DAIN-APP Application

This is the source code for the video interpolation application Dain-App, developed on top of the source code of DAIN Dain GIT Project

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Table of Contents

1. Introduction
2. Citation
3. Requirements and Dependencies
4. Docker
5. Installation
6. Running application with interface
7. Running application with command line
8. Slow-motion Generation
9. Training New Models
10. Google Colab Demo

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Introduction

Dain-App comes with a user interface and a command line script to help new users to start using it with little change to the code. You can also get the Windows binary from the build section.

You can see a few results from those Youtube videos. Turning animations to 60FPS. Turning Sprite Art to 60FPS. Turning Stop Motion to 60FPS. Turning ANIME P1 to 60FPS. Turning ANIME P2 to 60FPS.

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Citation

If you find the code and datasets useful in your research, please cite:

@article{Dain-App,
	title={Dain-App: Application for Video Interpolations},
	author={Gabriel Poetsch},
	year={2020}
}
@inproceedings{DAIN,
    author    = {Bao, Wenbo and Lai, Wei-Sheng and Ma, Chao and Zhang, Xiaoyun and Gao, Zhiyong and Yang, Ming-Hsuan}, 
    title     = {Depth-Aware Video Frame Interpolation}, 
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
    year      = {2019}
}
@article{MEMC-Net,
     title={MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement},
     author={Bao, Wenbo and Lai, Wei-Sheng, and Zhang, Xiaoyun and Gao, Zhiyong and Yang, Ming-Hsuan},
     journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
     doi={10.1109/TPAMI.2019.2941941},
     year={2018}
}	

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Requirements and Dependencies

• numba=0.51.2
• numpy=1.19.2
• opencv-python=4.4.0.46
• pillow=8.0.1
• pyqt5=5.15.1
• python=3.8.5
• scikit-learn=0.23.2
• scipy=1.5.4
• torch=1.7.0+cu110
• torchvision=0.8.1+cu110
• tqdm=4.51.0
• ffmpeg

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Docker

Containerised development enviroment using NVIDIA Container Toolkit.

Both Docker and NVIDIA Container Toolkit must be installed.

• Install Docker
• Install NVIDIA Container Toolkit

Then in the docker directory of the repository 4 scripts are provided to add convenience.

• docker_build.sh
• docker_destroy.sh
• docker_run.sh
• docker_stop.sh

TO-DO Get docker working Add nvidia ffmpeg Move patrion and discord links to about me section Add better auto detection for settings Reorginize UI

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Installation

Check out the Colab code:

Remember you need to build the .cuda scripts before the app can work.

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Running application with interface

python my_design.py

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Running application with command line

You can see all commands for CLI using this code:

python my_design.py -cli -h

A example of a working code:

python  my_design.py -cli --input "gif/example.gif" -o "example_folder/" -on "interpolated.gif" -m "model_weights/best.pth" -fh 3 --interpolations 2 --depth_awarenes 0 --loop 0 -p 0 --alpha 0 --check_scene_change 10 --png_compress 0 --crf 1 --pixel_upscale_downscale_before 1 --pixel_downscale_upscale_after 1 --pixel_upscale_after 1 --mute_ffmpeg 1 --split_size_x -1 --split_size_y -1 --split_pad 150 --half 0 --step_extract 1 --step_interpolate 1 --batch_size 1 --use_benchmark 0 --force_flow 1 --smooth_flow 0 --downscale -1 --fast_mode 0

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Training New Models

Currently Dain-App training code is broken, to train new models, use the DAIN github and import the models to Dain-App

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Google Colab Demo

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Contact

Gabriel Poetsch

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License

See MIT License