Add winter effect to images
- Add fog
- Add showfall
- Add fallen snow
Input | Fog | Snow | Fallen snow |
---|---|---|---|
Fog + Snow | Fog + Fallen snow | Fallen snow + Snow | Fog + Snow + Fallen snow |
---|---|---|---|
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
python3 main.py path_to_image
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
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},
}