mirror of https://github.com/n00mkrad/flowframes
Updated rife-ncnn and dain-ncnn to 20220728
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n00mkrad
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The MIT License (MIT)
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Copyright (c) 2020 nihui
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# DAIN ncnn Vulkan
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ncnn implementation of DAIN, Depth-Aware Video Frame Interpolation.
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dain-ncnn-vulkan uses [ncnn project](https://github.com/Tencent/ncnn) as the universal neural network inference framework.
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## [Download](https://github.com/nihui/dain-ncnn-vulkan/releases)
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Download Windows/Linux/MacOS Executable for Intel/AMD/Nvidia GPU
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**https://github.com/nihui/dain-ncnn-vulkan/releases**
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This package includes all the binaries and models required. It is portable, so no CUDA or Caffe runtime environment is needed :)
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## About DAIN
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DAIN (Depth-Aware Video Frame Interpolation) (CVPR 2019)
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https://github.com/baowenbo/DAIN
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Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang
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This work is developed based on our TPAMI work MEMC-Net, where we propose the adaptive warping layer. Please also consider referring to it.
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https://sites.google.com/view/wenbobao/dain
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http://arxiv.org/abs/1904.00830
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## Usages
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Input two frame images, output one interpolated frame image.
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### Example Command
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```shell
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./dain-ncnn-vulkan -0 0.jpg -1 1.jpg -o 01.jpg
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./dain-ncnn-vulkan -i input_frames/ -o output_frames/
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```
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### Video Interpolation with FFmpeg
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```shell
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mkdir input_frames
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mkdir output_frames
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# find the source fps and format with ffprobe, for example 24fps, AAC
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ffprobe input.mp4
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# extract audio
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ffmpeg -i input.mp4 -vn -acodec copy audio.m4a
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# decode all frames
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ffmpeg -i input.mp4 input_frames/frame_%06d.png
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# interpolate 2x frame count
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./dain-ncnn-vulkan -i input_frames -o output_frames
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# encode interpolated frames in 48fps with audio
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ffmpeg -framerate 48 -i output_frames/%06d.png -i audio.m4a -c:a copy -crf 20 -c:v libx264 -pix_fmt yuv420p output.mp4
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```
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### Full Usages
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```console
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Usage: dain-ncnn-vulkan -0 infile -1 infile1 -o outfile [options]...
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dain-ncnn-vulkan -i indir -o outdir [options]...
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-h show this help
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-v verbose output
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-0 input0-path input image0 path (jpg/png/webp)
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-1 input1-path input image1 path (jpg/png/webp)
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-i input-path input image directory (jpg/png/webp)
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-o output-path output image path (jpg/png/webp) or directory
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-n num-frame target frame count (default=N*2)
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-s time-step time step (0~1, default=0.5)
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-t tile-size tile size (>=128, default=256) can be 256,256,128 for multi-gpu
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-m model-path dain model path (default=best)
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-g gpu-id gpu device to use (default=auto) can be 0,1,2 for multi-gpu
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-j load:proc:save thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
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-f pattern-format output image filename pattern format (%08d.jpg/png/webp, default=ext/%08d.png)
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```
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- `input0-path`, `input1-path` and `output-path` accept file path
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- `input-path` and `output-path` accept file directory
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- `num-frame` = target frame count
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- `time-step` = interpolation time
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- `tile-size` = tile size, use smaller value to reduce GPU memory usage, must be multiple of 32, default 256
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- `load:proc:save` = thread count for the three stages (image decoding + dain interpolation + image encoding), using larger values may increase GPU usage and consume more GPU memory. You can tune this configuration with "4:4:4" for many small-size images, and "2:2:2" for large-size images. The default setting usually works fine for most situations. If you find that your GPU is hungry, try increasing thread count to achieve faster processing.
