RealSR-NCNN-Android

中文说明

RealSR-NCNN-Android is a simple Android application that based on Waifu2x-NCNN, SRMD-NCNN, RealCUGAN-NCNN, RealSR-NCNN, Real-ESRGAN, Anime4KCPP.
The application does not collect any private information from your device.
Download: Github Release

This repository contains 3 project:

1. RealSR-NCNN-Android-CLI can build programs that can be used by the console (for example, Termux) for Android. It contains 7 modules (Anime4k, MNN-SR, RealSR, RealCUGAN, SRMD, Waifu2x and Resize)
   • The RealSR program could use realsr models and real-esrgan models with ncnn format.
   • The MNN-SR program could use mnn format models.
   • The Resize program contains classical interpolation mode nearest bilinear bicubic and avir lancir.
2. RealSR-NCNN-Android-GUI can build a APK (has a GUI and easy to use). Actually it is a shell for the programs build from RealSR-NCNN-Android-CLI.
3. Resize-CLI just a demo like the Resize-NCNN-Android-CLI, but it not need ncnn and could build by VS.

How to use RealSR-NCNN-Android-GUI

Two ways of selecting files:

1. Share one or more images from other apps (e.g. Gallery) to this app
2. In this app, click Select Image to select an image

Two ways of running:

1. chose a model, click the Run button and wait some time. The photo view will show the result when the program finish its work. If you like the result, you could click the Save button.
2. input shell command and enter. (You can input help and get more info)

input & output

Add more models to RealSR-NCNN-Android-GUI

RealSR-NCNN-Android-GUI could load extra models from sdcard automatily in ver 1.7.6. You could download more models from https://huggingface.co/tumuyan2/realsr-models .

1. Make a directory in sdcard.
2. Input the directory path to Path for custom models (RealSR/ESRGAN/Waifu2x) and save.
3. Download and copy models to the directory you make.
4. Return the main activity, then you could select the new models.

Convert models to mnn format

✨ ver 1.11 support mnn models. MNN could support more models than ncnn. ver 1.12 could use -c param or model file name to config the color space. refer to https://mnn-docs.readthedocs.io/en/latest/tools/convert.html , you could convert ONNX, TFLITE, TorchScript, Tensorflow models to mnn format.

1. pip install mnn
2. (for example, convert onnx to mnn) MNNConvert -f ONNX --modelFile "{onnx_path}" --MNNModel "{mnn_path}" --bizCode biz --fp16 --info --detectSparseSpeedUp
3. modify mnn model filename, contains scale info just like4xabcdefg.mnn abc-x4-def.mnn or abc_4x_def.mnn, copy to your custom model folder. If the filename contains Grayscale Gray2YCbCr Gray2YUV YCbCr YUV, the App will apply these color space.

Convert pth models to ncnn format by yourself

Also you could convert ESRGAN pth models by yourself.

1. Download ESRGAN pytorch models from OpenModelDB and unzip it to somewhere.

2. Download cupscale and unzip it in your PC.

3. Convert pytorch models to ncnn. Open CupscaleData\bin\pth2ncnn, use pth2ncnn.exe to convert pth files to ncnn file.

4. Rename models, just like this:

   models-Real-ESRGAN-AnimeSharp  // directory should have a suffix of models-Real- or models-ESRGAN-
   ├─x4.bin                       // models name as x[n], n is scale
   ├─x4.param
   

• This tool can only convert ESRGAN models, not Real-ESRGAN models. If there are Real-ESRGAN models with perfect effect that need to be converted, I can help you convert them manually.

Another way to convert models:

1. Download the model from OpenModelDB.
2. Download and install chaiNNer (this requires a good internet connection).
3. Open chaiNNer, link the nodes, or use the project I provided: https://github.com/tumuyan/RealSR-NCNN-Android/raw/main/chainner-pth2ncnn.chn
4. Drag the model onto the leftmost node, click the run button, and wait. Note that not every model can be converted to an ncnn model.
5. Rename the file (same as the previous method). If you used the project file I provided, this step is unnecessary.

