light-side

⚡️PyTorch Lightning Implementations of Recent Low-Light Image Enhancement !

https://github.com/canturan10/light_side

Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
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DOI references
✓
Academic publication links
Links to: arxiv.org
○
Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (7.1%) to scientific vocabulary

Keywords

computer-vision deep-learning image-enhancement low-light-image-enhancement python pytorch pytorch-lightning state-of-the-art

Last synced: 6 months ago · JSON representation ·

Repository

⚡️PyTorch Lightning Implementations of Recent Low-Light Image Enhancement !

Basic Info

Host: GitHub
Owner: canturan10
License: mit
Language: Python
Default Branch: master
Homepage: https://canturan10.github.io/light_side/
Size: 5.13 MB

Statistics

Stars: 16
Watchers: 1
Forks: 3
Open Issues: 0
Releases: 2

Topics

computer-vision deep-learning image-enhancement low-light-image-enhancement python pytorch pytorch-lightning state-of-the-art

Created over 3 years ago · Last pushed over 3 years ago

Metadata Files

Readme License Citation

Light Side of the Night

Low-Light Image Enhancement

Website • Docs • Pypi

Light Side

TABLE OF CONTENTS

About The Light Side
Prerequisites
Installation
- From Pypi
- From Source
Usage Examples
Architectures
Datasets
Deployments
Training
Tests
Contributing
Contributors
Contact
License
References
Citations

About The Light Side

Light Side is an low-light image enhancement library that consist state-of-the-art deep learning methods. The light side of the Force is referenced. The aim is to create a light structure that will find the Light Side of the Night.

The light side of the Force, also known as Ashla, was one of two methods of using the Force. The light side was aligned with calmness, peace, and passiveness, and was used only for knowledge and defense. The Jedi were notable practitioners of the light, being selfless servants of the will of the Force, and their enemies, the Sith followed the dark side of the Force.

Source: Wookieepedia

Low-light image enhancement aims at improving the perception or interpretability of an image captured in an environment with poor illumination.

Source: paperswithcode

Prerequisites

Before you begin, ensure you have met the following requirements:

| requirement | version | | ----------------- | -------- | | imageio | ~=2.15.0 | | numpy | ~=1.22.0 | | pytorch_lightning | ~=1.7.0 | | scikit-learn | ~=1.0.2 | | torch | ~=1.9.1 |

Installation

To install Light Side, follow these steps:

From Pypi

bash pip install light_side

From Source

bash git clone https://github.com/canturan10/light_side.git cd light_side pip install .

From Source For Development

bash git clone https://github.com/canturan10/light_side.git cd light_side pip install -e ".[all]"

Usage Examples

```python import imageio import light_side as ls

img = imageio.imread("test.jpg")

model = ls.Enhancer.frompretrained("modelconfig_dataset") model.eval()

results = model.predict(img) ```

APIs

For more information, please refer to the APIs

Architectures

[x] Zero DCE
[ ] EnlightenGAN
[ ] MBLLEN
[ ] LLFlow

For more information, please refer to the Architectures

Datasets

[x] Zero DCE
[ ] LOL
[ ] DICM
[ ] MEF

For more information, please refer to the Datasets

Deployments

For more information, please refer to the Deployment

Training

To training, follow these steps:

For installing Light Side, please refer to the Installation.

bash python training/zerodce_training.py

For optional arguments,

bash python training/zerodce_training.py --help

Tests

During development, you might like to have tests run.

Install dependencies

bash pip install -e ".[test]"

Linting Tests

bash pytest light_side --pylint --pylint-error-types=EF

Document Tests

bash pytest light_side --doctest-modules

Coverage Tests

bash pytest --doctest-modules --cov light_side --cov-report term

Contributing

To contribute to Light Side, follow these steps:

Fork this repository.
Create a branch: git checkout -b <branch_name>.
Make your changes and commit them: git commit -m '<commit_message>'
Push to the original branch: git push origin
Create the pull request.

Alternatively see the GitHub documentation on creating a pull request.

Contributors

Oğuzcan Turan

You ?

Contact

If you want to contact me you can reach me at can.turan.10@gmail.com.

License

This project is licensed under MIT license. See LICENSE for more information.

References

The references used in the development of the project are as follows.

Citations

Click to expand!

