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DigitalFilm_dataset

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YAML Metadata Warning: The task_categories "image-style-transfer" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

DeepDigitalFilm

DigitalFilm: Use a neural network to simulate film style.


"DigitalFilm" Digital Film

Use a neural network to simulate film style.
Explore the documentation of this project »

View the demo · Report a bug · Propose a new feature

This README.md is for developers and users 简体中文

PowerPoint Presentation

source code

Sample

rollei_infrared_400

Figure 1 Sample rollei_infrared_400

kodak_gold_200

Figure 2 Sample kodak gold 200

fuji_color_200

Figure 3 Sample fuji color 200

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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