This fork add an option for HD and custom resolutions

FlowBlur-App

This code is build on top of RAFT: Recurrent All Pairs Field Transforms for Optical Flow, the original readme is also included in this file.

You can download the Windows build clicking here.

Requirement

pip install ffmpeg
pip install numba
pip install numpy
pip install opencv-python
pip install pillow
pip install PyQt5
pip install scikit-learn
pip install torch
pip install torchvision
pip install tqdm

Starting with GUI

First make sure to download the models from RAFT and add to the models folder. After that run this line to start the GUI:

python my_code.py

RAFT Readme:

This repository contains the source code for our paper:

RAFT: Recurrent All Pairs Field Transforms for Optical Flow
ECCV 2020
Zachary Teed and Jia Deng

Requirements

The code has been tested with PyTorch 1.6 and Cuda 10.1.

conda create --name raft
conda activate raft
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch
conda install matplotlib
conda install tensorboard
conda install scipy
conda install opencv

Demos

Pretrained models can be downloaded by running

./download_models.sh

or downloaded from google drive

You can demo a trained model on a sequence of frames

python demo.py --model=models/raft-things.pth --path=demo-frames

Required Data

To evaluate/train RAFT, you will need to download the required datasets.

By default datasets.py will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the datasets folder

├── datasets
    ├── Sintel
        ├── test
        ├── training
    ├── KITTI
        ├── testing
        ├── training
        ├── devkit
    ├── FlyingChairs_release
        ├── data
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow

Evaluation

You can evaluate a trained model using evaluate.py

python evaluate.py --model=models/raft-things.pth --dataset=sintel --mixed_precision

Training

We used the following training schedule in our paper (2 GPUs). Training logs will be written to the runs which can be visualized using tensorboard

./train_standard.sh

If you have a RTX GPU, training can be accelerated using mixed precision. You can expect similiar results in this setting (1 GPU)

./train_mixed.sh

(Optional) Efficent Implementation

You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension

cd alt_cuda_corr && python setup.py install && cd ..

and running demo.py and evaluate.py with the --alternate_corr flag Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass.

funghetto / FlowBlur-App