\n\n#: \"${VIDEO_MODE:=HAL"
},
{
"path": "scripts/install.sh",
"chars": 512,
"preview": "#!/bin/bash\nset -eux\n\n: \"${BUILD_NERFSTUDIO:=ON}\"\n: \"${INSTALL_SAI:=ON}\"\n\n# You may also need to run this\n# pip install "
},
{
"path": "scripts/process_and_train_sai_custom.sh",
"chars": 914,
"preview": "#!/bin/bash\n\n# Process and train a custom recording created with Spectacular Rec.\n#\n# This version uses both motion blur"
},
{
"path": "scripts/process_and_train_video.sh",
"chars": 835,
"preview": "#!/bin/bash\n\n# Process and train deblurred 3DGS from a video.\n# set ROLLING_SHUTTER=ON to train a rolling shutter compen"
},
{
"path": "scripts/process_smartphone_dataset.sh",
"chars": 2364,
"preview": "#!/bin/bash\nset -eux\n\n# Process raw input data. If set to OFF, then sai-cli and\n# colmap-sai-cli-imgs intermediary datas"
},
{
"path": "scripts/render_and_compile_comparison_video.sh",
"chars": 591,
"preview": "#!/bin/bash\nset -eux\n\nINPUT_BASE=\"$1\"\nINPUT_OURS=\"$2\"\n\n# zoom 2x original focal length to highlight details, slow speed "
},
{
"path": "scripts/render_and_train_comparison_sai_custom.sh",
"chars": 1088,
"preview": "#!/bin/bash\n\n# Process and train a custom recording created with Spectacular Rec.\n# Trains two versions: baseline and de"
},
{
"path": "scripts/render_and_train_comparison_video.sh",
"chars": 1090,
"preview": "#!/bin/bash\n\n# Process and train 3DGS from a video with and without deblurring\n# (or rolling shutter compensation if ROL"
},
{
"path": "train.py",
"chars": 12110,
"preview": "\"\"\"Train a single instance\"\"\"\nimport os\nimport subprocess\nimport shutil\nimport sys\nimport time\nimport datetime\nimport js"
},
{
"path": "train_eval_split_by_blur_score.py",
"chars": 3650,
"preview": "\"\"\"Combine COLMAP poses with sai-cli velocities\"\"\"\nimport os\nimport json\nimport shutil\n\ndef process(input_folder, output"
}
]
About this extraction
This page contains the full source code of the SpectacularAI/3dgs-deblur GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 26 files (121.4 KB), approximately 30.6k tokens, and a symbol index with 41 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.