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README.md
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flownet2.py
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README.md

OpticalFlow - FlowNet2

Load and run the pre-trained model in FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks by Ilg et al.

Given two images, the network is trained to predict the optical flow between these images.

  • Top: both input images from Flying Chairs, ground-truth, original FlowNet2 results (Caffe)
  • Bottom: Converted FlowNet2-C, FlowNet2-S, FlowNet2 results (this implementation)
Model AEE (sintel clean)
FlowNet-S 3.82
FlowNet-C 3.08
FlowNet2 2.10

The authors report the AEE of 2.03 (Caffe Model) on Sintel-clean and our implementation gives an AEE of 2.10, which is better than other TensorFlow implementations.

Usage

  1. Download the pre-trained model (converted from caffe):
wget http://models.tensorpack.com/OpticalFlow/flownet2.npz
wget http://models.tensorpack.com/OpticalFlow/flownet2-s.npz
wget http://models.tensorpack.com/OpticalFlow/flownet2-c.npz

Note: You are required to accept the author's license to use these weights.

  1. Run inference
python flownet2.py \
	--images frame0.png frame1.png frame2.png
	--load flownet2.npz --model flownet2

This command will show predictions for all the consecutive pairs one by one. Press any key to visualize the next prediction.

  1. Evaluate AEE (Average Endpoing Error) on Sintel dataset:
wget http://files.is.tue.mpg.de/sintel/MPI-Sintel-complete.zip
unzip MPI-Sintel-complete.zip
python flownet2.py --load flownet2.npz --model flownet2 --sintel_path /path/to/Sintel/training