Video frame interpolation using multiple separable convolution
pyflow: Fast optical flow estimation.
My fork of pyflow: No messy
logs / warnings that are printed to stdout.
- Youtube videos
- UCF Datasets:
- KITTI Scene Flow 2015
UCF dataset's quality is too low and contains watermark so not suitable. KITTI's images are stretched / warpped so not suitable.
So my data are only sampled youtube videos.
Useful tool to batch download youtube videos.
https://github.com/rg3/youtube-dl
Sample patch triples from videos.
Scan a directory and print a list of triples.
Accept a list of triples (output of list-valid-triple.py) and a threshold value. Filter out triples that potentially contains video shot boundary.
ref: http://www-nlpir.nist.gov/projects/tvpubs/tvpapers03/ramonlull.paper.pdf
Filter out triples that has low texture entropy.
ref: http://scikit-image.org/docs/dev/auto_examples/filters/plot_entropy.html
Estimate flow magnitude between first and third frame for each data.
Flow-guided heuristically sample data.
Plot histogram of distribution of data from the result of flow-estimate.py
and
flow-guided-sample.py
.