Video to frame:

video_processing.py line38: path = your video path; line39: name = your video name. Output frames to folder "~/original-frames/

Equirectangular image to perspective image based on the view:

class Enquirec2Perspec:

init(self, img): img: left eye's current frame of the panoramic video (top half of the video)

GetPerspective(self, FOV, THETA, PHI, height, width):

FOV: horizontal field of view in degrees. Here we need to set it as 104.
THETA: left/right angle in degrees of view center(right direction is positive, left direction is negative)
PHI: up/down angle in degrees of view center(up direction is positive, down direction is negative)
height, width: height/width of the output viewport image, should fit the resolution of each eye's viewport. Here we need to set them as 1440, 1600.

For the viewport at center, the THETA, PHI should be 0, 0. Once you have the perpective images from the ceter view for the left eye, use following code to get optical flow.

Perspective view:

360to2d.py

To get optical flow:

run.sh parameter.--inference_dataset_root --name --save_dir final directory structure: target_dir/name.npy

flownet2-pytorch

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks.

Multiple GPU training is supported, and the code provides examples for training or inference on MPI-Sintel clean and final datasets. The same commands can be used for training or inference with other datasets. See below for more detail.

Inference using fp16 (half-precision) is also supported.

For more help, type

python main.py --help

Network architectures

Below are the different flownet neural network architectures that are provided.
A batchnorm version for each network is also available.

FlowNet2S
FlowNet2C
FlowNet2CS
FlowNet2CSS
FlowNet2SD
FlowNet2

Custom layers

FlowNet2 or FlowNet2C* achitectures rely on custom layers Resample2d or Correlation.
A pytorch implementation of these layers with cuda kernels are available at ./networks.
Note : Currently, half precision kernels are not available for these layers.

Data Loaders

Dataloaders for FlyingChairs, FlyingThings, ChairsSDHom and ImagesFromFolder are available in datasets.py.

Loss Functions

L1 and L2 losses with multi-scale support are available in losses.py.

Installation

# get flownet2-pytorch source
git clone https://github.com/NVIDIA/flownet2-pytorch.git
cd flownet2-pytorch

# install custom layers
bash install.sh

Python requirements

Currently, the code supports python 3

numpy
PyTorch ( == 0.4.1, for <= 0.4.0 see branch python36-PyTorch0.4)
scipy
scikit-image
tensorboardX
colorama, tqdm, setproctitle

Converted Caffe Pre-trained Models

We've included caffe pre-trained models. Should you use these pre-trained weights, please adhere to the license agreements.

FlowNet2[620MB]
FlowNet2-C[149MB]
FlowNet2-CS[297MB]
FlowNet2-CSS[445MB]
FlowNet2-CSS-ft-sd[445MB]
FlowNet2-S[148MB]
FlowNet2-SD[173MB]

Inference

# Example on MPISintel Clean   
python main.py --inference --model FlowNet2 --save_flow --inference_dataset MpiSintelClean \
--inference_dataset_root /path/to/mpi-sintel/clean/dataset \
--resume /path/to/checkpoints

Training and validation

# Example on MPISintel Final and Clean, with L1Loss on FlowNet2 model
python main.py --batch_size 8 --model FlowNet2 --loss=L1Loss --optimizer=Adam --optimizer_lr=1e-4 \
--training_dataset MpiSintelFinal --training_dataset_root /path/to/mpi-sintel/final/dataset  \
--validation_dataset MpiSintelClean --validation_dataset_root /path/to/mpi-sintel/clean/dataset

# Example on MPISintel Final and Clean, with MultiScale loss on FlowNet2C model 
python main.py --batch_size 8 --model FlowNet2C --optimizer=Adam --optimizer_lr=1e-4 --loss=MultiScale --loss_norm=L1 \
--loss_numScales=5 --loss_startScale=4 --optimizer_lr=1e-4 --crop_size 384 512 \
--training_dataset FlyingChairs --training_dataset_root /path/to/flying-chairs/dataset  \
--validation_dataset MpiSintelClean --validation_dataset_root /path/to/mpi-sintel/clean/dataset

Results on MPI-Sintel

Reference

If you find this implementation useful in your work, please acknowledge it appropriately and cite the paper:

@InProceedings{IMKDB17,
  author       = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",
  title        = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",
  booktitle    = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
  month        = "Jul",
  year         = "2017",
  url          = "http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17"
}

@misc{flownet2-pytorch,
  author = {Fitsum Reda and Robert Pottorff and Jon Barker and Bryan Catanzaro},
  title = {flownet2-pytorch: Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks},
  year = {2017},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/NVIDIA/flownet2-pytorch}}
}

Related Optical Flow Work from Nvidia

Code (in Caffe and Pytorch): PWC-Net
Paper : PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume.

Acknowledgments

Parts of this code were derived, as noted in the code, from ClementPinard/FlowNetPytorch.

danieltao / vr_sickness_flownet2