https://github.com/qijiezhao/py-denseflow

Extract TVL1 optical flows in python (multi-process && multi-server)
https://github.com/qijiezhao/py-denseflow

optical-flow python tvl1

Last synced: 20 days ago
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Extract TVL1 optical flows in python (multi-process && multi-server)

• Host: GitHub
• URL: https://github.com/qijiezhao/py-denseflow
• Owner: qijiezhao
• Created: 2017-07-19T03:20:09.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2018-01-14T05:55:09.000Z (almost 7 years ago)
• Last Synced: 2023-11-07T19:01:40.991Z (about 1 year ago)
• Topics: optical-flow, python, tvl1
• Language: Python
• Homepage:
• Size: 8.79 KB
• Stars: 188
• Watchers: 6
• Forks: 68
• Open Issues: 9
• Metadata Files:
  • Readme: README.md

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README

        
# Py-denseflow

This is a python port of denseflow, which extract the videos' frames and **optical flow images** with **TVL1 algorithm** as default.

---

### Requirements:

- numpy

- cv2

- PIL.Image

- multiprocess

- scikit-video (optional)

- scipy

## Installation

#### Install the requirements:

```

pip install -r requirements.txt

```

---

## Usage

The denseflow.py contains two modes including '**run**' and '**debug**'.

here 'debug' is built for debugging the video paths and video-read methods. ([IPython.embed](http://ipython.org/ipython-doc/dev/interactive/reference.html#embedding) suggested)

Just simply run the following code:

```

python denseflow.py --new_dir=denseflow_py --num_workers=1 --step=1 --bound=20 --mode=debug

```

While in 'run' mode, here we provide multi-process as well as multi-server with manually s_/e_ IDs setting.

for example:  server 0 need to process 3000 videos with 4 processes parallelly working:

```

python denseflow.py --new_dir=denseflow_py --num_workers=4 --step=1 --bound=20 --mode=run --s_=0 --e_=3000

```

---

Just feel free to let me know if any bugs exist.