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- `pattern-format` = the filename pattern and format of the image to be output, png is better supported, however webp generally yields smaller file sizes, both are losslessly encoded
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If you encounter a crash or error, try upgrading your GPU driver:
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- Intel: https://downloadcenter.intel.com/product/80939/Graphics-Drivers
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- AMD: https://www.amd.com/en/support
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- NVIDIA: https://www.nvidia.com/Download/index.aspx
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## Build from Source
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1. Download and setup the Vulkan SDK from https://vulkan.lunarg.com/
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- For Linux distributions, you can either get the essential build requirements from package manager
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```shell
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dnf install vulkan-headers vulkan-loader-devel
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```
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```shell
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apt-get install libvulkan-dev
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```
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```shell
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pacman -S vulkan-headers vulkan-icd-loader
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```
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2. Clone this project with all submodules
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```shell
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git clone https://github.com/nihui/dain-ncnn-vulkan.git
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cd dain-ncnn-vulkan
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git submodule update --init --recursive
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```
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3. Build with CMake
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- You can pass -DUSE_STATIC_MOLTENVK=ON option to avoid linking the vulkan loader library on MacOS
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```shell
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mkdir build
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cd build
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cmake ../src
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cmake --build . -j 4
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```
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### TODO
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* test-time sptial augmentation aka TTA-s
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* test-time temporal augmentation aka TTA-t
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## Sample Images
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### Original Image
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### Interpolate with dain
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```shell
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dain-ncnn-vulkan.exe -0 0.png -1 1.png -o out.png
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```
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## Original DAIN Project
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- https://github.com/baowenbo/DAIN
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## Other Open-Source Code Used
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- https://github.com/Tencent/ncnn for fast neural network inference on ALL PLATFORMS
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- https://github.com/webmproject/libwebp for encoding and decoding Webp images on ALL PLATFORMS
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- https://github.com/nothings/stb for decoding and encoding image on Linux / MacOS
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- https://github.com/tronkko/dirent for listing files in directory on Windows
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The MIT License (MIT)
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Copyright (c) 2020 nihui
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
|
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in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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|
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# RIFE ncnn Vulkan
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ncnn implementation of RIFE, Real-Time Intermediate Flow Estimation for Video Frame Interpolation.
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rife-ncnn-vulkan uses [ncnn project](https://github.com/Tencent/ncnn) as the universal neural network inference framework.
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## [Download](https://github.com/nihui/rife-ncnn-vulkan/releases)
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Download Windows/Linux/MacOS Executable for Intel/AMD/Nvidia GPU
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**https://github.com/nihui/rife-ncnn-vulkan/releases**
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This package includes all the binaries and models required. It is portable, so no CUDA or PyTorch runtime environment is needed :)
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## About RIFE
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RIFE (Real-Time Intermediate Flow Estimation for Video Frame Interpolation)
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https://github.com/hzwer/arXiv2020-RIFE
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Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang
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https://rife-vfi.github.io
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https://arxiv.org/abs/2011.06294
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## Usages
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Input two frame images, output one interpolated frame image.
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### Example Commands
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```shell
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./rife-ncnn-vulkan -0 0.jpg -1 1.jpg -o 01.jpg
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./rife-ncnn-vulkan -i input_frames/ -o output_frames/
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```
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Example below runs on CPU, Discrete GPU, and Integrated GPU all at the same time. Uses 2 threads for image decoding, 4 threads for one CPU worker, 4 threads for another CPU worker, 2 threads for discrete GPU, 1 thread for integrated GPU, and 4 threads for image encoding.
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```shell
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./rife-ncnn-vulkan -i input_frames/ -o output_frames/ -g -1,-1,0,1 -j 2:4,4,2,1:4
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```
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### Video Interpolation with FFmpeg
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```shell
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mkdir input_frames
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mkdir output_frames
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# find the source fps and format with ffprobe, for example 24fps, AAC
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ffprobe input.mp4
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# extract audio
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ffmpeg -i input.mp4 -vn -acodec copy audio.m4a
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# decode all frames
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ffmpeg -i input.mp4 input_frames/frame_%08d.png
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# interpolate 2x frame count
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./rife-ncnn-vulkan -i input_frames -o output_frames
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# encode interpolated frames in 48fps with audio
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ffmpeg -framerate 48 -i output_frames/%08d.png -i audio.m4a -c:a copy -crf 20 -c:v libx264 -pix_fmt yuv420p output.mp4
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```
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### Full Usages
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```console
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Usage: rife-ncnn-vulkan -0 infile -1 infile1 -o outfile [options]...
