This is based on https://github.com/sampepose/flownet2-tf

To test my version

python -m src.flownet_sd.test --input_a a --input_b b --out ./output

FlowNet2 (TensorFlow)

This repo contains FlowNet2[1] for TensorFlow. It includes FlowNetC, S, CS, CSS, CSS-ft-sd, SD, and 2.

Installation

pip install enum
pip install pypng
pip install matplotlib
pip install image
pip install scipy
pip install numpy
pip install tensorflow

Linux: sudo apt-get install python-tk

You must have CUDA installed: make all

Download weights

To download the weights for all models (4.4GB), run the download.sh script in the checkpoints directory. All test scripts rely on these checkpoints to work properly.

Flow Generation (1 image pair)

python -m src.flownet2.test --input_a data/samples/0img0.ppm --input_b data/samples/0img1.ppm --out ./

Available models:

• flownet2
• flownet_s
• flownet_c
• flownet_cs
• flownet_css (can edit test.py to use css-ft-sd weights)
• flownet_sd

If installation is successful, you should predict the following flow from samples/0img0.ppm:

Training

If you would like to train any of the networks from scratch (replace flownet2 with the appropriate model):

python -m src.flownet2.train

For stacked networks, previous network weights will be loaded and fixed. For example, if training CS, the C weights are loaded and fixed and the S weights are randomly initialized.

Fine-tuning

TODO

Benchmarks

Benchmarks are for a forward pass with each model of two 512x384 images. All benchmarks were tested with a K80 GPU and Intel Xeon CPU E5-2682 v4 @ 2.30GHz. Code was executed with TensorFlow-1.2.1 and python 2.7.12 on Ubuntu 16.04. Resulting times were averaged over 10 runs. The first run is always slower as it sets up the Tensorflow Session.

	S	C	CS	CSS	SD	2
First Run	681.039ms	898.792ms	998.584ms	1063.357ms	933.806ms	1882.003ms
Subsequent Runs	38.067ms	78.789ms	123.300ms	161.186ms	62.061ms	276.641ms

Sources

[1] E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, T. Brox FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, IEEE Conference in Computer Vision and Pattern Recognition (CVPR), 2017.