OpticalFlow

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.

2. Run inference

python flownet2.py \
	--left left.png --right right.png \
	--load flownet2.npz --model flownet2

3. 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