Repository for different network models related to flow/disparity (ECCV 18)
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 We do not publish any training code and also do not offer support about questions for training networks.
python3 controller.py eval image0_path image1_path out_dir
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
.floformat. The other modalities use a custom binary format called
.float3. To read
.float3files to numpy arrays, please use the netdef_slim.utils.io module.
Example usage: ``` from netdefslim.utils.io import read occfile = 'occ.float3' occdata = read(occfile) # returns a numpy array
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.
import netdefslim as nd nd.loadmodule('FlowNet3/css/controller.py') c = Controller() out = c.net_actions.eval(img0, img1)
'occ.fwd', np.array[...], 'occ_soft.fwd', np.array[...], 'mb.fwd', np.array[...], 'mb_soft.fwd', np.array[...], ])
netdef_models is under the GNU General Public License v3.0