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netdef_models

Repository for different network models related to flow/disparity from the following papers:

NOTE: We only provide deployment code for these networks. We do not publish any training code and also do not offer support about questions for training networks.

• Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow
  (E. Ilg and T. Saikia and M. Keuper and T. Brox published at ECCV 2018) [paper] [video]

• Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
  (E. Ilg and Ö. Cicek and S. Galesso and A. Klein and O. Makansi and F. Hutter and T. Brox published at ECCV 2018) [paper] [video]

Setup

• Install tensorflow (1.4) (pip3 install tensorflow-gpu==1.4)
• Compile and install lmbspecialops. Please use the branch eccv18 instead of master
• Install netdef_slim
• Clone this repository

Running networks

• Change your directory to the network directory (Eg: FlowNet3)
• Run download_snapshots.sh. This takes a while to download all snapshots
• Now you should be ready to run the networks. Change your directory to a network type (Eg: css). Use the following command to test the network on an image pair: python3 controller.py eval image0_path image1_path out_dir

Output formats

The networks are executed using the controller.py scripts in the respective folders. Just running this controller will produce several output files in a folder (note that you can also obtain this output just as numpy arrays and write it to some custom files; see next section).

For optical flow we use the standard .flo format. The other modalities use a custom binary format called .float3. To read .float3 files to numpy arrays, please use the netdef_slim.utils.io module.

Example usage:

from netdef_slim.utils.io import read 
occ_file = 'occ.float3'
occ_data = read(occ_file) # returns a numpy array

# to visualize
import matplotlib.pyplot as plt
plt.imshow(occ_data[:,:,0])

Controller eval

The eval method of the controller writes to the disk by default. To avoid writing to disk, create a Controller object and use the eval method available in the net_actions member variable. This can be useful if you want to process the output of our networks in memory and not incur additional disk I/O.

Example usage:

import netdef_slim as nd
nd.load_module('FlowNet3/css/controller.py')
c = Controller() 
out = c.net_actions.eval(img0, img1)
# out is an OrderedDict with the following structure
#OrderedDict(['flow[0].fwd',     np.array[...],
              'occ[0].fwd',      np.array[...],
              'occ_soft[0].fwd', np.array[...],
              'mb[0].fwd',       np.array[...],
              'mb_soft[0].fwd',  np.array[...],
              ])       

License

netdef_models is under the GNU General Public License v3.0