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winterveil

Add winter effect to images

Features

  • Add fog
  • Add showfall
  • Add fallen snow

Example

Input Fog Snow Fallen snow
output_f output_f output_f output_f
Fog + Snow Fog + Fallen snow Fallen snow + Snow Fog + Snow + Fallen snow
output_f output_f output_f output_f

Usage

Install dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Winterize image

python3 main.py path_to_image

Options

usage: WinterVeil [-h] -i IMAGE [-f] [-s] [-ss SNOWFLAKE_SIZE] [-sc SNOWFLAKE_COUNT] [-fs]

options:
  -h, --help            show this help message and exit
  -i IMAGE, --image IMAGE
                        input image path
  -f, --fog             add fog to the image
  -s, --snow            add snow to the image
  -ss SNOWFLAKE_SIZE, --snowflake-size SNOWFLAKE_SIZE
                        size of snowflakes in pixel
  -sc SNOWFLAKE_COUNT, --snowflake-count SNOWFLAKE_COUNT
                        number of snowflakes on image (visibility depends on depth map!)
  -fs, --fallen-snow    add fallen snow to the image

Acknowledgments

This project uses the MiDaS depth estimation model by René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun, which is licensed under the MIT License. If you use this project, please also cite their original work:

@article{Ranftl2020,
	author    = {Ren\'{e} Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun},
	title     = {Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer},
	journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
	year      = {2020},
}
@article{Ranftl2021,
	author    = {Ren\'{e} Ranftl and Alexey Bochkovskiy and Vladlen Koltun},
	title     = {Vision Transformers for Dense Prediction},
	journal   = {ArXiv preprint},
	year      = {2021},
}

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