Package: light-side
Light Side of the NightLow-Light Image EnhancementWebsite•Docs•PypiTABLE OF CONTENTSAbout The Light SidePrerequisitesInstallationFrom PypiFrom SourceUsage ExamplesArchitecturesDatasetsDeploymentsTrainingTestsContributingContributorsContactLicenseReferencesCitationsAbout The Light SideLight Sideis 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 theLight 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:WookieepediaLow-light image enhancementaims at improving the perception or interpretability of an image captured in an environment with poor illumination.Source:paperswithcodePrerequisitesBefore you begin, ensure you have met the following requirements:requirementversionimageio~=2.15.0numpy~=1.22.0pytorch_lightning~=1.7.0scikit-learn~=1.0.2torch~=1.9.1InstallationTo install Light Side, follow these steps:From Pypipipinstalllight_sideFrom Sourcegitclonehttps://github.com/canturan10/light_side.gitcdlight_side
pipinstall.From Source For Developmentgitclonehttps://github.com/canturan10/light_side.gitcdlight_side
pipinstall-e".[all]"Usage Examplesimportimageioimportlight_sideaslsimg=imageio.imread("test.jpg")model=ls.Enhancer.from_pretrained("model_config_dataset")model.eval()results=model.predict(img)APIsAvailable ModelsAvailable Versions for a Spesific ModelLatest Version for a Spesific ModelPretrained ModelModel with Random Weight InitializationPretrained Arch ModelArch Model with Random Weight InitializationFor more information, please refer to theAPIsArchitecturesZero DCEEnlightenGANMBLLENLLFlowFor more information, please refer to theArchitecturesDatasetsZero DCELOLDICMMEFFor more information, please refer to theDatasetsDeploymentsFastAPIONNXDeepSparseTensorFlowTensorFlow LiteFor more information, please refer to theDeploymentTrainingTo training, follow these steps:For installing Light Side, please refer to theInstallation.pythontraining/zerodce_training.pyFor optional arguments,pythontraining/zerodce_training.py--helpTestsDuring development, you might like to have tests run.Install dependenciespipinstall-e".[test]"Linting Testspytestlight_side--pylint--pylint-error-types=EFDocument Testspytestlight_side--doctest-modulesCoverage Testspytest--doctest-modules--covlight_side--cov-reporttermContributingTo contribute toLight 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 originCreate the pull request.Alternatively see theGitHubdocumentation oncreating a pull request.ContributorsOğuzcan TuranYou ?ContactIf you want to contact me you can reach me atcan.turan.10@gmail.com.LicenseThis project is licensed underMITlicense. SeeLICENSEfor more information.ReferencesThe references used in the development of the project are as follows.SatellighteImg ShieldsGitHub PagesPyTorch LightningTorchvisionCitationsClick to expand!@misc{Turan_satellighte,author={Turan, Oguzcan},title={{satellighte}},url={https://github.com/canturan10/satellighte}}@article{DBLP:journals/corr/abs-2001-06826,author={Chunle Guo andChongyi Li andJichang Guo andChen Change Loy andJunhui Hou andSam Kwong andRunmin 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 thereadme-template