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DeepDigitalFilm
DigitalFilm: Use a neural network to simulate film style.
"DigitalFilm" Digital Film
Use a neural network to simulate film style.
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This README.md is for developers and users 简体中文
Sample
Run Demo
The length and width of the input photo need to be divisible by 32.
python digitalFilm.py [-v/-h/-g] -i <input> -o <ouput> -m <model>
- -v print version information
- -h help information
- -g graphical image selection
- -i input image directory
- -o output image directory
- -m model directory
training model
training model directly use cyclegan.ipynb. But you need to download the pre-trained model of resnet18 in advance. Prepare digital photos and film photos in two folders. The model are included in the Release.
Installation steps
git clone https://github.com/SongZihui-sudo/digitalFilm.git
It is best to create an environment in conda now and then install various dependencies.
pip install -r requirement.txt
Overall architecture
Converting digital photos to film style can be regarded as an image style conversion task. Therefore, the overall architecture adopts the cycleGAN network. pytorch-CycleGAN-and-pix2pix In addition, it is difficult to obtain large-scale digital photos and film-style photos, so an unsupervised approach is adopted to use unpaired data for training.
Dataset
The dataset consists of dual-source image data, the main part of which is collected from high-quality digital photos taken by Xiaomi 13 Ultra mobile phone, and the rest is selected from professional HDR image dataset. Film samples are collected from the Internet.
File directory description
- DigitalFilm.ipynb is used to train the model
- app is a demo
- digitalFilm.py
- mynet.py
- mynet2.py
Version Control
This project uses Git for version management. You can view the currently available version in the repository.
Author
151122876@qq.com SongZihui-sudo
Zhihu:Dr.who qq:1751122876
*You can also view all the developers involved in the project in the list of contributors. *
Copyright
This project is licensed under GPLv3. For details, please refer to LICENSE.txt
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