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--- |
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title: Video Background Removal |
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emoji: 👁 |
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colorFrom: green |
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colorTo: yellow |
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sdk: streamlit |
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sdk_version: 1.31.1 |
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app_file: app.py |
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pinned: false |
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--- |
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# AI Powered Video Background Removal Tool |
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[](https://github.com/killian31/VideoBackgroundRemoval/stargazers) |
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[](https://github.com/killian31/VideoBackgroundRemoval/actions/workflows/github-actions-black-formatting.yml) |
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[](https://wakatime.com/badge/github/killian31/VideoBackgroundRemoval) |
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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. |
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<p align="center"> |
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<table> |
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<tr> |
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<td> |
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<img src="assets/example.gif" width="385" height="216" /> |
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</td> |
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<td> |
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<img src="assets/output_example.gif" width="385" height="216" /> |
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</td> |
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</tr> |
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</table> |
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</p> |
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## Contents |
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Table of contents: |
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- [Installation](#installation) |
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- [Usage](#usage) |
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- [Example](#example) |
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- [Contribution](#how-to-contribute) |
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## Installation |
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### With pyenv and poetry |
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```bash |
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git clone https://github.com/killian31/VideoBackgroundRemoval.git |
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cd VideoBackgroundRemoval |
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pyenv virtualenv 3.11.9 vbr |
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pyenv activate vbr |
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pip install poetry |
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poetry install |
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``` |
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## Usage |
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### Using the Streamlit app (locally) |
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Run `streamlit run app.py` to launch the Streamlit app. Then, upload a video, draw a |
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bounding box around what you want to remove the background from, using the sliders, |
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and click on Segment Video. |
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### Command line |
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```bash |
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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] |
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[--mobile_sam_weights MOBILE_SAM_WEIGHTS] [--tracker_name {yolov7,yoloS}] [--output_dir OUTPUT_DIR] [--output_video OUTPUT_VIDEO] [--auto_detect] |
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[--background_color BACKGROUND_COLOR] |
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options: |
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-h, --help show this help message and exit |
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--video_filename VIDEO_FILENAME |
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path to the video |
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--dir_frames DIR_FRAMES |
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path to the directory in which all input frames will be stored |
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--image_start IMAGE_START |
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first image to be stored |
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--image_end IMAGE_END |
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last image to be stored, last one if 0 |
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--bbox_file BBOX_FILE |
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path to the bounding box text file |
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--skip_vid2im whether to write the video frames as images |
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--mobile_sam_weights MOBILE_SAM_WEIGHTS |
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path to MobileSAM weights |
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--tracker_name {yolov7,yoloS} |
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tracker name |
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--output_dir OUTPUT_DIR |
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directory to store the output frames |
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--output_video OUTPUT_VIDEO |
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path to store the output video |
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--auto_detect whether to use a bounding box to force the model to segment the object |
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--background_color BACKGROUND_COLOR |
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background color for the output (hex) |
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``` |
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## Example |
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The following command line is a working example from a video stored in the repo: |
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```bash |
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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 |
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``` |
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## How to Contribute |
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We welcome contributions from the community! To ensure a consistent code style, we ask contributors to follow these guidelines: |
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### Code Format |
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Please format your code using the `black` code formatter. |
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#### Installation |
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```bash |
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pip install black |
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``` |
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#### Usage |
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To format your code: |
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```bash |
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black . |
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``` |
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This setup will help maintain a consistent coding style throughout the project. |
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