# dense_flow

**Repository Path**: luca_guo/dense_flow

## Basic Information

- **Project Name**: dense_flow
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-05-02
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

Extracting dense flow field given a video.

#### Dependencies:
- LibZip:
to install on ubuntu ```apt-get install libzip-dev``` on mac ```brew install libzip```

#### For OpenCV 3 Users
Please see the [opencv-3.1](https://github.com/yjxiong/dense_flow/tree/opencv-3.1) branch. Many thanks to @victorhcm for the contributions!

### Install
```
git clone --recursive http://github.com/yjxiong/dense_flow
mkdir build && cd build
cmake .. && make -j
```

### Usage
```
./extract_gpu -f=test.avi -x=tmp/flow_x -y=tmp/flow_y -i=tmp/image -b=20 -t=1 -d=0 -s=1 -o=dir
```
- `test.avi`: input video
- `tmp`: folder containing RGB images and optical flow images
- `dir`: output generated images to folder. if set to `zip`, will write images to zip files instead.

### Warp Flow
The warp optical flow is used in the following paper

```
@inproceedings{TSN2016ECCV,
  author    = {Limin Wang and
               Yuanjun Xiong and
               Zhe Wang and
               Yu Qiao and
               Dahua Lin and
               Xiaoou Tang and
               Luc {Van Gool}},
  title     = {Temporal Segment Networks: Towards Good Practices for Deep Action Recognition},
  booktitle   = {ECCV},
  year      = {2016},
}
```

To extract warp flow, use the command
```
./extract_warp_gpu -f test.avi -x tmp/flow_x -y tmp/flow_y -i tmp/image -b 20 -t 1 -d 0 -s 1 -o dir
```