```bibtex @misc{Turan_satellighte, author = {Turan, Oguzcan}, title = {{satellighte}}, url = {https://github.com/canturan10/satellighte} } ``` ```bibtex @article{DBLP:journals/corr/abs-2001-06826, author = {Chunle Guo and Chongyi Li and Jichang Guo and Chen Change Loy and Junhui Hou and Sam Kwong and Runmin Cong}, title = {Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement}, journal = {CoRR}, volume = {abs/2001.06826}, year = {2020}, url = {https://arxiv.org/abs/2001.06826}, eprinttype = {arXiv}, eprint = {2001.06826}, timestamp = {Sat, 23 Jan 2021 01:20:17 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2001-06826.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

Give a ⭐️ if this project helped you!

This readme file is made using the readme-template

Owner

Name: Oğuzcan Turan
Login: canturan10
Kind: user
Company: turk.ai

Website: canturan10.github.io
Repositories: 2
Profile: https://github.com/canturan10

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this repository, please cite it as below."
preferred-citation:
  type: generic
  authors:
  - family-names: "Turan"
    given-names: "Oguzcan"
  title: "light_side"
  url: "https://github.com/canturan10/light_side"

GitHub Events

Total

Last Year

Committers

Last synced: about 2 years ago

All Time

Total Commits: 39
Total Committers: 2
Avg Commits per committer: 19.5
Development Distribution Score (DDS): 0.051

Past Year

Commits: 3
Committers: 1
Avg Commits per committer: 3.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Oğuzcan Turan	c**0@g**m	37
Oğuzcan Turan	3****0	2

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 0
Total pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Total issue authors: 0
Total pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

Total packages: 1
Total downloads:
- pypi 61 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 10
Total maintainers: 1

pypi.org: light-side

PyTorch Lightning Implementations of Recent Low-Light Image Enhancement !

Homepage: https://github.com/canturan10/light_side
Documentation: https://light-side.readthedocs.io/
License: MIT License
Latest release: 0.1.0
published over 3 years ago

Versions: 10
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 61 Last month

Rankings

Dependent packages count: 6.6%

Stargazers count: 15.3%

Forks count: 19.6%

Average: 20.7%

Dependent repos count: 30.6%

Downloads: 31.5%

Maintainers (1)

canturan10

Last synced: 6 months ago

Dependencies

requirements.txt pypi

PyYAML *
fire *
imageio *
numpy *
pytorch_lightning *
rich *
scikit-learn *
torch *
torchmetrics *
torchvision *
uniplot *

.github/workflows/release.yml actions

actions/checkout master composite
actions/setup-python v1 composite

deployment/fastapi/Dockerfile docker

python 3.8 build

deployment/deepsparse/requirements.txt pypi

deepsparse *

deployment/fastapi/requirements.txt pypi

fastapi ==0.74.1
python-multipart ==0.0.5
uvicorn ==0.17.5

deployment/onnx/requirements.txt pypi

onnx *
onnxruntime *

deployment/tensorflow/requirements.txt pypi

onnx *
onnx-tf *
tensorflow *
tensorflow-probability *

deployment/tensorflow_lite/requirements.txt pypi

onnx *
onnx-tf *
tensorflow *
tensorflow-probability *

setup.py pypi

light-side

Science Score: 54.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Light Side of the Night

Low-Light Image Enhancement

About The Light Side

Prerequisites

Installation

From Pypi

From Source

From Source For Development

Usage Examples

APIs

Architectures

Datasets

Deployments

Training

Tests

Linting Tests

Document Tests

Coverage Tests

Contributing

Contributors

Oğuzcan Turan

You ?

Contact

License

References

Citations

Owner

Citation (CITATION.cff)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: light-side

Rankings

Maintainers (1)

Dependencies