Advice: Ability to create video?

[gateway]

Hi, thanks for providing this amazing style transfer tool. In the past I was using cysmiths nerual style tf transfer which stopped working for the new titan RTX card I put in. I see similarities in what you guys have done and have been testing your version with much joy (planning on doing a blog post about some settings I found useful).

Anyhow I was wondering if we could use this for video some how because some of the video style transfer stuff out their is 4-5 years old and use optical flow and deepmatching which is mainly cpu intensive and any gpu ones dont see to work with the system.

Any thoughts on anyone using your code to create videos? is the seed value what I would need to render out various frames along with maybe nvidias new optical flow system or?

Anyhow thank you again!

[ProGamerGov]

@gateway A specific seed value would let you recreate the same stylized video. Specific seeds aren't required for stylizing video.

I haven't seen anyone using neural-style-pt with proper optical flow yet, but from what I understand it shouldn't be to difficult to do. Another good starting point for getting video working might be following after https://github.com/manuelruder/artistic-videos, though it may be a bit out of date these days. Like neural-style-pt, it's based on the original neural-style.

For optical flow, I'm not aware of what the latest implementations that you could use are. You try asking on the PyTorch forums, or depending on your skills PyTorch may make it easier to implement yourself.

[gateway]

@ProGamerGov thanks for your reply.. Im kinda a hacker vs a python programmer, I program other languages but never got into python so my skills are more setup stuff, hack in a few lines here but when it comes to PyTorch or other neural stuff I'm still in the learning and amazement period.

Nvidia has their optical Flow SDK for GPU's that support Turning which I have. https://developer.nvidia.com/opticalflow-sdk Also I believe this can be compiled into OpenCV as an add-on. From everything I have read with video even tried out the one you mentioned they all still rely on cpu's, some have found a way to create a cluster of cpus which is helpful but nowhere near what a gpu can do of course. This was done with artistic videos or umm cysmiths, cant remember now..

https://youtu.be/ktI3uTlqb8E

https://youtu.be/72FIC0zJPMs (ignore the audio sync, I forgot to render the frames in ffmpeg to 24fps).

Just cant way years for these frames to render, especially if you want do do at least 1080p.. anyhow was just trying to pick your brain on this..

[ProGamerGov]

@gateway PyTorch is light years ahead of other frameworks like TensorFlow in terms of how easy it is to do things. Python is also a pretty easy to learn language, at least compared to Java and Lua.

I haven't done a lot of experimentation with video just because of how long it takes to test the code, but I'm excited to see what you could come up with!

[JCBrouwer]

I've actually extended this repo to add flow-weighted video style transfer. https://github.com/JCBrouwer/maua-style/tree/working-video-transfer It uses multi-resolution + multi-pass transfer by default and can generate 1080p frames in a couple of minutes depending on the quality. You'll need to checkout the working-video-transfer branch though. The master is broken at the moment because I tried to add support for video styles. I think that broke some things in the optimization / other style transfer types. You can run it with python canvas.py +args, see the argparse in canvas.py for all the options.

[gateway]

@JCBrouwer I'm trying to give it a shot, have my conda env set up and all required modules, but when I try to run it I get

(maua) gateway@gateway-media:~/work/video/maua-style$ python canvas.py 
Traceback (most recent call last):
  File "canvas.py", line 10, in <module>
    import ffmpeg
ModuleNotFoundError: No module named 'ffmpeg'

Its installed, shows up in conda list, and also tried to install it via pip which it also shows up.. ill create a ticket in your repo.. also just something to think about, it would be better for most people to use their internal ffmpeg, since mine is usually compiled with the cuda and nvenc encoding/decoding on the gpu.

fixed by doing this, might be worth adding to the install.sh https://github.com/conda-forge/ffmpeg-python-feedstock

conda config --add channels conda-forge conda install ffmpeg-python anyhow moving all issues to your depo, lets see what I can come up with.. btw how about a sample command line args? :)