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rife-ncnn-vulkan -i indir -o outdir [options]...
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-h show this help
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-v verbose output
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-0 input0-path input image0 path (jpg/png/webp)
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-1 input1-path input image1 path (jpg/png/webp)
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-i input-path input image directory (jpg/png/webp)
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-o output-path output image path (jpg/png/webp) or directory
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-n num-frame target frame count (default=N*2)
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-s time-step time step (0~1, default=0.5)
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-m model-path rife model path (default=rife-v2.3)
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-g gpu-id gpu device to use (-1=cpu, default=auto) can be 0,1,2 for multi-gpu
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-j load:proc:save thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
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-x enable tta mode
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-u enable UHD mode
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-f pattern-format output image filename pattern format (%08d.jpg/png/webp, default=ext/%08d.png)
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```
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- `input0-path`, `input1-path` and `output-path` accept file path
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- `input-path` and `output-path` accept file directory
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- `num-frame` = target frame count
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- `time-step` = interpolation time
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- `load:proc:save` = thread count for the three stages (image decoding + rife interpolation + image encoding), using larger values may increase GPU usage and consume more GPU memory. You can tune this configuration with "4:4:4" for many small-size images, and "2:2:2" for large-size images. The default setting usually works fine for most situations. If you find that your GPU is hungry, try increasing thread count to achieve faster processing.
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- `pattern-format` = the filename pattern and format of the image to be output, png is better supported, however webp generally yields smaller file sizes, both are losslessly encoded
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If you encounter a crash or error, try upgrading your GPU driver:
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- Intel: https://downloadcenter.intel.com/product/80939/Graphics-Drivers
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- AMD: https://www.amd.com/en/support
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- NVIDIA: https://www.nvidia.com/Download/index.aspx
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## Build from Source
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1. Download and setup the Vulkan SDK from https://vulkan.lunarg.com/
|
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- For Linux distributions, you can either get the essential build requirements from package manager
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```shell
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dnf install vulkan-headers vulkan-loader-devel
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```
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```shell
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apt-get install libvulkan-dev
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```
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```shell
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pacman -S vulkan-headers vulkan-icd-loader
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```
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2. Clone this project with all submodules
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```shell
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git clone https://github.com/nihui/rife-ncnn-vulkan.git
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cd rife-ncnn-vulkan
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git submodule update --init --recursive
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```
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3. Build with CMake
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- You can pass -DUSE_STATIC_MOLTENVK=ON option to avoid linking the vulkan loader library on MacOS
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|
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```shell
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mkdir build
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cd build
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cmake ../src
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cmake --build . -j 4
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```
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### TODO
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* test-time temporal augmentation aka TTA-t
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### Model
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| model | upstream version |
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| rife | 1.2 |
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| rife-HD | 1.5 |
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| rife-UHD | 1.6 |
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| rife-anime | 1.8 |
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| rife-v2 | 2.0 |
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| rife-v2.3 | 2.3 |
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| rife-v2.4 | 2.4 |
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| rife-v3.0 | 3.0 |
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| rife-v3.1 | 3.1 |
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| rife-v4 | 4.0 |
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## Sample Images
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### Original Image
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### Interpolate with rife rife-anime model
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```shell
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rife-ncnn-vulkan.exe -m models/rife-anime -0 0.png -1 1.png -o out.png
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```
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### Interpolate with rife rife-anime model + TTA-s
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```shell
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rife-ncnn-vulkan.exe -m models/rife-anime -x -0 0.png -1 1.png -o out.png
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```
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## Original RIFE Project
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- https://github.com/hzwer/arXiv2020-RIFE
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## Other Open-Source Code Used
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- https://github.com/Tencent/ncnn for fast neural network inference on ALL PLATFORMS
|
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- https://github.com/webmproject/libwebp for encoding and decoding Webp images on ALL PLATFORMS
|
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- https://github.com/nothings/stb for decoding and encoding image on Linux / MacOS
|
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- https://github.com/tronkko/dirent for listing files in directory on Windows
|
||||
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