RIFE ncnn Vulkan Python

Introduction

rife-ncnn-vulkan is nihui's ncnn implementation of Real-World Super-Resolution via Kernel Estimation and Noise Injection super resolution.

rife-ncnn-vulkan-python wraps rife-ncnn-vulkan project by SWIG to make it easier to integrate rife-ncnn-vulkan with existing python projects.

Downloads

Linux/Windos/Mac X86_64 build releases are available now. However, for Linux you may need to compile it yourself if you are using distro with GLIBC < 2.29 .

Build

First, you have to install python, python development package (Python native development libs in Visual Studio), vulkan SDK and SWIG on your platform. And then:

Linux

git clone https://github.com/ArchieMeng/rife-ncnn-vulkan-python.git
cd rife-ncnn-vulkan-python
git submodule update --init --recursive
cmake -B build src
cd build
make

Windows

I used Visual Studio 2019 and msvc v142 to build this project for Windows.

Install visual studio and open the project directory, and build. Job done.

The only problem on Windows is that, you cannot use CMake for Windows to generate the Visual Studio solution file and build it. This will make the lib crash on loading.

The only way is use Visual Studio to open the project as directory, and build it from Visual Studio.

About RIFE

RIFE (Real-Time Intermediate Flow Estimation for Video Frame Interpolation)

https://github.com/hzwer/arXiv2020-RIFE

Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang

https://rife-vfi.github.io

https://arxiv.org/abs/2011.06294

Usages

Example Program

from PIL import Image
from rife_ncnn_vulkan import RIFE

im0, im1 = Image.open("0.png"), Image.open("1.png")
rife = RIFE(0)
im = rife.process(im0, im1)
im.save("interframe.png")