Skip to content

Ulderick/DeepDreamVideo

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

20 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DeepDreamVideo

Implementing #deepdream on video

Included experiment: Deep Dreaming Fear & Loathing in Las Vegas: the Great Fan Francisco Acid Wave

The results can be seen on youtube: https://www.youtube.com/watch?v=oyxSerkkP4o

(mp4 not yet fucked by youtube compression also at mega.nz) (original unprocessed mp4 at mega.nz)

deepdreamanim1 deepdreamanim2

##INSTALL Dependencies

A good overview (constantly being updated) on which software libraries to install & list of web resources/howto is at reddit: https://www.reddit.com/r/deepdream/comments/3cawxb/what_are_deepdream_images_how_do_i_make_my_own/

##Usage:

Extract 25 frames a second from the source movie

./1_movie2frames.sh input.mp4 frames

Let a pretrained deep neural network dream on it frames, one by one, taking each new frame and adding 0-50% of the old frame into it for continuity of the hallucinated artifacts, and go drink your caffe

./2_dreaming_time.py -i frames -o processed

Once enough frames are processed (the script will cut the audio to the needed length automatically) or once all frames are done, put the frames + audio back together:

./3_frames2movies.sh processed

##More information:

This repo implements a deep neural network hallucinating Fear & Loathing in Las Vegas. Visualizing the internals of a deep net we let it develop further what it think it sees.

We're using the #deepdream technique developed by Google, first explained in the Google Research blog post about Neural Network art.

Code:

parameters used (and useful to play with):

  • network: standard reference GoogLeNet model trained on ImageNet from the Caffe Model Zoo (https://github.com/BVLC/caffe/wiki/Model-Zoo)

  • iterations: 5

  • jitter: 32 (default)

  • octaves: 4 (default)

  • layers locked to moving upwards from inception_4c/output to inception_5b/output (only the output layers, as they are most sensitive to visualizing "objects", where reduce layers are more like "edge detectors") and back again

  • every next unprocessed frame in the movie clip is blended with the previous processed frame before being "dreamed" on, moving the alpha from 0.5 to 1 and back again (so 50% previous image net created, 50% the movie frame, to taking 100% of the movie frame only). This takes care of "overfitting" on the frames and makes sure we don't iteratively build more and more "hallucinations" of the net and move away from the original movie clip.

An investigation of using the MIT Places trained CNN (mainly landscapes):

https://www.youtube.com/watch?v=6IgbMiEaFRY

Installing DeepDream:

Enjoy! Roelof

About

implementing deep dream on video

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 85.0%
  • Shell 15.0%