How to build RealSR-NCNN-Android-GUI

Build RealSR-NCNN-Android-CLI by yourself or download assets.zip from github release page, the zip file contains models & ELF files. Unzip and put them to this folder, then build it with Android Studio. The direct download link for current version: https://github.com/tumuyan/RealSR-NCNN-Android/releases/download/1.11.1/assets.zip

RealSR-NCNN-Android-GUI\app\src\main\assets\
└─realsr
    │  Anime4k
    │  colors.xml
    │  delegates.xml
    │  libc++_shared.so
    │  libncnn.so
    │  libomp.so
    │  magick
    │  realcugan-ncnn
    │  realsr-ncnn
    │  resize-ncnn
    │  srmd-ncnn
    │  waifu2x-ncnn
    │  
    ├─models-nose
    │      up2x-no-denoise.bin
    │      up2x-no-denoise.param
    │      
    ├─models-pro
    │      up2x-conservative.bin
    │      up2x-conservative.param
    │      up2x-denoise3x.bin
    │      up2x-denoise3x.param
    │      up2x-no-denoise.bin
    │      up2x-no-denoise.param
    │      up3x-conservative.bin
    │      up3x-conservative.param
    │      up3x-denoise3x.bin
    │      up3x-denoise3x.param
    │      up3x-no-denoise.bin
    │      up3x-no-denoise.param
    │      
    ├─models-Real-ESRGAN
    │      x4.bin
    │      x4.param
    │      
    ├─models-Real-ESRGAN-anime
    │      x4.bin
    │      x4.param
    │      
    ├─models-Real-ESRGANv2-anime
    │      x2.bin
    │      x2.param
    │      x4.bin
    │      x4.param
    │      
    ├─models-Real-ESRGANv3-anime
    │      x2.bin
    │      x2.param
    │      x3.bin
    │      x3.param
    │      x4.bin
    │      x4.param
    │      
    ├─models-ESRGAN-Nomos8kSC
    │      x4.bin
    │      x4.param
    |
    └─models-se
           up2x-conservative.bin
           up2x-conservative.param
           up2x-denoise1x.bin
           up2x-denoise1x.param
           up2x-denoise2x.bin
           up2x-denoise2x.param
           up2x-denoise3x.bin
           up2x-denoise3x.param
           up2x-no-denoise.bin
           up2x-no-denoise.param
           up3x-conservative.bin
           up3x-conservative.param
           up3x-denoise3x.bin
           up3x-denoise3x.param
           up3x-no-denoise.bin
           up3x-no-denoise.param
           up4x-conservative.bin
           up4x-conservative.param
           up4x-denoise3x.bin
           up4x-denoise3x.param
           up4x-no-denoise.bin
           up4x-no-denoise.param

Limitations

This is a very simple tool, flexible and powerful, has the following shortcomings (and there are no plans for improvement):

About Batch Processing

1. You can load multiple images by opening the album, selecting multiple pictures, sharing, and choosing realsr, but you cannot select multiple images within the app.
2. After loading multiple images, only one image can be previewed; there is no image list.
3. After processing, the results will not be previewed and will be saved directly to the album.
4. Magick (including the quick menu in the top right corner) does not support batch processing.

About GIF Animation

1. Processing can only be done in GIF animation mode when one GIF is opened; otherwise, only one frame can be processed.
2. GIFs cannot be previewed.
3. After processing is complete, the results will not be previewed and will be saved directly to the album.
4. Magick (including the quick menu in the top right corner) does not support GIF processing.

Acknowledgement

original super-resolution projects

• https://github.com/xinntao/Real-ESRGAN
• https://github.com/jixiaozhong/RealSR
• https://github.com/cszn/SRMD
• https://github.com/bilibili/ailab/tree/main/Real-CUGAN
• https://github.com/bloc97/Anime4K

ncnn projects and models

Most of the C code is copied from Nihui, cause of the directory structure had to be adjusted, the original git was broken

• https://github.com/nihui/realsr-ncnn-vulkan
• https://github.com/nihui/srmd-ncnn-vulkan
• https://github.com/nihui/waifu2x-ncnn-vulkan
• https://github.com/nihui/realcugan-ncnn-vulkan
• https://github.com/TianZerL/Anime4KCPP

Others Open-Source Code Used

• https://github.com/Tencent/ncnn for fast neural network inference on ALL PLATFORMS
• https://github.com/alibaba/MNN A lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba.
• https://github.com/nothings/stb for decoding and encoding image on Linux / MacOS
• https://github.com/tronkko/dirent for listing files in directory on Windows
• https://github.com/webmproject/libwebp for encoding and decoding Webp images on ALL PLATFORMS
• https://github.com/avaneev/avir AVIR image resizing algorithm designed by Aleksey Vaneev
• https://github.com/ImageMagick/ImageMagick6 Use ImageMagick® to resize/convert images.
• https://github.com/MolotovCherry/Android-ImageMagick7 I also have use this fork https://github.com/Miseryset/Android-ImageMagick7

Others packaged models

• Real-ESRGAN model Nomos8kSC trained by Phhofm.
• Real-ESRGAN model MoeSR trained by luoyily.