Implementing #deepdream on video
Creative Request
It would be very helpful for other deepdream researchers, if you could include the used parameters in the description of your youtube videos. You can find the parameters in the image filenames.
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 destroyed by youtube compression also at mega.nz together with original video file.
All single processed + unprocessed frames are also at github
Advise also at https://github.com/graphific/DeepDreamVideo/wiki
##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 ffmpeg [movie.mp4] [directory]
or
./1_movie2frames.sh avconf [movie.mp4] [directory]
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
usage: 2_dreaming_time.py [-h] -i INPUT -o OUTPUT -it IMAGE_TYPE [--gpu GPU] [-t MODEL_PATH] [-m MODEL_NAME] [-p PREVIEW] [-oct OCTAVES] [-octs OCTAVESCALE] [-itr ITERATIONS] [-j JITTER] [-z ZOOM] [-s STEPSIZE] [-b BLEND] [-l LAYERS [LAYERS ...]] [-v VERBOSE] [-gi GUIDE_IMAGE] [-sf START_FRAME] [-ef END_FRAME] Dreaming in videos. optional arguments: -h, --help show this help message and exit -i INPUT, --input INPUT Input directory where extracted frames are stored -o OUTPUT, --output OUTPUT Output directory where processed frames are to be stored -it IMAGE_TYPE, --image_type IMAGE_TYPE Specify whether jpg or png --gpu GPU Switch for gpu computation. -t MODEL_PATH, --model_path MODEL_PATH Model directory to use -m MODEL_NAME, --model_name MODEL_NAME Caffe Model name to use -p PREVIEW, --preview PREVIEW Preview image width. Default: 0 -oct OCTAVES, --octaves OCTAVES Octaves. Default: 4 -octs OCTAVESCALE, --octavescale OCTAVESCALE Octave Scale. Default: 1.4 -itr ITERATIONS, --iterations ITERATIONS Iterations. Default: 10 -j JITTER, --jitter JITTER Jitter. Default: 32 -z ZOOM, --zoom ZOOM Zoom in Amount. Default: 1 -s STEPSIZE, --stepsize STEPSIZE Step Size. Default: 1.5 -b BLEND, --blend BLEND Blend Amount. Default: "0.5" (constant), or "loop" (0.5-1.0), or "random" -l LAYERS [LAYERS ...], --layers LAYERS [LAYERS ...] Array of Layers to loop through. Default: [customloop] - or choose ie [inception_4c/output] for that single layer -v VERBOSE, --verbose VERBOSE verbosity [0-3] -gi GUIDE_IMAGE, --guide_image GUIDE_IMAGE path to guide image -sf START_FRAME, --start_frame START_FRAME starting frame nr -ef END_FRAME, --end_frame END_FRAME end frame nr
gpu:
python 2_dreaming_time.py -i frames -o processed --gpu 0
cpu:
python 2_dreaming_time.py -i frames -o processed
different models can be loaded with:
python 2_dreaming_time.py -i frames -o processed --model_path ../caffe/models/Places205-CNN/ --model_name Places205.caffemodel --gpu 0
or
python 2_dreaming_time.py -i frames -o processed --model_path ../caffe/models/bvlc_googlenet/ --model_name bvlc_googlenet.caffemodel --gpu 0
(again eat your heart out, Not a free lunch, but free models are here)
and sticking to one specific layer:
python 2_dreaming_time.py -i frames -o processed -l inception_4c/output --gpu 0
(don't forget the --gpu 0 flag if you got a gpu to run on)
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_frames2movie.sh [frames_directory] [original_video_with_sound]
##Guided Dreaming
command:
python 2_dreaming_time.py -i frames -o processed -l inception_4c/output --guide-image flower.jpg --gpu 0
##Batch Processing with different parameters
python 2_dreaming_time.py -i frames -o processed -l inception_4c/output --guide-image flower.jpg --gpu 0 --start-frame 1 --end-frame 100; python 2_dreaming_time.py -i frames -o processed -l inception_4b/output --guide-image disco.jpg --gpu 0 --start-frame 101 --end-frame 200
##Blending Options The best results come from a well selected blending factor, used to blend each frame into the next, keeping consitancy between the frames and the dreamed up artefacts, but without the added dreamed artefacts overruling the original scene, or in the opposite case, switching too rapidly.
blending can be set by
--blendand can be a float, default 0.5, "random" (a random float between 0.5 and 1., where 1 means disregarding all info from the old frame and starting from scratch with dreaming up artefacts), and "loop" which loops back and forth from 0.5 to 1.0, as originally done in the Fear and Loathing clip.
Constant (default):
python 2_dreaming_time.py -i frames -o processed -b 0.5
Loop:
python 2_dreaming_time.py -i frames -o processed -b loop
Random:
python 2_dreaming_time.py -i frames -o processed -b random
##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:
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network: standard reference GoogLeNet model trained on ImageNet from the Caffe Model Zoo (https://github.com/BVLC/caffe/wiki/Model-Zoo)
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iterations: 5
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jitter: 32 (default)
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octaves: 4 (default)
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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
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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.
https://www.youtube.com/watch?v=6IgbMiEaFRY
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original Google code is relatively straightforward to use: https://github.com/google/deepdream/blob/master/dream.ipynb
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gist for osx: https://gist.github.com/robertsdionne/f58a5fc6e5d1d5d2f798
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docker image: https://registry.hub.docker.com/u/mjibson/deepdream/
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booting preinstalled ami + installing caffe at amazon: https://github.com/graphific/dl-machine
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general overview of convolutinal nets using Caffe: https://github.com/graphific/DL-Meetup-intro/blob/master/PyConSe15-cat-vs-dog.ipynb
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or using lasagne: http://www.slideshare.net/roelofp/python-for-image-understanding-deep-learning-with-convolutional-neural-nets
Roelof | KTH & Graph Technologies | @graphific