Neural Style Transfer Transition Video Processing

This code extends the neural style transfer image processing technique to video by generating smooth transitions between a sequence of reference style images across video frames. The generated output video is a highly altered, artistic representation of the input video consisting of constantly changing abstract patterns and colors that emulate the original content of the video. The user's choice of style reference images, style sequence order, and style sequence length allow for infinite user experimentation and the creation of an endless range of artistically interesting videos.

System Requirements

This algorithm is computationally intensive so I highly
recommend optimizing its performance by installing drivers for
Tensorflow GPU support
if you have access to a CUDA compatible GPU. Alternatively, you can
take advantage of the free GPU resources available through Google Colab Notebooks.
Even with GPU acceleration, the program may take several minutes to render a video.

Colab Notebook Version

Configuration

All configuration of the video properties and input/output file
locations can be set by the user in config.py

Configurable Variable in config.py	Description
ROOT_PATH	Path to input/output directory
FRAME_HEIGHT	Sets height dimension in pixels to resize the output video to. Video width will be calculated automatically to preserve aspect ratio. Low values will speed up processing time but reduce output video quality
INPUT_FPS	Defines the rate at which frames are captured from the input video
INPUT_VIDEO_NAME	Filename of input video
STYLE_SEQUENCE	List that contains the indices corresponding to the image files in the 'style_ref' folder. Defines the reference style image transition sequence. Can be arbitrary length, the rate at which the video transitions between styles will be adjusted to fit the video
OUTPUT_FPS	Defines the frame rate of the output video
OUTPUT_VIDEO_NAME	Filename of output video to be created
GHOST_FRAME_TRANSPARENCY	Proportional feedback constant for frame generation. Should be a value between 0 and 1. Affects the amount change that can occur between frames and the smoothness of the transitions.
CLEAR_INPUT_FRAME_CACHE	If True the program clears the captured input frames each run. If False, you can run multiple style sequences without having to recapture video frames
PRESERVE_COLORS	If True the output video will preserve the colors of the input video. If False the program will perform standard style transfer

The user must find and place their own style reference images in the style_ref directory.
Style reference images can be
arbitrary size. For best results, try to use style reference images with similar dimensions
and aspect ratios. Three example style reference images are given.

Minor video time effects can be created by setting INPUT_FPS and OUTPUT_FPS to different relative values

INPUT_FPS > OUTPUT_FPS creates a slowed time effect
INPUT_FPS = OUTPUT_FPS creates no time effect
INPUT_FPS < OUTPUT_FPS creates a timelapse effect

Usage

$ python3 -m venv env
$ source env/bin/activate
$ pip3 install -r requirements.txt
$ python3 style_frames.py

Neural Style Transfer Transition Video Processing