---
title: Video Background Removal
emoji: 👁
colorFrom: green
colorTo: yellow
sdk: streamlit
sdk_version: 1.31.1
app_file: app.py
pinned: false
---
# AI Powered Video Background Removal Tool

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The Video Background Removal Tool is designed to enable users to effortlessly remove backgrounds from videos by selecting a subject in a single frame. This powerful tool is optimized to run on CPUs and boasts a user-friendly interface, making it ideal for a wide range of users, especially online content creators like YouTubers.

<p align="center">
  <table>
    <tr>
      <td>
        <img src="assets/example.gif" width="385" height="216" />
      </td>
      <td>
        <img src="assets/output_example.gif" width="385" height="216" />
      </td>
    </tr>
  </table>
</p>

## Contents

Table of contents:

- [Installation](#installation)
- [Usage](#usage)
- [Example](#example)
- [Contribution](#how-to-contribute)

## Installation

### With pyenv and poetry

```bash
git clone https://github.com/killian31/VideoBackgroundRemoval.git
cd VideoBackgroundRemoval
pyenv virtualenv 3.11.9 vbr
pyenv activate vbr
pip install poetry
poetry install
```

## Usage

### Using the Streamlit app (locally)

Run `streamlit run app.py` to launch the Streamlit app. Then, upload a video, draw a
bounding box around what you want to remove the background from, using the sliders,
and click on Segment Video.

### Command line

```bash
usage: main.py [-h] [--video_filename VIDEO_FILENAME] [--dir_frames DIR_FRAMES] [--image_start IMAGE_START] [--image_end IMAGE_END] [--bbox_file BBOX_FILE] [--skip_vid2im]
               [--mobile_sam_weights MOBILE_SAM_WEIGHTS] [--tracker_name {yolov7,yoloS}] [--output_dir OUTPUT_DIR] [--output_video OUTPUT_VIDEO] [--auto_detect]
               [--background_color BACKGROUND_COLOR]

options:
  -h, --help            show this help message and exit
  --video_filename VIDEO_FILENAME
                        path to the video
  --dir_frames DIR_FRAMES
                        path to the directory in which all input frames will be stored
  --image_start IMAGE_START
                        first image to be stored
  --image_end IMAGE_END
                        last image to be stored, last one if 0
  --bbox_file BBOX_FILE
                        path to the bounding box text file
  --skip_vid2im         whether to write the video frames as images
  --mobile_sam_weights MOBILE_SAM_WEIGHTS
                        path to MobileSAM weights
  --tracker_name {yolov7,yoloS}
                        tracker name
  --output_dir OUTPUT_DIR
                        directory to store the output frames
  --output_video OUTPUT_VIDEO
                        path to store the output video
  --auto_detect         whether to use a bounding box to force the model to segment the object
  --background_color BACKGROUND_COLOR
                        background color for the output (hex)
```

## Example

The following command line is a working example from a video stored in the repo:

```bash
python3 main.py --video_filename assets/example.mp4 --dir_frames ./frames --bbox_file bbox.txt --mobile_sam_weights models/mobile_sam.pt --output_dir output_frames --output_video output.mp4
```

## How to Contribute

We welcome contributions from the community! To ensure a consistent code style, we ask contributors to follow these guidelines:

### Code Format

Please format your code using the `black` code formatter.

#### Installation

```bash
pip install black
```

#### Usage

To format your code:

```bash
black .
```

This setup will help maintain a consistent coding style throughout the project.